Demonstrate linear regression modelling applied towards predicting the compressive strength of concrete
BACKGROUND:
-Compressive strength is the capacity of a material or structure to withstand loads tending to reduce size, as opposed to tensile strength, which withstands loads tending to elongate.
-The concrete compressive strength is a highly non-linear function of age and ingredients .These ingredients include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate.
-The actual concrete compressive strength (MPa) for a given mixture under a specific age (days) was determined from laboratory.
PROBLEM STATEMENT:
-Based on values of components of concrete mixtures, can we predict the compressive strength of concrete?
MODELS TESTED:
-Simple Linear Regression – predicting Compressive Strength (CCS) based on ‘Cement’
-Multivariable Linear Regression, drawing features from optimal correlations
-Multivariable linear regression, with manually selected features
-Random Forest
-Simple Logistic Regression – predicting whether concrete is cured or fresh.