Concrete Compressive Strength Prediction using Machine Learning Concrete is one of the most important materials in Civil Engineering. Knowing the compressive strength of concrete is very important when constructing a building or a bridge. The Compressive Strength of Concrete is a highly nonlinear function of ingredients used in making it and their characteristics. Thus, using Machine Learning to predict the Strength could be useful in generating a combination of ingredients which result in high Strength.

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Please refer ConcreteCompressiveStrengthPrediction.ipynb for code.

1. Problem Statement Predicting Compressive Strength of Concrete given its age and quantitative measurements of ingredients.

2. Data Description

Number of instances - 1030

Number of Attributes - 9

Attribute breakdown

8 quantitative inputs, 1 quantitative output

Attribute information

Inputs

Cement

Blast Furnace Slag

Fly Ash

Water

Superplasticizer

Coarse Aggregate

Fine Aggregate

All above features measured in kg/$m^3$

Age (in days)

Output

Concrete Compressive Strength (Mpa)