Concrete-compressive-strength

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

Data is obtained from UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength

• 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)

3. Modelling and Evaluation

• Algorithms used

  • Linear regression
  • Lasso regression
  • Ridge regression
  • Decision Trees
  • Random Forests

• Metric - Since the target variable is a continuous variable, regression evaluation metric RMSE (Root Mean Squared Error) and R2 Score (Coefficient of Determination) have been used.

4. Results

Feature correlation

Feature importance

Final Comparison

5. References

1. https://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength