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.
Please read this blog for more explanation. Also leave some claps to show appreciation!
Please refer ConcreteCompressiveStrengthPrediction.ipynb for code.
Predicting Compressive Strength of Concrete given its age and quantitative measurements of ingredients.
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
- Cement
- Blast Furnace Slag
- Fly Ash
- Water
- Superplasticizer
- Coarse Aggregate
- Fine Aggregate
All above features measured in kg/$m^3$
- Age (in days)
- Concrete Compressive Strength (Mpa)
-
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.
