COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR PREDICTING CONCRETE COMPRESSIVE STRENGTH
DOI:
https://doi.org/10.69980/n873h086Keywords:
Concrete Compressive Strength, Machine Learning, Random Forest, Regression Models, Predictive Modeling, Civil EngineeringAbstract
Concrete compressive strength is a basic parameter in civil engineering that relates to the quality and longevity of structures. Traditional techniques of measuring compressive strength are based on laboratory tests which are usually time and resource consuming. The paper examines how machine learning methods can be used to estimate compressive strength of concrete in terms of mixture composition and curing age. They used a dataset of 1030 samples reflecting eight input variables; cement, water, aggregates and age. Three machine learning models, all regressions, were implemented: Linear Regression, Decision Tree Regressor, and Random Forest Regressor. The data was split into 80:20 training and testing data. Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and coefficient of determination (R2) were used to gauge model performance. The Regressor model that performed better was the Random Forest Regressor which had a R2 of 0.882 with an RMSE of 5.510. The analysis of importance of features showed that the most important factors in the compressive strength prediction are the curing age and cement content. The findings denote that the machine learning models, especially the ensemble-based models, may be useful in forecasting the compressive strength of concrete and provide an effective alternative to conventional experimental techniques.
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