Optimized machine learning algorithms with SHAP analysis for predicting compressive strength in high-performance concrete

Abstract

This research examines the application of eight different machine learning (ML) algorithms for predicting the compressive strength of high-performance concrete (HPC). Achieving precise predictions is crucial for enhancing structural reliability and optimizing resource usage in construction projects. The analysis utilized the “Concrete Compressive Strength” dataset, sourced from UC Irvine’s publicly available ML repository. The models evaluated include Gradient Boosting Regressor (GBR), Extreme Gradient Boosting Regression (XGBoost), Random Forest (RF), Support Vector Regression (SVR), Artificial Neural Network (ANN), Multilayer Perceptron (MLP), Lasso, and k-Nearest Neighbors (KNN). To enhance performance, critical data preprocessing steps were undertaken, which involved feature scaling, cleaning, and normalization. Hyperparameter tuning via Grid Search (GS) and K-fold cross-validation further optimized the models. Among those analyzed, XGBoost and GBR achieved the highest predictive accuracy, with R² values of 93.49% and 92.09% respectively, coupled with lower mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE). SHapley Additive exPlanations (SHAP) analysis revealed cement content and curing age as the most significant factors affecting compressive strength. Validation against experimental data confirmed the reliability of XGBoost and GBR through consistent prediction patterns and close alignment with empirical measurements. The results establish ML as an effective approach for HPC strength prediction, offering advantages in computational efficiency and accuracy over conventional analytical methods.

Keywords

Concrete strength / Machine learning / Prediction / Boosting / Regression / High performance concrete / Hyperparameter tuning / Grid search / Cross validation / SHapley Additive exPlanations

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