Unveiling non-steady chloride migration insights through explainable machine learning

Abstract

This study explores the influence of concrete mix ingredients on the non-steady chloride migration coefficient () using an explainable machine learning (XML) approach that integrates Extreme Gradient Boosting (XGBoost) and Shapley Additive Explanations (SHAP). The dataset, comprising 204 observations from literature, is utilized to train the XGBoost algorithm for predicting . The model demonstrates notable performance metrics with (MAE = 1.61 × 10−12 m2/s, RMSE = 2.38 × 10−12 m2/s, and = 0.95) in the training set and (MAE = 2.22 × 10−12 m2/s, RMSE = 3.18 × 10−12 m2/s, and = 0.87) and the test set. The SHAP method provides comprehensive insights into feature importance, offering valuable information about the relationships and dependencies among various features. The top five features identified as significant contributors include coarse aggregate, superplasticizer, concrete age, cement, and water. Visualization of SHAP values through diverse plots proves essential for obtaining a thorough understanding of feature influence. The explainability of the model's results contributes new insights, aiding in the development of optimal and sustainable concrete with enhanced resistance to chloride penetration. Furthermore, the model's explainability fosters trust in its predictions, facilitating seamless integration into real-world applications.