Explainable tuned machine learning models for assessing the impact of corrosion on bond strength in concrete

Abstract

This study mainly aims to evaluate the bond strength of corroded reinforcements in reinforced concrete members. In this regard, a comprehensive dataset containing a total of 285 specimens was collected from previous experiments. All collected specimens, including normal concrete, were subjected to pull-out tests to ensure consistent results. The features evaluated are associated with both concrete and rebar characteristics, corrosion rate, and duration. Six machine learning (ML) models were used to assess the dataset: Decision Tree, Random Forest, Light Gradient-Boosting Machine, Gradient Boosting, Extreme Gradient Boosting, and Extra Tree. Hyperparameter tuning was conducted using grid search to optimize model performance and enhance predictive accuracy. Additionally, the Shapley Values technique was utilized to interpret the significance of the features on bond strength.

The results show that Extreme Gradient Boosting and Extra tree methods outperformed the other models, with $R^2$ score of 0.9 each and RSME of 2.21 and 1.87, respectively. Furthermore, tuned models resulted in more accurate performance than the default models. Evaluating the significance of studied features indicated that the elevated levels of corrosion were associated with a negative impact on bond strength. In addition, the corrosion rate is considered to be the most influential factor affecting the bond strength.