Explainable artificial intelligence (XAI)-driven probabilistic image-based structural health monitoring of reinforced concrete beams with shear reinforcements

Abstract

This paper addresses the challenge of accurately evaluating structural damage in reinforced concrete (RC) beams with shear reinforcements using image-based data. The research question focuses on whether probabilistic and explainable machine learning models can effectively predict strength- and displacement-based damage indicators from crack images. A framework is developed that integrates Explainable Artificial Intelligence (XAI), probabilistic shear strength modeling, image processing, and feature selection to extract 41 critical damage-related features. Four machine learning models are trained and validated using 375 images from ten experimental studies, with Gaussian Process Regression achieving an R² value of 0.923 in strength-based prediction. These results offer a non-contact, scalable, and interpretable solution for structural health monitoring and safety assessment of RC members. The findings encourage further exploration of image-based and probabilistic SHM approaches under cyclic, seismic, or environmental loading conditions.