Interpretable ensemble learning framework for shear strength assessment of corroded reinforced concrete beams

The assessment of deterioration-affected reinforced concrete (RC) structures is an important task for maintaining structural safety and supporting maintenance and strengthening decisions in the built environment. As corrosion influences structural resistance through multiple coupled factors, reliable estimation of shear strength remains challenging in corroded reinforced concrete (CRC) beams. However, conventional empirical and analytical models often rely on simplified assumptions and may exhibit limited accuracy across different test conditions, while existing prediction models usually lack sufficient generalization and interpretability. To address this issue, this study develops an interpretable data-driven framework for the shear strength assessment of CRC beams using ensemble learning. A database comprising 180 experimentally reported CRC beam specimens was compiled by integrating material properties, geometric characteristics, reinforcement details, and corrosion-related parameters. Several machine-learning models were evaluated, among which the proposed Stacking model achieved the best overall predictive performance on the adopted dataset, with test-set predictions concentrated within a ± 20% error band and a test-set R2 of 0.9727. The proposed model was further examined through comparison with representative empirical and code-based prediction models, showing closer agreement with the experimental results in the examined literature datasets. Moreover, SHAP-based interpretation was employed to examine the relative influence of input variables and their contribution trends with respect to shear strength, thereby improving the transparency of the learned feature-response relationships. A simple user-oriented interface was also implemented to facilitate interaction with the trained model. The proposed framework provides a supplementary data-driven approach for assessing the shear strength of CRC beams.