Rapid shear capacity prediction of TRM-strengthened unreinforced masonry walls through interpretable machine learning deployed in a web app

Abstract

The presented study provides an efficient and reliable tool for rapid shear capacity estimation of TRM-strengthened unreinforced masonry walls. For this purpose, a data-driven methodology based on a machine learning system is proposed using a dataset constituted of experimental results selected from the bibliography. The outlier points of the dataset were detected using the Cook’s distance methodology and removed from the raw dataset, which consisted of 113 examples and 11 input variables. In the processed dataset, 17 machine learning methods were trained, optimized through hyperparameter tuning, and compared on the test set. The most effective models were the optimized instances of XGBoost and CatBoost methods, which combined into a voting model to leverage the predictive capacity of more than a single regressor. The final blended model shows remarkable predicting capacity with the determination factor (R2) equal to 0.95 and the mean absolute percentage error equal to 8.03%. Also, the model’s predictions are compared with those of existing analytical relationships, and it is found to perform the best of all. In sequence, machine learning interpretation methods are applied to find how the predictors influence the target output. Am, ft, and n⋅tf were identified as the most significant predictors with a mainly positive influence on the shear capacity. Finally, the built machine learning system is employed in a user-friendly web app for easy access and usage by professionals and researchers.