  The presented study aims to provide an efficient and reliable tool for rapid
estimation of the shear capacity of a TRM-strengthened masonry wall. 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 were detected using 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 are 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 ($R^2$)
equal to 0.95 and the mean absolute percentage error equal to 8.03\%. In
sequence, machine learning interpretation methods are applied to find how the
predictors influence the target output. $A_m$, $f_t$, and $n\cdot t_f$ were
identified as the most significant predictors with a mainly positive influence
on the shear capacity. Finally, the built ML system is employed in a
user-friendly web app for easy access and usage by professionals and
researchers.
