Data‑driven prediction of masonry flexural bond strength : Interpretability and implications
Flexural bond strength is a key parameter influencing the structural performance of masonry, yet its accurate prediction remains challenging due to the complex interplay of multiple material and testing variables. This study presents a machine learning (ML) framework for predicting flexural bond strength using a harmonized database comprising 1041 test specimens. Additionally, a review of 67 publi
