Quantifying Hybrid Failure Modes of Unreinforced Masonry Walls through Experimental Data Analysis

Failure mode identification in unreinforced masonry (URM) walls is a challenging task because of the presence of multiple damage mechanisms, including so-called hybrid modes. This paper employed a novel application of supervised and unsupervised learning to link URM wall design and mechanical properties to the failure mode. We used a database with 330 backbone curves from cyclically loaded URM walls as well as the associated images captured during the experiments. Based on the observations documented during the experiments, information on the cyclic curves, and the associated images, the walls were first manually classified into four failure modes: bed-joint sliding, diagonal tension, rocking, and toe crushing. Then k-means clustering was used to group the damaged walls into four classes based on critical points along the backbone curves. A hybridity index was introduced to quantify the contribution of each failure mode based on the distance from the centroid of the clusters. The design and mechanical properties of the URM walls were then used to predict the hybridity index using a multioutput regression model. The hybridity prediction model determines the contribution of the various failure modes to the ultimate behavior of a damaged URM wall. The proposed framework provides a robust approach to quantifying the relative contribution of each failure mechanism to the overall performance of the URM wall.