Abstract

The seismic evaluation of existing unreinforced masonry (URM) structures is deemed essential due to their historical value and the vulnerability they present when subjected to earthquake shaking. Most of these buildings, constructed in the last century, do not comply with the current seismic codes and masonry standard. This study focusses on the assessment of URM walls subjected to lateral loading and more in detail to the behaviour of structural components such as the piers and spandrels. The behavior of structural elements, comprising the shear and bending failure modes, are investigated; these failure modes can be detected through the crack propagation initiated in building’s masonry walls. Then, a parametric study on a series of capacity formulations of piers and spandrels is performed and each failure mode is investigated independently in the aim of assessing the accuracy in capturing their strength. Three experimental test data are used to validate the efficiency of formulations for diagonal shear and data from five testing programs are used to validate the proposed formulations used to predict the maximum shear force resulting from flexural behavior of piers. Subsequently, a deterministic model for piers is built using an open-source software to generate data required to build a linear relationship between different parameters and the performance criteria expressed in terms of strength and ductility. A case study comprising an URM facade wall of an existing 2-storey school building in Montreal is also conducted and certain strengthening interventions are presented. Further, the generated data is used to build probabilistic models that operates on Bayesian Networks. Nonlinear analysis using the target and the predictive variables are carried out. Machine learning algorithms are applied to acquire the entropy reduction factors which helps determining the most predictive variables used to assess the performance of piers.