Bayesian Methodology for Probabilistic Description of Mechanical Parameters of Masonry Walls

Abstract

In consideration of the high vulnerability of the built environment, the assessment of seismic behavior of existing masonry buildings is a key topic in view of their retrofitting and reuse. Because masonry’s behavior depends on complex nonhomogeneous, anisotropic, asymmetric, and nonlinear properties, the definition of suitable mechanical models is still a critical issue, especially for stone masonry. Structural analyses of existing masonry buildings in seismic-prone areas are thus significantly influenced by the adopted mechanical models and assumptions about their relevant masonry properties, which are characterized by large uncertainty. In this study, a procedure for the definition of masonry classes and probability density functions of relevant mechanical parameters, such as elastic modulus and shear modulus, is proposed. The general procedure is illustrated referring to a significant number of in situ double-flat-jack test results on stone masonry obtained by the authors during an ad hoc experimental campaign. Finally, combining information on masonry quality obtained by visual inspection with results of in situ tests, a Bayesian methodology is proposed for the updating of masonry mechanical parameters, thereby providing the basis for a more refined probabilistic assessment of the seismic risk index.