Seismic demand model class uncertainty in seismic loss analysis for a code-designed URM infilled RC frame building

Abstract

Probabilistic seismic risk is affected by several sources of uncertainty. Investigating their influence on loss analysis results is essential to obtain reliable quantitative estimations of seismic performance. Within the framework developed by the Pacific Earthquake Engineering Research (PEER) Center for probabilistic seismic loss analysis, this study incorporates the effect of seismic demand model class uncertainty on seismic risk performance metric estimation. The extended formulation is illustrated with an application example code-designed reinforced concrete moment resisting frame building with unreinforced masonry (URM) infill walls, where model class uncertainty related to URM infill walls modeling is propagated to the estimation of seismic financial losses. Model class uncertainty accounts for the variability arising from the use of different modeling solutions, such as the ones associated with the adoption of three equivalent strut macro-models and their modeling parameters. Probabilistic distributions are assigned to selected infill strut model parameters, and a large set of finite element models (FEMs) are generated for each infill strut model class by sampling the model parameter distributions through Latin Hypercube Sampling (LHS). Expected values of repair cost and life-cycle annualized loss are evaluated and compared for two sets of three building performance models. The first set considers the original (median) values of infill strut backbone parameters, while the second set includes model parameter uncertainty. The uncertainty propagation from structural response results to repair costs is also reported for the six performance models. Finally, the contribution of different structural and non-structural element categories to the financial losses is presented.