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Quantifying the effects of modeling uncertainty on the seismic performance assessment of a non-ductile reinforced concrete masonry-infilled frame structure


Quantifying and propagating aleatory and epistemic uncertainty in nonlinear structural response simulation is key to robust performance-based seismic assessments. In this thesis, the focus is on the probabilistic seismic performance assessment of a non-ductile three-story reinforced concrete infilled frame building. The uncertainty in ground motion records and the uncertainty embedded in structural model parameters are explicitly considered. An equivalent strut model is used for the masonry infill walls, where the six constitutive parameters that define its backbone curve are treated as random variables. The variability in these parameters is characterized by developing correlated and uncorrelated distributions of the deduced-to-predicted ratios using data from 113 experimental tests. The uncertainties are propagated using Latin hypercube sampling to generate randomized structural model realizations. Multiple stripe analysis is performed with hazard-consistent ground motions. The effect of considering modeling uncertainty is investigated in terms of the distributions of maximum story drift ratios and drift based fragility functions. It is shown that the inclusion of modeling uncertainty has significant effects on the seismic performance of the case study building. The dispersion in the response and the mean annual frequency of exceeding the drift-based limit states are increased when modeling uncertainty is included. The initial stiffness of the infill walls is observed to have the most significant contribution to the performance of the building.

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