Buildings characterized by torsional irregularity are notorious for their vulnerability to earthquakes. However, most fragility/vulnerability models used for regional seismic risk assessment fail to distinguish between buildings with torsionally balanced and unbalanced configurations. The present study aims to develop a seismic fragility model sensitive to torsional irregularity for low-rise non-ductile RC moment frame buildings. To this end, a numerical investigation centred on 13 three-storied plan-asymmetric models, each characterized by a distinct combination of normalized stiffness eccentricity, torsional radius to mass radius of gyration ratio and normalized strength eccentricity, is presented. Using high-dimensional model representation (HDMR), a metamodel for the maximum inter-storey drift under bi-directional seismic action is calibrated, considering average spectral acceleration and the above trio of torsional irregularity descriptors (TIDs) as the predictor variables. Carrying out numerical simulations on limit state functions formulated using the above demand model and appropriate capacity thresholds, the seismic fragility is estimated. The parameters of a lognormal fragility function compatible with the above estimates are determined using the method of maximum likelihood. A strong correlation is observed between the TIDs and the fragility function parameters. This reinforces the importance of accounting for all three TIDs while rating the seismic fragility of a building. Using least-square error minimization technique, a functional relationship is established between the fragility model parameters and the TIDs. The framework described herein provides a simple, yet rational means to develop the next generation of seismic fragility models accounting for one of the most critical factors governing seismic behaviour--'torsional irregularity'.
Cloud analysis has emerged as a popular tool for the seismic demand/fragility assessment of structures. The output of cloud analysis is a seismic demand model which relates an Engineering Demand Parameter (EDP) indicative of structural distress to an Intensity Measure (IM) signifying the severity of ground shaking. IMs commonly used for probabilistic seismic demand assessment are quite heterogenous with respect to their “efficiency”, i.e. their degree of correlation with a specific EDP. This feature has serious implications on the number of ground motion records that must be used to perform cloud analysis on a given structure in order to accurately describe the distribution of the EDP at various IM levels. In the current study, demand models for maximum interstorey drift (?max), based on a wide spectrum of IMs, are developed from the cloud analyses of a five-storey RC bare frame structure using a suite of fifty unscaled natural ground motion records. The method of bootstrap resampling is used to investigate the convergence of the regression coefficients in the demand model with the size of the bootstrap subsamples, each comprising of a limited subset of records drawn from the original suite with repetitions allowed. This procedure helps determine the minimum number of ground motion records necessary for the calibration of demand models without compromising its accuracy in predicting the drift demands. Results from the study indicate a strong correlation between the efficiency of various IMs and the optimal number of records required to produce reliable seismic demand models.
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