Clustering-based adaptive ground motion selection algorithm for efficient estimation of structural fragilities

To accurately predict the seismic demands of structural systems, a proper set of ground motions representing the seismic hazard of a given site is needed. In general, such a set includes a large number of ground motions, and thus may result in high computational cost. To address this computational challenge without compromising the accuracy of structural fragility, this paper proposes a clustering-based algorithm that can select a representative subset of ground motions adaptively from a given set of ground motions. First, critical features of ground motions that significantly affect seismic demands of the structural system are identified by Lasso regression of seismic responses of various single degree of freedom systems on existing intensity measures of ground motions. Second, ground motions are selected adaptively based on the hierarchical clustering of the critical features until the fragility curve converges. Applications to a reinforced concrete building and steel moment-resisting frames demonstrate the improved efficiency and wide applicability of the proposed method. The results of the numerical examples confirm the robust performance of the proposed algorithm against various ground motions, structural types, and definitions of the limit-states. The proposed algorithm enables us to obtain structural fragilities using a significantly reduced number of ground motions while keeping consistency with the available ground motion set.