One of the key steps in the seismic resilience assessment of buildings is gaining insight into their seismic response. The frequency content of ground motions and the dynamic characteristics of the building contribute mutually to the seismic response of the entire system (soil + structure). This study aims to present a new approach to assess the effects of the soil structure interaction (SSI) and of the frequency content of the ground motion. A case study of reinforced concrete moment frames (RCMFs) is herein presented by applying 280 ground motion time history records, which were registered on site classes A and B (based on ASCE 7–16). To this end, initially the characteristics of the input motions (e.g. amplitude and frequency content) were calculated, and then the time histories were partitioned into three clusters based on two optimal clustering features, namely Peak Ground Acceleration to Peak Ground Velocity ratio (PGA/PGV) and F5 (representing the frequency associated with 5% of total power in cumulative power spectral density, PSD). The clustering were done by implementing an unsupervised machine learning (ML) algorithm (i.e. K-means clustering), also the number of clusters was achieved using a well-known method in ML literature. Several time histories from each of the clusters were randomly selected to perform incremental dynamic analysis (IDA) on a 6- and a 10-story RCMF. Two different base conditions were considered: fixed-based (SSI neglected) and flexible-base (considering SSI effects). The findings demonstrate the contribution of frequency content and SSI to the seismic response of RCMFs, and also present an approach for considering frequency content in seismic analysis.
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