SEISMIC RESPONSE ASSESSMENT OF MULTI-STOREY BUILDINGS USING SAP2000, PYTHON, AND MODAL TIME-HISTORY ANALYSIS

Multi-story buildings are more susceptible to earthquakes than single-story structures because of differences in structure, stiffness, mass distribution and ground motion characteristics. Traditional structural assessment methods could be very time-consuming in determining seismic responses for various earthquake scenarios. This study proposes an integrated computational framework that integrates SAP2000 with Python to simulate as well as assess the seismic response of the structure and estimate its performance beforehand. The methodology is based on three-dimensional models for reinforced-concrete buildings with regular and irregular layouts, and involves modal analysis and modal time-history analysis of the structures under the FEMA P695 far-field ground-motion data set, which contains a set of 22 record pairs and 44 horizontal acceleration time histories. The fundamental period, spectral acceleration, maximum interstory drift, and roof displacement are obtained from the model and analyzed and predicted with the aid of machine-learning parameters and statistical analysis by using python programming. The results show that the fundamental period has the strongest positive correlation with the number of stories (r=0.83) and roof displacement has the highest correlation with maximum interstory drift (r=0.73). The predictive model successfully has an (R2) of 0.92 for fundamental period, 0.56 for roof displacement, 0.47 for maximum interstory drift, and 0.44 for spectral acceleration. The results indicate the value of using a combined approach of structural analysis and data-based prediction for enhanced assessment of seismic response and determination of structural demands.