Sharif Digital Repository

This research presents advanced methodologies for the seismic damage assessment of structures, focusing on the rapid and precise estimation of key engineering demand parameters. By integrating modern signal processing techniques with machine learning models, the study introduces a novel, data-driven framework that eliminates the reliance on traditional, computationally intensive modeling approaches. Leveraging real-time data from seismic sensors, these methodologies accurately predict critical performance metrics including residual and peak story drift ratios, peak floor accelerations, and fragility curves, significantly improving the accuracy of seismic assessments. The inclusion of...