The Sensitivity of Global Structural Parameters for Unreinforced Masonry Buildings Subjected to Simulated Ground Motions

Abstract

This research performs a parametric study based on Equivalent Single Degree of Freedom (ESDOF) models for simplified seismic analysis of unreinforced masonry (URM) structures. This is a necessary action due to the fact that it is not affordable to model and analyze populations of masonry buildings by using detailed continuum-based models during regional seismic damage and loss estimation studies. Hence, this study focuses on the sensitivity of major structural parameters of a selected idealized hysteretic model for URM buildings. The numerical models are subjected to region-specific simulated ground motion time histories generated using validated seismological parameters. The variations in dynamic analysis results are evaluated using statistical tools for major structural and seismological parameters. The results reveal that the strength factor is the most influential structural parameter, whereas magnitude and distance have a significant impact on the response of idealized URM models as seismological parameters. Furthermore, the specific seismic performance exhibiting limited ductility capacity and the narrow margin of safety between the initial state of inelastic behavior and the ultimate (collapse) state for URM buildings is verified by the statistical approaches employed in this study.

Utilising Artificial Neural Networks for Assessing Seismic Demands of Buckling Restrained Braces Due to Pulse-like Motions

“…Researchers have focused on developing innovative strategies to enhance building structural performance while also employing alternative approaches to estimate demands on structures and assess losses in the building stock [1][2][3][4][5][6][7][8]. In recent years, the rapid advancement of computer processors has led to a significant increase in the utilisation of machine learning capabilities across various engineering domains [9][10][11][12][13][14][15][16][17][18][19]. An important application of machine learning is to enable accurate and reliable estimation of seismic demands for single-degree-of-freedom or more complex multi-degree-of-freedom structures, an area of growing interest in recent times [20][21][22][23][24].…”