Utilizing Artificial Neural Networks and Random Forests to Forecast the Dynamic Amplification Factors of Non-Structural Components

Abstract

Soft stories in buildings are well-known to present structural vulnerabilities during seismic events, and the failure of non-structural components (NSCs) has been evident in past earthquakes, along with structural damage. This study seeks to investigate how the presence of a soft story in a building affects the criteria for elastic floor acceleration. The soft story is assumed to be at the top, middle, and bottom levels of the structure. To comprehend the behavior of NSCs, the researchers analyze the floor response spectra (FRSs) and component acceleration amplification. Remarkably, the results reveal that the position of the soft story strongly influences the floor response spectra, with structures featuring a middle soft story showing the most significant amplification of component acceleration. In constructing the FRSs, the component dynamic amplification factors (CDAFs) play a vital role as they accurately illustrate how NSCs amplify floor vibrations. Consequently, the study delves into exploring machine learning (ML) models like artificial neural networks (ANNs) and random forest (RF) to map the intricate relationship between CDAFs, the dynamic characteristics of the building, and the behavior of NSCs. Upon comparison of the two models, the random forest model emerges as the superior method in predicting the CDAFs.