Enhancing Seismic Design of Non-structural Components Implementing Artificial Intelligence Approach: Predicting Component Dynamic Amplification Factors

The seismic performance of non-structural components (NSCs) has been the focus of intensive study during the last few decades. Modern building codes define design forces on components using too simple relationships. The component accelerates faster than the floor acceleration to which it is connected. Therefore, component dynamic amplification factors (CDAFs) are calculated in this work to quantify the amplification in the acceleration of NSCs for the various damping ratios and tuning ratios of the NSC, and the primary structural periods. From the analysis results, it was observed that CDAF peaks are either underestimated or overestimated by the code-based formulae. A prediction model to ascertain the CDAFs was also developed using artificial neural networks (ANNs). Following that, the suggested model is contrasted with the established relationships from the past research. The ANN model's coefficient of correlation ( ) was 0.97. Hence, using an ANN algorithm reduces the necessity of laborious and complex analysis.

Enhancing Seismic Design of Non-structural Components Implementing Artificial Intelligence Approach: Predicting Component Dynamic Amplification Factors Keywords:

primary structure , Secondary structure , Dynamic interaction , Tuning ratio