Hilbert-Huang Transform-Based Seismic Intensity Parameters for Performance-Based Design of RC-Framed Structures

Abstract

This study aims to develop the optimal artificial neural networks (ANNs) capable of estimating the seismic damage of reinforced concrete (RC)-framed structures by considering several seismic intensity parameters based on the Hilbert–Huang Transform (HHT) analysis. The selected architecture of ANN is the multi-layer feedforward perceptron (MFP) network. The values of the HHT-based parameters were calculated for a set of seismic excitations, and a combination of five to twenty parameters was performed to develop input datasets. The output data were the structural damage expressed by the Park and Ang overall damage index (DIPA,global). The potential contribution of nine training algorithms to developing the most effective MFP was also investigated. The results confirm that the evolved MFP networks, utilizing the employed parameters, provide an accurate estimation of the target output of DIPA,global. As a result, the developed MFPs can constitute a reliable computational intelligence approach for determining the seismic damage induced on structures and, thus, a powerful tool for the scientific community for the performance-based design of buildings.