Application of Metaheuristic Algorithms in Ground Motion Selection and Scaling for Time History Analysis of Structures

Abstract

In this study, we utilize two metaheuristic algorithms, particle swarm optimization, and biogeography-based optimization, to select and scale ground motion (GM) records for use in the time history analysis of structures. This method ensures that there is no alteration to the phase or shape of the response spectra of the records. The proposed methodology demonstrates an ability to search through hundreds of earthquake records and propose a combination of 11 record pairs and scaling factors, resulting in a mean spectrum that aligns with the target spectrum. We applied the proposed research to two sites in separate geographical regions in the United States: Memphis and San Francisco, and we followed the ASCE 7-22 procedure. Selected ground motions underwent scaling adjustments represented by scalar values in a user-defined range. Furthermore, we present error metrics, comparing the target spectrum with the mean spectrum derived from the selected records. To showcase the effectiveness of our approach, we conducted a comparative analysis against results obtained from PEER-NGA methodology. The outcomes show that the methodology can be viewed as an effective and reliable approach for obtaining appropriate GM records for the time history analysis of structures.