A comprehensive categorization method for identifying near‐fault pulse‐like ground motions

Abstract

AbstractIdentifying near‐fault pulse‐like ground motions from extensive ground motion databases holds paramount importance, as it provides a pivotal foundation for further inquiries into this specific type of ground motions, including the modeling of such stochastic processes as well as thorough analysis of their potential impact on structures and infrastructure systems. Currently, a diverse array of quantitative methods for identifying pulse‐like ground motions have emerged, all of which demonstrate good accuracy within their respective research scopes. However, due to the limitations of each individual method in identifying specific cases, these diverse approaches often yield inconsistent results for certain ground motion records, posing a significant challenge in establishing a reliable classification criterion that relies solely on a single identification method. To address this issue, the present study adopts a multifaceted approach. Instead of improving a single time‐frequency analysis‐based identification method, it carefully conducts a selection of seven baseline methods through a systematic overview of the field. By leveraging the analytic hierarchy process (AHP), a comprehensive categorization method is developed that integrates the strengths of each approach, resulting in a more robust and credible classification criterion. According to the devised category indicator, ground motions can be classified into four categories: Category A comprises definitively pulse‐like ground motions; Category B comprises apparently pulse‐like ground motions; Category C consists of probably pulse‐like ground motions; and Category D encompasses ground motions unlikely to exhibit pulse‐like characteristics. It provides a more elaborate classification beyond the binary distinction of pulse‐like and non‐pulse‐like ground motions associated with traditional onefold classification methods. For validation purposes, a basic dataset comprising near‐fault ground motion records from the NGA‐West 2 database has been utilized. To verify the comprehensive categorization method, two datasets of pulse‐like ground motion records suggested by FEMA and PEER and one dataset of ground motion records collected during the 1999 Chi‐Chi earthquake are addressed. Numerical examples illustrate the remarkable effectiveness of the proposed method in identifying near‐fault pulse‐like ground motions based on their varying degrees of pulse‐like characteristics.