Automatic classification of near‐fault pulse‐like ground motions

This study presents an automated, quantitative classification method for near-fault pulse-like ground motions, distinguishing between forward-directivity and fling-step (FS) motions. The method introduces two novel parameters—the pulse velocity ratio and pulse area ratio—which transform the classification standard from a qualitative to a quantitative framework. Combined with an enhanced pulse extraction technique that captures permanent displacement characteristics, these parameters significantly improve classification efficiency and repeatability. This automated approach overcomes the limitations of manual classification, providing reproducible results. The identified FS ground motions can be applied to the dynamic analysis of cross-fault structures, enhancing the reliability of seismic hazard assessments.

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Publication Details

Journal
Computer-Aided Civil and Infrastructure Engineering
Published
2024-12-25
DOI
https://doi.org/10.1111/mice.13408
Citations
5
Primary Topic
Seismic Performance and Analysis
Type
article
Field-Weighted Citation Impact
1.30

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article

Automatic classification of near‐fault pulse‐like ground motions

Shuyan Ji, Hongwu Yang, Lei Hu, Yingmin Li et al.
5 citations
Computer-Aided Civil and Infrastructure Engineering
Seismic Performance and Analysis
1.30
article

Automatic classification of near‐fault pulse‐like ground motions

Shuyan Ji, Hongwu Yang, Lei Hu, Yingmin Li, Weihao Pan
article en
5 citations

Abstract

This study presents an automated, quantitative classification method for near-fault pulse-like ground motions, distinguishing between forward-directivity and fling-step (FS) motions. The method introduces two novel parameters—the pulse velocity ratio and pulse area ratio—which transform the classification standard from a qualitative to a quantitative framework. Combined with an enhanced pulse extraction technique that captures permanent displacement characteristics, these parameters significantly improve classification efficiency and repeatability. This automated approach overcomes the limitations of manual classification, providing reproducible results. The identified FS ground motions can be applied to the dynamic analysis of cross-fault structures, enhancing the reliability of seismic hazard assessments.

Computer-Aided Civil and Infrastructure EngineeringVol. 40(11)
Chongqing University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 21%
Seismic Performance and Analysis
1.30
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