Identification technique for impulsive seismic motions based on the combination of EAMRCM and SVM.
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| Title: | Identification technique for impulsive seismic motions based on the combination of EAMRCM and SVM. |
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| Authors: | Liang, Mingshi1 (AUTHOR), Jiang, Liqiang1 (AUTHOR) jianglq2019@csu.edu.cn, Zhang, Jin2 (AUTHOR), Liu, Xiang2 (AUTHOR) |
| Source: | Advances in Structural Engineering. Oct2026, Vol. 29 Issue 14, p2860-2874. 15p. |
| Subjects: | Support vector machines, Time-frequency analysis, Signal processing, Principal components analysis |
| Abstract: | This study proposes a novel method for identifying impulsive seismic motions by integrating the Enhanced Adaptive Multi-Resolution Chirplet Method (EAMRCM) with a Support Vector Machine (SVM) classifier. EAMRCM provides refined time-frequency representations that effectively extract intricate pulse characteristics from near-fault seismic motion records, thereby enhancing both the accuracy and robustness of the extraction process. Principal Component Analysis (PCA) is employed to distill key features from the seismic data, which is subsequently coupled with an SVM classifier to automatically distinguish between impulsive and non-impulsive events. Compared to existing approaches, the proposed method effectively adapts to complex seismic motion patterns, significantly reduces manual intervention, and enhances both automation and classification accuracy. Simulation tests performed in MATLAB demonstrate that this approach markedly improves the identification accuracy for impulsive seismic events, offering a novel tool and methodological foundation for seismic analysis and disaster mitigation engineering. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | This study proposes a novel method for identifying impulsive seismic motions by integrating the Enhanced Adaptive Multi-Resolution Chirplet Method (EAMRCM) with a Support Vector Machine (SVM) classifier. EAMRCM provides refined time-frequency representations that effectively extract intricate pulse characteristics from near-fault seismic motion records, thereby enhancing both the accuracy and robustness of the extraction process. Principal Component Analysis (PCA) is employed to distill key features from the seismic data, which is subsequently coupled with an SVM classifier to automatically distinguish between impulsive and non-impulsive events. Compared to existing approaches, the proposed method effectively adapts to complex seismic motion patterns, significantly reduces manual intervention, and enhances both automation and classification accuracy. Simulation tests performed in MATLAB demonstrate that this approach markedly improves the identification accuracy for impulsive seismic events, offering a novel tool and methodological foundation for seismic analysis and disaster mitigation engineering. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 13694332 |
| DOI: | 10.1177/13694332251399294 |
