Geomechanics and Engineering

  • 제37권6호
  • /
  • Pages.539-554
  • /
  • 2024
  • /
  • 2005-307X(pISSN)
  • /
  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Machine learning application to seismic site classification prediction model using Horizontal-to-Vertical Spectral Ratio (HVSR) of strong-ground motions

  • Francis G. Phi (Department of Civil and Environmental Engineering, Kongju National University) ;
  • Bumsu Cho (Department of Computer Science and Engineering, Kongju National University) ;
  • Jungeun Kim (Department of Computer Science and Engineering, Kongju National University) ;
  • Hyungik Cho (Department of Civil Systems Engineering, Andong National University) ;
  • Yun Wook Choo (Department of Civil and Environmental Engineering, Kongju National University) ;
  • Dookie Kim (Department of Civil and Environmental Engineering, Kongju National University) ;
  • Inhi Kim (Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology)
  • 투고 : 2024.01.24
  • 심사 : 2024.05.02
  • 발행 : 2024.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

This study explores development of prediction model for seismic site classification through the integration of machine learning techniques with horizontal-to-vertical spectral ratio (HVSR) methodologies. To improve model accuracy, the research employs outlier detection methods and, synthetic minority over-sampling technique (SMOTE) for data balance, and evaluates using seven machine learning models using seismic data from KiK-net. Notably, light gradient boosting method (LGBM), gradient boosting, and decision tree models exhibit improved performance when coupled with SMOTE, while Multiple linear regression (MLR) and Support vector machine (SVM) models show reduced efficacy. Outlier detection techniques significantly enhance accuracy, particularly for LGBM, gradient boosting, and voting boosting. The ensemble of LGBM with the isolation forest and SMOTE achieves the highest accuracy of 0.91, with LGBM and local outlier factor yielding the highest F1-score of 0.79. Consistently outperforming other models, LGBM proves most efficient for seismic site classification when supported by appropriate preprocessing procedures. These findings show the significance of outlier detection and data balancing for precise seismic soil classification prediction, offering insights and highlighting the potential of machine learning in optimizing site classification accuracy.

키워드

과제정보

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government and Ministry of Science and ICT (MSIT) (No.2021R1A4A1031509 and No.2021R1A2C2009985).

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