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2024 Vol.25, Issue 11
Study of Prediction of Liquefaction Potential Index Based on Machine Learning Method

기계학습기법을 통한 액상화 발생가능 지수 예측에 관한 연구

Junseo Jeon1
,
Jongkwan Kim2*
,
Jintae Han3
,
Seunghwan Seo4
,
Byeonghan Jeon5
전 준서1
,
김 종관2*
,
한 진태3
,
서 승환4
,
전 병한5
1Senior Researcher, Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology (KICT)
2Senior Researcher, Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology (KICT)
3Research Fellow, Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology (KICT)
4Senior Researcher, Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology (KICT)
5Manager, Institute of Technology, LT SAMBO
*Corresponding Author
1 November 2024. pp. 5-12
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Abstract

In this study, the liquefaction potential index was assessed using actual borehole data and seismic waves, and a predictive model was developed based on machine learning methods. A total of 10 features were selected including factors reflecting the characteristics of the seismic waves. To identify candidate methods, a preliminary test was conducted using commonly used machine learning methods for regression, followed by Bayesian optimization to optimize the hyperparameters for these candidate methods. Among artificial neural networks, Gaussian process regression, and random forest, it was found that the random forest effectively predicted the liquefaction potential index, as indicated by a low root mean square error, a high coefficient of determination, and considerations regarding overfitting. However, it was noted that the model tends to underestimate the liquefaction potential index when the index was 5 or higher.

Keywords
Liquefaction potential index
Borehole data
Machine learning
Bayesian optimization
Hyperparameter

실제 시추공 정보 및 지진파를 이용하여 액상화 발생가능 지수를 산정하고, 기계학습기법을 이용하여 액상화 발생가능 지수 예측모델을 학습하였다. 학습을 위해 지진파의 특징을 반영한 인자를 포함하여 총 10가지의 특징을 선택하였다. 일반적으로 이용되는 기계학습기법 중 사전학습을 통해 후보 모델을 선정하고, 후보 모델에 대해 베이지안 최적화를 적용하여 초매개변수를 최적화시켰다. 인공신경망, 가우시안 프로세스 회귀, 랜덤 포레스트 중 평균제곱근오차, 결정계수 및 과대적합 여부를 종합한 결과, 랜덤 포레스트가 액상화 발생가능 지수를 잘 예측하였다. 다만, 액상화 발생가능 지수가 5 이상에서는 액상화 발생가능 지수를 과소예측하는 경향을 보였다.

키워드
액상화 발생가능 지수
시추공 자료
기계학습
베이지안 최적화
초매개변수
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Information
  • Publisher :Korean Geo-Environmental Society
  • Publisher(Ko) :한국지반환경공학회
  • Journal Title :Journal of the Korean Geo-Environmental Society
  • Journal Title(Ko) :한국지반환경공학회 논문집
  • Volume : 25
  • No :11
  • Pages :5-12
  • Received Date : 2024-09-30
  • Revised Date : 2024-10-10
  • Accepted Date : 2024-10-22
  • DOI :https://doi.org/10.14481/jkges.2024.25.11.5
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