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2022 Vol.18, Issue 4 Preview Page

Original Article

Seismic Fragility of I-Shape Curved Steel Girder Bridge using Machine Learning Method

머신러닝 기반 I형 곡선 거더 단경간 교량 지진 취약도 분석

전준태1ㆍ주부석2*ㆍ손호영3

Juntai Jeon1, Bu-Seog Ju2*, Ho-Young Son3

1Professor, Department of Civil & Environmental Engineering, Inha Technical College, Incheon, Republic of Korea
2Professor, Department of Civil Engineering, Kyunghee University, Yongin, Republic of Korea
3Research Doctor, Department of Civil Engineering, Kyunghee University, Yongin, Republic of Korea
31 December 2022. pp. 899-907
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Abstract
Purpose: Although many studies on seismic fragility analysis of general bridges have been conducted using machine learning methods, studies on curved bridge structures are insignificant. Therefore, the purpose of this study is to analyze the seismic fragility of bridges with I-shaped curved girders based on the machine learning method considering the material property and geometric uncertainties. Method: Material properties and pier height were considered as uncertainty parameters. Parameters were sampled using the Latin hypercube technique and time history analysis was performed considering the seismic uncertainty. Machine learning data was created by applying artificial neural network and response surface analysis method to the original data. Finally, earthquake fragility analysis was performed using original data and learning data. Result: Parameters were sampled using the Latin hypercube technique, and a total of 160 time history analyzes were performed considering the uncertainty of the earthquake. The analysis result and the predicted value obtained through machine learning were compared, and the coefficient of determination was compared to compare the similarity between the two values. The coefficient of determination of the response surface method was 0.737, which was relatively similar to the observed value. The seismic fragility curve also showed that the predicted value through the response surface method was similar to the observed value. Conclusion: In this study, when the observed value through the finite element analysis and the predicted value through the machine learning method were compared, it was found that the response surface method predicted a result similar to the observed value. However, both machine learning methods were found to underestimate the observed values.
Keywords
Curved Bridge
Seismic Fragility
Machine Learning
Artificial Neural Net Work
Response Surface Method
연구목적: 기계학습법을 이용하여 일반적인 직선 교량의 지진 취약도 분석 연구는 다수 수행되었으나 곡선 교량 구조물에 대한 연구 사례는 미미하다. 따라서 본 연구의 목적은 기계학습법 기반 I형 곡선 거더를 갖는 교량의 재료 특성 및 기하학적 불확실성을 고려한 지진 취약도 분석하는 것이다. 연구방법: 강재 및 콘크리트의 재료 특성과 교각의 높이를 불확실성 매개변수로 고려하였다. 라틴하이퍼큐브 기법을 이용하여 매개변수를 샘플링하였으며 지진파의 불확실성을 고려하여 시간이력해석을 수행하였다. 해석결과를 원본데이터로 인공신경망, 반응표면분석법을 적용하여 학습 데이터를 생성하였다. 최종적으로 원본 데이터 및 학습데이터를 이용하여 지진 취약도 분석을 수행하였다. 연구결과: 라틴하이퍼큐브 기법을 이용하여 매개변수를 샘플링하였으며 지진파의 불확실성을 고려하여 총 160회의 시간이력해석을 수행하였다. 해석결과와 기계학습을 통해 얻어진 예측 값을 비교하였으며 두 값의 유사도를 비교하기 위해 결정계수를 비교하였다. 반응표변분석법의 결정계수가 0.737로 비교적 관측 값과 유사한 것으로 나타났다. 지진 취약도 곡선도 반응표면 분석법을 통해 예측된 값이 관측 값과 유사한 것으로 나타났다. 결론: 본 연구에서 유한요소 해석을 통해 관측된 값과 기계학습법을 통해 예측된 값을 비교하였을 때 반응표면분석법이 관측값과 유사한 결과를 예측하는 것으로 나타났다. 하지만 두 가지 기계학습법 모두 관측 값에 비해 과소평가되는 것으로 나타났다.
키워드
곡선교량
지진 취약도
기계학습법
인공신경망
반응표면분석법
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Information
  • Publisher :The Korean Society of Disaster Information
  • Publisher(Ko) :한국재난정보학회
  • Journal Title :Journal of the Society of Disaster Information
  • Journal Title(Ko) :한국재난정보학회논문집
  • Volume : 18
  • No :4
  • Pages :899-907
  • DOI :https://doi.org/10.15683/kosdi.2022.12.31.899
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