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http://dx.doi.org/10.15683/kosdi.2022.12.31.899

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

Juntai Jeon (Department of Civil & Environmental Engineering, Inha Technical College)
Bu-Seog Ju (Department of Civil Engineering, Kyunghee University)
Ho-Young Son (Department of Civil Engineering, Kyunghee University)
Publication Information
Journal of the Society of Disaster Information / v.18, no.4, 2022 , pp. 899-907 More about this Journal
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;
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