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Title |
Method of Earthquake Acceleration Estimation for Predicting Damage to Arbitrary Location Structures based on Artificial Intelligence
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Authors |
이경석(Kyeong-Seok Lee) ; 서영득(Young-Deuk Seo) ; 백은림(Eun-Rim Baek) |
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DOI |
https://doi.org/10.11112/jksmi.2023.27.3.71 |
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Keywords |
지진가속도 추정; 머신러닝 Earthquake acceleration estimation; Machine learning |
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Abstract |
It is not efficient to install a maintenance system that measures seismic acceleration and displacement on all bridges and buildings to evaluate the safety of structures after an earthquake occurs. In order to maintain this, an on-site investigation is conducted. Therefore, it takes a lot of time when the scope of the investigation is wide. As a result, secondary damage may occur, so it is necessary to predict the safety of individual structures quickly. The method of estimating earthquake damage of a structure includes a finite element analysis method using approved seismic information and a structural analysis model. Therefore, it is necessary to predict the seismic information generated at arbitrary location in order to quickly determine structure damage. In this study, methods to predict the ground response spectrum and acceleration time history at arbitrary location using linear estimation methods, and artificial neural network learning methods based on seismic observation data were proposed and their applicability was evaluated. In the case of the linear estimation method, the error was small when the locations of nearby observatories were gathered, but the error increased significantly when it was spread. In the case of the artificial neural network learning method, it could be estimated with a lower level of error under the same conditions.
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