Journal of Korean Association for Spatial Structures (한국공간구조학회논문집)

Korean Association for Spatial Structures (한국공간구조학회)
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Development of Machine Learning Based Seismic Response Prediction Model for Shear Wall Structure considering Aging Deteriorations

경년열화를 고려한 전단벽 구조물의 기계학습 기반 지진응답 예측모델 개발

  • Kim, Hyun-Su (Division of Architecture, Sunmoon University) ;
  • Kim, Yukyung (Division of Architecture, Sunmoon University) ;
  • Lee, So Yeon (Division of Architecture, Sunmoon University) ;
  • Jang, Jun Su (Division of Architecture, Sunmoon University)
  • 김현수 (선문대학교 건축학부) ;
  • 김유경 (선문대학교 건축학부) ;
  • 이소연 (선문대학교 건축학부) ;
  • 장준수 (선문대학교 건축학부)
  • Received : 2024.05.18
  • Accepted : 2024.05.31
  • Published : 2024.06.15
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Abstract

Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.

Keywords

Acknowledgement

본 논문은 2022년도 가동원전 안전성향상 핵심기술개발사업의 지원으로 수행되고 있는 과제(과제번호: 20224B10200080) 내용의 일부입니다. 산업통상자원부와 한국에너지기술평가원의 연구비 지원에 깊은 감사를 드립니다.

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