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Effectiveness of Data-Driven Section Shape Ratios for Seismic Performance-Based Artificial Intelligence of Piloti-Type Buildings

필로티 건축물의 인공지능 기반 내진성능 평가를 위한 데이터 기반 부재의 단면 형상비 연구

  • Lee, Gayoon (Deep Learning Architecture Research Center, Department of Architectural Engineering, Sejong University) ;
  • Quoc Bao To (Deep Learning Architecture Research Center, Department of Architectural Engineering, Sejong University) ;
  • Jo, Hye-rim (Deep Learning Architecture Research Center, Department of Architectural Engineering, Sejong University) ;
  • Shin, Jiuk (Department of Architecture, Gyeongsang National University) ;
  • Kihak Lee (Deep Learning Architecture Research Center, Department of Architectural Engineering, Sejong University)
  • 이가윤 (세종대학교 건축공학과 딥러닝 건축연구소) ;
  • 토바오웍 (세종대학교 건축공학과 딥러닝 건축연구소) ;
  • 조혜림 (세종대학교 건축공학과 딥러닝 건축연구소) ;
  • 신지욱 (경상국립대학교 건축공학과) ;
  • 이기학 (세종대학교 건축공학과 딥러닝 건축연구소)
  • Received : 2024.12.02
  • Accepted : 2024.12.04
  • Published : 2025.01.01

Abstract

Structures compromised by a seismic event may be susceptible to aftershocks or subsequent occurrences within a particular duration. Considering that the shape ratios of sections, such as column shape ratio (CSR) and wall shape ratio (WSR), significantly influence the behavior of reinforced concrete (RC) piloti structures, it is essential to determine the best appropriate methodology for these structures. The seismic evaluation of piloti structures was conducted to measure seismic performance based on section shape ratios and inter-story drift ratio (IDR) standards. The diverse machine-learning models were trained and evaluated using the dataset, and the optimal model was chosen based on the performance of each model. The optimal model was employed to predict seismic performance by adjusting section shape ratios and output parameters, and a recommended approach for section shape ratios was presented. The optimal section shape ratios for the CSR range from 1.0 to 1.5, while the WSR spans from 1.5 to 3.33, regardless of the inter-story drift ratios.

Keywords

Acknowledgement

이 논문은 국토교통부 국토교통과학기술진흥원 국토교통 기술사업화를 위한 이어달리기사업의 지원을 받아 수행된 연구임 (RS-2024-00410886). 이에 감사드립니다.

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