Journal of the Korean Geotechnical Society (한국지반공학회논문집)

Korean Geotechnical Society (한국지반공학회)
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Development of Artificial Neural Network Model for Prediction of Seismic Response of Building with Soil-structure Interaction

지반-상부 구조물 효과를 고려한 인공신경망 기반 지진 응답 예측 모델 개발

  • Won, Jongmuk (Dept. of Civil and Environmental Engrg., Univ. of Ulsan) ;
  • Shin, Jiuk (Korea BIM Research Center, Korea Institute of Civil Eng. and Building Tech)
  • 원종묵 (울산대학교 건설환경공학부) ;
  • 신지욱 (한국건설기술연구원)
  • Received : 2020.04.27
  • Accepted : 2020.06.15
  • Published : 2020.08.31
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Abstract

Constructing the maximum displacement and shear force database for the seismic performance of building with soil-structure interaction under varied earthquake scenarios and geotechnical conditions is critical in developing the neural network-based prediction models. However, using the available 3D FEM-based computer simulation techniques causes high computation costs in developing the database. This study introduces the framework of developing the artificial neural network (ANN) model to predict the seismic performance of building at given Poisson's ratio and shear wave velocity of soil. The simple Single-Degree-Of-Freedom system was used to develop the database and the performance of the developed neural network model is discussed through the evaluated coefficient of determination (R2). In addition, ANN models were developed for 90~100% percentile of the database to assess the accuracy of the developed ANN models in each percentile.

인공신경망(ANN) 지진응답 예측모델 구성을 위해 다양한 지진파 및 지반 조건 하에서 구조물의 최대변위 및 최대 전단력 데이터베이스 구축이 필요하다. 하지만 3차원 컴퓨터 해석을 활용한 데이터베이스 구축은 많은 시간 및 인력, 비용을 발생시킨다. 본 연구에서는 주어진 지반의 포아송비와 전단파 속도에 대하여 건물의 지진응답을 예측할 수 있는 ANN 모델 개발 프레임워크를 소개하였다. 데이터베이스 구축에는 지반-상부 구조물 효과를 고려할 수 있는 간단한 단자유도 모델을 이용하였고 개발된 ANN 모델의 정확도를 결정계수(R2)를 통하여 논의하였다. 또한 구축된 데이터베이스의 백분위 90~100에서 ANN 모델을 구성하고 결정계수를 통해 각 백분위에서 ANN 모델의 정확도에 대하여 논의하였다.

Keywords

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