Journal of the Korean Geotechnical Society (한국지반공학회논문집)

Korean Geotechnical Society (한국지반공학회)
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Development of an Ensemble Prediction Model for Lateral Deformation of Retaining Wall Under Construction

시공 중 흙막이 벽체 수평변위 예측을 위한 앙상블 모델 개발

  • 서승환 (한국건설기술연구원 지반연구본부) ;
  • 정문경 (한국건설기술연구원 지반연구본부)
  • Received : 2023.02.27
  • Accepted : 2023.03.27
  • Published : 2023.04.30
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Abstract

The advancement in large-scale underground excavation in urban areas necessitates monitoring and predicting technologies that can pre-emptively mitigate risk factors at construction sites. Traditionally, two methods predict the deformation of retaining walls induced by excavation: empirical and numerical analysis. Recent progress in artificial intelligence technology has led to the development of a predictive model using machine learning techniques. This study developed a model for predicting the deformation of a retaining wall under construction using a boosting-based algorithm and an ensemble model with outstanding predictive power and efficiency. A database was established using the data from the design-construction-maintenance process of the underground retaining wall project in a manifold manner. Based on these data, a learning model was created, and the performance was evaluated. The boosting and ensemble models demonstrated that wall deformation could be accurately predicted. In addition, it was confirmed that prediction results with the characteristics of the actual construction process can be presented using data collected from ground measurements. The predictive model developed in this study is expected to be used to evaluate and monitor the stability of retaining walls under construction.

도심지 지하굴착 공사가 대형화되면서 공사 중 안전사고에 대한 위험요인이 더욱 증가하고 있다. 이에 따라 공사현장의 위험요소를 모니터링하고 사전에 예측할 수 있는 기술이 필요하다. 굴착으로 인한 흙막이 벽체의 변형을 예측하는 방법에는 크게 경험식과 수치해석 두 가지 방법으로 분류할 수 있으며, 최근에는 인공지능 기술의 발달과 함께 머신러닝 기법을 활용한 예측 모델이 한 가지 방법으로 자리 잡고 있다. 본 연구에서는 예측력과 효율성이 우수한 부스팅 계열 알고리즘 및 앙상블 모델을 이용하여 시공 중 흙막이 벽체 변형을 예측하는 모델을 구축하였다. 지하흙막이 공사의 설계-시공-유지관리 과정에서 도출되는 자료들을 복합적으로 활용하여 데이터베이스를 구축하고, 이 자료를 토대로 학습모델을 만들고 성능을 평가하였다. 모델 성능 평가 결과, 높은 정확도로 흙막이 벽체 변형을 예측할 수 있었으며, 지반계측 자료를 학습에 활용함으로써 실제 시공과정의 특성이 반영된 예측결과를 제시할 수 있었다. 본 연구에서 구축한 예측 모델을 활용하여 시공 중 흙막이 벽체의 안정성 평가 및 모니터링에 활용할 수 있을 것으로 기대된다.

Keywords

Acknowledgement

본 연구는 과학기술정보통신부 한국건설기술연구원 주요사업(과제번호 20230105-001, 인공지능을 활용한 대심도 지하 대공간의 스마트 복합 솔루션 개발)으로 수행되었습니다.

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