Geomechanics and Engineering

  • 제38권5호
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  • Pages.507-515
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  • 2024
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  • 2005-307X(pISSN)
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  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Prediction models of rock quality designation during TBM tunnel construction using machine learning algorithms

  • Byeonghyun Hwang (School of Civil, Environmental and Architectural Civil Engineering, Korea University) ;
  • Hangseok Choi (School of Civil, Environmental and Architectural Civil Engineering, Korea University) ;
  • Kibeom Kwon (School of Civil, Environmental and Architectural Civil Engineering, Korea University) ;
  • Young Jin Shin (R&D division, Hyundai Engineering & Construction) ;
  • Minkyu Kang (Center for Defense Resource Management, Korea Institute for Defense Analyses)
  • 투고 : 2023.11.23
  • 심사 : 2024.02.04
  • 발행 : 2024.09.10
⟨ 이전 논문 다음 논문 ⟩

초록

An accurate estimation of the geotechnical parameters in front of tunnel faces is crucial for the safe construction of underground infrastructure using tunnel boring machines (TBMs). This study was aimed at developing a data-driven model for predicting the rock quality designation (RQD) of the ground formation ahead of tunnel faces. The dataset used for the machine learning (ML) model comprises seven geological and mechanical features and 564 RQD values, obtained from an earth pressure balance (EPB) shield TBM tunneling project beneath the Han River in the Republic of Korea. Four ML algorithms were employed in developing the RQD prediction model: k-nearest neighbor (KNN), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGB). The grid search and five-fold cross-validation techniques were applied to optimize the prediction performance of the developed model by identifying the optimal hyperparameter combinations. The prediction results revealed that the RF algorithm-based model exhibited superior performance, achieving a root mean square error of 7.38% and coefficient of determination of 0.81. In addition, the Shapley additive explanations (SHAP) approach was adopted to determine the most relevant features, thereby enhancing the interpretability and reliability of the developed model with the RF algorithm. It was concluded that the developed model can successfully predict the RQD of the ground formation ahead of tunnel faces, contributing to safe and efficient tunnel excavation.

키워드

과제정보

This research was conducted with the support of the "National R&D Project for Smart Construction Technology (No. RS-2020-KA157074)" funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure and Transport, and managed by the Korea Expressway Corporation.

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