Geomechanics and Engineering

  • 제26권3호
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  • Pages.235-251
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  • 2021
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  • 2005-307X(pISSN)
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  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Data-driven framework for predicting ground temperature during ground freezing of a silty deposit

  • Pham, Khanh (Department of Civil Engineering, International University) ;
  • Park, Sangyeong (School of Civil, Environmental, & Architectural Engineering, Korea University) ;
  • Choi, Hangseok (School of Civil, Environmental, & Architectural Engineering, Korea University) ;
  • Won, Jongmuk (Department of Civil and Environmental Engineering, University of Ulsan)
  • 투고 : 2020.12.17
  • 심사 : 2021.07.22
  • 발행 : 2021.08.10
⟨ 이전 논문 다음 논문 ⟩

초록

Predicting the frozen zone near the freezing pipe in artificial ground freezing (AGF) is critical in estimating the efficiency of the AGF technique. However, the complexity and uncertainty of many factors affecting the ground temperature cause difficulty in developing a reliable physical model for predicting the ground temperature. This study proposed a data-driven framework to accurately predict the ground temperature during the operation of AGF. Random forest (RF) and extreme gradient boosting (XGB) techniques were employed to develop the prediction model using the dataset of a field experiment in the silty deposit. The developed ensemble models showed relatively good performance (R2 > 0.96), yet the XGB model showed higher accuracy than the RF model. In addition, the evaluated mutual information and importance score revealed that the environmental attributes (ambient temperature, surface temperature, humidity, and wind speed) can be critical in predicting ground temperature during the AFG operation. The prediction models presented in this study can be utilized in evaluating freezing efficiency at the range of geotechnical and environmental attributes.

키워드

과제정보

This work was supported by the National Research Foundation of Korea (NRF) grants (2020R1A6A1A03045059 and 2019R1A2C2086647) funded by the Korea government.

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