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Predicting Frost Depth of Soils in South Korea Using Machine Learning Techniques

Author

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  • Hyun-Jun Choi

    (Northern Infrastructure Specialized Team, Korea Institute of Civil Engineering and Building Technology, Goyang 10223, Korea)

  • Sewon Kim

    (Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology, Goyang 10223, Korea)

  • YoungSeok Kim

    (Northern Infrastructure Specialized Team, Korea Institute of Civil Engineering and Building Technology, Goyang 10223, Korea)

  • Jongmuk Won

    (Department of Civil and Environmental Engineering, University of Ulsan, Ulsan 44610, Korea)

Abstract

Predicting the frost depth of soils in pavement design is critical to the sustainability of the pavement because of its mechanical vulnerability to frozen-thawed soil. The reliable prediction of frost depth can be challenging due to the high uncertainty of frost depth and the unavailability of geotechnical properties needed to use the available empirical- and analytical-based equations in literature. Therefore, this study proposed a new framework to predict the frost depth of soil below the pavement using eight machine learning (ML) algorithms (five single ML algorithms and three ensemble learning algorithms) without geotechnical properties. Among eight ML models, the hyperparameter-tuned gradient boosting model showed the best performance with the coefficient of determination (R 2 ) = 0.919. Furthermore, it was also shown that the developed ML model can be utilized in the prediction of several levels of frost depth and assessing the sensitivity of pavement-related predictors for predicting the frost depth of soils.

Suggested Citation

  • Hyun-Jun Choi & Sewon Kim & YoungSeok Kim & Jongmuk Won, 2022. "Predicting Frost Depth of Soils in South Korea Using Machine Learning Techniques," Sustainability, MDPI, vol. 14(15), pages 1-14, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9767-:d:883064
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    References listed on IDEAS

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    1. Raghu Garg & Himanshu Aggarwal & Piera Centobelli & Roberto Cerchione, 2019. "Extracting Knowledge from Big Data for Sustainability: A Comparison of Machine Learning Techniques," Sustainability, MDPI, vol. 11(23), pages 1-17, November.
    2. Qinglin Li & Haibin Wei & Leilei Han & Fuyu Wang & Yangpeng Zhang & Shuanye Han, 2019. "Feasibility of Using Modified Silty Clay and Extruded Polystyrene (XPS) Board as the Subgrade Thermal Insulation Layer in a Seasonally Frozen Region, Northeast China," Sustainability, MDPI, vol. 11(3), pages 1-15, February.
    3. Audrius Vaitkus & Judita Gražulytė & Egidijus Skrodenis & Igoris Kravcovas, 2016. "Design of Frost Resistant Pavement Structure Based on Road Weather Stations (RWSs) Data," Sustainability, MDPI, vol. 8(12), pages 1-13, December.
    4. Qinglin Li & Haibin Wei & Peilei Zhou & Yangpeng Zhang & Leilei Han & Shuanye Han, 2019. "Experimental and Numerical Research on Utilizing Modified Silty Clay and Extruded Polystyrene (XPS) Board as the Subgrade Thermal Insulation Layer in a Seasonally Frozen Region, Northeast China," Sustainability, MDPI, vol. 11(13), pages 1-15, June.
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    Cited by:

    1. Chao Liu & Han Li & Jiuzhe Xu & Weijun Gao & Xiang Shen & Sheng Miao, 2023. "Applying Convolutional Neural Network to Predict Soil Erosion: A Case Study of Coastal Areas," IJERPH, MDPI, vol. 20(3), pages 1-21, January.

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