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

Hyun-Jun Choi, Sewon Kim, YoungSeok Kim and Jongmuk Won ()
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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

Sustainability, 2022, vol. 14, issue 15, 1-14

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.

Keywords: frost depth; frozen-thawed; pavement; machine learning; hyperparameter (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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