Prediction of thaw settling coefficient of frozen soil using machine learning techniques
Ke Wang, Shuang Liu, Wenqiang Xing, LianZhen Zhang, Shuang Tian, Mengyao Qiu, Shanzhen Li · 2025
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Description
Ke Wang, Shuang Liu, and their co-authors used machine learning to predict how much frozen soil sinks when it thaws. Their 2025 study targets the thaw settling coefficient, a key number for building on frozen ground. Four inputs were studied: freezing temperature, dry density, water content, and the load pressing from above.
An experimental database of 841 samples fed several nonlinear models. Neural network and neuro fuzzy models predicted the coefficient most accurately. The fuzzy model was validated twice on fresh databases and held up to engineering needs. A mutual information analysis ranked how strongly each input moves the result. Sensitivity checks show which factors engineers must measure most carefully when they judge thaw risk under roads and foundations.
Catalog record
| CREATOR | Ke Wang, Shuang Liu, Wenqiang Xing, LianZhen Zhang, Shuang Tian, Mengyao Qiu, Shanzhen Li |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 42 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |