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The highway subgrade is the foundation for ensuring the safe operation of highways, and subgrade compaction degree is a crucial parameter in subgrade construction. This study employs field tests to investigate subgrade compaction degree, analyzing the effects of rolling passes, rolling speed, and moisture content of subgrade fill material on subgrade compaction degree. Subsequently, four machine learning models are used to establish prediction models for subgrade compaction degree: random forest model, sparrow search algorithm optimized random forest model (SSA-RF), AdaBoost model, and BP-AdaBoost model. The results indicate that: (1) Subgrade compaction degree increases with the number of rolling passes, decreases with rolling speed, and is greater when the moisture content of the subgrade fill material is closer to the optimum moisture content. (2) The AdaBoost model shows the poorest prediction performance and is not suitable for predicting subgrade compaction degree, whereas the SSA-RF model achieves the best training results, with correlation coefficients (R2) of 0.98 and 0.94 for the training and testing sets, respectively, closely matching the actual results, validating the feasibility of this method for predicting highway subgrade compaction degree.

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Research on a Machine Learning-Based Subgrade Compaction Degree Prediction Model

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Feng Li,
  • Jianfei Zhao,
  • Hongzhao Li,
  • Bing Hui,
  • Zhenkun Wang,
  • Wenjun Zhang,
  • Guangbo Liu

摘要

The highway subgrade is the foundation for ensuring the safe operation of highways, and subgrade compaction degree is a crucial parameter in subgrade construction. This study employs field tests to investigate subgrade compaction degree, analyzing the effects of rolling passes, rolling speed, and moisture content of subgrade fill material on subgrade compaction degree. Subsequently, four machine learning models are used to establish prediction models for subgrade compaction degree: random forest model, sparrow search algorithm optimized random forest model (SSA-RF), AdaBoost model, and BP-AdaBoost model. The results indicate that: (1) Subgrade compaction degree increases with the number of rolling passes, decreases with rolling speed, and is greater when the moisture content of the subgrade fill material is closer to the optimum moisture content. (2) The AdaBoost model shows the poorest prediction performance and is not suitable for predicting subgrade compaction degree, whereas the SSA-RF model achieves the best training results, with correlation coefficients (R2) of 0.98 and 0.94 for the training and testing sets, respectively, closely matching the actual results, validating the feasibility of this method for predicting highway subgrade compaction degree.