用机器学习预测改良土强度，XGBoost表现最佳。
Intelligent Gradient Boosting Algorithms for Estimating Strength of Modified Subgrade Soil
- 采用XGBoost等算法，基于121组实验数据预测土体强度。
- 对CBR、UCS和R的预测准确率均超99.9%，最高达99.99%。
- 适合道路工程中快速评估改良土性能，尤其适合参数敏感性分析。
路面性能取决于路基强度。传统实验测定加州承载比(CBR)、无侧限抗压强度(UCS)及抗力值(R)耗时费力且成本高，催生了基于机器学习的快速替代方法。本研究探索了分类提升（CatBoost）、极端梯度提升（XGBoost）和支持向量回归（SVR）在水化石灰活化稻壳灰（HARSH）改良土强度预测中的应用。以121组不同HARSH配比下的塑限、液限、塑性指数、黏土活性、最优含水率和最大干密度为输入，使用决定系数（R²）、均方根误差（RMSE）、平均绝对误差（MAE）和平均绝对百分比误差（MAPE）评估模型性能。结果表明，XGBoost在预测CBR、UCS和R方面均优于其他模型，对应R²分别为0.9994、0.9995和0.9999；SVR在预测CBR和R上优于CatBoost，R²达0.9997；而CatBoost在预测UCS上优于SVR，R²为0.9994。特征敏感性分析显示，三类模型一致认为提高HARSH掺量可提升预测强度值。与已有研究对比，XGBoost在预测路基性能方面更具优势。
原文摘要 · Abstract (English)
The performance of pavement under loading depends on the strength of the subgrade. However, experimental estimation of properties of pavement strengths such as California bearing ratio (CBR), unconfined compressive strength (UCS) and resistance value (R) are often tedious, time-consuming and costly, thereby inspiring a growing interest in machine learning based tools which are simple, cheap and fast alternatives. Thus, the potential application of two boosting techniques; categorical boosting (CatBoost) and extreme gradient boosting (XGBoost) and support vector regression (SVR), is similarly explored in this study for estimation of properties of subgrade soil modified with hydrated lime activated rice husk ash (HARSH). Using 121 experimental data samples of varying proportions of HARSH, plastic limit, liquid limit, plasticity index, clay activity, optimum moisture content, and maximum dry density as input for CBR, UCS and R estimation, four evaluation metrics namely coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) are used to evaluate the models' performance. The results indicate that XGBoost outperformed CatBoost and SVR in estimating these properties, yielding R2 of 0.9994, 0.9995 and 0.9999 in estimating the CBR, UCS and R respectively. Also, SVR outperformed CatBoost in estimating the CBR and R with R2 of 0.9997 respectively. On the other hand, CatBoost outperformed SVR in estimating the UCS with R2 of 0.9994. Feature sensitivity analysis shows that the three machine learning techniques are unanimous that increasing HARSH proportion lead to values of the estimated properties respectively. A comparison with previous results also shows superiority of XGBoost in estimating subgrade properties.
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