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.

翻译：路面在荷载作用下的性能取决于路基的强度。然而，通过实验方法估算路面强度特性，如加州承载比（CBR）、无侧限抗压强度（UCS）和抗力值（R），通常过程繁琐、耗时且成本高昂，这激发了人们对基于机器学习的工具日益增长的兴趣，这些工具提供了简单、廉价且快速的替代方案。因此，本研究同样探索了两种提升技术——类别提升（CatBoost）和极限梯度提升（XGBoost）以及支持向量回归（SVR）在估算经水合石灰活化稻壳灰（HARSH）改良的路基土特性方面的潜在应用。研究使用121个不同HARSH配比的实验数据样本，以塑限、液限、塑性指数、黏土活性、最优含水量和最大干密度作为输入，用于估算CBR、UCS和R，并采用四个评估指标——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）来评估模型的性能。结果表明，在估算这些特性时，XGBoost的表现优于CatBoost和SVR，在估算CBR、UCS和R时分别获得了0.9994、0.9995和0.9999的R2值。此外，在估算CBR和R时，SVR的表现优于CatBoost，R2值均为0.9997。另一方面，在估算UCS时，CatBoost的表现优于SVR，R2值为0.9994。特征敏感性分析表明，三种机器学习技术一致认为，增加HARSH的比例会分别导致估算特性值的提高。与先前结果的比较也显示了XGBoost在估算路基特性方面的优越性。