Accurate assessment of undrained shear strength (USS) for soft sensitive clays is a great concern in geotechnical engineering practice. This study applies novel data-driven extreme gradient boosting (XGBoost) and random forest (RF) ensemble learning methods for capturing the relationships between the USS and various basic soil parameters. Based on the soil data sets from TC304 database, a general approach is developed to predict the USS of soft clays using the two machine learning methods above, where five feature variables including the preconsolidation stress (PS), vertical effective stress (VES), liquid limit (LL), plastic limit (PL) and natural water content (W) are adopted. To reduce the dependence on the rule of thumb and inefficient brute-force search, the Bayesian optimization method is applied to determine the appropriate model hyper-parameters of both XGBoost and RF. The developed models are comprehensively compared with three comparison machine learning methods and two transformation models with respect to predictive accuracy and robustness under 5-fold cross-validation (CV). It is shown that XGBoost-based and RF-based methods outperform these approaches. Besides, the XGBoost-based model provides feature importance ranks, which makes it a promising tool in the prediction of geotechnical parameters and enhances the interpretability of model.

准确评估软敏感粘土的不排水剪切强度（USS）是岩土工程实践中非常关注的问题。这项研究应用新颖的数据驱动的极端梯度增强（XGBoost）和随机森林（RF）集成学习方法来捕获USS与各种基本土壤参数之间的关系。根据TC304数据库中的土壤数据集，使用上述两种机器学习方法开发了一种通用方法来预测软黏土的USS，其中五个特征变量包括预固结应力（PS），垂直有效应力（VES），液体极限（LL），塑性极限（PL）和自然水含量（W）被采用。为了减少对经验法则和低效的蛮力搜索的依赖，应用贝叶斯优化方法确定XGBoost和RF的适当模型超参数。将开发的模型与三种比较机器学习方法和两种转换模型进行全面比较，以预测在5倍交叉验证（CV）下的预测准确性和鲁棒性。结果表明，基于XGBoost和基于RF的方法优于这些方法。此外，基于XGBoost的模型提供了特征重要性等级，这使其成为岩土参数预测中的有前途的工具，并增强了模型的可解释性。结果表明，基于XGBoost和基于RF的方法优于这些方法。此外，基于XGBoost的模型提供了特征重要性等级，这使其成为岩土参数预测中的有前途的工具，并增强了模型的可解释性。结果表明，基于XGBoost和基于RF的方法优于这些方法。此外，基于XGBoost的模型提供了特征重要性等级，这使其成为岩土参数预测中的有前途的工具，并增强了模型的可解释性。