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基于贝叶斯集成学习算法的土体先期固结压力预测模型
李超, 汪磊, 陈洋, 李天义
PDF(5863 KB)
PDF(5863 KB)
基于贝叶斯集成学习算法的土体先期固结压力预测模型
Prediction Model of Soils’ Preconsolidation Pressure Based on Bayesian Ensemble Learning Algorithm
准确评估土体的先期固结压力（PS）是岩土工程实践中的一个重要问题.采用集成学习算法（XGBoost、RF）来捕捉各个土体参数之间的关系，建立先期固结压力预测模型.使用贝叶斯优化方法来确定模型的最优参数，并通过与SVR、KNN和MLP三种非集成算法进行对比，统计分析了不同模型在相关系数R2 、均方根误差RMSE和绝对平均误差MAPE三种误差指标下的表现；最后在5折交叉验证下，评估各个模型的预测精度及泛化性.结果表明基于XGBoost的预测精度最高，其RMSE及MAPE分别为20.80 kPa和18.29%；其次是RF，分别为24.532 kPa和19.15%.同时在PS作为回归变量的情况下，其特征重要性为：USS>VES>w>LL>PL.因此，在小规模数据集情况下，集成学习算法在预测精度及泛化性上要优于其他算法，且可作为岩土参数敏感性分析的有效方法.
Accurate assessment of soils’ preconsolidation stress (PS) is important in geotechnical engineering practice. In this paper it analyzes the influence of soils’ preconsolidation stress, uses ensemble learning algorithms (XGBoost, RF) to capture the relationship between soil parameters and establishes prediction models. A Bayesian optimization method was used to determine the optimal parameters of the models, three machin elearning algorithms, namely SVR, KNN, and MLP, are introduced for comparison, and the models were statistically analyzed by three error metrics, including correlation coefficient（R2 ）, root mean square error （RMSE） and mean absolute percentage error (MAPE). And finally, the prediction accuracy and generalization of each model were evaluated under 5-fold cross-validation (CV). The XGBoost-based prediction accuracy is the highest, with RMSE and MAPE of 20.80 kPa and 18.29%, respectively, followed by RF with 24.532 kPa and 19.15%, respectively. Meanwhile, in the case of PS as a regression variable, its characteristic importance is USS>VES>w>LL>PL. It shows that the ensemble learning algorithms (XGBoost, RF) are better than other algorithms in terms of prediction accuracy and generalization in the case of small-scale data sets, and can be used as an effective method for sensitivity analysis of geotechnical parameters.
先期固结压力 / 集成学习 / 贝叶斯优化 / 5折交叉验证 / XGBoost / 工程地质
preconsolidation stress / ensemble learning / Bayesian optimization / 5-fold cross-validation / XGBoost / engineering geology
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