1. 湖南交通职业技术学院
2. 湖南省交通科学研究院有限公司
3. 长沙理工大学土木与环境工程学院
纸质出版:2026
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郭芳, 顾维, 袁明. Stacking集成学习策略的花岗岩残积土抗剪强度参数预测[J]. 地质科技通报, 2026,45(4):340-350.
郭芳, 顾维, 袁明. Stacking集成学习策略的花岗岩残积土抗剪强度参数预测[J]. Bulletin of Geological Science and Technology, 2026, 45(4): 340-350.
郭芳, 顾维, 袁明. Stacking集成学习策略的花岗岩残积土抗剪强度参数预测[J]. 地质科技通报, 2026,45(4):340-350. DOI: 10.19509/j.cnki.dzkq.tb202603032.
郭芳, 顾维, 袁明. Stacking集成学习策略的花岗岩残积土抗剪强度参数预测[J]. Bulletin of Geological Science and Technology, 2026, 45(4): 340-350. DOI: 10.19509/j.cnki.dzkq.tb202603032.
花岗岩残积土工程性质空间离散性强,其抗剪强度参数传统试验测试周期长、成本高、效率偏低,同时单一机器学习模型普遍存在泛化能力有限的问题。本研究构建基于Stacking集成学习的预测模型,实现花岗岩残积土黏聚力与内摩擦角的高精度预测,并揭示各物理指标对抗剪强度参数的影响机制。依托文献及工程勘察实测土工试验数据建立样本集,经数据筛选与归一化预处理后,搭建2层Stacking集成学习框架;选取随机森林(RF)、支持向量机(SVM)、误差反向传播神经网络(BPNN)作为异构基学习器,采用五折交叉验证完成模型训练,以岭回归(Ridge回归)作为元学习器融合多模型输出结果;选取细粒含量、孔隙比、含水率、液限、塑限、比重6项指标作为输入,黏聚力与内摩擦角作为输出,并引入SHAP可解释性方法分析参数影响规律。模型在验证集上,黏聚力、内摩擦角的决定系数R
2
分别为0.88、0.90,均方根误差RMSE依次为3.60 kPa、2.46°,较最优单一基学习器R
2
分别提升1%、5%;独立工程测试集结果表明,黏聚力预测偏差为0~1.91 kPa,内摩擦角预测偏差为0°~0.67°,集成模型综合性能显著优于单一模型。SHAP分析证实,黏聚力主控因素为含水率、液限、细粒含量,内摩擦角主控因素为细粒含量、孔隙比、含水率,变化规律与土力学理论相符。Stacking集成学习能够充分发挥不同机器学习模型的优势,有效提升花岗岩残积土抗剪强度参数的预测精度与鲁棒性,可为该类土体分布区边坡稳定性评价、滑坡灾害防治提供高效、低成本的参数获取技术支撑。
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