基于可解释GWO-XGBoost的隧道挤压预测研究

Tunnel squeezing prediction using explainable GWO-XGBoost model

摘要

【目的】为了实现对隧道挤压的准确预测，【方法】构建了XGBoost分类预测模型，利用灰狼优化算法(GWO)对XGBoost模型进行超参数优化，基于经过插补和过采样处理的不平衡缺失数据集进行模型的训练和测试。模型的输入特征为隧道埋深(H)、岩石掘进质量指数(Q)、隧道直径(D)、强度应力比(SSR)和支护刚度(K),评价指标为准确率(ACC)、F1分数、Kappa系数和Matthews相关系数(MCC)。【结果】所构建的GWO-XGBoost模型在训练集和测试集上预测准确率均达到了98.94%,在测试集上的评价指标累计值达到了5.913 1,展现出了优越的预测性能。SSR、D、K、Q和H的平均Shapley可加性解释(Shapley Additive exPlanation, SHAP)值分别为3.06、1.07、0.82、0.73和0.51,表明SSR是对模型输出结果影响最大的特征。【结论】GWO-XGBoost模型在互助北山隧道和木寨岭隧道的挤压预测结果与实际情况相符，证明了该模型在隧道工程中具有较高的适用性和预测准确性。

Abstract

[Objective] To achieve accurate prediction of tunnel squeezing, [Methods] an eXtreme Gradient Boosting(XGBoost) model tuned by Grey Wolf Optimization(GWO) was constructed for tunnel squeezing prediction. Training and testing of the GWO-XGBoost model were conducted on an imbalanced dataset with missing data that had undergone imputation and oversampling techniques. The input features of the GWO-XGBoost model included tunnel burial depth(H), rock tunnelling quality index(Q), diameter(D), strength stress ratio(SSR), and support stiffness(K). The performance of the GWO-XGBoost model was rigorously evaluated using a suite of metrics, including accuracy(ACC), the F1 score, the Kappa coefficient, and the Matthews correlation coefficient(MCC). [Results] The result indicated that the presented GWO-XGBoost model achieved an impressive prediction accuracy of 98.94% on both the training set and the test set. Moreover, on the test set, the cumulative value of the evaluation metrics soared to 5.913 1, underscoring the model's exceptional predictive capabilities. The average Shapley Additive exPlanation(SHAP) values for SSR, D, K, Q, and H were 3.06, 1.07, 0.82, 0.73, and 0.51, respectively, indicating that SSR was the most influential feature affecting the model's output result. [Conclusion] The application of the GWO-XGBoost model to the Huzhubeishan Tunnel and Muzhailing Tunnel has yielded squeezing predictions that closely align with the actual conditions observed, proving the high applicability and predictive accuracy of the presented model in tunnel engineering.

关键词

隧道挤压预测 / XGBoost / 灰狼优化算法 / 模型解释 / 缺失数据集 / 变形 / 影响因素

Key words

tunnel squeezing prediction / XGBoost / grey wolf optimizer / model explanation / missing dataset / deformation / influencing factors

基金资助

国家自然科学基金青年科学基金项目(52008383)

湖北省自然科学基金项目(2023AFB369)