水电能源科学

针对现有盾构掘进速度预测方法存在的模型算法单一、精度不高和泛化性较差等问题，为了提高盾构掘进速度预测精度，建立一种基于变分模态分解(VMD)、蜣螂优化算法(DBO)和Stacking(VMD-DBO-Stacking)集成学习的盾构掘进速度预测模型。首先，为了得到更有效的数据，采用VMD对原始数据进行分解重构得到去噪后的施工参数数据用于后续模型预测；其次，基于集成学习策略，选取支持向量回归(SVR)模型、随机森林(RF)模型、极端梯度提升(XGBoost)模型作为基学习器，高斯过程回归(GPR)模型作为元学习器，从而构建预测精度更高、泛化性更强的Stacking集成学习预测模型；然后，为了进一步提高预测精度，采用DBO对集成学习模型进行超参数优化；最后，将此预测模型用于河南某引水隧洞工程盾构施工中并与其他预测模型进行对比。结果表明，与其他单一模型(SVR、RF、XGBoost)相比，所建模型具有更高的预测精度，平均精度分别提升7.76%、6.70%、4.97%,为盾构掘进速度预测提供一种新思路。

Addressing the issues of single model algorithm, low accuracy, and poor generalization in existing shield tunneling speed prediction methods, this study proposes a shield tunneling speed prediction approach to improve prediction accuracy based on Variational Mode Decomposition(VMD), Dung Beetle Optimizer(DBO), and Stacking ensemble learning. Firstly, to obtain more effective data, VMD is applied to decompose and reconstruct the original data to obtain denoised construction parameter data for subsequent model prediction. Secondly, based on the ensemble learning strategy, Support Vector Regression(SVR), Random Forest(RF), and Extreme Gradient Boosting(XGBoost) models are selected as base learners, while Gaussian Process Regression(GPR) is chosen as the meta-learner to construct a Stacking ensemble learning prediction model with higher prediction accuracy and stronger generalization ability. Thirdly, to further enhance prediction accuracy, DBO is employed to optimize the hyperparameters of the ensemble learning model. Finally, this prediction method is applied to the shield tunneling construction of a water diversion tunnel project in Henan Province and compared with other prediction methods. Compared to other single models(SVR, RF, XGBoost), the results indicate that the proposed method achieves higher prediction accuracy, with average accuracy improvements of 7.76%, 6.70%, and 4.97%, respectively, providing a new approach for shield tunneling speed prediction.