基于SVM集成学习的隧道掌子面稳定性预测方法

Prediction of Tunnel Face Stability Based on Support Vector Machine and Ensemble Learning

隧道掌子面稳定性受多种因素的影响。隧道开挖前需对掌子面稳定性进行评估,以制定相应措施,保证隧道施工过程的安全。为提高预测效率,提出一种基于集成学习和支持向量机的隧道掌子面稳定性快速预测方法。首先根据正交实验设计原理,选择最具代表性的训练样本;接着采用三维数值计算,对样本进行标定;然后基于支持向量机算法,采用不同的核函数,拟合隧道掌子面稳定性预测模型,并通过留一法对模型的预测精度进行验证;最后根据集成学习机制,通过投票法整合预测模型,实现隧道掌子面稳定性的综合预测。结果表明,采用集成学习机制,可以最大限度降低单个预测模型的泛化误差,提高隧道掌子面稳定性预测结果的可靠性。

The stability of a tunnel face is influenced by a variety of factors, and it is necessary to assess the stability of the tunnel face before tunnel excavation, so as to develop appropriate measures and ensure the safety of the tunnelling process. To improve the efficiency of prediction, this paper proposes a method for the fast prediction of tunnel face stability based on ensemble learning and support vector machine(SVM). First, the most typical training samples are selected based on the principle of orthogonal experimental design, and then the samples are calibrated through three-dimensional numerical calculations. Second, based on the SVM algorithm, different kernel functions are used to fit the prediction models of tunnel face stability and verify their prediction accuracy through the leave-one-out method. Finally, according to the ensemble learning mechanism, the prediction models are synthesized by the voting method to realize the integrated prediction of tunnel face stability. The results show that the ensemble learning mechanism can minimize the generalization error of individual prediction models and improve the reliability of the prediction results of tunnel face stability.