基于群智能算法优化支持向量回归的挤压性围岩隧道变形预测

徐剑波; 姚天宇; 王力; 朱颂阳; 罗学东

doi:10.19509/j.cnki.dzkq.tb20230675

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基于群智能算法优化支持向量回归的挤压性围岩隧道变形预测

更新时间：2026-11-05

- 基于群智能算法优化支持向量回归的挤压性围岩隧道变形预测
- Bulletin of Geological Science and Technology Vol. 44, Issue 5, Pages: 317-326(2025)
- 作者机构：
  
  1. 中国一冶集团有限公司
  2. 中国地质大学(武汉)工程学院
- 作者简介：
- 基金信息：
- DOI：10.19509/j.cnki.dzkq.tb20230675
  CLC： U457;TP18;U451.2
- Online First：21 March 2025，
  
  Published：21 March 2025
- 稿件说明：
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徐剑波, 姚天宇, 王力, et al. 基于群智能算法优化支持向量回归的挤压性围岩隧道变形预测[J]. Bulletin of Geological Science and Technology, 2025, 44(5): 317-326.
DOI：

徐剑波, 姚天宇, 王力, et al. 基于群智能算法优化支持向量回归的挤压性围岩隧道变形预测[J]. Bulletin of Geological Science and Technology, 2025, 44(5): 317-326. DOI： 10.19509/j.cnki.dzkq.tb20230675.

摘要

隧道工程中，隧道设计和施工安全的前提是准确评估隧道围岩变形量。将萤火虫算法（FA）、鲸鱼优化算法（WOA）和灰狼优化算法（GWO）与优化支持向量回归（SVR）结合起来，并基于此构建了3种混合群智能优化预测模型，以预测挤压性围岩隧道变形量。构建了一个包含62个样本的数据库，选取了7种隧道及围岩初始参数作为预测模型输入参数，将隧道径向变形量作为输出量。选择决定系数（R

）、均方根误差（RMSE）、平均绝对误差（MAE）模型预测效果的评价指标。最后，使用归一化互信息法评估不同输入参数对隧道围岩变形预测结果的影响。研究结果表明，FA-SVR模型在训练阶段和测试阶段的预测性能优于GWO-SVR模型和WOA-SVR模型，训练集和测试集对应的R

分别为0.963 4和0.964 8

RMSE分别为18.786和14.699

MAE分别为9.460和11.170，预测能力排序为：FA-SVR>WOA-SVR>GWO-SVR。萤火虫算法、鲸鱼优化算法和灰狼优化算法均能提高支持向量回归模型的预测性能，FA-SVR模型的预测效果最好，经过优化的混合预测模型性能显著优于经典模型。敏感性分析表明，节理密度是影响隧道围岩变形预测值的最重要参数。研究成果可为隧道工程安全控制提供重要参考。

Abstract

ObjectiveIn tunnel engineering

the prerequisite for tunnel design and construction safety is to accurately assess the amount of deformation of the rock surrounding a tunnel. MethodsIn this work

the firefly algorithm (FA)

whale optimization algorithm (WOA)

and gray wolf optimization algorithm (GWO) are combined with optimized support vector regression (SVR)

and three intelligent hybrid swarm optimization prediction models are constructed to predict the deformation of extruding surrounding rock tunnels. A database containing 62 samples was constructed

and seven initial parameters of tunnels and surrounding rock were selected as the input parameters of the prediction models

with the radial deformation of tunnels as the output quantity. The coefficient of determination (R2)

root-mean-square error (RMSE)

and mean absolute error (MAE) were selected as the evaluation indices of the model's prediction performance. Finally

the effects of different input parameters on the prediction results of tunnel rock deformation were evaluated via normalized mutual information values. ResultsCompared with the GWO-SVR and WOA-SVR models

the FA-SVR model demonstrated superior predictive performance during both the training and testing phases. The corresponding R2 values were 0.9634 and 0.9648

the RMSE values were 18.786 and 14.699

and the MAE values were 9.460 and 11.170 for the training and testing sets

respectively. The ranking of predictive capability was as follows: FA-SVR>WOA-SVR>GWO-SVR. The firefly algorithm

whale optimization algorithm

and gray wolf optimization algorithm can improve the prediction performance of the support vector regression model. The FA-SVR model has the best prediction effect

and the optimized hybrid prediction model performs significantly better than the classical models. The sensitivity analysis reveals that the joint frequency is the most important parameter affecting the predicted value of deformation of the rock surrounding a tunnel. ConclusionThe research results can provide an important reference for the safety control of tunnel engineering.

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