| Citation: | XU Jianbo，YAO Tianyu，WANG Li，et al. Prediction of Squeezing Surrounding Rock Tunnel Deformation Based on Support Vector Regression Optimized by Swarm Intelligence Algorithm[J]. Bulletin of Geological Science and Technology，2025，${article_volume}（0）：1-10 doi: 10.19509/j.cnki.dzkq.tb20230675
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In tunnel engineering, the prerequisite for tunnel design and construction safety is to accurately assess the amount of deformation of tunnel surrounding rock.
In this paper, the Firefly Algorithm (FA), Whale Optimization Algorithm (WOA) and Gray Wolf Optimization Algorithm (GWO) are combined with optimized Support Vector Regression (SVR), and based on which three hybrid swarm intelligent optimization prediction models are constructed to predict deformation of extruding surrounding rock tunnels. A database containing 62 samples was constructed, and seven initial parameters of tunnels and surrounding rocks were selected as the input parameters of the prediction models, and the radial deformation of tunnels as the output quantities. The coefficient of determination (
The FA-SVR model demonstrated superior predictive performance during both the training and testing phases compared to the GWO-SVR and WOA-SVR models. For the training set, the corresponding
The results show that the firefly algorithm, the whale optimization algorithm and the 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 shows that joint frequency is the most important parameter that affects the predicted value of deformation of tunnel surrounding rock.
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