Support vector regression with heuristic optimization algorithms for predicting the ground surface displacement induced by EPB shield tunneling

• Published in: Gondwana research 2023-11, Vol.123, p.3-15
• Main Authors: Lu, Dechun, Ma, Yiding, Kong, Fanchao, Guo, Caixia, Miao, Jinbo, Du, Xiuli
• Format: Article
• Language: English
• Subjects: Determination of hyperparameters; EPB shield tunneling; Ground surface displacement; Heuristic optimization algorithm; Support vector regression
• ISSN: 1342-937X; 1878-0571
• DOI: 10.1016/j.gr.2022.07.002

[Display omitted] •Support vector regression is used to predict tunnel-induced strata displacement.•Heuristic algorithms are applied to determine SVR hyperparameters.•Accuracy, stability and efficiency are used to evaluate the applicable algorithms. Machine learning method with heuristic optimization algorithms is proposed to predict the stratum displacement induced by earth pressure balanced shield tunneling. Support vector regression is used as the machine learning method. Four heuristic intelligent optimization algorithms, namely, genetic algorithm, particle swarm optimization, grey wolf optimizer and sparrow search algorithm, are applied to optimize the two hyperparameters of support vector regression model, namely, penalty factor and bandwidth term. Simulated annealing algorithm is introduced to show the necessity of using heuristic algorithms. Mean square error of k-fold cross validation is considered as the fitness function for optimization algorithms. Normalization method and dummy variables are used for data preprocessing. For 115 samples from field measurement, 92 samples are used as the training set, and 23 samples are used as the test set. Three categories of parameters, namely, shield tunneling parameters, tunnel geometrical parameters and stratum types, are used as input parameters for the proposed method. Correlations among parameters are analyzed by Pearson correlation coefficient. The prediction results show that grey wolf optimizer and sparrow search algorithm are suitable methods for determining hyperparameters of support vector regression due to higher accuracy, efficiency, and stability.