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Study on Time Series Prediction of the Tunnel Deformation Based on the Multivariable GP-DE Model
(1 Highway and Bridge Institute of Dalian Maritime University, Dalian 116026；2 Jilin Provincial Communication Planning and Design Institute, Changchun 130021)
Abstract Accurate prediction and controlling of the tunnel deformation is the key point in ensuring the tunnel con? struction safety. Aiming at the insufficiency of current time series prediction method of tunnel surrounding rock deformation, a time series prediction method of the tunnel deformation based on multivariable Gauss Process (GP) -Differential Evolution Algorithm (DE) is proposed. According to the results of tunnel automation monitoring, the multivariable phase space is reconstructed, and the input dimensions are reduced by principal component analysis. On this basis, the GP-DE model is used to predict the tunnel deformation. Taking the Gaoligou tunnel in Jilin province as an example, the surrounding rock displacement on the vault crown is predicted, and the prediction results are compared with that of BP neural network and SVM model. The results show that the GP-DE model of multi-variable time series has higher prediction accuracy, and the predicted value is in good agreement with the measured value,proving that it is an effective method for tunnel displacement prediction.
Abstract：
Accurate prediction and controlling of the tunnel deformation is the key point in ensuring the tunnel con? struction safety. Aiming at the insufficiency of current time series prediction method of tunnel surrounding rock deformation, a time series prediction method of the tunnel deformation based on multivariable Gauss Process (GP) -Differential Evolution Algorithm (DE) is proposed. According to the results of tunnel automation monitoring, the multivariable phase space is reconstructed, and the input dimensions are reduced by principal component analysis. On this basis, the GP-DE model is used to predict the tunnel deformation. Taking the Gaoligou tunnel in Jilin province as an example, the surrounding rock displacement on the vault crown is predicted, and the prediction results are compared with that of BP neural network and SVM model. The results show that the GP-DE model of multi-variable time series has higher prediction accuracy, and the predicted value is in good agreement with the measured value,proving that it is an effective method for tunnel displacement prediction.
ZHANG Fengrui1 JIANG Annan1 ZHAO Liang2 CHEN Wei2 GUO Kuo2
.Study on Time Series Prediction of the Tunnel Deformation Based on the Multivariable GP-DE Model[J] MODERN TUNNELLING TECHNOLOGY, 2021,V58(1): 109-116
2021, Vol. 58 