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| A BP neural network-based prediction method for seismic response of circular tunnel linings and parameter analysis |
| YU Haitao1, ZHU Chenyang2
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1. Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education, Tongji University, Shanghai 200092, China;
2. Department of Geotechnical Engineering, Tongji University, Shanghai 200092, China |
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Abstract A prediction method of seismic response of circular tunnel based on artificial neural network was proposed. Taking the peak value of bedrock ground motion, the buried depth of tunnel, the relative stiffness ratio of stratum to structure and the contact condition between tunnel and stratum as the basic input parameters, the key response indexes of lining stress and deformation including bending moment, axial force, shear force and diameter change rate of circular tunnel under different ground motions were predicted. Through the response acceleration method, 320 sets of numerical calculation models for seismic response of tunnel structures composed of different input parameter values were established. The results were extracted to obtain the data set of this research and used for the establishment and test of the prediction model. The results showed that the mean square error and correlation coefficient of the response prediction value and the reference value of each group of models performed well, which verified the feasibility of the analysis model. Based on the prediction model after data training, the seismic response of tunnel structure under arbitrary ground motion input could be obtained, and then the prediction formula of seismic response of circular tunnel could be fitted. The validity of the method was verified by comparing with the classical analytical solution of seismic response of deep buried circular tunnel under degraded conditions. This method could also conveniently and quickly analyze the sensitivity influence ranking and effect of basic input parameters, which provided a new analysis method for seismic design and analysis of underground structures.
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Received: 10 January 2023
Published: 20 September 2023
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