|Table of Contents|

[1] Li Zhaozhong, Chang Xiangyu, Wang Hao, et al. Back-analysis method of rock mass propertiesin tunnel engineering using multiple monitoring databased on LS-SVR algorithm [J]. Journal of Southeast University (English Edition), 2023, 39 (1): 1-7. [doi:10.3969/j.issn.1003-7985.2023.01.001]

Back-analysis method of rock mass propertiesin tunnel engineering using multiple monitoring databased on LS-SVR algorithm()

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

2023 1
Research Field:
Traffic and Transportation Engineering
Publishing date:


Back-analysis method of rock mass propertiesin tunnel engineering using multiple monitoring databased on LS-SVR algorithm
Li Zhaozhong1 2 Chang Xiangyu1 Wang Hao1 Mao Jianxiao1
1 Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 211189, China
2 China Railway 24th Bureau Group Co., Ltd., Shanghai 200071, China
tunnel engineering back-analysis method rock mass properties least-squares support vector regression algorithm
To accurately estimate the rock mass properties of a high-speed railway tunnel, a back-analysis method using multiple monitoring data based on the least-squares support vector regression(LS-SVR)algorithm is presented. The root mean square error(RMSE)and mean absolute percentage error(MAPE)are used as evaluation indices. The results of the parameter estimation are compared with those of the back propagation neural network(BPNN)and Gaussian process regression(GPR). The results show that for the single type of monitoring data, the LS-SVR model with vault settlement has the lowest RMSE and MAPE values. Moreover, as the data type increases, the RMSE value of the LS-SVR decreases, especially for the model with the mixed data of vault settlement, convergence, and floor heave. The comparison results show that the presented model has lower RMSE and MAPE values than BPNN and GPR. The LS-SVR model using multiple monitoring data shows better performance than existing back-analysis methods, improving the accuracy of the estimation of rock mass properties.


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Biographies: Li Zhaozhong(1984—), male, Ph. D. candidate; Wang Hao(corresponding author), male, doctor, professor, wanghao1980@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No. 51978155), the Science and Technology Program of Ministry of Housing and Urban-Rural Development(No. 2020-K-125).
Citation: Li Zhaozhong, Chang Xiangyu, Wang Hao, et al.Back analysis method of rock mass properties in tunnel engineering using multiple monitoring data based on LS-SVR algorithm[J].Journal of Southeast University(English Edition), 2023, 39(1):1-7.DOI:10.3969/j.issn.1003-7985.2023.01.001.
Last Update: 2023-03-20