Multiparameter Inversion Early Warning System of Tunnel Stress-Seepage Coupling Based on IA-BP Algorithm
Under construction disturbance, the surrounding rock of a soft rock tunnel shows obvious aging characteristics. The creep characteristics of a rock mass under stress-seepage coupling greatly influence the long-term stability of a project. How to simply, quickly, and accurately determine the creep pa...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/1566693 |
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| author | Junxiang Wang Jie Sun Haijun Kou Yaxian Lin |
| author_facet | Junxiang Wang Jie Sun Haijun Kou Yaxian Lin |
| author_sort | Junxiang Wang |
| collection | DOAJ |
| description | Under construction disturbance, the surrounding rock of a soft rock tunnel shows obvious aging characteristics. The creep characteristics of a rock mass under stress-seepage coupling greatly influence the long-term stability of a project. How to simply, quickly, and accurately determine the creep parameters of a rock mass under coupling conditions is significant to engineering structure design and construction. The optimal weights and thresholds of the BP neural network are sought through the immune algorithm to avoid the problem of slow convergence speed of the BP neural network and easy to fall into local optimum. Therefore, an intelligent back analysis method based on the IA-BP algorithm is established, which leads to the development of the corresponding intelligent back analysis program. The creep effect of the rock mass was simulated herein using the Drucker–Prager yield criterion and the time hardening creep law as the forward optimization method constitutive model. In addition, a sensitivity analysis of the parameters was performed to determine the optimal number of inversion parameters. By comparing and analyzing the residual between the inversion results of the IA-BP algorithm, PSO-BP algorithm, and the test values, the high precision of the IA-BP algorithm is proved. Taking the Lan Zhou-Hai Kou national expressway tunnel as an engineering example, a multiparameter creep inversion of the tunnel surrounding rock under the stress-seepage coupling condition was conducted using the inverse analysis method of the IA-BP algorithm. The results showed that the proposed IA-BP algorithm can effectively prevent the BP neural network from falling into a local minimum. Also, the algorithm is fast and accurate. The intelligent back analysis method based on the IA-BP algorithm is applied to the multifield coupling parameter back analysis, provides the basis and help for the structural design and construction of soft rock tunnel in water-rich stratum. |
| format | Article |
| id | doaj-art-4a87b7f0abb84febb4dfed0b4b9e42e5 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-4a87b7f0abb84febb4dfed0b4b9e42e52025-08-20T03:36:35ZengWileyAdvances in Civil Engineering1687-80861687-80942021-01-01202110.1155/2021/15666931566693Multiparameter Inversion Early Warning System of Tunnel Stress-Seepage Coupling Based on IA-BP AlgorithmJunxiang Wang0Jie Sun1Haijun Kou2Yaxian Lin3School of Architecture and Civil Engineering, Shenyang University of Technology, Shenyang, Liaoning 110870, ChinaSchool of Architecture and Civil Engineering, Shenyang University of Technology, Shenyang, Liaoning 110870, ChinaChina Railway 19th Bureau Group No. 5 Engineering Co., Ltd., Dalian 116100, Liaoning, ChinaSchool of Architecture and Civil Engineering, Shenyang University of Technology, Shenyang, Liaoning 110870, ChinaUnder construction disturbance, the surrounding rock of a soft rock tunnel shows obvious aging characteristics. The creep characteristics of a rock mass under stress-seepage coupling greatly influence the long-term stability of a project. How to simply, quickly, and accurately determine the creep parameters of a rock mass under coupling conditions is significant to engineering structure design and construction. The optimal weights and thresholds of the BP neural network are sought through the immune algorithm to avoid the problem of slow convergence speed of the BP neural network and easy to fall into local optimum. Therefore, an intelligent back analysis method based on the IA-BP algorithm is established, which leads to the development of the corresponding intelligent back analysis program. The creep effect of the rock mass was simulated herein using the Drucker–Prager yield criterion and the time hardening creep law as the forward optimization method constitutive model. In addition, a sensitivity analysis of the parameters was performed to determine the optimal number of inversion parameters. By comparing and analyzing the residual between the inversion results of the IA-BP algorithm, PSO-BP algorithm, and the test values, the high precision of the IA-BP algorithm is proved. Taking the Lan Zhou-Hai Kou national expressway tunnel as an engineering example, a multiparameter creep inversion of the tunnel surrounding rock under the stress-seepage coupling condition was conducted using the inverse analysis method of the IA-BP algorithm. The results showed that the proposed IA-BP algorithm can effectively prevent the BP neural network from falling into a local minimum. Also, the algorithm is fast and accurate. The intelligent back analysis method based on the IA-BP algorithm is applied to the multifield coupling parameter back analysis, provides the basis and help for the structural design and construction of soft rock tunnel in water-rich stratum.http://dx.doi.org/10.1155/2021/1566693 |
| spellingShingle | Junxiang Wang Jie Sun Haijun Kou Yaxian Lin Multiparameter Inversion Early Warning System of Tunnel Stress-Seepage Coupling Based on IA-BP Algorithm Advances in Civil Engineering |
| title | Multiparameter Inversion Early Warning System of Tunnel Stress-Seepage Coupling Based on IA-BP Algorithm |
| title_full | Multiparameter Inversion Early Warning System of Tunnel Stress-Seepage Coupling Based on IA-BP Algorithm |
| title_fullStr | Multiparameter Inversion Early Warning System of Tunnel Stress-Seepage Coupling Based on IA-BP Algorithm |
| title_full_unstemmed | Multiparameter Inversion Early Warning System of Tunnel Stress-Seepage Coupling Based on IA-BP Algorithm |
| title_short | Multiparameter Inversion Early Warning System of Tunnel Stress-Seepage Coupling Based on IA-BP Algorithm |
| title_sort | multiparameter inversion early warning system of tunnel stress seepage coupling based on ia bp algorithm |
| url | http://dx.doi.org/10.1155/2021/1566693 |
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