Local Stress Field Correction Method Based on a Genetic Algorithm and a BP Neural Network for In Situ Stress Field Inversion
The in situ stress field is the fundamental factor causing deformation and damage in geotechnical engineering, so it is the main basis for underground engineering design and excavation. However, it is difficult to accurately obtain the in situ stress through most existing inversion methods in areas...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/4396168 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832549185091534848 |
|---|---|
| author | Tianzhi Yao Zuguo Mo Li Qian Jianhua He Jianhai Zhang |
| author_facet | Tianzhi Yao Zuguo Mo Li Qian Jianhua He Jianhai Zhang |
| author_sort | Tianzhi Yao |
| collection | DOAJ |
| description | The in situ stress field is the fundamental factor causing deformation and damage in geotechnical engineering, so it is the main basis for underground engineering design and excavation. However, it is difficult to accurately obtain the in situ stress through most existing inversion methods in areas with complex geological conditions. For the problem of a relatively discrete and nonlinear relationship of measured stress in the Yebatan Hydropower Station area, a new in situ stress inversion method called the local stress field correction (LSFC) method combining a genetic algorithm (GA), backpropagation (BP) neural network, and submodel method is proposed. The inverted in situ stress results produced by this method show that the distribution of in situ stress is greatly influenced by tectonic movements in the Yebatan area, there is no obvious linear relationship with depth, and the stress release phenomenon occurs at the faults. By comparison with the multiple regression method, it is found that the method still has high inversion accuracy under complex geological conditions, and the average relative error of LSFC inversion results is 17.05%, which is much lower than the value of 43.58% via the multiple regression method. Therefore, the LSFC method can be used for the inversion of in situ stress in complex geological regions and provide a reference for engineering design and construction. |
| format | Article |
| id | doaj-art-a03dadbdf22549f1a8a1fa6623e6438d |
| 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-a03dadbdf22549f1a8a1fa6623e6438d2025-02-03T06:11:57ZengWileyAdvances in Civil Engineering1687-80861687-80942021-01-01202110.1155/2021/43961684396168Local Stress Field Correction Method Based on a Genetic Algorithm and a BP Neural Network for In Situ Stress Field InversionTianzhi Yao0Zuguo Mo1Li Qian2Jianhua He3Jianhai Zhang4State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resources and Hydropower, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resources and Hydropower, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resources and Hydropower, Sichuan University, Chengdu 610065, ChinaPower China Chengdu Engineering Corporation Limited, Chengdu 610072, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resources and Hydropower, Sichuan University, Chengdu 610065, ChinaThe in situ stress field is the fundamental factor causing deformation and damage in geotechnical engineering, so it is the main basis for underground engineering design and excavation. However, it is difficult to accurately obtain the in situ stress through most existing inversion methods in areas with complex geological conditions. For the problem of a relatively discrete and nonlinear relationship of measured stress in the Yebatan Hydropower Station area, a new in situ stress inversion method called the local stress field correction (LSFC) method combining a genetic algorithm (GA), backpropagation (BP) neural network, and submodel method is proposed. The inverted in situ stress results produced by this method show that the distribution of in situ stress is greatly influenced by tectonic movements in the Yebatan area, there is no obvious linear relationship with depth, and the stress release phenomenon occurs at the faults. By comparison with the multiple regression method, it is found that the method still has high inversion accuracy under complex geological conditions, and the average relative error of LSFC inversion results is 17.05%, which is much lower than the value of 43.58% via the multiple regression method. Therefore, the LSFC method can be used for the inversion of in situ stress in complex geological regions and provide a reference for engineering design and construction.http://dx.doi.org/10.1155/2021/4396168 |
| spellingShingle | Tianzhi Yao Zuguo Mo Li Qian Jianhua He Jianhai Zhang Local Stress Field Correction Method Based on a Genetic Algorithm and a BP Neural Network for In Situ Stress Field Inversion Advances in Civil Engineering |
| title | Local Stress Field Correction Method Based on a Genetic Algorithm and a BP Neural Network for In Situ Stress Field Inversion |
| title_full | Local Stress Field Correction Method Based on a Genetic Algorithm and a BP Neural Network for In Situ Stress Field Inversion |
| title_fullStr | Local Stress Field Correction Method Based on a Genetic Algorithm and a BP Neural Network for In Situ Stress Field Inversion |
| title_full_unstemmed | Local Stress Field Correction Method Based on a Genetic Algorithm and a BP Neural Network for In Situ Stress Field Inversion |
| title_short | Local Stress Field Correction Method Based on a Genetic Algorithm and a BP Neural Network for In Situ Stress Field Inversion |
| title_sort | local stress field correction method based on a genetic algorithm and a bp neural network for in situ stress field inversion |
| url | http://dx.doi.org/10.1155/2021/4396168 |
| work_keys_str_mv | AT tianzhiyao localstressfieldcorrectionmethodbasedonageneticalgorithmandabpneuralnetworkforinsitustressfieldinversion AT zuguomo localstressfieldcorrectionmethodbasedonageneticalgorithmandabpneuralnetworkforinsitustressfieldinversion AT liqian localstressfieldcorrectionmethodbasedonageneticalgorithmandabpneuralnetworkforinsitustressfieldinversion AT jianhuahe localstressfieldcorrectionmethodbasedonageneticalgorithmandabpneuralnetworkforinsitustressfieldinversion AT jianhaizhang localstressfieldcorrectionmethodbasedonageneticalgorithmandabpneuralnetworkforinsitustressfieldinversion |