Optimized Back-Analysis of Regional Tectonic Stress based on Genetic Algorithms
In this paper, we develop an optimized back-analysis technique based on genetic algorithms to determine the regional tectonic stress state for stability analysis of rock masses with nonlinear behavior around a tunnel. A real coded genetic algorithm (GA) is employed to find the optimal parameters by...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Published: |
Advances in Management
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/34029 |
| _version_ | 1848754110605557760 |
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| author | Yang, C. Zhu, Y. Wu, Yong Hong Su, X. Jin, C. Li, Y. |
| author_facet | Yang, C. Zhu, Y. Wu, Yong Hong Su, X. Jin, C. Li, Y. |
| author_sort | Yang, C. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper, we develop an optimized back-analysis technique based on genetic algorithms to determine the regional tectonic stress state for stability analysis of rock masses with nonlinear behavior around a tunnel. A real coded genetic algorithm (GA) is employed to find the optimal parameters by minimizing the discrepancy between the predicted results and field measurement. A nonlinear 2-D finite element model is used for the prediction of the behavior of the excavation system, in which the rock mass is numerically simulated as a non-tension elastic-plastic material. The optimized back analysis technique is then applied to a synthetic example of a deep tunnel in yielding rock. Measurements of tunnel wall displacements are used to identify the magnitude and orientation parameters of the regional tectonic stress. The results show that the present method is capable of estimating the regional tectonic stress state parameters with stable and good convergence. Numerical experiments are also carried out to check the influences of position and numbers of measurements to the reliability of the back-analysis results. Furthermore, the sensitivity analysis of the GAs optimization procedure is discussed in terms of identification of regional tectonic stress state. |
| first_indexed | 2025-11-14T08:35:12Z |
| format | Journal Article |
| id | curtin-20.500.11937-34029 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:35:12Z |
| publishDate | 2012 |
| publisher | Advances in Management |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-340292017-01-30T13:40:44Z Optimized Back-Analysis of Regional Tectonic Stress based on Genetic Algorithms Yang, C. Zhu, Y. Wu, Yong Hong Su, X. Jin, C. Li, Y. In this paper, we develop an optimized back-analysis technique based on genetic algorithms to determine the regional tectonic stress state for stability analysis of rock masses with nonlinear behavior around a tunnel. A real coded genetic algorithm (GA) is employed to find the optimal parameters by minimizing the discrepancy between the predicted results and field measurement. A nonlinear 2-D finite element model is used for the prediction of the behavior of the excavation system, in which the rock mass is numerically simulated as a non-tension elastic-plastic material. The optimized back analysis technique is then applied to a synthetic example of a deep tunnel in yielding rock. Measurements of tunnel wall displacements are used to identify the magnitude and orientation parameters of the regional tectonic stress. The results show that the present method is capable of estimating the regional tectonic stress state parameters with stable and good convergence. Numerical experiments are also carried out to check the influences of position and numbers of measurements to the reliability of the back-analysis results. Furthermore, the sensitivity analysis of the GAs optimization procedure is discussed in terms of identification of regional tectonic stress state. 2012 Journal Article http://hdl.handle.net/20.500.11937/34029 Advances in Management restricted |
| spellingShingle | Yang, C. Zhu, Y. Wu, Yong Hong Su, X. Jin, C. Li, Y. Optimized Back-Analysis of Regional Tectonic Stress based on Genetic Algorithms |
| title | Optimized Back-Analysis of Regional Tectonic Stress based on Genetic Algorithms |
| title_full | Optimized Back-Analysis of Regional Tectonic Stress based on Genetic Algorithms |
| title_fullStr | Optimized Back-Analysis of Regional Tectonic Stress based on Genetic Algorithms |
| title_full_unstemmed | Optimized Back-Analysis of Regional Tectonic Stress based on Genetic Algorithms |
| title_short | Optimized Back-Analysis of Regional Tectonic Stress based on Genetic Algorithms |
| title_sort | optimized back-analysis of regional tectonic stress based on genetic algorithms |
| url | http://hdl.handle.net/20.500.11937/34029 |