NSGA–III–XGBoost-Based Stochastic Reliability Analysis of Deep Soft Rock Tunnel
How to evaluate the reliability of deep soft rock tunnels under high stress is a very important problem to be solved. In this paper, we proposed a practical stochastic reliability method based on the third-generation non-dominated sorting genetic algorithm (NSGA–III) and eXtreme Gradient Boosting (X...
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MDPI AG
2024-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/5/2127 |
| _version_ | 1797264769934163968 |
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| author | Jiancong Xu Chen Sun Guorong Rui |
| author_facet | Jiancong Xu Chen Sun Guorong Rui |
| author_sort | Jiancong Xu |
| collection | DOAJ |
| description | How to evaluate the reliability of deep soft rock tunnels under high stress is a very important problem to be solved. In this paper, we proposed a practical stochastic reliability method based on the third-generation non-dominated sorting genetic algorithm (NSGA–III) and eXtreme Gradient Boosting (XGBoost). The proposed method used the Latin hypercube sampling method to generate the dataset samples of geo-mechanical parameters and adopted XGBoost to establish the model of the nonlinear relationship between displacements and surrounding rock mechanical parameters. And NSGA–III was used to optimize the surrogate model hyper-parameters. Finally, the failure probability was computed by the optimized surrogate model. The proposed approach was firstly implemented in the analysis of a horseshoe-shaped highway tunnel to illustrate the efficiency of the approach. Then, in comparison to the support vector regression method and the back propagation neural network method, the feasibility, validity and advantages of XGBoost were demonstrated for practical problems. Using XGBoost to achieve Monte Carlo simulation, a surrogate solution can be provided for numerical simulation analysis to overcome the time-consuming reliability evaluation of initial support structures in soft rock tunnels. The proposed method can evaluate quickly the large deformation disaster risks of non-circular deep soft rock tunnels. |
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| format | Article |
| id | doaj.art-4018fda3b2a847da997a53766c54f123 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-25T00:34:10Z |
| publishDate | 2024-03-01 |
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| spelling | doaj.art-4018fda3b2a847da997a53766c54f1232024-03-12T16:40:11ZengMDPI AGApplied Sciences2076-34172024-03-01145212710.3390/app14052127NSGA–III–XGBoost-Based Stochastic Reliability Analysis of Deep Soft Rock TunnelJiancong Xu0Chen Sun1Guorong Rui2Department of Geotechnical Engineering, Tongji University, Shanghai 200092, ChinaDepartment of Geotechnical Engineering, Tongji University, Shanghai 200092, ChinaChina Railway 20th Bureau Group Co., Ltd., Xi’an 710016, ChinaHow to evaluate the reliability of deep soft rock tunnels under high stress is a very important problem to be solved. In this paper, we proposed a practical stochastic reliability method based on the third-generation non-dominated sorting genetic algorithm (NSGA–III) and eXtreme Gradient Boosting (XGBoost). The proposed method used the Latin hypercube sampling method to generate the dataset samples of geo-mechanical parameters and adopted XGBoost to establish the model of the nonlinear relationship between displacements and surrounding rock mechanical parameters. And NSGA–III was used to optimize the surrogate model hyper-parameters. Finally, the failure probability was computed by the optimized surrogate model. The proposed approach was firstly implemented in the analysis of a horseshoe-shaped highway tunnel to illustrate the efficiency of the approach. Then, in comparison to the support vector regression method and the back propagation neural network method, the feasibility, validity and advantages of XGBoost were demonstrated for practical problems. Using XGBoost to achieve Monte Carlo simulation, a surrogate solution can be provided for numerical simulation analysis to overcome the time-consuming reliability evaluation of initial support structures in soft rock tunnels. The proposed method can evaluate quickly the large deformation disaster risks of non-circular deep soft rock tunnels.https://www.mdpi.com/2076-3417/14/5/2127soft rock tunnelstochastic finite difference methodreliabilityXGBoostNSGA-III |
| spellingShingle | Jiancong Xu Chen Sun Guorong Rui NSGA–III–XGBoost-Based Stochastic Reliability Analysis of Deep Soft Rock Tunnel Applied Sciences soft rock tunnel stochastic finite difference method reliability XGBoost NSGA-III |
| title | NSGA–III–XGBoost-Based Stochastic Reliability Analysis of Deep Soft Rock Tunnel |
| title_full | NSGA–III–XGBoost-Based Stochastic Reliability Analysis of Deep Soft Rock Tunnel |
| title_fullStr | NSGA–III–XGBoost-Based Stochastic Reliability Analysis of Deep Soft Rock Tunnel |
| title_full_unstemmed | NSGA–III–XGBoost-Based Stochastic Reliability Analysis of Deep Soft Rock Tunnel |
| title_short | NSGA–III–XGBoost-Based Stochastic Reliability Analysis of Deep Soft Rock Tunnel |
| title_sort | nsga iii xgboost based stochastic reliability analysis of deep soft rock tunnel |
| topic | soft rock tunnel stochastic finite difference method reliability XGBoost NSGA-III |
| url | https://www.mdpi.com/2076-3417/14/5/2127 |
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