Research on Dynamic Stability of Slopes Under the Influence of Heavy Rain Using an Improved NSGA-II Algorithm
As an important connecting channel between cities, roads are one of the main elements in urban development infrastructure. The stability evaluation of the roadbed slope runs through the entire life cycle, especially during the operation stage. However, under extreme weather conditions, especially he...
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MDPI AG
2025-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/12/6914 |
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| author | Bohu He Xiuli Du Mingzhou Bai Jinwen Yang Dong Ma |
| author_facet | Bohu He Xiuli Du Mingzhou Bai Jinwen Yang Dong Ma |
| author_sort | Bohu He |
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| description | As an important connecting channel between cities, roads are one of the main elements in urban development infrastructure. The stability evaluation of the roadbed slope runs through the entire life cycle, especially during the operation stage. However, under extreme weather conditions, especially heavy rainfall, the roadbed slope may become unstable, thus endangering operational safety. Therefore, it is necessary to conduct precise dynamic assessments of slope stability. However, due to site limitations, it is often not possible to obtain accurate mechanical parameters of a slope using traditional survey methods when deformation and failure have already occurred. In this study, building upon our existing parameter inversion model, the improved backpropagation genetic algorithm non-dominated sorting genetic algorithm II model (BPGA-NSGA-II), in-depth research was conducted on the selection of key parameters for the model. This study utilized monitoring data to perform an inversion analysis of the real-time mechanical parameters of the slope. Subsequently, the inverted parameters were applied to dynamically assess the stability of the slope. The calculation results demonstrate that the slope safety factor decreased from an initial value of 1.212 to 0.800, which aligns with actual monitoring data. This research provides a scientifically effective method for the dynamic stability assessment of slopes. |
| format | Article |
| id | doaj-art-88f3ef05d172498696b9c91b18ac311c |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-88f3ef05d172498696b9c91b18ac311c2025-08-20T03:26:21ZengMDPI AGApplied Sciences2076-34172025-06-011512691410.3390/app15126914Research on Dynamic Stability of Slopes Under the Influence of Heavy Rain Using an Improved NSGA-II AlgorithmBohu He0Xiuli Du1Mingzhou Bai2Jinwen Yang3Dong Ma4School of Emergency Technology and Management, North China Institute of Science and Technology, Beijing 101601, ChinaFaculty of Urban Construction, Beijing University of Technology, Beijing 100124, ChinaSchool of Civil Engineering, Beijing Jiaotong University, Beijing 100044, ChinaPostdoctoral Research Workstation, China Railway 16th Bureau Group Co., Ltd., Beijing 100018, ChinaPostdoctoral Research Workstation, China Railway 16th Bureau Group Co., Ltd., Beijing 100018, ChinaAs an important connecting channel between cities, roads are one of the main elements in urban development infrastructure. The stability evaluation of the roadbed slope runs through the entire life cycle, especially during the operation stage. However, under extreme weather conditions, especially heavy rainfall, the roadbed slope may become unstable, thus endangering operational safety. Therefore, it is necessary to conduct precise dynamic assessments of slope stability. However, due to site limitations, it is often not possible to obtain accurate mechanical parameters of a slope using traditional survey methods when deformation and failure have already occurred. In this study, building upon our existing parameter inversion model, the improved backpropagation genetic algorithm non-dominated sorting genetic algorithm II model (BPGA-NSGA-II), in-depth research was conducted on the selection of key parameters for the model. This study utilized monitoring data to perform an inversion analysis of the real-time mechanical parameters of the slope. Subsequently, the inverted parameters were applied to dynamically assess the stability of the slope. The calculation results demonstrate that the slope safety factor decreased from an initial value of 1.212 to 0.800, which aligns with actual monitoring data. This research provides a scientifically effective method for the dynamic stability assessment of slopes.https://www.mdpi.com/2076-3417/15/12/6914road slopeheavy rainfallimproved NSGA-II algorithmslope stabilitydynamic assessmentslope monitoring |
| spellingShingle | Bohu He Xiuli Du Mingzhou Bai Jinwen Yang Dong Ma Research on Dynamic Stability of Slopes Under the Influence of Heavy Rain Using an Improved NSGA-II Algorithm Applied Sciences road slope heavy rainfall improved NSGA-II algorithm slope stability dynamic assessment slope monitoring |
| title | Research on Dynamic Stability of Slopes Under the Influence of Heavy Rain Using an Improved NSGA-II Algorithm |
| title_full | Research on Dynamic Stability of Slopes Under the Influence of Heavy Rain Using an Improved NSGA-II Algorithm |
| title_fullStr | Research on Dynamic Stability of Slopes Under the Influence of Heavy Rain Using an Improved NSGA-II Algorithm |
| title_full_unstemmed | Research on Dynamic Stability of Slopes Under the Influence of Heavy Rain Using an Improved NSGA-II Algorithm |
| title_short | Research on Dynamic Stability of Slopes Under the Influence of Heavy Rain Using an Improved NSGA-II Algorithm |
| title_sort | research on dynamic stability of slopes under the influence of heavy rain using an improved nsga ii algorithm |
| topic | road slope heavy rainfall improved NSGA-II algorithm slope stability dynamic assessment slope monitoring |
| url | https://www.mdpi.com/2076-3417/15/12/6914 |
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