Displacement Back Analysis for a High Slope of the Dagangshan Hydroelectric Power Station Based on BP Neural Network and Particle Swarm Optimization
The right bank high slope of the Dagangshan Hydroelectric Power Station is located in complicated geological conditions with deep fractures and unloading cracks. How to obtain the mechanical parameters and then evaluate the safety of the slope are the key problems. This paper presented a displacemen...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/741323 |
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| author | Zhengzhao Liang Bin Gong Chunan Tang Yongbin Zhang Tianhui Ma |
| author_facet | Zhengzhao Liang Bin Gong Chunan Tang Yongbin Zhang Tianhui Ma |
| author_sort | Zhengzhao Liang |
| collection | DOAJ |
| description | The right bank high slope of the Dagangshan Hydroelectric Power Station is located in complicated geological conditions with deep fractures and unloading cracks. How to obtain the mechanical parameters and then evaluate the safety of the slope are the key problems. This paper presented a displacement back analysis for the slope using an artificial neural network model (ANN) and particle swarm optimization model (PSO). A numerical model was established to simulate the displacement increment results, acquiring training data for the artificial neural network model. The backpropagation ANN model was used to establish a mapping function between the mechanical parameters and the monitoring displacements. The PSO model was applied to initialize the weights and thresholds of the backpropagation (BP) network model and determine suitable values of the mechanical parameters. Then the elastic moduli of the rock masses were obtained according to the monitoring displacement data at different excavation stages, and the BP neural network model was proved to be valid by comparing the measured displacements, the displacements predicted by the BP neural network model, and the numerical simulation using the back-analyzed parameters. The proposed model is useful for rock mechanical parameters determination and instability investigation of rock slopes. |
| format | Article |
| id | doaj-art-d55d4ed994164821bfa640ebe8989848 |
| institution | OA Journals |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-d55d4ed994164821bfa640ebe89898482025-08-20T02:19:38ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/741323741323Displacement Back Analysis for a High Slope of the Dagangshan Hydroelectric Power Station Based on BP Neural Network and Particle Swarm OptimizationZhengzhao Liang0Bin Gong1Chunan Tang2Yongbin Zhang3Tianhui Ma4Institute of Rock Instability and Seismicity Research, Dalian University of Technology, Dalian, Liaoning 116024, ChinaInstitute of Rock Instability and Seismicity Research, Dalian University of Technology, Dalian, Liaoning 116024, ChinaInstitute of Rock Instability and Seismicity Research, Dalian University of Technology, Dalian, Liaoning 116024, ChinaInstitute of Rock Instability and Seismicity Research, Dalian University of Technology, Dalian, Liaoning 116024, ChinaInstitute of Rock Instability and Seismicity Research, Dalian University of Technology, Dalian, Liaoning 116024, ChinaThe right bank high slope of the Dagangshan Hydroelectric Power Station is located in complicated geological conditions with deep fractures and unloading cracks. How to obtain the mechanical parameters and then evaluate the safety of the slope are the key problems. This paper presented a displacement back analysis for the slope using an artificial neural network model (ANN) and particle swarm optimization model (PSO). A numerical model was established to simulate the displacement increment results, acquiring training data for the artificial neural network model. The backpropagation ANN model was used to establish a mapping function between the mechanical parameters and the monitoring displacements. The PSO model was applied to initialize the weights and thresholds of the backpropagation (BP) network model and determine suitable values of the mechanical parameters. Then the elastic moduli of the rock masses were obtained according to the monitoring displacement data at different excavation stages, and the BP neural network model was proved to be valid by comparing the measured displacements, the displacements predicted by the BP neural network model, and the numerical simulation using the back-analyzed parameters. The proposed model is useful for rock mechanical parameters determination and instability investigation of rock slopes.http://dx.doi.org/10.1155/2014/741323 |
| spellingShingle | Zhengzhao Liang Bin Gong Chunan Tang Yongbin Zhang Tianhui Ma Displacement Back Analysis for a High Slope of the Dagangshan Hydroelectric Power Station Based on BP Neural Network and Particle Swarm Optimization The Scientific World Journal |
| title | Displacement Back Analysis for a High Slope of the Dagangshan Hydroelectric Power Station Based on BP Neural Network and Particle Swarm Optimization |
| title_full | Displacement Back Analysis for a High Slope of the Dagangshan Hydroelectric Power Station Based on BP Neural Network and Particle Swarm Optimization |
| title_fullStr | Displacement Back Analysis for a High Slope of the Dagangshan Hydroelectric Power Station Based on BP Neural Network and Particle Swarm Optimization |
| title_full_unstemmed | Displacement Back Analysis for a High Slope of the Dagangshan Hydroelectric Power Station Based on BP Neural Network and Particle Swarm Optimization |
| title_short | Displacement Back Analysis for a High Slope of the Dagangshan Hydroelectric Power Station Based on BP Neural Network and Particle Swarm Optimization |
| title_sort | displacement back analysis for a high slope of the dagangshan hydroelectric power station based on bp neural network and particle swarm optimization |
| url | http://dx.doi.org/10.1155/2014/741323 |
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