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Displacement back analysis for a high slope of the Dagangshan Hydroelectric Power Station based on BP neural network and particle swarm optimization.

Liang Z, Gong B, Tang C, Zhang Y, Ma T - ScientificWorldJournal (2014)

Bottom Line: A numerical model was established to simulate the displacement increment results, acquiring training data for the artificial neural network model.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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Rock Instability and Seismicity Research, Dalian University of Technology, Dalian, Liaoning 116024, China.

ABSTRACT
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.

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Failure state of the slope model.
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Related In: Results  -  Collection


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fig13: Failure state of the slope model.

Mentions: Figure 12 shows the slope displacement distribution after excavating Step 10. The phenomena of ground heave appeared caused by the excavation and geostress unloading at EL 1100 m. Figure 13 shows the failure state of the slope model. The blue blocks were in elastic state, and blocks in the other colors were the plastic zone. It can be found that the plastic zones develop downward along the fissures XL316-1, especially in the area between fissures XL316-1 and XL9-15. The large plastic zone from EL 1135 to EL 1195 had considerable influence on the slope stability.


Displacement back analysis for a high slope of the Dagangshan Hydroelectric Power Station based on BP neural network and particle swarm optimization.

Liang Z, Gong B, Tang C, Zhang Y, Ma T - ScientificWorldJournal (2014)

Failure state of the slope model.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4129176&req=5

fig13: Failure state of the slope model.
Mentions: Figure 12 shows the slope displacement distribution after excavating Step 10. The phenomena of ground heave appeared caused by the excavation and geostress unloading at EL 1100 m. Figure 13 shows the failure state of the slope model. The blue blocks were in elastic state, and blocks in the other colors were the plastic zone. It can be found that the plastic zones develop downward along the fissures XL316-1, especially in the area between fissures XL316-1 and XL9-15. The large plastic zone from EL 1135 to EL 1195 had considerable influence on the slope stability.

Bottom Line: A numerical model was established to simulate the displacement increment results, acquiring training data for the artificial neural network model.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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Rock Instability and Seismicity Research, Dalian University of Technology, Dalian, Liaoning 116024, China.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus