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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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Numerical model of section IX-IX of the right bank slope.
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Related In: Results  -  Collection


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fig9: Numerical model of section IX-IX of the right bank slope.

Mentions: According to the geologic investigations and monitoring data, Profile LPIX-IX (0+161.19 from upstream to downstream) was selected as the typical profile for displacement back analysis. The model covered most of the disturbed zones of the high slope: 700 meters long toward the inner slope from the centerline of the riverbed in transverse direction (as X direction) and 625 meters in vertical direction from EL 900 m to EL 1525 m (as Y direction). Only a unit thickness in Z direction was considered by simplifying the model into a plan strain problem. The bottom boundary was fixed in the vertical direction and surrounding boundaries were fixed in their respective normal directions. The number of the numerical elements is 4923, and the number of nodes is 10244. All the monitoring points were located at the nodes. The weak geological structural planes were built in the model, namely, fault f231, fault f65, and unloading fractures such as XL9-15 and XL316-1. The mesh of the right bank slope is shown in Figure 9.


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)

Numerical model of section IX-IX of the right bank slope.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig9: Numerical model of section IX-IX of the right bank slope.
Mentions: According to the geologic investigations and monitoring data, Profile LPIX-IX (0+161.19 from upstream to downstream) was selected as the typical profile for displacement back analysis. The model covered most of the disturbed zones of the high slope: 700 meters long toward the inner slope from the centerline of the riverbed in transverse direction (as X direction) and 625 meters in vertical direction from EL 900 m to EL 1525 m (as Y direction). Only a unit thickness in Z direction was considered by simplifying the model into a plan strain problem. The bottom boundary was fixed in the vertical direction and surrounding boundaries were fixed in their respective normal directions. The number of the numerical elements is 4923, and the number of nodes is 10244. All the monitoring points were located at the nodes. The weak geological structural planes were built in the model, namely, fault f231, fault f65, and unloading fractures such as XL9-15 and XL316-1. The mesh of the right bank slope is shown in Figure 9.

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