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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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Convergence curve of the BP neural network.
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fig11: Convergence curve of the BP neural network.

Mentions: The training samples were used to train the BP network, while the PSO was adopted to search for the relatively matched network model in advance, making the summed square error between the displacements from the numerical simulation and those from the BP network a minimum. Only the last neural network model was described here. In the particle swarm optimization model, the particle number was 25, the total evolution number was 200, the inertia weight was varied linearly from 0.95 to 0.4, the learning factors (c1′ and c2′) were both 2.02, and upper speed limit was 0.5. Then, the BP neural network initialized by PSO was trained by the trainlm algorithm. The tansig function and purelin function were used as the transfer functions from the input layer to the hidden layer and from the hidden layer to the output layer, respectively. The convergence curve for the summed square error is shown in Figure 11. The BP neural network was finally established for the following back analysis when the mean square error tended to be a minimal value.


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)

Convergence curve of the BP neural network.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig11: Convergence curve of the BP neural network.
Mentions: The training samples were used to train the BP network, while the PSO was adopted to search for the relatively matched network model in advance, making the summed square error between the displacements from the numerical simulation and those from the BP network a minimum. Only the last neural network model was described here. In the particle swarm optimization model, the particle number was 25, the total evolution number was 200, the inertia weight was varied linearly from 0.95 to 0.4, the learning factors (c1′ and c2′) were both 2.02, and upper speed limit was 0.5. Then, the BP neural network initialized by PSO was trained by the trainlm algorithm. The tansig function and purelin function were used as the transfer functions from the input layer to the hidden layer and from the hidden layer to the output layer, respectively. The convergence curve for the summed square error is shown in Figure 11. The BP neural network was finally established for the following back analysis when the mean square error tended to be a minimal value.

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