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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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Displacement comparison between the measured displacements and the predicted displacements.
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Related In: Results  -  Collection


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fig8: Displacement comparison between the measured displacements and the predicted displacements.

Mentions: Usually, a three-layer BP neural network with M nodes in input layer, 2M + 1 nodes in hidden layer, and N nodes in output layer can be trained to express any functional relationship accurately [22]. Therefore, a BP neural network model with 10 input layer nodes and 1 output layer nodes was supposed to predict the elastic moduli of the rock masses of the high slope. The node number of the hidden layer was assigned to be 21 and the structure of the BP neural network model is 10-21-1. The BP neural network model was trained by using the samples created with the data of M410RBP monitored from March 27, 2010, to January 9, 2011. Then, other data were used for testing the validity of the BP neural network model. The comparison between measured displacements of M410RBP and the predicted displacements by the BP neural network is shown in Figure 8. It can be found that the proposed model can reflect the deformation trend with a good accuracy and basically meet the practical requirement in the slope engineering project.


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)

Displacement comparison between the measured displacements and the predicted displacements.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig8: Displacement comparison between the measured displacements and the predicted displacements.
Mentions: Usually, a three-layer BP neural network with M nodes in input layer, 2M + 1 nodes in hidden layer, and N nodes in output layer can be trained to express any functional relationship accurately [22]. Therefore, a BP neural network model with 10 input layer nodes and 1 output layer nodes was supposed to predict the elastic moduli of the rock masses of the high slope. The node number of the hidden layer was assigned to be 21 and the structure of the BP neural network model is 10-21-1. The BP neural network model was trained by using the samples created with the data of M410RBP monitored from March 27, 2010, to January 9, 2011. Then, other data were used for testing the validity of the BP neural network model. The comparison between measured displacements of M410RBP and the predicted displacements by the BP neural network is shown in Figure 8. It can be found that the proposed model can reflect the deformation trend with a good accuracy and basically meet the practical requirement in the slope engineering project.

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