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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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Accumulative displacement curves of the monitoring points TP28R and TP31R.
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fig6: Accumulative displacement curves of the monitoring points TP28R and TP31R.

Mentions: Displacement monitoring provides a helpful technique to predict slope stability. Trends of displacement variation can be traced during excavation and supporting periods. The excavation of the right bank slope of Dagangshan Hydroelectric Power Station caused lots of concrete surface cracks due to stress adjustments caused by the excavations. The monitoring displacement data of points TP28R and TP31R, which were located at Elevation 1100 m and Elevation 1070 m, are shown in Figure 6. It can be observed that the slope has large displacement obviously along the horizontal direction (X direction) under the excavations.


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)

Accumulative displacement curves of the monitoring points TP28R and TP31R.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig6: Accumulative displacement curves of the monitoring points TP28R and TP31R.
Mentions: Displacement monitoring provides a helpful technique to predict slope stability. Trends of displacement variation can be traced during excavation and supporting periods. The excavation of the right bank slope of Dagangshan Hydroelectric Power Station caused lots of concrete surface cracks due to stress adjustments caused by the excavations. The monitoring displacement data of points TP28R and TP31R, which were located at Elevation 1100 m and Elevation 1070 m, are shown in Figure 6. It can be observed that the slope has large displacement obviously along the horizontal direction (X direction) under the excavations.

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