Limits...
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.

Show MeSH

Related in: MedlinePlus

Survey of the Dagangshan Hydropower Station.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4129176&req=5

fig1: Survey of the Dagangshan Hydropower Station.

Mentions: The Dagangshan Hydroelectric Power Station is located at the midstream of Dadu River in Shimian County, west of Sichuan Province in China. It is one of the largest hydroelectric projects which are currently being constructed along the mainstream of the Dadu River. The normal water level of the station dam is 1130 m, the dead water level is 1120 m, and the storage capacity below the normal water level is about 0.742 billion m3. Its maximum height is nearly 210 m. The power station is installed with a capacity of 2600 MW [11]. The dam is located in a typical valley of a “V” shape with steep and high bank slopes (Figure 1).


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)

Survey of the Dagangshan Hydropower Station.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig1: Survey of the Dagangshan Hydropower Station.
Mentions: The Dagangshan Hydroelectric Power Station is located at the midstream of Dadu River in Shimian County, west of Sichuan Province in China. It is one of the largest hydroelectric projects which are currently being constructed along the mainstream of the Dadu River. The normal water level of the station dam is 1130 m, the dead water level is 1120 m, and the storage capacity below the normal water level is about 0.742 billion m3. Its maximum height is nearly 210 m. The power station is installed with a capacity of 2600 MW [11]. The dam is located in a typical valley of a “V” shape with steep and high bank slopes (Figure 1).

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