Improved BP neural network-based back analysis of displacements

2010 ◽  
Author(s):  
Zhang guihua ◽  
Ma xianmin ◽  
Chaijing
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Back Analysis

Back-Analysis of Indoor Test Parameters for Rock-Fill of Shui Bu Ya CFRD

2011 ◽  
Vol 243-249 ◽  
pp. 4581-4586
Author(s):  
Lei Ming He ◽  
Li Hui Du ◽  
Jian Yang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Back Analysis ◽  
Soil Parameters ◽  
Model Parameters ◽  
Analysis Method ◽  
Filling Process ◽  
Rock Fill ◽  
Test Parameters ◽  
Displacement Back Analysis

In the numerical calculation of geotechnical project, it’s difficult to confirm the parameters because of the complexity and the uncertainty of them as the time is changing. However, the back-analysis provides us an effective way. Based on the result of the triaxial test on rock-fill of Shui Bu Ya CFRD, the thesis adopts the direct back-analysis method which combines the BP Neural Network and Genetic Algorithm to calculate the Tsinghua non-linear K-G model parameters of the rock-fill. The back-analysis parameters are used to simulate the filling process of Shui Bu Ya CFRD and predict the displacement of the dam. The thesis provides a technical reference for displacement back-analysis of soil parameters for CFRD.

Application of GA-BP to Back Analysis of Rock’s Parameters

2013 ◽  
Vol 671-674 ◽  
pp. 175-179
Author(s):  
Guo Feng Wang ◽  
Wen Zhao ◽  
Yong Ping Guan ◽  
Shen Gang Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Back Analysis ◽  
Surrounding Rock ◽  
Railway Tunnel ◽  
Training Time ◽  
Material Parameters ◽  
Underground Caverns ◽  
Excavation Stability ◽  
Selection Of

The selection of material parameters relates to the excavation stability of the underground caverns. However, back analysis is an efficient method to evaluate mechanical parameters. Given the defects of BP neural network, such as low capability of generalization and long training time, by using GA, which have global optimization ability to optimize the BP neural network weights. The parameter of surrounding rock was designed by uniform and orthogonal method, not only reduced the iterative time also improved the accuracy of the prediction. The proposed method is further illustrated with its application to the underground cavern of Lvchunba railway tunnel. Based on the surrounding rock’s parameters obtained by back analysis, the displacement of the surrounding rock was predicted. The results showed that the error between numerical calculation value and actual monitoring value was 13.2%,-8.3%,-8.9%,9.4% respectively.

scholarly journals Multiparameter Inversion Early Warning System of Tunnel Stress-Seepage Coupling Based on IA-BP Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Junxiang Wang ◽  
Jie Sun ◽  
Haijun Kou ◽  
Yaxian Lin
Keyword(s):  
Neural Network ◽  
Rock Mass ◽  
Bp Neural Network ◽  
Back Analysis ◽  
Soft Rock ◽  
Surrounding Rock ◽  
Bp Algorithm ◽  
Analysis Method ◽  
Rock Tunnel ◽  
Design And Construction

Under construction disturbance, the surrounding rock of a soft rock tunnel shows obvious aging characteristics. The creep characteristics of a rock mass under stress-seepage coupling greatly influence the long-term stability of a project. How to simply, quickly, and accurately determine the creep parameters of a rock mass under coupling conditions is significant to engineering structure design and construction. The optimal weights and thresholds of the BP neural network are sought through the immune algorithm to avoid the problem of slow convergence speed of the BP neural network and easy to fall into local optimum. Therefore, an intelligent back analysis method based on the IA-BP algorithm is established, which leads to the development of the corresponding intelligent back analysis program. The creep effect of the rock mass was simulated herein using the Drucker–Prager yield criterion and the time hardening creep law as the forward optimization method constitutive model. In addition, a sensitivity analysis of the parameters was performed to determine the optimal number of inversion parameters. By comparing and analyzing the residual between the inversion results of the IA-BP algorithm, PSO-BP algorithm, and the test values, the high precision of the IA-BP algorithm is proved. Taking the Lan Zhou-Hai Kou national expressway tunnel as an engineering example, a multiparameter creep inversion of the tunnel surrounding rock under the stress-seepage coupling condition was conducted using the inverse analysis method of the IA-BP algorithm. The results showed that the proposed IA-BP algorithm can effectively prevent the BP neural network from falling into a local minimum. Also, the algorithm is fast and accurate. The intelligent back analysis method based on the IA-BP algorithm is applied to the multifield coupling parameter back analysis, provides the basis and help for the structural design and construction of soft rock tunnel in water-rich stratum.

scholarly journals Displacement Back Analysis for a High Slope of the Dagangshan Hydroelectric Power Station Based on BP Neural Network and Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Zhengzhao Liang ◽  
Bin Gong ◽  
Chunan Tang ◽  
Yongbin Zhang ◽  
Tianhui Ma
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Back Analysis ◽  
Hydroelectric Power Station ◽  
Mechanical Parameters ◽  
Hydroelectric Power ◽  
Power Station ◽  
Bp Neural Network Model

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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