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 An image denoising method based on BP neural network optimized by improved whale optimization algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chunzhi Wang ◽  
Min Li ◽  
Ruoxi Wang ◽  
Han Yu ◽  
Shuping Wang
Keyword(s):  
Neural Network ◽  
Image Denoising ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Weight Coefficient ◽  
Whale Optimization Algorithm ◽  
Denoising Method ◽  
Training Time ◽  
Whale Optimization ◽  
City Construction

AbstractAs an important part of smart city construction, traffic image denoising has been studied widely. Image denoising technique can enhance the performance of segmentation and recognition model and improve the accuracy of segmentation and recognition results. However, due to the different types of noise and the degree of noise pollution, the traditional image denoising methods generally have some problems, such as blurred edges and details, loss of image information. This paper presents an image denoising method based on BP neural network optimized by improved whale optimization algorithm. Firstly, the nonlinear convergence factor and adaptive weight coefficient are introduced into the algorithm to improve the optimization ability and convergence characteristics of the standard whale optimization algorithm. Then, the improved whale optimization algorithm is used to optimize the initial weight and threshold value of BP neural network to overcome the dependence in the construction process, and shorten the training time of the neural network. Finally, the optimized BP neural network is applied to benchmark image denoising and traffic image denoising. The experimental results show that compared with the traditional denoising methods such as Median filtering, Neighborhood average filtering and Wiener filtering, the proposed method has better performance in peak signal-to-noise ratio.

Study on Deformation and Damage of Rock Mass Subject to High In Situ Stress by Using a Elastoplastic Damage Model and Considering the Impact of Weak Planes

2013 ◽  
Vol 838-841 ◽  
pp. 705-709
Author(s):  
Yun Hao Yang ◽  
Ren Kun Wang
Keyword(s):  
Rock Mass ◽  
Large Scale ◽  
Damage Model ◽  
Back Analysis ◽  
In Situ Stress ◽  
Surrounding Rock ◽  
High In Situ Stress ◽  
Underground Caverns ◽  
The Impact

Large scale underground caverns are under construction in high in-situ stress field at Houziyan hydropower station. To investigate deformation and damage of surrounding rock mass, a elastoplastic orthotropic damage model capable of describing induced orthotropic damage and post-peak behavior of hard rock is used, together with a effective approach accounting for the presence of weak planes. Then a displacement based back analysis was conducted by using the measured deformation data from extensometers. The computed displacements are in good agreement with the measured ones at most of measurement points, which confirm the validities of constitutive model and numerical simulation model. The result of simulation shows that damage of surrounding rock mass is mainly dominated by the high in-situ stress rather than the weak planes and heavy damage occur at the cavern shoulders and side walls.

Back Analysis for Mass Parameters of Tunnel Surrounding Rock

2012 ◽  
Vol 170-173 ◽  
pp. 20-24 ◽  
Author(s):  
Kai Cui ◽  
Xue Kai Pan
Keyword(s):  
Deformation Mechanism ◽  
Strain Softening ◽  
Back Analysis ◽  
Surrounding Rock ◽  
Tunnel Engineering ◽  
Railway Tunnel ◽  
Displacement Back Analysis ◽  
Information Construction ◽  
Engineering Information ◽  
The Common

Tunnel engineering information construction has been widely used, and the back analysis is its core. As the common useful method, displacement back analysis is of special advantages. This paper introduces the calculative method based on the application in a railway tunnel. The result shows that strain softening model can be used to simulate the large deformation mechanism of surrounding rock.

A RBF Neural Network Approach for Fitting Creep Curve of Sandstone

2010 ◽  
Vol 171-172 ◽  
pp. 274-277
Author(s):  
Yun Liang Tan ◽  
Ze Zhang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Creep Curve ◽  
Bp Neural Network ◽  
Rbf Neural Network ◽  
Reference Value ◽  
Network Approach ◽  
Neural Network Approach ◽  
Training Time ◽  
Set Up

In order to quest an effective approach for predicate the rheologic deformation of sandstone based on some experimental data, an improved approaching model of RBF neural network was set up. The results show, the training time of improved RBF neural network is only about 10 percent of that of the BP neural network; the improved RBF neural network has a high predicating accuracy, the average relative predication error is only 7.9%. It has a reference value for the similar rock mechanics problem.

scholarly journals Fault Diagnosis of Transmission Line using Feed Forward Neural Network

2019 ◽  
Vol 8 (9S2) ◽  
pp. 263-268
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Transmission Lines ◽  
Field Work ◽  
Neural Nets ◽  
Feed Forward Neural Network ◽  
Training Time ◽  
Optimum Result ◽  
Human Effort ◽  
Selection Of

The implementation of neural network for the fault diagnosis is to improve the dependability of the proposed scheme by providing a more accurate, faster diagnosis relaying scheme as compared with the conventional relaying schemes. It is important to improve the relaying schemes regarding the shortcoming of the system and increase the dependability of the system by using the proposed relaying scheme. It also provide more accurate, faster relaying scheme. It also gives selective schemes as compared to conventional system. The techniques for survey employed some methods for the collection of data which involved a literature review of journals, from review on books, newspaper, magazines as well as field work, additional data was collected from researchers who are working in this field. To achieve optimum result we have to improve following things: (i) Training time, (ii) Selection of training vector, (iii) Upgrading of trained neural nets and integration of technologies. AI with its promise of adaptive training and generalization deserves scope. As a result we obtain a system which is more reliable, more accurate, and faster, has more dependability as well as it will selective according to the proposed relaying scheme as compare to the conventional relaying scheme. This system helps us to reduce the shortcoming like major faults which we faced in the complex system of transmission lines which will helps in reducing human effort, saves cost for maintaining the transmission system.

Time Series Forecasting of Tunnel Surrounding Rock Displacement

2011 ◽  
Vol 261-263 ◽  
pp. 1789-1793 ◽  
Author(s):  
Guang Xiang Mao ◽  
Yuan You Xia ◽  
Ling Wei Liu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Bp Neural Network ◽  
Time Series Prediction ◽  
Surrounding Rock ◽  
Tunnel Construction ◽  
Rock Stress ◽  
Rock Types ◽  
Network Time ◽  
Tunnel Depth

In the process of tunnel construction, because the rock stress redistribute, the vault and the two groups will generate displacement constantly. This paper adopts the genetic algorithm to optimize the weight and threshold of BP neural network, taking the tunnel depth, rock types and part measured values of displacement as input parameters to construct a neural network time series prediction model of tunnel surrounding rock displacement. The method proposed in the paper has been applied in the Ma Tou Tang tunnel construction successfully, and the results show that the model can predict the displacement of the surrounding rock quickly and accurately.

scholarly journals Study on selecting of prefabricated plant site basing on BP neural network

2018 ◽  
Vol 53 ◽  
pp. 03073
Author(s):  
Yao Gang ◽  
Yang Yang ◽  
Shen Xin ◽  
Li Jun
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
High Precision ◽  
Bp Neural Network ◽  
Raw Materials ◽  
Output Factor ◽  
Plant Site ◽  
Selection Of ◽  
Transportation Facilities

In this paper, the evaluation and prediction model of prefabricated plant site was established by BP neural network, which taking nine factors into consideration, such as location, topography, land scale, transportation facilities, availability of raw materials and labour. These nine factors were taken as input factors, and the normalized global value was taken as output factor. The normalized global value was used to evaluate the performance of prefabricated plant site. In addition, the model was verified to be accurate by analysing twelve prefabricated plant site samples. Therefore, it is obvious that the model is stable in operation with high precision, and can provide effective support in the selection of prefabricated plant site.

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