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.

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.

Experimental Study on Creep of Surrounding Rock Mass in Argillaceous Soft Rock Tunnel

2012 ◽  
Vol 193-194 ◽  
pp. 826-830 ◽  
Author(s):  
Qiu Yan Fan ◽  
Jia Yan Lu ◽  
Zhen Zhu
Keyword(s):  
Rock Mass ◽  
Rock Fracture ◽  
Soft Rock ◽  
Creep Damage ◽  
Surrounding Rock ◽  
Laboratory Simulation ◽  
Creep Process ◽  
Laboratory Model ◽  
Accelerated Creep ◽  
Rock Tunnel

Due to the complexity of underground engineering, study on creep characteristics of surrounding rock mainly adopts laboratory simulation experiment. Recently, the similar materials are used to do the experiment both at home and abroad, yet it can't reflect the original nature and others of rock fissure in original rock mass. Through the field collection of undisturbed sample of Tertiary mudstone, the conclusions are made by laboratory model test on surrounding rock of underground tunnel and creep process regularity and failure mechanism in the surrounding rock in this paper as follows: there are three obvious stages of creep under certain stress levels, which is measured on the surface of surrounding rock mass in argillaceous soft rock tunnel--decay creep ,steady creep and accelerated creep. The surrounding rock will produce the accelerated creep damage when the stress level is over a threshold value. Creep damage of surrounding rock mainly includes the forms of roof sinking, floor working up, and forming a macro- fracture surfaces etc. Test results showed that the undisturbed surrounding rock fracture would be dissimilar to the homogeneous surrounding rock without fracture in damage model, and original fissure have a larger effect on creep damage of surrounding rock.

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.

Rheology-Based Stability Analysis of Soft Rock Tunnel in Construction Suspension

2014 ◽  
Vol 934 ◽  
pp. 129-135
Author(s):  
Su Min Zhang ◽  
Yong Quan Zhu
Keyword(s):  
Stability Analysis ◽  
Cross Section ◽  
Back Analysis ◽  
Soft Rock ◽  
Calculation Model ◽  
Surrounding Rock ◽  
Tunnel Construction ◽  
Rock Tunnel ◽  
Primary Support ◽  
Water Contents

Suspension in tunnel construction usually cause tunnel callapse in weak ground if the suspension time is long. Dongkeling tunnel on Guizhou-Guangxi line being as the engineering background, the indoor rheological experiment and back analysis were performed to obtain the rheological model and parameters of the surrounding rock, and the 3D viscoelasto-plastic calculation model was established to find the evolving rules of deformation at critical locations under two different water contents. It was found that the primary support has little effect on rheology of tunnel work face but on the cross section, and decreasing the water content of the surrounding rock to medium or low saturation level favors the tunnel stabillity greatly for water abundent tunnels.

Application of BP Neural Network PID Algorithm in Refrigerated Space Temperature Control

2014 ◽  
Vol 599-601 ◽  
pp. 827-830 ◽  
Author(s):  
Wei Tian ◽  
Yi Zhun Peng ◽  
Pan Wang ◽  
Xiao Yu Wang
Keyword(s):  
Neural Network ◽  
Temperature Control ◽  
Bp Neural Network ◽  
Pid Control ◽  
Bp Algorithm ◽  
Pid Algorithm ◽  
Controller Performance ◽  
Neural Network Algorithm ◽  
Adaptive Ability ◽  
Neural Network Pid

Taking the temperature control of a refrigerated space as example, this paper designs a controller which is based on traditional PID operation and BP neural network algorithm. It has better steady-state precision and adaptive ability. Firstly, the article introduces the concepts of the refrigerated space, PID and BP algorithm. Then, the temperature control of refrigerated space is simulated in MATLAB. The PID parameters will be adjusted by simulation in BP Neural Network. The PID control parameters could be created real-time online, which makes the controller performance best.

Back Analysis Methods in Geotechnical Engineering

2014 ◽  
Vol 1020 ◽  
pp. 423-428 ◽  
Author(s):  
Eva Hrubesova ◽  
Marek Mohyla
Keyword(s):  
Rock Mass ◽  
Geotechnical Engineering ◽  
Back Analysis ◽  
Rheological Parameters ◽  
Analysis Method ◽  
Direct Optimization ◽  
Initial Stress State ◽  
Analytical Functions ◽  
Is Evaluation

The paper deals with the back analysis method in geotechnical engineering, that goal is evaluation the more objective and reliable parameters of the rock mass on the basis of in-situ measurements. Stress, deformational, strength and rheological parameters of the rock mass are usually determined by some inaccuracies and errors arising from the complexity and variability of the rock mass. This higher or lower degree of imprecision is reflected in the reliability of the mathematical modelling results. The paper presents the utilization of direct optimization back analysis method, based on the theory of analytical functions of complex variable and Kolosov-Muschelischvili relations, to the evaluation of initial stress state inside the rock massif.

Judgement Method for Surrounding Rock Stability of Guanjiao Tunnel Based on Catastrophe Theory

2011 ◽  
Vol 90-93 ◽  
pp. 2307-2312 ◽  
Author(s):  
Wen Jiang Li ◽  
Su Min Zhang ◽  
Xian Min Han
Keyword(s):  
Plastic Strain ◽  
Catastrophe Theory ◽  
Soft Rock ◽  
Surrounding Rock ◽  
In Situ Monitoring ◽  
Engineering Practice ◽  
Stability Of Surrounding Rock ◽  
The Stability ◽  
Rock Tunnel

The stability judgement of surrounding rock is one of the key jobs in tunnel engineering. Taking the Erlongdong fault bundle section of Guanjiao Tunnel as the background, the stability of surrounding rock during construction of soft rock tunnel was discussed preliminarily. Based on plastic strain catastrophe theory, and combining numerical results and in-situ data, the limit displacements for stability of surrounding rock were analyzed and obtained corresponding to the in-situ monitoring technology. It shows that the limit displacements obtained corresponds to engineering practice primarily. The plastic strain catastrophe theory under unloading condition provides new thought for ground stability of deep soft rock tunnel and can be good guidance and valuable reference to construction decision making and deformation managing of similar tunnels.

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.

Sign in / Sign up

Export Citation Format

Share Document