scholarly journals A Back Analysis Method Using Measured Displacements for Predicting Deformational Behavior of Discontinuous Rock Mass.

1996 ◽  
pp. 11-22
Author(s):  
Norikazu Shimizu ◽  
Hiroshi Kakihara ◽  
Hidekazu Terato ◽  
Koji Nakagawa
Keyword(s):  
Rock Mass ◽  
Back Analysis ◽  
Analysis Method ◽  
Deformational Behavior ◽  
Discontinuous Rock

Predication of Displacement of Tunnel Rock Mass Based on the Back-Analysis Method-BP Neural Network

2021 ◽  
Author(s):  
Wenzheng Cao ◽  
Yujing Jiang ◽  
Osamu Sakaguchi ◽  
Ningbo Li ◽  
Wei Han
Keyword(s):  
Neural Network ◽  
Rock Mass ◽  
Bp Neural Network ◽  
Back Analysis ◽  
Analysis Method

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.

Longitudinal deformation profile of a tunnel in weak rock mass by using the back analysis method

2018 ◽  
Vol 71 ◽  
pp. 478-493 ◽  
Author(s):  
Yanbin Luo ◽  
Jianxun Chen ◽  
Yi Chen ◽  
Pengsheng Diao ◽  
Xiong Qiao
Keyword(s):  
Rock Mass ◽  
Back Analysis ◽  
Weak Rock ◽  
Analysis Method ◽  
Longitudinal Deformation ◽  
Weak Rock Mass

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.

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