Tunnel Surrounding Rock Deformation Research Articles

Optimization design of pre-reinforcement scheme for large cross section shallow buried tunnel in Xi’an Metro

In order to control the deformation of the surrounding rock of the large section of the tunnel, the author analyzes the stratum displacement law caused by the tunnel excavation by means of on-site monitoring. And the optimized design of the existing tunnel pre-reinforcement scheme is carried out. The FLAC3D numerical simulation method is used to verify the feasibility of the pre-reinforcement optimization scheme. Field test showed that the tunnel surrounding rock deformation in the allowable range, and the deformation control measures of the surrounding rock are reasonable and effective. The research results have important guiding significance for future similar project.

Supporting Force Characteristics and Extrusion Deformation of Tunnel Surrounding Rock in Layered Stratum Under High Ground Stress Condition

Supporting Force Characteristics and Extrusion Deformation of Tunnel Surrounding Rock in Layered Stratum Under High Ground Stress Condition

Influence of Side-Pressure Coefficient on Deformation of Tunnel Surrounding Rock and Bolt Axial Force

Internal force and deformation of surrounding rock and supporting structure of the nonlinear research is the use of finite element software ADINA by ideal elastic-plastic constitutive model.Results show that the lateral pressure coefficient increased from 0 to 1, and even decrease sharply arch sedimentation of surrounding rock, side wall horizontal displacement towards the hole along the radial direction development gradually reduced to 0 and reverse to the hole, when the lateral pressure coefficient is less than 0.5, bolt axial force biggest change is not obvious, when lambda increases gradually, the largest bolt axial force significantly increased.

Research on Grey System Model and Its Application on Displacement Prediction in Tunnel Surrounding Rock

In the process of the highway tunnel construction, the stability and the reliability of tunnel rock is an important guarantee to ensure safety construction. Tunnel surrounding rock deformation monitoring is an important method for obtaining information on surrounding rock and controlling tunnel stability in the period of construction. Forecasting deformation of surrounding rock is the key to estimate shoring types, parameter and longtime stability after being commissioned for use. Considering the non-linear characteristic of deformation of the tunnel, the grey system prediction models were proposed. Based on the displacement of Guankouya rock tunnel, grey models of the GM(1, 1) and the Model DGM(2, 1) were established for the tunnel grey forecasting model of the rock tunnel displacement. The calculated results show that two models for tunnel displacement generally were predictable. GM(1, 1) and DGM(2, 1) models are similar to the tunnel displacement development model. Application examples demonstrate that it has extraordinary adaptability to the tunnel displacement forecast and all types of surrounding rock displacement can be predicted better by the grey model and the model has high simulation and prediction accuracy.

Study on the Deformation Forecasting for the Tunnel Surrounding Rock Using the Neural Network

The non-pillar sublevel caving method is used in Iron Mine in Banshi. In the mining area, there are many folds and faults, the inclination of ore body changes greatly, and ore and rock are fragmentized. The tunnel often collapsed and the surrounding rock deformation was getting large during the construction stage. Using the data of tunnel surrounding rock deformation, we adopt the neural network method to set up the mapping relation between the tunnel surrounding rock deformation and the project factors, including tunnel deepness, tunnel dimension, measuring time and surrounding rock quality. The analyzing results show that the maximum error between the forecast and the testing data is 13%, which indicates that this method is useful and feasible to the mining engineering. Key words: rock pressure; measure, deformation of the tunnel surrounding rock; neural network; data normalization; mapping

The Automatic and Real Time Measurement of Tunnel Surrounding Rock Deformation

We designed a tunnel surrounding rock(TSR) deformation automatic and real time measurement system .The long distance laser datum line instrument (LDLI) is fixed in stable ceiling rock of the tunnel. The LDLI transmits a bunch of lateral linear benchmark beam with small facula, and projects to the linear array CCD which mounted on instable surrounding rock. The linear array CCD detected the facula centre position real time. The facula displacement within a period is equal to the displacement of instable rock. We used the wireless data transmission module and optical fiber transceiver to transfer the displacement data to the on duty room computer. We compiled the data processing software to display the measure result in graphical and give a judge for the instable surrounding rock. If it is in danger, the alarm module gives audible and visible alarm signal in site and send a message to the manager’s mobile phone. The measurement accuracy is less than 1mm. We used this system to measure the tunnel surrounding rock deformation automatically and real time to realize intelligent management in several tunnel

Tunnel Surrounding Rock Deformation Characteristics and Control in Deep Coal Mining

In order to study the failure characteristics and control method of deep tunnel surrounding rock, based on the stress test, the structure and stress state of the main transportation tunnel surrounding rock in Mine Zhaogezhuang level 14 was analyzed, and it shows that the surrounding rock is exposed to an interphase hard and soft disadvantageous structure state and complex high stress repeated addition area; Through the theoretical analysis and the statistical data, the relation between the tunnel stress transformation and the surrounding rock deformation was proposed; Through the numerical simulation of the tunnel surrounding rock failure process with the help of RFPA procedure, the results show that the damage of the transportation tunnel level 14 mainly occurs in the bottom and the two coal ribs, and the failure process is spreading from the bottom to the two coal ribs, and the effect of the surrounding rock deformation control is obvious by using the way of 2.5 m anchor with 1.0 m grouting strengthening.

Research of Three-Exponential Smoothing Method in Tunnel Displacement Prediction

Prediction of tunnel surrounding rock displacement is a hot topic. To accurately predict the displacement of tunnel surrounding, we put forward three exponential smoothing method and describe its basic theory. Based on the measured displacement of a tunnel monitoring sections in Northwest area, compared with traditional regression analysis and grey model as GM (1, 1) to predict the displacement, the results show that the average error and variance about prediction of displacement using three exponential smoothing method is minimum. This method is suitable for the prediction of tunnel surrounding rock deformation and can be applied to practical engineering.

Novel High-Precision Grey Forecasting Model and its Application of Deformation of Tunnel Surrounding Rock

Accurately estimating the deformation of tunnel surrounding rock is a very important work for surveyors, and we adopted grey model as a forecasting means because of its fast calculation with as few as four data inputs needed, however, the original GM (1, 1) model is not fit for dynamic and long data prediction. For this purpose, we propose a novel approach to improve prediction accuracy of GM(1,1) model through optimization of the initial condition and adoption the technique of rolling modeling, the new forecasting model termed RnGM(1,1). By using the optimized model to analyze and predict the deformation of tunnel surrounding rock and comparing this optimized model with other models, we finally draw a conclusion that this optimized model is able to improve the precision of prediction and therefore can be applied to deformation data analysis.