Tunnel Surrounding Rock Displacement Research Articles

Support technology for mine roadways in extreme weakly cemented strata and its application

For the engineering geology conditions of bad mine roadway roof and floor lithology in extremely weak cemented strata, the best section shape of the roadway is determined from the study of tunnel surrounding rock displacement, plastic zone and stress distribution in rectangular, circle arch and arch wall sections, respectively. Based on the mining depth and thickness of the coal seam, roadway support technology solutions with different buried depth and thickness of coal seam are proposed. Support schemes are amended and optimized in time through monitoring data of the deformation of roadway, roof separation, I-beam bracket, bolt and anchor cable force to ensure the long-term stability and security of the roadway surrounding rock and support structure. The monitoring results show that mine roadway support schemes for different buried depth and section can be adapted to the characteristics of ground pressure and deformation of the surrounding rock in different depth well, effectively control the roadway surrounding rock deformation and the floor heave and guarantee the safety of construction and basic stability of surrounding rock and support structure.

Displacement Prediction of Tunnel Surrounding Rock: A Comparison of Support Vector Machine and Artificial Neural Network

Displacement prediction of tunnel surrounding rock plays an important role in safety monitoring and quality control tunnel construction. In this paper, two methodologies, support vector machines (SVM) and artificial neural network (ANN), are introduced to predict tunnel surrounding rock displacement. Then the two modes are texted with the data ofFangtianchongtunnel, respectively. The comparative results show that solutions gained by SVM seem to be more robust with a smaller standard error compared to ANN. Generally, the comparison between artificial neural network (ANN) and SVM shows that SVM has a higher accuracy prediction than ANN. Results also show that SVM seems to be a powerful tool for tunnel surrounding rock displacement prediction.

Key Problems on New Underpass Beneath the Existing Highway

This paper is a new crossing of a certain city through existing project based on the highway, the 3D numerical simulation is used to the new crossing the roads longitudinal effects of vertical scope, the settlement of surrounding rock and the road, the stress value, as well as the lining of the stress state of. Research shows that six excavation can effectively control the road and the new tunnel surrounding rock displacement and stress by using CRD method in construction; the existing road longitudinal impact area from both ends of road about 6m; lining structure in a safe state, but the wall removed, effect on the internal force of lining is larger, should give attention to construction.

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.

HYBRID MODEL FOR DISPLACEMENT PREDICTION OF TUNNEL SURROUNDING ROCK

This paper presents a hybrid method to predict tunnel surrounding rock displacement, which is one of the most important factors for quality control and safety during tunnel construction. The hybrid method comprises two phases, one is support vector machine (SVM)-based model for predicting the tunnel surrounding rock displacement, and the other is GA-based model for optimizing the parameters in the SVM. The proposed model is evaluated with the data of tunnel surrounding rock displacement on the tunnel of Wuhan-Guangzhou railway in China. The results show that genetic algorithm (GA) has a good convergence and relative stable performance. The comparison results also show that the hybrid method can generally provide a better performance than artificial neural network (ANN) and finite element method (FEM) for tunnel surrounding rock displacement prediction.

The Comparative Study on the Impact of Tunnel Excavation with Footage Different CRD to Testability of Surrounding Rock

This article used Geotechnical Engineering General Software FLAC3D to simulate the tunnel CRD method under 1.5m, 2.0m, 2.5 m, 3.0 m footage separately and analysis the influence to the tunnel surrounding rock stability in the different footage construction. This paper selected a real tunnel project as the research object and analysis comparatively the rule and characteristic of various methods of tunnel surrounding rock displacement, stress distribution and the plastic zone size through simulated computing the different footage construction. The results show that,Ⅰ.Considered from the surrounding rock stress control that the 1.5m footage adjacent formation stress adjustment effect is best, next is 2.0m, 2.5 m, 3.0 m;Ⅱ.Considered from the plastic zone size that the 1.5m footage control effect is best, next is in turn 2.0m, 2.5 m, 3.0 m;ⅢConsidered from the surrounding rock displacement control that 1.5 meter footage adjacent formation control effect is best, but 2.0 meter, 2.5 meters and 3.0 meter phase differences are not big.

Time Series Forecasting of Tunnel Surrounding Rock Displacement

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.

Tunnel Surrounding Rock Displacement Prediction Using Support Vector Machine

Multi-step-ahead prediction of tunnel surrounding rock displacement is an effective way to ensure the safe and economical construction of tunnels. This paper presents a multi-step-ahead prediction model, which is based on support vector machine (SVM), for tunnel surrounding rock displacement prediction. To improve the training efficiency of SVM, shuffled complex evolution algorithm (SCE-UA) is also performed through some exponential transformation. The data from the Chijiangchong tunnel are used to examine the performance of the prediction model. Results show that SVM is generally better than artificial neural network (ANN). This indicates that SVM is a feasible and effective multi-step method for tunnel surrounding rock displacement prediction.

Applying Support Vector Machines to Predict Tunnel Surrounding Rock Displacement

Displacement prediction of tunnel surrounding rock plays a significant role for safety estimation during tunnel construction. This paper presents an approach to use support vector machines (SVM) to predict tunnel surrounding rock displacement. A stepwise search is also introduced to optimize the parameters in SVM. The data of Fangtianchong tunnel is use to evaluate the proposed model. The comparison between artificial neural network (ANN) and SVM shows that SVM has a high-accuracy prediction than ANN. Results also show SVM seems to be a powerful tool for tunnel surrounding rock displacement prediction.