Mining Hazards to the Safety of Segment Lining for Tunnel Boring Machine Inclined Tunnels

The segment lining is a new type of support structure for mining tunnels. The disturbance of coal excavation leads to the deformation of segment lining and has great hazards to the safety of the tunnels. Based on the tunnel boring machine (TBM) inclined tunnels in Xinjie mine, the ultimate span L0 of the rock beam on the top slab of the coal seam was calculated according to the bending (tension) damage theory. A numerical model was built to simulate the bottom area of the inclined tunnels. During the coal mining, the additional displacements and additional stresses of the segment lining were analyzed, and then the safety factors of the support structure were calculated. Finally, the width of the coal pillar to protect the inclined tunnels was determined. The results showed that the ultimate span of the rock beam on the top of the coal seam is 31.7 m, the deformation of the inclined tunnel has a fish-belly shape, and the deformation leads to the increase of maximum axial force and bending moment. For the inclined tunnels in Xinjie coalmine, a total width of 91.3 m of coal pillar must be reserved to keep the safety factor of the structure higher than 2.0 and prevent the inclined tunnels from the mining hazards.

Advance prediction method for rock mass stability of tunnel boring based on deep neural network of time series

Geological layers excavated using tunnel boring machines are buried deeply and sampled difficultly, and the geological behavior exhibits high diversity and complexity. Excavating in uncertain geology conditions bears the risks of excessive damage to the equipment and facing geologic hazards. Many scholars have used various signals to predict the advance geology conditions, but accurate prediction of these conditions in real-time and without effecting operations has not been realized yet. In this article, based on a large amount of corresponding data, an advance prediction model of the rock mass category (RMC) is formulated. First, the problem is divided into two parts, which are modeled separately to reduce the complexity of design and training. Then, the two models are combined in a pre-trained model, which is retrained to as the final prediction model to avoid the problem of error accumulation. The final model can predict the advance RMC in real-time and without affecting operations. The accuracy of the prediction model reaches 99% at an advance time of 60 min. The advance RMC can be used to guide the selection of support modes and control parameters without additional detection equipment and excavation down-time.

The Study on Feasibility and Welding Characteristics of GMAW Surfacing Remanufacturing of H13 Steel Cutter Ring of TBM Hob

As H13 steel is a common material for cutters of Tunnel Boring Machine (TBM), the research on surfacing remanufacturing performance is of great value. In this paper, the phase composition of the surfacing layer of H13 steel after gas metal arc welding (GMAW) was analyzed by exploring the precipitation of hard phase in the molten pool, and the microstructure evolution of the surfacing layer was revealed. Then, we carried out simulation modeling analysis on H13 steel surfacing remanufacturing. Results show that: (1) the surfacing layer is combined with the base metal by physical metallurgy without obvious defects such as pores, inclusions and cracks in the surfacing layer; (2) the hardness of the surfacing layer is 60 HRC, which is about 1.5 times of that of the base metal; (3) the stress is mainly concentrated in the arc starting and ending points, followed by the external constraints on both sides of the surfacing layer; (4) the deformation of surfacing layer is slight, which does not affect the forming quality of base metal, while the deformation of base metal is relatively severe. This paper verifies the feasibility of H13 steel remanufacturing from experimental and simulation, providing theoretical basis for future engineering practice.

Assessment of rate of penetration of a tunnel boring machine in the longest railway tunnel of Turkey

One of the most important issues in tunnels to be constructed with tunnel boring machines (TBMs) is to predict the excavation time. Excavation time directly affects tunnel costs and feasibility. For this reason, studies on the prediction of TBM performance have always been interesting for tunnel engineers. Therefore, the purpose of the study is to develop models to predict the rate of penetration (ROP) of TBMs. In accordance with the purpose of the study, a new database including 5334 cases is obtained from the longest railway tunnel of Turkey. Each case includes uniaxial compressive strength, Cerchar Abrasivity Index, α angle, weathering degree and water conditions as input or independent variables. Two multiple regression models and two ANN models are developed in the study. The performances of the ANN models are considerably better than those of the multiple regression equations. Before deep tunnel construction in a metamorphic rock medium, the ANN models developed in the study are reliable and can be used. In contrast, the performances of the multiple regression equations are promising, but they predict lower ROP values than the measured ROP values. Consequently, the prediction models for ROP are open to development depending on the new data and new prediction algorithms.

Prediction of the number of consumed disc cutters of tunnel boring machine using intelligent methods

Purpose. Disc cutters are the main cutting tools for the Tunnel Boring Machines (TBMs). Prediction of the number of consumed disc cutters of TBMs is one of the most significant factors in the tunneling projects. Choosing the right model for predicting the number of consumed disc cutters in mechanized tunneling projects has been the most important mechanized tunneling topics in recent years. Methods. In this research, the prediction of the number of consumed disc cutters considering machine and ground conditions such as Power (KW), Revolutions per minute (RPM) (Cycle/Min), Thrust per Cutter (KN), Geological Strength Index (GSI) in the Sabzkooh water conveyance tunnel has been conducted by multiple linear regression analysis and multiple nonlinear regression, Gene Expression Programming (GEP) method and Support Vector Machine (SVM) approaches. Findings. Results showed that the number of consumed disc cutters for linear regression method is R2 = 0.95 and RMSE = 0.83, nonlinear regression method is – R2 = 0.95 and RMSE = 0.84, Gene Expression Programming (GEP) method is – R2 = 0.94 and RMSE = 0.95, Support Vector Machine (SVM) method is – R2 = 0.98 and RMSE = 0.45. Originality. During the analyses, in order to evaluate the accuracy and efficiency of predictive models, the coefficient of determination (R2) and root mean square error (RMSE) have been used. Practical implications. Results demonstrated that all four methods are effective and have high accuracy but the method of support vector machine has a special superiority over other methods.