Underground Space最新文献

Double-arch tunnels, as one of the popular forms of tunnels, might be exposed to boiling liquid expanding vapour explosions (BLEVEs) associated with transported liquified petroleum gas (LPG), which could cause damage to the tunnel and even catastrophic collapse of the tunnel in extreme cases. However, very limited study has investigated the performance of double-arch tunnels when exposed to internal BLEVEs and in most analyses of tunnel responses to accidental explosions. The TNT-equivalence method was used to approximate the explosion load, which may lead to inaccurate tunnel response predictions. This study numerically investigates the response of typical double-arch tunnels to an internal BLEVE resulting from the instantaneous rupture of a 20 m³ LPG tank. Effects of various factors, including in-situ stresses, BLEVE locations, and lining configurations on tunnel responses are examined. The results show that the double-arch tunnels at their early-operation ages are more vulnerable to severe damage when exposed to the BLEVE due to the low action of in-situ stress of rock mass on the response of early-age tunnels. It is also found that directing the LPG tank to different driving lanes inside tunnels can affect the BLEVE-induced tunnel response more significantly than varying the configurations of tunnel lining. Moreover, installing section-steel arches in the mid-wall can effectively improve the blast resistance of the double-arch tunnels against the internal BLEVE. In addition, the prediction models based on multi-variate nonlinear regressions and machine learning methods are developed to predict the BLEVE-induced damage levels of the double-arch tunnels without and with section-steel arches.

Many empirical and analytical methods have been proposed to predict fracturing pressure in cohesive soils. Most of them take into account three to four specific influencing factors and rely on the assumption of a failure mode. In this study, a novel data-mining approach based on the XGBoost algorithm is investigated for predicting fracture initiation in cohesive soils. This has the advantage of handling multiple influencing factors simultaneously, without pre-determining a failure mode. A dataset of 416 samples consisting of 14 distinct features was herein collected from past studies, and used for developing a regressor and a classifier model for fracturing pressure prediction and failure mode classification respectively. The results show that the intrinsic characteristics of the soil govern the failure mode while the fracturing pressure is more sensitive to the stress state. The XGBoost-based model was also tested against conventional approaches, as well as a similar machine learning algorithm namely random forest model. Additionally, several large-scale triaxial fracturing tests and an in-situ case study were carried out to further verify the generalization ability and applicability of the proposed data mining approach, and the results indicate a superior performance of the XGBoost model.

Clogging frequently occurs in the cutter head, excavation chamber or screw conveyor when an earth pressure balance (EPB) shield machine is tunneling in soft or silty clay ground with high clay mineral content. In this paper, montmorillonite, kaolinite, and illite were selected as research objects, and molecular dynamics (MD) simulation and laboratory experiment were adopted. At the microscopic scale, dynamic contact behavior and interfacial mechanical behavior of the interface between clay minerals and water/surfactant solution was simulated and the interfacial adhesion and conditioning mechanism between clay minerals and water/surfactant solution was revealed. Thus, sodium dodecyl benzene sulfonate (SDBS) surfactant was selected as the main composition of the soil conditioner. Then, the adhesion stress before and after soil conditioning and the contact angles between clay minerals and water/surfactant solution were tested and analyzed at the macroscopic scale. The result shows that the contact angle between droplet and clay mineral surface is an important parameter to characterize soil adhesion. The simulation rules of the microscopic contact angle are consistent with the experiment results. Furthermore, the adsorption energy between microscopic substances is dominated by electrostatic force, which can reflect the adhesion stress between macroscopic substances. Soil adhesion stress can be effectively decreased by adding the surfactant to the soil conditioner.

This study aims to predict the migration time of toxic fumes induced by excavation blasting in underground mines. To reduce numerical simulation time and optimize ventilation design, several back propagation neural network (BPNN) models optimized by honey badger algorithm (HBA) with four chaos mapping (CM) functions (i.e., Chebyshev (Che) map, Circle (Cir) map, Logistic (Log) map, and Piecewise (Pie) map) are developed to predict the migration time. 125 simulations by the computational fluid dynamics (CFD) method are used to train and test the developed models. The determination coefficient (R²), the variance accounted for (VAF), the Willmott’s index (WI), the root mean square error (RMSE), the mean absolute percentage error (MAPE), and the sum of squares error (SSE) are utilized to evaluate the model performance. The evaluation results indicate that the CirHBA-BPNN model has achieved the most satisfactory performance by reaching the highest values of R² (0.9945), WI (0.9986), VAF (99.4811%), and the lowest values of RMSE (15.7600), MAPE (0.0343) and SSE (6209.4), respectively. The wind velocity in roadway (W_v) is the most important feature for predicting the migration time of toxic fumes. Furthermore, the intrinsic response characteristic of the optimal model is implemented to enhance the model interpretability and provide reference for the relationship between features and migration time of toxic fumes in ventilation design.

When tunnel boring machines (TBMs) excavate through jointed rock masses, the cutting efficiency is strongly affected by the shear strength of joints, the mechanism of which, however, remains poorly understood. In this study, a series of disc-cutter indentation tests were conducted on granite rock mass specimens with different joint shear strengths. During the indentation, the cracking process was recorded by a digital image correlation (DIC) system. The deformation and strength of specimens, cracking behavior, rock breakage mode and cutting efficiency were quantitatively investigated. In addition, to investigate the combined effects of joint shear strength, orientation and spacing on the rock breakage mechanism, numerical rock mass models were established based on a particle flow code PFC2D. Experimental results reveal that the cracking of primary and secondary cracks changes from the mixed shear-tensile to tensile mode in the initial stage, while the joint shear strength does not affect the cracking mode in the subsequent propagation process. The rock breakage mode is classified to an internal block breakage mode, a cross-joint breakage mode and a cutters-dependent breakage mode. The cross-joint breakage mode is optimal for improving the cutting efficiency. Numerical simulation results reveal that the increase in the joint shear strength changes the internal block breakage mode to cross-joint breakage mode for rock masses of particular ranges of joint orientation and spacing. These findings provide basis for improving the TBM cutting efficiency through jointed rock masses.

This paper develops a new approach for reliability-based design (RBD) of tunnel face support pressure from a quantile value perspective. A surrogate model is constructed to calculate the collapse pressures of the random samples generated by a single run of Monte Carlo simulation (MCS). The cumulative distribution function (CDF) of the collapse pressure is then obtained and the support pressure aiming at a target failure probability is chosen as the upper quantile value of the collapse pressures. The proposed approach does not require repetitive reliability analyses compared to the existing methods. Moreover, a direct relationship between the target failure probability and the required support pressure is established. An illustrative example is used to demonstrate the implementation procedure. The accuracy of the reliability-based support pressures is verified by direct MCS incorporating with three-dimensional numerical simulations. Finally, the influencing factors, including the sample size of MCS, the correlation coefficient between random variables, the choice of experimental points, and the surrogate model, are investigated. This method can play a complementary role to available approaches due to its advantages of simplicity and efficiency.