Anisotropic shearing mechanism of Kangding slate:Experimental investigation and numerical analysis

The shear mechanical behavior is regarded as an essential factor affecting the stability of the surrounding rocks in underground engineering.The shear strength and failure mechanisms of layered rock are significantly affected by the foliation angles.Direct shear tests were conducted on cubic slate samples with foliation angles of 0°,30°,45°,60°,and 90°.The effect of foliation angles on failure patterns,acoustic emission(AE)characteristics,and shear strength parameters was analyzed.Based on AE characteristics,the slate failure process could be divided into four stages:quiet period,step-like increasing period,dramatic increasing period,and remission period.A new empirical expression of cohesion for layered rock was proposed,which was compared with linear and sinusoidal cohesion expressions based on the results made by this paper and previous experiments.The comparative analysis demonstrated that the new expression has better prediction ability than other expressions.The proposed empirical equation was used for direct shear simulations with the combined finite-discrete element method(FDEM),and it was found to align well with the experimental results.Considering both computational efficiency and accuracy,it was recommended to use a shear rate of 0.01 m/s for FDEM to carry out direct shear simulations.To balance the relationship between the number of elements and the simulation results in the direct shear simulations,the recommended element size is 1 mm.

An elasto-plastic and viscoplastic damage constitutive model for dilatancy and fracturing behavior of soft rock squeezing deformation

Soft rock squeezing deformation mainly consists of pre-peak damage-dilatancy and post-peak fracture-bulking at the excavation unloading instant,and creep-dilatancy caused by time-dependent damage and fracturing.Based on the classic elastoplastic and Perzyna over-stress viscoplastic theories,as well as triaxial unloading confining pressure test and triaxial unloading creep test results,an elastoplastic and viscoplastic damage constitutive model is established for the short-and long-term dilatancy and fracturing behavior of soft rock squeezing deformation.Firstly,the criteria for each deformation and failure stage are expressed as a linear function of confining pressure.Secondly,the total damage evolution equation considering time-dependent damage is proposed,including the initial damage produced at the excavation instant,in which the damage variable increases exponentially with the lateral strain,and creep damage.Thirdly,a transient five-stages elasto-plastic constitutive equation for the short-term deformation after excavation that comprised of elasticity,pre-peak damage-dilatancy,post-peak brittle-drop,linear strain-softening,and residual perfectly-plastic regimes is developed based on incremental elasto-plastic theory and the nonassociated flow rule.Fourthly,regarding the timedependent properties of soft rock,based on the Perzyna viscoplastic over-stress theory,a viscoplastic damage model is set up to capture creep damage and dilatancy behavior.Viscoplastic strain is produced when the stress exceeds the initial static yield surface fs;the distance between the static yield surface fs and the dynamic yield surface fd determines the viscoplastic strain rate.Finally,the established constitutive model is numerically implemented and field applied to the-848 m belt conveyer haulage roadway of Huainan Panyidong Coal Mine.Laboratory test results and in-situ monitoring results validate the rationality of the established constitutive model.The presented model takes both the transient and time-dependent damage and fracturing into consideration.

Molecular Dynamics Simulation of the Evolution of Interfacial Dislocation Network and Stress Distribution of a Ni-Based SingleCrystal Superalloy

The evolution of misfit dislocation network at γ/γ＇ phase interfaces and the stress distribution characteristics of Ni-based single-crystal superalloys under different temperatures of 0, 100 and 300 K are studied by molecular dynamics （MD） simulation. It was found that a closed three-dimensional misfit dislocation network appears on the γ/γ＇ phase interfaces, and the shape of the dislocation network is independent of the lattice mismatch. Under the influence of the temperature, the dislocation network gradually becomes irregular, a/2 [110] dislocations in the γ matrix phase emit and partly cut into the γ＇ phase with the increase in temperature. The dislocation evolution is related to the local stress field, a peak stress occurs at γ/γ＇ phase interface, and with the increase in temperature and relaxation times, the stress in the γ phase gradually increases, the number of dislocations in the γ phase increases and cuts into γ＇ phase from the interfaces where dislocation network is damaged. The results provide important information for understanding the temperature dependence of the dislocation evolution and mechanical properties of Ni-based single-crystal superalloys.

A real-time prediction method for tunnel boring machine cutter-head torque using bidirectional long short-term memory networks optimized by multi-algorithm

Based on data from the Jilin Water Diversion Tunnels from the Songhua River(China),an improved and real-time prediction method optimized by multi-algorithm for tunnel boring machine(TBM)cutter-head torque is presented.Firstly,a function excluding invalid and abnormal data is established to distinguish TBM operating state,and a feature selection method based on the SelectKBest algorithm is proposed.Accordingly,ten features that are most closely related to the cutter-head torque are selected as input variables,which,in descending order of influence,include the sum of motor torque,cutter-head power,sum of motor power,sum of motor current,advance rate,cutter-head pressure,total thrust force,penetration rate,cutter-head rotational velocity,and field penetration index.Secondly,a real-time cutterhead torque prediction model’s structure is developed,based on the bidirectional long short-term memory(BLSTM)network integrating the dropout algorithm to prevent overfitting.Then,an algorithm to optimize hyperparameters of model based on Bayesian and cross-validation is proposed.Early stopping and checkpoint algorithms are integrated to optimize the training process.Finally,a BLSTMbased real-time cutter-head torque prediction model is developed,which fully utilizes the previous time-series tunneling information.The mean absolute percentage error(MAPE)of the model in the verification section is 7.3%,implying that the presented model is suitable for real-time cutter-head torque prediction.Furthermore,an incremental learning method based on the above base model is introduced to improve the adaptability of the model during the TBM tunneling.Comparison of the prediction performance between the base and incremental learning models in the same tunneling section shows that:(1)the MAPE of the predicted results of the BLSTM-based real-time cutter-head torque prediction model remains below 10%,and both the coefficient of determination(R^(2))and correlation coefficient(r)between measured and predicted values exceed 0.95;and(2)the incremental learning method is suitable for realtime cutter-head torque prediction and can effectively improve the prediction accuracy and generalization capacity of the model during the excavation process.

Robust model for tunnel squeezing using Bayesian optimized classifiers with partially missing database

Accurately predicting and estimating the squeezing and ground response to tunneling remains challenging.Moreover,tunnel-squeezing hazards are much more likely to occur in deeply buried long tunnels with complex engineering-geological environments.There-fore,a high-performance predictive model for tunnel squeezing is necessary.A superior ensemble classifier is put forward in this study,which is composed of four individual classifiers(gradient boosting classifier,extra-trees classifier,AdaBoost classifier,and Logistic regression classifier)and two optimization algorithms(Bayesian optimization(BO)and sparrow search algorithm(SSA)).The training database covers five parameters:tunnel depth(H),rock tunneling quality index(Q),tunnel diameter(D),support stiffness(K),and strength stress ratio(SSR),about which the basic information is accessible at the early design phases.However,the dataset compiled from the literature is insufficient.Thus,the ten proposed methods are used to replace the missing values.During the model training pro-cess,BO shows its strong ability to optimize seventeen hyperparameters.When applied to tune the classifiers’weights,SSA achieves a fast and efficient performance.The novel Shapley Additive Explanations–LightGBM method indicates that the K is the most important input feature,followed by SSR,Q,H,and D,respectively.The ensemble classifier is then validated using the test set and additional his-torical case projects.The validation shows that the model can achieve an accuracy of 98%(i.e.,the error rate is 2%)on the test set,higher than those achieved by previous prediction models.Moreover,the predicted probability could provide warning information for timely support measures.Finally,the application results are illustrated through tests on the tunnel sections that have not yet been excavated in the line of the Sichuan–Tibet railway project.The applied predictive tendencies and laws are in line with the practical experience.In sum-mary,the proposed model’s prediction results are reasonable,and its prediction will be more accurate as more data is collected and trained for prewarning the tunnel squeezing hazard.