The Ireviken Event was the first Middle Paleozoic event consisting of synchronised faunal,isotopic and facies change to be recognised.An analysis of the conodont faunas throughout the Boree Creek/Borenore Limestone su...The Ireviken Event was the first Middle Paleozoic event consisting of synchronised faunal,isotopic and facies change to be recognised.An analysis of the conodont faunas throughout the Boree Creek/Borenore Limestone succession in the central western region of the Tasman fold belt of New South Wales(Australia)revealing all five conodont zones that comprise the event is presented.While some zonal boundaries are precise,allowing direct comparison of stratigraphic intervals on other paleo-continents,some can only be approximated.Conodont data from pre-Ireviken Event strata,in contrast,only permit the identification of a broad Telychian chronology.The identification of Wenlock post-Ireviken Event conodont zones is incomplete due to lithological variability,namely the presence of tuffaceous beds near the top of the formation and an unconformity between the Boree Creek and overlying Borenore Limestone.The Boree Creek Formation contains the only example of the Ireviken Event discovered to date from the Tasman fold belt of eastern Gondwanaland.展开更多
7075 aluminum alloy is often used as an important load-bearing structure in aircraft industry due to its superior mechanical properties.During the process of deep hole boring,the boring bar is prone to vibrate because...7075 aluminum alloy is often used as an important load-bearing structure in aircraft industry due to its superior mechanical properties.During the process of deep hole boring,the boring bar is prone to vibrate because of its limited machining space,bad environment and large elongation induced low stiffness.To reduce vibration and improve machined surface quality,a particle damping boring bar,filled with particles in its inside damping block,is designed based on the theory of vibration control.The theoretical damping coefficient is determined,then the boring bar structure is designed and trial-manufactured.Experimental studies through impact testing show that cemented carbide particles with a diameter of 5 mm and a filling rate of 70% achieve a damping ratio of 19.386%,providing excellent vibration reduction capabilities,which may reduce the possibility of boring vibration.Then,experiments are setup to investigate its vibration reduction performance during deep hole boring of 7075 aluminum alloy.To observe more obviously,severe working conditions are adopted and carried out to acquire the time domain vibration signal of the head of the boring bar and the surface morphologies and roughness values of the workpieces.By comparing different experimental results,it is found that the designed boring bar could reduce the maximum vibration amplitude by up to 81.01% and the surface roughness value by up to 47.09% compared with the ordinary boring bar in two sets of experiments,proving that the designed boring bar can effectively reduce vibration.This study can offer certain valuable insights for the machining of this material.展开更多
To fundamentally alleviate the excavation chamber clogging during slurry tunnel boring machine(TBM)advancing in hard rock,large-diameter short screw conveyor was adopted to slurry TBM of Qingdao Jiaozhou Bay Second Un...To fundamentally alleviate the excavation chamber clogging during slurry tunnel boring machine(TBM)advancing in hard rock,large-diameter short screw conveyor was adopted to slurry TBM of Qingdao Jiaozhou Bay Second Undersea Tunnel.To evaluate the discharging performance of short screw conveyor in different cases,the full-scale transient slurry-rock two-phase model for a short screw conveyor actively discharging rocks was established using computational fluid dynamics-discrete element method(CFD-DEM)coupling approach.In the fluid domain of coupling model,the sliding mesh technology was utilized to describe the rotations of the atmospheric composite cutterhead and the short screw conveyor.In the particle domain of coupling model,the dynamic particle factories were established to produce rock particles with the rotation of the cutterhead.And the accuracy and reliability of the CFD-DEM simulation results were validated via the field test and model test.Furthermore,a comprehensive parameter analysis was conducted to examine the effects of TBM operating parameters,the geometric design of screw conveyor and the size of rocks on the discharging performance of short screw conveyor.Accordingly,a reasonable rotational speed of screw conveyor was suggested and applied to Jiaozhou Bay Second Undersea Tunnel project.The findings in this paper could provide valuable references for addressing the excavation chamber clogging during ultra-large-diameter slurry TBM tunneling in hard rock for similar future.展开更多
The accurate selection of operational parameters is critical for ensuring the safety,efficiency,and automation of Tunnel Boring Machine(TBM)operations.This study proposes a similarity-based framework integrating model...The accurate selection of operational parameters is critical for ensuring the safety,efficiency,and automation of Tunnel Boring Machine(TBM)operations.This study proposes a similarity-based framework integrating model-based boring indexes(derived from rock fragmentation mechanisms)and Euclidean distance analysis to achieve real-time recommendations of TBM operational parameters.Key performance indicators-thrust(F),torque(T),and penetration(p)-were used to calculate three model-based boring indexes(a,b,k),which quantify dynamic rock fragmentation behavior.A dataset of 359 candidate samples,reflecting diverse geological conditions from the Yin-Chao water conveyance project in Inner Mongolia,China,was utilized to validate the framework.The system dynamically recommends parameters by matching real-time data with historical cases through standardized Euclidean distance,achieving high accuracy.Specifically,the mean absolute error(MAE)for rotation speed(n)was 0.10 r/min,corresponding to a mean absolute percentage error(MAPE)of 1.09%.For advance rate(v),the MAE was 3.4 mm/min,with a MAPE of 4.50%.The predicted thrust(F)and torque(T)values exhibited strong agreement with field measurements,with MAEs of 270 kN and 178 kN∙m,respectively.Field applications demonstrated a 30%reduction in parameter adjustment time compared to empirical methods.This work provides a robust solution for real-time TBM control,advancing intelligent tunneling in complex geological environments.展开更多
Accurately forecasting the operational performance of a tunnel boring machine(TBM)in advance is useful for making timely adjustments to boring parameters,thereby enhancing overall boring efficiency.In this study,we us...Accurately forecasting the operational performance of a tunnel boring machine(TBM)in advance is useful for making timely adjustments to boring parameters,thereby enhancing overall boring efficiency.In this study,we used the Informer model to predict a critical performance parameter of the TBM,namely thrust.Leveraging data from the Guangzhou Metro Line 22 project on the big data platform in China,the model’s performance was validated,while data from Line 18 were used to assess its generalization capability.Results revealed that the Informer model surpasses random forest(RF),extreme gradient boosting(XGB),support vector regression(SVR),k-nearest neighbors(KNN),back propagation(BP),and long short-term memory(LSTM)models in both prediction accuracy and generalization performance.In addition,the optimal input lengths for maximizing accuracy in the single-time-step output model are within the range of 8–24,while for the multiple-time-step output model,the optimal input length is 8.Furthermore,the last predicted value in the case of multiple-time-step outputs showed the highest accuracy.It was also found that relaxation of the Pearson analysis method metrics to 0.95 improved the performance of the model.Finally,the prediction results were most affected by earth pressure,rotation speed,torque,boring speed,and the surrounding rock grade.The model can provide useful guidance for constructors when adjusting TBM operation parameters.展开更多
In tunnel construction with tunnel boring machines(TBMs),accurate knowledge of disc-cutter failure states is crucial to ensure efficient operation and prevent delays and cost overruns.This study investigates the influ...In tunnel construction with tunnel boring machines(TBMs),accurate knowledge of disc-cutter failure states is crucial to ensure efficient operation and prevent delays and cost overruns.This study investigates the influence of disc-cutter partial wear on tunneling parameters and proposes a novel method for discriminating partial-wear ratio based on a stacking ensemble model.The time-domain features of torque and thrust,including the average value and standard deviation,are analyzed through a series of scaled-down experimental tests on partial wear.Torque and thrust values will increase when a disc cutter is trapped and partially worn.The impact of partial-wear ratio on tunneling parameters appears to be more significant than partial-wear depth.A total of 40 features are selected from the time domain,frequency domain,and time-frequency domain to describe the torque and thrust.The relationships between these features and the partial-wear ratio are analyzed using the Pearson coefficient and Copula entropy.The results reveal that,except for the form factor in the time-domain features,the remaining features exhibit certain linear or non-linear correlations with the partial-wear ratio.Lastly,the proposed model successfully achieves the discrimination of the partial-wear ratio and outperforms other commonly used models in terms of overall classification accuracy and differentiation capability in different categories.This research provides effective support for monitoring and health management of disc-cutter failure states.展开更多
To guarantee safe and efficient tunneling of a tunnel boring machine(TBM),rapid and accurate judgment of the rock mass condition is essential.Based on fuzzy C-means clustering,this paper proposes a grouped machine lea...To guarantee safe and efficient tunneling of a tunnel boring machine(TBM),rapid and accurate judgment of the rock mass condition is essential.Based on fuzzy C-means clustering,this paper proposes a grouped machine learning method for predicting rock mass parameters.An elaborate data set on field rock mass is collected,which also matches field TBM tunneling.Meanwhile,target stratum samples are divided into several clusters by fuzzy C-means clustering,and multiple submodels are trained by samples in different clusters with the input of pretreated TBM tunneling data and the output of rock mass parameter data.Each testing sample or newly encountered tunneling condition can be predicted by multiple submodels with the weight of the membership degree of the sample to each cluster.The proposed method has been realized by 100 training samples and verified by 30 testing samples collected from the C1 part of the Pearl Delta water resources allocation project.The average percentage error of uniaxial compressive strength and joint frequency(Jf)of the 30 testing samples predicted by the pure back propagation(BP)neural network is 13.62%and 12.38%,while that predicted by the BP neural network combined with fuzzy C-means is 7.66%and6.40%,respectively.In addition,by combining fuzzy C-means clustering,the prediction accuracies of support vector regression and random forest are also improved to different degrees,which demonstrates that fuzzy C-means clustering is helpful for improving the prediction accuracy of machine learning and thus has good applicability.Accordingly,the proposed method is valuable for predicting rock mass parameters during TBM tunneling.展开更多
The big data generated by tunnel boring machines(TBMs)are widely used to reveal complex rock-machine interactions by machine learning(ML)algorithms.Data preprocessing plays a crucial role in improving ML accuracy.For ...The big data generated by tunnel boring machines(TBMs)are widely used to reveal complex rock-machine interactions by machine learning(ML)algorithms.Data preprocessing plays a crucial role in improving ML accuracy.For this,a TBM big data preprocessing method in ML was proposed in the present study.It emphasized the accurate division of TBM tunneling cycle and the optimization method of feature extraction.Based on the data collected from a TBM water conveyance tunnel in China,its effectiveness was demonstrated by application in predicting TBM performance.Firstly,the Score-Kneedle(S-K)method was proposed to divide a TBM tunneling cycle into five phases.Conducted on 500 TBM tunneling cycles,the S-K method accurately divided all five phases in 458 cycles(accuracy of 91.6%),which is superior to the conventional duration division method(accuracy of 74.2%).Additionally,the S-K method accurately divided the stable phase in 493 cycles(accuracy of 98.6%),which is superior to two state-of-the-art division methods,namely the histogram discriminant method(accuracy of 94.6%)and the cumulative sum change point detection method(accuracy of 92.8%).Secondly,features were extracted from the divided phases.Specifically,TBM tunneling resistances were extracted from the free rotating phase and free advancing phase.The resistances were subtracted from the total forces to represent the true rock-fragmentation forces.The secant slope and the mean value were extracted as features of the increasing phase and stable phase,respectively.Finally,an ML model integrating a deep neural network and genetic algorithm(GA-DNN)was established to learn the preprocessed data.The GA-DNN used 6 secant slope features extracted from the increasing phase to predict the mean field penetration index(FPI)and torque penetration index(TPI)in the stable phase,guiding TBM drivers to make better decisions in advance.The results indicate that the proposed TBM big data preprocessing method can improve prediction accuracy significantly(improving R2s of TPI and FPI on the test dataset from 0.7716 to 0.9178 and from 0.7479 to 0.8842,respectively).展开更多
To reduce the uncertainty associated with the traditional definition of tunnel boring machine(TBM)utilization(U)and achieve an effective indicator of TBM performance,a new performance indicator called rock mass-relate...To reduce the uncertainty associated with the traditional definition of tunnel boring machine(TBM)utilization(U)and achieve an effective indicator of TBM performance,a new performance indicator called rock mass-related utilization(U_(r))is introduced;this variable considers only rock mass-related factors rather than all potential factors.This work aims to predict U_(r)by adopting the rock mass rating(RMR)and the moisture-dependent Cerchar abrasivity index(CAI).Substantial U_(r),RMR and CAI data are acquired from a 31.57 km northwestern Chinese water conveyance tunnel via tunnelling field recordings,geological investigations and Cerchar abrasivity tests.The moisture dependence of the CAI is explored across four lithologies:quartz schists,granites,sandstones and metamorphic andesites.The potential influences of RMR and CAI on Ur are then investigated.As the RMR increases,U_(r)initially increases and then peaks at an RMR of 56 before declining.U_(r)appears to decline with CAI.An investigation-based relation among U_(r),RMR and moisture-dependent CAI is developed for estimating U_(r).The developed relation can accurately predict U_(r)using RMR and moisture-dependent CAI in the majority of the tunnelling cases examined.This work proposes a stable indicator of TBM performance and provided a fairly accurate prediction method for this indicator.展开更多
In recent years,tunnel boring machines(TBMs)have been widely used in tunnel construction.Rockbursts,as a dynamic geological disaster,pose a serious threat to the safety and efficienttunneling of TBMs.The microseismic ...In recent years,tunnel boring machines(TBMs)have been widely used in tunnel construction.Rockbursts,as a dynamic geological disaster,pose a serious threat to the safety and efficienttunneling of TBMs.The microseismic monitoring technique provides an effective solution for rockburst warning.However,due to the complexity and variability of the TBM excavation environment,microseismic events induced by rock fracture are often accompanied by interference events,such as electrical noise,TBM vibration,and mechanical knock.This study proposes a multi-channel intelligent classification approach for microseismic events in TBM excavation scenarios,based on double-layer stacking learning,to identify rock fractures.In this approach,decision tree is used as the base classifieron each microseismic channel,while extreme learning machine is employed as the meta-classifierto aggregate all base classifiers.Additionally,mind evolutionary computation is integrated to optimize the built-in hyperparameters of various classifiers.Meanwhile,a comprehensive preprocessing and augmentation flowfor microseismic data has been developed,encompassing feature extraction,dimensionality reduction,outlier detection,and outlier substitution.The results reveal that the multi-channel stacking model,which combines classificationand regression tree and extreme learning machine,achieves optimal global and local generalization performance compared to other multi-channel stacking models and traditional single-channel models.The accuracy,Hamming loss,and Cohen’s kappa are 96.75%,0.0325,and 0.9148,respectively,and the F_(1)-score and AUC on rock fracture events are 0.9366 and 0.9818,respectively.Finally,a generative artificialintelligence-based scheme is invented to enhance the robustness of the model for signal-mixing events.展开更多
In tunnel construction,tunnel boring machine(TBM)tunnelling typically relies on manual experience with sub-optimal control parameters,which can easily lead to inefficiency and high costs.This study proposed an intelli...In tunnel construction,tunnel boring machine(TBM)tunnelling typically relies on manual experience with sub-optimal control parameters,which can easily lead to inefficiency and high costs.This study proposed an intelligent decision-making method for TBM tunnelling control parameters based on multiobjective optimization(MOO).First,the effective TBM operation dataset is obtained through data preprocessing of the Songhua River(YS)tunnel project in China.Next,the proposed method begins with developing machine learning models for predicting TBM tunnelling performance parameters(i.e.total thrust and cutterhead torque),rock mass classification,and hazard risks(i.e.tunnel collapse and shield jamming).Then,considering three optimal objectives,(i.e.,penetration rate,rock-breaking energy consumption,and cutterhead hob wear),the MOO framework and corresponding mathematical expression are established.The Pareto optimal front is solved using DE-NSGA-II algorithm.Finally,the optimal control parameters(i.e.,advance rate and cutterhead rotation speed)are obtained by the satisfactory solution determination criterion,which can balance construction safety and efficiency with satisfaction.Furthermore,the proposed method is validated through 50 cases of TBM tunnelling,showing promising potential of application.展开更多
The use of foam,as the most economical soil conditioning technique,in earth pressure balance tunnel boring machine(EPB-TBM)tunneling projects has significant effects on operation efficiency,excavation cost,and operati...The use of foam,as the most economical soil conditioning technique,in earth pressure balance tunnel boring machine(EPB-TBM)tunneling projects has significant effects on operation efficiency,excavation cost,and operation time.This study mainly focuses on developing models to predict the foam(surfactant)consumption.For this purpose,five empirical models are developed based on a database containing 11048 datasets of real-time foam consumption from three EPB-TBM tunneling projects in Iran.This database includes the most effective machine operational parameters and soil geomechanical properties on the foam consumption.Multiple linear regression analysis,multiple non-linear regression analysis,M5Prime decision tree,artificial neural network,and least squares support vector machine techniques are used to construct the models.To evaluate the performance of developed models,three performance evaluation criteria(including normalized root mean square error,variance account for,and coefficient of determination)are used based on the training and testing datasets.The results show that the developed models have high performance and their validity is guaranteed according to the testing dataset.Furthermore,the M5Prime model,which demonstrates the best performance compared to other models,is applied to predict the foam consumption in 19 excavation rings of Kohandezh station in Isfahan metro,Iran.After conducting an excavation operation in this station and comparing the results,it was found that the M5Prime model accurately predicts foam consumption with an average error of less than 13%.Therefore,the developed models,particularly M5Prime model,can be confidently applied in EPB-TBM tunneling projects for predicting foam consumption with a low error rate.展开更多
Substantial advancements have been achieved in Tunnel Boring Machine(TBM)technology and monitoring systems,yet the presence of missing data impedes accurate analysis and interpretation of TBM monitoring results.This s...Substantial advancements have been achieved in Tunnel Boring Machine(TBM)technology and monitoring systems,yet the presence of missing data impedes accurate analysis and interpretation of TBM monitoring results.This study aims to investigate the issue of missing data in extensive TBM datasets.Through a comprehensive literature review,we analyze the mechanism of missing TBM data and compare different imputation methods,including statistical analysis and machine learning algorithms.We also examine the impact of various missing patterns and rates on the efficacy of these methods.Finally,we propose a dynamic interpolation strategy tailored for TBM engineering sites.The research results show that K-Nearest Neighbors(KNN)and Random Forest(RF)algorithms can achieve good interpolation results;As the missing rate increases,the interpolation effect of different methods will decrease;The interpolation effect of block missing is poor,followed by mixed missing,and the interpolation effect of sporadic missing is the best.On-site application results validate the proposed interpolation strategy's capability to achieve robust missing value interpolation effects,applicable in ML scenarios such as parameter optimization,attitude warning,and pressure prediction.These findings contribute to enhancing the efficiency of TBM missing data processing,offering more effective support for large-scale TBM monitoring datasets.展开更多
A centrifuge modeling test and a three-dimensional finite element analysis（FEA）of super-long rock-socketed bored pile groups of the Tianxingzhou Bridge are proposed.Based on the similarity theory,different prototypi...A centrifuge modeling test and a three-dimensional finite element analysis（FEA）of super-long rock-socketed bored pile groups of the Tianxingzhou Bridge are proposed.Based on the similarity theory,different prototypical materials are simulated using different indicators in the centrifuge model.The silver sand,the shaft and the pile cap are simulated according to the natural density,the compressive stiffness and the bending stiffness,respectively.The finite element method（FEM）is implemented and analyzed in ANSYS,in which the stress field during the undisturbed soil stage,the boring stage,the concrete-casting stage and the curing stage are discussed in detail.Comparisons in terms of load-settlement,shaft axial force distribution and lateral friction between the numerical results and the test data are carried out to investigate the bearing behaviors of super-long rock-socketed bored pile groups under loading and unloading conditions.Results show that there is a good agreement between the centrifuge modeling tests and the FEM.In addition,the load distribution at the pile top is complicated,which is related to the stiffness of the cap,the corresponding assumptions and the analysis method.The shaft axial force first increases slightly with depth then decreases sharply,and the rate of decrease in rock is greater than that in sand and soil.展开更多
In this paper we address the dynamics of compensation cutting process from both Laplace s frequency domain and the time domain of the first time, using the two computer aided analyzing softwares: MATLAB and SIMULI...In this paper we address the dynamics of compensation cutting process from both Laplace s frequency domain and the time domain of the first time, using the two computer aided analyzing softwares: MATLAB and SIMULINK. Theoretical analysis and simulation experiments firstly show that not only the systematical stiffness of workpiece, spindle and tools, but also the regenerated coefficient affects the compensation displacement effect. The results show that the SREC is practicable in reality to decease the spindle induced errors in many engineering applications such as hard boring through simulation and the preliminary experiment results.展开更多
This paper is concerned with the work involved in improving the machining accuracy of a cantilever boring bar by on line compensation with a piezoelectric actuator. A boring bar is made into lever structure, with str...This paper is concerned with the work involved in improving the machining accuracy of a cantilever boring bar by on line compensation with a piezoelectric actuator. A boring bar is made into lever structure, with strain gauges attached to the bar for measuring its force induced deflections. The piezoelectric actuator is employed to compensate the deflections of the boring bar for accuracy improvement. Due to the mechanical advantage of the structure, the boring bar can be made into smaller size. The diameter of the bar implemented is 10 mm and the ratio of length to diameter (L/D) is larger than 8. It is found that the machining accuracy is improved considerably by using the piezoelectric actuator compensation system.展开更多
The methodology of predicting pile shaft skin ultimate friction has been studied in a systematic way. In the light of that, the analysis of the pile shaft resistance for bored and cast in situ piles in cohesive soil...The methodology of predicting pile shaft skin ultimate friction has been studied in a systematic way. In the light of that, the analysis of the pile shaft resistance for bored and cast in situ piles in cohesive soils was carried out thoroughly in the basis of field performance data of 10 fully instrumented large diameter bored piles (LDBPs) used as the bridge foundation. The undrained strength index μ in term of cohesive soils was brought forward in allusion to the cohesive soils in the consistence plastic state, and can effectively combine the friction angle and the cohesion of cohesive soils in undrained condition. And that the classical ' α method' was modified much in effect to predict the pile shaft skin friction of LDBPs in cohesive soils. Furthermore, the approach of standard penetration test (SPT) N value used to estimate the pile shaft skin ultimate friction was analyzed, and the calculating formulae were established for LDBPs in clay and silt clay respectively.展开更多
文摘The Ireviken Event was the first Middle Paleozoic event consisting of synchronised faunal,isotopic and facies change to be recognised.An analysis of the conodont faunas throughout the Boree Creek/Borenore Limestone succession in the central western region of the Tasman fold belt of New South Wales(Australia)revealing all five conodont zones that comprise the event is presented.While some zonal boundaries are precise,allowing direct comparison of stratigraphic intervals on other paleo-continents,some can only be approximated.Conodont data from pre-Ireviken Event strata,in contrast,only permit the identification of a broad Telychian chronology.The identification of Wenlock post-Ireviken Event conodont zones is incomplete due to lithological variability,namely the presence of tuffaceous beds near the top of the formation and an unconformity between the Boree Creek and overlying Borenore Limestone.The Boree Creek Formation contains the only example of the Ireviken Event discovered to date from the Tasman fold belt of eastern Gondwanaland.
基金supported by the Scientific Research Program of Tianjin Education Committee(No.2022ZD030)。
文摘7075 aluminum alloy is often used as an important load-bearing structure in aircraft industry due to its superior mechanical properties.During the process of deep hole boring,the boring bar is prone to vibrate because of its limited machining space,bad environment and large elongation induced low stiffness.To reduce vibration and improve machined surface quality,a particle damping boring bar,filled with particles in its inside damping block,is designed based on the theory of vibration control.The theoretical damping coefficient is determined,then the boring bar structure is designed and trial-manufactured.Experimental studies through impact testing show that cemented carbide particles with a diameter of 5 mm and a filling rate of 70% achieve a damping ratio of 19.386%,providing excellent vibration reduction capabilities,which may reduce the possibility of boring vibration.Then,experiments are setup to investigate its vibration reduction performance during deep hole boring of 7075 aluminum alloy.To observe more obviously,severe working conditions are adopted and carried out to acquire the time domain vibration signal of the head of the boring bar and the surface morphologies and roughness values of the workpieces.By comparing different experimental results,it is found that the designed boring bar could reduce the maximum vibration amplitude by up to 81.01% and the surface roughness value by up to 47.09% compared with the ordinary boring bar in two sets of experiments,proving that the designed boring bar can effectively reduce vibration.This study can offer certain valuable insights for the machining of this material.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.2023YJS053)the National Natural Science Foundation of China(Grant No.52278386).
文摘To fundamentally alleviate the excavation chamber clogging during slurry tunnel boring machine(TBM)advancing in hard rock,large-diameter short screw conveyor was adopted to slurry TBM of Qingdao Jiaozhou Bay Second Undersea Tunnel.To evaluate the discharging performance of short screw conveyor in different cases,the full-scale transient slurry-rock two-phase model for a short screw conveyor actively discharging rocks was established using computational fluid dynamics-discrete element method(CFD-DEM)coupling approach.In the fluid domain of coupling model,the sliding mesh technology was utilized to describe the rotations of the atmospheric composite cutterhead and the short screw conveyor.In the particle domain of coupling model,the dynamic particle factories were established to produce rock particles with the rotation of the cutterhead.And the accuracy and reliability of the CFD-DEM simulation results were validated via the field test and model test.Furthermore,a comprehensive parameter analysis was conducted to examine the effects of TBM operating parameters,the geometric design of screw conveyor and the size of rocks on the discharging performance of short screw conveyor.Accordingly,a reasonable rotational speed of screw conveyor was suggested and applied to Jiaozhou Bay Second Undersea Tunnel project.The findings in this paper could provide valuable references for addressing the excavation chamber clogging during ultra-large-diameter slurry TBM tunneling in hard rock for similar future.
基金supported by the National Key R&D Program of China(2022YFE0200400).
文摘The accurate selection of operational parameters is critical for ensuring the safety,efficiency,and automation of Tunnel Boring Machine(TBM)operations.This study proposes a similarity-based framework integrating model-based boring indexes(derived from rock fragmentation mechanisms)and Euclidean distance analysis to achieve real-time recommendations of TBM operational parameters.Key performance indicators-thrust(F),torque(T),and penetration(p)-were used to calculate three model-based boring indexes(a,b,k),which quantify dynamic rock fragmentation behavior.A dataset of 359 candidate samples,reflecting diverse geological conditions from the Yin-Chao water conveyance project in Inner Mongolia,China,was utilized to validate the framework.The system dynamically recommends parameters by matching real-time data with historical cases through standardized Euclidean distance,achieving high accuracy.Specifically,the mean absolute error(MAE)for rotation speed(n)was 0.10 r/min,corresponding to a mean absolute percentage error(MAPE)of 1.09%.For advance rate(v),the MAE was 3.4 mm/min,with a MAPE of 4.50%.The predicted thrust(F)and torque(T)values exhibited strong agreement with field measurements,with MAEs of 270 kN and 178 kN∙m,respectively.Field applications demonstrated a 30%reduction in parameter adjustment time compared to empirical methods.This work provides a robust solution for real-time TBM control,advancing intelligent tunneling in complex geological environments.
基金supported by the National Natural Science Foundation of China(No.41827807)the Foundation of the Guangdong Provincial Key Laboratory of Modern Civil Engineering Technology(No.2021B1212040003),China.
文摘Accurately forecasting the operational performance of a tunnel boring machine(TBM)in advance is useful for making timely adjustments to boring parameters,thereby enhancing overall boring efficiency.In this study,we used the Informer model to predict a critical performance parameter of the TBM,namely thrust.Leveraging data from the Guangzhou Metro Line 22 project on the big data platform in China,the model’s performance was validated,while data from Line 18 were used to assess its generalization capability.Results revealed that the Informer model surpasses random forest(RF),extreme gradient boosting(XGB),support vector regression(SVR),k-nearest neighbors(KNN),back propagation(BP),and long short-term memory(LSTM)models in both prediction accuracy and generalization performance.In addition,the optimal input lengths for maximizing accuracy in the single-time-step output model are within the range of 8–24,while for the multiple-time-step output model,the optimal input length is 8.Furthermore,the last predicted value in the case of multiple-time-step outputs showed the highest accuracy.It was also found that relaxation of the Pearson analysis method metrics to 0.95 improved the performance of the model.Finally,the prediction results were most affected by earth pressure,rotation speed,torque,boring speed,and the surrounding rock grade.The model can provide useful guidance for constructors when adjusting TBM operation parameters.
基金supported by the Natural Science Basic Research Program of Shaanxi Province(No.2019JLZ-13)the National Key R&D Program of China(No.2022YFC3802305)+1 种基金the National Natural Science Foundation of China(No.52105074)the Open Project of State Key Laboratory of Shield Machine and Boring Technology(No.SKLST-2021-K02),China.
文摘In tunnel construction with tunnel boring machines(TBMs),accurate knowledge of disc-cutter failure states is crucial to ensure efficient operation and prevent delays and cost overruns.This study investigates the influence of disc-cutter partial wear on tunneling parameters and proposes a novel method for discriminating partial-wear ratio based on a stacking ensemble model.The time-domain features of torque and thrust,including the average value and standard deviation,are analyzed through a series of scaled-down experimental tests on partial wear.Torque and thrust values will increase when a disc cutter is trapped and partially worn.The impact of partial-wear ratio on tunneling parameters appears to be more significant than partial-wear depth.A total of 40 features are selected from the time domain,frequency domain,and time-frequency domain to describe the torque and thrust.The relationships between these features and the partial-wear ratio are analyzed using the Pearson coefficient and Copula entropy.The results reveal that,except for the form factor in the time-domain features,the remaining features exhibit certain linear or non-linear correlations with the partial-wear ratio.Lastly,the proposed model successfully achieves the discrimination of the partial-wear ratio and outperforms other commonly used models in terms of overall classification accuracy and differentiation capability in different categories.This research provides effective support for monitoring and health management of disc-cutter failure states.
基金Natural Science Foundation of Shandong Province,Grant/Award Number:ZR202103010903Doctoral Fund of Shandong Jianzhu University,Grant/Award Number:X21101Z。
文摘To guarantee safe and efficient tunneling of a tunnel boring machine(TBM),rapid and accurate judgment of the rock mass condition is essential.Based on fuzzy C-means clustering,this paper proposes a grouped machine learning method for predicting rock mass parameters.An elaborate data set on field rock mass is collected,which also matches field TBM tunneling.Meanwhile,target stratum samples are divided into several clusters by fuzzy C-means clustering,and multiple submodels are trained by samples in different clusters with the input of pretreated TBM tunneling data and the output of rock mass parameter data.Each testing sample or newly encountered tunneling condition can be predicted by multiple submodels with the weight of the membership degree of the sample to each cluster.The proposed method has been realized by 100 training samples and verified by 30 testing samples collected from the C1 part of the Pearl Delta water resources allocation project.The average percentage error of uniaxial compressive strength and joint frequency(Jf)of the 30 testing samples predicted by the pure back propagation(BP)neural network is 13.62%and 12.38%,while that predicted by the BP neural network combined with fuzzy C-means is 7.66%and6.40%,respectively.In addition,by combining fuzzy C-means clustering,the prediction accuracies of support vector regression and random forest are also improved to different degrees,which demonstrates that fuzzy C-means clustering is helpful for improving the prediction accuracy of machine learning and thus has good applicability.Accordingly,the proposed method is valuable for predicting rock mass parameters during TBM tunneling.
基金The support provided by the Natural Science Foundation of Hubei Province(Grant No.2021CFA081)the National Natural Science Foundation of China(Grant No.42277160)the fellowship of China Postdoctoral Science Foundation(Grant No.2022TQ0241)is gratefully acknowledged.
文摘The big data generated by tunnel boring machines(TBMs)are widely used to reveal complex rock-machine interactions by machine learning(ML)algorithms.Data preprocessing plays a crucial role in improving ML accuracy.For this,a TBM big data preprocessing method in ML was proposed in the present study.It emphasized the accurate division of TBM tunneling cycle and the optimization method of feature extraction.Based on the data collected from a TBM water conveyance tunnel in China,its effectiveness was demonstrated by application in predicting TBM performance.Firstly,the Score-Kneedle(S-K)method was proposed to divide a TBM tunneling cycle into five phases.Conducted on 500 TBM tunneling cycles,the S-K method accurately divided all five phases in 458 cycles(accuracy of 91.6%),which is superior to the conventional duration division method(accuracy of 74.2%).Additionally,the S-K method accurately divided the stable phase in 493 cycles(accuracy of 98.6%),which is superior to two state-of-the-art division methods,namely the histogram discriminant method(accuracy of 94.6%)and the cumulative sum change point detection method(accuracy of 92.8%).Secondly,features were extracted from the divided phases.Specifically,TBM tunneling resistances were extracted from the free rotating phase and free advancing phase.The resistances were subtracted from the total forces to represent the true rock-fragmentation forces.The secant slope and the mean value were extracted as features of the increasing phase and stable phase,respectively.Finally,an ML model integrating a deep neural network and genetic algorithm(GA-DNN)was established to learn the preprocessed data.The GA-DNN used 6 secant slope features extracted from the increasing phase to predict the mean field penetration index(FPI)and torque penetration index(TPI)in the stable phase,guiding TBM drivers to make better decisions in advance.The results indicate that the proposed TBM big data preprocessing method can improve prediction accuracy significantly(improving R2s of TPI and FPI on the test dataset from 0.7716 to 0.9178 and from 0.7479 to 0.8842,respectively).
基金financially supported by the National Natural Science Foundation of China(Nos.41972270,52076198)the Key Research and Development Plan of Henan Province(No.182102210014)+2 种基金the Excellent Youth Foundation of Henan Scientific Committee(No.222300420078)the Youth Talent Promotion Project of Henan Province(No.2022HYTP019)the Open Foundation of State Key Laboratory of Shield Machine and Boring Technology(No.SKLST-2019-K06)。
文摘To reduce the uncertainty associated with the traditional definition of tunnel boring machine(TBM)utilization(U)and achieve an effective indicator of TBM performance,a new performance indicator called rock mass-related utilization(U_(r))is introduced;this variable considers only rock mass-related factors rather than all potential factors.This work aims to predict U_(r)by adopting the rock mass rating(RMR)and the moisture-dependent Cerchar abrasivity index(CAI).Substantial U_(r),RMR and CAI data are acquired from a 31.57 km northwestern Chinese water conveyance tunnel via tunnelling field recordings,geological investigations and Cerchar abrasivity tests.The moisture dependence of the CAI is explored across four lithologies:quartz schists,granites,sandstones and metamorphic andesites.The potential influences of RMR and CAI on Ur are then investigated.As the RMR increases,U_(r)initially increases and then peaks at an RMR of 56 before declining.U_(r)appears to decline with CAI.An investigation-based relation among U_(r),RMR and moisture-dependent CAI is developed for estimating U_(r).The developed relation can accurately predict U_(r)using RMR and moisture-dependent CAI in the majority of the tunnelling cases examined.This work proposes a stable indicator of TBM performance and provided a fairly accurate prediction method for this indicator.
基金supported by the National Natural Science Foundation of China(Grant Nos.42472351 and 42177140)the Young Elite Scientist Sponsorship Program by the China Association for Science and Technology(Grant No.YESS20230742).
文摘In recent years,tunnel boring machines(TBMs)have been widely used in tunnel construction.Rockbursts,as a dynamic geological disaster,pose a serious threat to the safety and efficienttunneling of TBMs.The microseismic monitoring technique provides an effective solution for rockburst warning.However,due to the complexity and variability of the TBM excavation environment,microseismic events induced by rock fracture are often accompanied by interference events,such as electrical noise,TBM vibration,and mechanical knock.This study proposes a multi-channel intelligent classification approach for microseismic events in TBM excavation scenarios,based on double-layer stacking learning,to identify rock fractures.In this approach,decision tree is used as the base classifieron each microseismic channel,while extreme learning machine is employed as the meta-classifierto aggregate all base classifiers.Additionally,mind evolutionary computation is integrated to optimize the built-in hyperparameters of various classifiers.Meanwhile,a comprehensive preprocessing and augmentation flowfor microseismic data has been developed,encompassing feature extraction,dimensionality reduction,outlier detection,and outlier substitution.The results reveal that the multi-channel stacking model,which combines classificationand regression tree and extreme learning machine,achieves optimal global and local generalization performance compared to other multi-channel stacking models and traditional single-channel models.The accuracy,Hamming loss,and Cohen’s kappa are 96.75%,0.0325,and 0.9148,respectively,and the F_(1)-score and AUC on rock fracture events are 0.9366 and 0.9818,respectively.Finally,a generative artificialintelligence-based scheme is invented to enhance the robustness of the model for signal-mixing events.
基金supported by the National Natural Science Foundation of China(Grant No.52179105)China Postdoctoral Science Foundation(Grant No.2024M762193)。
文摘In tunnel construction,tunnel boring machine(TBM)tunnelling typically relies on manual experience with sub-optimal control parameters,which can easily lead to inefficiency and high costs.This study proposed an intelligent decision-making method for TBM tunnelling control parameters based on multiobjective optimization(MOO).First,the effective TBM operation dataset is obtained through data preprocessing of the Songhua River(YS)tunnel project in China.Next,the proposed method begins with developing machine learning models for predicting TBM tunnelling performance parameters(i.e.total thrust and cutterhead torque),rock mass classification,and hazard risks(i.e.tunnel collapse and shield jamming).Then,considering three optimal objectives,(i.e.,penetration rate,rock-breaking energy consumption,and cutterhead hob wear),the MOO framework and corresponding mathematical expression are established.The Pareto optimal front is solved using DE-NSGA-II algorithm.Finally,the optimal control parameters(i.e.,advance rate and cutterhead rotation speed)are obtained by the satisfactory solution determination criterion,which can balance construction safety and efficiency with satisfaction.Furthermore,the proposed method is validated through 50 cases of TBM tunnelling,showing promising potential of application.
文摘The use of foam,as the most economical soil conditioning technique,in earth pressure balance tunnel boring machine(EPB-TBM)tunneling projects has significant effects on operation efficiency,excavation cost,and operation time.This study mainly focuses on developing models to predict the foam(surfactant)consumption.For this purpose,five empirical models are developed based on a database containing 11048 datasets of real-time foam consumption from three EPB-TBM tunneling projects in Iran.This database includes the most effective machine operational parameters and soil geomechanical properties on the foam consumption.Multiple linear regression analysis,multiple non-linear regression analysis,M5Prime decision tree,artificial neural network,and least squares support vector machine techniques are used to construct the models.To evaluate the performance of developed models,three performance evaluation criteria(including normalized root mean square error,variance account for,and coefficient of determination)are used based on the training and testing datasets.The results show that the developed models have high performance and their validity is guaranteed according to the testing dataset.Furthermore,the M5Prime model,which demonstrates the best performance compared to other models,is applied to predict the foam consumption in 19 excavation rings of Kohandezh station in Isfahan metro,Iran.After conducting an excavation operation in this station and comparing the results,it was found that the M5Prime model accurately predicts foam consumption with an average error of less than 13%.Therefore,the developed models,particularly M5Prime model,can be confidently applied in EPB-TBM tunneling projects for predicting foam consumption with a low error rate.
基金supported by the National Natural Science Foundation of China(Grant No.52409151)the Programme of Shenzhen Key Laboratory of Green,Efficient and Intelligent Construction of Underground Metro Station(Programme No.ZDSYS20200923105200001)the Science and Technology Major Project of Xizang Autonomous Region of China(XZ202201ZD0003G).
文摘Substantial advancements have been achieved in Tunnel Boring Machine(TBM)technology and monitoring systems,yet the presence of missing data impedes accurate analysis and interpretation of TBM monitoring results.This study aims to investigate the issue of missing data in extensive TBM datasets.Through a comprehensive literature review,we analyze the mechanism of missing TBM data and compare different imputation methods,including statistical analysis and machine learning algorithms.We also examine the impact of various missing patterns and rates on the efficacy of these methods.Finally,we propose a dynamic interpolation strategy tailored for TBM engineering sites.The research results show that K-Nearest Neighbors(KNN)and Random Forest(RF)algorithms can achieve good interpolation results;As the missing rate increases,the interpolation effect of different methods will decrease;The interpolation effect of block missing is poor,followed by mixed missing,and the interpolation effect of sporadic missing is the best.On-site application results validate the proposed interpolation strategy's capability to achieve robust missing value interpolation effects,applicable in ML scenarios such as parameter optimization,attitude warning,and pressure prediction.These findings contribute to enhancing the efficiency of TBM missing data processing,offering more effective support for large-scale TBM monitoring datasets.
基金The Natural Science Foundation of Hubei Province(No.2007ABA094)
文摘A centrifuge modeling test and a three-dimensional finite element analysis（FEA）of super-long rock-socketed bored pile groups of the Tianxingzhou Bridge are proposed.Based on the similarity theory,different prototypical materials are simulated using different indicators in the centrifuge model.The silver sand,the shaft and the pile cap are simulated according to the natural density,the compressive stiffness and the bending stiffness,respectively.The finite element method（FEM）is implemented and analyzed in ANSYS,in which the stress field during the undisturbed soil stage,the boring stage,the concrete-casting stage and the curing stage are discussed in detail.Comparisons in terms of load-settlement,shaft axial force distribution and lateral friction between the numerical results and the test data are carried out to investigate the bearing behaviors of super-long rock-socketed bored pile groups under loading and unloading conditions.Results show that there is a good agreement between the centrifuge modeling tests and the FEM.In addition,the load distribution at the pile top is complicated,which is related to the stiffness of the cap,the corresponding assumptions and the analysis method.The shaft axial force first increases slightly with depth then decreases sharply,and the rate of decrease in rock is greater than that in sand and soil.
文摘In this paper we address the dynamics of compensation cutting process from both Laplace s frequency domain and the time domain of the first time, using the two computer aided analyzing softwares: MATLAB and SIMULINK. Theoretical analysis and simulation experiments firstly show that not only the systematical stiffness of workpiece, spindle and tools, but also the regenerated coefficient affects the compensation displacement effect. The results show that the SREC is practicable in reality to decease the spindle induced errors in many engineering applications such as hard boring through simulation and the preliminary experiment results.
文摘This paper is concerned with the work involved in improving the machining accuracy of a cantilever boring bar by on line compensation with a piezoelectric actuator. A boring bar is made into lever structure, with strain gauges attached to the bar for measuring its force induced deflections. The piezoelectric actuator is employed to compensate the deflections of the boring bar for accuracy improvement. Due to the mechanical advantage of the structure, the boring bar can be made into smaller size. The diameter of the bar implemented is 10 mm and the ratio of length to diameter (L/D) is larger than 8. It is found that the machining accuracy is improved considerably by using the piezoelectric actuator compensation system.
文摘The methodology of predicting pile shaft skin ultimate friction has been studied in a systematic way. In the light of that, the analysis of the pile shaft resistance for bored and cast in situ piles in cohesive soils was carried out thoroughly in the basis of field performance data of 10 fully instrumented large diameter bored piles (LDBPs) used as the bridge foundation. The undrained strength index μ in term of cohesive soils was brought forward in allusion to the cohesive soils in the consistence plastic state, and can effectively combine the friction angle and the cohesion of cohesive soils in undrained condition. And that the classical ' α method' was modified much in effect to predict the pile shaft skin friction of LDBPs in cohesive soils. Furthermore, the approach of standard penetration test (SPT) N value used to estimate the pile shaft skin ultimate friction was analyzed, and the calculating formulae were established for LDBPs in clay and silt clay respectively.
