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
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
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
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
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
Advances in intelligent shield machines reflect an evolving trend from traditional tunnel boring machines(TBMs)to tunnel boring robots(TBRs).This shift aims to address the challenges encountered by the conventional sh...Advances in intelligent shield machines reflect an evolving trend from traditional tunnel boring machines(TBMs)to tunnel boring robots(TBRs).This shift aims to address the challenges encountered by the conventional shield machine industry arising from construction environment and manual operations.This study presents a systematic review of intelligent shield machine technology,with a particular emphasis on its smart operation.Firstly,the definition,meaning,contents,and development modes of intelligent shield machines are proposed.The development status of the intelligent shield machine and its smart operation are then presented.After analyzing the operation process of the shield machine,an autonomous operation framework considering both stand-alone and fleet levels is proposed.Challenges and recommendations are given for achieving autonomous operation.This study offers insights into the essence and developmental framework of intelligent shield machines to propel the advancement of this technology.展开更多
Rockburst disasters occur frequently during deep underground excavation,yet traditional concepts and methods can hardly meet the requirements for support under high geo-stress conditions.Consequently,rockburst control...Rockburst disasters occur frequently during deep underground excavation,yet traditional concepts and methods can hardly meet the requirements for support under high geo-stress conditions.Consequently,rockburst control remains challenging in the engineering field.In this study,the mechanism of excavation-induced rockburst was briefly described,and it was proposed to apply the excavation compensation method(ECM)to rockburst control.Moreover,a field test was carried out on the Qinling Water Conveyance Tunnel.The following beneficial findings were obtained:Excavation leads to changes in the engineering stress state of surrounding rock and results in the generation of excess energy DE,which is the fundamental cause of rockburst.The ECM,which aims to offset the deep excavation effect and lower the risk of rockburst,is an active support strategy based on high pre-stress compensation.The new negative Poisson’s ratio(NPR)bolt developed has the mechanical characteristics of high strength,high toughness,and impact resistance,serving as the material basis for the ECM.The field test results reveal that the ECM and the NPR bolt succeed in controlling rockburst disasters effectively.The research results are expected to provide guidance for rockburst support in deep underground projects such as Sichuan-Xizang Railway.展开更多
The technology of tunnel boring machine(TBM)has been widely applied for underground construction worldwide;however,how to ensure the TBM tunneling process safe and efficient remains a major concern.Advance rate is a k...The technology of tunnel boring machine(TBM)has been widely applied for underground construction worldwide;however,how to ensure the TBM tunneling process safe and efficient remains a major concern.Advance rate is a key parameter of TBM operation and reflects the TBM-ground interaction,for which a reliable prediction helps optimize the TBM performance.Here,we develop a hybrid neural network model,called Attention-ResNet-LSTM,for accurate prediction of the TBM advance rate.A database including geological properties and TBM operational parameters from the Yangtze River Natural Gas Pipeline Project is used to train and test this deep learning model.The evolutionary polynomial regression method is adopted to aid the selection of input parameters.The results of numerical exper-iments show that our Attention-ResNet-LSTM model outperforms other commonly-used intelligent models with a lower root mean square error and a lower mean absolute percentage error.Further,parametric analyses are conducted to explore the effects of the sequence length of historical data and the model architecture on the prediction accuracy.A correlation analysis between the input and output parameters is also implemented to provide guidance for adjusting relevant TBM operational parameters.The performance of our hybrid intelligent model is demonstrated in a case study of TBM tunneling through a complex ground with variable strata.Finally,data collected from the Baimang River Tunnel Project in Shenzhen of China are used to further test the generalization of our model.The results indicate that,compared to the conventional ResNet-LSTM model,our model has a better predictive capability for scenarios with unknown datasets due to its self-adaptive characteristic.展开更多
As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance le...As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers.On the other hand,a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule.The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications.The previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of accuracy.Hence,this study aims to introduce a hybrid randomforest(RF)technique optimized by global harmony search with generalized oppositionbased learning(GOGHS)for forecasting TBM advance rate(AR).Optimizing the RF hyper-parameters in terms of,e.g.,tree number and maximum tree depth is the main objective of using the GOGHS-RF model.In the modelling of this study,a comprehensive databasewith themost influential parameters onTBMtogetherwithTBM AR were used as input and output variables,respectively.To examine the capability and power of the GOGHSRF model,three more hybrid models of particle swarm optimization-RF,genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM AR.Evaluation of the developed models was performed by calculating several performance indices,including determination coefficient(R2),root-mean-square-error(RMSE),and mean-absolute-percentage-error(MAPE).The results showed that theGOGHS-RF is a more accurate technique for estimatingTBMAR compared to the other applied models.The newly-developedGOGHS-RFmodel enjoyed R2=0.9937 and 0.9844,respectively,for train and test stages,which are higher than a pre-developed RF.Also,the importance of the input parameters was interpreted through the SHapley Additive exPlanations(SHAP)method,and it was found that thrust force per cutter is the most important variable on TBMAR.The GOGHS-RF model can be used in mechanized tunnel projects for predicting and checking performance.展开更多
Tunnel boring machines(TBMs)have been widely utilised in tunnel construction due to their high efficiency and reliability.Accurately predicting TBM performance can improve project time management,cost control,and risk...Tunnel boring machines(TBMs)have been widely utilised in tunnel construction due to their high efficiency and reliability.Accurately predicting TBM performance can improve project time management,cost control,and risk management.This study aims to use deep learning to develop real-time models for predicting the penetration rate(PR).The models are built using data from the Changsha metro project,and their performances are evaluated using unseen data from the Zhengzhou Metro project.In one-step forecast,the predicted penetration rate follows the trend of the measured penetration rate in both training and testing.The autoregressive integrated moving average(ARIMA)model is compared with the recurrent neural network(RNN)model.The results show that univariate models,which only consider historical penetration rate itself,perform better than multivariate models that take into account multiple geological and operational parameters(GEO and OP).Next,an RNN variant combining time series of penetration rate with the last-step geological and operational parameters is developed,and it performs better than other models.A sensitivity analysis shows that the penetration rate is the most important parameter,while other parameters have a smaller impact on time series forecasting.It is also found that smoothed data are easier to predict with high accuracy.Nevertheless,over-simplified data can lose real characteristics in time series.In conclusion,the RNN variant can accurately predict the next-step penetration rate,and data smoothing is crucial in time series forecasting.This study provides practical guidance for TBM performance forecasting in practical engineering.展开更多
Understanding the undular tidal bores in the Qiantang River is essential for effective river management and maintenance.While breaking tidal bores have been studied extensively, reports on undular tidal bores in the Q...Understanding the undular tidal bores in the Qiantang River is essential for effective river management and maintenance.While breaking tidal bores have been studied extensively, reports on undular tidal bores in the Qiantang Riverremain limited. Furthermore, observed data on undular tidal bores fulfilling the requirements of short measurementtime intervals, and spring, medium, and neap tide coverage, and providing detailed data for the global vertical stratificationof flow velocity are quite limited. Based on field observations at Qige in the Qiantang estuary, we analyzedthe characteristics of undular tidal bores. The results showed that the flooding amplitude (a) of the first wave isalways larger than its ebbing amplitude (b). Moreover, the vertical distribution of the maximum flood velocity exhibitesthree shapes, influenced by the tidal range, while that of the maximum ebb velocity exhibites a single shape. Duringthe initial phase of the flood tide in the spring and medium tides, the upper water body experiences multiple oscillatingchanges along the flow direction, corresponding to the alternating process of the crest and trough of the tide levelupon the arrival of the tidal bore. The tidal range is a crucial parameter in tidal bore hydrodynamics. By establishingthe relationship between hydrodynamic parameters and tidal range, other hydrodynamic parameters, such as the tidalbore height, maximum flood depth–averaged velocity, maximum flood stratified velocity at the measurement points,and duration of the flood tide current, can be effectively predicted, thereby providing an important reference for rivermanagement and maintenance.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 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 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 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 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Advances in intelligent shield machines reflect an evolving trend from traditional tunnel boring machines(TBMs)to tunnel boring robots(TBRs).This shift aims to address the challenges encountered by the conventional shield machine industry arising from construction environment and manual operations.This study presents a systematic review of intelligent shield machine technology,with a particular emphasis on its smart operation.Firstly,the definition,meaning,contents,and development modes of intelligent shield machines are proposed.The development status of the intelligent shield machine and its smart operation are then presented.After analyzing the operation process of the shield machine,an autonomous operation framework considering both stand-alone and fleet levels is proposed.Challenges and recommendations are given for achieving autonomous operation.This study offers insights into the essence and developmental framework of intelligent shield machines to propel the advancement of this technology.
基金supported by the National Natural Science Foundation of China (41941018)the Foundation of State Key Laboratory for Geomechanics and Deep Underground Engineering (SKLGDUEK 2217)the Foundation of Collaborative Innovation Center for Prevention and Control of Mountain Geological Hazards of Zhejiang Province (PCMGH-2022-03).
文摘Rockburst disasters occur frequently during deep underground excavation,yet traditional concepts and methods can hardly meet the requirements for support under high geo-stress conditions.Consequently,rockburst control remains challenging in the engineering field.In this study,the mechanism of excavation-induced rockburst was briefly described,and it was proposed to apply the excavation compensation method(ECM)to rockburst control.Moreover,a field test was carried out on the Qinling Water Conveyance Tunnel.The following beneficial findings were obtained:Excavation leads to changes in the engineering stress state of surrounding rock and results in the generation of excess energy DE,which is the fundamental cause of rockburst.The ECM,which aims to offset the deep excavation effect and lower the risk of rockburst,is an active support strategy based on high pre-stress compensation.The new negative Poisson’s ratio(NPR)bolt developed has the mechanical characteristics of high strength,high toughness,and impact resistance,serving as the material basis for the ECM.The field test results reveal that the ECM and the NPR bolt succeed in controlling rockburst disasters effectively.The research results are expected to provide guidance for rockburst support in deep underground projects such as Sichuan-Xizang Railway.
基金The research was supported by the National Natural Science Foundation of China(Grant No.52008307)the Shanghai Sci-ence and Technology Innovation Program(Grant No.19DZ1201004)The third author would like to acknowledge the funding by the China Postdoctoral Science Foundation(Grant No.2023M732670).
文摘The technology of tunnel boring machine(TBM)has been widely applied for underground construction worldwide;however,how to ensure the TBM tunneling process safe and efficient remains a major concern.Advance rate is a key parameter of TBM operation and reflects the TBM-ground interaction,for which a reliable prediction helps optimize the TBM performance.Here,we develop a hybrid neural network model,called Attention-ResNet-LSTM,for accurate prediction of the TBM advance rate.A database including geological properties and TBM operational parameters from the Yangtze River Natural Gas Pipeline Project is used to train and test this deep learning model.The evolutionary polynomial regression method is adopted to aid the selection of input parameters.The results of numerical exper-iments show that our Attention-ResNet-LSTM model outperforms other commonly-used intelligent models with a lower root mean square error and a lower mean absolute percentage error.Further,parametric analyses are conducted to explore the effects of the sequence length of historical data and the model architecture on the prediction accuracy.A correlation analysis between the input and output parameters is also implemented to provide guidance for adjusting relevant TBM operational parameters.The performance of our hybrid intelligent model is demonstrated in a case study of TBM tunneling through a complex ground with variable strata.Finally,data collected from the Baimang River Tunnel Project in Shenzhen of China are used to further test the generalization of our model.The results indicate that,compared to the conventional ResNet-LSTM model,our model has a better predictive capability for scenarios with unknown datasets due to its self-adaptive characteristic.
基金the National Natural Science Foundation of China(Grant 42177164)the Distinguished Youth Science Foundation of Hunan Province of China(2022JJ10073).
文摘As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers.On the other hand,a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule.The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications.The previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of accuracy.Hence,this study aims to introduce a hybrid randomforest(RF)technique optimized by global harmony search with generalized oppositionbased learning(GOGHS)for forecasting TBM advance rate(AR).Optimizing the RF hyper-parameters in terms of,e.g.,tree number and maximum tree depth is the main objective of using the GOGHS-RF model.In the modelling of this study,a comprehensive databasewith themost influential parameters onTBMtogetherwithTBM AR were used as input and output variables,respectively.To examine the capability and power of the GOGHSRF model,three more hybrid models of particle swarm optimization-RF,genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM AR.Evaluation of the developed models was performed by calculating several performance indices,including determination coefficient(R2),root-mean-square-error(RMSE),and mean-absolute-percentage-error(MAPE).The results showed that theGOGHS-RF is a more accurate technique for estimatingTBMAR compared to the other applied models.The newly-developedGOGHS-RFmodel enjoyed R2=0.9937 and 0.9844,respectively,for train and test stages,which are higher than a pre-developed RF.Also,the importance of the input parameters was interpreted through the SHapley Additive exPlanations(SHAP)method,and it was found that thrust force per cutter is the most important variable on TBMAR.The GOGHS-RF model can be used in mechanized tunnel projects for predicting and checking performance.
文摘Tunnel boring machines(TBMs)have been widely utilised in tunnel construction due to their high efficiency and reliability.Accurately predicting TBM performance can improve project time management,cost control,and risk management.This study aims to use deep learning to develop real-time models for predicting the penetration rate(PR).The models are built using data from the Changsha metro project,and their performances are evaluated using unseen data from the Zhengzhou Metro project.In one-step forecast,the predicted penetration rate follows the trend of the measured penetration rate in both training and testing.The autoregressive integrated moving average(ARIMA)model is compared with the recurrent neural network(RNN)model.The results show that univariate models,which only consider historical penetration rate itself,perform better than multivariate models that take into account multiple geological and operational parameters(GEO and OP).Next,an RNN variant combining time series of penetration rate with the last-step geological and operational parameters is developed,and it performs better than other models.A sensitivity analysis shows that the penetration rate is the most important parameter,while other parameters have a smaller impact on time series forecasting.It is also found that smoothed data are easier to predict with high accuracy.Nevertheless,over-simplified data can lose real characteristics in time series.In conclusion,the RNN variant can accurately predict the next-step penetration rate,and data smoothing is crucial in time series forecasting.This study provides practical guidance for TBM performance forecasting in practical engineering.
基金supported by the National Natural Science Foundation of China(Grant No.42276176)the Joint Funds of the Zhejiang Provincial Natural Science Foundation of China(Grant No.LZJWZ23E090006)+2 种基金the Science and Technology Project of Zhejiang Provincial Department of Water Resources(Grant No.RC2233)the Key Project of Zhejiang Provincial Natural Science Foundation(Grant No.LZJWZ23E090003)the Joint Funds of the Zhejiang Provincial Natural Science Foundation of China(Grant No.LZJWY24E090002).
文摘Understanding the undular tidal bores in the Qiantang River is essential for effective river management and maintenance.While breaking tidal bores have been studied extensively, reports on undular tidal bores in the Qiantang Riverremain limited. Furthermore, observed data on undular tidal bores fulfilling the requirements of short measurementtime intervals, and spring, medium, and neap tide coverage, and providing detailed data for the global vertical stratificationof flow velocity are quite limited. Based on field observations at Qige in the Qiantang estuary, we analyzedthe characteristics of undular tidal bores. The results showed that the flooding amplitude (a) of the first wave isalways larger than its ebbing amplitude (b). Moreover, the vertical distribution of the maximum flood velocity exhibitesthree shapes, influenced by the tidal range, while that of the maximum ebb velocity exhibites a single shape. Duringthe initial phase of the flood tide in the spring and medium tides, the upper water body experiences multiple oscillatingchanges along the flow direction, corresponding to the alternating process of the crest and trough of the tide levelupon the arrival of the tidal bore. The tidal range is a crucial parameter in tidal bore hydrodynamics. By establishingthe relationship between hydrodynamic parameters and tidal range, other hydrodynamic parameters, such as the tidalbore height, maximum flood depth–averaged velocity, maximum flood stratified velocity at the measurement points,and duration of the flood tide current, can be effectively predicted, thereby providing an important reference for rivermanagement and maintenance.
