Predicting the tunneling-induced maximum ground surface settlement is a complex problem since the settlement depends on plenty of intrinsic and extrinsic factors.This study investigates the efficiency and feasibility ...Predicting the tunneling-induced maximum ground surface settlement is a complex problem since the settlement depends on plenty of intrinsic and extrinsic factors.This study investigates the efficiency and feasibility of six machine learning(ML)algorithms,namely,back-propagation neural network,wavelet neural network,general regression neural network(GRNN),extreme learning machine,support vector machine and random forest(RF),to predict tunneling?induced settlement.Field data sets including geological conditions,shield operational parameters,and tunnel geometry collected from four sections of tunnel with a total of 3.93 km are used to build models.Three indicators,mean absolute error,root mean absolute error,and coefficient of determination the(7?2)are used to demonstrate the performance of each computational model.The results indicated that ML algorithms have great potential to predict tunneling-induced settlement,compared with the traditional multivariate linear regression method.GRNN and RF algorithms show the best performance among six ML algorithms,which accurately recognize the evolution of tunneling-induced settlement.The correlation between the input variables and settlement is also investigated by Pearson correlation coefficient.展开更多
This paper mainly deals with the critical technology of earth pressure balance (EPB) control in shield tunneling. On the assumption that the conditioned soil in the working chamber of the shield is plasticized, a theo...This paper mainly deals with the critical technology of earth pressure balance (EPB) control in shield tunneling. On the assumption that the conditioned soil in the working chamber of the shield is plasticized, a theoretical principle for EPB control is proposed. Dynamic equilibrium of intake volume and discharge volume generated by thrust and discharge is modeled theoretically to simulate the earth pressure variation during excavating. The thrust system and the screw conveyor system for earth pressure control are developed based on the electro-hydraulic technique. The control models of the thrust speed regulation of the cylinders and the rotating speed adjustment of the screw conveyor are also presented. Simulation for earth pressure control is conducted with software AMESim and MATLAB/Simulink to verify the models. Experiments are carried out with intake control in clay soil and discharge control in sandy gravel section, respectively. The experimental results show that the earth pressure variations in the working chamber can be kept at the expected value with a practically acceptable precision by means of real-time tuning the thrust speed or the revolving speed of discharge system.展开更多
This paper introduces an intelligent framework for predicting the advancing speed during earth pressure balance(EPB)shield tunnelling.Five artificial intelligence(AI)models based on machine and deep learning technique...This paper introduces an intelligent framework for predicting the advancing speed during earth pressure balance(EPB)shield tunnelling.Five artificial intelligence(AI)models based on machine and deep learning techniques-back-propagation neural network(BPNN),extreme learning machine(ELM),support vector machine(SVM),long-short term memory(LSTM),and gated recurrent unit(GRU)-are used.Five geological and nine operational parameters that influence the advancing speed are considered.A field case of shield tunnelling in Shenzhen City,China is analyzed using the developed models.A total of 1000 field datasets are adopted to establish intelligent models.The prediction performance of the five models is ranked as GRU>LSTM>SVM>ELM>BPNN.Moreover,the Pearson correlation coefficient(PCC)is adopted for sensitivity analysis.The results reveal that the main thrust(MT),penetration(P),foam volume(FV),and grouting volume(GV)have strong correlations with advancing speed(AS).An empirical formula is constructed based on the high-correlation influential factors and their corresponding field datasets.Finally,the prediction performances of the intelligent models and the empirical method are compared.The results reveal that all the intelligent models perform better than the empirical method.展开更多
Estimating surface settlement induced by excavation construction is an indispensable task in tunneling,particularly for earth pressure balance(EPB)shield machines.In this study,predictive models for assessing surface ...Estimating surface settlement induced by excavation construction is an indispensable task in tunneling,particularly for earth pressure balance(EPB)shield machines.In this study,predictive models for assessing surface settlement caused by EPB tunneling were established based on extreme gradient boosting(XGBoost),artificial neural network,support vector machine,and multivariate adaptive regression spline.Datasets from three tunnel construction projects in Singapore were used,with main input parameters of cover depth,advance rate,earth pressure,mean standard penetration test(SPT)value above crown level,mean tunnel SPT value,mean moisture content,mean soil elastic modulus,and grout pressure.The performances of these soft computing models were evaluated by comparing predicted deformation with measured values.Results demonstrate the acceptable accuracy of the model in predicting ground settlement,while XGBoost demonstrates a slightly higher accuracy.In addition,the ensemble method of XGBoost is more computationally efficient and can be used as a reliable alternative in solving multivariate nonlinear geo-engineering problems.展开更多
Cutterhead torque is a crucial parameter for the design and operation of earth pressure balance （EPB） shield tunneling machine. However, the traditional calculation models of cutterhead torque are too rough or exist...Cutterhead torque is a crucial parameter for the design and operation of earth pressure balance （EPB） shield tunneling machine. However, the traditional calculation models of cutterhead torque are too rough or exist gross errors under variable geological conditions. In order to improve the precision of the calculation model of cutterhead torque, dynamic operation parameters are considered and a new model is proposed. Experiment is carried out on a ~1.8 m shield machine test rig and the calculating re- sult with the new model is compared with the experimental data to verify the validity of the new model. The relative error of the new model is as low as 4% at smooth stage and is reduced to 5% at the end of trembling stage. Based on the results of the new model and the test data obtained from the 001.8 m test rig and the construction site, the inner relationships between several operation parameters and cutterhead torque are investigated and some quantitative conclusions are obtained.展开更多
EPB TBMs(Earth pressure balance Tunneling Boring Machines) are extensively used in tunneling constructions because of its high efficiency and low disturbance on structures above ground. It is critically significant to...EPB TBMs(Earth pressure balance Tunneling Boring Machines) are extensively used in tunneling constructions because of its high efficiency and low disturbance on structures above ground. It is critically significant to predict the thrust acting on TBMs under different geological conditions for both the design of power system and the control of tunneling process. The interaction between the cutterhead and the ground is the core of excavation, through which geological conditions determine the thrust re-quirement combined with operating status and structural characteristics. This paper conducted a mechanical decoupling analysis to obtain a basic expression of the cutterhead-ground interactive stress. Then more engineering factors(such as cutterhead topological structure, underground overburden, thrusts on other parts, etc.) were further considered to establish a predicting model for the total thrust acting on a machine during tunneling. Combined with three subway projects under different geological conditions in China, the model was verified and used to analyze how geological, operating and structural parameters influence the acting thrust.展开更多
基金The present work was carried out with the support of Research Program of Changsha Science and Technology Bureau(cskq 1703051)the National Natural Science Foundation of China(Grant Nos.41472244 and 51878267)+1 种基金the Industrial Technology and Development Program of Zhongjian Tunnel Construction Co.,Ltd.(17430102000417)Natural Science Foundation of Hunan Province,China(2019JJ30006).
文摘Predicting the tunneling-induced maximum ground surface settlement is a complex problem since the settlement depends on plenty of intrinsic and extrinsic factors.This study investigates the efficiency and feasibility of six machine learning(ML)algorithms,namely,back-propagation neural network,wavelet neural network,general regression neural network(GRNN),extreme learning machine,support vector machine and random forest(RF),to predict tunneling?induced settlement.Field data sets including geological conditions,shield operational parameters,and tunnel geometry collected from four sections of tunnel with a total of 3.93 km are used to build models.Three indicators,mean absolute error,root mean absolute error,and coefficient of determination the(7?2)are used to demonstrate the performance of each computational model.The results indicated that ML algorithms have great potential to predict tunneling-induced settlement,compared with the traditional multivariate linear regression method.GRNN and RF algorithms show the best performance among six ML algorithms,which accurately recognize the evolution of tunneling-induced settlement.The correlation between the input variables and settlement is also investigated by Pearson correlation coefficient.
基金Supported by the National Outstanding Youth Foundation of China (Grant No. 50425518)the National Basic Research Program of China ("973" Project) (Grant No. 2007CB714004)
文摘This paper mainly deals with the critical technology of earth pressure balance (EPB) control in shield tunneling. On the assumption that the conditioned soil in the working chamber of the shield is plasticized, a theoretical principle for EPB control is proposed. Dynamic equilibrium of intake volume and discharge volume generated by thrust and discharge is modeled theoretically to simulate the earth pressure variation during excavating. The thrust system and the screw conveyor system for earth pressure control are developed based on the electro-hydraulic technique. The control models of the thrust speed regulation of the cylinders and the rotating speed adjustment of the screw conveyor are also presented. Simulation for earth pressure control is conducted with software AMESim and MATLAB/Simulink to verify the models. Experiments are carried out with intake control in clay soil and discharge control in sandy gravel section, respectively. The experimental results show that the earth pressure variations in the working chamber can be kept at the expected value with a practically acceptable precision by means of real-time tuning the thrust speed or the revolving speed of discharge system.
基金funded by“The Pearl River Talent Recruitment Program”in 2019(Grant No.2019CX01G338),。
文摘This paper introduces an intelligent framework for predicting the advancing speed during earth pressure balance(EPB)shield tunnelling.Five artificial intelligence(AI)models based on machine and deep learning techniques-back-propagation neural network(BPNN),extreme learning machine(ELM),support vector machine(SVM),long-short term memory(LSTM),and gated recurrent unit(GRU)-are used.Five geological and nine operational parameters that influence the advancing speed are considered.A field case of shield tunnelling in Shenzhen City,China is analyzed using the developed models.A total of 1000 field datasets are adopted to establish intelligent models.The prediction performance of the five models is ranked as GRU>LSTM>SVM>ELM>BPNN.Moreover,the Pearson correlation coefficient(PCC)is adopted for sensitivity analysis.The results reveal that the main thrust(MT),penetration(P),foam volume(FV),and grouting volume(GV)have strong correlations with advancing speed(AS).An empirical formula is constructed based on the high-correlation influential factors and their corresponding field datasets.Finally,the prediction performances of the intelligent models and the empirical method are compared.The results reveal that all the intelligent models perform better than the empirical method.
基金supported by the National Natural Science Foundation of China(No.51608071)Technology Plan Project(2019-0045).
文摘Estimating surface settlement induced by excavation construction is an indispensable task in tunneling,particularly for earth pressure balance(EPB)shield machines.In this study,predictive models for assessing surface settlement caused by EPB tunneling were established based on extreme gradient boosting(XGBoost),artificial neural network,support vector machine,and multivariate adaptive regression spline.Datasets from three tunnel construction projects in Singapore were used,with main input parameters of cover depth,advance rate,earth pressure,mean standard penetration test(SPT)value above crown level,mean tunnel SPT value,mean moisture content,mean soil elastic modulus,and grout pressure.The performances of these soft computing models were evaluated by comparing predicted deformation with measured values.Results demonstrate the acceptable accuracy of the model in predicting ground settlement,while XGBoost demonstrates a slightly higher accuracy.In addition,the ensemble method of XGBoost is more computationally efficient and can be used as a reliable alternative in solving multivariate nonlinear geo-engineering problems.
基金supported by the National Basic Research Program ("973"Program) of China (Grant No. 2007CB714004)
文摘Cutterhead torque is a crucial parameter for the design and operation of earth pressure balance （EPB） shield tunneling machine. However, the traditional calculation models of cutterhead torque are too rough or exist gross errors under variable geological conditions. In order to improve the precision of the calculation model of cutterhead torque, dynamic operation parameters are considered and a new model is proposed. Experiment is carried out on a ~1.8 m shield machine test rig and the calculating re- sult with the new model is compared with the experimental data to verify the validity of the new model. The relative error of the new model is as low as 4% at smooth stage and is reduced to 5% at the end of trembling stage. Based on the results of the new model and the test data obtained from the 001.8 m test rig and the construction site, the inner relationships between several operation parameters and cutterhead torque are investigated and some quantitative conclusions are obtained.
基金supported by the National Natural Science Foundation of China (Grant Nos. 11127202 & 11302146)
文摘EPB TBMs(Earth pressure balance Tunneling Boring Machines) are extensively used in tunneling constructions because of its high efficiency and low disturbance on structures above ground. It is critically significant to predict the thrust acting on TBMs under different geological conditions for both the design of power system and the control of tunneling process. The interaction between the cutterhead and the ground is the core of excavation, through which geological conditions determine the thrust re-quirement combined with operating status and structural characteristics. This paper conducted a mechanical decoupling analysis to obtain a basic expression of the cutterhead-ground interactive stress. Then more engineering factors(such as cutterhead topological structure, underground overburden, thrusts on other parts, etc.) were further considered to establish a predicting model for the total thrust acting on a machine during tunneling. Combined with three subway projects under different geological conditions in China, the model was verified and used to analyze how geological, operating and structural parameters influence the acting thrust.
