Ground settlement and tunnel response due to twin-curved shield tunnelling in soft ground with small clear distance

Twin curved tunnels are often encountered in shield tunnelling,where significant complexities in densely exploited underground space are observed.In this study,the ground settlement and tunnel deformation due to twin-curved shield tunnelling in soft ground were investigated using numerical simulation and field monitoring.Different curvature radii of twin curved tunnels and subsequent effects of tunnel construction were considered to reveal the tunnelling effect on ground surface settlement and tunnel deformation.The results show that the settlement trough yields one offset towards inside of curved shield tunnelling.The location of settlement trough and maximum settlement were affected by curvature radius but except for the shape and width of settlement trough.Adjacent parallel twin-curved shield tunnelling could increase the offset of existing settlement trough and maximum settlement.Then,an empirical prediction of surface settlement trough due to twin-curved shield tunnelling with same tunnel diameters in soft clay was proposed,which was applicable to curvature radius less than 800 m.Finally,a minimum radius of 600 m of curvature tunnel was proposed in terms of allowable convergence deformation of tunnel.The result could provide guidance on safety evaluation for twin curved shield tunnelling construction.

Three-dimensional pseudo-dynamic reliability analysis of seismic shield tunnel faces combined with sparse polynomial chaos expansion

To address the seismic face stability challenges encountered in urban and subsea tunnel construction,an efficient probabilistic analysis framework for shield tunnel faces under seismic conditions is proposed.Based on the upper-bound theory of limit analysis,an improved three-dimensional discrete deterministic mechanism,accounting for the heterogeneous nature of soil media,is formulated to evaluate seismic face stability.The metamodel of failure probabilistic assessments for seismic tunnel faces is constructed by integrating the sparse polynomial chaos expansion method(SPCE)with the modified pseudo-dynamic approach(MPD).The improved deterministic model is validated by comparing with published literature and numerical simulations results,and the SPCE-MPD metamodel is examined with the traditional MCS method.Based on the SPCE-MPD metamodels,the seismic effects on face failure probability and reliability index are presented and the global sensitivity analysis(GSA)is involved to reflect the influence order of seismic action parameters.Finally,the proposed approach is tested to be effective by a engineering case of the Chengdu outer ring tunnel.The results show that higher uncertainty of seismic response on face stability should be noticed in areas with intense earthquakes and variation of seismic wave velocity has the most profound influence on tunnel face stability.

On the critical particle size of soil with clogging potential in shield tunneling

Shield tunneling is easily obstructed by clogging in clayey strata with small soil particles.However,soil clogging rarely occurs in strata with coarse-grained soils.Theoretically,a critical particle size of soils should exist,below which there is a high risk of soil clogging in shield tunneling.To determine the critical particle size,a series of laboratory tests was carried out with a large-scale rotary shear apparatus to measure the tangential adhesion strength of soils with different particle sizes and water contents.It was found that the tangential adhesion strength at the soilesteel interface gradually increased linearly with applied normal pressure.When the particle size of the soil specimen was less than 0.15 mm,the interfacial adhesion force first increased and then decreased as the water content gradually increased;otherwise,the soil specimens did not manifest any interfacial adhesion force.The amount of soil mass adhering to the steel disc was positively correlated with the interfacial adhesion force,thus the interfacial adhesion force was adopted to characterize the soil clogging risk in shield tunneling.The critical particle size of soils causing clogging was determined to be 0.15 mm.Finally,the generation mechanism of interfacial adhesion force was explored for soils with different particle sizes to explain the critical particle size of soil with clogging risk in shield tunneling.

Prediction of subsurface settlement induced by shield tunnelling in sandy cobble stratum

This study focuses on the analytical prediction of subsurface settlement induced by shield tunnelling in sandy cobble stratum considering the volumetric deformation modes of the soil above the tunnel crown.A series of numerical analyses is performed to examine the effects of cover depth ratio(C/D),tunnel volume loss rate(h t)and volumetric block proportion(VBP)on the characteristics of subsurface settle-ment trough and soil volume loss.Considering the ground loss variation with depth,three modes are deduced from the volumetric deformation responses of the soil above the tunnel crown.Then,analytical solutions to predict subsurface settlement for each mode are presented using stochastic medium theory.The influences of C/D,h t and VBP on the key parameters(i.e.B and N)in the analytical expressions are discussed to determine the fitting formulae of B and N.Finally,the proposed analytical solutions are validated by the comparisons with the results of model test and numerical simulation.Results show that the fitting formulae provide a convenient and reliable way to evaluate the key parameters.Besides,the analytical solutions are reasonable and available in predicting the subsurface settlement induced by shield tunnelling in sandy cobble stratum.

Effects of data smoothing and recurrent neural network(RNN)algorithms for real-time forecasting of tunnel boring machine(TBM)performance

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.

Prediction of Disc Cutter Life During Shield Tunneling with AI via the Incorporation of a Genetic Algorithm into a GMDH-Type Neural Network

Disc cutter consumption is a critical problem that influences work performance during shield tunneling processes and directly affects the cutter change decision.This study proposes a new model to estimate the disc cutter life(Hf)by integrating a group method of data handling(GMDH)-type neural network(NN)with a genetic algorithm(GA).The efficiency and effectiveness of the GMDH network structure are optimized by the GA,which enables each neuron to search for its optimum connections set from the previous layer.With the proposed model,monitoring data including the shield performance database,disc cutter consumption,geological conditions,and operational parameters can be analyzed.To verify the performance of the proposed model,a case study in China is presented and a database is adopted to illustrate the excellence of the hybrid model.The results indicate that the hybrid model predicts disc cutter life with high accuracy.The sensitivity analysis reveals that the penetration rate(PR)has a significant influence on disc cutter life.The results of this study can be beneficial in both the planning and construction stages of shield tunneling.

Determination of minimum overburden depth for underwater shield tunnel in sands:Comparison between circular and rectangular tunnels

With the development of global urbanization,the utilization of underground space is more critical and attractive for civil purposes.Various shapes of shield tunnels have been gradually proposed to cope with different geological conditions and service purposes of underground structures.Generally,reducing the burial depth of shield tunnel is conducive to construction and cost saving.However,extremely small overburden depth cannot provide sufficient uplift resistance to maintain the stability and serviceability of the tunnel.To this end,this paper firstly reviewed the status of deriving the minimum sand over-burden depth of circular shield tunnel using mechanical equilibrium(ME)method.It revealed that the estimated depth is rather conservative.Then,the uplift resistance mechanism of both circular and rectangular tunnels was deduced theoretically and verified with the model tests.The theoretical uplift resistance is consistent with the experimental values,indicating the feasibility of the proposed equations.Furthermore,the determination of the minimum soil overburden depth of rectangular shield tunnel under various working conditions was presented through integrated ME method,which can provide more reasonable estimations of suggested tunnel burial depth for practical construction.Additionally,optimizations were made for calculating the uplift resistance,and the soil thickness providing uplift resistance is suggested to be adjusted according to the testing results.The results can provide reference for the design and construction of various shapes of shield tunnels in urban underground space exploitation.

Tunnelling performance prediction of cantilever boring machine in sedimentary hard-rock tunnel using deep belief network

Evaluating the adaptability of cantilever boring machine(CBM) through in-depth excavation and analysis of tunnel excavation data and rock mass parameters is the premise of mechanical design and efficient excavation in the field of underground space engineering.This paper presented a case study of tunnelling performance prediction method of CBM in sedimentary hard-rock tunnel of Karst landform type by using tunneling data and surrounding rock parameters.The uniaxial compressive strength(UCS),rock integrity factor(Kv),basic quality index([BQ]),rock quality index RQD,brazilian tensile strength(BTS) and brittleness index(BI) were introduced to construct a performance prediction database based on the hard-rock tunnel of Guiyang Metro Line 1 and Line 3,and then established the performance prediction model of cantilever boring machine.Then the deep belief network(DBN) was introduced into the performance prediction model,and the reliability of performance prediction model was verified by combining with engineering data.The study showed that the influence degree of surrounding rock parameters on the tunneling performance of the cantilever boring machine is UCS > [BQ] > BTS >RQD > Kv > BI.The performance prediction model shows that the instantaneous cutting rate(ICR) has a good correlation with the surrounding rock parameters,and the predicting model accuracy is related to the reliability of construction data.The prediction of limestone and dolomite sections of Line 3 based on the DBN performance prediction model shows that the measured ICR and predicted ICR is consistent and the built performance prediction model is reliable.The research results have theoretical reference significance for the applicability analysis and mechanical selection of cantilever boring machine for hard rock tunnel.

Estimation of tunnel axial orientation in the interlayered rock mass using a comprehensive algorithm

The axial selection of tunnels constructed in the interlayered soft-hard rock mass affects the stability and safety during construction.Previous optimization is primarily based on experience or comparison and selection of alternative values under specific geological conditions.In this work,an intelligent optimization framework has been proposed by combining numerical analysis,machine learning(ML)and optimization algorithm.An automatic and intelligent numerical analysis process was proposed and coded to reduce redundant manual intervention.The conventional optimization algorithm was developed from two aspects and applied to the hyperparameters estimation of the support vector machine(SVM)model and the axial orientation optimization of the tunnel.Finally,the comprehensive framework was applied to a numerical case study,and the results were compared with those of other studies.The results of this study indicate that the determination coefficients between the predicted and the numerical stability evaluation indices(STIs)on the training and testing datasets are 0.998 and 0.997,respectively.For a given geological condition,the STI that changes with the axial orientation shows the trend of first decreasing and then increasing,and the optimal tunnel axial orientation is estimated to be 87.This method provides an alternative and quick approach to the overall design of the tunnels.

Vibrations induced by tunnel boring machine in urban areas: In situ measurements and methodology of analysis

Excavation with tunnel boring machine(TBM)can generate vibrations,causing damages to neighbouring buildings and disturbing the residents or the equipment.This problem is particularly challenging in urban areas,where TBMs are increasingly large in diameter and shallow in depth.In response to this problem,four experimental campaigns were carried out in different geotechnical contexts in France.The vibration measurements were acquired on the surface and inside the TBMs.These measurements are also complemented by few data in the literature.An original methodology of signal processing is pro-posed to characterize the amplitude of the particle velocities,as well as the frequency content of the signals to highlight the most energetic bands.The levels of vibrations are also compared with the thresholds existing in various European regulations concerning the impact on neighbouring structures and the disturbance to local residents.

Real-time prediction of mechanical behaviors of underwater shield tunnel structure using machine learning method based on structural health monitoring data

Predicting the mechanical behaviors of structure and perceiving the anomalies in advance are essential to ensuring the safe operation of infrastructures in the long run.In addition to the incomplete consideration of influencing factors,the prediction time scale of existing studies is rough.Therefore,this study focuses on the development of a real-time prediction model by coupling the spatio-temporal correlation with external load through autoencoder network(ATENet)based on structural health monitoring(SHM)data.An autoencoder mechanism is performed to acquire the high-level representation of raw monitoring data at different spatial positions,and the recurrent neural network is applied to understanding the temporal correlation from the time series.Then,the obtained temporal-spatial information is coupled with dynamic loads through a fully connected layer to predict structural performance in next 12 h.As a case study,the proposed model is formulated on the SHM data collected from a representative underwater shield tunnel.The robustness study is carried out to verify the reliability and the prediction capability of the proposed model.Finally,the ATENet model is compared with some typical models,and the results indicate that it has the best performance.ATENet model is of great value to predict the realtime evolution trend of tunnel structure.

Mechanical performances of shield tunnel segments under asymmetric unloading induced by pit excavation

To explore the stress and deformation responses,as well as the failure characteristics of the shield tunnel segment of Hangzhou Metro under the influences of pit excavation and other surrounding projects,a self-developed“shield tunnel segment hydraulic loading system”was used to carry out full-scale loading tests on the three-ring staggered assembled segments.The structural performances and failure process of the tunnel segment under step-by-step asymmetric unloading were studied.A safety index was proposed to describe the bearing capacity of the segment.Next,a finite element model(FEM)was established to analyze the bearing capacity of segment using the test results.Finally,the effect of reinforcement with a steel plate on the deformation and bearing capacity of the segment was analyzed.The results showed that under asymmetric unloading,the peak value and amplitude of the bending moment on the near unloading side converged with a greater value than those on the far side.The concrete internal force exhibited a directional transformation at different load stages.Cracks first appeared at the 180inner arc surface of the bottom standard block and then expanded to both sides,while the rate of crack propagation of the outer arc surface was relatively lower.The bearing capacity of the segments can be evaluated by the combination of the factors,e.g.the residual bearing capacity coefficient,moment transfer coefficient,and characterization coefficient.The segments approaching failure can facilitate the increase in the residual bearing capacity coefficient by more than 50%.This can provide guidance for the service assessment of metro tunnel operations.

Research on visual differences of exits of different grades of tunnels based on machine learning

Tunnels are vital in connecting crucial transportation hubs as transportation infrastructure evolves.Variations in tunnel design standards and driving conditions across different levels directly impact driver visual perception and traffic safety.This study employs a Gaussian hybrid clustering machine learning model to explore driver gaze patterns in highway tunnels and exits.By utilizing contour coefficients,the optimal number of classification clusters is determined.Analysis of driver visual behavior across tunnel levels,focusing on gaze point distribution,gaze duration,and sweep speed,was conducted.Findings indicate freeway tunnel exits exhibit three distinct fixation point categories aligning with Gaussian distribution,while highway tunnels display four such characteristics.Notably,in both tunnel types,65%of driver gaze is concentrated on the near area ahead of their lane.Differences emerge in highway tunnels due to oncoming traffic,leading to 13.47%more fixation points and 0.9%increased fixation time in the right lane compared to regular highway tunnel conditions.Moreover,scanning speeds predominantly fall within the 0.25-0.3 range,accounting for 75.47%and 31.14%of the total sweep speed.

Analytical solution for longitudinal deformation of shield tunnel induced by overcrossing tunnelling considering circumferential joints

This paper presents a new analytical solution for assessing the longitudinal deformation of shield tunnel associated with overcrossing tunnelling in consideration of circumferential joints.A simplified longitudinal beam-spring model(SLBSM)is established to model the longitudinal behaviours of shield tunnel,which can consider the opening and dislocation between segmental rings simultaneously.Then,the existing tunnel is treated as the SLBSM resting on the elastic foundation.The state equations including tunnel displacements and internal forces are constructed to solve the discontinuous deformation of circumferential joint-segmental ring.The feasibility of the proposed solution is verified through three well-documented cases.The predictions from the proposed method are also compared with other analytical methods.It is found that the proposed method can well capture the deformation of tunnel segmental rings and joints,where the rigid displacement mainly occurs in the segmental rings while the rotation and dislocation occur in the circumferential joints.Some dominant parameters are also analysed to explore the effects on existing tunnel deformation,including the rotation stiffness and shearing stiffness of joints,the skew angle and the clearance between new and old tunnels.

Vote-Based Feature Selection Method for Stratigraphic Recognition in Tunnelling Process of Shield Machine

Shield machines are currently the main tool for underground tunnel construction. Due to the complexity and variability of the underground construction environment, it is necessary to accurately identify the ground in real-time during the tunnel construction process to match and adjust the tunnel parameters according to the geological conditions to ensure construction safety. Compared with the traditional method of stratum identifcation based on staged drilling sampling, the real-time stratum identifcation method based on construction data has the advantages of low cost and high precision. Due to the huge amount of sensor data of the ultra-large diameter mud-water balance shield machine, in order to balance the identifcation time and recognition accuracy of the formation, it is necessary to screen the multivariate data features collected by hundreds of sensors. In response to this problem, this paper proposes a voting-based feature extraction method (VFS), which integrates multiple feature extraction algorithms FSM, and the frequency of each feature in all feature extraction algorithms is the basis for voting. At the same time, in order to verify the wide applicability of the method, several commonly used classifcation models are used to train and test the obtained efective feature data, and the model accuracy and recognition time are used as evaluation indicators, and the classifcation with the best combination with VFS is obtained. The experimental results of shield machine data of 6 diferent geological structures show that the average accuracy of 13 features obtained by VFS combined with diferent classifcation algorithms is 91%;among them, the random forest model takes less time and has the highest recognition accuracy, reaching 93%, showing best compatibility with VFS. Therefore, the VFS algorithm proposed in this paper has high reliability and wide applicability for stratum identifcation in the process of tunnel construction, and can be matched with a variety of classifer algorithms. By combining 13 features selected from shield machine data features with random forest, the identifcation of the construction stratum environment of shield tunnels can be well realized, and further theoretical guidance for underground engineering construction can be provided.

Load-sharing Characteristic of Multiple Pinions Driving in Tunneling Boring Machine

The failure of the key parts, such as gears, in cutter head driving system of tunneling boring machine has not been properly solved under the interaction of driving motors asynchronously and wave tunneling torque load. A dynamic model of multi-gear driving system is established considering the inertia effects of driving mechanism and cutter head as well as the bending-torsional coupling. By taking into account the nonlinear coupling factors between ring gear and multiple pinions, the influence for meshing angle by bending-torsional coupling and the dynamic load-sharing characteristic of multiple pinions driving are analyzed. Load-sharing coefficients at different rotating cutter head speeds and input torques are presented. Numerical results indicate that the load-sharing coefficients can reach up to 1.2-1.3. A simulated experimental platform of the multiple pinions driving is carried out and the torque distributions under the step load in driving shaft of pinions are measured. The imbalance of torque distribution of pinions is verified and the load-sharing coefficients in each pinion can reach 1.262. The results of simulation and test are similar, which shows the correctness of theoretical model. A loop coupling control method is put forward based on current torque master slave control method. The imbalance of the multiple pinions driving in cutter head driving system of tunneling boring machine can be greatly decreased and the load-sharing coefficients can be reduced to 1.051 by using the loop coupling control method. The proposed research provides an effective solution to the imbalance of torque distribution and synchronous control method for multiple pinions driving of TBM.

Machine learning-based automatic control of tunneling posture of shield machine

For a tunnel driven by a shield machine,the posture of the driving machine is essential to the construction quality and environmental impact.However,the machine posture is controlled by the experienced driver of shield machine by setting hundreds of tunneling parameters empirically.Machine learning(ML)algorithm is an alternative method that can let the computer to learn from the driver’s operation and try to model the relationship between parameters automatically.Thus,in this paper,three ML algorithms,i.e.multi-layer perception(MLP),support vector machine(SVM)and gradient boosting regression(GBR),are improved by genetic algorithm(GA)and principal component analysis(PCA)to predict the tunneling posture of the shield machine.A set of the parameters for shield tunneling is extracted from the construction site of a Shanghai metro.In total,53,785 pairwise data points are collected for about 373 d and the ratio between training set,validation set and test set is 3:1:1.Each pairwise data point includes 83 types of parameters covering the shield posture,construction parameters,and soil stratum properties at the same time.The test results show that the averaged R2 of MLP,SVM and GBR based models are 0.942,0.935 and 0.6,respectively.Then the automatic control for the posture of shield tunnel is illustrated with an application example of the proposed models.The proposed method is proved to be helpful in controlling the construction quality with optimized construction parameters.

Three-dimensional displacement characteristics of adjacent pile induced by shield tunneling under influence of multiple factors

Shield tunneling inevitably passes through a large number of pile foundations in urban areas.Thus,an accurate assessment of tunneling-induced pile displacement and potential damage becomes a critical part of shield construction.This study presents a mechanism research of pile-soil-tunnel interaction through Pasternak-based two-stage analysis method.In the first stage,based on Mindlin’s solution,the soil displacement fields induced by shield thrust force,cutterhead frictions,shield shell frictions and grouting pressure are derived.The analytical solution of threedimensional soil displacement field is established by introducing Pinto’s three-dimensional volume loss formula,which solves the problems that shield construction factors are not taken into account in Loganathan’s formula and only twodimensional soil displacement field can be obtained.In the second stage,based on Pasternak’s two-parameter foundation model,the analytical solution of pile displacement induced by shield tunneling in layered soil is derived.A case was found in the project of interval tunnels from Wanjiali Square to Furong District Government of Changsha Metro Line 5,where the shield tunnels were constructed near viaduct piles.The reliability of the analytical solution proposed in this study is verified by comparing with the field measured data and the results of finite element method(FEM).In addition,the comparisons of longitudinal,horizontal and vertical displacements of soil and pile foundation analyzed by the analytical solution and FEM provide corresponding theoretical basis,which has significant engineering guidance for similar projects.

Comparison of machine learning methods for ground settlement prediction with different tunneling datasets

This study integrates different machine learning(ML) methods and 5-fold cross-validation(CV) method to estimate the ground maximal surface settlement(MSS) induced by tunneling.We further investigate the applicability of artificial intelligent(AI) based prediction through a comparative study of two tunnelling datasets with different sizes and features.Four different ML approaches,including support vector machine(SVM),random forest(RF),back-propagation neural network(BPNN),and deep neural network(DNN),are utilized.Two techniques,i.e.particle swarm optimization(PSO) and grid search(GS)methods,are adopted for hyperparameter optimization.To assess the reliability and efficiency of the predictions,three performance evaluation indicators,including the mean absolute error(MAE),root mean square error(RMSE),and Pearson correlation coefficient(R),are calculated.Our results indicate that proposed models can accurately and efficiently predict the settlement,while the RF model outperforms the other three methods on both datasets.The difference in model performance on two datasets(Datasets A and B) reveals the importance of data quality and quantity.Sensitivity analysis indicates that Dataset A is more significantly affected by geological conditions,while geometric characteristics play a more dominant role on Dataset B.

Effectiveness of predicting tunneling-induced ground settlements using machine learning methods with small datasets

Prediction of tunneling-induced ground settlements is an essential task,particularly for tunneling in urban settings.Ground settlements should be limited within a tolerable threshold to avoid damages to aboveground structures.Machine learning(ML)methods are becoming popular in many fields,including tunneling and underground excavations,as a powerful learning and predicting technique.However,the available datasets collected from a tunneling project are usually small from the perspective of applying ML methods.Can ML algorithms effectively predict tunneling-induced ground settlements when the available datasets are small?In this study,seven ML methods are utilized to predict tunneling-induced ground settlement using 14 contributing factors measured before or during tunnel excavation.These methods include multiple linear regression(MLR),decision tree(DT),random forest(RF),gradient boosting(GB),support vector regression(SVR),back-propagation neural network(BPNN),and permutation importancebased BPNN(PI-BPNN)models.All methods except BPNN and PI-BPNN are shallow-structure ML methods.The effectiveness of these seven ML approaches on small datasets is evaluated using model accuracy and stability.The model accuracy is measured by the coefficient of determination(R2)of training and testing datasets,and the stability of a learning algorithm indicates robust predictive performance.Also,the quantile error(QE)criterion is introduced to assess model predictive performance considering underpredictions and overpredictions.Our study reveals that the RF algorithm outperforms all the other models with the highest model prediction accuracy(0.9)and stability(3.0210^(-27)).Deep-structure ML models do not perform well for small datasets with relatively low model accuracy(0.59)and stability(5.76).The PI-BPNN architecture is proposed and designed for small datasets,showing better performance than typical BPNN.Six important contributing factors of ground settlements are identified,including tunnel depth,the distance between tunnel face and surface monitoring points(DTM),weighted average soil compressibility modulus(ACM),grouting pressure,penetrating rate and thrust force.