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.

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.

HSP超前探测技术在煤矿TBM掘进巷道中的应用研究

随着全断面掘进机TBM(Tunnel Boring Machine)逐渐应用于煤矿岩巷掘进,对不良地质构造进行超前准确快速预测的需求日益迫切。通过对主动源地震波超前探测方法的特点和TBM破岩震源超前探测技术的适用性进行分析,结合煤矿巷道地质和生产条件,提出了适用于煤矿巷道TBM掘进的HSP超前探测方法。以河南平顶山首山一矿TBM掘进底板瓦斯治理巷道为工程背景,选用防爆硬件一体化设计的探测仪器在煤矿巷道中进行应用。构建了空间型观测方式对煤矿巷道近水平煤线进行探测,优化了双护盾TBM掘进巷道狭小空间检波器阵列式布置参数;基于时频分析、互相关干涉处理、反射与散射联合反演等方法处理原始信号并进行探测结果成像。研究表明:采用空间型观测方式可实现与巷道小角度斜交煤线的识别,设计震源与首检波器间距离为15 m时最优。通过时频分析提取有效信号,利用互相关干涉法获取虚拟震源道和反射特征曲线,并结合反射与散射联合反演成像得到探测区域地层反射能量分布图,能够较准确地推测得到围岩存在的不良地质构造。通过比较现场开挖揭露情况与探测结果发现两者吻合度较高,表明HSP超前探测方法可实现掘进工作面前方100 m范围内超前无损地质预测,有助于提高煤矿岩巷TBM掘进速度。

地铁施工物化阶段TBM区间碳排放核算与减排

目前,国内外对土建工程碳排放的研究主要集中在住宅、办公建筑领域,对地铁建设工程碳排放的研究较少。为计量地铁TBM区间土建工程物化阶段碳排放,识别其排放特点,运用碳排放系数法建立了包含TBM区间土建实体、建造配套设备、运输施工机具、周转材料、劳动力在内的系统边界,将物化阶段划分为建材及预构件生产、建材及预构件运输、现场施工三个环节对碳排放进行计量。对碳排放因子进行了定义选取,构建了TBM区间各环节碳排放的计量模型并对青岛地铁典型TBM工段进行了案例分析,得到物化阶段三个环节的碳排放强度分别为4875.87 tCO_(2)/km、49.54 tCO_(2)/km、1780.04 tCO_(2)/km,总碳排放强度为6705.45 tCO_(2)/km。通过情景假设法,得出地铁TBM区间再生建材的使用可以带来1.21%~2.43%的减排效益。

Design Theory of Full Face Rock Tunnel Boring Machine Transition Cutter Edge Angle and Its Application

At present, the inner cutters of a full face rock tunnel boring machine (TBM) and transition cutter edge angles are designed on the basis of indentation test or linear grooving test. The inner and outer edge angles of disc cutters are characterized as symmetric to each other with respect to the cutter edge plane. This design has some practical defects, such as severe eccentric wear and tipping, etc. In this paper, the current design theory of disc cutter edge angle is analyzed, and the characteristics of the rock-breaking movement of disc cutters are studied. The researching results show that the rotational motion of disc cutters with the cutterhead gives rise to the difference between the interactions of inner rock and outer rock with the contact area of disc cutters, with shearing and extrusion on the inner rock and attrition on the outer rock. The wear of disc cutters at the contact area is unbalanced, among which the wear in the largest normal stress area is most apparent. Therefore, a three-dimensional model theory of rock breaking and an edge angle design theory of transition disc cutter are proposed to overcome the flaws of the currently used TBM cutter heads, such as short life span, camber wearing, tipping. And a corresponding equation is established. With reference to a specific construction case, the edge angle of the transition disc cutter has been designed based on the theory. The application of TBM in some practical project proves that the theory has obvious advantages in enhancing disc cutter life, decreasing replacement frequency, and making economic benefits. The proposed research provides a theoretical basis for the design of TBM three-dimensional disc cutters whose rock-breaking operation time can be effectively increased.

Theoretical prediction of wear of disc cutters in tunnel boring machine and its application

Predicting the cutter consumption and the exact time to replace the worn-out cutters in tunneling projects constructed with tunnel boring machine(TBM) is always a challenging issue. In this paper, we focus on the analyses of cutter motion in the rock breaking process and trajectory of rock breaking point on the cutter edge in rocks. The analytical expressions of the length of face along which the breaking point moves and the length of spiral trajectory of the maximum penetration point are derived. Through observation of rock breaking process of disc cutters as well as analysis of disc rock interaction, the following concepts are proposed: the arc length theory of predicting wear extent of inner and center cutters, and the spiral theory of predicting wear extent of gage and transition cutters. Data obtained from5621 m-long Qinling tunnel reveal that among 39 disc cutters, the relative errors between cumulatively predicted and measured wear values for nine cutters are larger than 20%, while approximately 76.9% of total cutters have the relative errors less than 20%. The proposed method could offer a new attempt to predict the disc cutter's wear extent and changing time.

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.

Performance characteristics of tunnel boring machine in basalt and pyroclastic rocks of Deccan traps–A case study

A12.24km long tunnel between Maroshi and Ruparel College is being excavated by tunnel boring machine(TBM)to improve the water supply system of Greater Mumbai,India.In this paper,attempt has been made to establish the relationship between various litho-units of Deccan traps,stability of tunnel and TBM performances during the construction of5.83km long tunnel between Maroshi and Vakola.The Maroshi–Vakola tunnel passes under the Mumbai Airport and crosses both runways with an overburden cover of around70m.The tunneling work was carried out without disturbance to the ground.The rock types encountered during excavation arefine compacted basalt,porphyritic basalt,amygdaloidal basalt pyroclastic rocks with layers of red boles and intertrappean beds consisting of various types of shales Relations between rock mass properties,physico-mechanical properties,TBM specifications and the cor responding TBM performance were established.A number of support systems installed in the tunne during excavation were also discussed.The aim of this paper is to establish,with appropriate accuracy the nature of subsurface rock mass condition and to study how it will react to or behave during under ground excavation by TBM.The experiences gained from this project will increase the ability to cope with unexpected ground conditions during tunneling using TBM.

基于VMD与加权RF的TBM掘进速度预测SHAP解释模型

为较准确地实现TBM掘进速度(PR)的预测,构建一套基于加权随机森林(RF)结合变分模态分解(VMD)的集成学习预测模型。模型建立过程中,通过收集来自KS隧洞与兰州水源地输水隧洞中涵盖不同岩性下的数据,利用VMD对数据进行4次模态分解,在保留数据特性的同时去除最高频噪音;采用SHAP对未加权传统RF从模型贡献角度进行特征度量,以此实现未加权传统RF加权,并使用RFECV与网格搜索对加权RF进行特征遴选、超参数优化;通过实际工程对模型的性能进行验证,基于SHAP理论对模型从全局与局部进行解释。结果表明:1)所建模型预测精度较高,其在测试集上的均方根误差(MSE)、平均绝对误差(MAE)与决定系数(R^(2))分别为0.0649(m/h)^(2)、0.1875 m/h、0.9254。2)在实际工程的验证中,模型取得了MSE=0.0503(m/h)^(2)、MAE=0.1613 m/h、R^(2)=0.9505的性能表现,精度理想,且性能均高于常用的深度神经网络、支持向量回归、未加权传统RF。3)经过VMD处理可有效提升PR的预测精度,处理后的模型在测试集上MSE、MAE、R^(2)分别提升了82.50%、59.00%、33.25%。4)岩石单轴抗压强度是精准预测PR时最重要的因素,地质参数在预测中的交互性明显优于掘进参数。预测分析重要洞段的PR时,需结合全局与局部2个角度进行综合分析。

基于图像分析的TBM掘进参数与岩碴特征关系研究

岩碴图像分析是隧道掘进机(tunnelboringmechine,TBM)智能掘进中实时破岩效率评价和掘进参数优化的重要手段。为了研究掘进参数与岩碴图像特征的关系,依托青岛地铁6号线创石区间TBM施工段,在一段较完整花岗岩地层中开展了分步推力掘进试验,并通过搭载的岩碴图像分析系统对岩碴图像进行采集和分析。结果表明,岩碴图像的中位粒径d50、最大粒径dmax和粗糙度指数CI与推力、扭矩和贯入度之间均表现出正相关,与现场贯入指数FPI和工程比能SE之间均呈现出良好的负相关。当推力超过破岩临界值后,推力的提升将显著提高岩碴的d50和CI,而只有当推力进一步增大到一定水平后dmax才显著增大。此外,随着推力的提升,大岩片的二维形状逐渐呈现出长条形,其平均长短轴比也将增加,但是推力超过一定水平后,大岩片长短轴将保持不变。上述研究结果表明,岩碴粒径和形状参数均反映出破岩效率,可作为实时TBM掘进参数优化的评价指标。

Real-time rock mass condition prediction with TBM tunneling big data using a novel rock-machine mutual feedback perception method

Real-time perception of rock mass information is of great importance to efficient tunneling and hazard prevention in tunnel boring machines(TBMs).In this study,a TBM-rock mutual feedback perception method based on data mining(DM) is proposed,which takes 10 tunneling parameters related to surrounding rock conditions as input features.For implementation,first,the database of TBM tunneling parameters was established,in which 10,807 tunneling cycles from the Songhua River water conveyance tunnel were accommodated.Then,the spectral clustering(SC) algorithm based on graph theory was introduced to cluster the TBM tunneling data.According to the clustering results and rock mass boreability index,the rock mass conditions were classified into four classes,and the reasonable distribution intervals of the main tunneling parameters corresponding to each class were presented.Meanwhile,based on the deep neural network(DNN),the real-time prediction model regarding different rock conditions was established.Finally,the rationality and adaptability of the proposed method were validated via analyzing the tunneling specific energy,feature importance,and training dataset size.The proposed TBM-rock mutual feedback perception method enables the automatic identification of rock mass conditions and the dynamic adjustment of tunneling parameters during TBM driving.Furthermore,in terms of the prediction performance,the method can predict the rock mass conditions ahead of the tunnel face in real time more accurately than the traditional machine learning prediction methods.

Hybrid ensemble soft computing approach for predicting penetration rate of tunnel boring machine in a rock environment

This study implements a hybrid ensemble machine learning method for forecasting the rate of penetration(ROP) of tunnel boring machine(TBM),which is becoming a prerequisite for reliable cost assessment and project scheduling in tunnelling and underground projects in a rock environment.For this purpose,a sum of 185 datasets was collected from the literature and used to predict the ROP of TBM.Initially,the main dataset was utilised to construct and validate four conventional soft computing(CSC)models,i.e.minimax probability machine regression,relevance vector machine,extreme learning machine,and functional network.Consequently,the estimated outputs of CSC models were united and trained using an artificial neural network(ANN) to construct a hybrid ensemble model(HENSM).The outcomes of the proposed HENSM are superior to other CSC models employed in this study.Based on the experimental results(training RMSE=0.0283 and testing RMSE=0.0418),the newly proposed HENSM is potential to assist engineers in predicting ROP of TBM in the design phase of tunnelling and underground projects.

Excavation of underground research laboratory ramp in granite using tunnel boring machine: Feasibility study

Underground research laboratory(URL)plays an important role in safe disposal of high-level radioactive waste(HLW).At present,the Xinchang site,located in Gansu Province of China,has been selected as the final site for China’s first URL,named Beishan URL.For this,a preliminary design of the Beishan URL has been proposed,including one spiral ramp,three shafts and two experimental levels.With advantages of fast advancing and limited disturbance to surrounding rock mass,the tunnel boring machine(TBM)method could be one of the excavation methods considered for the URL ramp.This paper introduces the feasibility study on using TBM to excavation of the Beishan URL ramp.The technical challenges for using TBM in Beishan URL are identified on the base of geological condition and specific layout of the spiral ramp.Then,the technical feasibility study on the specific issues,i.e.extremely hard rock mass,high abrasiveness,TBM operation,muck transportation,water drainage and material transportation,is investigated.This study demonstrates that TBM technology is a feasible method for the Beishan URL excavation.The results can also provide a reference for the design and construction of HLW disposal engineering in similar geological conditions.2020 Institute of Rock and Soil Mechanics,Chinese Academy of Sciences.Production and hosting by Elsevier B.V.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

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

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

Tunnel boring machine vibration-based deep learning for the ground identification of working faces

Tunnel boring machine(TBM) vibration induced by cutting complex ground contains essential information that can help engineers evaluate the interaction between a cutterhead and the ground itself.In this study,deep recurrent neural networks(RNNs) and convolutional neural networks(CNNs) were used for vibration-based working face ground identification.First,field monitoring was conducted to obtain the TBM vibration data when tunneling in changing geological conditions,including mixed-face,homogeneous,and transmission ground.Next,RNNs and CNNs were utilized to develop vibration-based prediction models,which were then validated using the testing dataset.The accuracy of the long short-term memory(LSTM) and bidirectional LSTM(Bi-LSTM) models was approximately 70% with raw data;however,with instantaneous frequency transmission,the accuracy increased to approximately 80%.Two types of deep CNNs,GoogLeNet and ResNet,were trained and tested with time-frequency scalar diagrams from continuous wavelet transformation.The CNN models,with an accuracy greater than 96%,performed significantly better than the RNN models.The ResNet-18,with an accuracy of 98.28%,performed the best.When the sample length was set as the cutterhead rotation period,the deep CNN and RNN models achieved the highest accuracy while the proposed deep CNN model simultaneously achieved high prediction accuracy and feedback efficiency.The proposed model could promptly identify the ground conditions at the working face without stopping the normal tunneling process,and the TBM working parameters could be adjusted and optimized in a timely manner based on the predicted results.

Utilizing partial least square and support vector machine for TBM penetration rate prediction in hard rock conditions

Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accuracy of prediction models employing partial least squares(PLS) regression and support vector machine(SVM) regression technique for modeling the penetration rate of TBM. To develop the proposed models, the database that is composed of intact rock properties including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and peak slope index(PSI), and also rock mass properties including distance between planes of weakness(DPW) and the alpha angle(α) are input as dependent variables and the measured ROP is chosen as an independent variable. Two hundred sets of data are collected from Queens Water Tunnel and Karaj-Tehran water transfer tunnel TBM project. The accuracy of the prediction models is measured by the coefficient of determination(R2) and root mean squares error(RMSE) between predicted and observed yield employing 10-fold cross-validation schemes. The R2 and RMSE of prediction are 0.8183 and 0.1807 for SVMR method, and 0.9999 and 0.0011 for PLS method, respectively. Comparison between the values of statistical parameters reveals the superiority of the PLSR model over SVMR one.

Soft ground tunnel lithology classification using clustering-guided light gradient boosting machine

During tunnel boring machine(TBM)excavation,lithology identification is an important issue to understand tunnelling performance and avoid time-consuming excavation.However,site investigation generally lacks ground samples and the information is subjective,heterogeneous,and imbalanced due to mixed ground conditions.In this study,an unsupervised(K-means)and synthetic minority oversampling technique(SMOTE)-guided light-gradient boosting machine(LightGBM)classifier is proposed to identify the soft ground tunnel classification and determine the imbalanced issue of tunnelling data.During the tunnel excavation,an earth pressure balance(EPB)TBM recorded 18 different operational parameters along with the three main tunnel lithologies.The proposed model is applied using Python low-code PyCaret library.Next,four decision tree-based classifiers were obtained in a short time period with automatic hyperparameter tuning to determine the best model for clustering-guided SMOTE application.In addition,the Shapley additive explanation(SHAP)was implemented to avoid the model black box problem.The proposed model was evaluated using different metrics such as accuracy,F1 score,precision,recall,and receiver operating characteristics(ROC)curve to obtain a reasonable outcome for the minority class.It shows that the proposed model can provide significant tunnel lithology identification based on the operational parameters of EPB-TBM.The proposed method can be applied to heterogeneous tunnel formations with several TBM operational parameters to describe the tunnel lithologies for efficient tunnelling.

An evolutionary adaptive neuro-fuzzy inference system for estimating field penetration index of tunnel boring machine in rock mass

Field penetration index(FPI) is one of the representative key parameters to examine the tunnel boring machine(TBM) performance.Lack of accurate FPI prediction can be responsible for numerous disastrous incidents associated with rock mechanics and engineering.This study aims to predict TBM performance(i.e.FPI) by an efficient and improved adaptive neuro-fuzzy inference system(ANFIS) model.This was done using an evolutionary algorithm,i.e.artificial bee colony(ABC) algorithm mixed with the ANFIS model.The role of ABC algorithm in this system is to find the optimum membership functions(MFs) of ANFIS model to achieve a higher degree of accuracy.The procedure and modeling were conducted on a tunnelling database comprising of more than 150 data samples where brittleness index(BI),fracture spacing,α angle between the plane of weakness and the TBM driven direction,and field single cutter load were assigned as model inputs to approximate FPI values.According to the results obtained by performance indices,the proposed ANFISABC model was able to receive the highest accuracy level in predicting FPI values compared with ANFIS model.In terms of coefficient of determination(R^(2)),the values of 0.951 and 0.901 were obtained for training and testing stages of the proposed ANFISABC model,respectively,which confirm its power and capability in solving TBM performance problem.The proposed model can be used in the other areas of rock mechanics and underground space technologies with similar conditions.

基于贯入度指数的TBM围岩可掘性分级研究

隧道掘进机(TBM)前期投入巨大,合理划分TBM可掘性分级并给出施工参数建议值对预测施工工期、控制成本等有重要意义。以深圳地铁6号线6101标段大浪站石岩站区间羊台山隧道TBM施工项目为背景,首先以贯入度指数FPI为掘进性能评价指标,通过Spearman相关性分析FPI作为评价指标的合理性;在此基础上以FPI为输入参数分别建立掘进贯入度、刀盘推力和掘进速度预测公式,并基于羊台山隧道右线掘进数据进行可靠性验证;基于k-均值聚类方法,以FPI为聚类指标,划分出可掘性等级,并根据掘进参数预测公式,给出了对应可掘性等级的掘进参数建议值。结果表明:(1)FPI能够在一定程度合理反映TBM施工中的岩机关系,可作为掘进性能评价指标;(2)建立的TBM掘进参数预测公式,相关性系数R2分别为0.75、0.98和0.97,相关性良好,能够准确为TBM掘进提供预测参数;(3)根据建立的TBM可掘性分级方法将围岩划分为5级,确定了不同等级下掘进参数值。

小断面土石组合地质条件下TBM施工围岩可掘性分级识别

围岩可掘性分级以及识别研究对隧道掘进机(TBM)高效率施工及智能化控制意义重大。依托南水北调安阳市西部调水工程TBM施工实际数据,利用掘进性能综合指标单位贯入度推力(FPI)、单位贯入度扭矩(TPI)建立了小断面土石组合地质条件下TBM施工围岩可掘性分级标准;提出了PCA-RF模型对围岩可掘性分级进行识别,并与BP、SVR和RF模型进行了比较讨论。结果表明:①建立的小断面土石组合围岩TBM施工可掘性分级标准是适用的,克服了土石组合围岩下传统围岩分类方法的局限性;②小断面土石组合围岩TBM施工可掘性分级PCA-RF识别模型的识别准确率达到了98.3%,高于BP、SVR和RF模型,可以满足工程施工需要。