Realtime prediction of hard rock TBM advance rate using temporal convolutional network (TCN) with tunnel construction big data

Real-time dynamic adjustment of the tunnel bore machine (TBM) advance rate according to the rock-machine interaction parameters is of great significance to the adaptability of TBM and its efficiency in construction. This paper proposes a real-time predictive model of TBM advance rate using the temporal convolutional network (TCN), based on TBM construction big data. The prediction model was built using an experimental database, containing 235 data sets, established from the construction data from the Jilin Water-Diversion Tunnel Project in China. The TBM operating parameters, including total thrust, cutterhead rotation, cutterhead torque and penetration rate, are selected as the input parameters of the model. The TCN model is found outperforming the recurrent neural network (RNN) and long short-term memory (LSTM) model in predicting the TBM advance rate with much smaller values of mean absolute percentage error than the latter two. The penetration rate and cutterhead torque of the current moment have significant influence on the TBM advance rate of the next moment. On the contrary, the influence of the cutterhead rotation and total thrust is moderate. The work provides a new concept of real-time prediction of the TBM performance for highly efficient tunnel construction.     

复杂岩石地层隧道掘进机操作特性分析

为提高掘进效率,改进掘进机与地层的匹配性设计,对掘进机的操作特性进行研究。基于重庆越江隧道盾构掘进试验,分析复杂岩石地层盾构刀盘扭矩、刀盘推力及转速的参数选用原则,在此基础上,分析主掘进参数与切深的关系。随后,对不同地层条件下刀盘扭矩与推力之间的匹配性进行分析,绘制了刀盘扭矩与推力之间的操作特性曲线,并用于分析和制定不同地层条件下的主掘进参数匹配方案。对于较软的泥岩,刀盘扭矩先达到最大值,而推力却不能发挥其最大能力;岩石强度增高,所需推力增加,在发挥扭矩能力的前提下,推力也能发挥其功能,从而使二者达到最佳操作关系;若岩石强度过高,推力保持在最大值,但切深很浅,滚刀旋转阻力减小,刀盘扭矩很难发挥最大能力。因此,应视地层软硬,根据刀盘推力与扭矩的操作特性对刀盘的主掘进参数进行合理设计,并在掘进过程中实时调整其匹配关系。     

Prediction of tunneling-induced ground movement with the multi-layer perceptron

This paper presents a method to predict ground movement around tunnels with artificial neural networks. Surface settlement above a tunnel and horizontal ground movement due to a tunnel construction are predicted with the help of input variables that have direct physical significance. A MATLAB based multi-layer backpropagation neural network model is developed, trained and tested with parameters obtained from the detailed investigation of different tunnel projects published in literature. The settlement is taken as a function of tunnel diameter, depth to the tunnel axis, normalized volume loss, soil strength, groundwater characteristics and construction methods. The output variables are settlement and trough width. Parameters for the prediction of horizontal ground movement include diameter to depth ratio (D/Z), unit weight of soil and cohesion. The neural network demonstrated a promising result and predicted the desired goal fairly successfully.     

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.     

TBM隧道施工配套设备改良技术探讨

为了使TBM能够更加顺利地完成隧道掘进工作,以深圳地铁六号线工程为依托,对TBM隧道施工配套设备改良技术进行总结.实践结果表明:TBM选型、刀盘优化以及洞内辅助设施优化是能够使TBM更加顺利掘进的关键.通过对TBM隧道掘进过程中对施工配套设备改良技术的总结说明,以期对其他类似工程提供借鉴.     

Modelling TBM performance with artificial neural networks

Assessing TBM performance is an important parameter for the successful accomplishment of a tunnelling project. This paper presents an attempt to model the advance rate of tunnelling with respect to the geological and geotechnical site conditions. The model developed for this particular task is implemented through the use of an artificial neural network (ANN) that allows the identification and understanding of both the way and the extent that the involved parameters affect the tunnelling process. The model described in the paper is customised for the construction of an interstation section of the Athens metro tunnels, where the ANN generalisations provided precise estimations regarding the anticipated advance rate.     

TBM施工条件下隧道净空位移监测

 以辽宁大伙房输水工程为背景,介绍TBM施工条件下隧道净空位移监测技术。根据试验,提出一种激光准直TBM隧道净空位移监测方法,它利用开敞式掘进机与隧道周边之间的纵向通视空间,通过对拱顶和两侧边墙三点位应用激光准直法来实现,从而解决TBM施工中现有监测方法难以有效实施净空位移监测的难题。同时,研制出SWL–I型TBM隧道激光位移监测系统,具有满意的量测精度和简便的操作特性,已在该工程Robbins和Wirth两种开敞式掘进机施工环境中得到应用验证,并获得掘进机刀盘尾部拱顶和两侧边墙围岩净空位移的变化特性曲线,实现了TBM施工条件下的净空位移监测。     

Correlation of tunnel convergence with TBM operational parameters and chip size in the Ghomroud tunnel, Iran

Evaluating the impact of rock mass properties on a tunneling operation is crucial, especially when using a tunnel boring machine (TBM). It is an integral part of machine selection and performance prediction in the design and bidding stage. Monitoring and analysis of ground conditions during the construction is also essential to allow the operator to take precautionary measures in adverse geological conditions. This involves adjusting TBM operational parameters such as machine thrust and penetration to avoid potential problems caused by face collapse or excessive convergence and subsequent machine seizure that can cause long delays. Tunnel wall convergence is a function of rock mass characteristics, in situ stresses, size of excavation, and rate of penetration (ROP). It is one of the main factors in determining the use of shielded machines in deep rock tunnel projects. The case study of the Ghomroud water conveyance tunnel project, under construction by a double shield TBM, is used to examine the effect of rock mass parameters on tunnel convergence and hence on the need for over excavation and shield lubrication to avoid problems such as shield seizure. Results of a preliminary analysis of field observations show that the amount of the tunnel convergence can have a direct relationship with the percentage of powder and large rock fragments in the muck. In addition, tunnel convergence has shown a strong relationship with the TBM thrust/torque and rate of penetration (ROP). These relationships have been examined and the results of the analysis as well as the resulting formulas will be explained in this paper.     

高地应力作用下大理岩岩体的TBM掘进试验研究

滚刀破岩效率的研究主要集中在室内线性试验机破岩试验和数值分析2个方面,在工地开展TBM掘进试验尚不普遍。锦屏二级水电站采用3台TBM开挖隧道群,3台TBM在不同洞深(不同地应力)条件对大理岩岩体进行TBM掘进试验、岩石渣片筛分试验及大渣片统计分析,研究岩体条件、TBM机器参数、TBM运行参数对TBM掘进速度的影响及高地应力作用下岩体可掘性指数的变化。研究结果表明:在高地应力条件下,尽管TBM掘进速度随推力增加而增大,但推力超过一定值后,TBM并不在优化状态下运行,TBM的运行需与岩体条件及地应力条件相匹配。     

Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling

Numerous empirical and analytical relations exist between shield tunnel characteristics and surface and subsurface deformation. Also, 2-D and 3-D numerical analyses have been applied to such tunneling problems. Similar but substantially fewer approaches have been developed for earth pressure balance (EPB) tunneling. In the Bangkok MRTA project, data on ground deformation and shield operation were collected. The tunnel sizes are practically identical and the subsurface conditions over long distances are comparable, which allow one to establish relationships between ground characteristics and EPB – operation on the one hand, and surface deformations on the other hand. After using the information to identify which ground- and EPB-characteristic have the greatest influence on ground movements, an approach based on artificial neural networks (ANN) was used to develop predictive relations. Since the method has the ability to map input to output patterns, ANN enable one to map all influencing parameters to surface settlements. Combining the extensive computerized database and the knowledge of what influences the surface settlements, ANN can become a useful predictive method. This paper attempts to evaluate the potential as well as the limitations of ANN for predicting surface settlements caused by EPB shield tunneling and to develop optimal neural network models for this objective.     

引洮单护盾TBM前盾改造分析

结合甘肃省引洮供水一期工程实践,针对单护盾TBM施工过程中遇到的饱水疏松砂岩特点,探讨了TBM的前盾改造问题,通过对几种前伸护盾方案进行比较及相关计算,得出了TBM的前盾改造能控制刀盘扭矩,减少出渣量的结论。     

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.     

Disc cutter wear prediction for a hard rock TBM cutterhead based on energy analysis

Disc cutter wear is a crucial problem that influences the working efficiency and security of hard rock tunnel boring machines (TBMs). This wear results from friction energy accumulation and conversion. In this study, the process of hard rock TBM disc cutter wear is identified and analyzed by quantifying the collective energy change. This study starts with an analysis of the friction process between the disc cutter and hard rock. The relationship between the rolling force work and thrust force work of the disc cutter is examined. As a result, the disc cutter energy equation is determined, and the meaning of the upper and lower bounds of this equation are discussed. Based on the above results, the hard rock TBM cutterhead energy equation is then deduced. A method to identify the friction work is developed. According to the energy wear theory, the cutter wear law on hard rock for a TBM cutterhead is revealed, and a method for predicting disc cutter wear for a hard rock TBM cutterhead is advanced. Furthermore, the validity of this prediction method is confirmed by utilizing data from project cases.     

Application of several optimization techniques for estimating TBM advance rate in granitic rocks

This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang-Selangor raw water transfer tunnel in Malaysia.Rock properties consisting of uniaxial compressive strength(UCS),Brazilian tensile strength(BTS),rock mass rating(RMR),rock quality designation(RQD),quartz content(q)and weathered zone as well as machine specifications including thrust force and revolution per minute(RPM)were measured to establish comprehensive datasets for optimization.Accordingly,to estimate the advance rate of TBM,two new hybrid optimization techniques,i.e.an artificial neural network(ANN)combined with both imperialist competitive algorithm(ICA)and particle swarm optimization(PSO),were developed for mechanical tunneling in granitic rocks.Further,the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice.To evaluate the accuracy of the proposed models for both testing and training datasets,various statistical indices including coefficient of determination(R~2),root mean square error(RMSE)and variance account for(VAF)were utilized herein.The values of R~2,RMSE,and VAF ranged in 0.939-0.961,0.022-0.036,and 93.899-96.145,respectively,with the PSO-ANN hybrid technique demonstrating the best performance.It is concluded that both the optimization techniques,i.e.PSO-ANN and ICA-ANN,could be utilized for predicting the advance rate of TBMs;however,the PSO-ANN technique is superior.     

Real-time analysis and prediction of shield cutterhead torque using optimized gated recurrent unit neural network

An accurate prediction of earth pressure balance (EPB) shield moving performance is important to ensure the safety tunnel excavation. A hybrid model is developed based on the particle swarm optimization (PSO) and gated recurrent unit (GRU) neural network. PSO is utilized to assign the optimal hyperparameters of GRU neural network. There are mainly four steps: data collection and processing, hybrid model establishment, model performance evaluation and correlation analysis. The developed model provides an alternative to tackle with time-series data of tunnel project. Apart from that, a novel framework about model application is performed to provide guidelines in practice. A tunnel project is utilized to evaluate the performance of proposed hybrid model. Results indicate that geological and construction variables are significant to the model performance. Correlation analysis shows that construction variables (main thrust and foam liquid volume) display the highest correlation with the cutterhead torque (CHT). This work provides a feasible and applicable alternative way to estimate the performance of shield tunneling.     

Training Artificial Neural Networks with the Aid of Fuzzy Sets

This article presents a technique of training artificial neural networks (ANNs) with the aid of fuzzy sets theory. The proposed ANN model is trained with field observation data for predicting the collapse potential of soils. This ANN model uses seven soil parameters as input variables. The output variable is the collapsibility (whether the soil is collapsible) or the collapse potential (if the soil is judged collapsible). The proposed technique involves a module for preprocessing input soil parameters and a module for postprocessing network output. The preprocessing module screens the input data through a group of predefined fuzzy sets, and the postprocessing module, on the other hand, "defuzzifies" the output from the network into a "nonfuzzy" collapse potential, a single value. The ANN with the proposed preprocessing and post-process techniques is shown to be superior to the conventional ANN model in the present study.     

Data mining for tunnel support stability: neural network approach

This paper presents a data mining approach to the prediction of tunnel support stability using artificial neural networks (ANN). The case data of a railway tunnel recently finished in Taiwan were used to establish the model. The main rock type was sedimentary rock. Rock mechanical and construction-related parameters with significant influences on support stability were filtered to train and test the ANN. Validation was also performed to show that the ANN outperformed the discriminant analysis and the multiple non-linear regression method in predicting tunnel support stability status.     

Probabilistic Neural Network for Reliability Assessment of Oil and Gas Pipelines

A fuzzy artificial neural network (ANN)–based approach is proposed for reliability assessment of oil and gas pipelines. The proposed ANN model is trained with field observation data collected using magnetic flux leakage (MFL) tools to characterize the actual condition of aging pipelines vulnerable to metal loss corrosion. The objective of this paper is to develop a simulation-based probabilistic neural network model to estimate the probability of failure of aging pipelines vulnerable to corrosion. The approach is to transform a simulation-based probabilistic analysis framework to estimate the pipeline reliability into an adaptable connectionist representation, using supervised training to initialize the weights so that the adaptable neural network predicts the probability of failure for oil and gas pipelines. This ANN model uses eight pipe parameters as input variables. The output variable is the probability of failure. The proposed method is generic, and it can be applied to several decision problems related with the maintenance of aging engineering systems.     

An appraisal of TBM performances in Turkey in difficult ground conditions and some recommendations

The geology of Turkey is very complex and major Northern and Eastern Faults including minor faults associated to these faults create tremendous problems, like squeezing of the TBM, excessive water ingress, TBM face collapses, as encountered in the Kargi power tunnel, the Dogancay energy tunnel, the Gerede water tunnel, and the Nur Dagi railway tunnel. Mixed ground conditions with ophiolites, graphitic schists and melanges with boulders are other fundamental difficulties leading to squeezing and blocking of the TBMs or even causing complete failures of the segments and abandoning of the tunnel. A typical example for tunnel abandoning is the Kosekoy high speed tunnel and an example for excessive TBM squeezing is the Uluabat energy tunnel. The affects of dykes in the Istanbul region is known well by practicing tunnel engineers. These andesitic rocks, make fractures in the country rock and cause several problems during TBM excavation like blocking the cutterhead and excessive disc cutter consumption. Typical examples are the Goztepe-Kadıkoy Metro tunnels, and the Melen water tunnel. The Beykoz utility tunnel is one of the most difficult tunnelling projects in Istanbul. Presence of clay minerals existing within the geologic formations is also one of the main reasons clogging the cutterhead of TBM as encountered in the Suruc water project. The effects of complex geology on the excavation efficiencies of different type of TBM’s used in the ten projects mentioned above are explained in this paper and some recommendations with a ground classification system for proper use of TBMs in faultyzones are given.