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.

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.

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.

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.

An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate

As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers.On the other hand,a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule.The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications.The previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of accuracy.Hence,this study aims to introduce a hybrid randomforest(RF)technique optimized by global harmony search with generalized oppositionbased learning(GOGHS)for forecasting TBM advance rate(AR).Optimizing the RF hyper-parameters in terms of,e.g.,tree number and maximum tree depth is the main objective of using the GOGHS-RF model.In the modelling of this study,a comprehensive databasewith themost influential parameters onTBMtogetherwithTBM AR were used as input and output variables,respectively.To examine the capability and power of the GOGHSRF model,three more hybrid models of particle swarm optimization-RF,genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM AR.Evaluation of the developed models was performed by calculating several performance indices,including determination coefficient(R2),root-mean-square-error(RMSE),and mean-absolute-percentage-error(MAPE).The results showed that theGOGHS-RF is a more accurate technique for estimatingTBMAR compared to the other applied models.The newly-developedGOGHS-RFmodel enjoyed R2=0.9937 and 0.9844,respectively,for train and test stages,which are higher than a pre-developed RF.Also,the importance of the input parameters was interpreted through the SHapley Additive exPlanations(SHAP)method,and it was found that thrust force per cutter is the most important variable on TBMAR.The GOGHS-RF model can be used in mechanized tunnel projects for predicting and checking performance.

Calculation of maximum surface settlement induced by EPB shield tunnelling and introducing most effective parameter

This study aims to predict ground surface settlement due to shallow tunneling and introduce the most affecting parameters on this phenomenon.Based on data collected from Shanghai LRT Line 2 project undertaken by TBM-EPB method,this research has considered the tunnel's geometric,strength,and operational factors as the dependent variables.At first,multiple regression(MR) method was used to propose equations based on various parameters.The results indicated the dependency of surface settlement on many parameters so that the interactions among different parameters make it impossible to use MR method as it leads to equations of poor accuracy.As such,adaptive neuro-fuzzy inference system(ANFIS),was used to evaluate its capabilities in terms of predicting surface settlement.Among generated ANFIS models,the model with all input parameters considered produced the best prediction,so as its associated R^2 in the test phase was obtained to be 0.957.The equations and models in which operational factors were taken into consideration gave better prediction results indicating larger relative effect of such factors.For sensitivity analysis of ANFIS model,cosine amplitude method(CAM) was employed; among other dependent variables,fill factor of grouting(n) and grouting pressure(P) were identified as the most affecting parameters.

Application of artificial neural networks to the prediction of tunnel boring machine penetration rate

Rate of penetration of a Tunnel Boring Machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project.This paper presents the results of a study into the application of an Artificial Neural Network(ANN) technique for modeling the penetration rate of tunnel boring machines.A database,including actual,measured TBM penetration rates,uniaxial compressive strengths of the rock,the distance between planes of weakness in the rock mass and rock quality designation was established.Data collected from three different TBM projects(the Queens Water Tunnel,USA,the Karaj-Tehran water transfer tunnel,Iran,and the Gilgel Gibe II hydroelectric project,Ethiopia).A five-layer ANN was found to be optimum,with an architecture of three neurons in the input layer,9,7 and 3 neurons in the first,second and third hidden layers,respectively,and one neuron in the output layer.The correlation coefficient determined for penetration rate predicted by the ANN was 0.94.

Reducing risk in long deep tunnels by using TBM and drill-and-blast methods in the same project-the hybrid solution

There are many examples of TBM tunnels through mountains, or in mountainous terrain, which have suffered the ultimate fate of abandonment, due to insufficient pre-investigation. Depth-of-drilling limitations are inevitable when depths approach or even exceed l or 2 km. Uncertainties about the geology, hydro-geology, rock stresses and rock strengths go hand-in-hand with deep or ultra-deep tunnels. Unfortunately, unexpected conditions tend to have a much bigger impact on TBM projects than on drill-and-blast projects. There are two obvious reasons. Firstly the circular excavation maximizes the tangential stress, making the relation to rock strength a higher source of potential risk. Secondly, the TBM may have been progressing fast enough to make probe-drilling seem to be unnecessary. If the stress-to-strength ratio becomes too high, or if faulted rock with high water pressure is unexpectedly encountered, the ＂unexpected events＂ may have a remarkable delaying effect on TBM. A simple equation explains this phenomenon, via the adverse local Q-value that links directly to utilization. One may witness dramatic reductions in utilization, meaning ultra-steep deceleration-of-the-TBM gradients in a log-log plot of advance rate versus time. Some delays can be avoided or reduced with new TBM designs, where belief in the need for probe-drilling and sometimes also pre-injection, have been fully appreciated. Drill-and-blast tunneling, inevitably involving numerous ＂probe-holes＂ prior to each advance, should be used instead, if investigations have been too limited. TBM should be used where there is lower cover and where more is known about the rock and structural conditions. The advantages of the superior speed of TBM may then be fully realized. Choosing TBM because a tunnel is very long increases risk due to the law of deceleration with increased length, especially if there is limited pre-investigation because of tunnel depth.

Energy release process of surrounding rocks of deep tunnels with two excavation methods

Numerical analysis of the total energy release of surrounding rocks excavated by drill-and-blast （D＆B） method and tunnel boring machine （TBM） method is presented in the paper. The stability of deep tunnels during excavation in terms of energy release is also discussed. The simulation results reveal that energy release during blasting excavation is a dynamic process. An intense dynamic effect is captured at large excavation footage. The magnitude of energy release during full-face excavation with D＆B method is higher than that with TBM method under the same conditions. The energy release rate （ERR） and speed （ERS） also have similar trends. Therefore, the rockbursts in tunnels excavated by D＆B method are frequently encountered and more intensive than those by TBM method. Since the space after tunnel face is occupied by the backup system of TBM, prevention and control of rockbursts are more difficult. Thus, rockbursts in tunnels excavated by TBM method with the same intensity are more harmful than those in tunnels by D＆B method. Reducing tunneling rate of TBM seems to be a good means to decrease ERR and risk of rockburst. The rockbursts observed during excavation of headrace tunnels at Jinping II hydropower station in West China confirm the analytical results obtained in this paper.

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.

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。

高海拔隧道施工粉尘运移特性及通风系统布置研究

为了降低高海拔隧道掘进机(Tunnel Boring Machine, TBM)在施工过程中产生的粉尘污染并进一步提高通风除尘效果,进而改善作业人员工作环境,研究以某高原TBM施工隧道为研究对象,利用流体仿真模拟软件Fluent对长压短抽方式下不同送风风管、除尘风管位置和抽压比参数进行计算,并将模拟结果与经验公式进行比较,分析粉尘在隧道内的运移特性。研究结果显示:长压短抽通风系统下的粉尘会在风流作用下向除尘风筒一侧运动,其中大部分粉尘会经过除尘风筒排出隧道外,小部分粉尘会在除尘风筒与隧道壁处形成聚集,且隧道内的机械设备对粉尘扩散有阻碍作用;当送风管口在距离掘进面约17 m时,隧道后程粉尘质量浓度较大,在距掘进面20 m处,隧道内的沿程粉尘质量浓度较小;当除尘风管在距掘进面12 m时,隧道内沿程粉尘质量浓度相对较大,作业环境恶劣。其中,作业人员集中区域的粉尘质量浓度最高,可达65.44 mg/m3,在距掘进面6 m位置处时,隧道环境较好。与抽压比为0.5相比,抽压比为0.8时隧道沿程粉尘质量浓度降低50%,除尘效果达到最佳。研究结论可为类似高原地区大断面TBM施工通风方案设计提供科学参考。

铁路隧道TBM施工物料运输方式对比分析

为便于选择TBM施工物料运输方式,以铁路隧道为工程背景,首先,简要总结有轨运输和无轨运输2种运输方式的特点及其工程应用情况;然后,根据施工实践拟定典型工况,研究典型工况下的运输方案及设备配置,并从设备配置和工期2个方面对有轨运输和无轨运输进行详细对比分析。研究结果表明:1)有轨运输和无轨运输在铁路隧道TBM施工中技术上都是可行的,总工期无明显差异。2)我国配合TBM施工的内燃机车质量明显不高且突破难度较大,应努力提升电动机车的工程适应性、可靠性、耐久性以及人性化设计;同时,需要规范有轨运输设备配置要求、轨道铺设和维护质量,以提升有轨运输的效率。3)需要研究适合TBM施工工况且成本低、通用性好的无轨运输设备。

小转弯曲线隧道TBM选型与掘进姿态调控方法

小转弯隧道施工时全断面隧道掘进机(full-section tunnel boring machine,TBM)的选型设计和掘进姿态控制具有特殊性,目前还缺乏通用的理论依据和指导方法。针对此问题,首先,对传统类型TBM小转弯选型进行研究,结合已有项目数据和几何模拟,确定传统类型TBM能适应的最小转弯半径。然后,对双盾敞开式TBM的推进系统和导向系统进行针对性设计,通过分析双盾敞开式TBM推进系统结构和实际施工,提出转弯时双盾敞开式TBM推进油缸内外侧行程差值的理论计算方法和施工过程中的姿态调控方法。最后,得出如下结论:1)当隧道转弯半径小于200 m时,敞开式TBM适应难度较大,需要采用双盾敞开式TBM;2)结合抚宁抽水蓄能电站项目实际施工情况,提出的双盾敞开式TBM的理论计算方法和姿态调控方法确保了转弯段隧道的轴线偏差在要求范围内。

外部随机激励干扰下掘进机振动信号多传感采集方法

掘进机运行过程中,在外部随机激励源的作用下,造成多传感器模式下的传感敏感性波动,结合振动信号不稳定,会导致采集的振动信号大幅度失真。为保证掘进机的稳定运行,提出多传感器模式下的随机激励振动信号采集方法。将掘进机看作多自由度系统,建立设备动力学模型;结合掘进机动力学特征,利用虚拟激励法构建符合外部源的随机激励函数;在建立的随机激励下,选择传感器类型和参数并安装多个传感器,设定采样频率;根据正弦稳态校准方法需求,设计校准传感器采集系统,提高传感器敏感性;在随机激励函数约束下预测传感器输出延时,计算每次测量的同步误差和误差补偿值,通过补偿确保多传感器实现振动信号的同步动态采集。实验结果表明:在多传感模式下,这种方法降低了外部的随机迟滞干扰,在随机激励作用下采集到的掘进机多传感振动信号波形与实际波形相符。

表面应变重构的视觉识别方法

针对全断面岩石隧道掘进机刀盘在恶劣工况下的应变监测,本文提出了一种表面应变重构的视觉识别方法。通过图像中圆形标距中心点之间像素点数量的变化,得到标距的实际变形,并在此基础上结合柯西-格林公式进行表面应力重构。本文以隧道掘进机刀盘刀座和筋板2个关键位置为研究对象,设计了4330V和Q345-D共2种材料不同尺寸标准样件,搭建了基于视觉识别和基于应变片的2套测量系统,进行了拉伸载荷作用下的应变对比及样件表面应力重构。研究表明:本文基于视觉识别方法测得的2种材料标准件变形的误差范围为1.28%~3.23%,在此基础上,进行的材料表面应力重构的误差范围为2.20%~4.76%。本文方法可以实现变形的准确测量及实现多方向的应变和变形重构,为实现复杂结构的应变和变形测量奠定了坚实基础。

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。

浅埋河谷段TBM施工应用与适宜性评价

TBM施工技术应用于隧洞开挖,技术上已经成熟,并广泛应用于深埋长大隧洞施工中。影响隧洞TBM施工的因素基本清楚,一般认为穿越浅埋河谷段隧洞采用TBM施工容易发生围岩塌方、涌水事故,不适宜采用TBM施工。为了研究浅埋河谷段TBM施工适宜性,通过两个典型的穿越浅埋河谷段隧洞TBM施工的工程实例,对比详细的工程地质条件,分析塌方的原因,进行TBM施工适宜性分析,结果表明,隧洞上覆弱风化岩体大于1倍洞径段进行适当的工程处理后,采用TBM施工是适宜性的。

三模式掘进机选型及模式转换技术研究——以广州市地铁7号线为例

为解决复杂多变地层条件下单一模式或双模式掘进机适应性不足的问题,依托广州地铁7号线萝岗站—水西站区间三模式掘进机施工案例,开展三模式掘进机选型及模式转换技术研究。该区间包含软土、富水砂层、硬岩等复杂多变的地质,通过对区间地质特征、掘进风险、模式适应性等方面的综合分析,结合三模式掘进机工程应用效果的反馈和进一步探究,确定三模式掘进机选型及模式转换方案。结果表明:1)长距离、高强度硬岩地层适合采用敞开式硬岩模式,沉降要求高的富水砂层适合采用泥水平衡模式,带孤石的软土地层适合采用土压平衡模式;2)集3种模式于一体的三模式掘进机能有效解决地铁盾构区间穿越多种复杂地层(软土地层、沉降控制要求高的地层以及硬岩地层)时的盾构选型和地层适应性问题,实现掘进机多模式一体、一键切换、一机多用;3)掘进模式转换流程、工艺的工程应用,形成了掘进模式快速、安全转换技术;4)硬岩掘进的双排渣方案能有效解决一般硬岩条件下的高效排渣(硬岩掘进螺旋输送机出渣模式)和硬岩富水条件下的防喷涌(硬岩掘进泥水出渣模式)问题。

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。