Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques

A reliable and accurate prediction of the tunnel boring machine(TBM) performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects.This research aims to develop six hybrid models of extreme gradient boosting(XGB) which are optimized by gray wolf optimization(GWO), particle swarm optimization(PSO), social spider optimization(SSO), sine cosine algorithm(SCA), multi verse optimization(MVO) and moth flame optimization(MFO), for estimation of the TBM penetration rate(PR).To do this, a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation, the rock mass rating, Brazilian tensile strength(BTS), rock mass weathering, the uniaxial compressive strength(UCS), revolution per minute and trust force per cutter(TFC), were set as inputs and TBM PR was selected as model output.Together with the mentioned six hybrid models, four single models i.e., artificial neural network, random forest regression, XGB and support vector regression were also built to estimate TBM PR for comparison purposes.These models were designed conducting several parametric studies on their most important parameters and then, their performance capacities were assessed through the use of root mean square error, coefficient of determination, mean absolute percentage error, and a10-index.Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of(0.1453, and 0.1325), R~2 of(0.951, and 0.951), mean absolute percentage error(4.0689, and 3.8115), and a10-index of(0.9348, and 0.9496) in training and testing phases, respectively.The developed hybrid PSO-XGB can be introduced as an accurate, powerful and applicable technique in the field of TBM performance prediction.By conducting sensitivity analysis, it was found that UCS, BTS and TFC have the deepest impacts on the TBM PR.     

TBM Performance Analysis in Pyroclastic Rocks: A Case History of Karaj Water Conveyance Tunnel

Karaj Water Conveyance Tunnel (KWCT) is 30-km long and has been designed for transferring 16 m³/s of water from Amir-Kabir dam to northwest of Tehran. Lot No. 1 of this long tunnel, with a length of 16 km, is under construction with a double shield TBM and currently about 8.7 km of the tunnel has been excavated/lined. This paper will offer an overview of the project, concentrating on the TBM operation and will review the results of field performance of the machine. In addition to analysis of the available data including geological and geotechnical information and machine operational parameters, actual penetration and advance rates will be compared to the estimated machine performance using prediction models, such as CSM, NTNU and Q_TBM. Also, results of analysis to correlate TBM performance parameters to rock mass characteristics will be discussed. This involves statistical analysis of the available data to develop new empirical methods. The preliminary results of this study revealed that the available prediction models need some corrections or modifications to produce a more accurate prediction in geological conditions of this particular project.     

Applying Optimized Support Vector Regression Models for Prediction of Tunnel Boring Machine Performance

One of the main factors in the effective application of a tunnel boring machine (TBM) is the ability to accurately estimate the machine performance in order to determine the project costs and schedule. Predicting the TBM performance is a nonlinear and multivariable complex problem. The aim of this study is to predict the performance of TBM using the hybrid of support vector regression (SVR) and the differential evolution algorithm (DE), artificial bee colony algorithm (ABC), and gravitational search algorithm (GSA). The DE, ABC and GSA are combined with the SVR for determining the optimal value of its user defined parameters. The optimization implementation by the DE, ABC and GSA significantly improves the generalization ability of the SVR. The uniaxial compressive strength (UCS), average distance between planes of weakness (DPW), the angle between tunnel axis and the planes of weakness (α), and intact rock brittleness (BI) were considered as the input parameters, while the rate of penetration was the output parameter. The prediction models were applied to the available data given in the literature, and their performance was assessed based on statistical criteria. The results clearly show the superiority of DE when integrated with SVR for optimizing values of its parameters. In addition, the suggested model was compared with the methods previously presented for predicting the TBM penetration rate. The comparative results revealed that the hybrid of DE and SVR yields a robust model which outperforms other models in terms of the higher correlation coefficient and lower mean squared error.     

Full-scale linear cutting test in Chongqing Sandstone and the comparison with field TBM excavation performance

In this study, determination of some machine parameters and performance prediction for tunnel boring machine (TBM) are conducted based on laboratory rock cutting test. Firstly, laboratory full-scale linear cutting test is carried out using 432-mm CCS (constant cross section) disc cutter in Chongqing Sandstone. Then, the input parameters for TBM cutterhead design are extracted; some TBM specifications are determined and then compared to the manufactured values. Finally, laboratory full-scale linear cutting test results are compared with the field TBM excavation performance data collected in Chongqing Yangtze River Tunnel. Results show that laboratory full-scale linear cutting test results, combined with some engineering considerations, can be used for the preliminary and rough design of TBM machine capacity. Meanwhile, combined with some modification factors, it can also well predict the field TBM excavation performance.

     

不同物探手段的超前地质预报效果研究——以藏东南某大埋深双护盾TBM隧洞为例

受双护盾TBM设备、施工工艺等的影响,目前钻爆法或其他形式TBM（全断面隧道掘进机）施工环境运用较为普遍的物探预报手段在双护盾TBM使用时适宜性有差异。根据双护盾TBM设备的技术特点,以藏东南地区某大埋深隧洞为背景,分析了不同物探预报手段在双护盾TBM施工的适宜性。研究EH4（高频大地电磁法）、ISP（综合地震成像系统法）、HSP（水平声波反射法）和改进的TRT（隧道反射成像法）4种物探手段在该隧洞超前预报的运用效果,EH4作为宏观预报手段,能够发现较大规模的不良地质体;ISP、HSP作为主动源和被动源手段,对隧道前方岩体条件进行预判分析具有一定的可靠度,且两种手段具较好的一致性;改进的TRT手段解决了双护盾环境下震源锤击激发和检波器的安装问题,能够发现隧洞前方的不良地质体,且TRT方法获取的波速曲线和正负反射界面信息更利于地质人员的分析和判断。研究成果表明,以EH4为宏观预报手段、ISP、HSP与TRT相结合的综合物探手段在藏东南大埋深的隧洞中超前地质预报效果良好,可为工程施工与技术处理提供依据。     

A spatial estimation model for continuous rock mass characterization from the specific energy of a TBM

Summary  Basic principles of the theory of rock cutting with rolling disc cutters are used to appropriately reduce tunnel boring machine (TBM) logged data and compute the specific energy (SE) of rock cutting as a function of geometry of the cutterhead and operational parameters. A computational code written in Fortran 77 is used to perform Kriging predictions in a regular or irregular grid in 1D, 2D or 3D space based on sampled data referring to rock mass classification indices or TBM related parameters. This code is used here for three purposes, namely: (1) to filter raw data in order to establish a good correlation between SE and rock mass rating (RMR) (or tunnelling quality index Q) along the chainage of the tunnel, (2) to make prediction of RMR, Q or SE along the chainage of the tunnel from boreholes at the exploration phase and design stage of the tunnel, and (3) to make predictions of SE and RMR or Q ahead of the tunnel’s face during excavation of the tunnel based on SE estimations during excavation. The above tools are the basic constituents of an algorithm to continuously update the geotechnical model of the rock mass based on logged TBM data. Several cases were considered to illustrate the proposed methodology, namely: (a) data from a system of twin tunnels in Hong Kong, (b) data from three tunnels excavated in Northern Italy, and (c) data from the section Singuerlin-Esglesias of the Metro L9 tunnel in Barcelona. Correspondence: G. Exadaktylos, Department of Mineral Resources Engineering, Technical University of Crete, Chania, Greece     

适合于TBM施工的HSP声波反射法地质超前预报

根据一般隧道地质超前预报方法无法在TBM工作面实施测试的情况、结合各种地质预报方法与TBM配合施工的优、缺点或应用效果基础上,开展适合于TBM施工的HSP快速地质超前预报技术研究。该技术利用TBM掘进机刀盘切割岩石所激发信号作为HSP声波反射法地质预报的激发信号,开展多次地质预报现场试验。实践证明,该技术在大伙房输水隧洞工程TBM施工地质预报的实践是基本成功的,实现了不停机条件下的TBM施工地质超前预报,对TBM施工起到了积极的指导作用。     

Experimental and analytical studies of the parameters influencing the action of TBM disc tools in tunnelling

The use of tunnel boring machines (TBMs) is increasingly popular in tunnelling. One of the most important aspects in the use of these machines is to assess with certain accuracy the effectiveness of the action of the discs on the cutter-head in the different rock types to be excavated. A specific machine, called an intermediate linear cutting machine (ILCM), has been developed at the Politecnico di Torino in order to study, on a reduced scale in detail in the laboratory, the interaction between the discs of the TBM and the rock: this machine allows a series of grooves to be cut on a rock sample of 0.5 × 0.3 × 0.2 m, through the rolling of a 6.5-in. disc, and evaluation, during testing, of the parameters associated with the action of the cutting tool. The parameters measured during the tests were compared with the results obtained employing two analytical methods widely used for predicting the performance of TBMs: the Colorado School of Mines (CSM) model and the Norwegian University of Science and Technology (NTNU) model. The latter showed a greater ability to reproduce tests conducted using the ILCM. However, as with the CSM model, it does not allow the optimal excavation condition (the ratio, which minimizes the specific energy of excavation, between the groove spacing and the penetration of the disc), necessary for the correct design of the TBM cutter-head, to be identified. An example, based on a real case of a tunnel in Northern Italy, allowed a demonstration of how the NTNU model provides results in line with the measurements taken during the excavation and represents, therefore, a model that is able to reliably simulate both laboratory tests and the action of a TBM on site. The NTNU model, together with the results of the tests with ILCM targeted on the identification of the optimal conditions of excavation, may allow the correct dimensioning of the TBM cutter-head to be attained in order to effectively implement the excavation.     

Advanced prediction of tunnel boring machine performance based on big data

Predicting the performance of a tunneling boring machine is vitally important to avoid any possible accidents during tunneling boring.The prediction is not straightforward due to the uncertain geological conditions and the complex rock-machine interactions.Based on the big data obtained from the 72.1 km long tunnel in the Yin-Song Diversion Project in China,this study developed a machine learning model to predict the TBM performance in a real-time manner.The total thrust and the cutterhead torque during a stable period in a boring cycle was predicted in advance by using the machine-returned parameters in the rising period.A long short-term memory model was developed and its accuracy was evaluated.The results show that the variation in the total thrust and cutterhead torque with various geological conditions can be well reflected by the proposed model.This real-time predication shows superior performance than the classical theoretical model in which only a single value can be obtained based on the single measurement of the rock properties.To improve the accuracy of the model a filtering process was proposed.Results indicate that filtering the unnecessary parameters can enhance both the accuracy and the computational efficiency.Finally,the data deficiency was discussed by assuming a parameter was missing.It is found that the missing of a key parameter can significantly reduce the accuracy of the model,while the supplement of a parameter that highly-correlated with the missing one can improve the prediction.     

大线速度下超高压水射流破岩试验研究

高压水射流的破岩效果对高压水射流辅助掘进机破岩技术至关重要。为提升隧道掘进机工况下高压水射流辅助破岩的效率,开展大线速度下超高压水射流破岩试验,分析喷嘴移动线速度、射流压力和喷嘴直径对破岩效果的影响规律,并探究加磨料和射流形式对破岩效果的影响。试验结果表明,随喷嘴移动线速度增加,高压水射流的切割深度和切割宽度均近似线性减小;随射流压力增加,切割深度近似线性增大,压力从200 MPa提高到280 MPa,切割深度增加了72%～82%;喷嘴直径从0.35 mm增大到0.60 mm,切割深度增加了60%～85%。大线速度下加磨料后射流变发散,加磨料的切割深度小于纯水的切割深度,加磨料的切割宽度大于纯水的切割宽度。砂管束流射流模式的能量利用率更高,砂管束流的切割深度比长线射流的切割深度大35%～42%,砂管束流的切割宽度比长线射流的切割宽度大78%～85%。基于Crow切割岩石理论,通过试验数据回归分析,得到大线速度下超高压水射流切割深度半理论半经验预测模型,可为高压水射流辅助掘进机破岩技术中射流切割参数优化提供参考依据。研究成果对提升隧道掘进机工况下超高压水射流辅助破岩的效率是很有意义的。     

Geostatistical Evaluation of the Mechanical Properties of Rock Mass for TBM Tunnelling by Seismic Reflection Method

Summary. The evaluation of the rock mass mechanical properties by the seismic reflection method and TBM driving is proposed for TBM tunnelling. The relationship between the reflection number derived from the three-dimensional seismic reflection method and the rock strength index (RSI) derived from TBM driving data is examined, and the methodology of conversion from the reflection number to the RSI is proposed. Furthermore a geostatistical prediction methodology to provide a three-dimensional geotechnical profile ahead of the tunnel face is proposed. The performance of this prediction method is verified by actual field data.     

Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system

The aim of this study is to predict the peak particle velocity (PPV) values from both presently constructed simple regression model and fuzzy-based model. For this purpose, vibrations induced by bench blasting operations were measured in an open-pit mine operated by the most important magnesite producing company (MAS) in Turkey. After gathering the ordered pairs of distance and PPV values, the site-specific parameters were determined using traditional regression method. Also, an attempt has been made to investigate the applicability of a relatively new soft computing method called as the adaptive neuro-fuzzy inference system (ANFIS) to predict PPV. To achieve this objective, data obtained from the blasting measurements were evaluated by constructing an ANFIS-based prediction model. The distance from the blasting site to the monitoring stations and the charge weight per delay were selected as the input parameters of the constructed model, the output parameter being the PPV. Valid for the site, the PPV prediction capability of the constructed ANFIS-based model has proved to be successful in terms of statistical performance indices such as variance account for (VAF), root mean square error (RMSE), standard error of estimation, and correlation between predicted and measured PPV values. Also, using these statistical performance indices, a prediction performance comparison has been made between the presently constructed ANFIS-based model and the classical regression-based prediction method, which has been widely used in the literature. Although the prediction performance of the regression-based model was high, the comparison has indicated that the proposed ANFIS-based model exhibited better prediction performance than the classical regression-based model.     

Application of Relevance Vector Machine for Prediction of Ultimate Capacity of Driven Piles in Cohesionless Soils

This paper examines the potential of relevance vector machine (RVM) in prediction of ultimate capacity of driven piles in cohesionless soils. RVM is a Bayesian framework for regression and classification with analogous sparsity properties to the support vector machine (SVM). In this study, RVM has been used as a regression tool. It can be seen as a probabilistic version of SVM. In this study, RVM model outperforms the artificial neural network (ANN) model based on root-mean-square-error (RMSE) and mean-absolute-error (MAE) performance criteria. It also estimates the prediction variance. An equation has been developed for the prediction of ultimate capacity of driven piles in cohesionless soils based on the RVM model. The results show that the RVM model has the potential to be a practical tool for the prediction of ultimate capacity of driven piles in cohesionless soils.     

隧道围岩破坏模式的进化神经网络识别

隧道围岩破坏受很多因素的影响,其破坏模式的识别是一个复杂的非线性系统辨识问题,采用一般方法很难得到好的解答。基于作者提出的免疫进化规划,并把它同神经网络(NN)相结合,提出了一种全新的结构及权值同时进化的进化神经网络(ENN)模型,用于围岩破坏模式的识别研究,用一个试验算例证明了进化神经网络具有解决此问题的良好性能。     

极限学习机模型在季冻区深基坑地表沉降预测中的应用

针对传统数据驱动模型存在收敛速度慢、过度拟合等问题,提出了基于极限学习机算法的基坑地表沉降预测方法。结合季冻区地铁车站基坑的特点,提取基坑开挖时间、开挖深度、围护桩顶位移、围护桩内力、支撑轴力及地表温度等特征信息,建立极限学习机回归预测模型,选用实例数据进行算例分析,并将其与传统回归预测模型进行对比,实验结果表明,极限学习机模型收敛速度快,泛化能力强,其预测精度优于传统预测模型,且在学习速度方面优势明显,对深基坑安全监控有一定的实用价值。     

Parameter Study on Prediction Methods for TBM Penetration Rate

Penetration rate prediction of Tunnel Boring Machine (TBM) is the first step to advance prediction process of mechanized tunnelling. In this research, influence of effective parameters on TBM penetration rate is investigated by sensitivity analysis of three main TBM performance prediction methods; Norwegian University of Science and Technology (NTNU), rock mass index (RMi) and Q_TBM. Based on these analyses, it is shown that applied thrust per disc and joint spacing in NTNU and RMi models have more influence on penetration rate. In Q_TBM model, Q value, applied thrust per disc and induced biaxial stress are more effective.     

卷积神经网络及其在矿床找矿预测中的应用——以安徽省兆吉口铅锌矿床为例

大数据人工智能地质学刚刚起步,基于大数据智能算法的地质研究是非常有意义的探索性实验。利用大数据和机器学习解决矿产预测问题,有助于人们克服不能全面考虑地质变量的困难及评估当前模型在已有数据中的可靠性。元素地表分布特征量主要受原岩成分、成矿作用影响和地表过程的影响,它们携带某些指示矿体就位的信息,即矿体在地下空间就位时在地表的响应,且未在地表过程中消失。以往的地球化学勘查工作仅仅识别异常,但未能发现矿体在地表响应的成矿特征量。本文以安徽省兆吉口铅锌矿床为例,通过机器学习,利用卷积神经网络算法,不断挖掘元素Pb分布特征与矿体地下就位空间的耦合相关性。经过1000次训练后,可以得到准确率0. 93,损失率0. 28的卷积神经网络模型。这种神经网络模型就是矿体在地下就位时元素在地表分布的响应,可以用来进行矿产资源预测。应用该模型对未知区进行预测,结果显示第53号区域具有很大概率存在尚未发现的矿体。     

TBM岩体可掘性预测及其分级研究

全断面硬岩隧道掘进机（tunnel boring machine, TBM）对岩体条件极其敏感,且其前期投入较大,准确地评估岩体可掘性、预测TBM掘进性能对TBM隧道施工至关重要。基于来自中国、伊朗两国涵盖3种不同岩性的5条TBM施工引水隧洞约300组现场数据,以现场贯入度指数FPI为岩体可掘性评价指标,分析了岩石单轴抗压强度UCS、岩体完整性指数 、岩体主要结构面与洞轴线的夹角?、隧洞直径D等与岩体可掘性之间的关系;探讨了适用于岩体可掘性研究的岩体参数统一方法,进一步建立了精度较高的（相关系数为0.768）岩体可掘性经验预测方法。基于该预测方法,运用K中心聚类分析方法,将岩体可掘性分为6类,探讨了不同岩体可掘性条件下TBM平均单刀推力、刀盘转速分布规律,相应成果可为实际工程中TBM施工隧洞岩体可掘性评估、掘进参数的选择、施工进度的安排提供一定的指导。     

Machine Learning Based Predictive Modeling of Debris Flow Probability Following Wildfire in the Intermountain Western United States

It has been recognized that wildfire, followed by large precipitation events, triggers both flooding and debris flows in mountainous regions. The ability to predict and mitigate these hazards is crucial in protecting public safety and infrastructure. A need for advanced modeling techniques was highlighted by re-evaluating existing prediction models from the literature. Data from 15 individual burn basins in the intermountain western United States, which contained 388 instances and 26 variables, were obtained from the United States Geological Survey (USGS). After randomly selecting a subset of the data to serve as a validation set, advanced predictive modeling techniques, using machine learning, were implemented using the remaining training data. Tenfold cross-validation was applied to the training data to ensure nearly unbiased error estimation and also to avoid model over-fitting. Linear, nonlinear, and rule-based predictive models including naïve Bayes, mixture discriminant analysis, classification trees, and logistic regression models were developed and tested on the validation dataset. Results for the new non-linear approaches were nearly twice as successful as those for the linear models, previously published in debris flow prediction literature. The new prediction models advance the current state-of-the-art of debris flow prediction and improve the ability to accurately predict debris flow events in wildfire-prone intermountain western United States.     

Method for prediction of landslide movements based on random forests

Prediction of landslide movements with practical application for landslide risk mitigation is a challenge for scientists. This study presents a methodology for prediction of landslide movements using random forests, a machine learning algorithm based on regression trees. The prediction method was established based on a time series consisting of 2 years of data on landslide movement, groundwater level, and precipitation gathered from the Kostanjek landslide monitoring system and nearby meteorological stations in Zagreb (Croatia). Because of complex relations between precipitations and groundwater levels, the process of landslide movement prediction is divided into two separate models: (1) model for prediction of groundwater levels from precipitation data and (2) model for prediction of landslide movements from groundwater level data. In a groundwater level prediction model, 75 parameters were used as predictors, calculated from precipitation and evapotranspiration data. In the landslide movement prediction model, 10 parameters calculated from groundwater level data were used as predictors. Model validation was performed through the prediction of groundwater levels and prediction of landslide movements for the periods from 10 to 90 days. The validation results show the capability of the model to predict the evolution of daily displacements, from predicted variations of groundwater levels, for the period up to 30 days. Practical contributions of the developed method include the possibility of automated predictions, updated and improved on a daily basis, which would be an important source of information for decisions related to crisis management in the case of risky landslide movements.