Evaluating D&B and TBM tunnelling using NTNU prediction models

Drill and Blast (D&B) and Tunnel Boring Machine (TBM) are the two dominating excavation methods in hard rock tunnelling. Selection of the cost effective excavation method for a tunnel is a function of tunnel cross section area, rock conditions, tunnel length, availability of skilled labour and proper equipment, and project schedule. Over the past few decades, major technological development and technical advances have been achieved in both methods. Yet, in many tunnelling projects, choosing the excavation method is still a challenge and requires considering pros and cons of each method and estimating construction time, costs, as well as post construction and operation & maintenance, and related risk in the planning phase. In this study, the productivity and efficiency of the D&B and TBM options for excavating certain size tunnels have been examined. The analysis is based on recent NTNU prediction models for advance rate and unit excavation cost for given ground conditions and tunnel geometry. For excavation of large size tunnels in very hard rock, the D&B method seems to be the cost effective choice irrespective to tunnel geometry. This is compared to smaller long tunnels with good boreability were the TBM has higher advance rate. The tunnel size and rock conditions have higher impact on the TBM performance and costs than for D&B. This refers to lower risk of using D&B where the use of this method is otherwise justified.     

Evaluation of common TBM performance prediction models based on field data from the second lot of Zagros water conveyance tunnel (ZWCT2)

Zagros water conveyance tunnel (ZWCT) is a 49 km tunnel designed for conveying 70 m³/s water from Sirvan River southward to Dashte Zahab plain in western Iran. This long tunnel has been divided in 3 Lots namely 1A, 1B, 2. By November 2014, about 22 km of the Lot 2 (with a total length of 26 km) has been excavated by two double shield TBMs from two southern and northern portals. The bored section of tunnel passed through different geological units of 3 main formations of Zagros mountain ranges which mainly consist of weak to moderately strong argillaceous-carbonate sedimentary rocks. In this paper, the operating and as-built geological data collected during construction phase of the Lot 2 of ZWCT project was used to compare the calculated machine performance by empirical methods such as the Hassanpour et al. (2011), Q_TBM, NTNU, Palmstrom, and theoretical model of Colorado School of Mines or CSM. The predicted penetration rates were then compared with the observed field performance of the machine and the variations of predicted rates were examined by statistical analysis. The results showed that the site-specific model, which was based on TBM performance in similar formations can provide estimates closer to actual machine performance.     

A new hard rock TBM performance prediction model for project planning

Among the models used for performance prediction of hard rock tunnel boring machines two stand out and are often used in the industry. They include the semi theoretical model by Colorado School of Mines and the empirical model by Norwegian University of Science and Technology in Trondheim (NTNU). While each have their strong points and area of applications, more accurate prediction has been sought by modifying one of the existing models or introduction of a new model. To achieve this, a database of actual machine performance from different hard rock TBM tunneling projects has been compiled and analyzed to develop a new TBM performance prediction model. To analyze the available data and offer new equations using statistical methods, relationships between different geological and TBM operational parameters were investigated. Results of analyzes show that there are strong relationships between geological parameters (like UCS, joint spacing and RQD) and TBM performance parameters specially Field Penetration Index (FPI). In this study, a boreability classification system and a new empirical chart, for preliminary estimation of rock mass boreability and TBM performance is suggested.     

A new model for TBM performance prediction in blocky rock conditions

The term “blocky rock conditions” is generally associated with face instabilities in blocky/jointed rock masses. These events are generally promoted by unfavorable rock mass structural conditions, in terms of joint frequency and orientation, and acting stresses. As a result, rock blocks are formed and then detach from the excavation face which becomes “blocky”, with a markedly irregular and uneven profile. This condition may have a paramount effect on TBM tunneling, leading to a high maintenance frequency and a low TBM advancement rate. Based on the TBM performance data recorded during excavation of tunnels in blocky rock conditions, a TBM performance prediction model has been developed. The model is based on the Field Penetration Index for blocky rock conditions, FPI_blocky, which was previously introduced to analyze the TBM performance in blocky grounds at the Lötschberg Base Tunnel. Through a multivariate regression analysis, a new expression has been introduced to predict the FPI_blocky based on the volumetric joint count (J_v) and the intact rock uniaxial compressive strength (UCS). An attempt has also been made to quantify the downtimes that may occur in blocky rock conditions and to estimate a reliable value of TBM daily advance.     

基于TBM掘进参数和渣料特征的岩体质量指标辨识

在工程实践和前人研究成果的基础上,综合分析了TBM法隧洞的围岩地质条件对TBM掘进过程的影响,提出了一种基于TBM掘进参数和渣料特征的围岩质量指标(RMR)辨识方法。该方法一方面可将TBM的实时工作参数输入到回归公式或神经网络模型中,实现岩体质量指标的辨识;另一方面可通过分析TBM渣料特征得到岩体的镶嵌结构、不连续面状态以及地下水等地质信息,进而对岩体的地质力学强度(GSITBM)和质量指标(RMRTBM)进行估计。鉴于这两种方式的显著特征,建议在地质条件较好时优先采用基于掘进参数的辨识方法,地质条件较差时采用基于渣料特征的评价方法。在对TBM法隧洞围岩质量指标辨识研究的基础上,对岩体基本力学参数的估计方法进行了研究。     

基于长短期记忆网络的TBM掘进预测模型及围岩等级对预测精度的影响

全断面隧道掘进机(TBM)施工过程可以采集大量丰富数据,这使得建立数据驱动的TBM掘进预测模型成为可能。为探究围岩等级信息对模型预测精度的影响,以TBM掘进循环上升段数据为主要输入,基于深度学习中的长短期记忆(LSTM)网络,建立两种考虑围岩等级信息的、一种不考虑围岩等级信息的TBM稳定段推力、扭矩预测模型。保持其他条件相同,对三种模型进行训练、测试。结果表明,在由于围岩等级分布不均匀而导致的训练样本数较少的情况下,可以通过精确的掘进循环参数提取与数据增强,使三种模型预测推力、扭矩时均能达到较高的精度(误差分别在8%、14%以内)。两种引入围岩等级信息作为输入参数的方法并不能明显提高模型的预测精度。三种模型中,以围岩等级信息作为LSTM网络初始状态的模型,其预测精度以不到1%的优势领先其他模型的预测精度。该优势主要由于该模型能够较好地预测V级围岩中的掘进循环稳定段参数。     

Evaluation of coupled partial models in structural engineering using graph theory and sensitivity analysis

The process of analysis and design in structural engineering requires the consideration of different partial models of loading, structural material, structural elements and analysis type, among others. All of these, need an adequate modelling as individuals and as coupled sets to catch a behaviour of interest. This paper proposes an innovative algorithm to facilitate quantitative measures to evaluate coupled partial models in structural engineering. Adapting graph theory and utilising variance based sensitivity analysis enable evaluation and drawing conclusions regarding the combinations of partial models in an engineering system. The algorithm is applied in bridge engineering, analysing bridge behaviour considering dynamic loading, creep and shrinkage material models and further considering geometric nonlinear effects.     

Disc cutting tests in Colorado Red Granite: Implications for TBM performance prediction

A series of full-scale laboratory disc cutting tests was conducted with a single disc cutter (432 mm diameter and a constant cross-section profile) and a single rock type (a coarse-grained red granite). Normal, rolling, and side forces were measured for a series of spacings and penetrations, from which other cutting parameters also were calculated. Although the increases of normal and rolling forces with increased spacing and penetration are as expected, the results illuminate additional aspects of performance prediction. Specific energy (SE) considerations indicate that a spacing of 76 mm is close to optimum in this hard, brittle crystalline rock. At this spacing, penetration has very little effect on SE. These results show why spacings near 76 mm are commonly found on tunnel boring machines operating in hard rock. The relationship of rolling force to normal force was close and consistent: A nearly linear rise of the ratio of rolling force to normal force with increased penetration, and, conversely, a nearly unchanged ratio with increases in spacing. The results tend to validate performance prediction methodologies based on normal force-penetration models.     

A fuzzy logic model to predict specific energy requirement for TBM performance prediction

Prediction of tunnel boring machine performance is a critical key for successful tunnel excavations. Specific energy requirement of disc cutters, which is defined as the amount of energy required to excavate a unit volume of rock, is one of the important parameters used for performance prediction of these machines. Much research has been conducted to predict cutting parameters of disc cutters using analytical, empirical and numerical approaches. In recent years alternative methods, such as fuzzy logic, have been extensively used to deal with subjects having ambiguities and uncertainties. In this study, a model was established to predict specific energy requirement of constant cross-section disc cutters in the rock cutting process by using fuzzy logic method. This model is based on experience and the database which consists of linear cutting test results that were generated over for many years at the Earth Mechanics Institute of the Colorado School of Mines. The model predicts specific energy requirements of disc cutters using uniaxial compressive and tensile strength of rocks, disc diameter and tip width, penetration and spacing of cuts.     

ANN and ANFIS performance prediction models for hydraulic impact hammers

Hydraulic impact hammers are mechanical excavators that can be used in tunneling projects economically under geologic conditions suitable for rock breakage by indentation. However, there is relatively less published material in the literature in relation to predicting the performance of that equipment employing rock properties and machine parameters. In tunnel excavation projects, there is often a need for accurate prediction the performance of such machinery. The poor prediction of machine performance can lead to very costly contractual claims. In this study, the application of soft computing methods for data analysis called artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) to predict the net breaking rate of an impact hammer is demonstrated. The prediction capabilities offered by ANN and ANFIS were shown by using field data of obtained from metro tunnel project in Istanbul, Turkey. For this purpose, two prediction models based on ANN and ANFIS were developed and the results obtained from those models were then compared to those of multiple regression-based predictions. Various statistical performance indexes were used to compare the performance of those prediction models. The results suggest that the proposed ANFIS-based prediction model outperforms both ANN model and the classical multiple regression-based prediction model, and thus can be used to produce a more accurate and reliable estimate of impact hammer performance from Schmidt hammer rebound hardness (SHRH) and rock quality designation (RQD) values obtained from the field tests.     

再生混凝土碳化预测模型参数及其敏感性分析

在现有再生混凝土碳化预测模型研究基础上,通过对比分析,给出了实用数学模型中的再生粗骨料影响系数建议值;采用数学回归分析方法,校订了经验模型中的拉应力影响系数;参数敏感性分析表明,再生混凝土碳化影响因素从大到小依次为保护层厚度、混凝土强度、湿度、温度、拉应力比、CO_2浓度。     

Analysis and prediction of TBM performance in blocky rock conditions at the Lötschberg Base Tunnel

This paper focuses on the analysis of the TBM performance recorded during the excavation of the Lötschberg Base Tunnel. The southern part of the tunnel was excavated by two gripper TBMs, partly through blocky rock masses at great depth. The jointed nature of the blocky rock mass posed serious problems concerning the stability of the excavation face. A detailed analysis has been carried out to obtain a relationship between the rock mass conditions and the TBM performance, using the Field Penetration Index (FPI). In blocky rock conditions, the FPI is defined as the ratio between the applied thrust force and the actual penetration rate. A database of the TBM parameters and the geological/geotechnical conditions for 160 sections along the tunnel has been established. The analysis reveals a relationship between the FPI and two rock mass parameters: the volumetric joint count (J_v) and the intact rock uniaxial compressive strength (UCS). Through a multivariate regression analysis, a prediction model for FPI in blocky rock conditions (FPI_blocky) is then introduced. Finally, other TBM performance parameters such as the penetration rate, the net advance rate and the total advance rate are evaluated using FPI_blocky.     

Evaluation and Bayesian dynamic prediction of deterioration of structural performance

The deterioration of structural performance is a time-variant process with a large amount of uncertainties and incompleteness of load and environmental effects. It seems inevitable that the prediction of structural deterioration should be based on a philosophy of information updating. In the present paper, a new model system for structural performance prediction is introduced based on Bayesian dynamic linear model (DLM) theory. The system can implement Bayesian updating on the prediction of the time series process of structural deterioration. It can effectively incorporate useful information through the deterioration process of structures to update the prediction. Intervention and monitoring techniques are also designed to ensure the stability of the model system. This paper also defines an indicator of the so-called ‘condition index’ to evaluate the structural performance. Using condition indexes, the qualitative condition rating of structural performance based on visual inspection in current engineering practice can be integrated into the Bayesian DLMs. Case studies validate the advantages of Bayesian DLMs. The information updating has a favourable effect on reliability analysis and life prediction. The condition indexes are simple and convenient used in practice.     

Evaluating the influence of engineering geological parameters on TBM performance during grinding process in limestone strata

Bulletin of Engineering Geology and the Environment - The chipping and grinding processes are the basis for understanding rock cutting during TBM excavation in hard rock conditions. The chips are...     

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.     

岩质边坡参数敏感度数值分析

利用有限元数值模拟技术,探讨了岩质边坡各参数的变动对边坡稳定性所造成的影响,并分析边坡稳定性对各参数的敏感程度,明确了粘聚力为边坡稳定的敏感参数,是边坡稳定性的控制因素;另外,内摩擦角对边坡稳定性影响也较明显。     

高性能混凝土自收缩机理及预测模型的研究进展

介绍了高性能混凝土自收缩的定义与发展,综合论述了国外各种关于混凝土的自收缩机理与预测模型,分析了其各自的特点、机理、适用范围以及各模型间的联系,并在此基础上提出了一些新见解,为模型的改进与完善提供了思路.     

基于可拓-Markov沥青路面使用性能预测模型实现

根据目前路面管理系统路面使用性能模型以及存在的问题,提出采用可拓概念区间化路面破损状况指标PCI的优、良、中、差划分,形成沥青路面在综合作用下的Markov过程,利用最小二乘法估计一步转移概率矩阵,从而建立PCI单指标评价可拓-Markov预测模型,并通过实例研究证实了可拓一Markov预测模型的优势。