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Advance rate (AR) estimation is a crucial factor at the conceptual phase of a tunneling project for preliminary estimation of tunnel boring machines' (TBMs') usage. The primary objective of this research is to develop order of magnitude performance charts for advance rate of TBMs that can be used using the limited information about the tunneling progress at the conceptual phase. The secondary objective of this dissertation is to evaluate factors that impact TBM progress in large diameter applications. Tunnel diameter and uniaxial compressive strength of ground are found to be some of the primary parameters for prediction of advance rate. Statistical analysis was used to produce an advance rate formula. Then performance charts were developed for specific rock conditions. The results were tested with use of case studies. The results of this dissertation showed that the highest amount of overestimation for case studies considered by these performance charts was 16%. The outcome of this dissertation can assist in prediction of tunnel boring machine's advance rate at the conceptual stage of a tunneling project based on uniaxial compressive strength of rock and diameter of the tunnel.
This book covers the fundamentals of tunneling machine technology: drilling, tunneling, waste removal and securing. It treats methods of rock classification for the machinery concerned as well as legal issues, using numerous example projects to reflect the state of technology, as well as problematic cases and solutions. The work is structured such that readers are led from the basics via the main functional elements of tunneling machinery to the different types of machine, together with their areas of application and equipment. The result is an overview of current developments. Close cooperation among the authors involved has created a book of equal interest to experienced tunnelers and newcomers.
A key factor in successful application of Tunnel Boring Machines (TBMs) technology in tunneling is the ability to develop accurate performance estimates for determining project schedule and costs. Over many years of TBM related research, the Earth Mechanics Institute of the Colorado School of Mines (CSM) has developed a computer based model to predict the performance of TBM in hard rock. Although the CSM model has proven reliable in massive rock conditions, its accuracy has been limited in rock mass exhibiting a high degree of fracturing. Quantifying the rock mass fracture and intact rock brittleness effects into the CSM model, Modified CSM Model has been developed. This book named as "Modified CSM Model for Predicting TBM Performance in Rock Mass" is related to rock mass-TBM interaction. The book reviews the well-known TBM performance prediction models such as; Norwegian University of Science and Technology (NTNU), QTBM, CSM, and Modified CSM. Further, it includes geotechnical and technological information related to the projects used for development of the Modified CSM model. The book is useful for engineers and scientists who deal with rock mechanics and TBM tunneling.
This practical guide describes the stage-by-stage development of a method for predicting the penetration rate (PR) and the advance rate (AR) for tunnel boring machines based on an expanded version of the Q-value, QTBM. The author analyzes 145 TBM tunnels that total 1,000km in length. He then develops simple formulae to estimate PR and AR from the QTBM value and to back-calculate QTBM from performance data. The book quantitatively explains actual advance rates as high as five m/hr for one day or as low as 0.005 m/hr for several months. It also covers logging methods, empirical TBM tunnel support design, and numerical verification of support.
Tunneling with tunnel boring machines (TBMs) is one of the most mechanized and sophisticated processes within the construction industry. However, there are considerable risks when venturing down several hundred feet where the conditions cannot be accurately determined in advance. Months and years of planning, engineering, profiling, researching, and scheduling go into a typical tunneling project before any ground breaking event. This thesis discussed in detail the growing technologies of the tunneling process using tunnel boring machines (TBM). First, some background information on tunneling and the use of a TBM was provided to introduce the process and the industry being discussed. A tunnel boring machine is unique to each project based on the ground conditions that will be encountered, the diameter and length of the tunnel, as well as several other technical and dynamic factors. After the introduction, the thesis examined a case study of a tunnel project, the Jollyville Transmission Main for the Water Treatment Plant #4 in Austin, Texas. More specifically, one of the TBMs used will be studied in order to maximize production. The details of this project provide an opportunity to discuss production improvements, scheduling impacts, and project costs. Different methods and their effect on the overall project outcome were compared. The case study information was compared with literature, as well as on-the-job information gained from discussions with the project manager, superintendents, and other on-site personnel. This research concluded that given certain circumstances, the use of a continuous conveyor, rather than the muck car/rail method, has potential for a quicker completion schedule and a greater profit for the contractor.
In this study is an attempt was made to improve our understanding of rock cutting process by hard rock Tunnel Boring Machines (TBM) and evaluate the impacts of rock mass properties on machine performance. To achieve these objectives, an extensive database on filed observations of TBM performance in various projects and rock mass conditions data has been established. This database includes the data from 10 different job sites with more 60 km of total length of bored tunnels. A set of new empirical performance prediction models have been developed with reasonably high degree of correlations by performing a series of multiple regression analysis. Also a new Rock Mass Adjustment factors for Colorado School of Mines (CSM) prognosis model has been de-rived from the statistical analysis. The accuracy of developed models and adjustment factors has been verified by comparing the observed data with estimations provided by NTNU and CSM models. A very close agreement has been obtained.
This book covers the tunnel boring machine (TBM) performance classifications, empirical models, statistical and intelligent-based techniques which have been applied and introduced by the researchers in this field. In addition, a critical review of the available TBM performance predictive models will be discussed in details. Then, this book introduces several predictive models i.e., statistical and intelligent techniques which are applicable, powerful and easy to implement, in estimating TBM performance parameters. The introduced models are accurate enough and they can be used for prediction of TBM performance in practice before designing TBMs.
Mechanised shield tunnelling has developed considerably since the publication of the first edition of this book. Challenging tunnel projects under difficult conditions demand innovative solutions, which has led to constant further development and innovation in process technology, constructions operations and the machines and materials used. The book collects the latest state of technology in mechanised shield tunnelling. It describes the basics of mechanised tunnelling technology and the various types of machines and gives calculation methods and constructural advice. Further chapters cover excavation tools, muck handling, tunnel support, surveying and steering as well as workplace safety. There is also detailled information about contractual aspects and process controlling.
This book covers the tunnel boring machine (TBM) performance classifications, empirical models, statistical and intelligent-based techniques which have been applied and introduced by the researchers in this field. In addition, a critical review of the available TBM performance predictive models will be discussed in details. Then, this book introduces several predictive models i.e., statistical and intelligent techniques which are applicable, powerful and easy to implement, in estimating TBM performance parameters. The introduced models are accurate enough and they can be used for prediction of TBM performance in practice before designing TBMs. .
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