高等岩石力学课程报告英文读书报告.doc

高等岩石力学英文读书报告 1530767 叶宇航

Reading report
Paper title: A new hard rock TBM performance prediction model for project planning

Major:
隧道与地下工程
Name:
叶 宇 航
Number:
1530767

3
Several models have been introduced over the years for prediction of hard rock TBM performance. The TBM performance prediction models are mostly based on an empirical or a semi-theoretical approach. Although they have advantages and area of applications, they also have disadvantages, such as CSM model don’t consider the main influencing parameter, NTNU model require special experiments originated from the drilling, QTBM are too complicated. The authors hope to better understand machine-rock interaction and to develop a more accurate model for performance estimate of hard rock TBMs. In order to achieve it, the authors investigate the field data of three main tunneling projects in Iran and Manapouri tunnel project in New Zealand. The data obtained from the projects as before mention including geological and performance parameters, have wide ranges of variations. But these wide ranges of geological and performance parameters helped in developing a more comprehensive TBM performance prediction model which has covered different geological conditions.
In general, to justify the use of TBM in any project and for planning purposes, a reasonably accurate estimation of rate of penetration (ROP), daily rate of advance (AR), and cutter cost/life estimate is necessary. But the authors chosen Field Penetration Index (FPI) which is a composite parameter as the machine parameter. In the text, both single and multi-variable regression analyzes were used to investigate relationship between engineering rock properties and TBM performance parameters and finally to develop empirical equation. The analysis of the data obtained from the projects proved that FPI is a suitable machine performance parameter for developing empirical relationships with geological parameters. And multi-variable regression analysis show good correlation between ln (FPI) as response parameter and UCS and RQD as predictors. In conclusion FPI is a good parameter for the evaluation of hard rock TBM performance. Therefore, the authors developed a chart of FPI prediction. This chart can be used for quick estimation of range of values for FPI in grounds with different rock strength and rock quality.
Excepts the FPI, the authors also concerned the boreability. Boreability is the term commonly used to express the ease or difficulty of rockmass excavation by a tunnel boring machine. Rock mass boreability depends on a number of influencing parameters including intact rock/rock mass properties, machine specifications and operational parameters. In tunneling projects, ground characteristics or boreability of the rockmass is an important parameter for selecting machine type and specifications. It is clear that proper evaluation of rock mass boreability can also play a major role in machine operation to achieve the best performance. FPI can be selected as an index for categorizing rock mass boreability. Based on the analysis of give projects, the authors defined six rock mass boreability classes, from most difficult for boring or B-0 class(Tough) to easiest for boring or B-V class (Excellent). Considered the relationship between FPI and boreability, the authors give a table of TBM performance estimation in rock masses with different boreability classes.
All in all, the paper proposed a simple model to evaluate rock mass boreability and TBM performance range. This model demonstrates that machine performance has been related to two main rock properties (UCS and RQD) and two operational parameters (average cutter head thrust and RPM).These Input parameters of the model are available in the preliminary stages of the tunnel design and planning. From this paper, I have a much better understanding of the estimation of TBM performance and the impact factors of FPI and boreability. And I think the model proposed in this paper can be applied as a useful tool for quick estimation of TBM performance in projects with different geological conditions and machine diameters. And this model is worth using widely. The new boreability classification which based on rock masses characteristics to allow for prediction of FPI values also worth learning. The authors adopt both single and multi-variable regression to analyze the relationship between engineering rock properties and TBM performance parameters. As a result, it obtains a good result. So, I think when we investigate a problem which influenced by various parameters, we can consider not just single parameter but multi-variable regression. In the process of developed model, varieties of charts which demonstrate the relationship between different parameter play an important role. Thus, chart is an important tool for research. In engineering project, theoretical model should be easy to use so that it can play an important role in project planning. So, I think the classes of rock mass boreability defined in the paper is useful to engineering field.