E. Farrokh
Abstract
In every tunnel boring machine (TBM) tunneling project, there is an initial low production phase so-called the Learning Phase Period (LPP), in which low utilization is experienced and the operational parameters are adjusted to match the working conditions. LPP can be crucial in scheduling and evaluating ...
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In every tunnel boring machine (TBM) tunneling project, there is an initial low production phase so-called the Learning Phase Period (LPP), in which low utilization is experienced and the operational parameters are adjusted to match the working conditions. LPP can be crucial in scheduling and evaluating the final project time and cost, especially for short tunnels for which it may constitute a major percentage of the total project completion time. The contractors are required to have a better understanding of the initial phase of a project to provide better estimates in the bidding documents. While evaluating and shortening of this phase of low production is important for increasing the productivity and daily advance rate of the machine, there has been limited a direct study and assessment of this period. In this work, we discuss the parameters impacting LPP, and introduce a new methodology for its evaluation. In this regard, an algorithm is introduced for estimation of the approximate extent of LPP based on some TBM tunneling case histories. On the basis of many statistical analyses conducted on the actual data and application of two different shapes of linear and polynomial for the description of LPP, a linear function is proposed for estimation of the learning phase parameters. The major parameters of this function are the learning conditions’ rating and the proportion of LPP to tunnel diameter (X1/D). Analysis of the correlation between these two parameters show a very good coefficient of determination (R2 = 92%). This function can be used for the evaluation of TBM advance rates in LPP and for adjusting the TBM utilization factor in the initial stages of a TBM tunneling project. The learning phase can affect the overall utilization rate and completion time of the tunnels, especially when their lengths are around a couple of kilometers. A true understanding of the LPP characteristics can help the contractors to come up with a more accurate bidding time and cost evaluation, and may also benefit the clients to arrange a better schedule for the final project delivery to the public.
Rock Mechanics
E. Farrokh
Abstract
The study of downtime and subsequently machine utilization in a given project is one of the major requirements of an accurate estimation of TBM performance and daily advance rate. Interestingly, while it is very common to report the components of downtime when discussing a tunneling project in the literature; ...
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The study of downtime and subsequently machine utilization in a given project is one of the major requirements of an accurate estimation of TBM performance and daily advance rate. Interestingly, while it is very common to report the components of downtime when discussing a tunneling project in the literature; there has not been a great amount of in-depth studies on this topic in the recent years. This work presents an in-depth analysis of the different components of hard rock TBM tunneling downtime on the basis of the information about several TBM tunneling projects from around the world including some that are underway or completed in the recent years. This includes the comparison of the recorded downtimes with those predicted by the existing models for these tunnels. The results of this comparison show that with the existing models, there is a poor correlation between the predicted and the actual downtime component values. This indicates that the existing models might be outdated or, in some cases, incompatible with the newly developed technologies. In order to provide a more accurate downtime model, an in-depth statistical analysis of the information about the same tunnels, used for the comparative studies, is conducted to develop the new “hard rock TBM downtime model”. This model includes a set of formulas and tables as well as some charts to predict different activities’ downtimes for three major hard TBM types including open TBM, single-shield TBM, and double-shield TBM. The comparison between the new model predictions and the actual values show a good agreement. The results of this work can be very helpful for the evaluation of time and cost to complete a TBM tunneling project, especially when the downtime is expected to be high.
H. Dehghani; N. Mikhak Beiranvand
Abstract
One of the most important parameters used for determining the performance of tunnel boring machines (TBMs) is their penetration rate. The parameters affecting the penetration rate can be divided in two categories. The first category is the controllable parameters such as the TBM technical characteristics, ...
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One of the most important parameters used for determining the performance of tunnel boring machines (TBMs) is their penetration rate. The parameters affecting the penetration rate can be divided in two categories. The first category is the controllable parameters such as the TBM technical characteristics, and type and geometry of the tunnel, and the second one is the uncontrollable parameters such as the intact rock properties and characteristics of the rock mass discontinuities. The aim of this work was to investigate the effects of rock mass properties on the penetration rate, and to present a new mathematical equation based on a statistical approach to estimate the TBM performance. To achieve this aim, the Monte-Carlo (MC) simulation method was used to model the TBM performance. Accordingly, the database consisting of the rock mechanics information such as the uniaxial compressive strength, Brazilian tensile strength, toughness and hardness of rock, spacing and orientation of discontinuities, and measured TBM penetration rate in 151 points out of a water tunnel was collected. Next, using the dimensional analysis, a comprehensive mathematical equation was obtained to calculate the TBM penetration rates using the developed database. Finally, using the MC simulation method, the probability distribution function of the TBM penetration rate was studied. The validation results obtained showed that the root mean square error (RMSE) of the proposed relationship was less than 0.3. The MC simulation results showed that hardness and density had the most and least effects on the penetration rate, respectively.
