• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.1
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• pp.57-70
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• 2017

In this study, we investigated the wear measurement methods for slurry pipe applied in the field of mining and oil sand industry and theoretical equations related to the prediction of wear in slurry pipe through literature review. Average daily wear rate and wear rate per excavated distance were determined from slurry discharge pipe thickness measurement data periodically measured at the actual slurry shield TBM site in Singapore. The wear rate of slurry pipe for Bukit Timah Granite was obtained. The wear rates for G (V) grade and mixed zone were 1.5 times higher than that of G (I) to G (IV) grade. Slurry pipe wear rate tends to increase in proportion to the slurry discharge velocity. The optimal slurry pipe replacement or rotation frequency can be estimated through the selection of the pipe wear rate considering geological condition and the reasonable pipe management thickness.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.2
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• pp.243-256
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• 2019

Formation of filter cake with little slurry penetration into the tunnel face ground is an essential factor to successfully apply the slurry shield tunnel boring machine (TBM) for tunnelling work. However, when the bentonite slurry is in contact with seawater, it is not easy to guarantee the filter cake formation due to decrease of the swelling volume and viscosity of the slurry. In this study, in order to evaluate the effect of the seawater on the applicability of the slurry shield TBM method, the slurry injection tests were carried out with the variation of seawater percentage contained in the slurry samples as well as the variation of soil types. And then, the effect of these two factors on the slurry clogging phenomena was theoretically and experimentally figure out. As a result, it was found that the value of the slurry clogging criteria (SCC) indicating the applicability of the slurry shield TBM significantly decreases up to 67% as the percentage of seawater increases from 0% up to 20%. In addition, it was found to be necessary to take into account both the characteristics of slurry and soil types together when judging the applicability of the slurry shield TBM method by assessing the slurry penetration characteristics that will occur during tunnelling work.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.3
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• pp.463-473
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• 2017

Bentonite swells when it comes into contact with water and makes it a viscous fluid. Thus it is widely used in civil engineering works for waterproofing. Utilizing the properties of bentonite, the slurry shield TBM supports excavated face with pressurized slurry as well as transporting excavated muck. When bentonite is in contact with seawater, due to the change of double layer thickness, its expandability and viscosity are lowered. This may cause problems for excavation stability and muck discharge due to the increase of sea water inflow when Slurry TBM is used under sea water conditions. In this study, the change of slurry condition caused by the inflow of sea water during tunnel excavation with Slurry TBM was investigated and a slurry management guideline was proposed. For this purpose, a laboratory test was carried out based on the slurry management criterions applied in the field, and a method applicable to the field where sea water is affected has been proposed.

• Journal of the Korean Society for Railway
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• v.16 no.1
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• pp.47-51
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• 2013

In this study, we carried out an experimental shield TBM excavation model test using a down-scale device in soft clay, to understand tunnel-face stability properties in relation to changes in slurry pressure. We performed five tests according to tunnel depth (0.5D, 0.75D, 1.0D, 1.25D, 1.5D), and compared theoretical tunnel-face pressure with model test results. The range in theoretical tunnel-face slurry pressure ($P_{min}{\leq}P_{slurry\;pressure}{\leq}P_{max}$), which is determined by earth pressure and water level, was very similar to the model test result. This result was due to the more isotropic condition of the soft clay ground, than of rocky ground.

• Journal of Korean Tunnelling and Underground Space Association
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• v.18 no.1
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• pp.61-74
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• 2016

This paper presents a case study on the ground settlement and volume loss estimation for slurry pressure balanced shield TBM tunnelling in weathered zone of granite rock. Settlement at each stage of shield tunnelling was analyzed and the volume losses and settlement trough factors were estimated from observations. In addition, using the existing volume loss evaluation method in literature, volume losses were estimated considering ground properties and actual driving parameters. Most of ground settlement occurred during passage of shield skin passage and after backfill grouting, and the measured total volume loss and trough curves appeared to coincide with literature. Shield and tail loss obtained from field measurement were found to be around 90% and 60% of the predictions, where tail loss indicated larger deviation than shield loss.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.502-517
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• 2022

As the use of TBM increases, research has recently increased to to analyze TBM data with machine learning techniques to predict the exchange cycle of disc cutters, and predict the advance rate of TBM. In this study, a regression prediction of disc cutte wear of slurry shield TBM site was made by combining machine learning based on the machine data and the geotechnical data obtained during the excavation. The data were divided into 7:3 for training and testing the prediction of disc cutter wear, and the hyper-parameters are optimized by cross-validated grid-search over a parameter grid. As a result, gradient boosting based on the ensemble model showed good performance with a determination coefficient of 0.852 and a root-mean-square-error of 3.111 and especially excellent results in fit times along with learning performance. Based on the results, it is judged that the suitability of the prediction model using data including mechanical data and geotechnical information is high. In addition, research is needed to increase the diversity of ground conditions and the amount of disc cutter data.

• Magazine of korean Tunnelling and Underground Space Association
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• v.25 no.3
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• pp.58-72
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• 2023

• Magazine of korean Tunnelling and Underground Space Association
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• v.25 no.2
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• pp.47-61
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• 2023

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.4
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• pp.687-699
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• 2018

The construction of subsea tunnel differs from that of inland tunnel because of high water pressure due to sea water level and difficulties to reinforce the ground under construction. Therefore, it is very important to prevent trouble in advance when the subsea tunnel is constructed. In this paper, we established lots of databases about characteristics of geological and mechanical parameters on the construction of subsea tunnel using micro slurry TBM which depth is about 60 m. The correlation analysis is conducted to confirm the effect of thrust, torque and RPM among the excavation database on the net penetration rate. Also, An empirical formula is suggested to predict the net penetration rate through the correlation analysis between FPI (Field Penetration Index) and specific energy from the subsea tunnel excavation database.

• Tunnel and Underground Space
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• v.31 no.6
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• pp.494-507
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• 2021

With the increasing use of TBM, research has recently been conducted in Korea to analyze TBM data with machine learning techniques to predict the ground in front of TBM, predict the exchange cycle of disk cutters, and predict the advance rate of TBM. In this study, classification prediction of rock characteristics of slurry shield TBM sites was made by combining traditional rock classification techniques and machine learning techniques widely used in various fields with machine data during TBM excavation. The items of rock characteristic classification criteria were set as RQD, uniaxial compression strength, and elastic wave speed, and the rock conditions for each item were classified into three classes: class 0 (good), 1 (normal), and 2 (poor), and machine learning was performed on six class algorithms. As a result, the ensemble model showed good performance, and the LigthtGBM model, which showed excellent results in learning speed as well as learning performance, was found to be optimal in the target site ground. Using the classification model for the three rock characteristics set in this study, it is believed that it will be possible to provide rock conditions for sections where ground information is not provided, which will help during excavation work.