• Geomechanics and Engineering
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• v.37 no.3
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• pp.223-241
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• 2024

Evaluating the performance of Tunnel Boring Machines (TBMs) stands as a pivotal juncture in the domain of hard rock mechanized tunneling, essential for achieving both a dependable construction timeline and utilization rate. In this investigation, three advanced artificial neural networks namely, gated recurrent unit (GRU), back propagation neural network (BPNN), and simple recurrent neural network (SRNN) were crafted to prognosticate TBM-rate of penetration (ROP). Drawing from a dataset comprising 1125 data points amassed during the construction of the Alborze Service Tunnel, the study commenced. Initially, five geomechanical parameters were scrutinized for their impact on TBM-ROP efficiency. Subsequent statistical analyses narrowed down the effective parameters to three, including uniaxial compressive strength (UCS), peak slope index (PSI), and Brazilian tensile strength (BTS). Among the methodologies employed, GRU emerged as the most robust model, demonstrating exceptional predictive prowess for TBM-ROP with staggering accuracy metrics on the testing subset (R² = 0.87, NRMSE = 6.76E-04, MAD = 2.85E-05). The proposed models present viable solutions for analogous ground and TBM tunneling scenarios, particularly beneficial in routes predominantly composed of volcanic and sedimentary rock formations. Leveraging forecasted parameters holds the promise of enhancing both machine efficiency and construction safety within TBM tunneling endeavors.

• Tunnel and Underground Space
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• v.16 no.4 s.63
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• pp.281-287
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• 2006

Intake of groundwater by tunneling in a mountainous area mostly results from groundwater flow through fractured parts of total rock mass. For reasonable analysis of this phenomenon the representative joint groups 1, 2, and 3 have been selected by previous investigations, geological/geophysical field tests and boring works. Three dimensional fractures were generated by the FracMan and MAFIC which is a three dimensional finite element model has been used to analyse a groundwater flow through fractured media. Monte Carlo simulation was applied to reduce the uncertainty of this study. The numerical results showed that the average and deviation of amounts of groundwater intaked into tunnel per unit length were $5.40{\times}10^{-1}$ and $3.04{\times}10^{-1}m^3/min/km$. It is concluded that tunnel would be stable on impact of groundwater environment by tunneling because of the lower value than $2.00{\sim}3.00m^3/min/km$ as previous and present standard on the application of tunnel construction.

• Geomechanics and Engineering
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• v.22 no.6
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• pp.489-496
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• 2020

This study proposes a new empirical model to effectively predict the excavation performance of a shield tunnel boring machine (TBM). The TBM performance is affected by the geological and geotechnical characteristics as well as the machine parameters of TBM. Field penetration index (FPI) is correlated with rock mass parameters to analyze the effective geotechnical parameters influencing the TBM performance. The result shows that RMR has a more dominant impact on the TBM performance than UCS and RQD. RMR also shows a significant relationship with the specific energy, which is defined as the energy required for excavating the unit volume of rock. Therefore, the specific energy can be used as an indicator of the mechanical efficiency of TBM. Based on these relationships with RMR, this study suggests an empirical performance prediction model to predict FPI, which can be derived from the correlation between the specific energy and RMR.

• Explosives and Blasting
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• v.23 no.1
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• pp.31-39
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• 2005

This study is based on the reseach about conte. track$(Dugso\~wonju)$ double line electric railway tunnel. Authors conducted test blasting to examine the effect of blasting pollution. To be conducted safe and effective work by using this result studies sailable substitution excavation method. No-vibration section generates continuously the vibration of breaker working to go abreast necessarily secondary fragmentation working and according to judging that application is actually difficult in case of the condition of study site, the period of construction, the cost of construction, the efficiency of construction, pre-heavy caliber horizontally boring working + line drilling method + vibration control secondary blasting method excavation working is possible from level within blast vibration standard.

• Tunnel and Underground Space
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• v.5 no.2
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• pp.114-122
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• 1995

The principle and applications of GPR(Ground Penetrating Radear) are familiar to engineering geologists and geophsicists as very attractive technique for continuous high resolution images of the subsurface. However, the main limitation of GPR is obviously related to presence of clayey or silty conductive soils, resulting in complete attenuation of radar signals. This difficulty gives hesitation for the exploration of the deeper targets for example detecting bedrock, particularly in Korean situation that most regions have conductive overburden. In order to prove usefulness of geological survey with GPR in that situation, the technique was tried to investigate depth of bedrock under thick conductive overburden and the other geolocgical informations for the constructionof foundation in the Dongbu apartment site, Kimhae. The reflection patterns on the processed GPR sections are well correlated with the geotechnical units-bedrock, alluvium, landfill unit and their internal layer-boundaries of boring data before GPR survey, except upper contact of bedrock. The isopach maps of the geotechnical units for the 3-D interpretations are made from GPR sections. The maps provided useful geological information that bedrock was distributed as plain and valley with 22~27m depth under alluvium unit (this depth is 5~8 m deeper than drill log) and sedimentary layers subsided and bended along growth fault with NNE strike/15$^{\circ}$SE dip in alluvium unit.

• Computers and Concrete
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• v.20 no.4
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• pp.429-437
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• 2017

In this study, the effect of the tensile strength and ratio of disc spacing to penetration depth on the efficiency of tunnel boring machine (TBM) is investigated using Particle flow code (PFC) in two dimensions. Models with dimensions of $150{\times}70mm$ made of rocks with four different tensile strength values of 5 MPa, 10 MPa, 15 MPa and 20 MPa were separately analyzed and two "U" shape cutters with width of 10 mm were penetrated into the rock model by velocity rate of 0.1 mm/s. The spacing between cutters was also varied in this study. Failure patterns for 5 different penetration depths of 3 mm, 4 mm, 5 mm, 6 mm, and 7 mm were registered. Totally 100 indentation test were performed to study the optimal tool-rock interaction. An equation relating mechanical rock properties with geometric characteristics for the optimal TBM performance is proposed. The results of numerical simulations show that the effective rock-cutting condition corresponding to the minimum specific energy can be estimated by an optimized disc spacing to penetration depth, which, in fact, is found to be proportional to the rock's tensile strength.

• Geomechanics and Engineering
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• v.29 no.3
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• pp.249-258
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• 2022

A disc cutter is an excavation tool on a tunnel boring machine (TBM) cutterhead; it crushes and cuts rock mass while the machine excavates using the cutterhead's rotational movement. Disc cutter wear occurs naturally. Thus, along with the management of downtime and excavation efficiency, abrasioned disc cutters need to be replaced at the proper time; otherwise, the construction period could be delayed and the cost could increase. The most common prediction models for TBM performance and for the disc cutter lifetime have been proposed by the Colorado School of Mines and Norwegian University of Science and Technology. However, design parameters of existing models do not well correspond to the field values when a TBM encounters complex and difficult ground conditions in the field. Thus, this study proposes a series of machine learning models to predict the disc cutter lifetime of a shield TBM using the excavation (machine) data during operation which is response to the rock mass. This study utilizes five different machine learning techniques: four types of classification models (i.e., K-Nearest Neighbors (KNN), Support Vector Machine, Decision Tree, and Staking Ensemble Model) and one artificial neural network (ANN) model. The KNN model was found to be the best model among the four classification models, affording the highest recall of 81%. The ANN model also predicted the wear rate of disc cutters reasonably well.

• Journal of Korean Tunnelling and Underground Space Association
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• v.14 no.5
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• pp.503-516
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• 2012

Rock mass rating (RMR) is the key factor when designing the appropriate support pattern of tunnel projects. Borehole drilling is usually performed along the tunnel route in order to determine the rock mass rating to be used for tunnel design. The rock mass rating at the non-boring region between boreholes is usually assessed through geophysical surveys such as electrical prospecting, seismic prospecting, etc. Many studies were carried out to find out the correlation between electrical resistivity and rock mass rating. However, most researches were aimed at obtaining the relationship between the two parameters utilizing experimental results obtained from laboratory tests or electrical prospectings. In this paper, efforts were made to analyze and obtain relationships between the electrical resistivity obtained from in-situ electrical resistivity logging data and the rock mass rating. Correlation studies using field data showed that the electrical resistivity is highly correlated with the rock mass rating with the determination coefficient more than 90%. The correlation analysis was also carried out between RMR classification parameters and the electrical resistivity. It was shown that the correlation between the condition of discontinuities and the electrical resistivity was very high with the determination coefficient more than 80%; that between the groundwater condition and the electrical resistivity was very low with the determination coefficient less than 57%.

• Tunnel and Underground Space
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• v.30 no.3
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• pp.214-225
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• 2020

Uniaxial compressive strength (UCS) of rock is one of the important factors to determine the advance speed during shield TBM tunnel excavation. UCS can be obtained through the Geotechnical Data Report (GDR), and it is difficult to measure UCS for all tunneling alignment. Therefore, the purpose of this study is to predict UCS by utilizing TBM machine driving data and machine learning technique. Several machine learning techniques were compared to predict UCS, and it was confirmed the stacking model has the most successful prediction performance. TBM machine data and UCS used in the analysis were obtained from the excavation of rock strata with slurry shield TBMs. The data were divided into 8:2 for training and test and pre-processed including feature selection, scaling, and outlier removal. After completing the hyper-parameter tuning, the stacking model was evaluated with the root-mean-square error (RMSE) and the determination coefficient (R²), and it was found to be 5.556 and 0.943, respectively. Based on the results, the sacking models are considered useful in predicting rock strength with TBM excavation data.

• Tunnel and Underground Space
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• v.28 no.6
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• pp.513-527
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• 2018

The Tunnel Boring Machine(TBM) is relatively insufficient to cope with unpredicted changes in ground conditions as compared with Conventional Tunnelling Methods. Therefore, it is very important to predict the TBM performance at the design stage and estimate the advance rate for the calculation of the construction period. In this study, we added data to 211 TBM databases constructed in the previous study and analyzed the correlation between TBM outer diameter, maximum thrust, maximum cutterhead torque, cutterhead driving power and RPM, which are the main design and manufacturing specifications of TBM. As a result of the analysis from results obtained in the previous studies, it was confirmed that TBM outer diameter is very effective and important in estimating maximum thrust, maximum cutterhead torque, and cutterhead driving power of the TBM. As a result of comparing the regression equations derived from other TBM databases outside the country and the regression equation obtained from the present study results, the maximum thrust showed a similar tendency to each other, but the maximum torque estimated from the regression equation of this study was higher than that of other countries in the case of the large scale TBM.