• Journal of Institute of Convergence Technology
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• v.11 no.1
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• pp.25-28
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• 2021

Recently, the fire safety of road tunnels has been important issues in South Korea. However, proper fire safety regulations has not made for road tunnels. Due to geographical challenges in South Korea, road tunnels should be constructed to secure stable traffic flows. In the Guidelines for Installing and Managing Disaster Prevention Facilities of Road Tunnels (NFSC 603), main target vehicles are passenger cars. This guidelines cannot support big fires from larger vehicles such as cargo, oil trucks. In this study, fire safety for a road tunnel in port area was analyzed with fire dynamics theory under cargo truck fire scenario. Sujunsan road tunnel in Busan city was chosen as a target tunnel, which links between Busan port and highways to increase cargo shipping. The results show the limitations of present guidelines (NFSC 603) for road tunnel from large fire situations.

• Journal of Korean Tunnelling and Underground Space Association
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• v.7 no.3
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• pp.187-195
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• 2005

When a tunnel is excavated below groundwater table, the groundwater flow occurs towards the tunnel resulting in the seepage pressure. In this paper, the effect of groundwater flows on the behavior of shotcrete lining installed between ground-liner interfaces was studied considering permeability ratio between the ground and the shotcrete into account. Three-dimensional coupled finite element analysis was performed for this assessment. Seepage forces will seriously affect the shotcrete behavior since arching phenomena do not occur in seepage forces. A parametric study was conducted on the various tunnelling situations including interfacial properties between ground and shotcrete lining, the shape of tunnel cross-section and the thickness of liner, etc. Moreover, the convergence-confinement method (CCM) of a NATM tunnel considering seepage forces was proposed. The result showed that the more water tight is the shotcrete, the smaller is the convergence and the larger is the internal pressure. Therefore, the watertight fiber-reinforced shotcrete is found to be even more advantageous when used in under water tunnel.

• Journal of the Korean Geophysical Society
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• v.7 no.4
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• pp.313-324
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• 2004

The finite element method and statistics of the convergence measurement are useful method of the stability analysis of the tunnel adjacent to the pier. It is the purpose of the this case study to certificate of validity of the application of those methods. The safety of the pilot tunnel method and LW pre-grouting has been evaluated from the FEM analysis. The three-dimensional finite element method is carried out for the decision of the level of stress redistribution at the two-dimensional numerical analysis. An analysis of the convergence is carried out by the estimation of preceding convergence at tunnel excavation. F-examination is applied for this estimation. As results of that analysis, The F-value is from 10.81 to 158.74 and the coefficient of determination is from 0.82 to 0.99. An analysis of convergence is carried out by using regression analysis. Consequently, it is shown that the convergence can be modeled as following function C(t) = a[1-exp(-bt)].

• Geomechanics and Engineering
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• v.31 no.6
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• pp.545-556
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• 2022

Evaluation and optimization of tunnel wall convergence (TWC) plays a vital role in preventing potential problems during tunnel construction and utilization stage. When convergence occurs at a high rate, it can lead to significant problems such as reducing the advance rate and safety, which in turn increases operating costs. In order to design an effective solution, it is important to accurately predict the degree of TWC; this can reduce the level of concern and have a positive effect on the design. With the development of soft computing methods, the use of deep learning algorithms and neural networks in tunnel construction has expanded in recent years. The current study aims to employ the long-short-term memory (LSTM) deep neural network predictor model to predict the TWC, based on 550 data points of observed parameters developed by collecting required data from different tunnelling projects. Among the data collected during the pre-construction and construction phases of the project, 80% is randomly used to train the model and the rest is used to test the model. Several loss functions including root mean square error (RMSE) and coefficient of determination (R²) were used to assess the performance and precision of the applied method. The results of the proposed models indicate an acceptable and reliable accuracy. In fact, the results show that the predicted values are in good agreement with the observed actual data. The proposed model can be considered for use in similar ground and tunneling conditions. It is important to note that this work has the potential to reduce the tunneling uncertainties significantly and make deep learning a valuable tool for planning tunnels.

• Journal of Korean Tunnelling and Underground Space Association
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• v.10 no.3
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• pp.233-243
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• 2008

A tunnel behavior predicted in the investigation and design stage is often different from its actual behavior due to mainly the complexity of ground conditions. In a tunnel construction, therefore, it is necessary to ensure the stability of the tunnel by predicting the behaviors of the ground and the supports through observations and measurements, and modifying immediately excavation and reinforcing methods when necessary. To do so, it is important to be able to predict the final tunnel behavior based on the initial tunnel behavior as early as possible. In this study, the correlations were obtained between the initial and the final convergence by analyzing statistically the convergence measurement data, collected from two domestic road tunnels under construction using NATM. In order to estimate the unknown displacements, occurred during the period between the excavation and the first measurement, two methods were used - one is the method by means of regression analysis using a modified exponential function and the other the method by a simple linear regression analysis using the data measured within the distance from tunnel face equal to the tunnel diameter (D). Finally, the relationships were obtained between the initial and final convergence, including the non-measured displacements estimated from the two different methods, by performing linear regression analyses. The regression analysis results showed that there are clear linear relationships between the initial and final convegence and the difference between the two linear regression equations was not that large for when using the exponential function and the simple linear function to estimate the non-measured displacements.

• Tunnel and Underground Space
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• v.13 no.6
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• pp.476-485
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• 2003

The purpose of this study is to present an analysis method for the prediction of the change of ground conditions. To this end, three-dimensional convergence displacements is analyzed in several ways to estimate the trend of displacement change. Three-dimensional arching effect is occurred around the unsupported excavation surface including tunnel face when a tunnel is excavated in a stable rock mass. If the ground condition ahead of tunnel face changes or a weak fracture zone exists a specific trend of displacement change is known to be occurred from the results of the existing researches. The existence of a discontinuity, whose change in front of the tunnel face, can be predicted from the ratio of L/C (longitudinal displacement at crown divided by settlement at crown) etc. Therefore, the change of ground condition and the existence of a fracture zone ahead of tunnel face can be predicted by monitoring three-dimensional absolute displacements during excavation, and applying the methodology presented in this study.

• Tunnel and Underground Space
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• v.3 no.2
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• pp.118-131
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• 1993

In this study, the results of elasto-plastic analysis for a subway tunnel using finite element method are presented. To determine input data for the analysis we carried out rock mass classificaton, insitu test and back analysis using measured displacements. Tunnel convergence, extension of yielding Zone and support load are described. By comparing the results of four different reinforcement patterns, the influence of those patterns on tunnel stability is presented. As a result of the analysis we suggest a ratonal reinforcement pattern.

• International Journal of Aeronautical and Space Sciences
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• v.2 no.2
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• pp.28-38
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• 2001

A numerical method for the assessment and correction of tunnel wall interference effects on forced-oscillation testing is presented. The method is based on the wall pressure signature method using computed wall pressure distributions. The wall pressure field is computed using unsteady three-dimensional full Navier-Stokes solver for a 70-degree pitching delta wing in a wind tunnel. Approximately-factorized alternate direction implicit (AF-ADI) scheme is advanced in time by solving block tri-diagonal matrices. The algebraic Baldwin-Lomax turbulence, model is included to simulate the turbulent flow effect. Also, dual time sub-iteration with, local, time stepping is implemented to improve the convergence. The computed wall pressure field is then imposed as boundary conditions for Euler re-simulation to obtain the interference flow field. The static computation shows good agreement with experiments. The dynamic computation demonstrates reasonable physical phenomena with a good convergence history. The effects of the tunnel wall in upwash and blockage are analyzed using the computed interference flow field for several reduced frequencies and amplitudes. The corrected results by pressure signature method agree well with the results of free air conditions.

• Tunnel and Underground Space
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• v.15 no.1 s.54
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• pp.1-8
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• 2005

Measurement of convergence was/is carried out manually throughout the world for tunnels under construction. However, manual method has certain limitations in terms of applicability for the tunnels in operation. This paper describes state of the art of convergence monitoring systems which are available for measuring displacement of existing tunnels. These technologies are analyzed as follows: 1 The Sofo system using the fiber optic sensors has been applied to the stress measurement of the tunnel lining. It has not yet been used for the monitoring of tunnel convergence because of its cost and reliability 2. A TPMS(Tunnel Profile Monitoring System) using tilt sensors and displacement sensors is used for the convergence monitoring of highway tunnels, subway tunnels and underground ducts. 3. A BCS(Bassett Convergence System) using a pair of tilt sensors can be used for the convergence monitoring of tunnels, however the accuracy of the measurement has to be improved because it uses AC input voltage during data acquisition. The system has to be validated before it can be applied to the tunnels in operation. Convergence monitoring systems using TPMS and/or BCS are recommended to be evaluated and improved by a series or tests in tunnels under construction in order to be applied to the main measuring section and the tunnels in operation.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.1
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• pp.25-37
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• 2024

The objective of this study was to create intelligent rail robots that are optimized for facility management and implement digital twin systems for smart road tunnel management. An autonomous surveillance system is formed by combining the sensing platform consisting of railing robots, fixed cameras and environmental detection sensors with the digital twin data platform technology for tunnel monitoring and early fire suppression. In order to develop mobile rail robots for fire extinguishing, we also designed and manufactured robots for extinguishing & monitoring and fire extinguishing devices, and then we examined the optimization of all parts. Our next step was to build a digital twin for road tunnel management by developing continuous image display system and implementing 3D modeling. After constructing prototypes, we attempted simulations by configuring abnormal symptom scenarios, such as vehicles fires. This study's proposal proposes high-accuracy risk prediction services that will enable intelligent management of risks in the tunnel with early response at each stage, using the data collected from the intelligent rail robots and digital twin systems.