(사)한국터널지하공간학회
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- P-ISSN2233-8292
- E-ISSN2287-4747
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ISSN : 2233-8292
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e-SubmissionVol.22 No.5
Abstract
Due to various advantages such as small facilities, ease of construction and so on, the grouting technology which is widely used in construction field has developed remarkably compared with the past. However, the efforts to improve the homogeneityof quality, long-term durability and environmental problems have been continued. In recent years, new grouting method has been developed in order to solve problems such as low strength, durability and leaching phenomenon of liquid glass (sodium silicate)grouting material in Korea. A newly developed method integrates the injection material with the ground by the self-healing material of crystallization growth type. For this reason, it is known that improvement of the durability and water quality of the ground, prevention of leaching, and environment friendliness can be expected. The present study applied a newly developed method to test sites and verified its effect such as injection range, improvement effect, waterproofing performance and so on. Standard penetration test, field permeability test, borehole shear test, pressuremetertest and pH test were conducted, and the results were compared between before and after developed method application. As results of tests, the field applicability and improvement effect of developed method were proved to be excellent.
Abstract
Cutter cutting tests for the cutter placement in the cutter head are being conducted through various studies. Although the cutter spacing at the minimum specific energy is mainly reflected in the cutter head design, since the optimum cutter spacing at the same cutter penetration depth varies depending on the rock conditions, studies on deciding the optimum cutter spacing should be actively conducted. The machine learning techniques such as the decision tree-based regression model and the SVM regression model were applied to predict the optimum cutter spacing ratio for the nonlinear relationship between cutter penetration depth and cutter spacing. Since the decision tree-based methods are greatly influenced by the number of data, SVM regression predicted optimum cutter spacing ratio according to the penetration depth more accurately and it is judged that the SVM regression will be effectively used to decide the cutter spacing when designing the cutter head if a large amount of data of the optimum cutter spacing ratio according to the penetration depth is accumulated.
Abstract
Underground concrete structures are usually designed to be used for decades, but in recent years, many of them are nearing their original life expectancy. As a result, it is necessary to promptly inspect and repair the structure, since it can cause lost of fundamental functions and bring unexpected problems. Therefore, personnel-based inspections and repairs have been underway for maintenance of underground structures, but nowadays, objective inspection technologies have been actively developed through the fusion of deep learning and image process. In particular, various researches have been conducted on developing a concrete crack detection algorithm based on supervised learning. Most of these studies requires a large amount of image data, especially, label images. In order to secure those images, it takes a lot of time and labor in reality. To resolve this problem, we introduce a method to increase the accuracy of crack area detection, improved by 0.25% on average by applying adversarial learning in this paper. The adversarial learning consists of a segmentation neural network and a discriminator neural network, and it is an algorithm that improves recognition performance by generating a virtual label image in a competitive
Abstract
Although TBM’s cutterhead requires design review for fatigue failure due to wearinduced section loss as well as heavy load during excavation, it is difficult to find a case of fatigue analysis for TBM cutterhead at present. In this study, a stress-life design review was conducted on cutter heads with a diameter of 8.2 m using S-N curves as a safety life design concept. Also, we introduced the fatigue design method of construction equipment and the method of assessing fatigue damage and explained the results of the fatigue analysis on the TBM cutter head with a diameter of 8.2 m. TheS-N curve has been shown to play a key role in fatigue design and can also be used to assess how much fatigue damage a structure is suffering from at this point in time. Inthe future, it is necessary to find out when fatigue problems occur during using theequipment and when it is good to conduct safety inspections of the equipment.
Abstract
This paper is the result of the study on the effect of the support structure of the tunnel steel rib. In tunnel excavation, the top and bottom half excavation methods result in subsidence of steel rib reinforcement due to insufficient support of steel rib reinforcement when the ground is poor after excavation. The foundation of the steel rib installed in the upper half excavates the bottom part of the base, causing the subsidence to occur due to various effects such as internal load and lateral pressure. As a result, the tunnel is difficult to maintain and its safety is problematic. To solve these problems, steel rib support structures have been developed. For the purpose of verification, the behavior of the supporting structure is verified by model experiments reduced to shotcrete and steel rib material similarity, the numerical analysis of ΔP and ΔP generated by bottom excavation by Terzaghi theoretical equation. As a result, it was found that the support structure of 20.100~198.423 kN is required for the 10~40 m section of the depth for each soil of weathered soil~soft rock. In addition, as a resultof the reduced model experiment, a fixed level of 50% steel rib deposit of steel rib support structure was installed. The study shows that the installation of steel rib support structures will compensate for uncertainties and various problems during construction. It is also thought that the installation of steel rib support structure will have many effects such as stability, economy, and air reduction.
Abstract
Currently, road tunnels and railroad tunnels are building smoke control systems to emit toxic gases and smoke from fires. Among the various smoke control systems, the transverse smoke control system has the disadvantage that air supply or exhaust is performed on only half of the cross-section, rather than air supply or exhaust on the entire cross-section of the tunnel as air is supplied or exhausted by partitioning the wind path. Therefore, this study analyzed the effect of exhaustion through numerical analysis and scaled model tests on the zoning smoke control system, which improved the limitations of the transverse smoke control system. As a result of the scaled model test, the transverse ventilation system exhibited a 25.6% smoke control rate based on the state where no smoke was controled, and zoning smoke control system showed asmoke control rate of 40.8%. In addition, as a result of numerical analysis, it was found that transverse ventilation system did not control fire smoke spreading from the tunnel and continued to spread. On the other hand, zoning smoke control system was found to be smoke controled within a certain section due to the air curtain effect and the flue gas effect.
Abstract
In recent years, Shield TBM construction has been continuously increasing in domestic tunnels. The main excavation tool in the shield TBM construction is a disccutter which naturally wears during the excavation process and significantly degrades the excavation efficiency. Therefore, it is important to know the appropriate time ofthe disc cutter replacement. In this study, it is proposed a predictive model that can determine yes/no of disc cutter replacement using machine learning algorithm. To dothis, the shield TBM machine data which is highly correlated to the disc cutter wears and the disc cutter replacement from the shield TBM field which is already constructedare used as the input data in the model. Also, the algorithms used in the study were the support vector machine, k-nearest neighbor algorithm, and decision tree algorithm areall classification methods used in machine learning. In order to construct an optimal predictive model and to evaluate the performance of the model, the classificationperformance evaluation index was compared and analyzed.
Abstract
Concrete with activated carbon and titanium dioxide (TiO2) has been used to reduce the particulate matter (PM) in underground structures (e.g., tunnels) due to the high performance of nitrogen oxides (NOx) abatement. Damage (e.g. crack, spalling, or detachment) can be caused by the environmental and ageing effects on the surface of the particulate matter reduction concrete, installed on the tunnel lining. Therefore, it is important to evaluate the existence of spalling on the concrete surface for maintaining performance of NOx reduction. In this study, a basic research was performed for feasibility of spalling evaluation using electrical resistivity characteristics. Given the test results, the electrical resistivity was decreased as the ratios of activated carbon (0~15%) and TiO2 (0~25%) were increased for specimens. Under a dry condition, electrical resistivity of cement specimens, mixed with activated carbon and TiO2, was decreased up to 2.3 times, compared with the normal cement specimen. In addition, under saturation conditions (degree of saturation: 85~98%), electrical resistivity of cement specimens with activated carbon, was decreased up to 3.5 times, compared with the normal cement specimen. Regardless of the condition (dry or saturated), the difference of electrical resistivity values shows the range of 2.3~2.8 times between the mixing specimen (with activated carbon (15%) and TiO2 (25%)) and the normal cement specimen. This study can help to provide basic knowledge for spalling evaluation using the electrical resistivity on the surface of the particulate matter reduction concrete in tunnels.