• Journal of Korean Tunnelling and Underground Space Association
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• v.15 no.5
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• pp.505-522
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• 2013

Previously, a new concept of indexing methodology has been proposed for quantitative assessment of tunnel collapse hazard level at each tunnel face with respect to the given geological data, design condition and the corresponding construction activity (Shin et al, 2009a). In this paper, 'linear' model, in which weights of influence factors are invariable, and 'non-linear' model, in which weights of influence factors are variable, are taken into account with some examples. Then, the 'non-linear' model is validated by using 100 tunnel collapse cases. It appears that 'non-linear' model allows us to have adapted weight values of influence factors to characteristics of given tunnel site. In order to make a better understanding and help for an effective use of the system, a series of operating processes of the system are built up. Then, by following the processes, the system is applied to a real-life tunnel project in very weak and varying ground conditions. Through this approach, it would be quite apparent that the tunnel collapse hazard indices are determined by well interlinked consideration of face mapping data as well as design/construction data. The calculated indices seem to be in good agreement with available electric resistivity distribution and design/construction status. In addition, This approach could enhance effective usage of face mapping data and lead timely and well corresponding field reactions to situation of weak tunnel faces.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.617-628
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• 2022

A ground support pattern should be designed by properly integrating various support materials in accordance with the rock mass grade when constructing a tunnel, and a technical decision must be made in this process by professionals with vast construction experiences. However, designing supports at the early stage of tunnel design, such as feasibility study or basic design, may be very challenging due to the short timeline, insufficient budget, and deficiency of field data. Meanwhile, the design of the support pattern can be performed more quickly and reliably by utilizing the machine learning technique and the accumulated design data with the rapid increase in tunnel construction in South Korea. Therefore, in this study, the design data and ground exploration data of 48 road tunnels in South Korea were inspected, and data about 19 items, including eight input items (rock type, resistivity, depth, tunnel length, safety index by tunnel length, safety index by rick index, tunnel type, tunnel area) and 11 output items (rock mass grade, two items for shotcrete, three items for rock bolt, three items for steel support, two items for concrete lining), were collected to automatically determine the rock mass class and the support pattern. Three machine learning models (S1, A1, A2) were developed using two machine learning algorithms (SVR, ANN) and organized data. As a result, the A2 model, which applied different loss functions according to the output data format, showed the best performance. This study confirms the potential of support pattern design using machine learning, and it is expected that it will be able to improve the design model by continuously using the model in the actual design, compensating for its shortcomings, and improving its usability.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.53-73
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• 2023

Although it is mandatory to wear a seat belt that can minimize human injury when traffic accident occurs, the number of traffic accident casualties not wearing seat belts still accounts for a significant proportion.The seat belt wearing detection system for all seats is a system that identifies whether all seat passengers wear a seat belt and encourages their usage, also it can be a useful technical countermeasure. Firstly, this study established the viability of system implementation by assessing the factors influencing the severity of injuries in traffic accidents through the development of an ordered probit model. Analysis results showed that the use of seat belts has statistically significant effects on the severity of traffic accidents, reducing the probability of death or serious injury by 0.054 times in the event of a traffic accident. Secondly, a meta-analysis was conducted based on prior research related to seat belts and injuries in traffic accidents to estimate the expected reduction in accident severity upon the implementation of the system.The analysis of the effect of accident severity reduction revealed that wearing seat belts would lead to a 63.3% decrease in fatal accidents, with the front seats showing a reduction of 75.7% and the rear seats showing a reduction of 58.1% in fatal accidents. Lastly, Using the results of the meta-analysis and traffic accident statistics, the expected decrease in the number of traffic accident casualties with the implementation of the system was derived to analyze the traffic safety effects of the proposed detection system. The analysis demonstrated that with an increase in the adoption rate of the system, the number of casualties in accidents where seat belts were not worn decreased. Specifically, at a system adoption rate of 60%, it is anticipated that the number of fatalities would decrease by more than three times compared to the current scenario. Based on the analysis results, operational strategies for the system were proposed to increase seat belt usage rates and reduce accident severity.