• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.5
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• pp.421-433
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• 2024

As the utilization of underground space increases, preventing collapse accidents during tunnel construction has become a significant challenge. This study aims to quantitatively assess the risk of tunnel collapse during construction by analyzing various influencing factors and proposing a tunnel collapse risk index based on these factors. For the 14 major influencing factors affecting tunnel collapse, weights were calculated using the analytic hierarchy process (AHP) method. Data from 27 collapse cases were collected, and Monte Carlo simulation was used to calculate the grade scores for each influencing factor. These scores were then synthesized to derive the tunnel collapse risk index. The average value of the tunnel collapse risk index was analyzed to be 49.359 points. Future comparisons with section-by-section evaluation results of tunnel collapse risk will allow for the assessment of whether a specific section has a lower or higher collapse risk. This study provides a systematic method for quantitatively evaluating the key factors of tunnel collapse risk, thereby contributing to the prevention of collapse accidents during tunnel construction and the establishment of appropriate countermeasures. Future research is expected to enhance the reliability of the tunnel collapse risk index by incorporating more field data and improving the accuracy of tunnel collapse risk assessment based on this index.

• Journal of Korean Tunnelling and Underground Space Association
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• v.11 no.4
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• pp.373-385
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• 2009

A new indexing methodology so called KTH-index was developed to quantitatively evaluate a potential level for tunnel collapse hazard, which has been successfully applied to tunnel construction sites to date. In this study, an attempt is made to apply this methodology for validating an outcome of tunnel design by checking the variation of KTH-index along longitudinal tunnel section. In this KTH-index simulation, it is the most important to determine the input factors reasonably. The design factor and construction condition are set up based on the designed outcome. Uncertain ground conditions are arranged based on borehole test and electro-resistivity survey data. Two scenarios for ground conditions, best and worst scenarios, are set up. From this simulation, it is shown that this methodology could be successfully applied for providing quantitative validity of a tunnel design and also potential hazard factors which should be carefully monitored in construction stage. The hazard factors would affect sensitively the hazard level of the tunnel site under consideration.

• 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.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.09a
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• pp.1294-1301
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• 2010

In this study, a case study, where a hazard management for tunnel collapses has been quantitatively undertaken based on the KICT Tunnel Hazard(KTH) index, is presented. From this, it was able to timely inform the field engineers when the more detailed investigation is required for checking if any risky factor is shown on the tunnel face. At the same time, variable additional information such as sensitivities of major influence factors are also provided to field engineers from the methodology given in this study. The additional information would be helpful for better understanding of tunnel hazard level at the current tunnelling stage and following the required actions for more detailed checks of risky factors.

• Journal of Korean Tunnelling and Underground Space Association
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• v.5 no.3
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• pp.241-250
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• 2003

Rock mass is very inhomogeneous in nature and data obtained by site investigations and tests are very limited. For this reason, many uncertainties are to be included in the process of constructing structures in rock mass. In the design of a tunnel, support pattern, advance rate, and excavation method, which are important design parameters, must be determined to be optimal. However, it is not easy to determine those parameters. Moreover if those parameters are determined incorrectly, unexpected risk occurs such as decrease in the stability of a tunnel or economic loss due to the excessive supports etc. In this study, how to determine an optimal support pattern and advance rate, which are the important tunnel design parameters, is introduced based on a risk analysis. It can be confirmed quantitatively that the more supported a tunnel is, the larger reliability index becomes and the more stable the tunnel becomes. Also an optimal support pattern and advance rate can be determined quantitatively by performing a risk analysis considering construction cost and the cost of loss which can be occurred due to the collapse of a tunnel.