• Geomechanics and Engineering
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• v.23 no.5
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• pp.441-454
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• 2020

The tunnel collapse, large deformation of surrounding rock, water and mud inrush are the major geological disasters in soft rock tunnel construction. Among them, tunnel collapse has the most serious impact on tunnel construction. Current research backed theories have certain limitations in identifying the collapse risk of soft rock tunnels. Examining the Zhengwan high-speed railway tunnel, eight soft rock tunnel collapse influencing factors were selected, and the combination of indicator weights based on the analytic hierarchy process and entropy weighting methods was obtained. The results show that the groundwater condition and the integrity of the rock mass are the main influencing factors leading to a soft rock tunnel collapse. A comprehensive fuzzy evaluation model for the collapse risk of soft rock tunnels is being proposed, and the real-time collapse risk assessment of the Zhengwan tunnel is being carried out. The results obtained via the fuzzy evaluation model agree well with the actual situation. A tunnel section evaluated to have an extremely high collapse risk and experienced a local collapse during excavation, verifying the feasibility of the collapse risk evaluation model. The collapse risk evaluation model proposed in this paper has been demonstrated to be a promising and innovative method for the evaluation of the collapse risk of soft rock tunnels, leading to safer construction.

• 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 the Earthquake Engineering Society of Korea
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• v.19 no.3
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• pp.93-102
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• 2015

This study evaluates collapse probabilities of structures which are designed according to a domestic seismic design code, KBC2009. In evaluating their collapse probabilities, to do this, probabilistic distribution models for seismic hazard and structural capacity are required. In this paper, eight major cities in Korea are selected and the demand probabilistic distribution of each city is obtained from the uniform seismic hazard. The probabilistic distribution for the structural capacity is assumed to follow a underlying design philosophy implicitly defined in ASCE 7-10. With the assumptions, the structural collapse probability in 50 years is evaluated based on the concept of a risk integral. This paper then defines an mean value of the collapse probabilities in 50 years of the selected major cities as the target risk. Risk-targeted spectral accelerations are finally suggested by modifying a current mapped spectral acceleration to meet the target risk.

• Earthquakes and Structures
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• v.18 no.3
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• pp.367-377
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• 2020

Incremental dynamic analysis (IDA) widely uses for the collapse risk assessment procedures of buildings. In this study, an IDA-based collapse risk assessment methodology is proposed, which employs a novel approach for detecting the near-collapse (NC) limit state. The proposed approach uses the modal pushover analysis results to calculate the maximum inter-story drift ratio of the structure. This value, which is used as the upper-bound limit in the IDA process, depends on the structural characteristics and global seismic responses of the structure. In this paper, steel midrise intermediate moment resisting frames (IMRFs) have selected as case studies, and their collapse risk parameters are evaluated by the suggested methodology. The composite action of a concrete floor slab and steel beams, and the interaction between the infill walls and the frames could change the collapse mechanism of the structure. In this study, the influences of the metal deck floor and autoclaved aerated concrete (AAC) masonry infill walls with uniform distribution are investigated on the seismic collapse risk of the IMRFs using the proposed methodology. The results demonstrate that the suggested modified IDA method can accurately discover the near-collapse limit state. Also, this method leads to much fewer steps and lower calculation costs rather than the current IDA method. Moreover, the results show that the concrete slab and the AAC infill walls can change the collapse parameters of the structure and should be considered in the analytical modeling and the collapse assessment process of the steel mid-rise intermediate moment resisting frames.

• Advances in Computational Design
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• v.2 no.2
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• pp.121-131
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• 2017

Collapse was one of the typical common geological hazards during the construction of tunnels. The risk assessment of collapse was an effective way to ensure the safety of tunnels. We established a prediction model of collapse based on Bayesian Network. 76 large or medium collapses in China were analyzed. The variable set and range of the model were determined according to the statistics. A collapse prediction software was developed and its veracity was also evaluated. At last the software was used to predict tunnel collapses. It effectively evaded the disaster. Establishing the platform can be subsequent perfect. The platform can also be applied to the risk assessment of other tunnel engineering.

• Geomechanics and Engineering
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• v.30 no.5
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• pp.413-422
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• 2022

TBM is widely used in the construction of various underground projects in the current world, and has the unique advantages that cannot be compared with traditional excavation methods. However, due to the high cost of TBM, the damage is even greater when geological disasters such as collapse occur during excavation. At present, there is still a shortage of research on various types of risk prediction of TBM tunnel, and accurate and reliable risk prediction model is an important theoretical basis for timely risk avoidance during construction. In this paper, a prediction model is proposed to evaluate the risk level of tunnel collapse by establishing a reasonable risk index system, using analytic hierarchy process to determine the index weight, and using the normal cloud model theory. At the same time, the traditional analytic hierarchy process is improved and optimized to ensure the objectivity of the weight values of the indicators in the prediction process, and the qualitative indicators are quantified so that they can directly participate in the process of risk prediction calculation. Through the practical engineering application, the feasibility and accuracy of the method are verified, and further optimization can be analyzed and discussed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.785-791
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• 2023

Risk management is essential for preventing accidents arising from uncertainties in TBM tunnel projects, especially concerning managing the risk of TBM tunnel collapse, which can cause extensive damage from the tunnel face to the ground surface. In addition, prioritizing risks is necessary to allocate resources efficiently within time and cost constraints. Therefore, this study aimed to establish a TBM risk database through case studies of TBM accidents and determine a risk priority for TBM tunnel collapse using the Bayes theorem. The database consisted of 87 cases, dealing with three accidents and five geological sources. Applying the Bayes theorem to the database, it was found that fault zones and weak ground significantly increased the probability of tunnel collapse, while the other sources showed low correlations with collapse. Therefore, the risk priority for TBM tunnel collapse, considering geological sources, is as follows: 1) Fault zone, 2) Weak ground, 3) Mixed ground, 4) High in-situ stress, and 5) Expansive ground. In practice, the derived risk priority can serve as a valuable reference for risk management, enhancing the safety and efficiency of TBM construction. It provides guidance for developing appropriate countermeasure plans and allocating resources effectively to mitigate the risk of TBM tunnel collapse.

• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.255-261
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• 2018

In the field of excavation, it is important to recognize and analyze the factors that cause the ground collapse in order to predict and cope with the ground subsidence. However, it is difficult for field engineers to predict ground collapse due to insufficient knowledge of ground subsidence influence factors. Although there are many cases and studies related to the ground subsidence, there is no manual to help practitioners. In this study, we present the criteria for describing and quantifying the influential factors to help the practitioners understand the existing ground collapse cases and classification of the ground subsidence factors revealed through the research. This study aims to improve the understanding of the factors affecting the ground collapse and to provide a GSRp for the ground subsidence risk assessment which can be applied quickly in the field.

• Tunnel and Underground Space
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• v.21 no.5
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• pp.333-340
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• 2011

RMR is most strongly adopted rock classification method to scheme support system in domestic tunnel. However the RMR, which is based on geological survey during design stage of tunnel, can't present the real ground accurately. In this study, authors suggested Weighted-RMR (W-RMR) which is considered weighted value of risk factors of collapse due to prevent collapse and roof falls during tunneling. According to the application of W-RMR to Bye-Gye tunnel, we could change support type flexibly by the risk factors on a face of tunnel.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.663-671
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• 2020

Recently, there have been many natural disasters in Korea, not only in forest areas but also in urban areas, and the national requirements for them are increasing. In particular, there is no pre-disaster information system that can systematically manage the collapse of the slope of the national highway. In this study, big data analysis was conducted on the factors causing slope collapse based on the detailed investigation report on the slope collapse of national roads in Gangwon-do and Gyeongsang-do areas managed by the Cut Slope Management System (CSMS) and the basic survey of slope failures. Based on the analysis results, a slope collapse risk prediction model was established through Adaboost, a classification-based machine learning model, reflecting the collapse slope location and weather information. It also developed a visualization map for the risk of slope collapse, which is a visualization program, to show that it can be used for preemptive disaster prevention measures by identifying the risk of slope due to changes in weather conditions.