• 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.

• 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.

• 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.

• 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.

• 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.22 no.4
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• pp.291-303
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• 2020

The deformation of the rock surrounding a tunnel manifests due to the stress redistribution within the surrounding rock. By observing the deformation of the surrounding rock, we can not only determine the stability of the surrounding rock and supporting structure but also predict the future state of the surrounding rock. In this paper, we used grey system theory to analyse the factors that affect the deformation of the rock surrounding a tunnel. The results show that the 5 main influencing factors are longitudinal wave velocity, tunnel burial depth, groundwater development, surrounding rock support type and construction management level. Furthermore, we used seismic prospecting data, preliminary survey data and excavated section monitoring data to establish a neural network learning model to predict the total amount of deformation of the surrounding rock during tunnel collapse. Subsequently, the probability of a change in deformation in each predicted section was obtained by using a Bayesian method for detecting change points. Finally, through an analysis of the distribution of the change probability and a comparison with the actual situation, we deduced the survey mark at which collapse would most likely occur. Surface collapse suddenly occurred when the tunnel was excavated to this predicted distance. This work further proved that the Bayesian method can accurately detect change points for risk evaluation, enhancing the accuracy of tunnel collapse forecasting. This research provides a reference and a guide for future research on the probability analysis of tunnel collapse.

• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.2
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• pp.155-170
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• 2020

In this paper, a risk management system applicable to NATM tunneling projects is proposed. After investigating case histories in NATM tunnel collapse, this paper analyzes the potential risk factors and their corresponding risk events during NATM tunnel construction. The risk factors are categorized into three groups: geological, design and construction risk factors. The risk events are also categorized into four types: excessive deformation, excessive deformation with subsidence, collapse inside tunnels, and collapse inside tunnels with subsidence. The paper identifies risk scenarios in consideration of the risk factors and proposes a risk analysis/evaluation method for the NATM tunnel risk scenarios. Based on the evaluation results, the optimal mitigation measure to handle the risk events is suggested. In order to effectively facilitate a series of risk management processes, it is necessary to develop a risk register and a management ledger for risk mitigation measures that are customized to NATM tunnels. Lastly, the risk management for an actual NATM tunnel construction site is performed to verify the validity of the proposed system.

• Earthquakes and Structures
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• v.16 no.6
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• pp.705-714
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• 2019

Seismic assessment of underground structures is one of the challenging problems in engineering design. This is because there are usually many sources of uncertainties in rocks and probable earthquake characteristics. Therefore, for decreasing of the uncertainties, seismic response of underground structures should be evaluated by sufficient number of earthquake records which is scarcely possible in common seismic assessment of underground structures. In the present study, a practical risk-based approach was performed for seismic risk assessment of an unsupported tunnel. For this purpose, Incremental Dynamic Analysis (IDA) was used to evaluate the seismic response of a tunnel in south-west railway of Iran and different analyses were conducted using 15 real records of earthquakes which were chosen from the PEER ground motion database. All of the selected records were scaled to different intensity levels (PGA=0.1-1.7 g) and applied to the numerical models. Based on the numerical modeling results, seismic fragility curves of the tunnel under study were derived from the IDA curves. In the next, seismic risk curve of the tunnel were determined by convolving the hazard and fragility curves. On the basis of the tunnel fragility curves, an earthquake with PGA equal to 0.35 g may lead to severe damage or collapse of the tunnel with only 3% probability and the probability of moderate damage to the tunnel is 12%.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.4
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• pp.315-326
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• 2024

In this study, the Delphi method and AHP (analytic hierarchy process) were used to evaluate tunnel collapse risk from a comprehensive and multifaceted perspective. Influence factors were established through literature reviews, previous studies, and brainstorming sessions with expert groups, resulting in the construction of five main classification systems. A panel of 21 experts was formed, and three rounds of Delphi surveys were conducted to prevent errors and biases in the expert judgment process, thereby enhancing reliability. Ultimately, 14 influence factors were identified through CVR (content validity ratio) and COV (coefficient of variation) analyses of the experts' responses. Subsequently, the AHP method was applied to assess the relative importance of each influence factor and calculate the final composite weights. The timing of support and reinforcement had the highest priority, followed by groundwater inflow, joint conditions, support pattern levels, and auxiliary methods. These findings help identify the key factors affecting tunnel collapse risk and provide a foundation for developing strategies to improve tunnel safety.