Technical Analysis of Collapse in Tunnel Excavation and Suggestion of Preventing Appropriate Applicable Methods (Case Study: Sardasht Dam Second Diversion Tunnel)

Esmailzadeh, Akbar; Shirzad, Peyman Jafar; Haghshenas, Sina Shaffiee

doi:10.21859/cej-03095

Cited by 4 publications

(3 citation statements)

References 2 publications

(1 reference statement)

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“…Previous studies (Li et al , 2014; Aliabadian et al , 2015; Senent and Jimenez, 2015; Chen et al , 2016; Jiao et al , 2016; Esmailzadeh et al , 2017) focused more on mathematical methods. Not only did this cause a lack of exploration of engineering practice and mining of engineering data, but more crucially, also caused the research on the collapse of loess tunnel to be limited and outdated.…”

Section: Discussionmentioning

confidence: 99%

Influence of factors on collapse risk of loess tunnel: a multi-index assessment model

Zhang

Wang

Zhou

et al. 2018

JEDT

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Purpose Because of the properties of loess, the occurrence of collapse following deformation of a large settlement is a common problem during the excavation of tunnels on loess ground. Hence, risk management for safer loess tunnel construction is of great significance. The purpose of this paper is to explore the influence of factors on collapse risk of loess tunnels and establish a risk assessment model using rough set theory and extension theory. Design/methodology/approach The surrounding rock level, groundwater conditions, burial depth, excavation method and support close time were selected as the factors and settlement deformation was the verification index for risk assessment. First, using rough set theory, the influence of risk factors on the collapse risk of loess tunnels was calculated by researching engineering data of excavated sections. Then, a collapse risk assessment model was developed based on extension theory. As the final step, the model was applied to practical engineering in the Loess Plateau of China. Findings The weights of surrounding rock level, groundwater conditions, burial depth, excavation method and support close time obtained using rough set theory were respectively 10.811 per cent, 18.919 per cent, 24.324 per cent, 40.541 per cent and 5.406 per cent. The assessment results obtained using the model were in good agreement with field observations. Originality/value This study highlights key points in collapse risk management of loess tunnels, which could be very useful for future construction methods. The model, using easily obtained parameters, helps in predicting the collapse risk level of loess tunnels excavated under different geological conditions and by different construction organizations and provides a reference for future studies.

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Section: Discussionmentioning

confidence: 99%

Influence of factors on collapse risk of loess tunnel: a multi-index assessment model

Zhang

Wang

Zhou

et al. 2018

JEDT

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“…Their results were validated with the drilling rate index (DRI), which indicated the high ability of the fuzzy c-means (FCM) algorithm in the clustering of geological units in tunneling projects and the appropriate provision of the condition of the region for evaluating the risk management in the project [24]. Esmailzadeh et al [25] carried out technical analysis of collapse in tunnel excavation. Based on their results, they made some recommendations in order to stabilize of tunnel crown [25].…”

Section: Introductionmentioning

confidence: 99%

“…Esmailzadeh et al [25] carried out technical analysis of collapse in tunnel excavation. Based on their results, they made some recommendations in order to stabilize of tunnel crown [25]. Haghshenas et al evaluated tunneling projects risks using the fuzzy analytical hierarchy process.…”

Section: Introductionmentioning

confidence: 99%

Assessment of risks of tunneling project in Iran using artificial bee colony algorithm

et al. 2019

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The soft computing techniques have been widely applied to model and analyze the complex and uncertain problems. This paper aims to develop a novel model for the risk assessment of tunneling projects using artificial bee colony algorithm. To this end, the risk of the second part of the Emamzade Hashem tunnel was assessed and analyzed in seven sections after testing geotechnical characteristics. Five geotechnical and hydrological properties of study zone are considered for the clustering of geological units in front of tunneling project including length of tunnel, uniaxial compressive strength, rock mass rating, tunneling index Q, density and underground water condition. These sections were classified in two lowrisk and high-risk groups based on their geotechnical characteristics and using clustering technique. It was resulted that three sections with lithologies Durood Formation, Mobarak Formation, and Ruteh Formation are placed in the high risk group and the other sections with lithologies Baroot Formation, Elika Formation, Dacite tuff of Eocene, and Shear Tuff, and Lava Eocene are placed in the low risk group. In addition, the underground water condition and density with 0.722 and 1 Euclidean distances have the highest and lowest impacts in the high risk group, respectively. Therefore, comparing the obtained results of modelling and actual excavation data demonstrated that this technique can be applied as a powerful tool for modeling risks of tunnel and underground constructions.

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Feasibility of Intelligent Models for Prediction of Utilization Factor of TBM

et al. 2020

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Technical Analysis of Collapse in Tunnel Excavation and Suggestion of Preventing Appropriate Applicable Methods (Case Study: Sardasht Dam Second Diversion Tunnel)

Cited by 4 publications

References 2 publications

Influence of factors on collapse risk of loess tunnel: a multi-index assessment model

Influence of factors on collapse risk of loess tunnel: a multi-index assessment model

Assessment of risks of tunneling project in Iran using artificial bee colony algorithm

Feasibility of Intelligent Models for Prediction of Utilization Factor of TBM

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