An interval risk assessment method and management of water inflow and inrush in course of karst tunnel excavation
Introduction
Karst areas are highly scattered throughout the world and the total of karst area is estimated to be distributed across approximately 5 × 107 km2. In particular, karst areas in China account for one-third of the total land area. Geological hazards frequently occur during the construction of highways, mines, and tunnels in these karst areas (Sweeting, 2012). More specifically, water inflow and inrush are one of the more serious geological hazards that may occur (Beard, 2010, Wang et al., 2012b). Groundwater is the driving force behind the development of karst topography and is also retained within numerous karst passages and fissures of the strata. Groundwater will be disturbed during tunnel excavation and water inrush is therefore likely to occur. In recent years, water inflow and inrush have occurred frequently during the construction of tunnels in Southwest China. Throughout the world, water inflow and inrush during tunnelling not only present difficulties in the construction process but can seriously threaten the safety of equipment and personnel (Cui et al., 2015, Li et al., 2015).
A large number of hydrogeological models and numerical simulations were previously performed to study the water inrush mechanism, in particular, potential paths of groundwater flow (Zhai, 2011, Reeves et al., 2013, Li et al., 2016b, Wang et al., 2017). Despite a number of in-depth research studies, the mechanism of water inrush remain challenging to evaluate, since influencing factors are not easy to quantify, and a lack of geological data related to specific sites compounds the issue. In recent decades, the development and application of advanced measurement techniques and geological prediction technology have made it possible to obtain more information about surrounding rock (Wang et al., 2017).
Numerous studies have been carried out to assess and predict the risk of water inflow and inrush in karst tunnels. A two-class fuzzy comprehensive evaluation method was adopted based on an evaluation index system consisting of four first-class and twelve second-class indices were adopted (Chu et al., 2017). Based on normal cloud theory, Wang et al. (2016b) constructed a comprehensive model to solve the randomness and fuzziness during the evaluation of water inrush risk. Furthermore, a risk assessment system using the attribute recognition method has been investigated (Li et al., 2013) and geographic information system (GIS) technology used to dynamically predict the risk of water inrush risk and develop suitable protective measures (Li and Li, 2014).
The risk factors of water inrush in karst tunnelling have been examined in detail using various methods. However, the methods lack certain features and influencing factors and have not been comprehensively analyzed. Therefore, the models do not accurately reflect the influence of key factors on water inrush in karst tunnels. Furthermore, the evaluation index, membership degree, and weight vector often vary, making it difficult to describe the uncertainty of influencing factors accurately. Besides, risk must be dynamically adjusted according to the excavation schedule, however, despite the dynamic nature of the tunnel construction process, similar dynamic risk assessment and management methods have not yet been proposed.
In the present study, a three-stage risk assessment model of water inflow and inrush in karst tunnels is proposed and a construction permission mechanism is established. Values of the evaluation indices, membership degrees and weight vector are presented as an interval number. The interval membership functions and weights of each factor were quantified, and a relative superiority analysis of the interval matrix was carried out. A case study to evaluate the engineering practices carried out in the Qiyueshan tunnel is also presented and may serve as a practical reference for the risk assessment and management of future karst tunnel construction projects.
Section snippets
Interval risk assessment method of water inflow and inrush
A three-stage risk assessment model of water inflow and inrush is established to analyze the risk due to the surroundings, factors, and feedback information and a computational model is then attained based on the analytic hierarchical process and fuzzy method. Moreover, the interval membership function and weight of each factor are quantified, and a relative superiority analysis of interval matrix subsequently is carried out. Finally, the interval risk assessment method of water inflow and
Construction permit mechanism and risk management
In view of the high risk and seriousness of disasters caused by water inflow and inrush in karst tunnels, the construction permission mechanism (Xu et al., 2011a) was established based on the dynamic modification of the construction information, dynamic adjustment of the construction plan and dynamic assessment of construction risk.
Engineering background
Qiyueshan tunnel is located in Hubei Province, a typical karst mountain area in China. The total length of the deep-buried Qiyueshan tunnel is 6.7 km, and the left line and the right line are 3386 m and 3375 m, respectively, with a maximum overburden thickness of 567 m in the right line. Moreover, Qiyueshan tunnel crosses a complex geological environment. The main geologic formation in YK19+370 to YK20+090 section is shale, siliceous limestone, sandstone, and dolomitic limestone. Qiyueshan
Conclusions
A three-stage risk assessment model of water inflow and inrush for karst tunnels is established, consisting of a conceptual model and computational model. In the present study, risk surroundings, construction factors and feedback information are analyzed. The construction permission mechanism of water inflow and inrush in karst tunnels is established based on the construction information, construction plan and dynamic risk assessment throughout the construction process.
The conceptual model
Acknowledgment
Much of the work presented in this paper was supported by the National Natural Science Foundation of China (Grant Nos.: 51879153; 41672281) and the China Postdoctoral Science Foundation (Grant Nos.: 2019M650789; 2019T120124).
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