Abstract
Water inrush is one of the main disasters occurring during tunnel construction in complex geological areas: once it happens, it can cause economic losses, casualties, and delay. Based on risk analysis and management, a risk control scheme is proposed as an effective means to control the risk of such a disaster; however, there are some deficiencies in existing research because the impacts of human factors on the risk of water inrush, dynamic changes in risk information during construction, and the diversity of types of water inrush are neglected. To enrich the research results of water inrush risk control and improve the effect of water inrush risk control, we first use the advantages of Bayesian network to analyse risk events, construct a Bayesian network structure chart of water inrush risk during construction, and propose a fuzzy probability risk analysis model for water inrush. The model can quickly track changes in risk information and diagnose the cause of water inrush disasters while providing an early warning thereof. In addition, considering that the diversity of water inrush types leads to differences in water inrush mechanisms, we believe that the formulation of any water inrush risk control scheme must be combined with water inrush mechanism analysis; therefore, we take a nondefect generated water inrush in front of the tunnel as a representative case and analyse the possible mechanism of water inrush through the stability analysis of the water-resisting strata. Then, based on the results of risk analysis and an analysis of the water inrush mechanism, a reasonable risk control scheme for water inrush is derived.
Highlights
As an important component of a nation’s infrastructure, the value and importance of the tunnels is of great concern around the world [1]
It can be said that the world is witnessing the advent of a prosperous era of tunnel engineering [4]; various geological disasters such as collapses [5], rock bursts [6], and water inrush [7] caused by tunnel construction problems have increased, which has caused problems such as property damage, construction delays, cost overruns, and even casualties [8]
A Bayesian network, as an important mathematical analysis method, offers the following advantages [32, 33]: (i) e causal relationship in the network structure diagram is clear and suitable for complex systems with multiple factors (ii) It can predict the changing trend of events by forward reasoning technology, and allow rapid diagnosis of accident disasters by reverse reasoning to find the causes of accidents (iii) Expert knowledge and empirical data can be combined, which is effective for the risk prediction of large projects (iv) e system can update the analysis results in time according to the changes in root node information and judge the changing trend of the system by predicting changes in the results
Summary
As an important component of a nation’s infrastructure, the value and importance of the tunnels is of great concern around the world [1]. Building a Bayesian network water inrush risk assessment model considering geological and construction factors can further improve the existing water inrush risk assessment system, effectively solve the problem of water inrush risk analysis during construction, and provide a scientific basis for the formulation of risk control technology. The proposed fuzzy Bayesian network model for water inrush risk considers geological factors and construction factors It can accurately reflect the influence of risk factors information changes on the water inrush risk by forward reasoning, and the main factors that cause disasters can be identified by reverse reasoning.
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