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Tunnel designers persistently experience issues regarding the impact of tunnel drainage on groundwater that can affect the safety of tunnels. From an environment-oriented approach, tunnel drainage affects the groundwater table and leads to alterations in the ecological environment in the long term. Accordingly, novel methods have been established to estimate drainage-induced drawdown concerning groundwater balance and environmental factors. However, the existing methods ignore the spatial characteristics of hydrogeological parameters and complex topography, leading to over-simplification of the groundwater flow; moreover, they lack a strategy to represent drawdown uncertainty. This paper presents a stochastic modeling method for inferring drainage-induced drawdown predictions conditional on tunnel inflow estimates. The method uses a numerical groundwater model to include the rock mass properties, fault zones, and complex topography of the tunnel area. The stochastic model processes the outcomes of the numerical model conditional on the absolute error of groundwater inflow based on Bayes' theorem. The contributions of the proposed method are as follows: (1) The method considers complex topography such as an inclined water table, fault zones, and rock type distribution. Accordingly, the groundwater simulation results indicate that the spatial distribution of rock types determines the magnitude of drawdown. Furthermore, the drawdown response was irregular and variable along the tunnel axis. (2) The Bayesian method allows the prediction of drawdown estimates with minimum data before tunnel construction with approximate drainage. The predictions can be updated using additional data during construction. (3) The application of the predictions is made flexible by arranging absolute error intervals, in which the decision-maker may consider the uncertainty of the parameters on the predicted solutions. The stochastic model was applied to a tunnel project in a poorly sampled ungauged mountainous catchment. The method provides information on parts of the tunnel that have the most significant potential impact with minimal input. The proposed method can be extended for long-term environmental impact assessment of tunnels by including surface features such as soil type, land use, and land cover. • Drawdown estimation was conducted using an environment-oriented approach. • The data scarcity problem was addressed using a stochastic modeling method. • The stochastic model estimates drawdown conditional to tunnel inflow measurements. • Drawdown is irregularly distributed and variable along the tunnel axis. [ABSTRACT FROM AUTHOR]

Collapse is one of the main hazards during tunnel construction by the drill-and-blast method. In order to evaluate the collapse risk and provide a basis for risk control, a failure probability evaluation method for collapse of drill-and-blast tunnels based on the multistate fuzzy Bayesian network is proposed in this paper. First, the typical tunnel collapse cases are analyzed statistically based on the risk breakdown structure method, a fault tree model is built for drill-and-blast tunnel collapses and the causal relationships between the tunnel collapse and the influential factors, such as natural conditions, engineering geology and construction etc., are revealed. Secondly, the multiple fault states of nodes, including rock mass grade and groundwater, are described by the fuzzy numbers. The fuzzy subsets are utilized to describe the failure probability of nodes and the uncertain logic relationship between nodes described by the multistate fuzzy conditional probability table is established. In order to ensure the reliability of the survey data when evaluating the possibility intervals of the multistate fuzzy conditional probability tables and the fuzzy failure probability of root nodes, as well as taking the expert judgment ability level and subjective reliability level into consideration, an expert investigation method based on the confidence indicator is proposed. Finally, in order to fully exploit expert knowledge and empirical data, the α-weighted valuation method is adopted for defuzzification so as to obtain precise parameters for the conditional probability tables. The 3σ criterion is employed to calculate the characteristic values of triangular fuzzy numbers so as to determine the prior fuzzy failure probability of root nodes. By means of the fuzzy Bayesian inference, the proposed method is capable of calculating the probability distribution of tunnel collapse and identifying the critical risk factors under both prior knowledge and given evidence circumstances. Taking the collapse failure probability evaluation for the Xiucun Tunnel passing through the fault F18 as an example, the application results demonstrated the feasibility and efficiency of the proposed method and it can be utilized as a decision-making tool for safety risk management during tunnel construction. • The influential factors causing tunnel collapse are identified. • The multiple fault states of factors and uncertain logic relationship between factors are considered. • A multistate fuzzy BN based method is proposed for collapse failure probability analysis of drill-and-blast tunnels. • The proposed method can handle multistate vagueness and uncertainty compared to the traditional BN. [ABSTRACT FROM AUTHOR]