A novel model for risk prediction of water inrush and its application in a tunnel in Xinjiang, China

Abstract

Read online

Tunnel water inrush may not only cause hundreds of millions of economic losses and serious casualties, but also leads to a series of ecological and environmental problems such as the decline of groundwater level, soil salinization and surface vegetation degradation. In this study, considering hydrogeology, construction, and dynamic monitoring factors, a new risk prediction model of water inrush is proposed based on fuzzy mathematical theory. The element of novelty is that this approach comprehensively considers nonlinearity and randomness factors, and the index values, weights, and membership are expressed as interval numbers instead of constant values. The interval membership degree of each index is calculated by an improved sigmoid membership function (SMF). A coupling algorithm of improved analytic hierarchy process and entropy method is used to calculate the index weight. In addition, the Boolean matrix is introduced into the relative advantage analysis of the interval vector, and the final risk level of water inrush is determined by the ranking result. The proposed model is applied to the analysis of the water inrush risk in the Ka−Shuang 2 (KS2) tunnel in Xinjiang, China. The predicted results align well with the actual excavation results, which indicates that this novel model has high accuracy and reliability. Simultaneously, a risk management response mechanism for different risk levels of water inrush is discussed, which is expected to provide a new research perspective for risk control of other related projects and promote regional sustainable development.

Keywords

• water inrush
• SMF
• comprehensive weight
• interval number
• risk prediction