Risk Control Technology for Water Inrush during the Construction of Deep, Long Tunnels

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.

Applications of Bayesian approaches in construction management research: a systematic review

“…BN has advantages of showing the propagation influence of risks in a network and updating the interdependency among risks when new information is available, overcoming the limitation of SEM, artificial neural networks and other simulation techniques in analyzing risks (Qazi et al , 2020). Therefore, it has been widely applied to risk assessment in construction-related research (Liu et al , 2019; Sun et al , 2018; Eshtehardian and Khodaverdi, 2016; Wu et al , 2015a). The integration of a normal Cloud model and BN for risk assessment allows discovering structure automatically from data and develop network dynamically, which predicts risks better than conventional BNs (Chen et al , 2020).…”

Risk Assessment of Water Inrush in Tunnels: A Case Study of a Tunnel in Guangdong Province, China

“…The degree of probability between the fuzzy synthesis degree S i and S j is defined as Pr ij = Pr S * i ≥ S * j . The matrix of possibility degree P = Pr ij n×n was calculated using Equation (10). Finally, subjective weights for the evaluation attributes were obtained by…”

“…There are g evaluation attributes of the benefit category and n-g evaluation attributes of the cost category, while ω j denotes the combined weight of the j-th evaluation attribute. y * i is defuzzified according to Equation (10) to obtain Y i . A larger Y i indicates a higher risk factor for the sample to water inrush.…”

“…Water-rich fracture zones are one of the main causes of water inrush in tunnels [6]. The occurrence of water hazards can be a serious threat to the safety of life and property of construction workers [7][8][9], in addition to involving groundwater drainage and environmental protection issues [10]. The risk assessment of the water inrush hazard is, therefore, of great importance for the next step in risk control [11].…”