A risk assessment method for subsea tunnel collapse based on cloud Bayesian network

Subsea tunnels are different from mountain tunnels in many aspects. During the construction process using drilling and blasting methods, there are complex environments such as unlimited seawater replenishment and complex geological structures that pose serious challenges to construction safety. In order to more accurately assess the safety of subsea tunnels so as to reduce the probability of disasters, this article proposes the characteristic indicators of "overburden thickness/depth of overlying seawater" (RSR) for subsea tunnels, and combines cloud model theory with Bayesian networks to establish a risk assessment method for subsea tunnel collapse. Taking the Jiaozhou Bay Second subsea tunnel as an example, prior risk reasoning and ex-post risk diagnosis are carried out. The maximum disaster risk section of the tunnel has been determined, and the key influencing factors of the collapse risk of the subsea tunnel have been identified, providing assurance for construction safety. The main contributions of the research results are as follows: (a) analyzed the factors affecting the risk of subsea tunnel; (b) the characteristic indicator for subsea tunnel collapse risk assessment was proposed; and (c) combined the Bayesian network and cloud model, and established the risk assessment method of subsea tunnel by drilling and blasting method.