Bayesian Networks for Estimating Hydrodynamic Forces on a Submerged Floating Tunnel

Abstract

A submerged floating tunnel (SFT) is a novel structure that allows crossing waterways where immersed tunnels or bridges are not viable. However, no SFT has been built yet mainly, due to lack of experience. In consequence, there are several uncertainties regarding its design and construction. An effect that should be further investigated is the structural response of the SFT under the simultaneous action of waves and currents. For this purpose, extreme values of waves and currents that were generated through a vine-copula model are used as input in a statistical model based on Bayesian Networks (BNs). The BNs are used to study the conditional correlation (i.e the correlation between random variables conditionalized on a given event) between the hydrodynamic forces acting on the SFT and metocean variables such as waves and currents. This methodology was applied to a case study in China for a SFT aimed to be built at the Qiongzhou Strait. Moreover, the BN model was used to test twelve different configurations of the SFT, with varying submergence depths and diameter sizes. The proposed methodology can be used to provide a more realistic estimation of the forces on the SFT by considering the dependence between the variables of interest. Moreover, this methodology can be extended to test different configurations of the SFT and other hydraulic or maritime structures subjected to simultaneous loading.