Fractal Dimension as an Effective Feature for Characterizing Hard Marine Growth Roughness from Underwater Image Processing in Controlled and Uncontrolled Image Environments

Evaluation of Hydrodynamic Force Coefficients in Presence of Biofouling on Marine/Offshore Structures, a Review and New Approach

Novel attempts to optimize the design and requalification of offshore structures draws attention to the importance of updating information about the environmental forces. One of the important steps to design or re-assess offshore structures is the re-evaluation/evaluation of bio-colonization’s effects. This paper presents a review of studies that considered biofouling in marine/offshore structures. Most of the previous researchers conducted the effects of biofouling as a surface roughness; however, some others proved that despite the surface roughness, other marine fouling components such as surface coverage ratio, biofouling species, and aggregation, may significantly influence hydrodynamic force coefficients, particularly at higher Reynolds numbers (Re). In addition, a new approach is proposed in this paper to estimate the drag coefficient of circular members covered by biofouling. The new approach relies on a multiple parameter equation and builds on the existing measurement of the drag force coefficient. Two relationships between biofouling parameters and drag coefficient are given for hard biofouling at the post-critical Re regime.

Reliability Updating of Offshore Structures Subjected to Marine Growth

Marine growth is a known problem for oceanic infrastructure and has been shown to negatively impact the reliability of bottom-fixed or floating offshore structures submitted to fatigue or extreme loading. Among other effects, it has been shown to change drag forces by increasing member diameters and modifying the roughness. Bio-colonization being highly random, the objective of this paper is to show how one-site inspection data increases reliability by decreasing uncertainties. This can be introduced in a reliability-based inspection framework for optimizing inspection and maintenance (here, cleaning). The modeling and computation are illustrated through the reliability analysis of a monopile in the European Atlantic area subjected to marine growth and according to the plastic collapse limit state. Based on surveys of structures in the North Sea, long-term stochastic modeling (space and time) of the marine growth thickness is first suggested. A Dynamic Bayesian Network is then developed for reliability updating from the inspection data. Finally, several realistic (10–20 measurements) inspection strategies are compared in terms of reliability improvement and the accuracy of reliability assessment.