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There are several factors to account for marine growth including but not limited to temperature, salinity, chlorophyll-a content, existing species in the environment and predating. This paper proposes a model of biological growth for hard species on marine structures, which can be compatible with site-specific and realistic ecology while also being able to translate the results for analyses linked to lifetime hydrodynamic or structural effects via commercial software or computing. The model preserves fundamentals of ecological aspects rather than using heuristics or random sampling to data fitting on sparsely collected information. The coefficients used in the proposed model align to the real world, with location-specific values, and can be adapted to new information. The growth model is demonstrated for Mythulis Edulis (blue mussel) colonisation to assess the lifetime hydrodynamic effects for the West Coast of Ireland and the Gulf of Guinea. The model can be extended to any hard growth approach.

Reinforced concrete (RC) structures in harbors are designed for long life (50–70 years) and are subjected to chloride-induced corrosion. Diagnosis of these structures usually relies on inspections which consist of ponding salt in the concrete cover with semi-destructive testing (SDT). Uncertainties in inspection result in bad diagnosis, and consequently in bad decisions. This study aims at developing a methodology for quantifying the uncertainty of measurements taken on-site where the random properties of concrete play a role. This assessment was performed through multiple measurements by three operators from two laboratories on the same cores extracted from a 27-year-old existing bridge located in Ireland. A total of 566 measurements are available. The effect of the operator, laboratory, and protocols are highlighted and then modeled. It is shown that the error of assessment is a function of the chloride content and follows a student distribution. The standard deviation of this error reached approximately 15% of the chloride content. This error is further propagated through the Fick law parameters for measuring its effect on the diagnosis: a 17% change of the probability of corrosion initiation is shown. This is the first study of this type with in situ inspections and the work is partly transferable to other coastal or offshore structures.

Offshore wind is planned to become the first source of energy by 2050. That requires installing turbines in deeper seas. It is shown that only floating wind turbines will allow dealing with this challenge while keeping a reasonable cost of energy production and transport according to the levelized cost of electricity. A Floating Offshore Substation will be needed in many sites. This technology is still at a low technology readiness level. This paper aims to analyze the system reliability of such a structure for which the failure rates of structural components such as mooring lines and dynamic power cables are close to the ones of electro-technical systems. Consequently, only a system reliability assessment of the floating offshore substation will allow accurately quantifying its availability and the most sensitive components. Usually, structural reliability aims at quantifying the probability of failures, while electro-technical reliability relies on feedback and observed failure rates. The paper first unifies these two concepts in a single formulation and then evaluates the system’s reliability and availability. This methodology is illustrated in a study case localized in the French coasts of the Mediterranean Sea, where the effect of several mooring and substation designs on the system reliability is compared.

The objective of this paper is to propose tools for the lifecycle management of infrastructure by showing the slow degradation processes for which inspection data are accessible, especially the data obtained from non-destructive testing (NDT) and structural health monitoring (SHM). One major characteristic of these degradation processes is their multiphasic nature; consequently, they can be discretised into different phases with specific physical kinematics where specific maintenance actions and measurement techniques can be performed. Within this framework, we propose implementing a degradation meta-modelling approach fed with measurements (NDT, SHM). This approach is based on state-dependent stochastic processes for modelling the degradation and maintenance of reinforced concrete structures that are subjected to chloride-induced deterioration. The benefit of using multiphasic degradation meta-models in the lifecycle management of infrastructure is illustrated through numerical examples that include single and multi-action management policies.

Wind energy is expected to play a significant role in meeting emission targets over the next 20 years. Offshore wind turbines in deep water (>150 m) must be developed due to resource quality, environmental, and activity constraints. Floating offshore wind turbines (FOWT) will be the best technology for reaching these targets. The dynamic submarine electrical cable (DSEC) is a key component of FOWT. Its electric insulation system is intended to withstand a maximum conductor temperature of 90 °C. However, biofouling growth, particularly mussels, can modify the heat transfer around the cable and thus its maximum conductor temperature, as well as temperature fluctuation, affecting the fatigue lifetime. In our work we estimate the effective thermal conductivity of mussels of various ages, as well as the heat transfer coefficient of the water around them. The results revealed that the effective thermal conductivity of juvenile mussels is lower than that of mix (both juvenile and adult) and only adult mussels. This variation in effective thermal conductivity with mussel age is related to the water porosity of the mussel’s layer. Then, the thermal effect of the resulting global thermal resistance can lead the DSEC conductor wire to either overheat (colonized by juvenile and mixed mussels) or cool down (colonized by adult mussels). Numerical simulations are used to quantify this effect.

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.

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.

Hard marine growth is an important process that affects the design and maintenance of floating offshore wind turbines. A key parameter of hard biofouling is roughness since it considerably changes the level of drag forces. Assessment of roughness from on-site inspection is required to improve updating of hydrodynamic forces. Image processing is rapidly developing as a cost effective and easy to implement tool for observing the evolution of biofouling and related hydrodynamic effects over time. Despite such popularity; there is a paucity in literature to address robust features and methods of image processing. There also remains a significant difference between synthetic images of hard biofouling and their idealized laboratory approximations in scaled wave basin testing against those observed in real sites. Consequently; there is a need for such a feature and imaging protocol to be linked to both applications to cater to the lifetime demands of performance of these structures against the hydrodynamic effects of marine growth. This paper proposes the fractal dimension as a robust feature and demonstrates it in the context of a stereoscopic imaging protocol; in terms of lighting and distance to the subject. This is tested for synthetic images; laboratory tests; and real site conditions. Performance robustness is characterized through receiver operating characteristics; while the comparison provides a basis with which a common measure and protocol can be used consistently for a wide range of conditions. The work can be used for design stage as well as for lifetime monitoring and decisions for marine structures, especially in the context of offshore wind turbines.

Corrosion defects affect the structural integrity of onshore pipelines making them prone to a Loss of Containment (LOC). LOCs may trigger significant consequences over the surrounding people and environment. Therefore, In-Line (ILI) inspections are commonly implemented to measure indicators of the metal loss along the pipe due to corrosion to support future intervention decisions. However, ILI tools are subject to detection uncertainties that hide the real number of defects because of the accuracy of the technique and the complexity of the spatially distributed corrosion degradation. This paper presents a framework in which a corroding pipeline is assessed spatially and temporally based on ILI measurements. This framework includes new defects generated over time, which are clustered with those already detected, degraded, and assessed regarding the reliability. To this end, the number of new defects is estimated with a Homogeneous Poisson Process. The defects are clustered with the DNV RP-F101 criterion, and their degradation is predicted using Levy processes. Finally, reliability is assessed temporally and spatially using Monte Carlo simulations based on a dynamic segmentation and a failure region. Following a real case study, the approach results allow us to identify critical segments of the pipeline.

After a few weeks, underwater components of offshore structures are colonized by marine species and after few years this marine growth can be significant. It has been shown that it affects the hydrodynamic loading of cylinder components such as legs and braces for jackets, risers and mooring lines for floating units. Over a decade, the development of Floating Offshore Wind Turbines highlighted specific effects due to the smaller size of their components. The effect of the roughness of hard marine growth on cylinders with smaller diameter increased and the shape should be representative of a real pattern. This paper first describes the two realistic shapes of a mature colonization by mussels and then presents the tests of these roughnesses in a hydrodynamic tank where three conditions are analyzed: current, wave and current with wave. Results are compared to the literature with a similar roughness and other shapes. The results highlight the fact that, for these realistic roughnesses, the behavior of the rough cylinders is mainly governed by the flow and not by their motions.

This paper investigates the role that virtual environments can play in assisting engineers and divers when performing subsea inspections. We outline the current state of research and technology that is relevant to the development of effective virtual environments. Three case studies are presented demonstrating how the inspection process can be enhanced through the use of virtual data. The first case study looks at how immersive virtual underwater scenes can be created to help divers and inspectors plan and implement real-world inspections. The second case study shows an example where deep learning-based computer vision methods are trained on datasets comprised of instances of virtual damage, specifically instances of barnacle fouling on the surface of a ship hull. The trained deep models are then applied to detect real-world instances of biofouling with promising results. The final case study shows how image-based damage detection methods can be calibrated using virtual images of damage captured under various simulated levels of underwater visibility. The work emphasizes the value of virtual data in creating a more efficient, safe and informed underwater inspection campaign for a wide range of built infrastructure, potentially leading to better monitoring, inspection and lifetime performance of such underwater structures.

Bio-colonisation affects the ageing of materials and the behaviour of offshore structures. Mooring systems and umbilicals belong to the family of slender bodies which are components sensitive to bio-colonisation because of a change of dynamic behaviour due to shape, roughness and mass modifications. However, this stochastic process in time and space is hard to predict. The purpose is then twofold: first, to provide a stochastic spatial model of the bio-colonisation on a mooring line; second, to show that in some defined environmental conditions, such as low wave height, low wind and current velocities, the monitoring of mooring lines tension can help to assess and reduce uncertainty on this model. Therefore, a comprehensive stochastic modelling based on mussels colonisation was carried out using on-site videotapes, experimental campaigns and expert knowledge. We studied the efficiency of a virtual sensing network using this model and a conditional entropy metric. It is first shown that the spatial model fits well with experimental data, and second that a denser medium accuracy sensor network is to be preferred to a single high accuracy fairlead sensor to reduce the uncertainty on the model parameters. It is then worth updating bio-colonisation on mooring lines during the life-time of a floating wind turbine.

Optimization of maintenance and design of coastal steel infrastructure needs for long-term predictive degradation models. The phenomenon of corrosion in the offshore and coastal environment is very complex due to the stochastic and changing nature of the environment (temperature, water chemical properties) and the multiple involved processes in competition. In the GEROM French project, the objective was to build a database of the residual thickness measured from ultrasonic measurements on sheet piles and piles carried out during 40 years in commercial and military harbors along the French coast. A total amount of 35,460 measurements were gathered. After a detailed analysis of the data and statistical modeling, a probabilistic model of corrosion is proposed for sheet piles. It relies on a piecewise description of the process with the depth depending on the exposed zone and a time-dependent evolution of parameters of the gamma probability density function.

The present paper deals with the stochastic modeling of bio-colonization for the computation of stochastic hydrodynamic loading on jacket-type offshore structures. It relies on a multidisciplinary study gathering biological and physical research fields that accounts for uncertainties at all the levels. Indeed, bio-colonization of offshore structures is a complex phenomenon with two major but distinct domains: (i) marine biology, whose processes are modeled with biomathematics methods, and (ii) hydrodynamic processes. This paper aims to connect these two domains. It proposes a stochastic model for the marine organism’s growth and then continues with transfers for the assessment of drag coefficient and forces probability density functions that account for marine growth evolution. A case study relies on the characteristics (growth and shape) of the blue mussel (Mytilus edulis) in the northeastern Atlantic.

This paper presents the Structural Health Monitoring (SHM) system developed for a port wharf of a freight terminal, in Saint-Nazaire, France. This concrete structure has been equipped with a multi-sensor system for the monitoring of concrete ageing. The measurement chain is designed to detect the penetration of chloride ions in order to quantify the risk of reinforcement bars corrosion. Modifications of the mechanical behavior of the structural elements of the wharf are also monitored. At first, the sensors embedded within the structure and the acquisition devices are described. The data from the monitoring performed during the first months of the structure service life are then presented. The concrete monitoring at early age providing data like temperature history, strain and resistivity is useful both for the wharf owner and the construction company since it indicates where concrete shrinkage is likely to cause cracking and gives an indicator of material hardening. These data were compared to the results of material tests carried out on concrete. The study shows that a measurement chain dedicated to the SHM could be a useful tool for validating the quality of the construction of a reinforced concrete structure before being used in the framework of long-term monitoring.

Recent breakthroughs in the computer vision community have led to the emergence of efficient deep learning techniques for end-to-end segmentation of natural scenes. Underwater imaging stands to gain from these advances, however, deep learning methods require large annotated datasets for model training and these are typically unavailable for underwater imaging applications. This paper proposes the use of photorealistic synthetic imagery for training deep models that can be applied to interpret real-world underwater imagery. To demonstrate this concept, we look at the specific problem of biofouling detection on marine structures. A contemporary deep encoder–decoder network, termed SegNet, is trained using 2500 annotated synthetic images of size 960 × 540 pixels. The images were rendered in a virtual underwater environment under a wide variety of conditions and feature biofouling of various size, shape, and colour. Each rendered image has a corresponding ground truth per-pixel label map. Once trained on the synthetic imagery, SegNet is applied to segment new real-world images. The initial segmentation is refined using an iterative support vector machine (SVM) based post-processing algorithm. The proposed approach achieves a mean Intersection over Union (IoU) of 87% and a mean accuracy of 94% when tested on 32 frames extracted from two distinct real-world subsea inspection videos. Inference takes several seconds for a typical image.

Chloride presence affects different environments (soil, water, concrete) decreasing their qualities. In order to assess chloride concentration this paper proposes a novel sensor for detecting and measuring it. This sensor is based on electric changes of commercial monocalcium aluminate (CA) when it interacts with chloride aqueous solutions. CA is used as a dielectric material between two coplanar capacitors. The geometry proposed for this sensor allows to assess the chloride content profile, or to make four times the same measurement. Besides, the experimental design gives us the possibility of study not just the chloride effect, but also the time and some geometric effects due to the sensor design. As a result, this sensor shows a limit of detection, sensitivity, and response time: 0.01 wt % Cl− and 0.06 wt % Cl−, and 2 min, respectively, comparable with other non invasive techniques as optical fibre sensors.