One of the biggest problems the maritime industry is currently experiencing is corrosion, resulting in short and long-term damages. Early prediction and proper corrosion monitoring can reduce economic losses. Traditional approaches used in corrosion prediction and detection are time-consuming and challenging to execute in inaccessible areas. Due to these reasons, artificial intelligence-based algorithms have become the most popular tools for researchers. This study discusses state-of-the-art artificial intelligence (AI) methods for marine-related corrosion prediction and detection: (1) predictive maintenance approaches and (2) computer vision and image processing approaches. Furthermore, a brief description of AI is described. The outcomes of this review will bring forward new knowledge about AI and the development of prediction models which can avoid unexpected failures during corrosion detection and maintenance. Moreover, it will expand the understanding of computer vision and image processing approaches for accurately detecting corrosion in images and videos.

Corrosion is an undesirable phenomenon resulting in material deterioration and degradation through electrochemical or chemical reactions with the surrounding environment. Additionally, corrosion presents considerable threats in both the short and long term because of its ability to create failures, leakages, and damage to materials, equipment, and environment. Despite swift technological developments, it remains difficult to determine the degrees of corrosion due to the different textures and the edgeless boundary of corrosion surfaces. Hence, there is a need to investigate the robust corrosion detection algorithms that are suitable for all degrees of corrosion. Recently, many computer vision and image processing algorithms have been developed for corrosion prediction, assessment, and detection, such as filtering, texture, color, pixelation, image enhancement, wavelet transformation, segmentation, classification, and clustering approaches. As a result, this paper reviews and discusses the state-of-the-art computer vision and image processing methods that have been developed for corrosion detection in various applications, industries, and academic research. The challenges for corrosion detection using computer vision and image processing algorithms are also explored. Finally, recommendations for future research are also detailed.

Offshore installations must be built to resist fatigue as well as extreme forces caused by severe environmental conditions. The structural reliability analysis is the popular practise to assess a variety of natural waves determined by the long-term probability distribution of wave heights and corresponding periods on the site. In truth, however, these structures are subjected to arbitrary wave-induced forces in the open ocean. Hence, it is much more reasonable to account for the changed loading characteristics by determining the probabilistic characteristics of the random loads and outcomes responses. The key challenges are uncertainties and the non-linearity of Morison’s drag element, which results in non-Gaussian loading and response distributions. This study would analyze advances achieved to date in a comprehensive probabilistic review of offshore fixed jacket-type platforms.

The structural integrity of offshore platforms is affected by degradation issues such as subsidence. Subsidence involves large settlement areas, and it is one of the phenomena that may be experienced by offshore platforms throughout their lives. Compaction of the reservoir is caused by pressure reduction, which results in vertical movement of soils from the reservoir to the mud line. The impact of subsidence on platforms will lead to a gradually reduced wave crest to deck air gap (insufficient air gap) and cause wave-in-deck. The wave-in-deck load can cause significant damage to deck structures, and it may cause the collapse of the entire platform. This study aims to investigate the impact of wave-in-deck load on structure response for fixed offshore structure. The conventional run of pushover analysis only considers the 100-year design crest height for the ultimate collapse. The wave height at collapse is calculated using a limit state equation for the probabilistic model that may give a different result. It is crucial to ensure that the reserve strength ratio (RSR) is not overly estimated, hence giving a false impression of the value. This study is performed to quantify the wave-in-deck load effects based on the revised RSR. As part of the analysis, the Ultimate Strength for Offshore Structures (USFOS) software and wave-in-deck calculation recommended by the International Organization for Standardization (ISO) as practised in the industry is adopted to complete the study. As expected, the new revised RSR with the inclusion of wave-in-deck load is lower and, hence, increases the probability of failure (POF) of the platform. The accuracy and effectiveness of this method will assist the industry, especially operators, for decision making and, more specifically, in outlining the action items as part of their business risk management.

Offshore structures are exposed to random wave loading in the ocean environment, and hence the probability distribution of the extreme values of their response to wave loading is required for their safe and economical design. In most cases, the dominant load on offshore structures is due to wind-generated random waves where the ocean surface elevation is defined using appropriate ocean wave energy spectra. Several spectral models have been proposed to describe a particular sea state that is used in the design of offshore structures. These models are derived from analysis of observed ocean waves and are thus empirical in nature. The spectral models popular in the offshore industry include Pierson-Moskowitz spectrum and JONSWAP spectrum. While the offshore industry recognizes that different methods of simulating ocean surface elevation lead to different estimation of design wave height, no systematic investigation has been conducted. Hence, the aim of this study is to investigate the effects of predicting the 100-year responses from various wave spectrum models. In this paper, the Monte Carlo time simulation (MCTS) procedure has been used to compare the magnitude of the 100-year extreme responses derived from different spectral models. Additionally, the linear random wave theory (LRWT) was implemented to simulate the offshore structural responses due to random wave loading. The models have been tested for three different environmental conditions represented by Hs = 15m, 10m and 5m respectively. The accuracy of the predictions of the 100-year responses from Pierson-Moskowitz and JONSWAP spectrums will then be investigated.

An offshore structural design should accurately calculate wave loads and structural responses acting on slender cylinders. The hydrodynamic drag-dominated force was always challenging, hence the hydrodynamic wave loading became a complex solution; it led to a nonlinear relation between the wave force and responses caused by the diffracted and radiated waves, which was included in Morison’s equation. For more consistency in the structural assessment, the linearised drag–inertia force was considered in model development, such as an improved version of the efficient time simulation regression (ETS-Reg) procedure that was introduced. The study aimed to improve the prediction of structural responses using the predetermined linear, polynomial, and cubic regression models. These simulations were performed focusing on high sea state conditions without wave-induced current effects. In order to evaluate the level of accuracy, the recent ETS-Reg models were compared and validated using the Monte Carlo time simulation (MCTS) method. An amended ETS-Reg model, known as the ETS-Reg_LR model, was also compared with the previous results obtained using the conventional ETS-Reg models (ETS-Reg_SE), leading to better structural response calculations.