Corrosion in ship structures is influenced by a variety of factors that are varying in time and space. Existing corrosion models used in practice only partially address the spatial variability of the corrosion process. Typical estimations of corrosion model parameters are based on averaging measurements for one ship type over structural elements from different ships and operational conditions. Most models do not explicitly predict the variability and correlation of the corrosion process among multiple locations in the structure. This correlation is of relevance when determining the necessary inspection coverage, and it can influence the reliability of the ship structure. In this paper, we develop a probabilistic spatiotemporal corrosion model based on a hierarchical approach, which represents the spatial variability and correlation of the corrosion process. The model includes as hierarchical levels vessel–compartment–frame–structural element–plate element. At all levels, variables representing common influencing factors (e.g., coating life) are introduced. Moreover, at the lowest level, which is the one of the plate element, the corrosion process can be modeled as a spatial random field. For illustrative purposes, the model is trained through Bayesian analysis with measurement data from a group of tankers. In this application, the spatial dependence among corrosion processes in different parts of the ships is identified and quantified using the proposed hierarchical model. Finally, how this spatial dependence can be exploited when making inference on the future condition of the ships is demonstrated.
Spatial Probabilistic Modeling of Corrosion in Ship Structures
Manuscript received August 4, 2015; final manuscript received October 12, 2016; published online June 12, 2017. Assoc. Editor: Michael Beer.
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Luque, J., Hamann, R., and Straub, D. (June 12, 2017). "Spatial Probabilistic Modeling of Corrosion in Ship Structures." ASME. ASME J. Risk Uncertainty Part B. September 2017; 3(3): 031001. https://doi.org/10.1115/1.4035399
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