Spatial probabilistic modeling of corrosion in ships structures

[+] Author and Article Information
Jesus Luque

Engineering Risk Analysis Group, Technische Universität München, Theresienstr. 90, 80333 Munich Germany

Rainer Hamann

DNV GL, Brooktorkai 18, 20457 Hamburg, Germany

Daniel Straub

Engineering Risk Analysis Group, Technische Universität München, Theresienstr. 90, 80333 Munich Germany

1Corresponding author.

ASME doi:10.1115/1.4035399 History: Received August 04, 2015; Revised October 12, 2016


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 variability 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 spatio-temporal corrosion model based on a hierarchical approach, which represents the spatial variability 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 it is found that there is significant spatial dependence among corrosion processes in different parts of the ships, which the proposed hierarchical model can capture. Finally, it is demonstrated how this spatial dependence can be exploited when making inference on the future condition of the ships.

Copyright (c) 2016 by ASME
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