Research Papers

Spatial Probabilistic Modeling of Corrosion in Ship Structures

[+] Author and Article Information
Jesus Luque

Engineering Risk Analysis Group,
Technische Universität München,
Theresienstr. 90,
Munich 80333, Germany
e-mail: jesus.luque@tum.de

Rainer Hamann

Brooktorkai 18,
Hamburg 20457, Germany
e-mail: rainer.hamann@dnvgl.com

Daniel Straub

Engineering Risk Analysis Group,
Technische Universität München,
Theresienstr. 90,
Munich 80333, Germany
e-mail: straub@tum.de

Manuscript received August 4, 2015; final manuscript received October 12, 2016; published online June 12, 2017. Assoc. Editor: Michael Beer.

ASME J. Risk Uncertainty Part B 3(3), 031001 (Jun 12, 2017) (12 pages) Paper No: RISK-15-1088; 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 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.

Copyright © 2017 by ASME
Topics: Corrosion , Vessels , Ships
Your Session has timed out. Please sign back in to continue.



Grahic Jump Location
Fig. 1

Example of a hierarchical Bayesian deterioration model (D: deterioration, R: corrosion rate, μ: mean, σ: standard deviation) using (a) explicit and (b) hierarchical representations

Grahic Jump Location
Fig. 2

Hierarchical structure of the spatial corrosion model

Grahic Jump Location
Fig. 3

Hierarchical representation of the thickness margin

Grahic Jump Location
Fig. 4

Hierarchical representation of the coating life

Grahic Jump Location
Fig. 5

Hierarchical representation of the corrosion model parameters

Grahic Jump Location
Fig. 6

Hierarchical representation of the full corrosion model, including measurements

Grahic Jump Location
Fig. 7

Hierarchical representation of the simulated example

Grahic Jump Location
Fig. 8

Exemplary estimated marginal PDFs of model parameters

Grahic Jump Location
Fig. 9

(a) Location of the structural element floor in tankers and (b) locations of thickness measurements in one of the measurement campaigns

Grahic Jump Location
Fig. 10

Exemplary posterior distribution of parameters of the corrosion model for the tankers example

Grahic Jump Location
Fig. 11

Measurement campaign in tanker 5 at year 17 used for model updating (the diameter of the circle is proportional to the measured thickness diminution)

Grahic Jump Location
Fig. 12

Comparison between the original (prior) and the updated (posterior) distribution

Grahic Jump Location
Fig. 13

Spatial distribution of the expected margin, coating life, and corrosion rate per measured location. The grayscale of a circle represents the mean estimate and the size of a circle reflects the deviation of the value at this location from the total mean value (i.e., from all measured points). The larger circles are values in the tails of the distribution.




Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Articles from Part A: Civil Engineering
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In