Zhang, Yi, Kim, Chul-Woo and Tee, Kong Fah ORCID: 0000-0003-3202-873X (2016) Maintenance management of offshore structures using Markov process model with random transition probabilities. Structure and Infrastructure Engineering, 13 (8). pp. 1068-1080. ISSN 1573-2479 (Print), 1744-8980 (Online) (doi:https://doi.org/10.1080/15732479.2016.1236393)

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Abstract

The robustness of an offshore engineering design is highly dependent on the maintenance management, where the latter needs a full knowledge of engineering analysis and predictions. An accurate estimation of offshore structural performance with time-varying effect is a keen technical issue. The traditional Markov chain model used for structural strength predictions suffers from the difficulty that some of the measurements or inspection data are largely different from the predicted damage condition. This paper presents a deterioration prediction method for maintenance planning in offshore engineering using the Markov models. Instead of traditional deterministic approaches, the Markov chain model is refined by expressing the transition probabilities as random variables. Through such development, the proposed model is able to estimate an interval for the deterioration of an offshore structure. An existing offshore structure located in South China Sea is used in this study for the demonstration purpose. The selection of transition periods of the Markov chain model is investigated. The use of the stochastic model in the prediction of maintenance timing is also discussed. The results show that the proposed approach can provide more reliable information on structural integrity compared to the conventional method.

Item Type:	Article
Uncontrolled Keywords:	Markov chain; Maintenance; Repair and replacement; Structural health assessment; Reliability; Deterioration; Offshore engineering; Robust design
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Engineering (ENG)
Last Modified:	27 Sep 2017 13:17
URI:	http://gala.gre.ac.uk/id/eprint/17396

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