Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/83438
Title: Optimal sustainable life cycle maintenance strategies for port infrastructures
Authors: Zhang, Yi
Kim, Chul-Woo
Tee, Kong Fah
Lam, Jasmine Siu Lee
Keywords: Sustainability
Life cycle analysis
Issue Date: 2016
Source: Zhang, Y., Kim, C.-W., Tee, K. F., & Lam, J. S. L. (2017). Optimal sustainable life cycle maintenance strategies for port infrastructures. Journal of Cleaner Production, 142, 1693-1709.
Series/Report no.: Journal of Cleaner Production
Abstract: Port operations are highly important in the central economic and industrial regions which rely heavily on the use of port infrastructures. An economic and efficient maintenance strategy is essential to govern the normal running of port infrastructures and thus seaborne transportation. Many agencies worldwide have managed to develop maintenance strategies to ensure optimal levels of serviceability and safety for port infrastructures. However, there is not much information about how sustainable issues can be implemented in the maintenance planning. This paper proposes a methodology for evaluating, comparing and improving sustainability of maintenance strategies for port infrastructures. The method is developed based on a proposed randomized structural deterioration model. The costs due to retrofitting, operating loss and environmental loss are considered in the total life cycle cost estimation. The concept of utility function is utilized to serve as a criterion for finding the optimal strategy among the alternative maintenance strategies. An investigation is performed on a Tokyo wharf to demonstrate the proposed approach. The maintenance strategies for different structural elements in the port infrastructures are discussed. The results show that the proposed approach can provide more reliable information on the maintenance timing. The predicted cost bounds allow owners/risk managers to understand the current condition of the structure in several ways, which include both safe-side prediction and average prediction.
URI: https://hdl.handle.net/10356/83438
http://hdl.handle.net/10220/42588
ISSN: 0959-6526
DOI: 10.1016/j.jclepro.2016.11.120
Rights: © 2016 Elsevier Ltd. This is the author created version of a work that has been peer reviewed and accepted for publication by Journal of Cleaner Production, Elsevier Ltd. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.jclepro.2016.11.120].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CEE Journal Articles

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