Life cycle utility-informed maintenance planning based on lifetime functions: optimum balancing of cost, failure consequences and performance benefit. Issue 7 (2nd July 2016)

• Record Type: Journal Article
• Authors: Sabatino, Samantha; Frangopol, Dan M.; Dong, You
• Is Part Of: Structure and infrastructure engineering. Volume 12:Issue 7(2016)
• Page Start: 830
• Page End: 847
• Publication Date: 2016-07-02
• Subjects: Lifetime functions -- highway bridge -- correlation -- risk assessment -- maintenance optimisation -- risk attitudes -- decision-making; Structural analysis (Engineering) -- Periodicals; Structural engineering -- Periodicals; Buildings -- Performance -- Periodicals; 620.005
• DOI: 10.1080/15732479.2015.1064968
• Languages: English
• ISSNs: 1573-2479

Abstract

Abstract : Decision-making regarding the optimum maintenance of civil infrastructure systems under uncertainty is a topic of paramount importance. This topic is experiencing growing interest within the field of life cycle structural engineering. Embedded within the decision-making process and optimum management of engineering systems is the structural performance evaluation, which is facilitated through a comprehensive life cycle risk assessment. Lifetime functions including survivor, availability, and hazard at component and system levels are utilised herein to model, using closed-form analytical expressions, the time-variant effect of intervention actions on the performance of civil infrastructure systems. The presented decision support framework based on lifetime functions has the ability to quantify maintenance cost, failure consequences and performance benefit in terms of utility by considering correlation effects. This framework effectively employs tri-objective optimisation procedures in order to determine optimum maintenance strategies under uncertainty. It provides optimum lifetime intervention plans allowing for utility-informed decision-making regarding maintenance of civil infrastructure systems. The effects of the risk attitude, correlation among components and the number of maintenance interventions on the optimum maintenance strategies are investigated. The capabilities of the proposed decision support framework are illustrated on five configurations of a four-component system and an existing highway bridge.