Effective optimum maintenance planning with updating based on inspection information for fatigue-sensitive structures. (October 2019)

• Record Type: Journal Article
• Authors: Kim, Sunyong; Ge, Baixue; Frangopol, Dan M.
• Is Part Of: Probabilistic engineering mechanics. Volume 58(2019)
• Publication Date: 2019-10
• Subjects: Fatigue crack -- Updating -- Inspection -- Maintenance -- Delay -- Multi-objective -- Optimization -- Cost -- Uncertainty; Engineering -- Statistical methods -- Periodicals; Mechanics, Applied -- Statistical methods -- Periodicals; Probabilities -- Periodicals; Ingénierie -- Méthodes statistiques -- Périodiques; Mécanique appliquée -- Méthodes statistiques -- Périodiques; Probabilités -- Périodiques; 620.100727
• DOI: 10.1016/j.probengmech.2019.103003
• Languages: English
• ISSNs: 0266-8920

Abstract

Abstract: Optimum inspection and maintenance planning is essential for the successful management of deteriorating structures. The reliability and accuracy of inspection and maintenance planning can be significantly improved by integrating new information obtained from inspections. For this reason, the inspection and maintenance planning should include an updating process after each inspection. This paper presents such a probabilistic approach for optimum inspection and maintenance planning. The proposed approach includes two multi-objective optimization (MOOP) processes before and after damage detection for effective updating. The first MOOP before damage detection is performed to determine the optimum inspection times by minimizing both the expected damage detection delay and expected total inspection cost. The fatigue crack detected at the inspection time scheduled from the first MOOP is used to update the probabilistic fatigue crack propagation. The updated crack propagation is applied to formulate the second MOOP, which determines the optimum times for maintenance with the objectives of minimizing both the expected maintenance delay and expected total inspection cost. The decision making process is applied to select the best Pareto solution from the Pareto optimal solutions of the first and second MOOPs. The proposed approach is applied to a fatigue critical detail of an existing steel bridge.