Decision support framework for bridge condition assessments. (November 2019)

• Record Type: Journal Article
• Authors: Björnsson, Ivar; Larsson Ivanov, Oskar; Honfi, Dániel; Leander, John
• Is Part Of: Structural safety. Volume 81(2019)
• Publication Date: 2019-11
• Subjects: Bridge operation and maintenance -- Condition assessment -- Decision support -- Bayesian decision theory -- Framework; Structural stability -- Periodicals; Safety factor in engineering -- Periodicals; Reliability (Engineering) -- Periodicals; Constructions -- Stabilité -- Périodiques; Coefficient de sécurité en ingénierie -- Périodiques; Fiabilité -- Périodiques; 620.86
• DOI: 10.1016/j.strusafe.2019.101874
• Languages: English
• ISSNs: 0167-4730

Abstract

Highlights: A Bayesian decision framework for condition assessment of bridges is presented. The framework considers three common attributes of condition assessments. The framework integrates assessment choices with decisions concerning interventions. A numerical example is calculated to illustrate the usefulness of the framework. Abstract: An essential aspect in the maintenance of existing bridges is the ability to adequately and accurately assess and evaluate the condition of the structure. Condition assessments, which can be carried out in any number of ways, provide valuable information concerning the actual state of a bridge, including the severity of potential damages, and form the basis for further maintenance decisions. Any decision support concerning the management of existing structures thus requires attention towards the uncertainties associated with the assessment methods when applied in practice as well as the maintenance actions these support. These uncertainties cannot be solely described as model uncertainties but are also a result of the variation in engineering performance observed in practice. In the current paper a rational and systematic framework is presented which provides practical decision support concerning whether condition assessments are necessary, what assessment methods are recommended, if invasive actions are needed, or if some other non-invasive option may be more appropriate. The framework takes into account three main attributes of an enhanced condition assessment, namely, modelling sophistication, considerations of uncertainties and risks, and knowledge/information content. Increasing the level of one or more of these attributes may be advantageous only if the expected benefits or added value of information is considered appropriate in relation to the cost of implementation in practice. A decision making model, based on Bayesian decision theory, is adopted to evaluate this problem. Two case studies, in which the framework is applied, are provided for illustrative purposes; the first is a generic numerical example and the second a decision scenario related to the fatigue assessment of an existing railway bridge.