Updating probabilities of bridge reinforcement corrosion using health monitoring data. (1st July 2019)

• Record Type: Journal Article
• Authors: Heitner, Barbara; OBrien, Eugene J.; Yalamas, Thierry; Schoefs, Franck; Leahy, Cathal; Décatoire, Rodrigue
• Is Part Of: Engineering structures. Volume 190(2019)
• Page Start: 41
• Page End: 51
• Publication Date: 2019-07-01
• Subjects: Bridge -- Safety -- Bayesian updating -- Damage indicators -- SHM; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2019.03.103
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: The paper establishes a link between structural health monitoring data and the level of corrosion damage. A Bayesian framework is used to update the level of corrosion in rein-forced concrete structure using the data. Alternative bridge damage indicators, based on monitoring data, are compared in terms of sensitivity to damage. Measurements on the bridge are simulated using a 6-year Weigh-in-Motion database. Abstract: In this paper a methodology is presented to update estimated level of corrosion damage using routine short-term health monitoring measurement data. The case study bridge is a reinforced concrete (RC) slab. It is subject to traffic loading from a weigh-in-motion database and time-dependent deterioration is modelled through corrosion. Different damage indicators based on different virtual sensor measurements (strain, deflection and rotation) are investigated. A method of comparing the performance of the damage indicators is presented and their data is used to achieve a Bayesian update of prior estimates of bridge reinforcement corrosion. It is shown how, with few and uncertain data, the estimation of deterioration level can be significantly improved, particularly with damage indicators based on rotation.