A novel pro-active approach towards SHM-based bridge management supported by FE analysis and Bayesian methods. Issue 2 (1st February 2020)

• Record Type: Journal Article
• Authors: Sousa, Helder; Rozsas, Arpad; Slobbe, Arthur; Courage, Wim
• Is Part Of: Structure and infrastructure engineering. Volume 16:Issue 2(2020)
• Page Start: 233
• Page End: 246
• Publication Date: 2020-02-01
• Subjects: Bridges -- damage -- structural health monitoring -- finite element modelling -- response surface modelling -- measurement uncertainty -- Bayesian statistics -- bridge management; Structural analysis (Engineering) -- Periodicals; Structural engineering -- Periodicals; Buildings -- Performance -- Periodicals; 620.005
• DOI: 10.1080/15732479.2019.1649287
• Languages: English
• ISSNs: 1573-2479

Abstract

Abstract: Europe has an extensive transport infrastructure network where bridges play a vital role. Most of them were built as part of the post-World War II reconstruction effort, meaning that we, as society, are already facing the beginning of the end of their design life. This shows the necessity of efficient approaches, complementing visual inspections, for early detection of damage that might jeopardise structural integrity and ultimately might lead to loss of life. This work introduces a novel, pro-active structural health monitoring (SHM) approach to better identify and quantify representative damage types, on prestressed concrete bridges. Based on a numerical simulation of a comprehensive case study available in the literature: the Lezíria Bridge, the results show that damage can be identified with good accuracy for early stages of damaged bridges. Pier settlements and prestress losses are the damage types where the severity is quite accurately quantified even for lower damage severity levels. Once the damage type is identified, it is found that a pair of two vertical displacements reveals to be enough to quantify the damage extent. The results also show potential in the utilisation of the approach for a rational and efficient design of monitoring systems towards damage identification.