Evaluation of Corrosion Protection of Steel Bridges

Kreislová, Kateřina; Geiplová, Hana

doi:10.1016/j.proeng.2012.07.085

Cited by 26 publications

(13 citation statements)

References 1 publication

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“…When comparing electrochemical results obtained from different coatings it is important to keep in mind that the barrier effect of a coating is directly proportional to the coating thickness. Industrial coating systems designed to provide corrosion protection, for example to bridges, have normally a thickness ranging from several hundreds of nanometers up to one mm [39,40]. Polymeric coatings used for corrosion protection as discussed in the scientific literature (for example: epoxy, polyester, acrylate and polyurethane coatings) range from a few micrometers up to several hundreds of micrometers [1,41].…”

Section: Pea-tio 2 -Hmdsomentioning

confidence: 99%

Long-term corrosion protection by a thin nano-composite coating

Ejenstam

Tuominen²,

Haapanen

et al. 2015

Applied Surface Science

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Section: Pea-tio 2 -Hmdsomentioning

confidence: 99%

Long-term corrosion protection by a thin nano-composite coating

Ejenstam

Tuominen²,

Haapanen

et al. 2015

Applied Surface Science

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“…Naturally, the barrier effect also increases with the coating thickness [22], and the commercial coating systems used to protect for example the steel in bridges are commonly as thick as several hundreds of micrometres up to 1 mm [23,24].…”

Section: Introductionmentioning

confidence: 99%

Corrosion protection by hydrophobic silica particle-polydimethylsiloxane composite coatings

et al. 2015

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“…The CPTs are hereafter defined by considering that the degradation of a bridge can be modelled by adopting a linear model. 36–38 Assume that a bridge element, described by node X , is in state x i . There are N parent nodes of this element, denoted as a set Y k .…”

Section: The Proposed Bayesian Belief Network Methods For Bridge Degradation Detectionmentioning

confidence: 99%

A Bayesian Belief Network method for bridge deterioration detection

Vagnoli

Remenyte‐Prescott

Andrews

2020

Proceedings of the Institution of Mechanical Engineers, Part O:

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Bridges are one of the most important assets of transportation networks. A closure of a bridge can increase the vulnerability of the geographic area served by such networks, as it reduces the number of available routes. Condition monitoring and deterioration detection methods can be used to monitor the health state of a bridge and enable detection of early signs of deterioration. In this paper, a novel Bayesian Belief Network (BBN) methodology for bridge deterioration detection is proposed. A method to build a BBN structure and to define the Conditional Probability Tables (CPTs) is presented first. Then evidence of the bridge behaviour (such as bridge displacement or acceleration due to traffic) is used as an input to the BBN model, the probability of the health state of whole bridge and its elements is updated and the levels of deterioration are detected. The methodology is illustrated using a Finite Element Model (FEM) of a steel truss bridge, and for an in-field post-tensioned concrete bridge.

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Evaluation of Corrosion Protection of Steel Bridges

Cited by 26 publications

References 1 publication

Long-term corrosion protection by a thin nano-composite coating

Long-term corrosion protection by a thin nano-composite coating

Corrosion protection by hydrophobic silica particle-polydimethylsiloxane composite coatings

A Bayesian Belief Network method for bridge deterioration detection

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