Abstract
Metallic corrosion is a big challenge affecting many sectors in a nation’s economy. Necessary corrosion prevention actions have to be taken in order to maintain the integrity of engineering assets susceptible to corrosion. This paper proposes a holistic framework to support the management of corrosion in metallic structures. It is a fully automation corrosion assessment process, with risk updated by Bayesian theory. Through analyzing the thickness data measured by non-destructive testing (NDT) techniques, the influence of corrosion on the component can be estimated using statistical methods, which will enable users to make decisions on maintenance based on quantitative information. A case study using corrosion data from a steel bridge is included to demonstrate the proposed framework. It improved the conventional corrosion analysis method by the proposed statistical approach using representative thickness data, which aims to take full use of the remaining life. This model can be adapted to a wide range of metallic structure suffering from corrosion damage.
Highlights
A Age corrosion rate (CR) confidence interval (CI) p S time tmm tnom T x Xp μ μ σ σSurface area of the object Service time, year Corrosion rate Confidence interval, % Probability of the quantile p of a statistical distribution Small specimens that are sampled randomly from this object Failure time, independent variables Minimum measured thickness Nominal thickness Return period Random variable from the statistical distribution generalized extreme value distribution (GEVD) return level Mean of a statistical distribution / population Location parameter of an EVD Standard deviation of a statistical distribution Scale parameter of a EVD Remaining useful life
Steel bridges play an important role in the transportation network and support the nation’s economy and traffic [31]
The paper is based on an Innovate UK (IUK) funded project – ASSAI, using thickness data from the Ultrasonic testing (UT) sensor installed on an aerial robot to carry out corrosion estimation for steel bridges
Summary
Surface area of the object Service time, year Corrosion rate Confidence interval, % Probability of the quantile p of a statistical distribution Small specimens that are sampled randomly from this object Failure time, independent variables Minimum measured thickness Nominal thickness Return period Random variable from the statistical distribution GEVD return level Mean of a statistical distribution / population Location parameter of an EVD Standard deviation of a statistical distribution Scale parameter of a EVD Remaining useful life.
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