 Stochastic modeling of corrosion growthChangxi Wang and Elsayed A. Elsayed Reliability Engineering and System Safety, 2020, vol. 204, issue C Abstract: Corrosion is a major threat to the structural integrity and safe operation of infrastructures throughout the world. Corrosion growth modeling is important in structure maintenance planning. Existing models focus on the maximum corrosion pit depth growth which may lead to inaccurate life predictions since the corrosion volume growth may result in failures before the depth reaches its failure threshold. We develop a stochastic model that characterizes both corrosion volume and depth growth. The distribution of volume growth increments with time and the reliability estimate based on the corrosion depth, the corrosion volume growth and their combined effect are obtained. The influence of stresses, which include the relative humidity, pH level and temperature is incorporated into the model based on the physics of the corrosion reaction mechanisms. Field corrosion data are used for the model's validation. The proposed model results in more accurate predictions of the remaining lives compared with the existing models. Keywords: Corrosion volume growth; Gamma process; Corrosion pit; Remaining life prediction; Corrosion growth under stresses (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:204:y:2020:i:c:s0951832020306219 DOI: 10.1016/j.ress.2020.107120 Access Statistics for this article Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares More articles in Reliability Engineering and System Safety from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.