Stochastic Modelling of Corrosion Process for Service Life Prediction of Civil Infrastructure
Lead Research Organisation:
University of Greenwich
Department Name: Sch of Archit and Construction
Abstract
This proposal seeks support for a Visiting Fellow to enable a joint investigation to be carried out for the development of a stochastic model for the whole process of reinforcement corrosion in realistic concrete structures. The developed model will be readily used in the service life prediction of corrosion prone infrastructure, particularly existing infrastructure for which life extension is of great significance. The proposed research is a follow-up to a recently completed EPSRC project (GR/R28348) on developing a risk-cost optimised maintenance strategy for coastal concrete structures. As probability-based frameworks for service life prediction of corrosion affected infrastructure become more established, the paucity of rational and useable models for corrosion processes / the cause of the problem / becomes apparent. The proposed work will address this gap by delivering a quantitative model for corrosion process. Research in this area is necessary and timely because the reinforcement corrosion in concrete infrastructure will eventually reduce its load carrying capacity, whereas demands for infrastructure to carry greater load are increasing. This poses a potential risk of premature and/or unexpected failures of infrastructure to the public at large. The development of a stochastic model for corrosion can assist asset managers to accurately predict the safe remaining service life of infrastructure and as such mitigate undue risk to the public.
Organisations
People |
ORCID iD |
Chun-Qing Li (Principal Investigator) |
Publications
Li C
(2011)
Prediction of Concrete Crack Width under Combined Reinforcement Corrosion and Applied Load
in Journal of Engineering Mechanics
Melchers R
(2009)
Observations and analysis of a 63-year-old reinforced concrete promenade railing exposed to the North Sea
in Magazine of Concrete Research