  Corrosion is a major problem affecting the durability of reinforced concrete
structures. Corrosion related maintenance and repair of reinforced concrete
structures cost multibillion USD per annum globally. It is often triggered by
the ingression of carbon dioxide and/or chloride into the pores of concrete.
Estimation of these corrosion causing factors using the conventional models
results in suboptimal assessment since they are incapable of capturing the
complex interaction of parameters. Hygrothermal interaction also plays a role
in aggravating the corrosion of reinforcement bar and this is usually
counteracted by applying surface-protection systems. These systems have
different degree of protection and they may even cause deterioration to the
structure unintentionally. The overall objective of this dissertation is to
provide a framework that enhances the assessment reliability of the corrosion
controlling factors. The framework is realized through the development of
data-driven carbonation depth, chloride profile and hygrothermal performance
prediction models. The carbonation depth prediction model integrates neural
network, decision tree, boosted and bagged ensemble decision trees. The
ensemble tree based chloride profile prediction models evaluate the
significance of chloride ingress controlling variables from various
perspectives. The hygrothermal interaction prediction models are developed
using neural networks to evaluate the status of corrosion and other unexpected
deteriorations in surface-treated concrete elements. Long-term data for all
models were obtained from three different field experiments. The performance
comparison of the developed carbonation depth prediction model with the
conventional one confirmed the prediction superiority of the data-driven model.
The variable ...
