Change point detection in corrosion health monitoring using statistical techniques

Abstract

With the development of more comprehensive strategies for on-line monitoring and the developments in smart sensor technology and digital data acquisition, there is a need to develop change point detection algorithms which can be used on-line for automated diagnosis of structural health. This is especially required in the case of bridge stocks consisting of large number of bridges which are monitored simultaneously. For structures affected by chloride-induced corrosion of reinforcement, the ability to detect corrosion of reinforcement in its early stages is critical in directing repairs to the most at-risk structures and will help in optimizing the use of limited funds. In this study, the identification of time of corrosion initiation is modeled as a problem of change point detection in online monitored electrochemical current noise data. For change point detection, an algorithm based on maximum likelihood approach is considered. Formulations are made for the determination of window size for the online monitored data points to be considered and the threshold value for the decision function. The usefulness of the algorithm is studied by considering an example problem of identification of time of corrosion initiation in a reinforced concrete bridge girder, in a Monte Carlo simulation framework. The studies presented in this paper are towards realizing sustainable infrastructure considering service life planning and declaration and assessment of sustainability aspects.