Abstract

In the highly corrosive environment where marine structures operate, the current industry practice is to overcompensate for corrosion induced thickness loss (CITL) and replace any parts that score below specified allowable limits during scheduled maintenance. Hence, there would be immediate benefits from the implementation of a Structural Health Monitoring (SHM) system that would allow for predictive, condition-based maintenance. The objective of this study is to investigate and assess the effectiveness of state-of-the-art statistical pattern recognition (SPR) and machine learning (ML) methods in association with alternative sensor grid architectures as an SHM scheme for detecting CITL, under highly variable operational conditions. For this purpose, a simple rectangular plate at different corrosion levels (uniform and pitting) was considered as a reference structural element, which was subjected to a stochastic pressure load. Strain response data were produced using Finite Element (FE) simulations and were treated under a probabilistic framework. Elements from detection theory and ML were taken under consideration in order to construct alternative detectors and assess their performance.

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