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Open Access Publications from the University of California

Scholarly Works (2 results)

  • Thesis
  • Peer Reviewed
UC San Diego Electronic Theses and Dissertations (2023)

Corrosion, particularly pitting corrosion, stands as one of the most prevalent forms of deterioration in large infrastructure, known for its slow, continuous, capacity-degrading nature. With the aging of structures, there is an increasing demand for the assessment and monitoring of corrosion status to facilitate system life-cycle management and optimal maintenance. However, due to the intricacies involved in the corrosion process, several challenges impede effective diagnosis and prognosis. These challenges include the inadequate coupling of mechanical stress into existing corrosion simulations, the computational intensity of most physics-based corrosion simulations limiting probabilistic studies and real-time predictions, and the difficulty in detecting pitting corrosion due to its localized nature, making it impractical to measure individual pits. This dissertation presents a comprehensive prognosis and diagnosis framework applied to a miter gate case study. The framework integrates multi-scale simulation including mesoscale Phase-Field (PF) simulation and macroscale structural analysis with Machine Learning (ML) methods to enable real-time corrosion diagnosis and prognosis. It enables for effective structural health monitoring (SHM) with digital twin (DT) for corrosion damage. The key components of this framework encompass the development of a multi-scale simulation for simulating pitting corrosion in large structures, the construction of ML-based surrogate models to expedite simulations, the implementation of uncertainty quantification (UQ), and the integration of pitting corrosion diagnosis and prognosis.

    • Article
    • Peer Reviewed
    UC San Diego Previously Published Works (2022)
    Corrosion can initiate cracking that leads to structural integrity reduction. Quantitative corrosion assessment is challenging, and the modeling of corrosion-induced crack initiation is essential for model-based corrosion reliability analysis of various structures. This paper proposes a probabilistic computational analysis framework for corrosion-to-crack transitions by integrating a phase-field model with machine learning and uncertainty quantification. An electro-chemo-mechanical phase-field model is modified to predict pitting corrosion evolution, in which stress is properly coupled into the electrode chemical potential. A crack initiation criterion based on morphology is proposed to quantify the pit-to-cracking transition. A spatiotemporal surrogate modeling method is developed to facilitate this, consisting of a Convolution Neural Network (CNN) to map corrosion morphology to latent spaces, and a Gaussian Process regression model with a nonlinear autoregressive exogenous model (NARX) architecture for prediction of corrosion dynamics in the latent space over time. It enables the real-time prediction of corrosion morphology and crack initiation behaviors (whether, when, and where the corrosion damage triggers the crack initiation), and thus makes it possible for probabilistic analysis, with uncertainty quantified. Examples at various stress and corrosion conditions are presented to demonstrate the proposed computational framework.
      Cover page: A probabilistic computational framework for the prediction of corrosion-induced cracking in large structures
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