Automated site-specific assessment of steel structures through integrating machine learning and fracture mechanics.

[Display omitted] • An automated workflow is proposed to assess the fracture mechanism from crack images. • A U-Net is developed to find the pixel-location of a crack. • A systematic study on various U-Net architectures is conducted. • A surrogate model is built to predict stress intensity factors from crack shapes. Despite the recent advances in using machine learning for crack identification of structures, the usefulness of artificial intelligence in guiding repair strategies is still limited due to the lack of information on the mechanism of crack formation and propagation. In this context, this paper develops an automated workflow to assess the fracture mechanism of steel structures directly from inspection images. Firstly, a U-Net is developed to find the pixel-location of a crack. Then, a Gaussian Process surrogate model is trained to predict stress intensity factors, K (indicator for fracture mechanism), for cracks with various dimensions using data from finite element simulations. In the end, by taking the dimensions of identified cracks as input, the trained Gaussian Process is used to estimate the K factors. The proposed workflow empowers an inspector to easily identify steel cracks, predict their propagation from a collection of raw images, and make rapid repair decisions on-site. [ABSTRACT FROM AUTHOR]