Assessment of Cracking Development in Concrete Precast Crane Beams Using Optical and Deep Learning Methods

The longevity and safety of concrete precast crane beams significantly impact the operational integrity of industrial infrastructure. Assessment of surface cracks development in concrete structural elements during laboratory tests is performed mainly by applying standard tools such as linear-variable-differential transformers and strain gauges. This paper presents a novel assessment methodology combining deep convolutional neural network for image segmentation with digital image correlation method to evaluate the structural health of precast crane beams after more than fifty years of service. The study first outlines the adaptation of the deep learning U-Net architecture for detecting and segmentation of surface cracks in crane beams. Concurrently, DIC technique is employed to measure surface strains and displacements under load. The integration of these technologies enables a non-destructive, accurate, and detailed analysis, facilitating early detection of deterioration that may compromise structural safety. Initial results from field tests validate the effectiveness of our approach, demonstrating its potential as a tool for predictive maintenance of aging industrial infrastructure.