Non-destructive testing of concrete for defects detection, using acoustic techniques, is currently performed mainly by human inspection of recorded images. The images consist of the inside of the examined elements obtained from testing devices such as the ultrasonic tomograph. However, such an automatic inspection is time-consuming, expensive, and prone to errors. To address some of these problems, this paper aims to evaluate a convolutional neural network (CNN) toward an automated detection of flaws in concrete elements using ultrasonic tomography. There are two main stages in the proposed methodology. In the first stage, an image of the inside of the examined structure is obtained and recorded by performing ultrasonic tomography-based testing. In the second stage, a convolutional neural network model is used for automatic detection of defects and flaws in the recorded image. In this work, a large and pre-trained CNN is used. It was fine-tuned on a small set of images collected during laboratory tests. Lastly, the prepared model was applied for detecting flaws. The obtained model has proven to be able to accurately detect defects in examined concrete elements. The presented approach for automatic detection of flaws is being developed with the potential to not only detect defects of one type but also to classify various types of defects in concrete elements.

This paper shows how 2D digital image correlation (2D DIC) and region-based convolutional neural network (R-CNN) can be combined for image-based automated monitoring and assessment of surface crack development of concrete structural elements during laboratory quasi-static tests. In the presented approach, the 2D DIC-based monitoring enables estimation of deformation fields on the surface of the concrete element and measurements of crack width. Moreover, the R-CNN model provides unmanned simultaneous detection and localization of multiple cracks in the images. The results show that the automatic monitoring and evaluation of crack development in concrete structural elements is possible with high accuracy and reliability.

This paper investigates composite-to-brick strengthening systems with flexible adhesive made of polyurethane (Carbon Fibre Reinforced Polyurethane (CFRPU) and Steel Reinforced Polyurethane (SRPU)) and epoxy resin (Carbon Fibre Reinforced Polymer (CFRP) and Steel Reinforced Polymer (SRP). The specimens were tested in a single lap shear test (SLST). LVDT displacement transducers (LVDT – Linear Variable Differential Transformer) and digital image correlation method (DIC) based measurement systems were used to measure displacements and strains. The obtained results were applied in a numerical analysis of the 3D model of the SLST specimen, with flexible adhesives modeled as a hyper-elastic model. The DIC and LVDT based systems demonstrated a good correlation. Experimental and numerical analysis confirmed that composite-to-brick strengthening systems with flexible adhesives are more effective on brittle substrates than stiff ones, as they are able to reduce stress concentrations and more evenly distribute stress along the entire bonded length, thus having a higher load carrying capacity.

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.