Imaging-based detection of AAR induced map-crack damage in concrete structure

摘要

Imaging-based inspection methods are increasingly being employed for crack detection in concretestructures, because they provide quantitative information compared to inspections based solely onconventional visual approaches. However, efficient image analysis methods are needed. This studyproposes the application of the grey level co-occurrence matrix (GLCM) texture analysis approach andan artificial neural network (ANN) classifier to obtain surface damage information, such as the totalamount of superficial cracking, as well as the total length, and range of crack widths. These methodswere applied to thermographic, visual colour and greyscale images of concrete blocks from CANMETthat were exposed outdoors for ten years, as well as slabs from GRAI that were kept indoors, allspecimens exhibiting various levels of alkali-aggregate reaction (AAR) damage. Results of theclassifications show that the greyscale imagery performed fairly well, with an overall classificationaccuracy range of 72.3-76.5% for the CANMET blocks, and 68.7-75.3% for the GRAI slabs. Classificationsusing the colour imagery were slightly better than the greyscale imagery, with accuracies ranging from71.4% to 75.2% for CANMET blocks and 70.9-72.0% for the GRAI slabs. The thermographic imagery,however, produced the highest overall classification accuracies, which range from 73.1% to 76.3% for the CANMET blocks and 742-76.9% for the GRAI slabs. The results show that all three types of imageryare relatively effective in characterizing and quantifying crack damage; however, the infraredthermography produced more accurate results compared to the visual colour, and greyscale images.