Computer Vision and Sensor Measurements for Crack Monitoring of Reinforced Concrete Beams: A Critical Review

Visible cracking is the earliest and most accessible evidence of distress in reinforced concrete beams, yet in most laboratories and inspections it is still recorded by hand-marking cracks and reading their widths with a comparator, a process that is slow, depends on the inspector and is hard to relate to the load carried by the member. Computer vision and deep learning can now locate and outline cracks in ordinary photographs, while inexpensive sensors record load, deflection and strain during a test. This paper presents a narrative critical review of thirty sources on vision-based and sensor-based crack monitoring, with emphasis on reinforced concrete beams. The literature is grouped into five strands: monitoring principles, classical image processing, deep-learning classification, pixel-level segmentation, and crack-width measurement on loaded members. The comparison shows that learning-based segmentation consistently suppresses the background noise that limits edge detectors and thresholding, and that encoder-decoder networks of the U-Net family give the boundary detail needed for width estimation. Reported scores, however, are seldom comparable, because studies differ in task definition, image resolution, reference measurement and data splitting. Few studies place image-derived crack metrics beside measured deflection or strain at matching load stages, and pixel size is rarely treated as a design parameter of the test. Six research gaps are identified, together with the elements of a reproducible laboratory protocol that could address them. The review is intended to help structural engineers plan vision-assisted beam tests whose outputs can be read alongside conventional instrumentation.