Early damage detection of fatigue failure for RC deck slabs under wheel load moving test using image analysis with artificial intelligence. (1st November 2021) - Adel, Mohamed; Yokoyama, Hiroshi; Tatsuta, Hitoshi; Nomura, Takanori; Ando, Yuki; Nakamura, Takuro; Masuya, Hiroshi; Nagai, Kohei
Highlights: Un-damaged and ASR-damaged RC deck slabs are tested under moving wheel loading. Detection of small delaminations (pits) along cracks that arise from out-of-plane shear deformations. U-Net architecture is proposed as machine learning model AI for crack's detection. Pit density could be considered an early index of upcoming fatigue failure RC deck slabs. Abstract: Reinforced concrete (RC) bridge decks suffer from cracking damages caused by traffic loading and environmental-related defects, such as the alkali-silica reaction (ASR). These require inspections involving measuring crack width and density followed by essential maintenance and repair works, however, there are no signs for fatigue failure. In this study, the out-of-plane shear deformations which cause small delaminations (pits) along surface cracks are proposed as an early indicator for fatigue failure. Thus, un-damaged and ASR-damaged RC deck slabs are tested under moving wheel loading and, using image-recognition for surface cracks detection, the pits along surface cracks are captured using an artificial intelligence (AI) model. The results show that, while both crack and pit density increase over the fatigue life of un-damaged slabs, there is an earlier sudden increase in pit density. In the case of the ASR-damaged slab, surface cracking is almost constant over the fatigue life until a sudden increase just prior to failure. Pit density, however, increases over the fatigue life with an earlier rapid increase before failure. The density of pits along cracks would be, therefore, a significantly earlier indicator of fatigue failure than crack density, offering the potential for more efficient and automatable bridge inspections.