Shear Condition Classification of Cracked Reinforced Concrete Beams Using Machine Learning

RC bridges represent about 40% of the US bridge inventory, with many of these bridges reaching or surpassing their design service life. As a result, there is a significant number of structures that require fast and accurate structural evaluation. Shear deficiencies can pose a higher safety risk than flexure deficiencies since shear failures are sudden. This study correlates shear crack width with shear condition and proposes a machine-learning framework to place RC beams into shear condition categories using quantitative estimates of shear, stiffness, and stirrup strain histories. The results of the proposed framework are compared with those from existing quantitative and qualitative assessment methodologies. The quantitative predictions of residual shear capacity and stiffness by the proposed framework are closer to experimental measurements than the ones by the existing methodologies. The qualitative condition classifications of the framework indicate less urgency for repair compared with the ones of the existing methodologies. The proposed framework enables the ranking of bridges within the same shear condition category due to its quantitative nature, and it has been implemented in a software application and can be used to set priorities for repair.