Probabilistic Load-bearing Capacity Estimation of RC Beams Using Corrosion Crack Distributions Obtained by UAV and Machine Learning

Abstract

Instead of visual inspection, it is now possible to conduct inspections based on images taken by an UAV. However, the current inspection using the UAV is limited to the identification of cracks, and their images have not yet been used to evaluate the structural performance, especially the load-bearing capacity. Since corrosion cracks due volumetric expansion of iron oxide appear on the surface of reinforced concrete (RC) structures in chloride-laden environments, it would be possible to evaluate the load-bearing capacity of deteriorated RC members if the amount of rebar corrosion inside the RC members can be estimated using images of these cracks taken by the UAV. In this study, probabilistic load-bearing capacity of deteriorated RC members is evaluated using information on the width of corrosion cracks obtained by UAV photography, in addition to finite element analysis, stochastic field theory, and machine learning. The UAV images are more difficult to identify corrosion cracks than the close-up images, and their effects on the estimated load-bearing capacity of RC members are investigated.