Abstract For steel bridges, corrosion has historically led to bridge failures, resulting in fatalities and injuries.To enhance public safety and prevent such incidents, authorities mandate in-situ evaluation and reporting of corroded members.The current inspection and evaluation protocol is characterized by intense labor, traffic delays, and poor capacity predictions.Here we combine full-scale experimental testing of a decommissioned girder, 3D laser scanning, and convolutional neural networks (CNNs) to Self Tanner introduce a continuous inspection and evaluation framework.
Classification and regression CNNs are trained on a databank of 1,421 naturally inspired corrosion scenarios, generated computationally based on point clouds of three corroded girders collected in lab conditions.Results indicate low errors of up to 2.0% and 3.3%, respectively.
The methodology is validated on eight real corroded ends and implemented for the evaluation of an in-service Caulking Guns bridge.This framework promises significant advancements in assessing aging bridge infrastructure with higher accuracy and efficiency compared to analytical or semi-analytical approaches.