Evaluating the structural condition of aging assets is a key challenge for resilient infrastructure. For steel bridges, corrosion has historically triggered bridge failures resulting to fatalities and injuries and to enhance public safety and prevent such incidents, authorities mandate in-situ evaluation and reporting of corroded members. Intense labour, traffic delays and poor capacity predictions characterise the current inspection and evaluation protocol. This work combines full scale experimental testing of a decommissioned girder, 3D laser scanning and convolutional neural networks (CNN) to introduce a continuous inspection and evaluation framework. By training both classification and regression CNNs on a databank of 1421 naturally inspired corrosion scenarios, low errors of up to 2.0% and 3.3%, respectively, are achieved. The methodology is validated on eight real corroded ends and implemented for the evaluation of an in-service bridge. This framework promises significant advancements in assessing aging bridge infrastructure with higher accuracy and efficiency compared to analytical or semi-analytical approaches