@article{ref1,
title="Structural integrity of aging steel bridges by 3D laser scanning and convolutional neural networks",
journal="Communications engineering",
year="2024",
author="Tzortzinis, Georgios and Filippatos, Angelos and Wittig, Jan and Gude, Maik and Provost, Aidan and Ai, Chengbo and Gerasimidis, Simos",
volume="3",
number="1",
pages="e106-e106",
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 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. <br><br>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 bridge. This framework promises significant advancements in assessing aging bridge infrastructure with higher accuracy and efficiency compared to analytical or semi-analytical approaches.<p /> <p>Language: en</p>",
language="en",
issn="2731-3395",
doi="10.1038/s44172-024-00255-8",
url="http://dx.doi.org/10.1038/s44172-024-00255-8"
}