TY  - JOUR
PY  - 2024//
TI  - Structural integrity of aging steel bridges by 3D laser scanning and convolutional neural networks
JO  - Communications engineering
A1  - Tzortzinis, Georgios
A1  - Filippatos, Angelos
A1  - Wittig, Jan
A1  - Gude, Maik
A1  - Provost, Aidan
A1  - Ai, Chengbo
A1  - Gerasimidis, Simos
SP  - e106
EP  - e106
VL  - 3
IS  - 1
N2  - 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>
LA  - en
SN  - 2731-3395
UR  - http://dx.doi.org/10.1038/s44172-024-00255-8
ID  - ref1
ER  -