Structural integrity of aging steel bridges by 3D laser scanning and convolutional neural networks

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

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. 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.

Details

OriginalspracheEnglisch
Aufsatznummer106
Seitenumfang14
FachzeitschriftCommunications Engineering
Jahrgang3
Ausgabenummer1
PublikationsstatusVeröffentlicht - 1 Aug. 2024
Peer-Review-StatusJa

Externe IDs

ORCID /0000-0003-0311-1745/work/165062192
ORCID /0000-0003-1370-064X/work/165062269
PubMed 39090208
Scopus 85201625192

Schlagworte