Back to Search Start Over

Advancing a Non-Contact Structural and Prognostic Health Assessment of Large Critical Structures.

Authors :
Chiu, Wing Kong
Kuen, Thomas
Vien, Benjamin Steven
Aitken, Hugh
Rose, Louis Raymond Francis
Buderath, Matthias
Source :
Sensors (14248220); Jun2024, Vol. 24 Issue 11, p3297, 17p
Publication Year :
2024

Abstract

This paper presents an overview of integrating new research outcomes into the development of a structural health monitoring strategy for the floating cover at the Western Treatment Plant (WTP) in Melbourne, Australia. The size of this floating cover, which covers an area of approximately 470 m × 200 m, combined with the hazardous environment and its exposure to extreme weather conditions, only allows for monitoring techniques based on remote sensing. The floating cover is deformed by the accumulation of sewage matter beneath it. Our research has shown that the only reliable data for constructing a predictive model to support the structural health monitoring of this critical asset is obtained directly from the actual floating cover at the sewage treatment plant. Our recent research outcomes lead us towards conceptualising an advanced engineering analysis tool designed to support the future creation of a digital twin for the floating cover at the WTP. Foundational work demonstrates the effectiveness of an unmanned aerial vehicle (UAV)-based photogrammetry methodology in generating a digital elevation model of the large floating cover. A substantial set of data has been acquired through regular UAV flights, presenting opportunities to leverage this information for a deeper understanding of the interactions between operational conditions and the structural response of the floating cover. This paper discusses the current findings and their implications, clarifying how these outcomes contribute to the ongoing development of an advanced digital twin for the floating cover. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14248220
Volume :
24
Issue :
11
Database :
Complementary Index
Journal :
Sensors (14248220)
Publication Type :
Academic Journal
Accession number :
177859948
Full Text :
https://doi.org/10.3390/s24113297