Remote Sensing Techniques for Bridge Deformation Monitoring at Millimetric Scale: Investigating the Potential of Satellite Radar Interferometry, Airborne Laser Scanning ... View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2022-06-30

AUTHORS

Matthias Schlögl, Peter Dorninger, Maciej Kwapisz, Marian Ralbovsky, Roland Spielhofer

ABSTRACT

In light of ageing infrastructure, structural condition assessment is a key prerequisite for the provision of reliable, safe and performant infrastructure networks. However, full systematic condition inspections across large transport networks are extremely resource intensive. Thus, network-wide continuous structural monitoring is hardly feasible using classical engineering assessment methods. Modern remote sensing techniques open up new possibilities for infrastructure assessment and monitoring. Three different methods for rapid, contactless and non-invasive infrastructure deformation monitoring are evaluated: (1) satellite radar interferometry (InSAR), (2) airborne laser scanning (ALS) using unmanned aerial vehicles (UAV) and (3) vehicle-mounted mobile laser scanning (MLS). All methods are tested at an integral concrete bridge in Vienna, Austria, and results are contrasted to reference measurements available from several water-level gauges. In addition, thermal deformation is modelled based on the prevailing environmental conditions. Results show that all methods are well capable of detecting general deformation trends, albeit exhibiting different stages of maturity. While the main application of InSAR lies in long-term continuous deformation measurement of the overall structure, MLS and ALS have the benefit of providing a wealth of data through measurement campaigns. All three contactless measurement methods are suitable for supplementing current structural condition assessments. More... »

PAGES

391-411

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s41064-022-00210-2

DOI

http://dx.doi.org/10.1007/s41064-022-00210-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1149110466


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