0
  • DE
  • EN
  • FR
  • Internationale Datenbank und Galerie für Ingenieurbauwerke

Anzeige

Bridge Surface Roughness Detection Based on Double-Vehicle-Double-Pass Technique

Bridge Surface Roughness Detection Based on Double-Vehicle-Double-Pass Technique
Autor(en): ,
Beitrag für IABSE Conference: Engineering the Past, to Meet the Needs of the Future, Copenhagen, Denmark, 25-27 June 2018, veröffentlicht in , S. 385-392
DOI: 10.2749/copenhagen.2018.385
Preis: € 25,00 inkl. MwSt. als PDF-Dokument  
ZUM EINKAUFSWAGEN HINZUFÜGEN
Vorschau herunterladen (PDF-Datei) 0.16 MB
Bridge surface roughness encompasses dents, cracks, bulges and other defects due to construction as well as wear and tear in service. It significantly affects the traffic load on bridge, costs extra...
Weiterlesen

Bibliografische Angaben

Autor(en): (The University of Hong Kong, Hong Kong, China)
(The University of Hong Kong, Hong Kong, China)
Medium: Tagungsbeitrag
Sprache(n): Englisch
Tagung: IABSE Conference: Engineering the Past, to Meet the Needs of the Future, Copenhagen, Denmark, 25-27 June 2018
Veröffentlicht in:
Seite(n): 385-392 Anzahl der Seiten (im PDF): 8
Seite(n): 385-392
Anzahl der Seiten (im PDF): 8
DOI: 10.2749/copenhagen.2018.385
Abstrakt: Bridge surface roughness encompasses dents, cracks, bulges and other defects due to construction as well as wear and tear in service. It significantly affects the traffic load on bridge, costs extra vehicle fuel consumption and tire wear, and therefore is a major concern of bridge monitoring and maintenance. As it considerably affects the contact force between the vehicle and the bridge, it is also a major obstacle of using vehicle measured data to identify bridge parameters. This paper presents a method to estimate the surface roughness profile of a bridge. With the acceleration data gathered from two different vehicles running on the bridge successively at the same speed, the roughness profile of the bridge can be measured with satisfactory accuracy. The method is verified with finite element simulation.
Stichwörter:
Parameteridentifikation