4.7 Article

SeeBridge as next generation bridge inspection: Overview, Information Delivery Manual and Model View Definition

期刊

AUTOMATION IN CONSTRUCTION
卷 90, 期 -, 页码 134-145

出版社

ELSEVIER
DOI: 10.1016/j.autcon.2018.02.033

关键词

Building Information Modeling; Bridge inspection; Information Delivery Manual; Model View Definition; Semantic enrichment; Remote sensing

资金

  1. European Union [31109806.0007]
  2. ERA-NET Plus Infravation
  3. European Commission
  4. Ministerie van Infrastructuur en Milieu, Rijkswaterstaat, Bundesministerium fur Verkehr, Bau
  5. Stadtentwicklung
  6. Danish Road Directorate
  7. Statens Vegvesen Vegdirektoratet
  8. Trafikverket - Trv
  9. Vegageroin
  10. Ministere de L'ecologle, du Developpement Durable et de L'energie
  11. Centro para el Desarrollo Tecnologico Industrial
  12. Anas S.P.A.
  13. Netivei Israel - National Transport Infrastructure Company Ltd.
  14. Federal Highway Administration USDOT
  15. EPSRC [EP/N021614/1] Funding Source: UKRI

向作者/读者索取更多资源

Innovative solutions for rapid and intelligent survey and assessment methods are required in maintenance, repair, retrofit and rebuild of enormous numbers of bridges in service throughout the world. Motivated by this need, a next-generation integrated bridge inspection system, called SeeBridge, has been proposed. An Information Delivery Manual (IDM) was compiled to specify the technical components, activities and information exchanges in the SeeBridge process, and a Model View Definition (MVD) was prepared to specify the data exchange schema to serve the IDM. The MVD was bound to the IFC4 Add2 data schema standard. The MM and MVD support research and development of the system by rigorously defining the information and data that structure bridge engineers' knowledge. The SeeBridge process is mapped, parts of the data repositories are presented, and the future use of the IDM is discussed. The development underlines the real potential for automated inspection of infrastructure at large, because it demonstrates that the hurdles in the way of automated acquisition of detailed and semantically rich models of existing infrastructure are computational in nature, not instrumental, and are surmountable with existing technologies.

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