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A New Methodology for the Prioritization of Visual Inspections of Bridges and Viaducts

Author(s): (Department of Civil and Environmental Engineering University of Perugia Perugia Italy)
(Department of Structural Mechanics and Hydraulic Engineering University of Granada Granada Spain)
(Department of Civil and Environmental Engineering University of Perugia Perugia Italy)
(Department of Civil and Environmental Engineering University of Perugia Perugia Italy)
Medium: journal article
Language(s): English
Published in: ce/papers, , n. 5, v. 6
Page(s): 753-759
DOI: 10.1002/cepa.2064
Abstract:

The assessment of risk conditions of bridges and viaducts allows management authorities to ensure the safety of the users and the proper allocation of economic resources for maintenance and retrofitting interventions. A common circumstance in practice regards the lack of information as well as the need to update it, causing delays in the risk evaluation and, consequently, in the entire decision‐making process. Especially in the managing of large bridge assets, the retrieval/updating of information can be costly, both in terms of time and economic resources. In this regard, the execution of visual inspections to determine the defect level of bridges and viaducts is the most resource‐intensive operation on the part of the management authorities. In this paper, a new methodology to prioritize visual inspections of bridges according to their risk condition and operating costs is proposed and illustrated with a real case study. A novel aspect of the proposed methodology regards the use of information gain criteria to refine the outcomes from the risk assessment and prioritization process. Finally, the outcomes of the methodology are implemented in a GIS framework to ensure high interoperability and facilitate the construction of intuitive risk maps to assist inspection plans.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1002/cepa.2064.
  • About this
    data sheet
  • Reference-ID
    10767212
  • Published on:
    17/04/2024
  • Last updated on:
    17/04/2024
 
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