Technologies and Platforms for Remote and Autonomous Bridge Inspection–Review

Anna M. Rakoczy, Diogo Ribeiro, Vedhus Hoskere, Yasutaka Narazaki, Piotr Olaszek, Wojciech Karwowski, Rafael Cabral, Yanlin Guo, Marcos Massao Futai, Pietro Milillo, Ricardo Santos, Adriana Trias, Luis Gonzalez, José Campos Matos, Franziska Schmidt

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Recent scientific and technological advancements have enabled a more efficient structural condition assessment of bridges, mainly through the implementation of intelligent inspection strategies. These intelligent strategies can provide early identification of critically damaged components before failure, and will therefore play a key role in extending the life of infrastructure. The latest inspection technologies can provide inspection plans with damage conditions, create a damage report as well as provide statistics and comparisons to the previous inspection findings. The new and existing inspection technologies are directed to help with the digitalization of the Bridge Management System (BMS). The complexity of maintenance/inspection requires organized, automated, open and transparent digital processes, which should consider both—structure and asset management data. Further, the inspection/monitoring findings serve as a source for decision-making models. The digitalized aspects of autonomous inspection provide better performance prediction models and guarantee safety for the users. This paper presents the latest findings in the field of remote inspection of bridges. In particular, the main technologies for inspection and geometrical assessment are depicted, especially those based on computer vision systems installed in UAVs and robots, LiDAR, radar, satellites and other non-contact systems including on-board monitoring. The accompanying article entitled: “Methodologies for remote bridge inspection” deals with the methodologies used for data processing based on Artificial Intelligence (AI).

Original languageEnglish (US)
JournalStructural Engineering International
DOIs
StateAccepted/In press - 2024

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction

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