Finite Element–Based Machine-Learning Approach to Detect Damage in Bridges under Operational and Environmental Variations

In the last decades, the long-term structural health monitoring of civil structures has been mainly performed using two approaches: model based and data based. The former approach tries to identify damage by relating the monitoring data to the prediction of numerical (e.g., finite-element) models of the structure. The latter approach is data driven, where measured data from a given state condition are compared to the baseline or reference condition. A challenge in both approaches is to make the distinction between the changes of the structural response caused by damage and environmental or operational variability. This issue was tackled here using a hybrid technique that integrates model- and data-based approaches into structural health monitoring. Data recorded in situ under normal conditions were combined with data obtained from finite-element simulations of more extreme environmental and operational scenarios and input into the training process of machine-learning algorithms for damage detection. The addition of simulated data enabled a sharper classification of damage by avoiding false positives induced by wide environmental and operational variability. The procedure was applied to the Z-24 Bridge, for which 1 year of continuous monitoring data were available.

• Record URL:
  https://doi.org/10.1061/(ASCE)BE.1943-5592.0001432
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/32947845
• Supplemental Notes:
  • © 2019 American Society of Civil Engineers.
• Authors:
  • Figueiredo, Eloi
  • Moldovan, Ionut
  • Santos, Adam
  • Campos, Pedro
  • Costa, João C W A
• Publication Date: 2019-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 04019061
• Serial:
  • Journal of Bridge Engineering
  • Volume: 24
  • Issue Number: 7
  • Publisher: American Society of Civil Engineers
  • ISSN: 1084-0702
  • Serial URL: http://ojps.aip.org/beo

Subject/Index Terms

• TRT Terms: Bridges; Data analysis; Deterioration by environmental action; Field tests; Mathematical models; Structural health monitoring
• Identifier Terms: Z-24 Bridge (Switzerland)
• Subject Areas: Bridges and other structures; Data and Information Technology; Highways; Maintenance and Preservation;

Filing Info

• Accession Number: 01706659
• Record Type: Publication
• Files: TRIS, ASCE
• Created Date: May 29 2019 9:22AM