A Hybrid Data Interpretation Framework for Automated Performance Monitoring of Infrastructure

The objective of this study is to establish and propose a hybrid data interpretation framework for automated performance monitoring of infrastructures. The framework is established by integrating non-parametric and model-based approaches. In fact, these approaches are merged in such a way that they can cover the associated drawbacks. The proposed framework consists of three main steps including analytical simulation of critical damage scenarios for a given structure, advanced statistical analysis and finally damage classification. In order to verify the efficiency of the proposed framework, a 4-Span Bridge is selected and exposed to the common damage scenarios. The framework has shown promising results in terms of continuous monitoring (detection of the damage and classifying the critical abnormal behaviors).

• Record URL:
  • https://doi.org/10.1061/9780784479117.037
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784479117
• Supplemental Notes:
  • © 2015 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Malekzadeh, M
  • Atia, G
  • Catbas, F N
• Conference:
  • Structures Congress 2015
  • Location: Portland Oregon, United States
  • Date: 2015-4-23 to 2015-4-25
• Publication Date: 2015-4

Language

• English

Media Info

• Media Type: Web
• Pagination: pp 426-434
• Monograph Title: Structures Congress 2015

Subject/Index Terms

• TRT Terms: Automation; Bridges; Classification; Monitoring; Simulation; Statistical analysis
• Uncontrolled Terms: Damage (Bridges); Damage detection; Data interpretation
• Subject Areas: Bridges and other structures; Highways; Maintenance and Preservation; I24: Design of Bridges and Retaining Walls; I61: Equipment and Maintenance Methods;

Filing Info

• Accession Number: 01561778
• Record Type: Publication
• ISBN: 9780784479117
• Files: TRIS, ASCE
• Created Date: Apr 27 2015 5:10PM