Improved Element-Level Bridge Inspection Criteria for Better Bridge Management and Preservation

Bridge inspection data is an essential step in the bridge asset management operation and the bridge management system. As such, a reliability-based, holistic framework was proposed to effectively collect reliable data and perform data fusion and information fusion of sensory data used for element-level inspection and conditional assessment. A comprehensive literature review was conducted to better understand the current state of the research of and practice in the bridge element inspection. To overcome the limitations of the visual inspection used in the routine bridge inspections, an unmanned aerial vehicle (UAV) was used to supplement the traditional visual inspection data by providing high quality, real-time, reliable data. Moreover, effective data fusion and information process methods were proposed to enhance the features extraction for sensor data for in-depth/special/damage inspections. This study explored the new data fusion methods based on three representative feature extraction techniques, while the kernel function-based support vector machine (SVM) was used to facilitate pattern recognition and improve identification. The effectiveness of these methods was verified even in conditions with high levels of noise interference. In addition, this study attempted to unveil and reduce the structural uncertainty experienced in in-depth/special/damage inspection. The deep Bayesian belief network (DBBN) was herein used to extract statistical representation from vast amount of structural data, for probabilistically determining structural condition and health state for decision making.

  • Record URL:
  • Supplemental Notes:
    • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
  • Corporate Authors:

    North Dakota State University, Fargo

    Department of Civil and Environmental Engineering
    Fargo, ND  United States  58105

    Mountain-Plains Consortium

    North Dakota State University
    Fargo, ND  United States  58108

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Authors:
    • Lin, Zhibin
    • Pan, Hong
    • Wang, Xingyu
    • Li, Mingli
  • Publication Date: 2019-12


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; Photos; References; Tables;
  • Pagination: 105p

Subject/Index Terms

Filing Info

  • Accession Number: 01741574
  • Record Type: Publication
  • Report/Paper Numbers: MPC-19-403
  • Contract Numbers: MPC-504
  • Created Date: May 31 2020 5:55PM