• DocumentCode
    265951
  • Title

    Practical application of bridge rating expert system to an aged bridge

  • Author

    Emoto, Hisao ; Takahashi, Junji ; Miyamoto, Ayaho

  • Author_Institution
    Dept. of Environ. Sci. & Eng., Yamaguchi Univ., Ube, Japan
  • fYear
    2014
  • fDate
    27-29 Aug. 2014
  • Firstpage
    213
  • Lastpage
    220
  • Abstract
    This paper describes the details of how to predict the remaining service life of an aged RC-T girder bridge and how to make verification of the predicted results based upon a practical bridge management system (J-BMS) which integrated with the concrete bridge rating expert system (BREX) with either visual inspection data and concrete core test results. The authors have been developing the J-BMS that is able to predict the deterioration process of existing bridge members. The remaining service life of the aged RC-T girder bridge (KT bridge) can be quantitatively estimated by applying the BREX system, which is a sub-system of the J-BMS with field inspection data. In this paper, it was found that both the main girder and concrete deck had a remaining service life not exceeding a decade by using the BREX system. Additionally, the influence of the soundness score (safety indices) was shown by selecting the learning (supervised) data. The remaining service life prediction was also verified using the concrete core specimen test.
  • Keywords
    beams (structures); bridges (structures); expert systems; inspection; learning (artificial intelligence); remaining life assessment; structural engineering computing; supports; BREX system; J-BMS; KT bridge; aged RC-T girder bridge; bridge management system; bridge members; bridge rating expert system; concrete core specimen test; concrete core test; deterioration process prediction; field inspection data; remaining service life; safety indices; soundness score; supervised learning data; visual inspection data; Bridges; Concrete; Inspection; Maintenance engineering; Slabs; Structural beams; Visualization; Aged RC-T Girder Bridge; Expert system; Field Inspections; J-BMS; Neural Network; Remaining Service Life;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Science and Information Conference (SAI), 2014
  • Conference_Location
    London
  • Print_ISBN
    978-0-9893-1933-1
  • Type

    conf

  • DOI
    10.1109/SAI.2014.6918192
  • Filename
    6918192