STRUCTURAL DAMAGE DETECTION OF A MULTI-SPAN CONTINUOUS STEEL-TRUSS BRIDGE FOCUSING ON TRAFFIC VIBRATION

• KIM Chul-Woo

  京都大学大学院 工学研究科社会基盤工学専攻

• KITAUCHI Sotaro

  京都大学大学院 工学研究科社会基盤工学専攻

• SUGIURA Kunitomo

  京都大学大学院 工学研究科社会基盤工学専攻

• KAWATANI Mitsuo

  神戸大学大学院 工学研究科市民工学専攻

• KAI Masayoshi

  NECシステムテクノロジー株式会社

Bibliographic Information

Other Title

• 多径間連続鋼トラス橋の交通振動を用いた損傷検知手法

Abstract

 This study is intended to investigate feasibility of bridge health monitoring utilizing dynamic parameters and a linear system parameter of a time series model identified from traffic-induced vibration data of bridge through a moving vehicle experiment on a real continuous steel-truss bridge. In addition to the natural frequency and damping constant of the bridge, this study adopts Damage Indicator (DI) which consists of autoregressive (AR) coefficients as a parameter to detect possible anomaly of the bridge. First, this study assesses the changes in frequency, damping constant, mode shape and DI. Second, Mahalano-bis-Taguchi System (MTS) is used to judge potential changes in frequency, damping constant and DI. DI can be estimated stably and its change due to damage is relatively large. Mahalanobis-Taguchi System (MTS) is successfully applied to make a decision on not only bridge condition but damage location. Observation demonstrates feasibility of structural diagnosis of bridges from the identified system parameters. However, the sensor location is still an important factor in identifying damage location which needs further investigations.

Journal

• Journal of Japan Society of Civil Engineers, Ser. A1 (Structural Engineering & Earthquake Engineering (SE/EE))

  Journal of Japan Society of Civil Engineers, Ser. A1 (Structural Engineering & Earthquake Engineering (SE/EE)) 69 (3), 557-571, 2013

  Japan Society of Civil Engineers

Keywords

• damage indicator
• fault detection
• linear system model
• Mahalanobis-Taguchi System (MTS)
• traffic-induced vibration
• truss bridge

Details

• CRID: 1390282680328145792
• NII Article ID: 130004961276
• DOI: 10.2208/jscejseee.69.557
• ISSN: 21854653
• Text Lang: ja
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed