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Development of Robotic Nondestructive Testing of Steel Corrosion of Prestressed Concrete Bridge Girders using Magnetic Flux Leakage System

Author(s):


Medium: journal article
Language(s): English
Published in: Transportation Research Record: Journal of the Transportation Research Board, , n. 8, v. 2674
Page(s): 466-476
DOI: 10.1177/0361198120925471
Abstract:

This paper describes the development and validation of a new magnetic-based corrosion detection device integrated in a robotic system. The system nondestructively scans the length of AASHTO-type prestressed concrete I-girders of bridges. The system includes two primary subsystems: an independent magnetic flux leakage (MFL) system for nondestructive testing, and a robotic rover to transport the MFL system along the girder’s length with navigation around transverse diaphragms. The MFL unit inspects prestressing steel strands embedded in concrete and detects cross-section losses caused by corrosion. The MFL is designed with two permanent magnets to magnetize embedded strands and multiple Hall-effect sensors to detect normal and axial magnetic flux leakage. The system is evaluated by testing a laboratory mockup girder and further applied on a field girder. Resulting magnetic signals from both normal and axial Hall-effect sensors are recorded using a newly developed and integrated data acquisition system. Finite element simulations through multi-physics 3D transient analysis aided the development of the new MFL system components, including appropriate magnets strength and dimensions, as well as the verification of the developed system. The effects of lateral reinforcements on nearby section loss are obtained from tests and analyzed with numerical models. Results indicate that the system can successfully disclose magnetic leakage signals at the corrosion zone.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1177/0361198120925471.
  • About this
    data sheet
  • Reference-ID
    10777932
  • Published on:
    12/05/2024
  • Last updated on:
    12/05/2024
 
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