NJDOT Research

NJ #
Detection of Damage Precursors in Steel Components for Life-Cycle Assessments

I) Novel inspection and monitoring technique of damage precursor and cracks

The motivation for this research is the lack of a single methodology for detection of damage precursors on the surface or in metal structural components within efficient large-area scanning, which combines the critical attributes of accuracy, efficiency, cost-effectiveness and overall robustness. We therefore propose to develop a damage precursor sensing and monitoring technique for steel bridges which provides the following advantages compared to other monitoring technologies, specifically:

  • Damage precursor, i.e. micro-size defect, detection and measurement
  • Crack growth rate monitoring over large length scale range, i.e. from micro-sized flaws to large-sized crack
  • Large coverage sensing of defects by placing only ONE sensor/transducer pair on the structural component
  • Easily applicable measurement, ensuring cost-saving, time-effective inspection
  • Extendable into real time monitoring

II) Linking the damage precursor and large crack detection to fatigue assessment methodologies
The second part of the research objectives refers to developing an efficient, reliable fatigue-life calculation procedure based on the measurement of damage precursors and cracks.

  • Representing damage state of the component by using ONE single parameter, i.e. the damage index DI, and answering the main questions in respect to structural health, i.e. what is the size/extent, type and growth rate of the damage.
  • Using the damage index DI to estimate the remaining fatigue life of the component
  • Optimization of maintenance and repair scheduling and related cost assessment based accurate prediction of expected damage accumulation in time
  • Validating existing fatigue/fracture assessment methodologies based on the S-N curves, the accumulated damage index after Palmgren-Miner, the fatigue serviceability index and the Paris law
Performing Organization
Stevens Institute of Technology
Principal Investigator(s)
Hassiotis, Sophia
Key Word(s)

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