Remote monitoring of fatigue-sensitive details on bridges : [part I and II].
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2015-03-01
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Abstract:Fatigue is one of the most critical problems for steel bridges as well as for any steel structures that needs ; to be considered during design and operation. The objectives of this study are to explore monitoring ; technologies, and to develop effective structural and data analysis strategies as well as implementation ; recommendations for evaluating performance of fatigue-sensitive details and retrofits in steel bridges. ; Acoustic emission (AE) was selected as a candidate inspection technology, and a monitoring system was ; installed on a bridge. In general, the performance of the monitoring system and associated software is ; satisfactory. The majority of AE monitoring challenges are associated with AE data analysis and ; interpretation of results. In this study, cluster analysis and non-linear mapping signal analysis techniques ; are used to group AE data with similar waveform characteristics. The presence of the signals that ; resemble the characteristics of crack opening signals, noise, and structural resonance is identified through ; waveform analysis. Once the presence of crack opening signals is confirmed, the source location plots ; are utilized to assess the concentration and the level of activity at the locations of interest. ; A significant difference is observed in the fatigue life calculated using measured stress, and the stresses ; calculated using finite element models loaded with a fatigue truck. Hence, a two-tier implementation ; process is recommended. Tier I process includes the assessment of bridges with repaired details. Tier II ; process recommends evaluating the entire bridge population with fatigue-sensitive details. Additional ; recommendations include implementing AE data interpretation capability in an on-line system to provide ; reliable input with minimal interpretation requirements for inspection-based maintenance management.
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Main Document Checksum:urn:sha256:8d286bc8b421971766aedeab556c396b718748eef97796955ccf83478f5a6352