Structural Damage Prognosis on Truss Bridges with End Connector Bolts
Publication: Journal of Engineering Mechanics
Volume 143, Issue 3
Abstract
The successful development of structural damage prognosis (SDP) capability requires the further development and integration of many technology areas, such as hardware, software, modeling capabilities, and so forth. By combining the damage-sensitive feature extraction, the higher statistical moments of structural responses and the fuzzy c-means (FCM) clustering algorithm with the traditional time-series analysis-based SDP method, an integrated method is proposed for the SDP of a truss bridge model with end connector bolts under environmental and operational variability simulated in laboratory. The theoretical formulation of the integrated method is developed first. A six-bay truss bridge model is then designed and fabricated in laboratory for assessing the performance of the proposed method. Various damage cases are simulated by partially or completely loosening some end connecter bolts of the truss bridge under the environmental variability. The performance of the integrated SDP method is assessed based on the time-series acceleration response data measured at 16 ball nodes of the truss bridge model in each reference state and test state. The illustrated results show that the proposed integrated SDP method can effectively identify the damages of a truss bridge with end connector bolts. The introduction of higher statistical moments can provide a beneficial supplement to the traditional damage-sensitive indexes.
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Acknowledgments
This research is jointly supported by National Natural Science Foundation of China (51278226 and 50978123) and the Project of Science and Technology on Reliability Physics and Application Technology of Electronic Component Laboratory (Nos. 9140C030605140C03015 and ZHD201207).
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© 2016 American Society of Civil Engineers.
History
Received: Dec 29, 2014
Accepted: Oct 30, 2015
Published online: Feb 25, 2016
Discussion open until: Jul 25, 2016
Published in print: Mar 1, 2017
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