20 October 2009 Modal macro-strain vector based damage detection methodology with long-gauge FBG sensors
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Proceedings Volume 7493, Second International Conference on Smart Materials and Nanotechnology in Engineering; 749331 (2009) https://doi.org/10.1117/12.843403
Event: Second International Conference on Smart Materials and Nanotechnology in Engineering, 2009, Weihai, China
Abstract
Advances in optic fiber sensing technology provide easy and reliable way for the vibration-based strain measurement of engineering structures. As a typical optic fiber sensing techniques with high accuracy and resolution, long-gauge Fiber Bragg Grating (FBG) sensors have been widely employed in health monitoring of civil engineering structures. Therefore, the development of macro strain-based identification methods is crucial for damage detection and structural condition evaluation. In the previous study by the authors, a damage detection algorithm for a beam structure with the direct use of vibration-based macro-strain measurement time history with neural networks had been proposed and validated with experimental measurements. In this paper, a damage locating and quantifying method was proposed using modal macrostrain vectors (MMSVs) which can be extracted from vibration induced macro-strain response measurement time series from long-gage FBG sensors. The performance of the proposed methodology for damage detection of a beam with different damage scenario was studied with numerical simulation firstly. Then, dynamic tests on a simply-supported steel beam with different damage scenarios were carried out and macro-strain measurements were employed to detect the damage severity. Results show that the proposed MMSV based structural identification and damage detection methodology can locate and identify the structural damage severity with acceptable accuracy.
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Bin Xu, Chongwu W. Liu, Sami F. Masri, "Modal macro-strain vector based damage detection methodology with long-gauge FBG sensors", Proc. SPIE 7493, Second International Conference on Smart Materials and Nanotechnology in Engineering, 749331 (20 October 2009); doi: 10.1117/12.843403; https://doi.org/10.1117/12.843403
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