A real-time structural parametric identification system based on fiber optic sensing and neural network algorithms

Zhishen Wu; Bin Xu

doi:10.1117/12.484060

1 August 2003 A real-time structural parametric identification system based on fiber optic sensing and neural network algorithms

Zhishen Wu, Bin Xu

Author Affiliations +

Proceedings Volume 5047, Smart Nondestructive Evaluation and Health Monitoring of Structural and Biological Systems II; (2003) https://doi.org/10.1117/12.484060
Event: NDE for Health Monitoring and Diagnostics, 2003, San Diego, California, United States

Abstract

A structural parametric identification strategy based on neural networks algorithms using dynamic macro-strain measurements in time domain from a long-gage strain sensor by fiber optic sensing technique such as Fiber Bragg Grating (FBG) sensor is developed. An array of long-gage sensors is bounded on the structure to measure reliably and accurately macro-strains. By the proposed methodology, the structural parameter of stiffness can be identified. A beam model with known mass distribution is considered as an object structure. Without any eigenvalue analysis or optimization computation, the structural parameter of stiffness can be identified. First an emulator neural network is presented to identify the beam structure in current state. Free vibration macro-strain responses of the beam structure are used to train the emulator neural network. The trained emulator neural network can be used to forecast the free vibration macro-strain response of the beam structure with enough precision and decide the difference between the free vibration macro-strain responses of other assumed structure with different structural parameters and those of the original beam structure. The root mean square (RMS) error vector is presented to evaluate the difference. Subsequently, corresponding to each assumed structure with different structural parameters, the RMS error vector can be calculated. By using the training data set composed of the structural parameters and RMS error vector, a parametric evaluation neural network is trained. A beam structure is considered as an existing structure, based on the trained parametric evaluation neural network, the stiffness of the beam structure can be forecast. It is shown that the parametric identification strategy using macro-strain measurement from long-gage sensors has the potential of being a practical tool for a health monitoring methodology applied to civil engineering structures.

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Zhishen Wu and Bin Xu "A real-time structural parametric identification system based on fiber optic sensing and neural network algorithms", Proc. SPIE 5047, Smart Nondestructive Evaluation and Health Monitoring of Structural and Biological Systems II, (1 August 2003); https://doi.org/10.1117/12.484060

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