Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/10014
Title: Identification of damage in dome-like structures using hybrid sensor measurements and artificial neural networks
Authors: Lu, W
Teng, J
Xu, Y 
Su, Z 
Issue Date: 2013
Publisher: Institute of Physics Publishing
Source: Smart materials and structures, 2013, v. 22, no. 10, 105014 How to cite?
Journal: Smart materials and structures 
Abstract: A damage detection scheme using multi-type sensor-based hybrid sensing and artificial-neural-network- (ANN-) based information processing was developed for dome-like structures used in civil infrastructure. Accelerometers and strain sensors were used to provide a hybrid measurement with the purpose of acquiring rich information associated with structural damage. The optimal placement of multiple sensors was explored so as to capture the most appropriate and sensitive signal features (damage parameter vectors) for damage characterization. A back-propagation ANN was constructed with the inputs extracted from the hybrid measurement. To validate the capacity of the proposed damage identification scheme, finite element analysis was conducted to identify damage in a Schwedler dome structure as an example. The performance of ANNs, trained by three kinds of damage parameter vector extracted from signals captured by (i) a sole accelerometer, (ii) a sole strain sensor, and (iii) both kinds of sensor was compared, to observe that the one trained by hybrid sensor measurement outperformed the others. Error analysis for a series of parametric studies, in which noise at different levels was included in the training input, was further carried out, and robustness of the proposed damage identification scheme under noisy measurement was demonstrated.
URI: http://hdl.handle.net/10397/10014
ISSN: 0964-1726
EISSN: 1361-665X
DOI: 10.1088/0964-1726/22/10/105014
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