Research of EMI Structural Health Monitoring Based on BP Neural Networks

Fu Hou Xu; Dong Dong Wen; Yu Xiang Zhang; Hua Cheng Li

doi:10.4028/www.scientific.net/AMM.188.231

Paper Titles

Discussion of Ceramsite Concrete Shaped Column in a Structural System
p.205

Relationships between Compressive Strength and Microstructure in Mortars with Iron Ore Tailings as Fine Aggregate
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Asphalt Pavement Performance Evaluation Based on SOM Neural Network
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Analysis on Seismic Response of Portal Segment of Highway Tunnel in High-Intensity Earthquake Areas
p.226

Research of EMI Structural Health Monitoring Based on BP Neural Networks
p.231

The Application of BIM in Intelligent Construction
p.236

Reliance of Embedment Strength on Dowel Diameter in Joints of Cement-Splinter Boards
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Bitumen Sliding Joints for Friction Elimination in Footing Bottom
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Study on the Drafting and Early-Warning of Temperature Double-Control Index of Different Sections & Periods of Concrete Block
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 188Research of EMI Structural Health Monitoring Based...

Research of EMI Structural Health Monitoring Based on BP Neural Networks

Abstract:

Based on the coupling characteristic of piezoelectric ceramics (PZT) and electro-mechanical, impedance changes were measured by the impedance analyzer. Aluminum plate’s impedance response under different load conditions was analyzed with electromechanical impedance technique. BP neural networks were established to identify the structural damage status and the RMSDR was calculated as neural network input data, then the networks was trained and validated. Experiment results show that the trained network can successfully identify the structural load state.

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Periodical:

Applied Mechanics and Materials (Volume 188)

Pages:

231-235

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.188.231

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Online since:

June 2012

Authors:

Fu Hou Xu, Dong Dong Wen, Yu Xiang Zhang, Hua Cheng Li

Keywords:

BP Neural Network (BPNN), Electromechanical Impedance Technique, Structural Health Monitoring (SHM)

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