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Proceedings Paper

Application of adaptive probabilistic neural network to damage detection of Tsing Ma suspension bridge
Author(s): Yi-Qing Ni; S. F. Jiang; Jan Ming Ko
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Paper Abstract

Since the probabilistic neural network (PNN) describes measurement data in a Bayesian probabilistic approach, it shows great promise for structural damage detection in noisy conditions. In the traditional PNN, the smoothing parameter is unique to all pattern classes and is specified artificially, which may result in inaccuracy of identification results and computational inefficiency. In this study, we explore the damage localization of the Tsing Ma suspension bridge by use of an adaptive PNN that optimally determines different smoothing parameters for different pattern classes through an iteration scheme. A series of pattern classes are defined for the Tsing Ma Bridge to depict different damage locations.

Paper Details

Date Published: 3 August 2001
PDF: 10 pages
Proc. SPIE 4337, Health Monitoring and Management of Civil Infrastructure Systems, (3 August 2001); doi: 10.1117/12.435610
Show Author Affiliations
Yi-Qing Ni, Hong Kong Polytechnic Univ. (Hong Kong)
S. F. Jiang, Hong Kong Polytechnic Univ. (Hong Kong)
Jan Ming Ko, Hong Kong Polytechnic Univ. (Hong Kong)


Published in SPIE Proceedings Vol. 4337:
Health Monitoring and Management of Civil Infrastructure Systems
Steven B. Chase; A. Emin Aktan, Editor(s)

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