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Structural Engineering and Mechanics   Volume 17, Number 3, March 2004, pages 483-492
Damage assessment of cable stayed bridge using probabilistic neural network
Hyo-Nam Cho, Young-Min Choi, Sung-Chil Lee and Choon-Kun Hur

Abstract     [Buy Article]
    This paper presents an efficient algorithm for the estimation of damage location and severity in bridge structures using Probabilistic Neural Network (PNN). Generally, the Back Propagation Neural Network (BPNN)-based damage detection methods need a lot of training patterns for neural network learning process and the optimum architecture of a BPNN is selected by trial and error. In this paper, the PNN instead of the conventional BPNN is used as a pattern classifier. The modal properties of damaged structure are somewhat different from those of undamaged one. The basic idea of proposed algorithm is that the PNN classifies a test pattern which consists of the modal characteristics from damaged structure, how close it is to each training pattern which is composed of the modal characteristics from various structural damage cases. In this algorithm, two PNNs are sequentially used. The first PNN estimates the damage location using mode shape and the results of the first PNN are put into the second PNN for the damage severity estimation using natural frequency. The proposed damage assessment algorithm using the PNN is applied to a cable-stayed bridge to verify its applicability.
Key Words
    probabilistic neural network; damage identification; cable-stayed bridge.
Department of Civil & Environmental Engineering, Hanyang University, Ansan, Kyunggi 425-791, South Korea

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