Abstract
Vibration-based method has been widely applied for damage identification of bridge. Natural frequency, mode shape, and their derivatives are sensitive parameters to damage. However, these parameters can be affected not only by the health of structure, but also by the changing temperature. It is essential to eliminate the influence of temperature in practice. Therefore, a fuzzy neural network-based damage assessment method is proposed in this paper. Uniform load surface curvature is used as damage indicator. Elasticity modulus of concrete is assumed to be temperature dependent in the numerical simulation of bridge model. Through selecting temperature and uniform load surface curvature as input variables of fuzzy neural network, the algorithm can distinguish the damage from temperature effect. Comparative analysis between fuzzy neural network and BP network illustrates the superiority of the proposed method.
Highlights
Bridge structure is playing significant role in modern transport system and economic development
Temperature effect can cause abnormal changes of modal parameters, which will lead to incorrect damage identification results
This paper presents an effective strategy for eliminating temperature effect in damage identification of bridge
Summary
Bridge structure is playing significant role in modern transport system and economic development. Vibration tests have been widely performed in bridge health monitoring The dynamic characteristics such as eigenfrequencies, modal shapes, and damping ratios of structure contain effective information on bridge health status [5]. Sohn et al [14] presented a linear adaptive model to discriminate the changes of natural frequencies due to temperature changes from those caused by structural damage or other environmental effects. It would be difficult for sensors to monitor environmental variables over a long time Another group of methods can minimize the environmental effect without measuring temperatures. Yan et al [16] proposed a principal-component-analysis- (PCA-) based method to distinguish between changes of modal data due to environmental variation and structural damage under linear or weakly nonlinear cases. A fuzzy neural network-based damage assessment method which can eliminate the temperature effect is proposed. Comparative analysis between fuzzy neural network and BP network is conducted
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