English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.

Fuzzy Neural Network-Based Damage Assessment of Bridge under Temperature Effect