Structural Damage Localization and Quantification Based on a CEEMDAN Hilbert Transform Neural Network Approach: A Model Steel Truss Bridge Case Study

Structural Damage Localization and Quantification Based on a CEEMDAN Hilbert Transform Neural Network Approach: A Model Steel Truss Bridge Case Study

Vibrations of complex structures such as bridges mostly present nonlinear and non-stationary behaviors. Recently, one of the most common techniques to analyze the nonlinear and non-stationary structural response is Hilbert−Huang Transform (HHT). This paper aims to evaluate the performance...

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Bibliographic Details
Main Authors: Asma Alsadat Mousavi, Chunwei Zhang, Sami F. Masri, Gholamreza Gholipour
Format: Article
Language:English
Published: MDPI AG 2020-02-01
Series:Sensors
Subjects:
damage detection
signal processing
steel-truss bridge
artificial neural network
hilbert–huang transform (hht)
complete ensemble empirical mode decomposition with adaptive noise (ceemdan)
Online Access:https://www.mdpi.com/1424-8220/20/5/1271

Internet

https://www.mdpi.com/1424-8220/20/5/1271

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