Experimental Study on Identification of Structural Changes Using Wavelet Energy Features

Xiaobang Zhang, Yong Lu*, Zachariah Wynne, Thomas P.S. Reynolds

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Wavelet packet transformation has been used widely in the damage identification and structural health monitoring communities. In particular, the use of wavelet packet node energy (WPNE) as damage sensitive features has attracted much research interest in more recent years. WPNE features tend to contain detailed information which can be highly sensitive to local damage or other forms of structural changes. However, most of the existing studies in the literature on using wavelet energy based features have been numerical and involved idealised assumptions such as perfect and identical excitations among different tests. This paper presents an experimental investigation into the viability of WPNE based techniques for detection and localisation of the structural changes in a real measurement environment. Vibration signals are acquired firstly from the test structures with different alterations to the structural states, realized mainly through the use of additional masses, and WPNE features are extracted. These features and the corresponding structural states form a dataset, from which supervised machine learning with neural network is carried out. The trained neural network is subsequently tested for its prediction capability. The experimental structures include a free-ended steel I beam, a flat beam with fixed ends and MX3D Bridge, the world’s first 3D-printed metal bridge. Different forms of excitation are involved for different test structures, including hammer impact and controlled heel drops and impacts from pedestrian footfall. Results indicate that the WPNE based neural network approach is capable of detecting and localising the structural changes in all tested structures. The accuracy is generally higher in a better controlled excitation situation, where structural changes at a level equivalent to incipient damage is detectable.

Original languageEnglish
Title of host publicationExperimental Vibration Analysis for Civil Engineering Structures
Subtitle of host publicationSelect Proceedings of the EVACES 2021
EditorsZhishen Wu, Tomonori Nagayama, Ji Dang, Rodrigo Astroza
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages15
ISBN (Print)9783030932350
Publication statusPublished - 24 Aug 2022
Event9th International Conference on Experimental Vibration Analysis for Civil Engineering Structures, EVACES 2021 - Virtual, Online
Duration: 17 Sept 202120 Sept 2021

Publication series

NameLecture Notes in Civil Engineering
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565


Conference9th International Conference on Experimental Vibration Analysis for Civil Engineering Structures, EVACES 2021
CityVirtual, Online

Keywords / Materials (for Non-textual outputs)

  • Damage identification
  • Machine learning
  • Vibration testing
  • Wavelet packet transform (WPT)
  • WPT energy features


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