Data-driven structure health monitoring (SHM) using wireless sensor network

Gao, Mingzhe

Data-driven structure health monitoring (SHM) using wireless sensor network

Gao, Mingzhe

Permalink

Publication Type:: Thesis
Issue Date:: 2025

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download thesisAdobe PDF (6.98 MB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Gao, Mingzhe
dc.date.accessioned	2025-12-22T03:40:26Z
dc.date.available	2025-12-22T03:40:26Z
dc.date.issued	2025
dc.identifier.uri	http://hdl.handle.net/10453/191094
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	This paper develops a novel VBI-based SHM approach using wireless sensor networks to monitor vehicle-bridge interaction dynamics. Advanced signal processing techniques extract crucial time-varying features for bridge condition assessment: a second-order synchrosqueezing transform (SST2) analyzes vehicle responses, while a multi-synchrosqueezing transform (MSST) analyzes bridge responses under moving vehicles. Finally, a dual-stream convolutional neural network (CNN) fuses features from both vehicle and bridge dynamic responses based synchrosqueezing transform for robust bridge damage classification. The methodology enables real-time, decentralized structural evaluation. Numerical and experimental studies verify the approach's effectiveness in providing reliable, accurate, and rapid condition assessment, particularly for short and medium-span road bridges.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/191094/1/thesis.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© 2025 Mingzhe Gao
dc.rights	au.edu.uts.lib/cph
dc.title	Data-driven structure health monitoring (SHM) using wireless sensor network	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

This paper develops a novel VBI-based SHM approach using wireless sensor networks to monitor vehicle-bridge interaction dynamics. Advanced signal processing techniques extract crucial time-varying features for bridge condition assessment: a second-order synchrosqueezing transform (SST2) analyzes vehicle responses, while a multi-synchrosqueezing transform (MSST) analyzes bridge responses under moving vehicles. Finally, a dual-stream convolutional neural network (CNN) fuses features from both vehicle and bridge dynamic responses based synchrosqueezing transform for robust bridge damage classification. The methodology enables real-time, decentralized structural evaluation. Numerical and experimental studies verify the approach's effectiveness in providing reliable, accurate, and rapid condition assessment, particularly for short and medium-span road bridges.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/191094