Load identification and structural damage detection of bridges

Zhang, Bing

Load identification and structural damage detection of bridges

Zhang, Bing

Permalink

Publication Type:: Thesis
Issue Date:: 2022

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download contents and abstractAdobe PDF (464.99 kB)

Adobe PDF

Download thesisAdobe PDF (9.92 MB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Zhang, Bing
dc.date.accessioned	2023-07-11T03:30:44Z
dc.date.available	2023-07-11T03:30:44Z
dc.date.issued	2022
dc.identifier.uri	http://hdl.handle.net/10453/171437
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	Load identification and structural damage detection are two important research areas in bridge structural health monitoring (SHM). In practice, incomplete measurement information, variable service environments and other uncertainties make the structural load and damage identification difficult. Currently, many identification methods for load identification and bridge structural damage detection cannot effectively serve under operating conditions. This study will focus on these two areas including the following contents. Regarding the load identification, one truncated transfer matrix-based regularization method is proposed for impact force identification. Impact location is identified first and then the force value is reconstructed using this method. To improve the impact force localization and value identification method, one low rank transfer submatrix-based group sparse regularization method is proposed to localize and reconstruct the impact force simultaneously. Low rank transfer submatrix-based group sparse regularization constructs a structured regularization on the unknown forces, by binding the unknown amplitudes associated with different potential locations into separate groups and promoting the group-level sparsity between the potential locations. Similarly, Group sparse feature also exist in the equivalent nodal force which is transferred from the moving force. Based on this feature, one group weighted Tikhonov regularization method is proposed for the moving force identification via the equivalent nodal force. These proposed methods for load identification are validated numerically and experimentally. In terms of structural damage detection, a new interface slip monitoring system is developed to directly measure the relative displacement between the concrete slab and steel girder and the integrity of the shear connectors has been assessed by the slip measurements. The finite element numerical model has been developed to study the interface damage detection of the steel-concrete composite structure under the pseudo moving vehicular load. The results show that the slippage divergence ratio is very sensitive to the shear connector damage. In practice, the cable force of the cable-stayed bridge is difficult to be monitored for its damage detection. Based on the relationship between the cable force and the strain of the bridge deck, a new method is proposed for the localization and quantitative identification of cable damage using the strain measurements of the bridge deck. Here the damage cable identification problem is treated as a multi-classification problem and the damage degree identification problem as a nonlinear regression problem using support vector machine. The proposed method has strong anti-noise performance and can be easily adapted to field health monitoring system.	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/10453/171437/2/02whole.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	© 2022 Bing Zhang
dc.rights	au.edu.uts.lib/nph
dc.title	Load identification and structural damage detection of bridges	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

Load identification and structural damage detection are two important research areas in bridge structural health monitoring (SHM). In practice, incomplete measurement information, variable service environments and other uncertainties make the structural load and damage identification difficult. Currently, many identification methods for load identification and bridge structural damage detection cannot effectively serve under operating conditions. This study will focus on these two areas including the following contents. Regarding the load identification, one truncated transfer matrix-based regularization method is proposed for impact force identification. Impact location is identified first and then the force value is reconstructed using this method. To improve the impact force localization and value identification method, one low rank transfer submatrix-based group sparse regularization method is proposed to localize and reconstruct the impact force simultaneously. Low rank transfer submatrix-based group sparse regularization constructs a structured regularization on the unknown forces, by binding the unknown amplitudes associated with different potential locations into separate groups and promoting the group-level sparsity between the potential locations. Similarly, Group sparse feature also exist in the equivalent nodal force which is transferred from the moving force. Based on this feature, one group weighted Tikhonov regularization method is proposed for the moving force identification via the equivalent nodal force. These proposed methods for load identification are validated numerically and experimentally. In terms of structural damage detection, a new interface slip monitoring system is developed to directly measure the relative displacement between the concrete slab and steel girder and the integrity of the shear connectors has been assessed by the slip measurements. The finite element numerical model has been developed to study the interface damage detection of the steel-concrete composite structure under the pseudo moving vehicular load. The results show that the slippage divergence ratio is very sensitive to the shear connector damage. In practice, the cable force of the cable-stayed bridge is difficult to be monitored for its damage detection. Based on the relationship between the cable force and the strain of the bridge deck, a new method is proposed for the localization and quantitative identification of cable damage using the strain measurements of the bridge deck. Here the damage cable identification problem is treated as a multi-classification problem and the damage degree identification problem as a nonlinear regression problem using support vector machine. The proposed method has strong anti-noise performance and can be easily adapted to field health monitoring system.

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

http://hdl.handle.net/10453/171437