Laory, Irwanda, Trinh, Thanh N., Smith, Ian F.C. and Brownjohn, James M.W. (2014) Methodologies for predicting natural frequency variation of a suspension bridge. Engineering Structures, Volume 80 . pp. 211-221. doi:10.1016/j.engstruct.2014.09.001

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Abstract

In vibration-based structural health monitoring, changes in the natural frequency of a structure are used to identify changes in the structural conditions due to damage and deterioration. However, natural frequency values also vary with changes in environmental factors such as temperature and wind. Therefore, it is important to differentiate between the effects due to environmental variations and those resulting from structural damage. In this paper, this task is accomplished by predicting the natural frequency of a structure using measurements of environmental conditions. Five methodologies – multiple linear regression, artificial neural networks, support vector regression, regression tree and random forest – are implemented to predict the natural frequencies of the Tamar Suspension Bridge (UK) using measurements taken from 3 years of continuous monitoring. The effects of environmental factors and traffic loading on natural frequencies are also evaluated by measuring the relative importance of input variables in regression analysis. Results show that support vector regression and random forest are the most suitable methods for predicting variations in natural frequencies. In addition, traffic loading and temperature are found to be two important parameters that need to be measured. Results show potential for application to continuously monitored structures that have complex relationships between natural frequencies and parameters such as loading and environmental factors.

Item Type:	Journal Article
Subjects:	T Technology > TG Bridge engineering
Divisions:	Faculty of Science > Engineering
Library of Congress Subject Headings (LCSH):	Suspension bridges -- Maintenance and repair -- Environmental aspects -- Mathematical models
Journal or Publication Title:	Engineering Structures
Publisher:	Elsevier Science Ltd.
ISSN:	0141-0296
Official Date:	November 2014
Dates:	Date Event November 2014 Published 22 September 2014 Available 1 September 2014 Accepted 15 November 2011 Submitted
Date of first compliant deposit:	9 February 2017
Volume:	Volume 80
Page Range:	pp. 211-221
DOI:	10.1016/j.engstruct.2014.09.001
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access

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