This paper proposes a novel bridge damage detection method using a moving force identification algorithm to detect changes in the predicted forces applied by vehicles crossing a bridge. A two-dimensional vehicle-bridge interaction model is used in simulations to evaluate how sensitive the method is to detecting changes in bridge stiffness due to damage. The results show that the proposed method is more sensitive than direct displacement measurements and can detect a loss of stiffness from a crack as small as 6% of the beam depth by analyzing changes in the most frequently occurring force pattern from simulated fleets of vehicles.

1. Bridge damage detection using ambient traffic and moving force
identification
Eugene OBrien1
, Ciaran Carey1
and Jennifer Keenahan2,
*,†
1
Department of Civil, Structural and Environmental Engineering, University College Dublin, Dublin, Ireland
2
Arup, 50 Ringsend Road, Ringsend, Dublin 4, Ireland
SUMMARY
This paper investigates a novel method for damage detection using a moving force identification algorithm. The
method aims to detect the changes in the predicted forces applied by vehicles crossing a bridge, which are shown
to be sensitive to damage. A two-dimensional vehicle–bridge interaction model is used in theoretical simulations
to assess the effectiveness of the method in detecting changes in stiffness. Fleets of similar vehicles are simulated,
and the force pattern of greatest frequency is used as the damage indicator. Results indicate that the method is more
sensitive to damage than direct measurements of displacement and can detect a loss in stiffness due to a crack with
a depth of as little as 6% of the beam depth. Copyright © 2015 John Wiley & Sons, Ltd.
Received 27 September 2013; Revised 23 February 2015; Accepted 7 March 2015
KEY WORDS: Moving Force Idnetification; Vehicle Bridge Interaction; Bridge Damage; Bridge Dynamics; Bridge
Displcement; Structural Health Monitoring
1. INTRODUCTION
In most developed countries, bridge condition is assessed based on visual inspections. Such inspections
are, however, labour intensive and often an unreliable way of determining the true condition. Examples
of the importance of effective bridge inspection include the collapse of the Interstate 34 Bridge in
Minneapolis on 1st August 2007 [1] and the collapse of an Interstate 5 bridge over the Skagit River
in Seattle on 27th May 2012 [2]. In other cases, over-precautionary measures are taken and unnecessary
repairs result. The shortcomings of visual inspections, combined with the increase in computational
power and signal processing capacity, have resulted in a move towards sensor-based monitoring of
bridge condition in recent years. A brief literature review is provided here on damage detection methods
for bridges. More comprehensive literature reviews can be found in [3–6].
The physically tangible relation between the changes in stiffness or mass and changes in natural fre-
quency has led many in the field of damage detection to use natural frequency as an indicator of dam-
age [7]. Cerda et al. [8] carry out an experimental work where damage is simulated as a change in
bridge mass, and changes in frequency are tested as the damage indicator. Messina et al. [9] present
a method based on linearised shifts in frequency due to damage and consider the changes in stiffness
only, capable of tackling multiple damage locations and intensities.
A damage detection algorithm, where information on the location and severity of damage is inferred
directly from changes in mode shapes, is presented by Kim and Stubbs [10]. In an effort to compare
frequency with mode shape methods, Kim et al. [11] applied both frequency-based and modal strain
energy-based method to identify damage location and severity in a simulated beam. The two methods
*Correspondence to: Jennifer Keenahan, Arup, 50 Ringsend Road, Ringsend, Dublin 4.
†
E-mail: jennifer.keenahan@gmail.com
STRUCTURAL CONTROL AND HEALTH MONITORING
Struct. Control Health Monit. 2015; 22:1396–1407
Published online 27 April 2015 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/stc.1749
Copyright © 2015 John Wiley & Sons, Ltd.