This document discusses using Moving Force Identification (MFI) theory to detect bridge damage. MFI identifies changes in predicted axle force histories that occur due to loss of bridge stiffness from damage. Numerical simulations use a vehicle-bridge interaction model to assess MFI's effectiveness at detecting stiffness changes. Fleets of vehicles are simulated and the mean force pattern is used as the damage indicator. Results show MFI is more sensitive to damage than direct displacement measurements. The paper also explores using force history as an indicator of damage location.

1. Investigating the use of Moving Force Identification Theory in Bridge
Damage Detection
Ciaran H. Carey1, a
, Eugene J. OBrien1, b
and Jennifer Keenahan1, c
1
School of Civil, Structural and Environmental Engineering, University College Dublin,
Newstead, Belfield, Dublin 4, Ireland
a
ciaran.carey@ucd.ie, b
eugen.obrien@ucd.ie, c
jennifer.ni-choinneachain@ucdconnect.ie
Keywords: Damage Detection; Moving Force Identification; MFI;
Abstract
This paper investigates the use of Moving Force Identification as a method of bridge damage
detection. It identifies changes in the predicted axle force histories that occur as a result of loss in
bridge element stiffness, i.e. as a result of bridge damage. A 2-dimensional Vehicle-Bridge
Interaction model is used in numerical simulations to assess the effectiveness of the method in
detecting changes in stiffness. Fleets of similar vehicles are simulated and the mean force pattern is
used as the damage indicator. Results show that the method is more sensitive to damage than direct
measurements of displacement. The paper also explores the use of the force history as an indicator
of damage location.
Introduction
The maintenance of bridges is central to the structural integrity and cost-effectiveness of any
transportation system. Early damage detection plays a central role in any maintenance programme.
The Malahide Viaduct collapse in Ireland on the 21st August 2009 and the collapse of the Interstate
34 Bridge in Minneapolis on 1st August 2007 [1] are examples of the challenges associated with the
current visual-based bridge inspection regime.
In recent years, sensor based monitoring of bridge condition has been made possible by
improvements in computational power and signal processing capacity. While many in the field have
investigated methods of detecting the presence of damage [2], success has been limited and few
have investigated detecting damage location. A brief literature review is provided here into methods
for detecting the location of damage in bridges. More comprehensive literature reviews can be
found elsewhere [3].
In numerical vehicle-bridge-interaction simulations, Kim et al. [4] identify the location of
damage directly from changes in the Element Stiffness Index (ratio of flexural rigidity of finite
element at damaged location to rigidity elsewhere) using a pseudo-static formulation derived from
the equations of motion for coupled vibration. The physically tangible relationship between changes
in stiffness and changes in natural frequency has led many in the field to use natural frequency as an
indicator of damage. Messina et al. [5] present a method based on a linearised sensitivity of
frequency shifts to damage and assumes changes in stiffness only. The method is capable of
identifying multiple damage locations. Nandwana et al. [6] model a crack in a cantilever as a
rotational spring and present plots of spring stiffness against crack location for any three natural
modes through the characteristic equation. The point of intersection of the three curves gives the
crack location.
Kim and Stubbs [7] present a method of detecting damage location which infers location
information directly from changes in mode shapes. Kim et al. [8], in an attempt to compare
frequency and mode shape-based methods, apply both methods to identify damage location in a
simulated beam. The two methods are used for several damage scenarios and it is found that the