Projects: Past Projects
Effects of Ambient Temperature
on FE Model Updating
Research Team: Babak Moaveni (PI), Iman Behmanesh (grad student)
Funding: NSF-BRIGE (1125624)
Duration: 1/2011 – 4/2012
Relevant publications to date:
Moaveni, B., and Behmanesh, I. (2012). "Effects of changing ambient temperature
on finite element model updating of the Dowling Hall Footbridge."
Engineering Structures, 43, 58-68.
FE model updating has been extensively used for structural health monitoring. In
this method, the physical parameters of a FE model of the structure are updated
to match the measured modal properties of the structure as damage evolves, and
the updated modeling parameters are used to detect, locate, and quantify damage.
The accuracy and spatial resolution of the damage identification results depend
significantly on the accuracy and completeness of the identified modal
parameters. The focus of this section is (1) to quantify the variation of FE
model updating results for the Dowling Hall Footbridge induced by the measured
ambient temperatures, and (2) to reduce this variation through removing the
temperature effects from identified natural frequencies.
An initial FE model of the Dowling Hall Footbridge is created based on the
design drawings and visual inspection of the footbridge, using the MATLAB-based
structural analysis software FEDEASLab. Careful attention is paid to model
geometry since the dimensions in the design drawings do not exactly match the
actual dimensions for a few components of the structure. In the FE model
updating process, a limited number of physical parameters of the FE model are
updated so the modal parameters (natural frequencies and mode shapes) of the FE
model match their experimentally identified counterparts. The updating
parameters are the substructure updating factors, which are defined as the
relative changes in the effective moduli of elasticity of elements in considered
substructures (i.e., groups of elements). In this study, the footbridge is
divided into five substructures as shown in Figure 1. The substructures are
defined based on the locations of accelerometers.
Figure 1. Considered substructures along the footbridge
Effects of temperature on the FE model updating are investigated by comparing
the results based on two sets of identified natural frequencies: before and
after removing temperature effects. The temperature effects are removed from the
identified natural frequencies using the above-mentioned fourth-order polynomial
regression model. A total number of 2×2,088 = 4,176 model updating runs are
performed and the statistics of the substructure updating factors are studied.
Figure 2 shows the variation of updating factors versus the measured steel
temperature by sensor S3 for both cases of model updating (black dots correspond
to before and grey dots correspond to after removing the temperature effects).
The variations of updating parameters are reduced after the temperature effects
are removed. The standard deviations of three out of five updating factors are
reduced up to 50.4%, but the standard deviation of the other two factors
remained almost unchanged. It was observed that the reduction in variation of
substructure updating factors after removing the temperature effects is
significantly less than the reduction in identified natural frequencies (29.7%
to 71.3%). Natural frequency residuals of mode 1, 3, and 5 are also
significantly reduced after removing the temperature effects. This indicates
better fit between the updated FE models and experimental data. Removing
temperature effects results in (a) smaller variability in the updated stiffness
parameters of FE models, (b) lower natural frequency residuals, and (c) updating
factors closer to zero. This yields more accurate results when FE model updating
is used for localization and quantification of damage as loss of stiffness. It
is worth noting that the non-zero updating factors after removing temperature
effects are due to other sources of uncertainty/variability such as FE modeling
errors, estimation errors of modal parameters, frequency-temperature modeling
errors, and pedestrian traffic on the bridge. Effects of estimation and modeling
errors on the FE model updating results can be accounted for in probabilistic
model updating procedures such as Bayesian FE model updating.
Figure 2. Variation of substructure updating factors versus temperature of
sensor S3 (black dots refer to updating results using identified modal
parameters, while grey dots correspond to results using temperature-removed