An Analysis of Skewed Bridge/Vehicle Interaction Using the Grillage Method
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An Analysis of Skewed Bridge/Vehicle Interaction Using the Grillage Method
An Analysis of Skewed Bridge/Vehicle Interaction Using the Grillage Method

Author: H. Zeng, J. Kuehn, J. Sun, H. Stalford | Size: 49 KB | Format: PDF | Quality: Original preprint | Publisher: H. Zeng, J. Kuehn, J. Sun, H. Stalford | pages: 6

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The 1998 Bridge Inventory classified approximately 68,000 of the 280,000
American highway bridges as substandard. One way to extend the useful service life of a
bridge is to reduce peak vehicle loads. Field tests conducted at Walnut Creek Bridge on
Interstate 35 near Purcell, Oklahoma revealed that it is common for vehicles to exert peak
dynamic loads 1.3-1.7 times their static weights on the bridge. The focus of this work is
to model the dynamic interaction between vehicles and the bridge to facilitate the
development of strategies aimed at reducing dynamic loads applied to the bridge.
Walnut Creek Bridge is a two-lane, four-span, continuous steel girder bridge with a
reinforced concrete deck. The bridge structure is modeled as an assembly of grillage
members, consisting of longitudinal and transverse torsion beams. The finite element
model includes 205 nodes and 425 elements. The vehicle used in the analysis is a
36,000kg tractor-trailer, which is the heaviest vehicle allowed on this bridge without a
permit. The vehicle model is a 7 degree-of-freedom planar representation that accounts
for both the heave and pitch. The equations of motion of the vehicle and the bridge are
treated as two subsystems and are solved separately using the fourth order Runge-Kutta
integration method in state space. The compatibility equations at the interface between
the vehicle tires and bridge deck are satisfied by an iterative procedure.
The simulation results are compared to experimental results obtained by using a
tractor-trailer for both quasi-static and dynamic tests. A response of a typical point of the
bridge has a peak error of 8.2% and an RMS error of 12.4% for the quasi-static case, and
a 17.6% peak error and a 24.5% RMS error for the dynamic case, respectively. The close
agreement between the simulations and experiments enables a study of the influence of
various parameters which contribute to the response of the interacted system.

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