Vehicle axle load identification on bridge deck with irregular road surface profile

The vehicular axle load on top of a bridge deck is estimated in this paper including the effect of the road surface roughness which is modeled as a Gaussian random process represented by the Karhunen-Loève expansion. The bridge is modeled as a simply supported planar Euler-Bernoulli beam and the vehicle is modeled by a four degrees-of-freedom mass-spring system. A stochastic force identification algorithm is proposed in which the statistics of the moving interaction forces can be accurately identified from a set of samples of the random responses of the bridge deck. Numerical simulations are conducted in which the Gaussian assumption for the road surface roughness, the response statistics calculation and the stochastic force identification technique for the proposed bridge-vehicle interaction model are verified. Both the effect of the number of samples used and the effect of different road surface profiles on the accuracy of the proposed stochastic force identification algorithm are investigated. Results show that the Gaussian assumption for the road surface roughness is correct and the proposed algorithm is accurate and effective.