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In this work, a method for vehicle load identification is proposed. It involves Bayesian
Analysis and (quasi-)static importance functions in order to estimate vehicle positions,
velocities and weights. The structure is modeled with finite elements in order to generate
model predictions for different load configurations. The model predictions are compared to
the actual measured data to identify the most probable loading configuration for that
measurement. This involves the use of enhanced Monte Carlo simulations such as MCMC to
reduce the computational effort.