A methodology for deriving a probabilistic braking force model from traffic data

This study presents a methodology for deriving a probabilistic model for estimating the braking force, which, on the contrary, is traditionally based on deterministic approaches in bridge design codes. The stochastic model utilizes the weight-in-motion dataset collected from a provincial road bridge for observing real traffic load probabilistic distributions in terms of vehicle gross weight, vehicle length, and inter-vehicle distance. Using Monte Carlo simulations, traffic convoys are generated for calculating the resultant braking force by assuming deceleration profiles available in literature and different scenarios to account for various braking combinations among the vehicles within a convoy. Starting from the obtained empirical cumulative distribution function, the probabilistic model provides the resultant braking force associated with a given return period, incorporating dynamic amplification factors as well. Comparisons are made to highlight that, within the span lengths investigated, the probabilistic model proposed provides higher resultant braking forces than those given by the deterministic model adopted by the Eurocode and the Italian Standards in cases of high return periods and low nominal lives (i.e., in cases of high no-occurrence probability). Conversely, values in agreement with or lower than those calculated using the deterministic models considered are obtained in other cases. Finally, some simplified design equations for the resultant braking forces are proposed for three different nominal lives, which are useful for assessing existing bridges or designing new ones.