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Description
Abstract. This study focuses on the deterioration of road pavement, particularly the formation of rutting, which adversely affects vehicle braking performance, especially in wet conditions. The research aims to predict the combined effects of rutting and intense precipitation on the vehicle braking performances by using a model based on Back-Propagation Neural Network (BPNN) algorithm. The model calculates vehicle braking distances on wet bituminous pavement, during rainfall with variable intensity, coupled with the effect of rutting, considering the presence of various Water Film Thicknesses (WFTs) over the pavement. It addresses real-world scenarios, incorporating factors like precipitation intensity, lane characteristics, rutting depths, and accumulated WFT. It can be seen that the effect of rutting on vehicle performance, particularly during rainy days when the rutting depressions are filled with rainwater, results in longer braking distances compared to dry conditions. The model's applicability is demonstrated through validations, examining its performance against existing methods. Additionally, the developed model is verified by simulating a vehicle's performance in a real case study, considering varying rutting depths every 10 meters during intense precipitation.
