Modeling a Hybrid Pavement Conditions Performance Framework for Botswana District Road Transportation Networks

摘要

Road conditions performance modeling is required in order to predict the future conditions and provide information that can be applied to transportation planning, decision making processes and identification of future maintenance interventions. As extension of knowledge in existing gravel road condition models, improved artificial intelligent gravel road performance models which best capture the effects of gravel loss condition influencing factors were developed using feed forward neural network (FFNN) hybrid with a district GIS-based map using linear referencing approach to display gravel loss conditions as a threshold to trigger optimal maintenance interventions. The developed FFNN gravel loss condition (GVL) prediction model yielded R2 = 0.95 > 0.9 benchmark based on minimum MSE = 0.055 < 0.1. Threshold value = 3 (fair condition) was specified on the GIS map for triggering maintenance interventions when gravel road subgrade exposure due to gravel loss is between 10 - 25% as condition monitoring innovative tools.