Modeling the Pavement Condition Index (PCI) of Pavement by Multiple linear regression and neural network back propagation

One of the most important goals of a pavement management system is determination of optimal priorities and time for repairs, through prediction of pavement status. In fact, the purpose of the PMS system is to repair and maintain the early stages of cost savings and savings. . Therefore, in this study, in order to determine the Pavement Condition Index (PCI), two linear regression models and back propagation neural network models were fitted and their power estimates were compared.  In this regard, the damages of three freeways of Karbala, Paul Zal and Tehran - Qom have been studied to identify the appropriate method for predicting pavement status index in order to identify the optimal maintenance time to reduce its costs. . Micropaver software and MATLAB and SPSS software were used for modeling and evaluation of components. This track was sampled in order to capture the failure of sample units at 100 m intervals and parts at 500 m intervals. The variables considered in the model analysis included: segment lifetime at inspection time (month), unit width, average AADT at segment lifetime, average percentage of heavy vehicles at segment lifetime, maximum temperature at segment lifetime in 1396, minimum Temperature is the lifetime of the piece in 1396 and the thickness of the pavement (cm). . The results show that the performance of neural network model based on mean square error index (MSE) as well as R2 index is 0.95 and 0.87, respectively, which is more valid than the multiple linear regression model (0.139). The future is paved. In addition, according to the neural network model, the lifetime of the segment can be most important in neural network construction (0.55) and then maximum temperature (0.122) and percentage of heavy vehicles (0.120) are the next important variables in predicting pavement status of roads.