Non-structural parameters like surface defects and ride quality were frequently used, as a practical index for the rehabilitation selection process. The key purpose of this study was the assessment of using artificial network technology as a support for decision-makers about paving maintenance concerning the structural condition compared to the conventional, time-consuming, effort, and costly methods. The structural model was established based on the deflections from the FWD, (asphalt and base) layers thickness, surface temperature, precipitation rate, AADTT, traffic volume of class 9 and base layer type. The data used in building the developed ANN model is related to a previous study of flexible pavement structures on the M4 highway in the Russian highways network during the five years 2013-2017. The ANN model was built, trained, and tested by the Matlab program. The focus was on calculating roughness, fatigue, and rutting values as they are the most common pavement distress on site. We used the logistic model equations, developed by the Federal Highway Administration’s Long-Term pavement Performance (LTPP) to calculate the three pavement distress that will be used as output variables while training the ANN model. The ANN model presented a high performance in predicting the three pavement distress (fatigue, roughness, and rutting) where the R- squared value was equal (1, 0,999, and 1), respectively for the forecasting sections.

Statement of the problem. The article is devoted to the use of artificial neural networks in solving the problems of processing the results of instrumental recording of bowls of flexible pavement deflections using FWD shock loading settings. Results. The analysis was carried out, the shortcomings of the existing processing methods were noted, in particular the “backcalculation” method, which consists of a long calculation time, and the instability of the results obtained. The structure of the artificial neural network was built to determine the elastic moduli of the pavement layers. Training of an artificial neural network was carried out using the method of back propagation of error. Conclusions. The developed neural network has shown good results in training on the test data set, as well as high accuracy of prediction of the elastic moduli of the pavement.

Introduction. This paper studies the capability of different types of artificial neural networks (ANN) to predict the modulus of elasticity of pavement layers for flexible asphalt pavement under operating conditions. The falling weight deflectometer (FWD) was selected to simulate the dynamic traffic loads and measure the flexural bowls on the road surface to obtain the database of ANN models.Materials and Methods. Artificial networks types (the feedforward backpropagation, layer-recurrent, cascade back- propagation, and Elman backpropagation) are developed to define the optimal ANN model using Matlab software. To appreciate the efficiency of every model, we used the constructed ANN models for predicting the elastic modulus values for 25 new pavement sections that were not used in the process of training, validation, or testing to ensure its suitability. The efficiency measures such as mean absolute error (MAE), the coefficient of multiple determinations R2, Root Mean Square Error (RMSE), Mean Absolute Percent Error (MAPE) values were obtained for all models results.Results. Based on the performance parameters, it was concluded that among these algorithms, the feed-forward model has a better performance compared to the other three ANN types. The results of the best four models were compared to each other and to the actual data obtained to determine the best method.Discussion and Conclusions. The differences between the results of the four best models for the four types of algorithms used were very small, as they showed the closeness between them and the actual values. The research results confirm the possibility of ANN-based models to evaluate the elastic modulus of pavement layers speedily and reliably for using it in the structural assessment of (NDT) flexible pavement data at the appropriate time.