APPLICATION OF STATISTICAL ANALYSIS AND ARTIFICIAL INTELLIGENCE METHODS IN NONDESTRUCTIVE COMPACTION CONTROL SYSTEMS FOR ASPHALT PAVERS
The results of the study are presented to create the theoretical foundations of non-destructive control of asphalt compaction in real time on the basis of neural network and mathematical-statistical methods. The methods and algorithms obtained in the process allow to predict in real time the indicator of the quality of the sealing of asphalt concrete coatings. Theoretical models of forecasting are considered: based on methods of artificial intelligence - neural network with direct communication and reverse distribution of error (ANN); using the multiple regression method. Comparative analysis of the forecasting results using parameters that affect the quality of the seal of road materials - the speed of the paver, the frequency of impacts of the trambing beam, the effort in the pusher of the tram beam and the type of mixture. It is shown that the accuracy of forecasting based on the ANN model with a direct link is better than the result based on the multiple regression model. The resulting ANN model with a direct link can explain more than 98% of the measured data.