Model for Estimating the Modulus of Elasticity of Asphalt Layers Using Machine Learning

The management of roads, as well as their maintenance, calls for an adequate assessment of the load-bearing capacity of the pavement structure. This serves as the basis on which future maintenance requirements are planned and plays a significant role in determining whether the rehabilitation or reconstruction of the pavement structure is required. The stability of the pavement structure depends on a large number of parameters, and it is not possible to fully assess all of them when making an estimation. One of the most significant parameters is the modulus of elasticity of asphalt layers (EAC). The goal of this study is to use models based on machine learning to perform a quick and efficient assessment of the modulus of elasticity of asphalt layers, as well as to compare the formed models. The paper defines models for EAC estimation using machine learning, in which the input data include the measured deflections and the temperature of the upper surface of the asphalt layer. Analyses of modeling using artificial neural networks (ANNs), support vector machines (SVMs) and boosted regression trees (BRT) were compared. The SVM method showed a higher accuracy in estimating the EAC modulus, with a mean absolute percentage error (MAPE) of 7.64%, while the ANN method and the BRT achieved accuracies of 9.13% and 8.84%, respectively. Models formed in this way can be practically implemented in the management and maintenance of roads. They enable an adequate assessment of the remaining load-bearing capacity and the level of reliability of the pavement structure using non-destructive methods, at the same time reducing the financial costs.