Solving the Problem of Determining the Mechanical Properties of Road Structure Materials Using Neural Network Technologies

Abstract

Introduction. Determination of mechanical properties of layered structures of highways is an urgent task. This is due, firstly, to the need to control the quality of new sections during the construction of highways. Secondly, to assess the condition of existing roads with the accumulation of damage and defects is of interest. The formation of multiple defects (cracks) changes the averaged viscoelastic properties of the components of the structure, specifically, the surface asphalt-concrete layers. The article discusses the use of neural network technologies to improve the accuracy of the recovery of viscoelastic properties. This approach is based on experimental methods. As an example, we can give the definition of the dynamic deflection of a structure from a falling weight, FWD.Materials and Methods. The elastic modulus of a three-layer structure was determined on the basis of a neural network. To find out the solution accuracy, it was compared to the results of mathematical modeling and experimental data.Results. The experimental and calculated parameters of the elastic modulus of individual layers of the road structure turned out to be very close. The proposed approach to determining the mechanical properties of materials of road structures allowed us to apply the obtained results to examination of the condition of individual elements and the entire road structure.Discussion and Conclusions. The prospects of using artificial intelligence to determine the mechanical properties of layered structures was shown. Further improvement of methods and tools for analyzing the behavior of road structures under dynamic loading will expand existing approaches to assessing the condition of road structures.

Prediction of the Properties of Vibro-Centrifuged Variatropic Concrete in Aggressive Environments Using Machine Learning Methods

“…XGBoost (XGB) is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable [57,58]. It is an ensemble learning method that sequentially builds shallow trees that are further ensembled to achieve a more accurate and reliable prediction.…”

Application of neural network technologies to solving the problem of materials classification of two-layer structure by hardness parameter

“…First of all, they are aimed at developing systems for assessing the strength, rigidity, and stability of the structure. A special place is occupied by studies of the stress-strain state of structures under the influence of shock loads [11,12]. In the article [13], the classification analysis of the properties of such materials as mild steel, stainless steel and aluminum of various thicknesses was carried out by means of neural networks.…”