Comparison of forecasting methods for solving problems of managing the stability of asphalt concrete mixture

Abstract

Comparison of forecasting methods for solving problems of managing the stability of asphalt concrete mixture

Suvorov D.N., Ilyukhin A.V., Nguyen X.V., Duong D.T.

Incoming article date: 20.09.2023

Prompt adjustment of the composition of the asphalt concrete mixture is key to achieving high quality asphalt concrete. To enable easy and rapid adjustment of the asphalt concrete mixture formulation, predicting the properties of asphalt concrete (Marshall stability) is critically important. There are many methods for predicting the properties of asphalt concrete, but the choice of one method or another is a very pressing problem. This article proposes two methods for forecasting Marshall stability: forecasting using a multiple linear regression model and forecasting using an autoregressive model. To evaluate the forecasting accuracy of models, we use two metrics: average absolute error (MAE) and average absolute percentage error (MAPE). The results of the study show that the autoregressive model exhibits better forecasting results, especially the second-order autoregressive model.

Keywords: asphalt concrete, control, composition adjustment, forecasting, multiple linear regression model, autoregression model, Marshall stability, forecast accuracy, mean absolute error, mean absolute percentage error