Mechanical performance prediction of asphalt mixtures: a baseline study of linear and non-linear regression compared with neural network modeling

Accurate predictions of asphalt mixtures' mechanical performance are crucial to improve the conventional mix-design procedures and to optimize both pavements' performance and service life. This research explores this issue by means of a comparative analysis between different modeling approaches: conventional regressions, both linear and non-linear, and artificial neural networks. Theformerare widelyused but may lack the flexibility to capture complex relationships between testing conditions and the corresponding mechanical behavior. The latterrepresent promising alternatives duetotheircapabilityto successfully model non-linear interactions between variables. This research compares the predictive accuracy of these different modeling approaches using experimental data resulting from 4-point bending tests carried out underseveral temperatures and loadingfrequencies.The outcomes suggest that neural networks outperform conventional regression models in capturing complex relationships, highlighting the strengths and limitations of each modeling approach and providing insights for selecting optimal models in road pavement engineering applications.