| description abstract | Asphalt pavement performance is crucial for the sustainable management of road infrastructure. However, achieving accurate predictions remains challenging due to the complex interactions among materials, environmental factors, and traffic loads. In this study, the optical microscope algorithm–least squares moment balanced machine (OMA-LSMBM), an AI-based inference engine, was developed to enhance the accuracy of asphalt performance prediction. This approach integrates machine-learning techniques with optimization algorithms. In the proposed model, LSMBM considers moments to determine the optimal hyperplane, a backpropagation neural network assigns weights to each datapoint, and an OMA optimizes the LSMBM hyperparameters and identifies the optimal feature subset combination. The proposed model was tested using three simulations, i.e., benchmark functions, pavement surface temperature, and asphalt mixture flow number. OMA-LSMBM demonstrated the best function approximation performance, improving the performance metrics and achieving a root mean square error value for pavement temperature prediction that was 6.49%–72.62% less than the comparison models. In terms of predicting flow number, the proposed model showed superior performance over the comparison models with a 11.15%–54.83% lower error rate. These results demonstrate the OMA-LSMBM significantly enhances the accuracy of asphalt performance predictions, which may be directly applied to improving road maintenance strategies and planning activities. | |
