A Novel Approach for Advancing Asphalt Pavement Temperature and Flow Number Predictions Using Optical Microscope Algorithm–Least Square Moment Balanced Machine

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.

• Record URL:
  • https://doi.org/10.1061/JCCEE5.CPENG-5923
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/08873801
• Supplemental Notes:
  • © 2024 American Society of Civil Engineers.
• Authors:
  • Cheng, Min-Yuan
  • Khasani, Riqi Radian
• Publication Date: 2024-11

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 04024034
• Serial:
  • Journal of Computing in Civil Engineering
  • Volume: 38
  • Issue Number: 6
  • Publisher: American Society of Civil Engineers
  • ISSN: 0887-3801

Subject/Index Terms

• TRT Terms: Algorithms; Asphalt pavements; Pavement performance; Predictive models; Temperature
• Subject Areas: Data and Information Technology; Highways; Maintenance and Preservation; Pavements; Planning and Forecasting;

Filing Info

• Accession Number: 01927583
• Record Type: Publication
• Files: TRIS, ASCE
• Created Date: Aug 20 2024 4:17PM