Abstract
Asphalt-paved Road junctions frequently encounter deformation and degradation challenges due to heavy vehicular traffic and varying climatic conditions, such as temperature fluctuations and precipitation. This study employs a multifaceted approach, incorporating a Multilayer Perceptron (MLP) model, ancillary machine learning techniques, and optimization methodologies, to address these challenges effectively. The primary objectives are the prediction and analysis of pavement deformation, the optimization of maintenance strategies, and the evaluation of road effectiveness. Our findings underscore the substantial contribution of heavy vehicles to road erosion and the profound impact of vehicular retention and braking at intersections. A Multilayer Perceptron (MLP) model is utilized to simulate future pavement degradation accurately at a specific intersection, leveraging real-time traffic flow data. This approach showcases the advantages of using real-world traffic data to model the lifecycle of asphalt dependencies dynamically at the intersection level. Mitigation of road deterioration is proposed via controlled traffic flow and optimization of relevant parameters, such as minimization of intersection wait times. The integration of machine learning substantially enhances road conditions and reduces vehicular waiting times at intersections. The implementation of this study's findings in pavement design and preservation practices could enable transportation authorities to improve road safety, reduce maintenance costs, and decrease the incidence of road accidents. Overall, this paper presents a comprehensive approach towards sustainable and efficient road infrastructure management, highlighting the potential of AI in tackling pressing infrastructure challenges.
Recommended Citation
S. Vrtagic et al., "AI-Enabled Assessment of Roadway Integrity: Forecasting Bitumen Deformation and Road Stability Throughout the Lifecycle Under Traffic Impact," International Journal of Transportation Development and Integration, vol. 7, no. 4, pp. 321 - 329, International Journal of Transportation Development and Integration, Dec 2023.
The definitive version is available at https://doi.org/10.18280/ijtdi.070406
Department(s)
Materials Science and Engineering
Publication Status
Open Access
Keywords and Phrases
Regression; Modeling erosion coefficients; Simulation; Back propagation; Lath and nail method
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2026 International Journal of Transportation Development and Integration, All rights reserved
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
2023-12-28
