A Machine Learning Tool for Pavement Design and Analysis

The AASHTOWare Pavement ME Design program is a pavement analysis tool, which is typically used for design purposes through an iterative trial-and-error process. To help the designer with a reasonable starting point in this iterative process, this paper introduces a machine learning method to embrace...

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Bibliographic Details
Published in:KSCE journal of civil engineering 2023, 27(1), , pp.207-217
Main Authors: Yang, Guangwei, Mahboub, Kamyar C., Renfro, Ryan L., Graves, Clark, Wang, Kelvin C. P.
Format: Article
Language:English
Subjects:
Asphalt
Civil Engineering
Concrete pavements
Design
Design analysis
Engineering
Flexible pavements
Geotechnical Engineering & Applied Earth Sciences
Highway Engineering
Industrial Pollution Prevention
Iterative methods
Learning algorithms
Machine learning
Pavement design
Rigid pavements
Software
Thickness
Traffic volume
토목공학
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Summary:The AASHTOWare Pavement ME Design program is a pavement analysis tool, which is typically used for design purposes through an iterative trial-and-error process. To help the designer with a reasonable starting point in this iterative process, this paper introduces a machine learning method to embrace the recently updated models in AASHTOWare Pavement ME Design software for pavement design. A total number of 79,600 pavement design scenarios (55,800 for flexible pavements and 23,800 for rigid pavements) were performed using the AASHTOWare Pavement ME Design software to consider various design inputs, such as: design life, traffic volume, climate zone, thickness, and modulus of pavement layers. The inputs and outputs of these design scenarios were used to develop the multioutput Random Forests model to simultaneously predict multiple pavement distresses and thicknesses of pavement layers. The results indicate that the multi-output Random Forests model can accurately predict pavement distresses and thicknesses for asphalt and concrete pavements. This tool will simplify pavement design procedure based on the models in the AASHTOWare Pavement ME Design software.
ISSN:1226-7988
1976-3808
DOI:10.1007/s12205-022-0448-z