Coupling asphalt construction process quality into product quality using data-driven methods

The long-term quality of the asphalt layer is crucial for maintaining the functionality of roads. Despite extensive research on predicting pavement failure modes and the effect of design and road use on the quality of the asphalt layer, there is limited understanding of how the quality of road const...

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Bibliographic Details
Published inISARC. Proceedings of the International Symposium on Automation and Robotics in Construction Vol. 40; pp. 349 - 356
Main Authors Shen, Q, Vahdatikhaki, F, Miller, S R, Dorée, A G
Format Conference Proceeding
LanguageEnglish
Published Waterloo IAARC Publications 01.01.2023
Subjects
Asphalt pavements
Case studies
Datasets
Failure modes
Highway construction
Internet of Things
Machine learning
Methods
Product quality
Road construction
Roads & highways
Trucks
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Summary:The long-term quality of the asphalt layer is crucial for maintaining the functionality of roads. Despite extensive research on predicting pavement failure modes and the effect of design and road use on the quality of the asphalt layer, there is limited understanding of how the quality of road construction impacts the long-term quality of asphalt pavement. This paper presents a data-driven approach to studying the impact of construction process quality on the International Roughness Index (IRI) of roads. Two machine learning models (Random Forest and Gated Recurrent Unit) were compared in a case study, with the GRU model (R2 of 0.8284) outperforming the RF model (R2 of 0.5498). Results showed that construction process quality was the third most significant factor affecting IRI.