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

Q. Shen; F. Vahdatikhaki; S. R. Miller; A. G. Dorée

doi:10.22260/ISARC2023/0048

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

Q. Shen, F. Vahdatikhaki, S. R. Miller, A. G. Dorée

Market Dynamics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

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.

Original language	English
Title of host publication	Proceedings of the 40th International Symposium on Automation and Robotics in Construction, ISARC 2023
Editors	Borja Garcia de Soto, Vicente Gonzalez, Ioannis Brilakis
Publisher	International Association for Automation and Robotics in Construction (IAARC)
Pages	349-356
Number of pages	8
ISBN (Electronic)	978-0-6458322-0-4
DOIs	https://doi.org/10.22260/ISARC2023/0048
Publication status	Published - 2023
Event	40th International Symposium on Automation and Robotics in Construction, ISARC 2023 - Indian Institute of Technology (IIT) Madras, Chennai, India Duration: 5 Jul 2023 → 7 Jul 2023 Conference number: 40

Publication series

Name	Proceedings of the International Symposium on Automation and Robotics in Construction
ISSN (Electronic)	2413-5844

Conference

Conference	40th International Symposium on Automation and Robotics in Construction, ISARC 2023
Abbreviated title	ISARC
Country/Territory	India
City	Chennai
Period	5/07/23 → 7/07/23

Keywords

Asphalt construction
construction process quality
data-driven methods
international roughness index (IRI)
machine learning
regression
2024 OA procedure

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10.22260/ISARC2023/0048

046_ISARC 2023_Paper_213Final published version, 1.24 MBLicence: Taverne

Cite this

Shen, Q., Vahdatikhaki, F., Miller, S. R., & Dorée, A. G. (2023). Coupling asphalt construction process quality into product quality using data-driven methods. In B. Garcia de Soto, V. Gonzalez, & I. Brilakis (Eds.), Proceedings of the 40th International Symposium on Automation and Robotics in Construction, ISARC 2023 (pp. 349-356). (Proceedings of the International Symposium on Automation and Robotics in Construction). International Association for Automation and Robotics in Construction (IAARC). https://doi.org/10.22260/ISARC2023/0048

Shen, Q. ; Vahdatikhaki, F. ; Miller, S. R. et al. / Coupling asphalt construction process quality into product quality using data-driven methods. Proceedings of the 40th International Symposium on Automation and Robotics in Construction, ISARC 2023. editor / Borja Garcia de Soto ; Vicente Gonzalez ; Ioannis Brilakis. International Association for Automation and Robotics in Construction (IAARC), 2023. pp. 349-356 (Proceedings of the International Symposium on Automation and Robotics in Construction).

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title = "Coupling asphalt construction process quality into product quality using data-driven methods",

abstract = "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.",

keywords = "Asphalt construction, construction process quality, data-driven methods, international roughness index (IRI), machine learning, regression, 2024 OA procedure",

author = "Q. Shen and F. Vahdatikhaki and Miller, {S. R.} and Dor{\'e}e, {A. G.}",

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year = "2023",

doi = "10.22260/ISARC2023/0048",

language = "English",

series = "Proceedings of the International Symposium on Automation and Robotics in Construction",

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Shen, Q , Vahdatikhaki, F , Miller, SR & Dorée, AG 2023, Coupling asphalt construction process quality into product quality using data-driven methods. in B Garcia de Soto, V Gonzalez & I Brilakis (eds), Proceedings of the 40th International Symposium on Automation and Robotics in Construction, ISARC 2023. Proceedings of the International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction (IAARC), pp. 349-356, 40th International Symposium on Automation and Robotics in Construction, ISARC 2023, Chennai, India, 5/07/23. https://doi.org/10.22260/ISARC2023/0048

Coupling asphalt construction process quality into product quality using data-driven methods. / Shen, Q.; Vahdatikhaki, F.; Miller, S. R. et al.
Proceedings of the 40th International Symposium on Automation and Robotics in Construction, ISARC 2023. ed. / Borja Garcia de Soto; Vicente Gonzalez; Ioannis Brilakis. International Association for Automation and Robotics in Construction (IAARC), 2023. p. 349-356 (Proceedings of the International Symposium on Automation and Robotics in Construction).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AB - 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.

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Shen Q , Vahdatikhaki F , Miller SR , Dorée AG. Coupling asphalt construction process quality into product quality using data-driven methods. In Garcia de Soto B, Gonzalez V, Brilakis I, editors, Proceedings of the 40th International Symposium on Automation and Robotics in Construction, ISARC 2023. International Association for Automation and Robotics in Construction (IAARC). 2023. p. 349-356. (Proceedings of the International Symposium on Automation and Robotics in Construction). doi: 10.22260/ISARC2023/0048

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

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