Artificial Neural Networks for Flexible Pavement

Bayat, R; Talatahari, S; Gandomi, AH; Habibi, M; Aminnejad, B

Artificial Neural Networks for Flexible Pavement

Bayat, R Talatahari, S Gandomi, AH Habibi, M Aminnejad, B

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Information (Switzerland), 2023, 14, (2)
Issue Date:: 2023-02-01

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Field	Value	Language
dc.contributor.author	Bayat, R
dc.contributor.author	Talatahari, S
dc.contributor.author	Gandomi, AH
dc.contributor.author	Habibi, M
dc.contributor.author	Aminnejad, B
dc.date.accessioned	2024-04-01T22:12:38Z
dc.date.available	2024-04-01T22:12:38Z
dc.date.issued	2023-02-01
dc.identifier.citation	Information (Switzerland), 2023, 14, (2)
dc.identifier.issn	2078-2489
dc.identifier.issn	2078-2489
dc.identifier.uri	http://hdl.handle.net/10453/177396
dc.description.abstract	Transportation agencies are primarily responsible for building new roads and maintaining current roads. The main focuses of these agencies are to prioritize maintenance and make significant rehabilitation decisions to handle serious problems facing road authorities. Considerable efforts and an abundance of studies have been performed to determine the nature, mechanisms, test methods, and measurement of pavements for preservation and improvements of roadways. The presented study reports a state-of-the-art review on recent advances in the application of artificial intelligence in various steps of flexible pavement, including pavement construction, performance, cost, and maintenance. Herein, the challenges of gathering large amounts of data, parameter optimization, portability, and low-cost data annotating are discussed. According to the findings, it is suggested that greater attention should be paid to integrating multidisciplinary roadway engineering techniques to address existing challenges and opportunities in the future.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Information (Switzerland)
dc.relation.isbasedon	10.3390/info14020062
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences
dc.subject.classification	46 Information and computing sciences
dc.title	Artificial Neural Networks for Flexible Pavement
dc.type	Journal Article
utslib.citation.volume	14
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2024-04-01T22:12:36Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	2

Abstract:

Transportation agencies are primarily responsible for building new roads and maintaining current roads. The main focuses of these agencies are to prioritize maintenance and make significant rehabilitation decisions to handle serious problems facing road authorities. Considerable efforts and an abundance of studies have been performed to determine the nature, mechanisms, test methods, and measurement of pavements for preservation and improvements of roadways. The presented study reports a state-of-the-art review on recent advances in the application of artificial intelligence in various steps of flexible pavement, including pavement construction, performance, cost, and maintenance. Herein, the challenges of gathering large amounts of data, parameter optimization, portability, and low-cost data annotating are discussed. According to the findings, it is suggested that greater attention should be paid to integrating multidisciplinary roadway engineering techniques to address existing challenges and opportunities in the future.

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http://hdl.handle.net/10453/177396