Piezoresistivity assessment of self-sensing asphalt-based pavements with machine learning algorithm

Deng, Z; Nguyen, QD; Mahmood, AH; Pang, Y; Shi, T; Sheng, D

Piezoresistivity assessment of self-sensing asphalt-based pavements with machine learning algorithm

Deng, Z Nguyen, QD Mahmood, AH Pang, Y Shi, T Sheng, D

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Construction and Building Materials, 2025, 468
Issue Date:: 2025-03-21

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Field	Value	Language
dc.contributor.author	Deng, Z
dc.contributor.author	Nguyen, QD
dc.contributor.author	Mahmood, AH
dc.contributor.author	Pang, Y
dc.contributor.author	Shi, T
dc.contributor.author	Sheng, D https://orcid.org/0000-0002-1665-6931
dc.date.accessioned	2025-04-14T00:06:14Z
dc.date.available	2025-04-14T00:06:14Z
dc.date.issued	2025-03-21
dc.identifier.citation	Construction and Building Materials, 2025, 468
dc.identifier.issn	0950-0618
dc.identifier.issn	1879-0526
dc.identifier.uri	http://hdl.handle.net/10453/186870
dc.description.abstract	Due to its fast-curing process, asphalt binder has been increasingly used to replace conventional cementitious materials and to produce asphalt-based self-sensing sensors (ASS). The asphalt mixture does not require consideration of curing age, as asphalt-based samples can be cured at room temperature. This is because the asphalt binder transitions from a Newtonian liquid to a solid state upon cooling. The relationship between strain and electrical response is one of the factors that influences the self-sensing performance. In this study, four-electrode method was applied to ASS and 10 V of applied voltage was used to measure the piezoresistivity. The percolation thresholds of ASS, in the range of 0.5–1.0 wt%, was found based on study results. In order to analyse the strain of ASS, digital image correlation (DIC) method was applied, and the cyclic loading process was used to simulate the practical pavement situation. Furthermore, to enable the application of ASS and enhance the efficiency of the self-sensing system, a machine learning approach was applied in this study to establish the relationship between strain changes and the electrical response of ASS. The trained algorithm, exhibiting a high determination coefficient (reached 0.965), can be utilized to predict strain changes in self-sensing sensors based on fractional resistance changes. Artificial intelligence significantly enhanced the application potential of self-sensing sensors.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation	http://purl.org/au-research/grants/arc/IH180100010
dc.relation.ispartof	Construction and Building Materials
dc.relation.isbasedon	10.1016/j.conbuildmat.2025.140291
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0905 Civil Engineering, 1202 Building
dc.subject.classification	Building & Construction
dc.subject.classification	3302 Building
dc.subject.classification	4005 Civil engineering
dc.subject.classification	4016 Materials engineering
dc.title	Piezoresistivity assessment of self-sensing asphalt-based pavements with machine learning algorithm
dc.type	Journal Article
utslib.citation.volume	468
utslib.for	0905 Civil Engineering
utslib.for	1202 Building
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 Civil and Environmental Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Transport Research Centre (TRC)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-04-14T00:06:13Z
pubs.publication-status	Published
pubs.volume	468

Abstract:

Due to its fast-curing process, asphalt binder has been increasingly used to replace conventional cementitious materials and to produce asphalt-based self-sensing sensors (ASS). The asphalt mixture does not require consideration of curing age, as asphalt-based samples can be cured at room temperature. This is because the asphalt binder transitions from a Newtonian liquid to a solid state upon cooling. The relationship between strain and electrical response is one of the factors that influences the self-sensing performance. In this study, four-electrode method was applied to ASS and 10 V of applied voltage was used to measure the piezoresistivity. The percolation thresholds of ASS, in the range of 0.5–1.0 wt%, was found based on study results. In order to analyse the strain of ASS, digital image correlation (DIC) method was applied, and the cyclic loading process was used to simulate the practical pavement situation. Furthermore, to enable the application of ASS and enhance the efficiency of the self-sensing system, a machine learning approach was applied in this study to establish the relationship between strain changes and the electrical response of ASS. The trained algorithm, exhibiting a high determination coefficient (reached 0.965), can be utilized to predict strain changes in self-sensing sensors based on fractional resistance changes. Artificial intelligence significantly enhanced the application potential of self-sensing sensors.

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http://hdl.handle.net/10453/186870