Double-layered granular soil modulus extraction for intelligent compaction using extended support vector machine learning considering soil-structure interaction

Xu, Z; Khabbaz, H; Fatahi, B; Wu, D

Double-layered granular soil modulus extraction for intelligent compaction using extended support vector machine learning considering soil-structure interaction

Xu, Z Khabbaz, H Fatahi, B

Wu, D

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Engineering Structures, 2023, 274
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Xu, Z
dc.contributor.author	Khabbaz, H
dc.contributor.author	Fatahi, B https://orcid.org/0000-0002-7920-6946
dc.contributor.author	Wu, D https://orcid.org/0000-0002-7284-5024
dc.date.accessioned	2023-01-31T07:15:10Z
dc.date.available	2023-01-31T07:15:10Z
dc.date.issued	2023-01-01
dc.identifier.citation	Engineering Structures, 2023, 274
dc.identifier.issn	0141-0296
dc.identifier.issn	1873-7323
dc.identifier.uri	http://hdl.handle.net/10453/165687
dc.description.abstract	Intelligent Compaction (IC) has been acquiring a growing interest in real-time quality control of compacted soil layers because of its high efficiency and full-area coverage. The current intelligent compaction technology allows the determination of the uniformity level of compaction over large areas according to the dynamic response of the roller. However, accurate real-time determination of the soil modulus during compaction based on roller acceleration has been challenging due to the multi-layered composite nature of the soil and the nonlinearities of the governing dynamic equations of motion and soil response. This study adopts a double-layered soil profile, and a three-dimensional finite element model, accounting for soil-drum interaction, is utilised for the analysis. The isotropic hardening elastoplastic hysteretic model was implemented to simulate the soil behaviour subjected to cyclic loading ranging from small to large strain amplitudes and account for stiffness degradation. The comprehensive dataset composed of the roller acceleration response and ground characteristics is then used to correlate the predicted soil modulus via an advanced machine learning approach. The adopted machine learning method incorporating Gaussian Kernel and Generalised Gegenbauer Kernel functions can reasonably predict the double-layered soil modulus during roller compaction. Additional analyses were conducted to observe the proper training size and number of iterations to achieve real-time quality control to be used by site engineers. Furthermore, the influences of the relative modulus ratio, drum length and top layer modulus on the soil surface dynamic displacement are discussed.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Engineering Structures
dc.relation.isbasedon	10.1016/j.engstruct.2022.115180
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0905 Civil Engineering, 0912 Materials Engineering, 0915 Interdisciplinary Engineering
dc.subject.classification	Civil Engineering
dc.title	Double-layered granular soil modulus extraction for intelligent compaction using extended support vector machine learning considering soil-structure interaction
dc.type	Journal Article
utslib.citation.volume	274
utslib.for	0905 Civil Engineering
utslib.for	0912 Materials Engineering
utslib.for	0915 Interdisciplinary Engineering
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/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	closed_access	*
dc.date.updated	2023-01-31T07:15:08Z
pubs.publication-status	Published
pubs.volume	274

Abstract:

Intelligent Compaction (IC) has been acquiring a growing interest in real-time quality control of compacted soil layers because of its high efficiency and full-area coverage. The current intelligent compaction technology allows the determination of the uniformity level of compaction over large areas according to the dynamic response of the roller. However, accurate real-time determination of the soil modulus during compaction based on roller acceleration has been challenging due to the multi-layered composite nature of the soil and the nonlinearities of the governing dynamic equations of motion and soil response. This study adopts a double-layered soil profile, and a three-dimensional finite element model, accounting for soil-drum interaction, is utilised for the analysis. The isotropic hardening elastoplastic hysteretic model was implemented to simulate the soil behaviour subjected to cyclic loading ranging from small to large strain amplitudes and account for stiffness degradation. The comprehensive dataset composed of the roller acceleration response and ground characteristics is then used to correlate the predicted soil modulus via an advanced machine learning approach. The adopted machine learning method incorporating Gaussian Kernel and Generalised Gegenbauer Kernel functions can reasonably predict the double-layered soil modulus during roller compaction. Additional analyses were conducted to observe the proper training size and number of iterations to achieve real-time quality control to be used by site engineers. Furthermore, the influences of the relative modulus ratio, drum length and top layer modulus on the soil surface dynamic displacement are discussed.

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http://hdl.handle.net/10453/165687