Physics-informed neural networks for consolidation of soils

Zhang, S; Lan, P; Li, HC; Tong, CX; Sheng, D

Physics-informed neural networks for consolidation of soils

Zhang, S Lan, P Li, HC Tong, CX Sheng, D

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Publisher:: EMERALD GROUP PUBLISHING LTD
Publication Type:: Journal Article
Citation:: Engineering Computations (Swansea, Wales), 2022, 39, (7), pp. 2845-2865
Issue Date:: 2022-07-05

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Field	Value	Language
dc.contributor.author	Zhang, S
dc.contributor.author	Lan, P
dc.contributor.author	Li, HC
dc.contributor.author	Tong, CX
dc.contributor.author	Sheng, D https://orcid.org/0000-0002-1665-6931
dc.date.accessioned	2023-04-11T03:01:46Z
dc.date.available	2023-04-11T03:01:46Z
dc.date.issued	2022-07-05
dc.identifier.citation	Engineering Computations (Swansea, Wales), 2022, 39, (7), pp. 2845-2865
dc.identifier.issn	0264-4401
dc.identifier.issn	1758-7077
dc.identifier.uri	http://hdl.handle.net/10453/169519
dc.description.abstract	Purpose: Prediction of excess pore water pressure and estimation of soil parameters are the two key interests for consolidation problems, which can be mathematically quantified by a set of partial differential equations (PDEs). Generally, there are challenges in solving these two issues using traditional numerical algorithms, while the conventional data-driven methods require massive data sets for training and exhibit negative generalization potential. This paper aims to employ the physics-informed neural networks (PINNs) for solving both the forward and inverse problems. Design/methodology/approach: A typical consolidation problem with continuous drainage boundary conditions is firstly considered. The PINNs, analytical, and finite difference method (FDM) solutions are compared for the forward problem, and the estimation of the interface parameters involved in the problem is discussed for the inverse problem. Furthermore, the authors also explore the effects of hyperparameters and noisy data on the performance of forward and inverse problems, respectively. Finally, the PINNs method is applied to the more complex consolidation problems. Findings: The overall results indicate the excellent performance of the PINNs method in solving consolidation problems with various drainage conditions. The PINNs can provide new ideas with a broad application prospect to solve PDEs in the field of geotechnical engineering, and also exhibit a certain degree of noise resistance for estimating the soil parameters. Originality/value: This study presents the potential application of PINNs for the consolidation of soils. Such a machine learning algorithm helps to obtain remarkably accurate solutions and reliable parameter estimations with fewer and average-quality data, which is beneficial in engineering practice.
dc.language	English
dc.publisher	EMERALD GROUP PUBLISHING LTD
dc.relation.ispartof	Engineering Computations (Swansea, Wales)
dc.relation.isbasedon	10.1108/EC-08-2021-0492
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0905 Civil Engineering, 0913 Mechanical Engineering, 0915 Interdisciplinary Engineering
dc.subject.classification	Applied Mathematics
dc.title	Physics-informed neural networks for consolidation of soils
dc.type	Journal Article
utslib.citation.volume	39
utslib.for	0905 Civil Engineering
utslib.for	0913 Mechanical 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
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-11T03:01:45Z
pubs.issue	7
pubs.publication-status	Published
pubs.volume	39
utslib.citation.issue	7

Abstract:

Purpose: Prediction of excess pore water pressure and estimation of soil parameters are the two key interests for consolidation problems, which can be mathematically quantified by a set of partial differential equations (PDEs). Generally, there are challenges in solving these two issues using traditional numerical algorithms, while the conventional data-driven methods require massive data sets for training and exhibit negative generalization potential. This paper aims to employ the physics-informed neural networks (PINNs) for solving both the forward and inverse problems. Design/methodology/approach: A typical consolidation problem with continuous drainage boundary conditions is firstly considered. The PINNs, analytical, and finite difference method (FDM) solutions are compared for the forward problem, and the estimation of the interface parameters involved in the problem is discussed for the inverse problem. Furthermore, the authors also explore the effects of hyperparameters and noisy data on the performance of forward and inverse problems, respectively. Finally, the PINNs method is applied to the more complex consolidation problems. Findings: The overall results indicate the excellent performance of the PINNs method in solving consolidation problems with various drainage conditions. The PINNs can provide new ideas with a broad application prospect to solve PDEs in the field of geotechnical engineering, and also exhibit a certain degree of noise resistance for estimating the soil parameters. Originality/value: This study presents the potential application of PINNs for the consolidation of soils. Such a machine learning algorithm helps to obtain remarkably accurate solutions and reliable parameter estimations with fewer and average-quality data, which is beneficial in engineering practice.

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http://hdl.handle.net/10453/169519