Prediction of resilient modulus of ballast under cyclic loading using machine learning techniques

Indraratna, B; Armaghani, DJ; Gomes Correia, A; Hunt, H; Ngo, T

Prediction of resilient modulus of ballast under cyclic loading using machine learning techniques

Indraratna, B

Armaghani, DJ Gomes Correia, A Hunt, H Ngo, T

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Transportation Geotechnics, 2023, 38, pp. 100895
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Indraratna, B https://orcid.org/0000-0002-2346-8250
dc.contributor.author	Armaghani, DJ
dc.contributor.author	Gomes Correia, A
dc.contributor.author	Hunt, H
dc.contributor.author	Ngo, T
dc.date.accessioned	2023-02-23T03:25:36Z
dc.date.available	2023-02-23T03:25:36Z
dc.date.issued	2023-01-01
dc.identifier.citation	Transportation Geotechnics, 2023, 38, pp. 100895
dc.identifier.issn	2214-3912
dc.identifier.issn	2214-3912
dc.identifier.uri	http://hdl.handle.net/10453/166373
dc.description.abstract	The resilient modulus (MR) of ballast is one of the key output parameters in any rail design project because it controls the elastic magnitude of track deformation under cyclic loading. This study investigates the response of MR under cyclic conditions as a function of four key parameters, i.e., the loading magnitude, the number of loading cycles, the loading frequency, and the confining pressure. To do so, two non-linear predictive models, namely, the artificial neural network (ANN), and the adaptive neuro-fuzzy inference system (ANFIS), are used to predict the MR values under different loading conditions. To evaluate and predict MR, an experimental database with 196 data samples is considered in this study. A series of sensitivity analyses is carried out to investigate the most effective parameters in each non-linear model and also predict the highest performance model. Although the results from the primary validation phase are satisfactory for the ANN and ANFIS models, ANFIS proves better (i.e., the coefficient of determination = 0.709) at estimating the MR during the secondary validation phase, using an independent dataset. Hence, it can be used as a powerful and practical model for predicting the magnitude of MR. On the basis of the ANFIS model, this study also offers some design considerations in terms of MR of ballast under a practical range of cyclic loading parameters.
dc.language	en
dc.publisher	Elsevier
dc.relation	http://purl.org/au-research/grants/arc/IC170100006
dc.relation.ispartof	Transportation Geotechnics
dc.relation.isbasedon	10.1016/j.trgeo.2022.100895
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0905 Civil Engineering
dc.title	Prediction of resilient modulus of ballast under cyclic loading using machine learning techniques
dc.type	Journal Article
utslib.citation.volume	38
utslib.for	0905 Civil 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	embargoed	*
utslib.copyright.embargo	2025-01-01T00:00:00+1000Z
dc.date.updated	2023-02-23T03:25:18Z
pubs.publication-status	Published
pubs.volume	38

Abstract:

The resilient modulus (MR) of ballast is one of the key output parameters in any rail design project because it controls the elastic magnitude of track deformation under cyclic loading. This study investigates the response of MR under cyclic conditions as a function of four key parameters, i.e., the loading magnitude, the number of loading cycles, the loading frequency, and the confining pressure. To do so, two non-linear predictive models, namely, the artificial neural network (ANN), and the adaptive neuro-fuzzy inference system (ANFIS), are used to predict the MR values under different loading conditions. To evaluate and predict MR, an experimental database with 196 data samples is considered in this study. A series of sensitivity analyses is carried out to investigate the most effective parameters in each non-linear model and also predict the highest performance model. Although the results from the primary validation phase are satisfactory for the ANN and ANFIS models, ANFIS proves better (i.e., the coefficient of determination = 0.709) at estimating the MR during the secondary validation phase, using an independent dataset. Hence, it can be used as a powerful and practical model for predicting the magnitude of MR. On the basis of the ANFIS model, this study also offers some design considerations in terms of MR of ballast under a practical range of cyclic loading parameters.

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http://hdl.handle.net/10453/166373