Data-driven machine learning approach for predicting volumetric moisture content of concrete using resistance sensor measurements

Thiyagarajan, K; Kodagoda, S; Ulapane, N

Data-driven machine learning approach for predicting volumetric moisture content of concrete using resistance sensor measurements

Thiyagarajan, K

Kodagoda, S

Ulapane, N

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016, 2016, pp. 1288 - 1293
Issue Date:: 2016-10-19

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Field	Value	Language
dc.contributor.author	Thiyagarajan, K https://orcid.org/0000-0002-4044-1711	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.contributor.author	Ulapane, N https://orcid.org/0000-0003-3432-3943	en_US
dc.date.issued	2016-10-19	en_US
dc.identifier.citation	Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016, 2016, pp. 1288 - 1293	en_US
dc.identifier.isbn	9781509026050	en_US
dc.identifier.uri	http://hdl.handle.net/10453/91324
dc.description.abstract	© 2016 IEEE. In sewerage industry, hydrogen sulphide induced corrosion of reinforced concretes is a global problem. To achieve a comprehensive knowledge of the propagation of concrete corrosion, it is vital to monitor the critical factors such as moisture. In this context, this paper investigates the use of resistance measuring and processing for estimating the concrete moisture content. The behavior of concrete moisture with resistance and surface temperature are studied and the effects of pH concentration on concrete are analyzed. Gaussian Process regression modeling is carried out to predict volumetric moisture content of concrete, where the results from experimental data are used to train the prediction model.	en_US
dc.relation.ispartof	Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016	en_US
dc.relation.isbasedon	10.1109/ICIEA.2016.7603783	en_US
dc.title	Data-driven machine learning approach for predicting volumetric moisture content of concrete using resistance sensor measurements	en_US
dc.type	Conference Proceeding
utslib.for	0913 Mechanical Engineering	en_US
pubs.embargo.period	Not known	en_US
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 Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. In sewerage industry, hydrogen sulphide induced corrosion of reinforced concretes is a global problem. To achieve a comprehensive knowledge of the propagation of concrete corrosion, it is vital to monitor the critical factors such as moisture. In this context, this paper investigates the use of resistance measuring and processing for estimating the concrete moisture content. The behavior of concrete moisture with resistance and surface temperature are studied and the effects of pH concentration on concrete are analyzed. Gaussian Process regression modeling is carried out to predict volumetric moisture content of concrete, where the results from experimental data are used to train the prediction model.

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http://hdl.handle.net/10453/91324