Analytical model and data-driven approach for concrete moisture prediction

Thiyagarajan, K; Kodagoda, S

Analytical model and data-driven approach for concrete moisture prediction

Thiyagarajan, K

Kodagoda, S

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Publication Type:: Conference Proceeding
Citation:: ISARC 2016 - 33rd International Symposium on Automation and Robotics in Construction, 2016, pp. 298 - 306
Issue Date:: 2016-01-01

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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.date.issued	2016-01-01	en_US
dc.identifier.citation	ISARC 2016 - 33rd International Symposium on Automation and Robotics in Construction, 2016, pp. 298 - 306	en_US
dc.identifier.uri	http://hdl.handle.net/10453/102995
dc.description.abstract	The advent of smart sensing technologies has opened up new avenues for addressing the billion dollar problem in the wastewater industry of H2S corrosion in concrete sewer pipes, where there is a growing interest in monitoring the environmental properties that govern the rate of corrosion. In this context, this paper proposes a methodology to predict the moisture content of concretes through data-driven approach by using Gaussian Process Regression modeling. The experimental program in this study practices measurements during wetting and drying phases of concrete. The obtained moisture data is used to train the prediction model against interpreted electrical resistivity data. The data of analytical model formulated from Archie's Law is then analyzed with experimental and Gaussian Process prediction data.	en_US
dc.relation.ispartof	ISARC 2016 - 33rd International Symposium on Automation and Robotics in Construction	en_US
dc.title	Analytical model and data-driven approach for concrete moisture prediction	en_US
dc.type	Conference Proceeding
utslib.for	091303 Autonomous Vehicles	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	open_access
pubs.publication-status	Published	en_US

Abstract:

The advent of smart sensing technologies has opened up new avenues for addressing the billion dollar problem in the wastewater industry of H2S corrosion in concrete sewer pipes, where there is a growing interest in monitoring the environmental properties that govern the rate of corrosion. In this context, this paper proposes a methodology to predict the moisture content of concretes through data-driven approach by using Gaussian Process Regression modeling. The experimental program in this study practices measurements during wetting and drying phases of concrete. The obtained moisture data is used to train the prediction model against interpreted electrical resistivity data. The data of analytical model formulated from Archie's Law is then analyzed with experimental and Gaussian Process prediction data.

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http://hdl.handle.net/10453/102995