Gaussian markov random fields for localizing reinforcing bars in concrete infrastructure

Thiyagarajan, K; Kodagoda, S; Van Nguyen, L; Wickramanayake, S

Gaussian markov random fields for localizing reinforcing bars in concrete infrastructure

Thiyagarajan, K

Kodagoda, S

Van Nguyen, L

Wickramanayake, S

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Publication Type:: Conference Proceeding
Citation:: ISARC 2018 - 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things, 2018
Issue Date:: 2018-01-01

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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.contributor.author	Van Nguyen, L https://orcid.org/0000-0001-5360-886X	en_US
dc.contributor.author	Wickramanayake, S	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	ISARC 2018 - 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things, 2018	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127811
dc.description.abstract	© ISARC 2018 - 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things. All rights reserved. Sensor technologies play a significant role in monitoring the health conditions of urban sewer assets. Currently, the concrete sewer systems are undergoing corrosion due to bacterial activities on the concrete surfaces. Therefore, water utilities use predictive models to estimate the corrosion by using observations such as relative humidity or surface moisture conditions. Surface moisture conditions can be estimated by electrical resistivity based moisture sensing. However, the measurements of such sensors are influenced by the proximal presence of reinforcing bars. To mitigate such e ects, the moisture sensor needs to be optimally oriented on the concrete surface. This paper focuses on developing a machine learning model for localizing the reinforcing bars inside the concrete through non-invasive measurements. This work utilizes a resistivity meter that works based on the Wenner technique to obtain electrical measurements on the concrete sample by taking measurements at di erent angles. Then, the measured data is fed to a Gaussian Markov Random Fields based spatial prediction model. The spatial prediction outcome of the proposed model demonstrated the feasibility of localizing the reinforcing bars with reasonable accuracy for the measurements taken at di erent angles. This information is vital for decision-making while deploying the moisture sensors in sewer systems.	en_US
dc.relation.ispartof	ISARC 2018 - 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things	en_US
dc.title	Gaussian markov random fields for localizing reinforcing bars in concrete infrastructure	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	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:

© ISARC 2018 - 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things. All rights reserved. Sensor technologies play a significant role in monitoring the health conditions of urban sewer assets. Currently, the concrete sewer systems are undergoing corrosion due to bacterial activities on the concrete surfaces. Therefore, water utilities use predictive models to estimate the corrosion by using observations such as relative humidity or surface moisture conditions. Surface moisture conditions can be estimated by electrical resistivity based moisture sensing. However, the measurements of such sensors are influenced by the proximal presence of reinforcing bars. To mitigate such e ects, the moisture sensor needs to be optimally oriented on the concrete surface. This paper focuses on developing a machine learning model for localizing the reinforcing bars inside the concrete through non-invasive measurements. This work utilizes a resistivity meter that works based on the Wenner technique to obtain electrical measurements on the concrete sample by taking measurements at di erent angles. Then, the measured data is fed to a Gaussian Markov Random Fields based spatial prediction model. The spatial prediction outcome of the proposed model demonstrated the feasibility of localizing the reinforcing bars with reasonable accuracy for the measurements taken at di erent angles. This information is vital for decision-making while deploying the moisture sensors in sewer systems.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/124287