Validated ground penetrating radar simulation model for estimating rebar location in infrastructure monitoring

Alvarez, JK; Sutjipto, S; Kodagoda, S

Validated ground penetrating radar simulation model for estimating rebar location in infrastructure monitoring

Alvarez, JK Sutjipto, S Kodagoda, S

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017, 2018, 2018-February pp. 1460 - 1465
Issue Date:: 2018-02-05

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Field	Value	Language
dc.contributor.author	Alvarez, JK	en_US
dc.contributor.author	Sutjipto, S	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.date.available	2017-02-28	en_US
dc.date.issued	2018-02-05	en_US
dc.identifier.citation	Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017, 2018, 2018-February pp. 1460 - 1465	en_US
dc.identifier.isbn	9781538621035	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133064
dc.description.abstract	© 2017 IEEE. Biogenic sulphide corrosion of reinforced concrete sewer pipes is an ongoing problem for wastewater governing bodies. Ensuring Workplace Health and Safety (WHS) is also an issue due to the harsh nature of sewer environments. As such, research into technologies that allow for automatic unmanned site assessments are of major priority to wastewater managing utilities. The use of Ground Penetrating Radar (GPR) is currently being investigated for it's ability to provide subsurface images. However, the GPR technology has not been tested and validated in harsh sewer environments. It is anticipated that the GPR interpretation can be hindered by low signal to noise ratio. As data driven machine learning techniques have proven to work in higly challenging data, our intenetion is to apply such techniques in GPR data processing. However, this is hindered by the lack of large amount of training data as it is prohibitively hard to collect such real experimental testing data. Thus, the aim of this study is to validate a ground penetrating radar simulation software, gprMax, and test it for suitability in generating realistic, big data sets with which to train the aforementioned data driven machine learning models supplemented with actual sewer crown data. The results of the study is the validation of the GPR simulator, tuned and able to generate reasonably realistic data. A novel concrete analog was also developed to allow for ease of testing of various parameters such as rebar cover depths and rebar spacing.	en_US
dc.relation.ispartof	Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017	en_US
dc.relation.isbasedon	10.1109/ICIEA.2017.8283069	en_US
dc.title	Validated ground penetrating radar simulation model for estimating rebar location in infrastructure monitoring	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-February	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)
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
pubs.volume	2018-February	en_US

Abstract:

© 2017 IEEE. Biogenic sulphide corrosion of reinforced concrete sewer pipes is an ongoing problem for wastewater governing bodies. Ensuring Workplace Health and Safety (WHS) is also an issue due to the harsh nature of sewer environments. As such, research into technologies that allow for automatic unmanned site assessments are of major priority to wastewater managing utilities. The use of Ground Penetrating Radar (GPR) is currently being investigated for it's ability to provide subsurface images. However, the GPR technology has not been tested and validated in harsh sewer environments. It is anticipated that the GPR interpretation can be hindered by low signal to noise ratio. As data driven machine learning techniques have proven to work in higly challenging data, our intenetion is to apply such techniques in GPR data processing. However, this is hindered by the lack of large amount of training data as it is prohibitively hard to collect such real experimental testing data. Thus, the aim of this study is to validate a ground penetrating radar simulation software, gprMax, and test it for suitability in generating realistic, big data sets with which to train the aforementioned data driven machine learning models supplemented with actual sewer crown data. The results of the study is the validation of the GPR simulator, tuned and able to generate reasonably realistic data. A novel concrete analog was also developed to allow for ease of testing of various parameters such as rebar cover depths and rebar spacing.

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

http://hdl.handle.net/10453/115431