Smart sensing technology for concrete Infrastructure sub-surface condition assessment

Acharya, Parikshit

Smart sensing technology for concrete Infrastructure sub-surface condition assessment

Acharya, Parikshit

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Publication Type:: Thesis
Issue Date:: 2024

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Field	Value	Language
dc.contributor.author	Acharya, Parikshit
dc.date.accessioned	2025-03-20T03:29:41Z
dc.date.available	2025-03-20T03:29:41Z
dc.date.issued	2024
dc.identifier.uri	http://hdl.handle.net/10453/186037
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	Currently, underground sewer pipes serve as the backbone for hygiene and cleanliness for all urbanized communities. Most of the sewers are old and due to microbial-induced concrete corrosion, the service life of the sewers is at stake. This serious global problem can affect public health as well as the economic growth of the country. There is growing interest among researchers to develop robust sensors to non-invasively estimate the depth of intact concrete in real-time. Corrosion in concrete structures is partly controlled by movement of ions along the concrete microstructure. Thus, a connection could be expected between the corrosion process in concrete due to acid permeation and the electrical resistivity of concrete. This thesis attempts to non-invasively estimate the acid permeation in concrete structures through electrical resistivity. In this thesis, acid-permeated concrete is considered as a two-resistivity layer problem, where the top resistivity layer is assumed to be acid permeated and the bottom resistivity layer is assumed to be normal moisture conditions after fully curing. Hence the focus of this thesis is to determine the depth of the top layer based on the resistivity value of a two-layered material. The sensor based on the four-probe Wenner method was modelled and validated with the previous scientific studies. The data generated from the sensor model was used for the study of the effects of electrode contact area, and electrode spacing distance. The study was also conducted for the measurement of apparent resistivity for a two-resistivity layered concrete. All the simulations were carried out by varying the depth of the top resistivity layer of concrete and resistivity measurements were taken with a selective interval of the depth of the top layer of a two-layered concrete. Lab experiments were carried out with different materials to analyse the changes in resistivity values with water ingress. The validated simulation data was used in a machine-learning framework to estimate the depth of the top layer. In the simulation squared exponential kernels along with Gaussian Process regression provided accurate depth predictions for depths more than 3mm. Depth predictions with top layer depths less than 3mm gave rise to erroneous predictions. Due to practical application considerations, in this dissertation, the scope was restricted to between 3mm to 10mm of acid ingress layer leaving future studies beyond the range. This dissertation reports non-invasively measuring the depth of acid permeation by using electrical resistivity method through implementation of a Machine Learning approach.	en_US.UTF-8
dc.format	Thesis (ME (Res))
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/186037/1/thesis.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© 2024 Parikshit Acharya
dc.rights	au.edu.uts.lib/cph
dc.title	Smart sensing technology for concrete Infrastructure sub-surface condition assessment	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

Currently, underground sewer pipes serve as the backbone for hygiene and cleanliness for all urbanized communities. Most of the sewers are old and due to microbial-induced concrete corrosion, the service life of the sewers is at stake. This serious global problem can affect public health as well as the economic growth of the country. There is growing interest among researchers to develop robust sensors to non-invasively estimate the depth of intact concrete in real-time. Corrosion in concrete structures is partly controlled by movement of ions along the concrete microstructure. Thus, a connection could be expected between the corrosion process in concrete due to acid permeation and the electrical resistivity of concrete. This thesis attempts to non-invasively estimate the acid permeation in concrete structures through electrical resistivity. In this thesis, acid-permeated concrete is considered as a two-resistivity layer problem, where the top resistivity layer is assumed to be acid permeated and the bottom resistivity layer is assumed to be normal moisture conditions after fully curing. Hence the focus of this thesis is to determine the depth of the top layer based on the resistivity value of a two-layered material. The sensor based on the four-probe Wenner method was modelled and validated with the previous scientific studies. The data generated from the sensor model was used for the study of the effects of electrode contact area, and electrode spacing distance. The study was also conducted for the measurement of apparent resistivity for a two-resistivity layered concrete. All the simulations were carried out by varying the depth of the top resistivity layer of concrete and resistivity measurements were taken with a selective interval of the depth of the top layer of a two-layered concrete. Lab experiments were carried out with different materials to analyse the changes in resistivity values with water ingress. The validated simulation data was used in a machine-learning framework to estimate the depth of the top layer. In the simulation squared exponential kernels along with Gaussian Process regression provided accurate depth predictions for depths more than 3mm. Depth predictions with top layer depths less than 3mm gave rise to erroneous predictions. Due to practical application considerations, in this dissertation, the scope was restricted to between 3mm to 10mm of acid ingress layer leaving future studies beyond the range. This dissertation reports non-invasively measuring the depth of acid permeation by using electrical resistivity method through implementation of a Machine Learning approach.

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