Abstract

In the field of structural health monitoring, self-sensing cementitious binders have gained great attention over the past decades due to their high sensing performance and durability. In particular, self-sensing cementitious coatings have seen increased interest due to their high compatibility with concrete structures and their ability to monitor existing infrastructure while using low amounts of material and at lower costs. Geopolymer coatings display favorable characteristics for this application due to their innate electrical properties and high bond strength with concrete structures. Despite the research that has been carried out on self-sensing coatings, the effect of the interfacial bond between the coating and substrate on the coating’s sensing performance has not been investigated. Poor bonding between the two materials can lead to low-quality sensing measurements and data misinterpretation. In this paper, we aim to investigate the bonding effect on the sensing performance of self-sensing geopolymer coatings. For this study fly ash-metakaolin geopolymer coatings were applied onto concrete substrates; the concrete surfaces were treated by employing three different surface preparation methods: mechanical brooming, chemical treatment and the untreated cast surface. The bond strength for each preparation technique was measured with the splitting tensile bond test and the sensing response for the geopolymer coatings under repeated loading was also characterized. Through proper understanding of the interface between cementitious materials, sensing coatings can be tailored accordingly to achieve high sensing performance and thus allowing high-quality monitoring and proactive maintenance in civil infrastructure.

Details

Title
Investigation of the interfacial bonding effect on self-sensing cementitious coatings for infrastructure monitoring
Author
Vlachakis, Christos; Yen-Fang, Su; Al-Tabbaa, Abir
Section
Self-Sensing, Monitoring and Inspection of Concrete Structures and Infrastructure
Publication year
2023
Publication date
2023
Publisher
EDP Sciences
ISSN
22747214
e-ISSN
2261236X
Source type
Conference Paper
Language of publication
English
ProQuest document ID
3185967372
Copyright
© 2023. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and conditions, you may use this content in accordance with the terms of the License.