Mechanical response and predictive modelling of vascular self-healing cementitious materials using novel healing agents

De Nardi, Cristina ORCID: https://orcid.org/0000-0002-7991-2357, Freeman, Brubeck, Gardner, Diane ORCID: https://orcid.org/0000-0002-2864-9122 and Jefferson, Tony ORCID: https://orcid.org/0000-0002-2050-2521 2023. Mechanical response and predictive modelling of vascular self-healing cementitious materials using novel healing agents. Cement and Concrete Composites 142 , 105143. 10.1016/j.cemconcomp.2023.105143

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Abstract

Self-healing systems represent an effective means of increasing the resilience of cementitious structures, extending service life and reducing cement production. This is achieved through the mitigation of cracking related durability problems. The success of a self-healing system is critically dependent on the selection of an appropriate healing agent, which depends upon the specific application, as well as a number of criteria including crack filling ability and the degree of mechanical healing required. In the present study, we develop modified formulations of a cyanoacrylate-based adhesive, suitable for use in a vascular self-healing cementitious material. The aim is to develop an ‘ideal’ healing agent for the self-healing system that has an extended shelf life and maximises load recovery. To this end, modified cyanoacrylates are tailored using a combination of predictive modelling and physical testing. The physical tests investigate both the mechanical, flow and chemical properties of the different healing agent formulations, including tensile strength, viscosity and curing. The predictive modelling employs a coupled chemo-mechanical model that is used to guide the physical testing programme through the prediction of the performance of different formulations. The results of the investigation show that a tailored formulation of a cyanoacrylate based healing agent increases the load recovery by 48% relative to the best performing original formulation. In addition, it is shown that the numerical model is able to predict the load response of new formulations with good accuracy.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0958-9465
Funders:	EPSRC
Date of First Compliant Deposit:	24 May 2023
Date of Acceptance:	15 May 2023
Last Modified:	13 Sep 2023 21:13
URI:	https://orca.cardiff.ac.uk/id/eprint/159897

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