Microcapsule triggering mechanics in cementitious materials: a modelling and machine learning approach

Ricketts, Evan John ORCID: https://orcid.org/0000-0001-8056-070X, de Souza, Lívia Ribeiro, Freeman, Brubeck Lee, Jefferson, Anthony ORCID: https://orcid.org/0000-0002-2050-2521 and Al-Tabbaa, Abir 2024. Microcapsule triggering mechanics in cementitious materials: a modelling and machine learning approach. Materials 17 (3) , 764. 10.3390/ma17030764

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Abstract

Self-healing cementitious materials containing microcapsules filled with healing agents can autonomously seal cracks and restore structural integrity. However, optimising the microcapsule mechanical properties to survive concrete mixing whilst still rupturing at the cracked interface to release the healing agent remains challenging. This study develops an integrated numerical modelling and machine learning approach for tailoring acrylate-based microcapsules for triggering within cementitious matrices. Microfluidics is first utilised to produce microcapsules with systematically varied shell thickness, strength, and cement compatibility. The capsules are characterised and simulated using a continuum damage mechanics model that is able to simulate cracking. A parametric study investigates the key microcapsule and interfacial properties governing shell rupture versus matrix failure. The simulation results are used to train an artificial neural network to rapidly predict the triggering behaviour based on capsule properties. The machine learning model produces design curves relating the microcapsule strength, toughness, and interfacial bond to its propensity for fracture. By combining advanced simulations and data science, the framework connects tailored microcapsule properties to their intended performance in complex cementitious environments for more robust self-healing concrete systems.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	MDPI
ISSN:	1996-1944
Date of First Compliant Deposit:	5 March 2024
Date of Acceptance:	28 January 2024
Last Modified:	04 Apr 2024 08:57
URI:	https://orca.cardiff.ac.uk/id/eprint/166512

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