Microcapsule Triggering Mechanics in Cementitious Materials: A Modelling and Machine Learning Approach.

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.

Development of 3D Printed Networks in Self-Healing Concrete.

This paper presents a new form of biomimetic cementitious material, which employs 3D-printed tetrahedral mini-vascular networks (MVNs) to store and deliver healing agents to damage sites within cementitious matrices. The MVNs are required to not only protect the healing agent for a sufficient period of time but also survive the mixing process, release the healing agent when the cementitious matrix is damaged, and have minimal impact on the physical and mechanical properties of the host cementitious matrix. A systematic study is described which fulfilled these design requirements and determined the most appropriate form and material for the MVNs. A subsequent series of experiments showed that MVNs filled with sodium silicate, embedded in concrete specimens, are able to respond effectively to damage, behave as a perfusable vascular system and thus act as healing agent reservoirs that are available for multiple damage-healing events. It was also proved that healing agents encapsulated within these MVNs can be transported to cracked zones in concrete elements under capillary driving action, and produce a recovery of strength, stiffness and fracture energy.

An indicator-based problem reduction scheme for coupled reactive transport models.

A number of effective models have been developed for simulating chemical transport in porous media; however, when a reactive chemical problem comprises multiple species within a substantial domain for a long period of time, the computational cost can become prohibitively expensive. This issue is addressed here by proposing a new numerical procedure to reduce the number of transport equations to be solved. This new problem reduction scheme (PRS) uses a predictor-corrector approach, which "predicts" the transport of a set of non-indicator species using results from a set of indicator species before "correcting" the non-indicator concentrations using a mass balance error measure. The full chemical transport model is described along with experimental validation. The PRS is then presented together with an investigation, based on a 16-species reaction-advection-diffusion problem, which determines the range of applicability of different orders of the PRS. The results of a further study are presented, in which a set of PRS simulations is compared with those from full model predictions. The application of the scheme to the intermediate-sized problems considered in the present study showed reductions of up to 82% in CPU time, with good levels of accuracy maintained.