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.

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.