Meta-Analysis and Machine Learning Models to Optimize the Efficiency of Self-Healing Capacity of Cementitious Material

Concrete and cement-based materials inherently possess an autogenous self-healing capacity. Despite the huge amount of literature on the topic, self-healing concepts still fail to consistently enter design strategies able to effectively quantify their benefits on structural performance. This study aims to develop quantitative relationships through statistical models and artificial neural network (ANN) by establishing a correlation between the mix proportions, exposure type and time, and width of the initial crack against suitably defined self-healing indices (SHI), quantifying the recovery of material performance. Furthermore, it is intended to pave the way towards consistent incorporation of self-healing concepts into durability-based design approaches for reinforced concrete structures, aimed at quantifying, with reliable confidence, the benefits in terms of slower degradation of the structural performance and extension of the service lifespan. It has been observed that the exposure type, crack width and presence of healing stimulators such as crystalline admixtures has the most significant effect on enhancing SHI and hence self-healing efficiency. However, other parameters, such as the amount of fibers and Supplementary Cementitious Materials have less impact on the autogenous self-healing. The study proposes, through suitably built design charts and ANN analysis, a straightforward input-output model to quickly predict and evaluate, and hence design, the self-healing efficiency of cement-based materials.

Automated summary

Although concrete and cement-based materials possess self-healing capacity, the consistent incorporation of self-healing concepts into design strategies is lacking. This study aims to quantify the recovery of material performance through statistical models and artificial neural network (ANN), and to establish a correlation between mix proportions, exposure type, time, and crack width against self-healing indices. Enhanced self-healing efficiency is mainly influenced by factors such as exposure type, crack width, and the presence of healing stimulators, while parameters like fibers and Supplementary Cementitious Materials have less impact on autogenous self-healing. The study proposes a straightforward input-output model through design charts and ANN analysis to predict and evaluate the self-healing efficiency of cement-based materials.