Data Mining Strategies to Handle State of Art Knowledge on Self-healing Capacity of Cementitious Materials

Abstract

Concrete and cement-based materials inherently possess an autogenous self-healing capacity, which is even high in High and Ultra-High-Performance Concretes because of the high amount of cement and supplementary cementitious materials (SCM) and low water/cementitious material (w/c) ratio. Despite the huge amount of literature on the topic self-healing concepts still fail to consistently enter design strategies to effectively quantify their benefits on the structural performance. In this study, quantitative relationships have been developed through design charts and artificial neural network (ANN) models. The employed approaches aimed at establishing a correlation between the mix proportions, exposure time, the width of the initial crack, and volume of fibers against suitably defined self-healing indices (SHI), quantifying the recovery of material performances which can be of interest for intended applications. Therefore, this study provides, for the first time in the literature to the authors’ knowledge, a holistic investigation on the autogenous self-healing capacity of cement-based materials based on extensive articles focused on the literature data mining. The design charts are developed 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. Finally, through ANN, a straightforward input-output model is developed to quickly predict and evaluate the self-healing efficiency of cement-based materials which can significantly reduce, in the design stage, the time, money, and efforts of laboratory investigation.