Resumen
Empowering materials with self-healing capabilities is an attractive approach for sustainable development. This strategy involves using different methods to automatically heal microcracks and damages that occur during the service life of materials or structures. Initially, this study begins with an in-depth exploration of self-healing characteristics found in materials such as concrete, asphalt, and polymers. The differences and comparative merits and demerits between autogenous (intrinsic) healing and autonomic (extrinsic) healing are discussed, and it is found that intrinsic healing is more promising. Subsequently, the study explores how models are applied to assess self-healing efficiency. The results indicate that time and temperature have significant impacts on the self-healing process. However, there is a scarcity of research exploring the effects of load factors during service life. Computational simulation methodologies for microcapsules and asphalt within self-healing materials are investigated. Multiscale characterization and machine learning can further elucidate the healing mechanisms and facilitate the establishment of computational models. This study endeavors to realize the maximum capabilities of self-healing materials, paving the way for the design of sustainable and more effective self-repairing materials for various applications.