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Recent Advances of Self-Healing Materials for Civil Engineering: Models and Simulations

Autor(en):



ORCID
Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Buildings, , n. 4, v. 14
Seite(n): 961
DOI: 10.3390/buildings14040961
Abstrakt:

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.

Copyright: © 2024 by the authors; licensee MDPI, Basel, Switzerland.
Lizenz:

Dieses Werk wurde unter der Creative-Commons-Lizenz Namensnennung 4.0 International (CC-BY 4.0) veröffentlicht und darf unter den Lizenzbedinungen vervielfältigt, verbreitet, öffentlich zugänglich gemacht, sowie abgewandelt und bearbeitet werden. Dabei muss der Urheber bzw. Rechteinhaber genannt und die Lizenzbedingungen eingehalten werden.

  • Über diese
    Datenseite
  • Reference-ID
    10773762
  • Veröffentlicht am:
    29.04.2024
  • Geändert am:
    05.06.2024
 
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