Open Access
REVIEW
Emerging Trends in Damage Tolerance Assessment: A Review of Smart Materials and Self-Repairable Structures
1 Department of Civil Engineering, Faculty of Engineering & Technology, University of Botswana, Gaborone, UB0061, Botswana
2 Department of Civil Engineering, University Kebangsaan Malaysia (UKM), Bangi, 43600, Malaysia
* Corresponding Author: Ali Akbar Firoozi. Email:
(This article belongs to the Special Issue: Health Monitoring and Rapid Evaluation of Infrastructures)
Structural Durability & Health Monitoring 2024, 18(1), 1-18. https://doi.org/10.32604/sdhm.2023.044573
Received 03 August 2023; Accepted 24 October 2023; Issue published 11 January 2024
Abstract
The discipline of damage tolerance assessment has experienced significant advancements due to the emergence of smart materials and self-repairable structures. This review offers a comprehensive look into both traditional and innovative methodologies employed in damage tolerance assessment. After a detailed exploration of damage tolerance concepts and their historical progression, the review juxtaposes the proven techniques of damage assessment with the cutting-edge innovations brought about by smart materials and self-repairable structures. The subsequent sections delve into the synergistic integration of smart materials with self-repairable structures, marking a pivotal stride in damage tolerance by establishing an autonomous system for immediate damage identification and self-repair. This holistic approach broadens the applicability of these technologies across diverse sectors yet brings forth unique challenges demanding further innovation and research. Additionally, the review examines future prospects that combine advanced manufacturing processes with data-centric methodologies, amplifying the capabilities of these ‘intelligent’ structures. The review culminates by highlighting the transformative potential of this union between smart materials and self-repairable structures, promoting a sustainable and efficient engineering paradigm.Keywords
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