Fiber and matrix-level damage detection and assessments for natural fiber composites

Fiber-reinforced polymer composites (FRPCs) have emerged as key materials in various engineering applications due to their remarkable properties. However, ensuring the structural integrity of these materials throughout their entire lifecycle remains a challenge. Over the years, several damage detection techniques (DDTs) have been employed for damage identification in FRPCs, with progress also made in developing self-sensing composites capable of detecting and repairing damage on their own. Despite advancements, sensible techniques are required for detecting damages in FRPCs to prevent catastrophic failures. The existing DDTs have proven to be effective for damage detection in synthetic fiber-reinforced composites (SFRCs). However, the increasing focus on sustainable and zero emission materials requires the implementation of these DDTs for natural fiber-reinforced composites (NFRCs). This study explores the capabilities of DDTs including nondestructive testing, structural health monitoring and the self-sensing for detecting fiber-, matrix- and interface-level damages in FRPCs. As a result, this review provides a framework for developing effective DDTs to detect damage in NFRCs by integrating them with machine learning models.