Advances in embedded sensor technologies for structural health monitoring of composite structures: a comprehensive review

Structural Health Monitoring (SHM) is critical for ensuring the safety and reliability of advanced composite structures. While embedded sensors offer a transformative approach to real-time monitoring, a critical challenge lies at the sensor-material interface, where integration processes and environmental factors govern long-term system performance. This review adds to existing knowledge by providing a focused, critical analysis of the specific challenges and advancements related to the integration and in-situ performance of embedded sensors in composites. A systematic literature search of the Web of Science and Scopus databases was conducted, focusing on peer-reviewed articles from the last five years related to embedded sensors, composites, and SHM. From an initial pool of over 1300 articles, 93 were selected for in-depth analysis based on specific inclusion criteria, focusing on experimental findings and novel integration techniques. The analysis reveals a clear predominance of piezoresistive sensors, particularly those using carbon-based nanomaterials, due to their excellent compatibility with composites and suitability for additive manufacturing. Key advancements in sensor integration via 3D printing (FDM, DIW, SLA) are identified, alongside significant challenges, including process-induced defects, thermal mismatch, and signal degradation from environmental factors like moisture and temperature. The findings indicate that while sensor technology has advanced, the reliability of the entire SHM system is often limited by the sensor-material interface. The move towards multifunctional, wireless, and self-powered sensors is a clear trend, but long-term durability and the inability to repair embedded sensors remain significant barriers to widespread adoption. This review concludes that future research must prioritise the development of robust, resilient sensor materials and standardised testing protocols. Integrating advanced materials like metamaterials and leveraging machine learning for predictive data analysis are crucial next steps to creating truly autonomous and reliable SHM systems for composite structures.