Article

Embedded Sensors for In-Situ Strain Monitoring in Composite Structures

Elias Randjbaran ORCID , Darya Khaksari ORCID , Hamid Mehrabi ORCID , Rizal Zahari ORCID , Dayang L. Majid ORCID , Mohamed T.H. Sultan ORCID , Norkhairunnisa Mazlan ORCID
    Received
    31 May 2025
    Published
    16 Oct 2025

    Abstract

    Continuous in-situ strain monitoring is vital for assessing the structural integrity and in-service performance of large-scale composite structures in sectors like aerospace and wind energy. This review provides a comprehensive analysis of methodologies for integrating sensor technologies to facilitate such monitoring. It encompasses established and emerging approaches, including Fibre Bragg Gratings (FBGs), piezoelectric transducers, and novel solutions like graphene-based sensors and MXene fibres. Beyond their operating principles, the review pays particular attention to vibration-based techniques that exploit nonlinear dynamic responses induced by damage. A critical appraisal is presented of the challenges of embedding these technologies, addressing manufacturing integration and the preservation of functional reliability under operational stressors. The article also considers key system-level requirements, including
    robust data acquisition, effective signal processing, and long-term durability. A central finding is the inherent trade off between sensor performance and structural integrity; FBGs offer high precision but can reduce interlaminar shear strength, whilst emerging solutions like MXene fibres show exceptional sensitivity but face durability challenges. The synthesis underscores significant advancements—from high-accuracy sensor localisation and nanotechnology in sensor fabrication, to autonomous, self-powered frameworks—alongside persistent, multidisciplinary challenges in creating validated and scalable systems. We conclude that the convergence of advanced sensing materials with intelligent data analytics is decisively transforming composites into intelligent, self-diagnosing systems.

    Keywords

    Composite SHM, In-Situ Strain Monitoring, Machine Learning Applications, Sensor IntegrationS, Smart Structures, Vibration Monitoring

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