STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Polymer & Composites Original Research Special issue

ML-Enhanced Smart Sensing Framework for IoT- Based Structural Health Monitoring Using Conductive Polymer Composites

  1. Nagaraj G. Department of Mechanical Enigneering , Sethu Institute of Technology
  2. Amaravarapu Pramod Kumar Department of Computer Science and Engineering - Data Science, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering &Technology, Hyderabad
  3. Basavaraju Kachapuram Department of Artificial Intelligence, Anurag University, Majarguda
  4. Muthukumar Subramanian Department of Computer Science and Engineering, Hindustan Institute of Technology & Science (Deemed to be University)
  5. Ashish Avasthi Department of Computer Science and Engineering, Poornima University, Jaipur
  6. Gandikota Dhana Lakshmi Department of Mathematics and Statistics, Vignan's Foundation for Science, Technology & Research (Deemed to be University), Guntur

Abstract

The growing demand for intelligent structural health monitoring (SHM) in dynamic infrastructures necessitates flexible sensing systems that are not only mechanically robust but also capable of real-time interpretation. Conventional SHM frameworks often rely on brittle sensor configurations and cloud-dependent processing pipelines, which suffer from latency, limited durability, and poor adaptability under variable loading conditions. Despite recent advances in composite materials and machine learning, current approaches lack a unified framework that seamlessly integrates material-level sensitivity with embedded predictive intelligence. To address this limitation, we propose CPC-Net, a novel fabrication-to-inference pipeline combining piezoresistive polymer composites with an edge-deployable hybrid CNN-LSTM model. The composite is engineered with graphene-reinforced elastomer matrices to ensure high gauge sensitivity and repeatable electromechanical behavior under cyclic strain. A dedicated embedded architecture supports in-situ signal preprocessing, real-time ML inference, and adaptive calibration, optimized for constrained IoT environments. The proposed system achieved an R² of 0.984 in strain-resistance prediction, with a gauge factor (GF) between 8–12. Machine learning inference yielded an accuracy of 93.4%, an F1-score of 0.91, and inference latency of 41 ms, outperforming existing SHM baselines across all tested metrics. This integrated solution demonstrates strong potential for deployment in next-generation SHM applications, offering a scalable, low-latency, and mechanically resilient alternative for real-time monitoring of civil infrastructure, soft robotics, and biomechanical systems.

Keywords

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