Digital Twin-Driven Structural Health Monitoring and Energy Management of IoT-Enabled Energy-Storing Polymer Composites Using Explainable Machine Learning

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Year : 2026 | Volume : 14 | 04 | Page :
By

Harish Reddy Gantla,

Bhavana R Maale,

Diksha Dani,

Tejaswini Mallavarapu,

Sanjeev Kumar,

Manna Sheela Rani Chetty,

  1. Associate Professor, Department of Computer Science and Engineering, Vignan Institute of Technology and Science, Hyderabad, Telangana, India
  2. Assistant Professor, Department of Computer Science and Engineering, Visvesvaraya Technological University, Muddenahalli, Karnataka, India
  3. Assistant Professor, Department of Computer Engineering, Mukesh Patel School of Technology, Engineering and Management, SVKM’s NMIMS, Mumbai, Maharashtra, India
  4. Sr. Data Scientist, The Judge Group, Georgia, USA
  5. Professor, Department of Electronics & Communication Engineering, Aditya University, Surampalem, Andhra Pradesh, India
  6. Professor, Department of Computer Science and Engineering, KLEF, Green Fields, Vaddeswaram, Guntur, Andhra Pradesh, India

Abstract

Energy storing polymer composites are widely utilized in intelligent structural systems, because of their mechanical and electrochemical properties. But, under varying thermo-mechanical and environmental conditions, it is important to have accurate degradation monitoring for real-time industrial process is challenging. In this work, an explainable machine-learning framework for structural health monitoring and adaptive energy management of polymer composites with energy storage capacity is proposed in an IoT environment with the help of a digital twin. The framework is based on a multimodal IoT sensing, synchronized cyber–physical digital twin state estimation and explainable machine learning degradation prediction, all in one cyber–physical framework. The structural strain, impedance variation, vibration response, thermal behavior and residual energy-state parameters analysis under uncertain sensing condition were conducted on a continuous basis using the explainable learning method – SHAP. The results of the experiments revealed that the structural degradation prediction accuracy was 96.42%, the F1-score was 94.87% and the RMSE was 0.061. The synchronized digital twin has an average state-transitions latency of 0.41 s during dynamic degradation evolution. Furthermore, the prediction performance was statistically validated and found to be stable with an average prediction stability index of more than 0.93 for Gaussian sensor perturbation up to 12%. The results validate the effectiveness of the digital twins, explainable AI and multimodal IoT sensing as means to ensure reliable structural monitoring and adaptive energy-management of multifunctional polymer composite systems.

Keywords: Digital Twin, Structural Health Monitoring, Energy-Storing Polymer Composites, Explainable Machine Learning, IoT-Enabled Smart Materials.

How to cite this article: Harish Reddy Gantla, Bhavana R Maale, Diksha Dani, Tejaswini Mallavarapu, Sanjeev Kumar, Manna Sheela Rani Chetty. Digital Twin-Driven Structural Health Monitoring and Energy Management of IoT-Enabled Energy-Storing Polymer Composites Using Explainable Machine Learning. Journal of Polymer & Composites. 2026; 14(04):-.
How to cite this URL: Harish Reddy Gantla, Bhavana R Maale, Diksha Dani, Tejaswini Mallavarapu, Sanjeev Kumar, Manna Sheela Rani Chetty. Digital Twin-Driven Structural Health Monitoring and Energy Management of IoT-Enabled Energy-Storing Polymer Composites Using Explainable Machine Learning. Journal of Polymer & Composites. 2026; 14(04):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=252992

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Ahead of Print Subscription Original Research
Volume 14
04
Received 14/06/2026
Accepted 11/08/2026
Published 20/08/2026
Publication Time 67 Days

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