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

Journal of Polymer & Composites Original Research Special issue

IoT-Based Structural Health Monitoring and Damage Detection in Fiber Reinforced Polymer Composite Structures

  1. Seemanthini K. Department of Machine Learning (AIML), B.M.S College of Engineering, Bengaluru
  2. Medikonda Swapna Department of Computer Science, NITTE Deemed to be University, Bengaluru
  3. Abrar Ahmed Syed Gainwell Technologies LLC
  4. Swarna Kuchibhotla Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Greenfields, Vaddeswaram, Guntur
  5. Pritam Paul Department of Computer Science and Engineering – AI, Brainware University
  6. Sivabalakrishnan R. Department of Mechatronics Engineering, Bannari Amman Institute of Technology, Sathyamangalam

Abstract

Applications of fiber-reinforced polymer (FRP) composite in the aerospace, civil infrastructure and renewable energy systems are increasing due to the fact that the composite possesses high ratio of strength to weight and can resist corrosion. However, processes of internal damages such as the cracking of the matrix, delamination and fiber fracture, are likely to take place without being visible on the surface and therefore a periodic check of the structure is done. It will propose an Internet of Things (IoT)-based structural health monitoring system to be used to detect the real-time structural construction damage in FRP composite structural construction. This system is composed of a combination of distributed sensing technologies e.g. strain, acoustic emission and impedance sensors combined with IoT communication architecture to collect and view data in real time. A multimodal damage-based damage fusion model was developed that has the capability to combine sensor signals in unison to deliver one structural health index to evaluate damage. Experimental evaluation demonstrated that the proposed framework achieved a detection accuracy of 95.4%, outperforming impedance tomography (85.3%) and Lamb-wave-based monitoring approaches (88.9%). The system also exhibited a precision of 94.1%, recall of 93.6%, and AUC value of 0.97, representing good performance in classification. Moreover, the monitoring system was able to identify the composite damage at an early stage with an average detection time of 3.8-4.2 seconds. These findings indicate that a combination of IoT-based sensing networks and multimodal data fusion is an effective method of scalable and reliable structural health measurements of advanced polymer composite structures.

Keywords

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