Artificial Intelligence-Enabled Blockchain Technology and Digital Twin for Smart Hospitals

16Cloud-Based Digital Twinning for Structural Health Monitoring Using Deep Learning

K. Renugadevi¹, T. Jayasankar^2* and J. ArputhaVijaya Selvi³

¹Department of ECE, Government College of Engineering, Thanjavur, Tamil Nadu, India

²Department of ECE, University College of Engineering, BIT Campus, Anna University, Tiruchirappalli, Tamil Nadu, India

³Department of ECE, Kings College of Engineering, Pudukkottai, Tamil Nadu, India

Abstract

This study explores the revolutionary potential of cloud-based digital twinning in combination with deep learning methodologies within the context of structural health monitoring (SHM) applications. “Digital twinning” is the practice of creating digital representations of physical assets in order to track, analyze, and improve their operation within the framework of the IoT paradigm. To enhance the efficacy and precision of SHM systems, we broaden the scope of SHM to include the use of cloud computing and advanced deep learning techniques. Cloud computing infrastructure was used to do this. Machine learning techniques like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are applied in a cloud setting to extract useful insights from streams of sensor data. These algorithms are trained using historical data and real-time sensor inputs to detect anomalies, diagnose structural problems, and forecast future failure modes. By constantly updating and improving their models based on sensor data, digital twins may provide structural ...