Application of Numerical Methods in Structural Health Monitoring Using IoT Sensors

Abstract

In the context of ageing bridges, buildings, and transportation frameworks, Structural Health Monitoring (SHM) has evolved as an important tool for evaluating the integrity and performance of infrastructure systems. This is particularly true in the context of SHM. It is now possible to do real-time monitoring thanks to the incorporation of sensors that are connected to the Internet of Things (IoT). This capability enables continuous data collecting from important sites of infrastructure. The quality and interpretability of these sensor data, on the other hand, are primarily dependent on the use of robust numerical approaches for signal processing, anomaly detection, and predictive modelling. The purpose of this work is to offer a methodical use of numerical techniques, especially the Finite Element Method (FEM), Euler's Method, and Least Squares Estimation, in the interpretation and utilization of data acquired by Internet of Things-based structural health monitoring (SHM) systems. These mathematical algorithms are shown to be able to identify stress buildup, vibrational anomalies, and deterioration trends in structures by making use of validated datasets derived from well-documented case studies. The technique is verified by means of two rigorous numerical examples that are matched with real-world datasets. These examples demonstrate the effectiveness of the proposed framework in improving the resilience of infrastructure. It has been shown via the findings that the incorporation of numerical computing into SHM that is allowed by the Internet of Things offers a solution for infrastructure management that is scalable, accurate, and proactive. As a result, our study bridges the gap between rigorous mathematical analysis and technical application in the contemporary age of intelligent monitoring.