Integrating WSN and IoT for enhanced structural health monitoring in real-time using neural networks: a novel approach

Abstract

Structural health monitoring (SHM) of buildings is critically important as it directly affects human safety and economic activities. This paper proposes a real-time system design for SHM to monitor the building’s status through WSNs, make intelligent inferences, and predict the risk. The system architecture consists of multiple stages. The first stage is the transmitting side attached directly to the building’s structure, which comprises several sensors, including ADXL345, SW-420, LVDT, and strain gauge. LoRa wireless communication technology is established to transfer the sensors’ data from the transmitter side to an on-site central node. The central node processes and transmits the data to the cloud via Wi-Fi. The artificial neural networks (ANN) algorithm is employed to classify healthy and abnormal data to determine the damage severity value of the building’s status based on the peak ground acceleration (PGA), which ensures high accuracy in determining the damage value exposed to the building. The system utilizes the ThingSpeak IoT platform, which integrates the trained neural network and central node for storing sensors’ data and damage severity value to enable real-time monitoring. The system was validated using a shake table experiment by applying three PGA values, 0.05 g, 0.15 g, and 0.32 g, to the building model. The results demonstrate that the system is reliable and more effective for damage prediction, achieving a mean absolute error (MAE) of 0.0126 and 0.014 for neural network training and testing, respectively. Moreover, the ANN performed a correlation coefficient (R²) of 0.95892 and 0.95961 for training and testing. The main achievement of this research involves developing an advanced integrated system that combines sensors with an IoT platform and neural networks to track building damage severity in real-time.