Development of IoT-Based Structural Health Monitoring System for Bridges

Thesis Abstract

The integrity and safety of bridges are critical components of urban infrastructure, yet traditional structural health monitoring (SHM) approaches often face limitations related to manual inspections, data siloes, and delayed response times, thereby increasing the risk of catastrophic failures and maintenance costs. In response to these challenges, this study aims to develop an Internet of Things (IoT)-based structural health monitoring system specifically designed for bridges to enable real-time, continuous assessment of structural conditions. The specific objectives are to design an integrated sensor network capable of capturing vital structural parameters such as strain, stress, vibration, and temperature; to develop a wireless data transmission framework that ensures reliable, tamper-proof communication; to establish data analytics algorithms for early detection of anomalies; and to validate the system through field deployment on a selected bridge. The research adopts a mixed-methods approach, combining quantitative experimental design with qualitative assessments to ensure comprehensive system validation. The study population comprises IoT sensors, data processing modules, and the structural bridge itself within an urban setting, with a sample size of 50 high-precision accelerometers and strain gauges deployed across critical load-bearing points of the bridge. Data collection instruments include commercially available IoT hardware components, custom-developed sensor nodes, cloud-based data storage solutions, and analytical software such as MATLAB and Python for data analysis. To assess the reliability and validity of the data collection instruments, calibration procedures and repeated measurements were employed, ensuring high measurement accuracy (with a target error margin of less than 5%). Analytical techniques include descriptive statistics, regression analysis for correlating sensor data with structural parameters, and machine learning algorithms—such as support vector machines and neural networks—to enable anomaly detection and predictive maintenance. Expected findings include the demonstration of the system's capability to detect early signs of structural distress, such as micro-cracks or abnormal vibration patterns, with high sensitivity and specificity. The system’s integration of sensor data and machine learning is anticipated to outperform traditional periodic inspections by providing continuous, real-time condition monitoring. These findings are expected to reveal correlations between environmental factors, load conditions, and structural responses, thereby enhancing predictive maintenance strategies and reducing both maintenance costs and safety risks. This research significantly contributes to knowledge by offering a scalable, technology-driven framework for SHM that can be adapted to various bridge types and urban contexts, bridging the gap between emerging IoT innovations and civil engineering practices. The study advances the theoretical understanding of integrative sensor networks within complex structural systems and applies theories of cyber-physical systems and sensor fusion paradigms within civil infrastructure monitoring. The main conclusion underscores that IoT-enabled SHM systems serve as a crucial tool in proactive bridge maintenance, extending service life and safeguarding public safety. Based on the findings, recommendations include broader adoption of IoT-based monitoring across different types of transportation infrastructure, policy formulations for data security and privacy, and the development of standardized protocols for sensor deployment and data analysis. Future research directions are suggested to explore the integration of energy harvesting techniques for sensor power sustainability, enhancement of wireless communication robustness in harsh environments, and the application of advanced artificial intelligence models for autonomous decision-making. This study paves the way for smarter, more resilient civil infrastructure systems, leveraging cutting-edge IoT technology to transform traditional maintenance paradigms into proactive, data-driven processes.