15. Bridge Health Monitoring: A Review of Utilizing the Internet of Things, Digital Twin, and Advanced Technologies

The chapter begins by addressing the inherent safety concerns and structural challenges faced by bridges due to various environmental and operational factors. Traditional visual inspections, while foundational, are labor-intensive and reliant on human expertise, often leading to delayed maintenance and increased costs. The advent of Structural Health Monitoring (SHM) has revolutionized this field, employing sensor data, feature extraction, and analytical phases to evaluate structural conditions in real-time. The integration of IoT, with its network of interconnected sensors, has significantly enhanced the accuracy and efficiency of bridge monitoring. IoT enables real-time data collection on parameters such as acceleration, displacement, and temperature, facilitating proactive maintenance and extending the service life of bridges. Digital Twin (DT) technology further augments this capability by creating virtual replicas of physical bridges, synchronized with real-world data. This allows for comprehensive simulations and in-depth analyses, providing valuable insights into structural behavior and potential issues. The chapter also explores advanced 3D modeling techniques, including LiDAR, UAVs, TLS, and photogrammetry, which enable the creation of detailed 3D models for precise structural assessments. These technologies not only improve the accuracy of monitoring but also enhance the practicality and effectiveness of maintenance strategies. The synthesis of IoT, DT, and 3D modeling presents a dynamic framework for capturing and analyzing data in real-time, paving the way for more resilient and sustainable infrastructure. The chapter concludes with a forward-looking perspective, emphasizing the potential of AI integration to further revolutionize bridge monitoring and maintenance practices.