Smart Sensor Networks for Real-Time Structural Health Monitoring of Bridges

Thesis Abstract

The increasing demand for structural safety and maintenance efficiency of bridges necessitates advanced monitoring systems capable of providing continuous, real-time data on structural integrity. Traditional inspection methods are often labor-intensive, sporadic, and subject to human error, underscoring the need for innovative solutions that leverage ICT and sensor technologies to enhance early fault detection and facilitate predictive maintenance. This study aims to develop and evaluate a comprehensive smart sensor network system designed for real-time structural health monitoring (SHM) of bridges, with the primary objectives of designing an optimal sensor placement strategy, implementing a robust data acquisition framework, and analyzing the influence of environmental and load factors on structural response. Employing a mixed-methods research design, the study integrates quantitative data collection through the deployment of wireless sensor nodes on a representative 12-span reinforced concrete bridge in the metropolitan area, featuring a sample size of 150 sensor units placed strategically along critical stress points. Data collection instruments include high-precision accelerometers, strain gauges, and environmental sensors, integrated within a custom sensor network architecture utilizing Internet of Things (IoT) protocols such as MQTT for data transmission. To ensure data validity and reliability, calibration procedures are rigorously followed, and sensor redundancy is incorporated. The quantitative data are analyzed using advanced signal processing techniques and machine learning algorithms, notably convolutional neural networks (CNNs) for anomaly detection, and statistical analysis such as multiple regression to correlate environmental variables with structural responses. Complementing the quantitative analysis, qualitative insights are gathered through interviews with structural engineers and maintenance personnel to contextualize sensor data and validate findings. The theoretical framework is anchored on the Systems Theory to model the interconnected sensor network as an integrated system, and the Theory of Structural Reliability to assess the probabilistic behavior of the monitored bridge under various load and environmental conditions. The analytical framework emphasizes the integration of sensor data analytics with structural assessment models to develop a decision-support system for proactive maintenance. The anticipated findings reveal that the strategic placement of sensors significantly enhances the detection of early structural anomalies, and environmental factors such as temperature fluctuations and humidity substantially influence the bridge’s dynamic responses. The application of machine learning techniques demonstrates high accuracy levels (above 92%) in identifying structural anomalies, thus validating the efficacy of the sensor network in real-time SHM. Additionally, the study elucidates the critical relationship between environmental conditions and structural health indicators, providing insights for adaptive sensor calibration. This research contributes novel empirical evidence on the integration of IoT-enabled sensor networks with predictive analytics for bridge SHM, advancing both theoretical understanding and practical implementation in civil engineering. Furthermore, the study introduces a scalable, cost-effective sensor deployment methodology adaptable to various bridge typologies, addressing current gaps in literature concerning sensor optimization and data analytics for real-time structural monitoring. The main conclusion emphasizes that the deployment of intelligent sensor networks markedly improves the timeliness and accuracy of structural health assessments, thereby supporting proactive maintenance regimes and prolonging the service life of bridges. Recommendations include adopting integrated sensor systems as standard practice in bridge management policies, investing in AI-driven analytics for enhanced predictive capabilities, and extending research to encompass long-term monitoring under diverse environmental conditions. Future studies should explore the integration of drone-based visual inspections with sensor data for comprehensive SHM and investigate the socio-economic impacts of automated monitoring systems in infrastructure management.