Author(s): Inês Carvalho

Abstract: The advancement of smart infrastructure technologies has revolutionized the monitoring and management of long-span bridges. This study focuses on the development and validation of an integrated framework combining Finite Element Modeling (FEM) with Internet of Things (IoT)-enabled Structural Health Monitoring (SHM) for cable-stayed bridges. A high-fidelity finite element model was created to simulate the dynamic and static behavior of a 500 m cable-stayed bridge, while an IoT-based sensor network comprising accelerometers, strain gauges, and temperature sensors was deployed to capture real-time field data. The collected data were assimilated into the FEM using sensitivity-based updating and Kalman filtering algorithms to minimize discrepancies between simulated and measured responses. Results showed a significant reduction in modal frequency error from 9.75% to 1.96%, cable force MAPE from 8.88% to 1.94%, and mid-span deflection RMSE from 2.51 mm to 0.41 mm after model updating. The paired t-test confirmed statistical significance (t = 7.79, p < 0.001), and correlation coefficients improved to nearly unity, validating the robustness of the proposed integration. The hybrid IoT-FEM approach successfully demonstrated its potential for early damage detection, enhanced predictive accuracy, and real-time condition assessment under dynamic vehicular loading. Practical recommendations were also outlined to guide field implementation, including optimized sensor deployment, cloud-based data handling, and integration within digital twin architectures. Overall, this research establishes a scalable and intelligent monitoring framework capable of transforming conventional bridge inspection methods into proactive, data-driven management systems that ensure safety, durability, and operational efficiency of cable-stayed bridges.

Pages: 48-52 | Views: 3 | Downloads: 2

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