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Monitoring, Modeling, and Assessment of a Self-Sensing Railway Bridge during Construction

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Butler, LJ 
Lin, W 
Xu, J 
Elshafie, MZEB 


This study demonstrates how integrating fibre optic sensor (FOS) networks into bridges during their construction can be used to quantify their pre-service performance. Details of the installation of a large FOS network on a new steel-concrete composite railway bridge are presented. An overview of the FOS technology, installation techniques, and monitoring program is presented and the monitoring results from several construction stages are discussed. A finite element (FE) model was developed and a phased analysis was carried out to simulate strain development in the bridge during consecutive construction stages. The response of the self-sensing bridge to the time dependent properties of the concrete deck was evaluated by comparing FOS measurements to predicted results based on several model code formulations (Eurocode 2, ACI 209, and CEB-fip) implemented in the FE model. Pre-service strain distribution due to dead loading is typically assumed to act uniformly along the bridges' span, however the monitoring results revealed these to be highly variable as a result of the complex interaction between gravity loading, bridge geometry, time-dependent concrete properties and temperature effects. Moment utilisation of the main steel girders and transverse composite beams at their pre-service condition was assessed and found to be between 19.3% and 24.9% of their design section capacities. Quantifying pre-service performance of bridges via integrated sensing also establishes a well-defined baseline which enables future data-driven condition assessment.



Bridge monitoring, Fiber optic sensors, Preservice assessment, Finite-element modeling, Railway bridges

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Journal of Bridge Engineering

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American Society of Civil Engineers (ASCE)
Engineering and Physical Sciences Research Council (EP/L010917/1)
Technology Strategy Board (920035)
Engineering and Physical Sciences Research Council (EP/N021614/1)
The authors gratefully acknowledge the Engineering and Physical Sciences Research Council (EPSRC) and Innovate UK for funding this research through the Cambridge Centre for Smart Infrastructure and Construction (CSIC) Innovation and Knowledge Centre (EPSRC Grant EP/L010917/1)