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

• Published in: Journal of bridge engineering 2018-10, Vol.23 (10), p.1762-1770
• Main Authors: Butler, Liam J, Lin, Weiwei, Xu, Jinlong, Gibbons, Niamh, Elshafie, Mohammed Z. E. B, Middleton, Campbell R
• Format: Article
• Language: English
• Subjects: Bridge construction; Civil engineering; Modelling; Monitoring; Railroads; Railway bridges; Railway construction; Technical Papers
• ISSN: 1084-0702; 1943-5592
• DOI: 10.1061/(ASCE)BE.1943-5592.0001288

AbstractThis study shows how integrating fiber optic sensor (FOS) networks into bridges during the construction stage can be used to quantify preservice performance. Details of the installation of a large FOS network on a new steel–concrete composite railway bridge in the United Kingdom are presented. An overview of the FOS technology, installation techniques, and monitoring program is also 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 according to several model code formulations implemented in the FE model. The preservice strain distribution due to dead loading is typically assumed to act uniformly along the bridge length; however, the monitoring results revealed that the distribution was highly variable as a result of the complex interactions between gravity loading, bridge geometry, time-dependent concrete properties, and temperature effects. Moment utilization of the main girders and composite beams, during preservice conditions, was assessed and found to be between 19.3 and 24.9% of the respective design cross-section capacities. Quantifying preservice performance via integrated sensing also provided a critical baseline for the bridge, which enables future data-driven condition assessments.