Robust fibre optic sensor arrays for monitoring early-age performance of mass-produced concrete sleepers
Structural Health Monitoring
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Butler, L., Xu, J., He, P., Gibbons, N., Dirar, S., Middleton, C., & Elshafie, M. (2017). Robust fibre optic sensor arrays for monitoring early-age performance of mass-produced concrete sleepers. Structural Health Monitoring https://doi.org/10.1177/1475921717714615
This study investigates integrating fibre optic sensing technology into the production process of concrete railway sleepers. Robust fibre Bragg grating strain and temperature sensor arrays were developed specifically for this application and were designed for long-term monitoring of sleeper performance. The sensors were used to monitor sleeper production and to help gain a deeper understanding of their early-age behaviour which can highly influence long-term performance. In total, 12 sleepers were instrumented and strain data were collected during the entire manufacturing process including concrete casting and curing, prestressing strand detensioning and qualification testing. Following the production process, sleepers were stored temporarily and monitored for 4 months until being placed in service. The monitoring results highlight the intrinsic variability in strain development among identical sleepers, despite high levels of production quality control. Using prestress loss as a quality control indicator, the integrated sensing system demonstrated that sleepers were performing within Eurocode-based design limits prior to being placed in service. A three-dimensional nonlinear finite element model was developed to provide additional insight into the sleepers’ early-age behaviour. Based on the fibre Bragg grating–calibrated finite element model, more realistic estimates for the creep coefficient were provided and found to be 48% of the Eurocode-predicted values.
fibre optic sensors, early-age concrete behaviour, prestress losses, finite element analysis, concrete sleepers
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors thank the EPSRC and Innovate UK for funding this research through the Cambridge Centre for Smart Infrastructure and Construction (CSIC) Innovation and Knowledge Centre (EPSRC grant reference number EP/L010917/1).
External DOI: https://doi.org/10.1177/1475921717714615
This record's URL: https://www.repository.cam.ac.uk/handle/1810/265789