## 6 Conclusions  
The correct design of the prestressing force (PF) distribution is very important to ensure the safety and serviceability of PSC structures. However, the prestress losses closely related to the PF distribution have generally been estimated using predictive equations specified in design codes or manuals, which were mostly derived from small-scale experiments. The validity of these equations at the scale of actual full-scale structures has not been sufficiently investigated because of the poor reliability and durability, and the difficulty in installation, when using conventional sensing technologies.  
Therefore, Smart Strands with embedded fiber optic sensors were developed and applied to a 20 m-long full-scale specimen and a 60 m-long PSC girder bridge to investigate the characteristics of the PF distribution affected by long-term prestress losses. The measured data were compared with the theoretical values obtained by the predictive equations for long-term prestress losses that are specified in several design provisions. The results revealed that temperature correction or compensation is very important to obtain the PFs based on true mechanical strains, which are not affected by a seasonal or daily temperature variation in the long-term monitoring.  
Although the two approaches of measurement and theory produced a similar long-term trend of the PF distribution, the difference between them varied depending on the design equations. The equation of Eurocode 2 to estimate the long-term prestress losses showed relatively good agreement with the measurement, when compared to that of ACI 209R and ACI 423.10R, for both the full-scale specimen and the actual PSC girder bridge. This can be attributed to the more refined form of the predictive equation of Eurocode 2 that addresses the time-dependency of the PF. The difference in the time function between ACI 209R and Eurocode 2 did not significantly affect the decreasing trend of the PF over time. The ultimate values of creep and shrinkage, together with a number of correction factors, greatly affected the long-term PF. In addition to the analysis of the long-term prestress losses, the Smart Strands also provided useful data in terms of the PF variation at each major stage during construction.  
We expect our developed Smart Strand to be applied practically in PF measurement for the reasonable safety assessment and maintenance of structures, such as in structural health monitoring (SHM).  
Conceptualization, S.-H.K. and S.-J.J.; methodology, S.-H.K. and S.-J.J.; software, S.-H.K.; validation, S.-H.K. and S.-J.J.; formal analysis, S.-H.K.; investigation, S.-H.K. and S.-J.J.; resources, S.Y.P.; data curation, S.-H.K. and S.-J.J.; writing--original draft preparation, S.-H.K.; writing--review and editing, S.-J.J.; visualization, S.-H.K.; supervision, S.-J.J.; project administration, S.-J.J.; funding acquisition, S.Y.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Acknowledgments:** This research was supported by a grant from a Strategic Research Project (Smart Monitoring System for Concrete Structures Using FRP Nerve Sensors) funded by the Korea Institute of Civil Engineering and Building Technology. The efforts of [PERSON] in the Korea Institute of Civil Engineering and Building Technology for preparing and monitoring the Smart Strands are also greatly appreciated.  
**Conflicts of Interest:** The authors declare no conflict of interest.