Abstract
Advanced fiber optic monitoring technologies are increasingly applied in structural health monitoring (SHM) to provide continuous, durable, and spatially distributed measurements in reinforced concrete structures. This study presents an experimental evaluation of a distributed fiber optic sensing (DFOS) approach based on inner-fixed-point cable architecture, aimed at supporting long-term crack monitoring in critical reinforced concrete elements. The proposed system is designed to deliver high-resolution distributed strain measurements while preserving sensing integrity under localized deformation conditions.In-situ commissioning measurements were carried out on a DFOS system installed along the horizontal cable route beneath Block R in the raft foundation of Abu Dhabi Plaza (Astana, Kazakhstan). Distributed strain and temperature data were acquired using a Brillouin Optical Time Domain Analyzer (BOTDA) during two commissioning stages conducted under different construction and operational conditions. The segmented cable configuration, incorporating predefined fixed points separated by free sensing lengths, enables localized strain assessment while maintaining mechanical robustness.The results demonstrate the system’s ability to reliably capture distributed strain and temperature variations and to resolve changes associated with construction-stage thermal and mechanical disturbances. Comparison of the two commissioning stages highlights the influence of concrete casting activities and measurement conditions on signal variability and allows the establishment of a stable baseline response. This baseline provides a foundation for subsequent long-term SHM, in which persistent localized strain anomalies relative to the reference state may be interpreted as indicators of potential cracking or damage evolution.Overall, the study confirms the suitability of the proposed DFOS approach for deployment in large-scale reinforced concrete structures and its potential integration into long-term monitoring frameworks, including Building Information Modeling (BIM) and digital twin platforms, to support informed decision-making throughout the service life of infrastructure.