A novel multi-sensor framework for post-strengthening assessment and health monitoring of FRP-retrofitted half-century-old reinforced concrete bridge

Reliable structural health monitoring (SHM) of fiber-reinforced polymer (FRP)-strengthened bridges is crucial for enabling timely maintenance interventions and extending the service life. Although existing studies primarily focus on SHM techniques using vibration sensing, a significant gap remains in correlating and integrating measurements from multiple sensing methods. Therefore, a comprehensive multi-sensor framework is necessary to enhance data reliability and structural health assessment. For the first time, a novel multi-sensor framework has been introduced for assessing FRP-strengthened half-century-old reinforced concrete bridge located in Alibaug, Raigad, India. The study involves instrumenting a span of the bridge with the lead zirconate titanate sensor for electromechanical impedance (EMI) sensing along with other conventional sensors such as accelerometer, Linear Variable Differential Transformer (LVDT), strain gauges, crack gauges, and tilt meters, targeting critical structural elements. Impedance signatures have been acquired along with other global structural responses using nearby sensors for various loading conditions. The obtained signatures are further processed using different statistical and wavelet-based variational indicators, for quantification of load severity. Subsequently, it is observed that the application of incremental loading on the structure is manifested as the variation in conductance peaks, along with an increase in the signal amplitude of the conventional sensors. Furthermore, a correlation analysis is performed between EMI-based variational indices and the conventional sensor magnitude. This correlation selection is constrained by appropriate assumptions regarding monotonicity and the improvement in accuracy of nonlinear over linear correlation. Results shows that the load induced variations for different sensing techniques is majorly following linear correlation with each other. The proposed multi-sensor fusion framework aims at setting up a mathematical correlation between different sensor measurement variation under incremental loading. The outcomes of this study highlight the capability of a multi-sensor framework to form a reliable assessment protocol for monitoring FRP-retrofitted bridges, enabling effective predictive maintenance.