Comparative Study of An RCC Bridge Under IRC Class AA Loading and Analysis of Deflections Using Sensors

Bridges are critical components of transportation infrastructure, requiring accurate evaluation of their structural performance under increasing traffic demands. This study focuses on the analysis of a Reinforced Cement Concrete (RCC) bridge subjected to IRC Class AA loading, which represents one of the heaviest design load cases for national and state highways. The bridge was modelled and analysed using STAAD.Pro to determine theoretical structural responses, including bending moments, shear forces, and deflections under moving loads. To validate the analytical results, sensor-based measurements were obtained from a scaled prototype model to record real-time deflection behaviour under applied loads. A machine learning approach was further employed to process sensor data and improve prediction accuracy by identifying deviation patterns between analytical and experimental observations. The comparative analysis demonstrated that while STAAD.Pro provides conservative and reliable predictions for design, real-world measurements capture additional behavioural variations influenced by material properties and dynamic effects. The study emphasizes the importance of integrating structural health monitoring systems and data-driven models in bridge engineering to enhance safety, durability, and predictive maintenance strategies.