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@article{207535,
        author = {Rithik Bidkar and Girish Joshi},
        title = {AI-IoT Enabled Structural Health Monitoring of an Old Reinforced Concrete Bridge Using SAP2000 Digital Twin and FFT-Based Vibration Response},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {3},
        pages = {1383-1387},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207535},
        abstract = {Ageing reinforced concrete bridges require continuous and intelligent monitoring because periodic inspection alone cannot reliably capture progressive deterioration, abnormal vibration or serviceability-related deflection. This paper develops a Scopus-style original research manuscript on structural health monitoring of old bridges using an integrated Internet of Things sensor network, artificial-intelligence-based anomaly interpretation and SAP2000 digital twin modelling. The framework combines accelerometers, displacement sensors, strain-oriented measurements and low-cost ESP32-based data acquisition. The Ozar Bridge in Pune is used as a case-study structure, with a 107 m length, four piers and 21.4 m short span. Field vibration responses were interpreted through Fast Fourier Transform (FFT) indicators at left, middle and right bridge locations under higher-load and light-load conditions. The middle zone produced the dominant amplitude response, reaching 4159.7 at 14.80 Hz under higher load and 1377.5 at 14.47 Hz under light load. These results show that the mid-span/central region requires higher attention during condition assessment. The proposed SHM framework offers a practical route for continuous bridge maintenance by integrating low-cost sensing, numerical validation and data-driven interpretation.},
        keywords = {Structural health monitoring; Old bridges; IoT sensors; SAP2000; Digital twin; FFT; Vibration response; Predictive maintenance},
        month = {August},
        }