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@article{203949,
        author = {Snehal Rajaram Gore and Pranali  Mahadev Mahadik and Prof Dr. Manisha V. Bhanuse},
        title = {a bridge structural health monitoring using machine learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {2009-2012},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203949},
        abstract = {in modern transportation systems, bridges play a vital role in ensuring safe and efficient movement of people and goods. Due to aging infrastructure, heavy traffic loads, environmental conditions, corrosion, earthquakes, and natural disasters, bridges are prone to structural damage and failure. Continuous monitoring of bridge health is therefore essential to prevent accidents and reduce maintenance costs. This paper presents a Machine Learning-based Bridge Structural Health Monitoring (BSHM) system for detecting and predicting structural abnormalities in bridges. The proposed system uses various sensors such as vibration sensors, strain gauges, temperature sensors, and load sensors to collect real-time structural data from the bridge. The collected data is processed and analyzed using Machine Learning algorithms to identify patterns related to cracks, excessive vibrations, stress, and structural weaknesses. The system applies supervised learning algorithms such as Random Forest, Support Vector Machine (SVM), and Decision Tree for damage detection and condition classification. The trained model predicts the structural condition of the bridge with improved accuracy and provides early warning alerts for maintenance requirements. The integration of IoT and wireless communication technologies enables remote monitoring and real-time data transmission to a central monitoring station.},
        keywords = {},
        month = {June},
        }