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@article{186796,
        author = {Prof. Abhale B.A. and Prof. Vairal D.B. and Prof. Vairal D.B. and Miss.Durgesha Chavan and Miss. Sanjivani Bare and Miss. Nikita Deore and Miss. Prerna Deshmukh},
        title = {Artificial Intelligence-Based Structural Health Monitoring System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {2912-2917},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186796},
        abstract = {As infrastructure ages and experiences environmental stress, overloading, and material degradation, bridge and building safety and durability have grown to be major concerns. Conventional inspection techniques mainly rely on manual surveys and recurring visual inspections, which are expensive, time-consuming, and frequently ineffective at spotting hidden or early- stage damage. Artificial intelligence (AI) is being used more and more in structural health monitoring (SHM) to address these issues by offering a continuous, data-driven evaluation of structural conditions. AI-based SHM systems gather and evaluate real-time data on vibration, strain, displacement, cracks, and material behaviour using sensors, cameras, IoT networks, and sophisticated algorithms. Automatic identification of structural irregularities and early warnings of possible failures are made possible by technologies like machine learning, deep learning, computer vision, and predictive analytics.},
        keywords = {Structural Health Monitoring (SHM), Artificial Intelligence (AI), Machine Learning, Deep Learning, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM)},
        month = {November},
        }