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<p>Structural health monitoring (SHM) has witnessed a transformative evolution with the integration of Artificial Intelligence (AI) and data analytics. This review synthesizes recent developments in the realm of AI-powered SHM, elucidating key findings and emphasizing the pivotal role of these technologies in shaping the future of infrastructure monitoring. The review highlights the efficacy of AI in processing and analyzing vast structural datasets, leading to improved detection, diagnosis, and prediction of structural issues. Machine learning algorithms contribute to a proactive approach, enabling the identification of subtle patterns indicative of deterioration. The symbiosis of AI and SHM not only enhances accuracy in anomaly detection but also holds promise in revolutionizing maintenance strategies. This abstract encapsulates the significance of AI and data analytics in SHM, concluding with insights into future research directions to address challenges and unlock untapped potentials in this dynamic field.</p>

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Artificial Intelligence and Data Analytics for Structural Health Monitoring: A Review of Recent Developments

  • 期刊论文

  • 发表日期：

    2025年4月7日

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  • Shrikant M. Harle,
  • Amol Bhagat,
  • Ruchita Ingole,
  • Nilesh Zanjad

摘要

Structural health monitoring (SHM) has witnessed a transformative evolution with the integration of Artificial Intelligence (AI) and data analytics. This review synthesizes recent developments in the realm of AI-powered SHM, elucidating key findings and emphasizing the pivotal role of these technologies in shaping the future of infrastructure monitoring. The review highlights the efficacy of AI in processing and analyzing vast structural datasets, leading to improved detection, diagnosis, and prediction of structural issues. Machine learning algorithms contribute to a proactive approach, enabling the identification of subtle patterns indicative of deterioration. The symbiosis of AI and SHM not only enhances accuracy in anomaly detection but also holds promise in revolutionizing maintenance strategies. This abstract encapsulates the significance of AI and data analytics in SHM, concluding with insights into future research directions to address challenges and unlock untapped potentials in this dynamic field.