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<p>Bridge rehabilitation is essential for maintaining and restoring existing bridges, addressing safety deficiencies, and reducing life-cycle maintenance costs. Innovative solutions are crucial with aging infrastructure increasingly vulnerable to structural damage from natural disasters and climate change. This review examines the role of Artificial Intelligence (AI) in transforming bridge rehabilitation, offering enhanced resilience through optimized repair procedures, cost reduction, and improved public safety. AI technologies, including machine learning, neural networks, and computer vision, enable real-time monitoring, early detection of structural issues, and precise data analysis. The study synthesizes existing literature, emphasizing AI’s potential to enhance the assessment, design, and execution phases of rehabilitation while also reducing reliance on manual, subjective inspections. It highlights the integration of AI with digital twins, IoT devices, and predictive models, which enable autonomous inspection systems and data-driven decision-making. Additionally, the review explores future research directions, such as improving model interpretability and data quality. The findings underscore AI’s transformative impact on bridge rehabilitation, contributing to sustainable practices and extending the service life of critical infrastructure while ensuring safety and efficiency in transportation systems.</p>

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Revolutionizing bridge rehabilitation through artificial intelligence: a comprehensive review and future directions

  • 期刊论文

  • 发表日期：

    2025年4月16日

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  • Salma Ouhmida,
  • Hanane Moulay Abdelali,
  • Nouzha Lamdouar

摘要

Bridge rehabilitation is essential for maintaining and restoring existing bridges, addressing safety deficiencies, and reducing life-cycle maintenance costs. Innovative solutions are crucial with aging infrastructure increasingly vulnerable to structural damage from natural disasters and climate change. This review examines the role of Artificial Intelligence (AI) in transforming bridge rehabilitation, offering enhanced resilience through optimized repair procedures, cost reduction, and improved public safety. AI technologies, including machine learning, neural networks, and computer vision, enable real-time monitoring, early detection of structural issues, and precise data analysis. The study synthesizes existing literature, emphasizing AI’s potential to enhance the assessment, design, and execution phases of rehabilitation while also reducing reliance on manual, subjective inspections. It highlights the integration of AI with digital twins, IoT devices, and predictive models, which enable autonomous inspection systems and data-driven decision-making. Additionally, the review explores future research directions, such as improving model interpretability and data quality. The findings underscore AI’s transformative impact on bridge rehabilitation, contributing to sustainable practices and extending the service life of critical infrastructure while ensuring safety and efficiency in transportation systems.