Recent advances in structural health diagnosis: a machine learning perspective
// Последние достижения в области диагностики состояния конструкций: перспективы машинного обучения //
Yuequan Bao et al (China)
В статье рассматриваются современные достижения в области мониторинга состояния мостов с точки зрения машинного обучения. Поднимаются такие темы, как очистка данных, полученных в ходе мониторинга от шумов и других "аномалий", оценка модальных параметров конструкций, идентификация повреждений конструкций, технология цифровых двойников и оценка надежности конструкций с использованием искусственного интеллекта.
Abstract. Structural health monitoring (SHM) is the most direct and advanced method for understanding the evolution laws of structures and ensuring structural safety. The essence of SHM lies in diagnosing structural health by analyzing monitoring data. Since the introduction of machine learning paradigm for SHM, using machine learning methods to analyze the monitoring data, identify, and evaluate structural health status has become a prominent research topic in this field. For complex bridge structures, diagnosing structural health based on highly incomplete monitoring data presents an inherent high-dimensional problem. Machine learning methods are particularly well-suited for addressing these issues due to their capabilities in effective feature extraction, efficient optimization, and robust scalability. This article provides a brief review of the developments in machine learning-based structural health diagnosis, including data cleaning, structural modal parameters estimation, structural damage identification, digital twin technology, and structural reliability assessment. Additionally, the paper discusses related open questions and potential directions for future research.
Recent advances in structural health diagnosis: a machine learning perspective // Bao Y. et al. / Advances in Bridge Engineering. 2025. Vol. 6, № 1. P. 7.
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