Machine Learning–Based Structural Health Monitoring

Dear Colleagues,

Recent advances in machine learning have opened vast possibilities for the development of disruptive innovations in the field of structural health monitoring (SHM). Machine learning provides advanced mathematical frameworks and algorithms that can help to discover and model the performance and conditions of a structure through deep mining of monitoring data—for example, machine learning applications in building structural design and performance assessment.

To be specific, this Special Issue will publish study results and research papers that present innovative uses of machine learning for processing SHM data. Additionally, it also encourages papers that provide comprehensive reviews of the literature on this topic.

Prof. Dr. Jun Teng
Prof. Dr. Fei Kang
Dr. Yu Tang
Guest Editors

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