Data-driven structural damage detection approach for Digital twin-Structural health monitoring

As part of the project activity funded by the Newton Fund Institutional Links through the U.K. Department of Business, Energy, and Industrial Strategy and managed by the British Council under Grant 429715093, Research paper ” Data-Driven Structural Health Monitoring using feature Fusion and Hybrid Deep Learning ” was accepted for publication in Q1 journal “IEEE Transactions on Automation Science and Engineering” in October 2020.

Toward a Digital Twin model automatically monitoring the operational state of large-scale structures in a near-real-time fashion, it requires a Structural Damage Detection (SDD) approach, both time-efficient, and data-efficient, while still maintaining accurate detection results. Conventional methods either resort to time-consuming sophisticated preprocessing techniques such as experimental modal analysis or data-eager image-based deep learning duplicating multiple times raw measured data. Continue reading “Data-driven structural damage detection approach for Digital twin-Structural health monitoring”