A novel physics-informed framework for real-time adaptive modal parameters estimation of offshore structures

Singular value decomposition provides a rigorous mathematical foundation for these monitoring methods, such as modal analysis, damage detection, etc. As substantial limitations including computational efficiency and adaptive capabilities have been identified in the matrix process, data-driven algorithms with enhanced efficiency and accuracy to adapt the rapid development of information and technologies have been highly required in offshore structures monitoring. To meet this demand, a novel physics-informed frame work enabling the real-time adaptive monitoring has been proposed in this paper. The working principle of the developed framework has been represented by the smart conversion of physics-informed modal identification into the optimal process of fast and accurate solving an eigensystem governed by differential equations via recurrent neural network. The ingenious design of the proposed framework has complied with the rule of singular value decomposition used for modal identification and therefore, learning capabilities of the physics-informed framework have been remarkably enhanced by successfully addressing two bottleneck problems including proper initialized input values and the optimal time increment of the developed recurrent neural network. Both numerical simulations and the field-data based study of a mono-pile offshore wind turbine structure have been presented to examine the superior performance of the proposed framework. Results have shown that the proposed framework has the ability to adaptively identify modal parameters with a higher level of computational efficiency as compared with traditional methods. Furthermore, the computational advantage of the develop framework has demonstrated the potential to integrate with sensor networks and edge computing for smart monitoring and maintenance in various engineering subjects.