Structural performance evaluation via digital-physical twin and multi-parameter identification

The performance of existing structures is often compromised by damage and condition changes, challenging current evaluation methods in accurately assessing their service status. This paper introduces a structural performance evaluation method via digital-physical twin and multi-parameter identification. Key features include: (1) a digital twin framework that integrates non-contact sensing data with finite element models. (2) a technique for local stiffness reduction using intelligent crack inspection data, where deep learning extracts crack information and a mechanical model calculates stiffness reduction coefficients. (3) a multi-parameter identification approach combining non-contact monitoring data with twin substructure models, employing substructure interaction technology and an enhanced unscented Kalman filter algorithm to identify critical parameters like support stiffness. The method's feasibility is demonstrated through a case study involving a frame structure, offering a new paradigm for the safety assessment of existing structures.