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.

中文翻译：

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通过数字物理孪生和多参数识别进行结构性能评估


现有结构的性能经常受到损坏和状况变化的影响，这给当前的评估方法准确评估其服务状态提出了挑战。本文介绍了一种基于数字物理孪生和多参数识别的结构性能评估方法。主要功能包括：（1） 将非接触式传感数据与有限元模型集成的数字孪生框架。（2） 一种使用智能裂纹检测数据进行局部刚度折减的技术，其中深度学习提取裂纹信息，机械模型计算刚度折减系数。（3） 一种将非接触式监测数据与孪生子结构模型相结合的多参数识别方法，采用子结构交互技术和增强的无迹卡尔曼滤波算法来识别支撑刚度等关键参数。该方法的可行性通过涉及框架结构的案例研究得到证明，为现有结构的安全评估提供了新的范式。