基于数字孪生和深度学习的砌体结构动态裂缝检测

Dynamic crack detection of masonry structures based on digital twins and deep learning

  • 摘要: 砌体结构材料特性、本构关系和几何构造的复杂性导致结构抗震性能分析过程繁琐且复杂,而且现有方法无法有效识别追踪结构动态裂缝演化。为提高结构抗震性能损伤评估的效率与精度,本文提出一种基于数字孪生和深度学习的砌体结构动态裂缝检测方法。为准确反映结构的非线性力学行为,通过集成结构监测数据与精细化有限元建模,建立了可动态更新的砌体结构数字孪生模型。通过砌体结构振动台试验,为数字孪生模型提供了校准与验证依据,结果表明模型可准确模拟结构在地震作用下的响应与损伤状态。通过数字孪生模型与振动台试验对比分析了砌体结构受地震波作用下的裂缝演化,所提出的方法可以准确追踪裂缝变化规律,砌体结构动态裂缝追踪检测的准确度可达96.2%。

     

    Abstract: The complexity of material properties, constitutive relationships, and geometric construction of masonry structures leads to a cumbersome and intricate process for analyzing their seismic performance. Moreover, existing methods cannot effectively identify the dynamic crack evolution of structures or accurately track the closure of cracks that cause structural damage. To improve the efficiency and accuracy of damage assessment for structural seismic performance, this paper proposes a crack evolution detection method for masonry structures based on digital twins. To accurately reflect the nonlinear mechanical behavior of the structure, a dynamically updated digital twin model of masonry structures is established by integrating structural monitoring data with refined finite element modeling. The shaking table test of masonry structures provides a basis for calibrating and validating the digital twin model, and the results show that the model can accurately simulate the response and damage state of the structure under seismic action. By comparing the digital twin model with the shaking table test, the crack evolution of masonry structures under seismic wave action is analyzed. The proposed method can accurately track the crack variation pattern, and the accuracy of dynamic crack tracking and detection in masonry structures can reach 96.2%.

     

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