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Spectral algorithm for non-destructive damage localisation: Application to an ancient masonry arch model

机译:非破坏性损伤定位的谱算法:在古代砌体拱模型中的应用

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摘要

Structural monitoring and vibration-based damage identification methods are fundamental tools for condition assessment and early-stage damage identification, especially when dealing with the conservation of historical constructions and the maintenance of strategic civil structures. However, although the substantial advances in the field, several issues must still be addressed to broaden the application range of such tools and to assert their reliability. This study deals with the experimental validation of a novel method for non-destructive damage identification purposes. This method is based on the use of spectral output signals and has been recently validated by the authors through a numerical simulation. After a brief insight into the basic principles of the proposed approach, the spectral-based technique is applied to identify the experimental damage induced on a masonry arch through statically increasing loading. Once the direct and cross spectral density functions of the nodal response processes are estimated, the system's output power spectrum matrix is built and decomposed in eigenvalues and eigenvectors. The present study points out how the extracted spectral eigenparameters contribute to the damage analysis allowing to detect the occurrence of damage and to locate the target points where the cracks appear during the experimental tests. The sensitivity of the spectral formulation to the level of noise in the modal data is investigated and discussed. As a final evaluation criterion, the results from the spectrum-driven method are compared with the ones obtained from existing non-model based damage identification methods.
机译:结构监测和基于振动的损伤识别方法是状态评估和早期损伤识别的基本工具,尤其是在处理历史建筑保护和战略土木结构维护时。但是,尽管在该领域取得了长足的进步,但仍必须解决几个问题,以扩大此类工具的应用范围并确定其可靠性。这项研究涉及一种用于非破坏性损伤识别的新颖方法的实验验证。该方法基于频谱输出信号的使用,并且最近已通过数值模拟得到作者的验证。在对提出的方法的基本原理进行了简短的了解之后,基于光谱的技术被应用于识别通过静态增加的载荷在砌体拱上引起的实验损伤。一旦估计了节点响应过程的直接和互谱密度函数,就建立了系统的输出功率谱矩阵,并将其分解为特征值和特征向量。本研究指出了提取的光谱特征参数如何有助于损伤分析,从而能够检测出损伤的发生并确定在实验测试中出现裂纹的目标点。研究和讨论了频谱公式对模态数据中噪声水平的敏感性。作为最终评估标准,将频谱驱动方法的结果与现有非模型损伤识别方法的结果进行比较。

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