Structural health monitoring is usually implemented by model-driven or data-driven methods. Both of them have their advantages and disadvantages. This article proposes an innovative hybrid strategy as a combination of model-driven and data-driven approaches to detecting and locating damage in civil structures. In this regard, modal flexibility matrices of the undamaged and damaged conditions are initially derived from their modal frequencies and mode shapes. Subsequently, the discrepancy between these matrices is proposed as a damage-sensitive feature. To increase damage detectability and localizability, the modal flexibility discrepancy matrix is expanded by the Kronecker product and then converted into a vector by a simple vectorization algorithm yielding vector-style feature samples. To detect and locate damage, this article introduces the k-medoids and density-based spatial clustering of applications with noise techniques. The vector-style feature samples are incorporated into these clustering methods to obtain two different damage indices including the direct clustering outputs and their Frobenius norms. The great novelty of this article is to develop an innovative hybrid strategy for damage detection and localization under noise-free and noisy conditions so that the damage-sensitive feature is obtained from a model-driven scheme and the decision-making is carried out by a data-driven strategy. A shear-building frame and the numerical model of the ASCE benchmark structure are used to validate the accuracy and performance of the proposed methods. Results demonstrate that the hybrid strategy presented here is influentially able to detect and locate damage in the presence of noisy modal data.

结构健康监控通常通过模型驱动或数据驱动的方法来实现。两者都有其优点和缺点。本文提出了一种创新的混合策略，将模型驱动和数据驱动方法相结合，以检测和定位土木结构中的损坏。在这方面，未损坏和损坏状态的模态柔性矩阵最初是从其模态频率和模态形状得出的。随后，这些矩阵之间的差异被提出为损伤敏感特征。为了提高损伤的可检测性和可定位性，模态柔性差异矩阵由Kronecker乘积扩展，然后通过简单的矢量化算法转换为矢量，从而产生矢量样式的特征样本。为了检测和定位损坏，本文介绍了ķ-质体和基于噪声的应用程序基于密度的空间聚类。将向量样式特征样本合并到这些聚类方法中，以获得两个不同的破坏指数，包括直接聚类输出及其Frobenius范数。本文的一个新奇之处是开发一种创新的混合策略，用于在无噪声和嘈杂的条件下进行损伤检测和定位，以便从模型驱动的方案中获得损伤敏感特征，并由决策者执行决策。数据驱动的策略。一个剪切构建框架和ASCE基准结构的数值模型被用来验证所提出方法的准确性和性能。