A popular method to conduct structural health monitoring is the spatio-temporal study of vibration signatures, where vibration properties are extracted from collected vibration responses. In this article, a novel methodology for extracting and analyzing distributed acceleration data for condition assessment of bridge girders is proposed. Three different techniques are fused, enabling robust damage detection, localization, and quantification. First, stochastic subspace identification is used as an output-only method to extract modal properties of the monitored structure. Second, a reduced-order stiffness matrix is reconstructed from the stochastic subspace identification data using the system equivalent reduction expansion process. Third, a particle swarm optimization algorithm is used to update a finite element model of the bridge girder to match the extracted reduced-order stiffness matrix and modal properties. The proposed approach is first verified through numerically simulated data of the girder and then validated using experimental data obtained from a full-scale pretensioned concrete beam that experienced two distinct states of damage. Results show that the method is capable of localizing and quantifying damages along the girder with good accuracy, and that results can be used to create a high-fidelity finite element model of the girder that could be leveraged for condition prognosis and forecasting.

中文翻译：

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使用随机子空间识别和粒子群模型更新的预应力混凝土梁中基于振动的损伤定位和量化

进行结构健康监测的一种流行方法是振动特征的时空研究，其中从收集的振动响应中提取振动特性。在本文中，提出了一种用于提取和分析用于桥梁状态评估的分布式加速度数据的新方法。融合了三种不同的技术，实现了稳健的损伤检测、定位和量化。首先，随机子空间识别被用作仅输出方法来提取受监控结构的模态特性。其次，使用系统等效缩减展开过程从随机子空间识别数据重建降阶刚度矩阵。第三，粒子群优化算法用于更新桥梁的有限元模型，以匹配提取的降阶刚度矩阵和模态属性。所提出的方法首先通过大梁的数值模拟数据进行验证，然后使用从经历了两种不同损坏状态的全尺寸预应力混凝土梁获得的实验数据进行验证。结果表明，该方法能够以良好的精度定位和量化大梁上的损坏，并且该结果可用于创建大梁的高保真有限元模型，可用于状况预测和预测。所提出的方法首先通过大梁的数值模拟数据进行验证，然后使用从经历了两种不同损坏状态的全尺寸预应力混凝土梁获得的实验数据进行验证。结果表明，该方法能够以良好的精度定位和量化大梁上的损坏，并且该结果可用于创建大梁的高保真有限元模型，可用于状况预测和预测。所提出的方法首先通过大梁的数值模拟数据进行验证，然后使用从经历了两种不同损坏状态的全尺寸预应力混凝土梁获得的实验数据进行验证。结果表明，该方法能够以良好的精度定位和量化大梁上的损坏，并且该结果可用于创建可用于状况预测和预测的高保真大梁有限元模型。