Abstract

Application of massive data to structural health monitoring (SHM) may lead to serious problems such as difficulty, computational inefficiency, and low damage detectability. This paper proposes innovative hybrid methods in order to detect damage under massive data, ambient vibration, and environmental and/or operational variability. Each of the proposed methods consists of a three-stage algorithm including response modelling via a time series representation, Gaussian mixture model (GMM) for dimensionality reduction, and an outlier detector. Due to the importance of response modelling under ambient vibration, this article employs the combination of AR with ARX called the ARARX model. In the second stage, a GMM is individually fitted to residual samples extracted from ARARX regarding the undamaged and damaged conditions to obtain low-dimensional features from massive data. Eventually, outlier analyses are carried out by the Mahalanobis distance and an auto-associative neural network to make a decision about the occurrence of damage. Two different kinds of threshold limits are also considered to examine the performances of the proposed methods. A large-scale cable-stayed bridge is applied to validate the reliability and effectiveness of these methods with several comparative studies. Results demonstrate that both proposed methods are effective tools for damage detection.

摘要

将海量数据应用于结构健康监测（SHM）可能会导致严重的问题，例如难度、计算效率低和损坏可检测性低。本文提出了创新的混合方法，以检测海量数据、环境振动以及环境和/或操作可变性下的损坏。所提出的每种方法都包含一个三阶段算法，包括通过时间序列表示的响应建模、用于降维的高斯混合模型 (GMM) 和异常值检测器。由于环境振动下响应建模的重要性，本文采用AR与ARX的组合称为ARARX模型。在第二阶段，将 GMM 单独拟合到从 ARARX 提取的关于未损坏和损坏条件的残差样本，以从海量数据中获取低维特征。最终，通过马哈拉诺比斯距离和自关联神经网络进行异常值分析，以做出关于损坏发生的决定。还考虑了两种不同的阈值限制来检查所提出方法的性能。以大型斜拉桥为例，通过多项比较研究验证了这些方法的可靠性和有效性。结果表明，所提出的两种方法都是用于损伤检测的有效工具。异常值分析是通过马氏距离和自关联神经网络进行的，以做出关于损坏发生的决定。还考虑了两种不同的阈值限制来检查所提出方法的性能。以大型斜拉桥为例，通过多项比较研究验证了这些方法的可靠性和有效性。结果表明，所提出的两种方法都是用于损伤检测的有效工具。异常值分析是通过马氏距离和自关联神经网络进行的，以做出关于损坏发生的决定。还考虑了两种不同的阈值限制来检查所提出方法的性能。以大型斜拉桥为例，通过多项比较研究验证了这些方法的可靠性和有效性。结果表明，所提出的两种方法都是用于损伤检测的有效工具。