Irrespective to how well structures were built, they all deteriorate. Herein, deterioration is defined as a slow and continuous reduction of structural performance, which if prolonged can lead to damage. Deterioration occurs due to different factors such as ageing, environmental and operational (E&O) variations including those due to service loads. Structural performance can be defined as load-carrying capacity, deformation capacity, service life and so on. This paper aims to develop an effective method to detect and locate deterioration in the presence of E&O variations and high measurement noise content. For this reason, a novel vibration-based deterioration assessment method is developed. Since deterioration alters the unique vibration characteristics of a structure, it can be identified by tracking the changes in the vibration characteristics. This study uses enhanced autoregressive (AR) time-series models to fit the vibration response data of a structure. Then, the statistical hypotheses of chi-square variance test and two-sample [Formula: see text]-test are applied to the model residuals. To precisely evaluate changes in the vibration characteristics, an integrated deterioration identification (DI) is defined using the calculated statistical hypotheses and a Hampel filter is used to detect and remove false positive and negative results. Model residual is the difference between the predicted signal from the time series model and the actual measured response data at each time interval. The response data of two numerically simulated case studies of 3-storey and 20-storey reinforced concrete (RC) shear frames contaminated with different noise contents demonstrate the efficacy of the proposed method. Multiple deterioration and damage locations, as well as preventive maintenance actions, are also considered in these case studies. Furthermore, the method was successfully verified utilizing measured data from an experiment carried out on a box-girder bridge (BGB) structure.

中文翻译：

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使用增强的自回归时间序列分析进行结构恶化定位

无论建筑物建造得多么好，它们都会恶化。在此，劣化被定义为结构性能的缓慢和持续降低，如果长期如此，可能会导致损坏。由于老化、环境和运营 (E&O) 变化等不同因素（包括服务负载引起的因素）而发生恶化。结构性能可定义为承载能力、变形能力、使用寿命等。本文旨在开发一种有效的方法来检测和定位存在 E&O 变化和高测量噪声含量的恶化。为此，开发了一种新的基于振动的劣化评估方法。由于劣化会改变结构的独特振动特性，它可以通过跟踪振动特性的变化来识别。本研究使用增强的自回归 (AR) 时间序列模型来拟合结构的振动响应数据。然后，将卡方方差检验和双样本[公式：见正文]-检验的统计假设应用于模型残差。为了精确评估振动特性的变化，使用计算出的统计假设定义了综合劣化识别 (DI)，并使用 Hampel 滤波器来检测和去除假阳性和假阴性结果。模型残差是时间序列模型的预测信号与每个时间间隔的实际测量响应数据之间的差异。受不同噪声含量污染的 3 层和 20 层钢筋混凝土 (RC) 剪力框架的两个数值模拟案例研究的响应数据证明了所提出方法的有效性。这些案例研究还考虑了多个劣化和损坏位置以及预防性维护措施。此外，利用在箱梁桥 (BGB) 结构上进行的实验的测量数据成功验证了该方法。