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This article aims at developing a new framework to identify and assess progressive structural damage. The method relies solely on output measurements to establish the frequency response functions of a structure using cepstrum-based operational modal analysis. Two different damage indicative features are constructed using the established frequency response functions. The first damage feature takes the residual frequency response function, defined as the difference in frequency response function between evolving states of the structure, and then reduces its dimension using principle component analysis; while in the second damage indicator, a new feature based on the area under the residual frequency response function curve is proposed. The rationale behind this feature lies in the fact that damage often affects a number of modes of the system, that is, it affects the frequency response function over a wide range of frequencies; as a result, this quantity has higher sensitivity to any structural change by combining all contributions from different frequencies. The obtained feature vectors serve as inputs to a novel multi-stage neural network ensemble designed to assess the severity of damage in the structure. The proposed method is validated using extensive experimental data from a laboratory four-girder timber bridge structure subjected to gradually progressing damage at various locations with different severities. In total, 13 different states of the structure are considered, and it is demonstrated that the new damage feature outperforms the conventional principle component analysis–based feature. The contribution of the work is threefold: first, the application of cepstrum-based operational modal analysis in structural health monitoring is further validated, which has potential for real-life applications where only limited knowledge of the input is available; second, a new damage feature is proposed and its superior performance is demonstrated; and finally, the comprehensive test framework including extensive progressive damage cases validates the proposed technique. [ABSTRACT FROM AUTHOR]

A large-scale cable-stayed bridge in the state of New South Wales, Australia, has been extensively instrumented with an array of accelerometer, strain gauge, and environmental sensors. The real-time continuous response of the bridge has been collected since July 2016. This study aims at condition assessment of this bridge by investigating three aspects of structural health monitoring including damage detection, damage localization, and damage severity assessment. A novel data analysis algorithm based on incremental multi-way data analysis is proposed to analyze the dynamic response of the bridge. This method applies incremental tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies. A total of 15 different damage scenarios were investigated; damage was physically simulated by locating stationary vehicles with different masses at various locations along the span of the bridge to change the condition of the bridge. The effect of damage on the fundamental frequency of the bridge was investigated and a maximum change of 4.4% between the intact and damage states was observed which corresponds to a small severity damage. Our extensive investigations illustrate that the proposed technique can provide reliable characterization of damage in this cable-stayed bridge in terms of detection, localization and assessment. The contribution of the work is threefold; first, an extensive structural health monitoring system was deployed on a cable-stayed bridge in operation; second, an incremental tensor analysis was proposed to analyze time series responses from multiple sensors for online damage identification; and finally, the robustness of the proposed method was validated using extensive field test data by considering various damage scenarios in the presence of environmental variabilities. [ABSTRACT FROM AUTHOR]

The motivation behind this paper is to develop a spectral-based damage detection and damage localization scheme using in-service ambient vibration in the context of non-model-based damage characterization. In this regard, a response parameter known as spectral moment is implemented for structural damage identification. The damage identification procedure starts with developing response power spectral density (PSD). The principal structural response features are then extracted from the frequency distribution of the spectrum using spectral moments. It is demonstrated that, although, spectral moment is a nonmodal characteristic of a process, it is related to modal parameters of a response signal since the spectral moment at each location is proportional to its corresponding modal vector. Hence, it is expected that due to damage occurrence spectral moment undergoes a variation. On this point, a damage sensitive feature is defined by comparing the spectral moments of two successive states of the structure. It is demonstrated that at a damage location there is a pronounced distinctive change in the indicative feature which is implemented for damage localization. The major advantages of the method include, first, the method works based on output-only measurement data, and second, it does not require any representative model of the structure and finally, unlike modal data that provides information at specific frequencies, the presented method is a broadband approach using information in a wide frequency range. The effectiveness and robustness of the proposed technique was validated by numerical simulations and an experimental case study. A series of numerical simulations with different damage scenarios was carried out on an Euler-Bernoulli beam subject to a band-limited random Gaussian excitation. Damage was simulated by reducing the flexural rigidity of elements at particular locations in single or multiple states. An experimental case study was also investigated utilizing a reinforced concrete jack arch which is one of the major structural components of the Sydney Harbor Bridge. Damage was introduced by creating a crack via applying gradual load using load cell. The numerical and experimental analyses indicated a promising sensitivity of the approach to detect and localize structural damage. [ABSTRACT FROM AUTHOR]