A Review on Vibration-Based Damage Detection Methods for Civil Structures

Sun, Xutao; Ilanko, Sinniah; Mochida, Yusuke; Tighe, Rachael C.

doi:10.3390/vibration6040051

Cited by 13 publications

(3 citation statements)

References 106 publications

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“…It is crucial to detect structural damage as early as possible to prevent catastrophic outcomes. Structural Health Monitoring (SHM) aims to fulfil this requirement [1]. The process involves detecting the occurrence of damage, identifying its location, and assessing its severity.…”

Section: Introductionmentioning

confidence: 99%

Identifying Damage in Structures: Definition of Thresholds to Minimize False Alarms in SHM Systems

Ditommaso,

Ponzo

2024

Buildings

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In recent years, the development of quick and streamlined methods for the detection and localization of structural damage has been achieved by analysing key dynamic parameters before and after significant events or as a result of aging. Many Structural Health Monitoring (SHM) systems rely on the relationship between occurred damage and variations in eigenfrequencies. While it is acknowledged that damage can affect eigenfrequencies, the reverse is not necessarily true, particularly for minor frequency variations. Thus, reducing false positives is essential for the effectiveness of SHM systems. The aim of this paper is to identify scenarios where observed changes in eigenfrequencies are not caused by structural damage, but rather by non-stationary combinations of input and system response (e.g., wind effects, traffic vibrations), or by stochastic variations in mass, damping, and stiffness (e.g., environmental variations). To achieve this, statistical variations of thresholds were established to separate linear non-stationary behaviour from nonlinear structural behaviour. The Duffing oscillator was employed in this study to perform various nonlinear analyses via Monte Carlo simulations.

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Section: Introductionmentioning

confidence: 99%

Identifying Damage in Structures: Definition of Thresholds to Minimize False Alarms in SHM Systems

Ditommaso,

Ponzo

2024

Buildings

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“…The disparity in the responses Eng 2024, 5 is minimized by formulating an inverse problem, followed by the updating of the identified potential parameters that encapsulate the information change [3][4][5][6][7]. In this regard, natural frequency and modal parameters are the most commonly extracted features to indicate damage [8,9]. The primary challenge associated with this approach is the difficulty in obtaining a reliable simulated model that accurately matches the corresponding experimental model [10].…”

Section: Introduction and State Of The Artmentioning

confidence: 99%

Damage Detection with Data-Driven Machine Learning Models on an Experimental Structure

Alemu,

Lahmer,

Walther

2024

Eng

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Various techniques have been employed to detect damage in civil engineering structures. Apart from the model-based approach, which demands the frequent updating of its corresponding finite element method (FEM)-built model, data-driven methods have gained prominence. Environmental and operational effects significantly affect damage detection due to the presence of damage-related trends in their analyses. Time-domain approaches such as autoregression and metrics such as the Mahalanobis squared distance have been utilized to mitigate these effects. In the realm of machine learning (ML) models, their effectiveness relies heavily on the type and quality of the extracted features, making this aspect a focal point of attention. The objective of this work is therefore to deploy and observe potential feature extraction approaches used as input in training fully data-driven damage detection machine learning models. The most damage-sensitive segment (MDSS) feature extraction technique, which potentially treats signals under multiple conditions, is also proposed and deployed. It identifies potential segments for each feature coefficient under a defined criterion. Therefore, 680 signals, each consisting of 8192 data points, are recorded using accelerometer sensors at the Los Alamos National Laboratory in the USA. The data are obtained from a three-story 3D building frame and are utilized in this research for a mainly data-driven damage detection task. Three approaches are implemented to replace four missing signals with the generated ones. In this paper, multiple fast Fourier and wavelet-transformed features are employed to evaluate their performance. Most importantly, a power spectral density (PSD)-based feature extraction approach that considers the maximum variability criterion to identify the most sensitive segments is developed and implemented. The performance of the MDSS selection technique, proposed in this work, surpasses that of all 18 trained neural networks (NN) and recurrent neural network (RNN) models, achieving more than 80% prediction accuracy on an unseen prediction dataset. It also significantly reduces the feature dimension. Furthermore, a sensitivity analysis is conducted on signal segmentation, overlapping, the treatment of a training dataset imbalance, and principal component analysis (PCA) implementation across various combinations of features. Binary and multiclass classification models are employed to primarily detect and additionally locate and identify the severity class of the damage. The collaborative approach of feature extraction and machine learning models effectively addresses the impact of environmental and operational effects (EOFs), suppressing their influences on the damage detection process.

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“…Modal-based techniques observe changes in modal parameters with respect to a predefined reference condition to detect structural damage; that is, the correlation between two models is studied. Several methods have been proposed to detect damage in the past decades, the most common techniques being those based on natural frequencies and those based on mode shapes and their derivatives [36,37].…”

Section: Introductionmentioning

confidence: 99%

Mass and Stiffness Correlation Using a Transformation Matrix

García Fernández,

Fernández Fernandez,

Brincker

et al. 2024

Infrastructures

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Model correlation techniques are methods used to compare two different models, usually a numerical model and an experimental model. According to the structural dynamic modification theory, the experimental mode shapes estimated by modal analysis can be expressed as a linear combination of the numerical mode shapes through a transformation matrix T. In this paper, matrix T is proposed as a novel model correlation technique to detect discrepancies between the numerical and the experimental models in terms of mass. The discrepancies in stiffness can be identified by combining the numerical natural frequencies and the matrix T. This methodology can be applied to correlate the numerical and experimental results of civil (bridges, dams, towers, buildings, etc.), aerospace and mechanical structures and to detect damage when using structural health monitoring techniques. The technique was validated by numerical simulations on a lab-scaled two-span bridge considering different degradation scenarios and experimentally on a lab-scaled structure, which was correlated with two numerical models.

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A Review on Vibration-Based Damage Detection Methods for Civil Structures

Cited by 13 publications

References 106 publications

Identifying Damage in Structures: Definition of Thresholds to Minimize False Alarms in SHM Systems

Identifying Damage in Structures: Definition of Thresholds to Minimize False Alarms in SHM Systems

Damage Detection with Data-Driven Machine Learning Models on an Experimental Structure

Mass and Stiffness Correlation Using a Transformation Matrix

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