Damage Identification in Reinforced Concrete Beams Using Wavelet Transform of Modal Excitation Responses

Soleymani, Atefeh; Jahangir, Hashem; Rashidi, Maria; Mojtahedi, Farid Fazel; Bahrami, Michael; Javanmardi, Ahad

doi:10.3390/buildings13081955

Cited by 6 publications

(3 citation statements)

References 46 publications

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“…The wavelet transform usually has an advantage over the Fourier transform in that the former can capture coefficients using high time and frequency resolutions, while the latter operates fully within the frequency domain. This enables WT coefficients to capture both low-frequency content that requires a high frequency resolution and high-frequency content that requires a high time resolution [53].…”

Section: Wavelet Coefficientsmentioning

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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Section: Wavelet Coefficientsmentioning

confidence: 99%

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

Alemu,

Lahmer,

Walther

2024

Eng

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“…The methods of damage identification demand high accuracy in recognizing frequency parameters [21]. When applied to environmental excitation, the engineering community generally believes that the modal parameter method can precisely determine the system frequency [22][23][24]. However, accuracy in recognizing frequency parameters is often intimately linked to response signals and recognition algorithms.…”

Section: Introductionmentioning

confidence: 99%

The Accuracy of Frequency Estimation of Structure Vibration under Ambient Excitation: Problems, Causes, and Methods

Deng,

Wen,

Tang

et al. 2024

Buildings

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Accurate identification of building structure frequencies forms the basis for damage detection. The structural dynamic response signal, under ambient excitation, can be transformed into a superposition of multiple single-frequency exponentially damped sinusoids combined with random white noise. However, the peak power spectrum of the response signal tends to exhibit line splitting, compromising the precision of frequency identification. This study examines the accuracy characteristics of the single-frequency free damping vibration signal (SFFDVS) and derives the Cramer–Rao lower bound for the frequency estimator. It thoroughly analyzes the factors influencing the accuracy of SFFDVS frequency identification. The study reveals that the primary cause of spectral line splitting is the random delay inherent in SFFDVS. Based on the maximum likelihood method (MLM), this research introduces the MLM algorithm for SFFDVS and provides a simulation analysis. The findings indicate that the MLM estimation algorithm for frequency parameters effectively addresses spectral line splitting and offers robust noise resistance and recognition accuracy.

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“…The core of this method lies in the ability of wavelet packet transform to extract subtle anomalies from vibration signals, enabling structural damage identification. Soleymani et al [18] utilized time-domain modal testing and wavelet analysis to identify damage in reinforced concrete beams. They generated various damage scenarios of different severity and locations using a numerical model of an RC beam, recorded acceleration time histories of damaged and undamaged structures, and ultimately determined the location and severity of damage through wavelet analysis.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study on Bridge Structural Damage Identification Based on Quasi-Static Displacement Effects and Wavelet Packet Decomposition

Chen,

Zhang,

et al. 2023

Buildings

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Based on the displacement-induced linearity error curve and the theory of wavelet packet analysis, a bridge structural damage identification method is proposed, integrating two damage indicators: Quasi-Static Displacement-Induced Linearity Error Curve (QSDIL) and Relative Energy Rate of Wavelet Packet Energy Spectrum (RES). This method first constructs the QSDIL damage indicator based on a quasi-static displacement-induced linearity error, which is used for the preliminary localization of the bridge structural damage. Subsequently, relying on the principles of wavelet packet analysis, the method constructs the RES damage indicator for accurate positioning of the damage location in the bridge structure. The proposed method is experimentally validated, and the impact of factors such as single-point damage, multi-point damage, signal noise, lane position, and vehicle weight on the experimental results is investigated. The results indicate that the proposed method exhibits excellent identification performance for the location of structural damage in both single-point and multi-point damage scenarios, with good agreement between experimental and theoretical values. As the signal-to-noise ratio decreases, the accuracy and precision of the RES curve in locating the bridge structural damage position exhibit a nonlinear decreasing trend, with relatively small identification errors observed at noise levels of 90 dB to 100 dB. Different lane positions have a minimal impact on the damage identification effectiveness. With increasing vehicle weight, both QSDIL and RES curves show an increasing trend in peak values, facilitating the localization of bridge structural damage positions.

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Damage Identification in Reinforced Concrete Beams Using Wavelet Transform of Modal Excitation Responses

Cited by 6 publications

References 46 publications

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

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

The Accuracy of Frequency Estimation of Structure Vibration under Ambient Excitation: Problems, Causes, and Methods

Experimental Study on Bridge Structural Damage Identification Based on Quasi-Static Displacement Effects and Wavelet Packet Decomposition

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