On the performance of vibro‐acoustic‐modulation‐based diagnosis of breathing cracks in thick, elastic slabs

Karve, Pranav; Mahadevan, Sankaran

doi:10.1002/stc.2470

Cited by 10 publications

(20 citation statements)

References 23 publications

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“…VAM is a damage diagnosis methodology that utilizes damage (delamination or crack)-induced nonlinear dynamic signatures to perform damage detection as well as localization. [6][7][8][9][10] In a VAM test, the component of interest is excited using a bi-harmonic excitation. The higher excitation frequency is called the probing frequency, whereas the lower frequency is termed as the pumping frequency.…”

Section: Vibro-acoustic Modulation-based Damage Localizationmentioning

confidence: 99%

“…As the effect of the geometric nonlinearity is pronounced near the location of the flaw, the relative magnitude of its (nonlinear) dynamic signature(the damage index, sum of sidebands, referred to as SBSum) enables localization of the hidden defect. 8 This technique was demonstrated for concrete slabs, numerically at first, 9 and then experimentally to localize (in 2D) ASR-induced damage in thick concrete specimens. 10 Note that the VAM-based diagnosis method cannot distinguish between the types of nonlinearities (geometric, material, or hybrid) that produce sidebands; however, it can help localize the site of dynamic nonlinearity creation (crack or delamination).…”

Section: Introductionmentioning

confidence: 99%

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Diagnosis of internal cracks in concrete using vibro-acoustic modulation and machine learning

Miele

Karve

Mahadevan

et al. 2022

Structural Health Monitoring

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This paper investigates the utility of physics-informed machine learning models for vibro-acoustic modulation (VAM)–based damage localization in concrete structures. Vibro-acoustic modulation is a nonlinear dynamics-based non-destructive testing method, which was initially developed to perform damage detection and later extended to accomplish damage localization. The VAM-based damage (hidden crack) diagnosis is performed by analyzing the damage index pattern on the surface of the component to arrive at the size and location of the hidden damage. Past investigations have employed heuristically selected damage index thresholds as well as computationally expensive Bayesian estimation methods for VAM-based damage localization in two (surface) dimensions. Compared to these studies, the proposed methodology automates the threshold selection (algorithmic instead of heuristic), increases the speed of the probabilistic damage diagnosis process, and enables the estimation of damage depth. We generate training data (damage index) for the machine learning models using the pertinent nonlinear dynamics (finite element) models using different combinations of test parameters. The (supervised) machine learning models are thus informed by computational physics models. These include two types of artificial neural network (ANN) models: classification models that identify whether a sensor location is damaged or not and regression models that enable Bayesian estimation to obtain the posterior probability distribution of damage location and size. The accuracy of machine learning-based diagnosis is evaluated using both numerical and laboratory experiments. The proposed physics-informed machine learning models for VAM-based damage diagnosis are able to achieve an accuracy of about 60–64% in the validation experiments, indicating the potential of these methods for internal crack detection. The results show that for complex (nonlinear dynamics-driven) diagnostic methods, damage index patterns learned from physics models could be successfully used for damage detection as well as localization.

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Section: Vibro-acoustic Modulation-based Damage Localizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diagnosis of internal cracks in concrete using vibro-acoustic modulation and machine learning

Miele

Karve

Mahadevan

et al. 2022

Structural Health Monitoring

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“…The yielding of frame structures under seismic action is an example of this type of non-linear damage [ 9 , 10 , 11 ]; (ii) generation of secondary vibrational resource complicating structural dynamic behavior. This type of non-linear damage is exemplified by breathing cracks [ 12 , 13 , 14 ] and material stratification [ 15 , 16 ]. Wavelet-based time-frequency methods are representative techniques for characterizing structural progressive degradation or deterioration, the first type of non-linear damage, with its ability to detect the relationship between frequencies and its time dependence, and has a certain noise robustness [ 17 , 18 , 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Use of Bispectrum Analysis to Inspect the Non-Linear Dynamic Characteristics of Beam-Type Structures Containing a Breathing Crack

Cui

Ge³

et al. 2021

Sensors

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A breathing crack is a typical form of structural damage attributed to long-term dynamic loads acting on engineering structures. Traditional linear damage identification methods suffer from the loss of valuable information when structural responses are essentially non-linear. To deal with this issue, bispectrum analysis is employed to study the non-linear dynamic characteristics of a beam structure containing a breathing crack, from the perspective of numerical simulation and experimental validation. A finite element model of a cantilever beam is built with contact elements to simulate a breathing crack. The effects of crack depth and location, excitation frequency and magnitude, and measurement noise on the non-linear behavior of the beam are studied systematically. The result demonstrates that bispectral analysis can effectively identify non-linear damage in different states with strong noise immunity. Compared with existing methods, the bispectral non-linear analysis can efficiently extract non-linear features of a breathing crack, and it can overcome the limitations of existing linear damage detection methods used for non-linear damage detection. This study’s outcome provides a theoretical basis and a paradigm for damage identification in cracked structures.

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“…single tone harmonic vibration). Karve and Mahadevan (2020) and Karve et al (2020) extended the VAM/sideband based damage mapping algorithm for damage localisation due to loss of stiffness and as well as localisation of alkali-silica reaction-induced damage in concrete structures. However, majority of the works utilising bitone harmonic vibration response or VAM, employ only the first sideband on either side of probing frequency or first intermodulation peaks for breathing crack identification (Kim et al, 2011; Klepka et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Breathing crack damage diagnostic strategy using improved MFCC features

Prawin

2021

Journal of Intelligent Material Systems and Structures

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In this paper, a new two-stage damage diagnostic technique for breathing crack identification in using improved Mel frequency Cepstral Analysis is proposed for engineering structures. The improvements such as the centre frequencies of Mel-filter bank around the resonant frequencies and the automatic selection of cut-off frequency for frequency conversion (i.e. from Mel-scale to frequency-scale) based on the energy of the response is employed in the present work to customise the estimation of Mel-frequency Cepstral Coefficients (popularly being used for speech signals) for structural vibration responses. In the first stage of the proposed improved Mel-frequency Cepstral Coefficients (MFCC) approach for breathing crack identification, the measured acceleration time history responses are converted into Mel-frequency Cepstral Coefficients using improved Mel frequency Cepstral Analysis. The Mahanabolis distance-based measure between the improved Mel-frequency Cepstral coefficients of the healthy structure and the structure with localized damage is used for confirming the presence of breathing crack using ambient vibration data during online monitoring. In the second stage, the spatial location of breathing crack is established through offline monitoring, by exciting the structure with bitone harmonic excitation. The improved Mel-filter bank energy measured spatially across the structure is used to identify the spatial location(s) of breathing crack. The effectiveness of the proposed approach is verified using the synthetic datasets of the benchmark simply supported beam with a breathing crack, provided by Helsinki Metropolia University of Applied Sciences and a numerically simulated cantilever beam with varied spatial locations and different depths of breathing crack. Finally, experimental investigations have been carried out to demonstrate the practical viability of the proposed MFCC approach. Numerical and experimental studies concluded that the proposed damage diagnostic approach is capable of detecting and localising multiple and also subtle cracks even under varying environmental conditions with noise-contaminated measurements.

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On the performance of vibro‐acoustic‐modulation‐based diagnosis of breathing cracks in thick, elastic slabs

Cited by 10 publications

References 23 publications

Diagnosis of internal cracks in concrete using vibro-acoustic modulation and machine learning

Diagnosis of internal cracks in concrete using vibro-acoustic modulation and machine learning

Use of Bispectrum Analysis to Inspect the Non-Linear Dynamic Characteristics of Beam-Type Structures Containing a Breathing Crack

Breathing crack damage diagnostic strategy using improved MFCC features

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