Experimental Investigation on Crack Localization in Steel and Composite Structures by Intersection of First Three Normalized Mode Shape Curves

Ramakrishna, S.; Sathish, J.; Kumar, V. D. Raj; Vamsi, S. Raghu

doi:10.1007/s11668-022-01486-7

J Fail. Anal. and Preven.

2022

DOI: 10.1007/s11668-022-01486-7

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Experimental Investigation on Crack Localization in Steel and Composite Structures by Intersection of First Three Normalized Mode Shape Curves

S. Ramakrishna¹,

J. Sathish²,

V. D. Raj Kumar³

et al.

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“…Chen et al (2022) proposed a multiobjective identification method for structural damage detection, leveraging modal feature extraction and a hybrid particle swarm optimization solver for accurate results. Ramakrishna et al (2022) introduced the normalized mode shape curve algorithm for damage detection and localization in steel beams. It was found that there is a good agreement between the actual and estimated crack location, with the percentage of error being less than 1%.…”

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confidence: 99%

Crack localization in glass fiber composite beams by experimental modal analysis and multi variable Gaussian process regression method

Rama Krishna,

Sathish,

Rahul Mani Datta

et al. 2023

IJSI

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PurposeEnsuring the early detection of structural issues in aircraft is crucial for preserving human lives. One effective approach involves identifying cracks in composite structures. This paper employs experimental modal analysis and a multi-variable Gaussian process regression method to detect and locate cracks in glass fiber composite beams.Design/methodology/approachThe present study proposes Gaussian process regression model trained by the first three natural frequencies determined experimentally using a roving impact hammer method with crystal four-channel analyzer, uniaxial accelerometer and experimental modal analysis software. The first three natural frequencies of the cracked composite beams obtained from experimental modal analysis are used to train a multi-variable Gaussian process regression model for crack localization. Radial basis function is used as a kernel function, and hyperparameters are optimized using the negative log marginal likelihood function. Bayesian conditional probability likelihood function is used to estimate the mean and variance for crack localization in composite structures.FindingsThe efficiency of Gaussian process regression is improved in the present work with the normalization of input data. The fitted Gaussian process regression model validates with experimental modal analysis for crack localization in composite structures. The discrepancy between predicted and measured values is 1.8%, indicating strong agreement between the experimental modal analysis and Gaussian process regression methods. Compared to other recent methods in the literature, this approach significantly improves efficiency and reduces error from 18.4% to 1.8%. Gaussian process regression is an efficient machine learning algorithm for crack localization in composite structures.Originality/valueThe experimental modal analysis results are first utilized for crack localization in cracked composite structures. Additionally, the input data are normalized and employed in a machine learning algorithm, such as the multi-variable Gaussian process regression method, to efficiently determine the crack location in these structures.

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confidence: 99%

Crack localization in glass fiber composite beams by experimental modal analysis and multi variable Gaussian process regression method

Rama Krishna,

Sathish,

Rahul Mani Datta

et al. 2023

IJSI

Self Cite

View full text Add to dashboard Cite

show abstract

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Experimental Investigation on Crack Localization in Steel and Composite Structures by Intersection of First Three Normalized Mode Shape Curves

Cited by 1 publication

References 20 publications

Crack localization in glass fiber composite beams by experimental modal analysis and multi variable Gaussian process regression method

Crack localization in glass fiber composite beams by experimental modal analysis and multi variable Gaussian process regression method

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