Two-Stage Inverse Method to Detect Delamination in Composite Beam Using Vibration Responses

Barman, Swarup K.; Jebieshia, T. R.; Tiwari, Pratik; Maiti, Dipak Kumar; Maity, Damodar

doi:10.2514/1.j057471

Cited by 8 publications

(7 citation statements)

References 30 publications

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“…Among these features, the capability of the present approach to accurately locate the delaminated region is one of the most important features, which is a rather far-fetched objective when using VSHM techniques [ 25 ]. On the other hand, some of the delamination detection techniques, such as the ones proposed in [ 23 , 24 ], have tried to alleviate this issue of the VSHM methods in beam problems; however, to the best of the authors’ knowledge, their methodology has not been implemented to problems involving plates/shells. Moreover, vibrational modes will become more complex as the dimensions of the domain increase, whereas the iFEM–RZT-based method is very versatile in terms of numerical/practical implementation thanks to its well-developed library of inverse elements [ 33 , 44 , 47 , 50 ].…”

Section: Numerical Examplesmentioning

confidence: 99%

“…In the work of Ratcliffe and Bagaria [ 23 ], fundamental vibrational mode of a composite beam was used to define a damage index, which facilitated locating delamination damage in the plate. This study inspired a study by Barman et al [ 24 ], where they used the bending modes of the composite beam and defined damage indices based on [ 23 ]. Next, by solving a minimization problem based on the vibrational characteristics of the beam, the exact location of the damage was detected.…”

Section: Introductionmentioning

confidence: 99%

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Delamination Detection and Localization in Vibrating Composite Plates and Shells Using the Inverse Finite Element Method

Ganjdoust,

Kefal,

Tessler

2023

Sensors

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Delamination damage is one of the most critical damage modes of composite materials. It takes place through the thickness of the laminated composites and does not show subtle surface effects. In the present study, a delamination detection approach based on equivalent von Mises strains is demonstrated for vibrating laminated (i.e., unidirectional fabric) composite plates. In this context, the governing relations of the inverse finite element method were recast according to the refined zigzag theory. Using the in situ strain measurements obtained from the surface and through the thickness of the composite shell, the inverse analysis was performed, and the strain field of the composite shell was reconstructed. The implementation of the proposed methodology is demonstrated for two numerical case studies associated with the harmonic and random vibrations of composite shells. The findings of this study show that the present damage detection method is capable of real-time monitoring of damage and providing information about the exact location, shape, and extent of the delamination damage in the vibrating composite plate. Finally, the robustness of the proposed method in response to resonance and extreme load variations is shown.

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Section: Numerical Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Delamination Detection and Localization in Vibrating Composite Plates and Shells Using the Inverse Finite Element Method

Ganjdoust,

Kefal,

Tessler

2023

Sensors

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“…Thus, the method developed in the present studyaims towards alleviate these shortcomings. Also Unified Particle Swarm Optimization has not been used asa cost optimization method for multistory building design in the previous studies, although it has been used very effectively in cost optimization of RC foundation [20], damage detection problems [33][34][35][36][37][38], magnetoencephalography problem [39] etc. Hence, UPSO have been found to the appropriate optimization method for multistory building design and cost optimization.…”

Section: Iv)mentioning

confidence: 99%

“…'Tackling magnetoencephalography with particle swarm optimization'. International Journal of Bio-Inspired Computation, 1(1-2),[32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49] [40] IS 456(2000).Plain and Reinforced Concrete -Code of Practice, Bureau of Indian Standards, New Delhi, India [41] IS 875 (Part I) (1987). Code of practice for design loads (other than earthquake)for buildings and structures: part 1 dead loads -unit weights of building material and stored materials, Bureau of Indian Standards, New Delhi, India [42] IS 875 (Part II) (1987).…”

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confidence: 99%

Cost Effective Design of RC Building Frame Employing Unified Particle Swarm Optimization

Chaudhuri¹,

Barman²,

Maity³

et al. 2021

Preprint

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Present paper deals with the cost effective design of reinforced concrete building frame employing unified particle swarm optimization (UPSO). Two building frames with G + 8 stories and G + 10 stories have been adopted to demonstrate the effectiveness of the present algorithm. Effect of seismic loads and wind load have been considered as per Indian Standard (IS) 1893 (Part-I) and IS 875 (Part-III) respectively. Analysis of the frames has been carried out in STAAD Pro software. The design loads for all the beams and columns obtained from STAAD Pro have been given as input of the optimization algorithm. Next, cost optimization of all beams and columns have been carried out in MATLAB environment using UPSO, considering the safety and serviceability criteria mentioned in IS 456. Cost of formwork, concrete and reinforcement have been considered to calculate the total cost. Reinforcement of beams and columns has been calculated with consideration for curtailment and feasibility of laying the reinforcement bars during actual construction. The numerical analysis ensures the accuracy of the developed algorithm in providing the cost optimized design of RC building frames considering safety, serviceability and constructional feasibilities.Further, Monte Carlo simulations performed on the numerical results, proved the consistency and robustness of the developed algorithm. Thus, the present algorithm is capable of giving a cost effective design of RC building frame, which can be adopted directly in construction site without making any changes.

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“…Contemporary research on delamination assessment (i.e., detection, quantification, and localization) in laminated composites focuses mainly on the use of high-frequency guided-waves (e.g., Lamb waves), acoustic emission/ultrasonic, and mode shape curvatures [61][62][63][64]. However, dealing with high-frequency signals may often cause difficulties in terms generation in a specific high-frequency band, high sampling rate, acquisition, storage, a requirement of the controlled experimental environment, and complex signal processing [41,65].…”

mentioning

confidence: 99%

A Deep Learning Framework for Vibration-Based Assessment of Delamination in Smart Composite Laminates

Khan

Shin

Lim

et al. 2020

Sensors

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Delamination is one of the detrimental defects in laminated composite materials that often arose due to manufacturing defects or in-service loadings (e.g., low/high velocity impacts). Most of the contemporary research efforts are dedicated to high-frequency guided wave and mode shape-based methods for the assessment (i.e., detection, quantification, localization) of delamination. This paper presents a deep learning framework for structural vibration-based assessment of delamination in smart composite laminates. A number of small-sized (4.5% of total area) inner and edge delaminations are simulated using an electromechanically coupled model of the piezo-bonded laminated composite. Healthy and delaminated structures are stimulated with random loads and the corresponding transient responses are transformed into spectrograms using optimal values of window size, overlapping rate, window type, and fast Fourier transform (FFT) resolution. A convolutional neural network (CNN) is designed to automatically extract discriminative features from the vibration-based spectrograms and use those to distinguish the intact and delaminated cases of the smart composite laminate. The proposed architecture of the convolutional neural network showed a training accuracy of 99.9%, validation accuracy of 97.1%, and test accuracy of 94.5% on an unseen data set. The testing confusion chart of the pre-trained convolutional neural network revealed interesting results regarding the severity and detectability for the in-plane and through the thickness scenarios of delamination.

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Two-Stage Inverse Method to Detect Delamination in Composite Beam Using Vibration Responses

Cited by 8 publications

References 30 publications

Delamination Detection and Localization in Vibrating Composite Plates and Shells Using the Inverse Finite Element Method

Delamination Detection and Localization in Vibrating Composite Plates and Shells Using the Inverse Finite Element Method

Cost Effective Design of RC Building Frame Employing Unified Particle Swarm Optimization

A Deep Learning Framework for Vibration-Based Assessment of Delamination in Smart Composite Laminates

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