On quantifying the effect of noise in surrogate based stochastic free vibration analysis of laminated composite shallow shells

Mukhopadhyay, T.; Naskar, Susmita; Dey, Sudip; Adhikari, Sondipon

doi:10.1016/j.compstruct.2015.12.037

Cited by 68 publications

(17 citation statements)

References 31 publications

(28 reference statements)

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“…The effective elastic properties of the individual lamina are obtained using Eq. (17). Thus, the natural frequency problem can be stated as = ( , , , , , 1 , 2 , … .…”

Section: Uncertainty Analysis Of Natural Frequencies Of a Ud Flax /Epmentioning

confidence: 99%

“…Instead, surrogate model approaches, which are constructed based on a limited set of actual input/output data points, are a suitable method when dealing with such complex problems. Several methods of application of surrogate models have been reported in the literature for evaluation of uncertainties in composite laminates, such as Kriging method [15,16], radial basis function [17], polynomial chaos expansion (PCE) [18][19][20], and artificial neural network (ANN) [21,22]. State-of-the-art reviews on the surrogate models for evaluating the uncertainty in structural responses of composite laminates can be found in [23].…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty analysis of mechanical behavior of flax fiber composites

Ravandi¹,

Banu²,

Noorian³

2020

Preprint

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The natural origin of the fibers combined with random production flaws results in significant uncertainties in the properties of natural fiber reinforced composites. A probabilistic assessment can help to characterize the uncertainties and evaluate the reliability of natural fiber composites, enabling their use in engineering designs. Toward this end, this study aims to quantify the uncertainties in the tensile strength and frequency response of a unidirectional flax/epoxy composite due to the variability of various input parameters, including the fiber material properties and manufacturing flaws. Based on the available data in the literature, the non-deterministic input variables were divided into normal and uniform variables using a statistical test. A computationally efficient response surface approach based on the polynomial chaos expansion was adopted to conduct the uncertainty analysis with multi-type uncertain variables. Moreover, the results were validated by the direct Monte Carlo simulation to demonstrate the accuracy and efficiency of the surrogate model.

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“…The effective elastic properties of the individual lamina are obtained using Eq. (17). Thus, the natural frequency problem can be stated as = ( , , , , , 1 , 2 , … .…”

Section: Uncertainty Analysis Of Natural Frequencies Of a Ud Flax /Epmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Uncertainty analysis of mechanical behavior of flax fiber composites

Ravandi¹,

Banu²,

Noorian³

2020

Preprint

View full text Add to dashboard Cite

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“…Moreover, the actual field vibration measurements may suffer from certain unavoidable errors, which make the damage identification process even more difficult. There is substantial amount of literature available dealing with the issue of noise in different SDI techniques and other engineering problems (Aiko et al, 2006;Cao et al, 2014;Dey et al, 2016b;Li and Law, 2008;Mukhopadhyay et al, 2016;Udwadia, 2005). These studies consider primarily the uncertainties associated with system parameters and measurement noise in SDI.…”

Section: Performance Of Rs-hdmr-based Damage Identification Algorithmmentioning

confidence: 99%

Structural damage identification: A random sampling-high dimensional model representation approach

Mukhopadhyay

Chowdhury

Chakrabarti

2016

Advances in Structural Engineering

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Structural damage identification and quantification of damage using non-destructive methods are important aspects for any civil, mechanical and aerospace engineering structures. In this study, a novel damage identification algorithm has been developed using random sampling-high dimensional model representation approach. A global sensitivity analysis based on random sampling-high dimensional model representation is adopted for important parameter screening purpose. Three different structures (spring mass damper system, simply supported beam and fibre-reinforced polymer composite bridge deck) have been used for various single and multiple damage conditions to validate the proposed algorithm. The performance of this method is found to be quite satisfactory in the realm of damage detection in structures. The random sampling-high dimensional model representation-based approach for meta-model formation is particularly useful in damage identification as it works well when large numbers of input parameters are involved. In this study, two different optimization methods have been used and their relative capability to identify damage has been discussed. Performance of this damage identification algorithm under the influence of noise has also been addressed in this article.

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“…Stochastic natural frequency of noise-induced support vector regression (SVR) model for laminated composite curved panels is analysed by Dey et al [49]. The effect of noise in surrogate model is quantified by Mukhopadhyay et al [50], whereas Dey et al [44] presented the effect of noise using polynomial neural network approach in uncertainty quantification of natural frequency, and Chakraborty [51] predicted delamination in laminated composite using ANN approach. Lee et al [52] determined the equivalent material properties of glass/epoxy composite by applying the homogenization method, and stochasticity in the equivalent material properties are assessed by MCS and found that variation in individual material properties has a significant effect on the equivalent material properties.…”

Section: Introductionmentioning

confidence: 99%

Stochastic impact responses analysis of functionally graded plates

Karsh

Kumar

Dey

2019

J Braz. Soc. Mech. Sci. Eng.

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The present paper portrays the mapping for stochasticity in low-velocity impact responses of functionally graded material (FGM) plates by employing multivariate adaptive regression splines (MARS) surrogate model in conjunction with finite element (FE) approach. The unavoidable stochastic variabilities (caused due to numerous errors involved in manufacturing processes) in material properties of FGM plates are considered in order to map the effect of elemental variabilities on global response of the structure. The material properties of FGM plates are considered to follow the rule of mixture in conjunction to power law. The Newmark's time integration scheme and modified Hertzian contact law are employed to solve the timedependent equation. The present FE formulation is based on an eight noded isoparametric element in which each element has five degrees of freedom. The effects of variability in temperature and power-law exponent on stochastic low-velocity impact responses are also portrayed. The maximum contact force, plate and impactor displacement are considered as the response parameters. The present MARS model is coupled with the finite element to achieve the higher efficiency with adequate accuracy as compared to the FE-based full-scale Monte Carlo simulation. The statistical results illustrate that the stochasticity in material properties significantly influences the low-velocity impact responses of FGM plates.

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On quantifying the effect of noise in surrogate based stochastic free vibration analysis of laminated composite shallow shells

Cited by 68 publications

References 31 publications

Uncertainty analysis of mechanical behavior of flax fiber composites

Uncertainty analysis of mechanical behavior of flax fiber composites

Structural damage identification: A random sampling-high dimensional model representation approach

Stochastic impact responses analysis of functionally graded plates

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