Reliability and sensitivity analysis of composite structures by an adaptive Kriging based approach

Zhou, Changcong; Li, Chen; Zhang, Hanlin; Zhao, Haodong; Zhou, Chunping

doi:10.1016/j.compstruct.2021.114682

Cited by 25 publications

(7 citation statements)

References 60 publications

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“…The theoretical background on Rényi entropy can be found in [59,60]. Rényi entropy is one of the extensions of the Shannon entropy from Equation (8). For a binary random variable, the relationship of Rényi entropy can be written as:…”

Section: Global Sensitivity Analysis Based On Rényi Entropymentioning

confidence: 99%

“…Reliability-oriented sensitivity analysis (ROSA) based on the Sobol-Hoeffding decomposition of P f (1 − P f ) was first introduced in [3,4]. Since then, ROSAs have been applied in studies of the structural reliability of steel [5,6], concrete [7] and composite [8] beams. The concept [3,4] builds on Sobol [9,10], but with its focus on P f , it surpasses classical methods examining the correlation [11,12], variance [13,14] or distribution [15,16] of model output without a clear relationship to structural reliability.…”

Section: Introductionmentioning

confidence: 99%

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Quantification of Model Uncertainty Based on Variance and Entropy of Bernoulli Distribution

Kala

2022

Mathematics

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This article studies the role of model uncertainties in sensitivity and probability analysis of reliability. The measure of reliability is failure probability. The failure probability is analysed using the Bernoulli distribution with binary outcomes of success (0) and failure (1). Deeper connections between Shannon entropy and variance are explored. Model uncertainties increase the heterogeneity in the data 0 and 1. The article proposes a new methodology for quantifying model uncertainties based on the equality of variance and entropy. This methodology is briefly called “variance = entropy”. It is useful for stochastic computational models without additional information. The “variance = entropy” rule estimates the “safe” failure probability with the added effect of model uncertainties without adding random variables to the computational model. Case studies are presented with seven variants of model uncertainties that can increase the variance to the entropy value. Although model uncertainties are justified in the assessment of reliability, they can distort the results of the global sensitivity analysis of the basic input variables. The solution to this problem is a global sensitivity analysis of failure probability without added model uncertainties. This paper shows that Shannon entropy is a good sensitivity measure that is useful for quantifying model uncertainties.

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Section: Global Sensitivity Analysis Based On Rényi Entropymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantification of Model Uncertainty Based on Variance and Entropy of Bernoulli Distribution

Kala

2022

Mathematics

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“…The Kriging model demonstrated high accuracy when compared to the experimental results. Zhou et al [ 14 ] developed an approach based on the Kriging model and the U learning function to predict the mechanical properties of composite structures. The results indicate that the prediction model, which is based on the adaptive Kriging method, has an acceptable level of computational accuracy and can significantly enhance computational efficiency.…”

Section: Introductionmentioning

confidence: 99%

A Fast and Efficient Approach to Strength Prediction for Carbon/Epoxy Composites with Resin-Missing Defects

Li,

Zhu

2024

Polymers

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A novel method is proposed to quickly predict the tensile strength of carbon/epoxy composites with resin-missing defects. The univariate Chebyshev prediction model (UCPM) was developed using the dimension reduction method and Chebyshev polynomials. To enhance the computational efficiency and reduce the manual modeling workload, a parameterization script for the finite element model was established using Python during the model construction process. To validate the model, specimens with different defect sizes were prepared using the vacuum assistant resin infusion (VARI) process, the mechanical properties of the specimens were tested, and the model predictions were analyzed in comparison with the experimental results. Additionally, the impact of the order (second–ninth) on the predictive accuracy of the UCPM was examined, and the performance of the model was evaluated using statistical errors. The results demonstrate that the prediction model has a high prediction accuracy, with a maximum prediction error of 5.20% compared to the experimental results. A low order resulted in underfitting, while increasing the order can improve the prediction accuracy of the UCPM. However, if the order is too high, overfitting may occur, leading to a decrease in the prediction accuracy.

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“…They concluded that more efficient and lighter vessels could be designed with their approach. Zhou et al 22 proposed an adaptive Kriging metamodeling approach for the failure probability, and local and global sensitivity of composite radome structures. There are numerous SA approaches available and each has its benefits and drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Efficient strategies for reliability analysis and uncertainty quantification for filament-wound cylinders under internal pressure

Azizian

Almeida

2023

Journal of Composite Materials

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Induced uncertainties during the filament winding process may cause a significant stochastic variation in the mechanical behaviour of composite shells. This paper aims to develop a novel and deep uncertainty quantification (UQ), sensitivity and reliability analyses of filament wound shells considering manufacturing uncertainties. Firstly, a progressive damage analysis is performed to estimate their deterministic burst pressure. Then, a signal-to-noise (SNR) approach is employed using the Taguchi method for sensitivity analysis and screening uncertainties arising from manufacturing. Initial results reveal that the shells are more sensitive to thickness uncertainties for thinner structures. Then, probabilistic and reliability analyses are carried out using the Boosted Decision Trees Regression (BDTR) approach from machine learning algorithms. Despite the complexity and non-linear relationships in the problem, the developed BDTR-based metamodel shows powerful predictive performance. A comparative study shows that ply thickness uncertainty leads to a significant underestimation of failure probability. For expensive and time-consuming models in that only a few runs can be affordable, a modified approximation method for reliability analysis is proposed. Results indicate a high capability at estimating failure probability with high accuracy.

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Reliability and sensitivity analysis of composite structures by an adaptive Kriging based approach

Cited by 25 publications

References 60 publications

Quantification of Model Uncertainty Based on Variance and Entropy of Bernoulli Distribution

Quantification of Model Uncertainty Based on Variance and Entropy of Bernoulli Distribution

A Fast and Efficient Approach to Strength Prediction for Carbon/Epoxy Composites with Resin-Missing Defects

Efficient strategies for reliability analysis and uncertainty quantification for filament-wound cylinders under internal pressure

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