Bounds of mechanical properties of fibre reinforced polymer composites with hybrid random and interval uncertainties

Zhou, Xiaochao; Wang, Neng-Wei; Gao, Kang; Natarajan, Sundararajan; Xiong, Wen; Jiang, Chao; Qian, Sheng-Yu; Cai, Chaocan

doi:10.1016/j.tws.2022.110158

Cited by 10 publications

(3 citation statements)

References 58 publications

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“…Rather, they investigated the effects of uncertainty sources on the different behaviors (especially dynamic behaviors) of these structures. A summary of these studies [15][16][17][18][19][20][21][22][23][24][25][26][27] is presented in Fig. 2.…”

Section: Figmentioning

confidence: 99%

A novel hybrid multi-stage probabilistic algorithm for the damage detection of carbon nanotube panels by considering the uncertainty of materials and mechanical properties

Khayat,

Amoushahi

2024

Preprint

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Composite materials have a high level of uncertainty (intrinsic and non-intrinsic) due to the manufacturing process as well as the placement of different phases of their constituent materials. These uncertainties can be identified in both macro and micro scales. Identifying the behavior of structures made of composite materials without taking into account the uncertainties, whether due to identification or modeling, can lead to unrealistic results, especially in the dynamic behavior of structures. One of these cases is the identification of damage types in composite structures which is usually done by using dynamic responses. Damages in composite materials or structures usually occur during construction or operation. The correct modeling of uncertainty sources is one of the most important factors in identifying the geometry, location, and severity of damages accurately. The uncertainties related to the position and placement of carbon nanotubes (CNTs) can cause noticeable changes in the characteristics of composite materials reinforced with CNTs. For this reason, in the present study, we identified damages in CNT panels by considering all possible sources of uncertainty. A probabilistic multi-stage reliability-based method was proposed in this study to detect damage in these structures. In order to model the intrinsic and non-intrinsic sources of uncertainty, a modified point estimation method (MPEM) was used. In addition, an enhanced differential quadrature (DQ) method was used to model the CNT panels. In each step of the proposed algorithm, the probability of damage in each element of the panels was calculated by analyzing the possible damages. According to the results of the previous step, the elements with a low failure probability were gradually sifted in the next steps. The sieved elements in each step were considered as intact elements in the next step. This systematic filtering of design variables can simultaneously reduce the dimensions and speed up the optimization problem. Finally, the probability of damage was calculated based on the probability density function of various damage severities and positions. The developed approach was applied for damage detection on a laboratory-tested plate to illustrate the efficiency of the proposed method. The effects of using different damage positions and severity levels on the diagnosis results were discussed. The results demonstrated that the number of frequencies and modes of vibration required to identify the position and severity of damages accurately is different according to the damage scenarios and the percentage of uncertainty.

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Section: Figmentioning

confidence: 99%

A novel hybrid multi-stage probabilistic algorithm for the damage detection of carbon nanotube panels by considering the uncertainty of materials and mechanical properties

Khayat,

Amoushahi

2024

Preprint

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“…In practical engineering applications, small fluctuations in these parameters may have an influence on the safety of the structure and lead to structural failure. [5][6][7] For example, the uncertainty of load and winding angle of multi-layer composite laminates is the main reason for the failure of composite structures. 8 Therefore, it is necessary to quantitatively evaluate the uncertainty in its parameters and consider the influence of uncertainty on the reliability of composite structures.…”

Section: Introductionmentioning

confidence: 99%

Reliability and sensitivity analysis of delamination growth of composite laminate structures using two efficient sampling methods

Liu,

Xiao,

2024

AIP Advances

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In recent years, composite structures have been used in a large number of applications in aerospace, machinery, marine, and civil engineering. However, there are inevitably many uncertainties in the whole life cycle of composite structures, which can easily lead to structural damage and failure. Therefore, it is important to analyze the reliability and sensitivity of composite structures. At present, most of the contributions use the first-order reliability method (FORM) and the second-order reliability method (SORM) to study the reliability of composite structures and compare them with the results of the Monte Carlo simulation. However, both methods have their limitations. FORM cannot guarantee the calculation accuracy for the highly nonlinear limit state equation, and the calculation efficiency of SORM is too low. Therefore, this paper proposes to use importance sampling (IS) and backpropagation neural network-based Monte Carlo (MC-BPNN) to study the reliability, sensitivity, and dispersion of delamination growth of composite laminates. The results show that compared with FORM and SORM, IS and MC-BPNN have higher calculation accuracy and efficiency and can more accurately evaluate the failure degree of composite structures and ensure their safe operation in the field of aerospace equipment. The universality of this method can make it being widely used in the reliability and sensitivity analysis of different composite materials as well as dispersion analysis.

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“…Probabilistic and non-probabilistic uncertainties may exist simultaneously in reliability analyses [24,25], and when defining material properties [26], for example. However, their combination has been seldom considered in the stochastic dynamic analysis of composite structures.…”

Section: Introductionmentioning

confidence: 99%

A multiscale uncertainty propagation method for dynamic analysis of laminated FRP composite plates with hybrid random and interval uncertainties

Zhou

Qian

Wang

et al. 2023

Composite Structures

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Bounds of mechanical properties of fibre reinforced polymer composites with hybrid random and interval uncertainties

Cited by 10 publications

References 58 publications

A novel hybrid multi-stage probabilistic algorithm for the damage detection of carbon nanotube panels by considering the uncertainty of materials and mechanical properties

A novel hybrid multi-stage probabilistic algorithm for the damage detection of carbon nanotube panels by considering the uncertainty of materials and mechanical properties

Reliability and sensitivity analysis of delamination growth of composite laminate structures using two efficient sampling methods

A multiscale uncertainty propagation method for dynamic analysis of laminated FRP composite plates with hybrid random and interval uncertainties

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