Including Shear in a Neural Network Constitutive Model for Architectural Textiles

Colasante, Giada; Gosling, Peter

doi:10.1016/j.proeng.2016.08.011

Cited by 12 publications

(3 citation statements)

References 5 publications

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“…In this model ANN, the following data were used: the 104 experimental data for the training and 20 experimental data, which the network has never seen before, for the tests. The training set was used to calculate the gradient and update the network weights and biases while the test set was used to evaluate the model's suitability [20]. These data were distributed at random using a Python program.…”

Section: Methodology Of Ann Architecturementioning

confidence: 99%

Modeling of Artificial Neural Networks for Hydrogen Production via Water Electrolysis

Bilgiç

Öztürk

2023

ECJSE

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Artificial neural networks have emerged as a promising tool for estimating hydrogen production process variables for reaction condition optimization. Here we aim to predict complex nonlinear systems that use of artificial neural networks for modeling hydrogen production via water electrolysis and to evaluate the common challenges that arise. To estimate the effect of different electrolyzer systems input parameters such as electrolyte material, electrolyte type, supplied power (voltage and current), temperature, and time on hydrogen production, a predictive model was developed. The percentage contributions of the input parameters to hydrogen production and the best network architecture to minimize computation time and maximize network accuracy were shown. The results show that the hydrogen production parameters from electrolysis and the predicted safety explosive limit are 7% of the average root mean square error. Furthermore, coefficient of determination value was found 0.93. This predicted value is very close to the observed values. The neural network algorithm developed in this study could be used to make critical decisions in the electrolysis process for parameters affecting hydrogen production.

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Section: Methodology Of Ann Architecturementioning

confidence: 99%

Modeling of Artificial Neural Networks for Hydrogen Production via Water Electrolysis

Bilgiç

Öztürk

2023

ECJSE

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“…Lin et al 19 constructed an ANN model to depict the flow behaviours of TC18 titanium ally. Colasante et al 20 designed a neural network capable of modelling the shear behaviour of coated fabrics employed in architecture. Jung et al 21 presented a new rate-dependent neural network constitutive model formulation and implemented it in finite element software, which has good potential to describe any rate-dependent material behaviour.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network–based constitutive relation modelling for the laminated fabric used in stratospheric airship

Gao

Meng

et al. 2022

Composites and Advanced Materials

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There have been gradually-increasing interests in the stratospheric airship (SSA) as a cost-effective alternative to earth orbit satellites for telecommunication and high-resolution earth observation. Lightweight and high strength envelopes are the keys to the design of SSAs, as it directly determines the endurance flight performance and loading deformation characteristics of the airship. Typical SSA envelope material is a laminated fabric, which is composed of fabric layer and other functional layers. Compared with conventional composite structures, the laminated fabric has complex nonlinear mechanical characteristics. Artificial neural network (ANN) has good processing ability to nonlinear information so that it is suitable to model the constitutive relation of laminated fabrics. In this work, an ANN based on the Scaled Conjugate Gradient (SCG) algorithm is proposed firstly to model the constitutive relation of fabric Uretek3216LV. Considering significant errors of the SCG ANN results, the network model is optimized through methods of selecting the number of hidden-layer nodes and training algorithms. Results show that the improved network model based on Bayesian Regularization (BR) algorithm and eight nodes of single hidden layer can better describe the constitutive relation of the laminated fabric than other conventional training algorithms. The proposed constitutive modelling method with ANN is expected to gain a deeper understanding of the mechanical mechanism and guide structural design of envelope material in further work.

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“…Overall, fabrics present engineers with a nonlinear anisotropic behaviour, making modelling the material behaviour accurately challenging and still subject to ongoing research. [3,4,5] While the establishment of numerical models is important to allow engineers to simulate the textile's material behaviour in depth, these models still require experimental input data regarding the material's response under loading. Up until recently a standardised test procedure for coated textiles did however not exist, resulting in the utilisation of different test approaches which can yield a considerable variation on the material parameters derived from these different tests [6,7,8].…”

Section: Introductionmentioning

confidence: 99%

Analysis and statistical interpretation of biaxial material constants derived according to EN 17117-1:2018

Craenenbroeck

Mollaert

Laêt

2021

Composite Structures

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Utilising fabrics as an engineering material allows for the creation of lightweight structures, but poses some challenges when it comes to deriving material properties for use in numerical models due to the complex composite-like nature of these coated fabrics.Until recently, no international standard regarding testing these materials and deriving their material properties existed. With the publication of EN 17117-1:2018, guidelines to biaxially test fabrics and derive material constants are now available.To evaluate the methodology presented in EN 17117-1:2018 an analysis of the test results was carried out. A statistical interpretation allowed for the

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Including Shear in a Neural Network Constitutive Model for Architectural Textiles

Cited by 12 publications

References 5 publications

Modeling of Artificial Neural Networks for Hydrogen Production via Water Electrolysis

Modeling of Artificial Neural Networks for Hydrogen Production via Water Electrolysis

Artificial neural network–based constitutive relation modelling for the laminated fabric used in stratospheric airship

Analysis and statistical interpretation of biaxial material constants derived according to EN 17117-1:2018

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