Assessment of critical buckling load of functionally graded plates using artificial neural network modeling

Duong, Huan Thanh; Phan, Hieu Chi; Tran, Tu Minh; Dhar, Ashutosh Sutra

doi:10.1007/s00521-021-06238-6

Cited by 13 publications

(9 citation statements)

References 36 publications

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“…Also, the R 2 values almost reach the absolute value of 1.0. Compared to the non-optimized model from, 26 the present model is considerably more accurate, with the errors around 10 times less than those found by Duong et al 26 The difference between MAE on the training and testing dataset is also reduced from roughly 50% (i.e. 0.6296 vs 0.3135) to almost equal (i.e.…”

Section: Optimal Parameterscontrasting

confidence: 51%

“…Based on first-order shear deformation theory (FSDT), the critical load of a simply supported plate can be found by solving equation (2). 26 det jKj ¼ 0 ð2Þ…”

Section: Ultimate Load Of Functionally Graded Structuresmentioning

confidence: 99%

“…NNet has often been applied to predict the mechanical behavior of FGM structures. 21,[26][27][28] Duong et al 26 used Monte Carlo simulation to stochastically assess the critical buckling load for FGM plate. The paper noted that FE or analytical models were unable to cope with the millions of predictions required.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Neural Network architecture and derivation of closed-form equation to predict ultimate load of functionally graded material plate

Duong

Phan

2023

Advances in Mechanical Engineering

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Functionally Graded Material (FGM) plate is a complicated structure with complex allocation of spatially changing proportions of ceramic and metal within the matter. Various analytical and numerical methods have been applied with a view to evaluating the critical load of FGM plate. However, these conventional methods struggle when the computational complexity is significant, which represents an obstacle to incorporation with other advanced techniques where computational power is required (e.g. optimization or random simulations). The Neural Network (NNet) model has been successfully applied to resolve this issue. However, the conventional NNet requires proper configuration to take advantage of the model, and thus, careful parameter tuning is required. Furthermore, the NNet is typically a “black box,” where the prediction mechanism is hidden. This paper establishes an optimized architecture for NNet, with parametric study of the model’s hyperparameters. Variance propagation is also applied to observe the variation of the model’s performance on random sub-databases splintered from the database. To this end, the explicit expression of the trained NNet model is provided after mathematically deploying the hidden algebra behind an NNet prediction. The developed model has very promising evaluation metrics: R2, MAE, and RMSE on the test set are 0.999925, 0.067516, and 0.146438, respectively.

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Section: Optimal Parameterscontrasting

confidence: 51%

“…Based on first-order shear deformation theory (FSDT), the critical load of a simply supported plate can be found by solving equation (2). 26 det jKj ¼ 0 ð2Þ…”

Section: Ultimate Load Of Functionally Graded Structuresmentioning

confidence: 99%

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Optimization of Neural Network architecture and derivation of closed-form equation to predict ultimate load of functionally graded material plate

Duong

Phan

2023

Advances in Mechanical Engineering

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“…Artificial neural networks have been studied comparatively in many studies with other methods. As a result, it has been observed to produce highly consistent and reliable results, especially in non-fixed and discrete data series [65,71,72].…”

Section: Advantages Of Artificial Neural Networkmentioning

confidence: 89%

The Neuromarketing Concept in Artificial Neural Networks: A Case of Forecasting and Simulation from the Advertising Industry

et al. 2022

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This research aims to examine a neural network (artificial intelligence) as an alternative model to examine the neuromarketing phenomenon. Neuromarketing is comparatively new as a technique for designing marketing strategies, especially advertising campaigns. Marketers have used a variety of different neuromarketing tools, for instance functional magnetic resonance imaging (fMRI), eye tracking, electroencephalography (EEG), steady-state probe topography (SSPT), and other expensive gadgets. Similarly, researchers have been using these devices to carry out their studies. Therefore, neuromarketing has been an expensive project for both companies and researchers. We employed 585 human responses and used the neural network (artificial intelligence) technique to examine the predictive consumer buying behavior of an effective advertisement. For this purpose, we employed two neural network applications (artificial intelligence) to examine consumer buying behavior, first taken from a 1–5 Likert scale. A second application was run to examine the predicted consumer buying behavior in light of the neuromarketing phenomenon. The findings suggest that a neural network (artificial intelligence) is a unique, cost-effective, and powerful alternative to traditional neuromarketing tools. This study has significant theoretical and practical implications for future researchers and brand managers in the service and manufacturing sectors.

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“…With the development of artificial intelligence (AI) technology, the use of deep learning methods to obtain better corrosion predictions for oil and gas pipelines has also become a focus of current research [10][11][12]. For example, Jain et al [13] proposed a quantitative evaluation model for the external corrosion rate of oil and gas pipelines based on Bayesian networks.…”

Section: Introductionmentioning

confidence: 99%

Metal Corrosion Rate Prediction of Small Samples Using an Ensemble Technique

Yang¹,

Zheng²,

Zeng³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Accurate prediction of the internal corrosion rates of oil and gas pipelines could be an effective way to prevent pipeline leaks. In this study, a proposed framework for predicting corrosion rates under a small sample of metal corrosion data in the laboratory was developed to provide a new perspective on how to solve the problem of pipeline corrosion under the condition of insufficient real samples. This approach employed the bagging algorithm to construct a strong learner by integrating several KNN learners. A total of 99 data were collected and split into training and test set with a 9:1 ratio. The training set was used to obtain the best hyperparameters by 10-fold cross-validation and grid search, and the test set was used to determine the performance of the model. The results showed that the Mean Absolute Error (MAE) of this framework is 28.06% of the traditional model and outperforms other ensemble methods. Therefore, the proposed framework is suitable for metal corrosion prediction under small sample conditions.

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Assessment of critical buckling load of functionally graded plates using artificial neural network modeling

Cited by 13 publications

References 36 publications

Optimization of Neural Network architecture and derivation of closed-form equation to predict ultimate load of functionally graded material plate

Optimization of Neural Network architecture and derivation of closed-form equation to predict ultimate load of functionally graded material plate

The Neuromarketing Concept in Artificial Neural Networks: A Case of Forecasting and Simulation from the Advertising Industry

Metal Corrosion Rate Prediction of Small Samples Using an Ensemble Technique

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