Study of Neural Network Models for the Ultimate Capacities of Cellular Steel Beams

Sharifi, Yasser; Moghbeli, Adel; Hosseinpour, Mahmoud; Sharifi, Hojjat

doi:10.1007/s40996-019-00281-z

Cited by 32 publications

(6 citation statements)

References 21 publications

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“…Selecting the appropriate activation function and learning method is essential for constructing neural networks [5]. The system's starting function includes linear and nonlinear starting functions.…”

Section: Common Startup and Learning Methodsmentioning

confidence: 99%

The Characteristics and Monitoring System of Athletes’ Physical Strength Training Based on Tunable Neuron Mathematical Model

2023

Applied Mathematics and Nonlinear Sciences

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In this paper, an adjustable neural network model is developed to predict the physical training load of athletes. This paper takes 48 contestants as experimental subjects. Then the average speed prediction method, nonlinear regression model prediction method and neural network model prediction method model are established to predict the training load. This paper uses three different prediction models to compare the training results. The relative error rate calculated by the average rate prediction method is about 23%. The close error rate calculated by the linear regression method is about 32%. The relative error rate calculated by the adjustable neural network is around 8%. The e flexible neural network model has good prediction accuracy.

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Section: Common Startup and Learning Methodsmentioning

confidence: 99%

The Characteristics and Monitoring System of Athletes’ Physical Strength Training Based on Tunable Neuron Mathematical Model

2023

Applied Mathematics and Nonlinear Sciences

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“…This process is called back-propagation of multilayer feed-forward ANN. The network architecture used in this article is a multilayer perceptron network (MLPN) as it has been shown to be an efficient tool to model various structural members [41,56]. The neural network toolbox with MATLAB [57] solves a data fitting problem with a two-layer feed-forward neural network and is used in this article.…”

Section: Neural Network Architecturementioning

confidence: 99%

Buckling resistance of hot‐finished CHS beam‐columns using FE modelling and machine learning

2023

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The use of circular hollow sections (CHS) has increased in recent years owing to its excellent mechanical behaviour including axial compression and torsional resistance as well as its aesthetic appearance. They are popular in a wide range of structural members, including beams, columns, trusses and arches. The behaviour of hot‐finished CHS beam‐columns made from normal‐ and high‐strength steels is the main focus of this article. A particular attention is given to predict the ultimate buckling resistance of CHS beam‐columns using the recent advancement of the artificial neural network (ANN). Finite element (FE) models were established and validated to generate an extensive parametric study. The ANN model is trained and validated using a total of 3439 data points collected from the generated FE models and experimental tests available in the literature. A comprehensive comparative analysis with the design rules in Eurocode 3 is conducted to evaluate the performance of the developed ANN model. It is shown that the proposed ANN‐based design formula provides a reliable means for predicting the buckling resistance of the CHS beam‐columns. This formula can be easily implemented in any programming software, providing an excellent basis for engineers and designers to predict the buckling resistance of the CHS beam–columns with a straightforward procedure in an efficient and sustainable manner with least computational time.

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“…This method has been used by many researchers. 36,37 Fig. 11 shows the relative contribution of each input on the output.…”

Section: Measuring the Relative Importance Of Each Input Variablementioning

confidence: 99%

The development of an artificial neural network – genetic algorithm model (ANN-GA) for the adsorption and photocatalysis of methylene blue on a novel sulfur–nitrogen co-doped Fe₂O₃ nanostructure surface

et al. 2020

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Study of Neural Network Models for the Ultimate Capacities of Cellular Steel Beams

Cited by 32 publications

References 21 publications

The Characteristics and Monitoring System of Athletes’ Physical Strength Training Based on Tunable Neuron Mathematical Model

The Characteristics and Monitoring System of Athletes’ Physical Strength Training Based on Tunable Neuron Mathematical Model

Buckling resistance of hot‐finished CHS beam‐columns using FE modelling and machine learning

The development of an artificial neural network – genetic algorithm model (ANN-GA) for the adsorption and photocatalysis of methylene blue on a novel sulfur–nitrogen co-doped Fe₂O₃ nanostructure surface

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Study of Neural Network Models for the Ultimate Capacities of Cellular Steel Beams

Cited by 32 publications

References 21 publications

The Characteristics and Monitoring System of Athletes’ Physical Strength Training Based on Tunable Neuron Mathematical Model

The Characteristics and Monitoring System of Athletes’ Physical Strength Training Based on Tunable Neuron Mathematical Model

Buckling resistance of hot‐finished CHS beam‐columns using FE modelling and machine learning

The development of an artificial neural network – genetic algorithm model (ANN-GA) for the adsorption and photocatalysis of methylene blue on a novel sulfur–nitrogen co-doped Fe2O3 nanostructure surface

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The development of an artificial neural network – genetic algorithm model (ANN-GA) for the adsorption and photocatalysis of methylene blue on a novel sulfur–nitrogen co-doped Fe₂O₃ nanostructure surface