Structural optimization by gradient-based neural networks

Iranmanesh, Amir; Kaveh, A.

doi:10.1002/(sici)1097-0207(19990920)46:2<297::aid-nme679>3.0.co;2-c

Cited by 97 publications

(13 citation statements)

References 8 publications

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“…1), which are approximation of objective or constraint function, and the solution of structural optimization problems. Recently, various ANN-OM methods have been proposed [2,3]. Nevertheless, all these approaches are based on the general concept described above (Fig.…”

Section: Basic Concept Of Ann-ommentioning

confidence: 99%

“…Among these methods, the artificial neural network (ANN) is considered to be one of the most widely used methods in the structural optimization. Over the past decade, contributions from numerous studies have brought ANN to fruition as an optimization method [1][2][3][4][5][6][7][8][9][10][11]. Consequently, ANN has become a basic tool for structural optimization.…”

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confidence: 99%

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A hybrid artificial neural network method with uniform design for structural optimization

Cheng

2008

Comput Mech

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This paper presents a new hybrid artificial neural network (ANN) method for structural optimization. The method involves the selection of training datasets for establishing an ANN model by uniform design method, approximation of the objective or constraint functions by the trained ANN model and yields solutions of structural optimization problems using the sequential quadratic programming method (SQP). In the proposed method, the use of the uniform design method can improve the quality of the selected training datasets, leading to a better performance of the ANN model. As a result, the ANN dramatically reduces the number of required trained datasets, and shows a good ability to approximate the objective or constraint functions and then provides an accurate estimation of the optimum solution. It is shown through three numerical examples that the proposed method provides accurate and computationally efficient estimates of the solutions of structural optimization problems.

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Section: Basic Concept Of Ann-ommentioning

confidence: 99%

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confidence: 99%

A hybrid artificial neural network method with uniform design for structural optimization

Cheng

2008

Comput Mech

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“…In the last decade neural networks are extensively employed in structural mechanics, Refs [4][5][6][7][8][9]. In the following, some basic concepts of neural networks is summarized, however, the interested reader may also refer to excellent textbooks on this subject [13][14][15].…”

Section: Bp and Rbf Neural Networkmentioning

confidence: 99%

“…Neural networks provide a powerful tool for approximate analysis and design of space structures. Such networks are trained using backpropagation [4][5][6] and counterpropagation networks [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Neural Networks for the Analysis and Design of Domes

Kaveh

Dehkordi

2003

International Journal of Space Structures

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In this paper, efficient neural networks are trained for the analysis, design and prediction of the displacements of domes using the Backpropagation and Radial Basis Functions neural networks. The performance of these networks is compared when applied to domes. Programs are developed for accurate distribution of applied forces and wind load to the nodal points of domes. Training and testing pairs are prepared by ANSYS software.

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“…ANN had a widespread application in the field of civil engineering since long. It was applied in the development of the backpropagation neural net and the improved counter-propagation neural net for the analysis and design of large scale space structures [17] and also for the presentation of a neurocomputing strategy combining neural networks and numerical structural optimization [18]. Again ANN was used to train efficient backpropagation neural networks for design of double-layer grids [19], to train neural networks that predict M-ф diagrams for the type of connection considered and for saddle-like connection with sufficient accuracy [20,21].…”

Section: Introductionmentioning

confidence: 99%

Prognosis of Wind-tempted Mean Pressure Coefficients of Cross-shaped Tall Buildings Using Artificial Neural Network

Paul

Dalui

2020

Period. Polytech. Civil Eng.

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The present paper focuses on the study of wind-induced responses of cross-plan shaped tall buildings. Initially, three parametric building models are studied for the purpose with a constant plan area 22500 mm2. The length and velocity scales are taken as 1:300 and 1:5, respectively. Wind angle of attack (WAA) is considered from 0° to 330° with an increment of 30°. At first, the external surface pressure coefficients (Cp) at different faces of the models are carried out for different wind occurrence angles employing Computational Fluid Dynamics method of simulated wind flow. Again, Fast Fourier Transform (FFT) fitted expressions as the sine and cosine function of WAA are proposed for attaining mean wind pressure coefficient on the building faces. The accuracy of the Fourier series expansions is justified by presenting histograms of sum square error (SSE), R2 value and root mean square error (RMSE). The results are also compared by training Artificial Neural Networks (ANN). Training is continued till Regression (R) values are more than 0.99 and Mean Squared Error (MSE) tends to 0, ensuring a close relationship among the outputs and targets. The face-wise value of (Cp) obtained using all three methods, are plotted. The error histograms of the ANN models show that the fitting data errors are spread within a reasonably good range. It is observed that the deviation in the result is not more than 5% in any case. Finally, the ANN predictions are presented for nine parametric models to cover a wide range of possible cross-shaped buildings.

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Structural optimization by gradient-based neural networks

Cited by 97 publications

References 8 publications

A hybrid artificial neural network method with uniform design for structural optimization

A hybrid artificial neural network method with uniform design for structural optimization

Neural Networks for the Analysis and Design of Domes

Prognosis of Wind-tempted Mean Pressure Coefficients of Cross-shaped Tall Buildings Using Artificial Neural Network

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