Optimal Design of Steel Structures Using Artificial Neural Networks
| Autor: | Yu-Chen Xue, 薛宇辰 |
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| Rok vydání: | 2013 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 101 In the past, the mathematical programming method is often used to solve the structural optimization problems. This method requires calculating the complex gradient function and sometime its answer is only a local optimum. The artificial neural network differs from this one. It is a parallel distributed processing mode of calculation. It can obtain more accurate results than those of regression analysis because its analytical model that has characteristics of nonlinear. Therefore, this thesis uses the artificial neural network method to optimize the design of steel structures. On the one hand, it is used to understand artificial neural network method in solving optimization of structures problems of applicability, on the other hand it is used to establish the optimal design patterns for beams, columns and frame structures of steel. Firstly, for beams, columns and frame structures of steel, this thesis uses two types of H-beams section to establish two different test sets. One type of the H-beams section is commonly used in the steel-structure design manual and the other is generated by the uniform random number. Next, this thesis composes the software to calculate the structural strength of the test samples, then using ETABS software for structural analysis and artificial neural network to build predictive models. Finally, the optimum structural design results are obtained by using CAFE software. The CAFE software used in this thesis is an optimization design system based on artificial neural network and design method of experiments. The result of the thesis have shown that using the design pattern in this article together with the CAFE software will result in getting a lighter structural design than the previous literature result. This research has significantly improved the practicality of using the artificial neural network on structural optimization. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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