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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/94331

    Title: 以類神經網路作鋼結構最佳化設計
    Other Titles: Optimal design of steel structures using artificial neural networks
    Authors: 薛宇辰;Xue, Yu-chen
    Contributors: 淡江大學土木工程學系碩士班
    高金盛;Kao, Chin-Sheng
    Keywords: 鋼結構;最佳化;類神經網路法;實驗設計法;Steel Structural;Optimization design;Artificial Neural Network;Design of experiments
    Date: 2013
    Issue Date: 2014-01-23 14:22:04 (UTC+8)
    Abstract: 論文提要內容:
    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.
    Appears in Collections:[土木工程學系暨研究所] 學位論文

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