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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/94323

    Title: 以類神經網路作桁架及構架結構最佳化設計
    Other Titles: Optimal design of truss and frame structures using artificial neural networks
    Authors: 施智勇;Shih, Chih-Yung
    Contributors: 淡江大學土木工程學系碩士班
    Keywords: 結構最佳化;單位力法;中心斷面;類神經網路;滿載應力法;Structure optimization;Unit load method;Central section;Artificial Neural Network;Fully stressed design
    Date: 2013
    Issue Date: 2014-01-23 14:21:16 (UTC+8)
    Abstract: 由於以高階演算法進行結構最佳化設計時,往往會因參數設定因素或收斂速度太快與太慢,導致最終結果不一或僅找到局部最佳解,一般常需另外加入其他策略方可減少計算量及獲得較佳結果。類神經網路法有別於這些方法,是一種平行分散處理的計算模式,其所建構的分析模型具有非線性特性,可獲得較一般迴歸分析更準確的結果,它已廣泛應用於各個領域。
    The advanced algorithms for structural optimization design often leads to problems which the final result is not unique and the result is local optium due to the influence of parameter setting or too fast and too slow convergence speed. In general, adding other strategies in analyzing is often necessary in order to reduce the amount of calculation and obtain better results. Artificial neural network (ANN), a parallel distributed processing computing model which differs from advanced algorithms can construct nonlinear characteristics, which creates more accurate results of regression analysis and it has been widely used in various fields.
    CAFE software has been used with SAP2000 in this study for optimum design of truss and frame structures. There are two methods of samples collecting design recommended in this research for both truss and frame structures. First method proposes sifting out all members from the zero bars by FSD, and manifesting a quick central section evaluation formula by unit load method. Central sections for other members except zero bars are calculated with the formula. As for truss structure, the central position in the search space is first established, then an appropriate range from central position, which it finds samples randomly. The second approach is a trial and error method, which a range of parameters near to constraint criteria is used, and random sampling is taken as well. A number of procedures were done in order to reduce the number of samples, the number of iterations and computation time.
    The results have shown that the use of both the CAFE software and the random sampling method can indeed significantly reduce the structural optimization calculation time and get better results compared to the literature. Due to the CAFE software we can find the significances of input variables that impacts constraints, this shows that two kinds of sample design approach is most favorable, significantly increasing the neural network method to structural optimization of practicality.
    Appears in Collections:[土木工程學系暨研究所] 學位論文

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