淡江大學機構典藏:Item 987654321/104647
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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/104647


    Title: Optimal Design of Reinforced Concrete Plane Frames Using Artificial Neural Network
    Authors: Kao, Chin-Sheng;Yeh, I-Cheng
    Keywords: artificial neural networks;optimization;reinforced concrete;plane frame
    Date: 2014-10-30
    Issue Date: 2016-01-06 11:05:58 (UTC+8)
    Abstract: To solve structural optimization problems, it is necessary to integrate a structural analysis package and an optimization package. There have been many packages that can be employed to analyze reinforced concrete plane frames. However, because most structural analysis packages suffer from closeness of systems, it is very difficult to integrate them with optimization packages. To overcome the difficulty, we proposed a possible alternative, DAMDO, which integrates Design, Analysis, Modeling, Definition, and Optimization phases into an integration environment as follows. (1) Design: first generate many possible structural design alternatives. Each design alternative consists of many design variables X. (2) Analysis: employ the structural analysis software to analyze all structural design alternatives to obtain their internal forces and displacements. They are the response variables Y. (3) Modeling: employ artificial neural networks to build the models Y=f(X) to obtain the relationship functions between the design variables X and the response variables Y. (4) Definition: employ the design variables X and the response variables Y to define the objective function and constraint functions. (5) Optimization: employ the optimization software to solve the optimization problem consisting of the objective function and the constraint functions to produce the optimum design variables. The RC frame optimization problem was examined to evaluate the DAMDO approach, and the empirical results showed that it can be solved by the approach.
    Relation: Computers and Concrete 14(4), p.445-462
    DOI: 10.12989/cac.2014.14.4.445
    Appears in Collections:[Graduate Institute & Department of Civil Engineering] Journal Article

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