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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/106191

    Title: Using Neural Networks to Integrate Structural Analysis Package and Optimization Package
    Authors: Kao, Chin-Sheng;Yeh, I-Cheng
    Keywords: Artificial neural networks;optimization;truss structure
    Date: 2015/03/25
    Issue Date: 2016-04-22 13:23:49 (UTC+8)
    Publisher: Springer London
    Abstract: To solve structural optimization problems, it is necessary to integrate a structural analysis package and an optimization package. Since most structural analysis packages suffer from closeness of system, it is very difficult to integrate it with an optimization package. To overcome the difficulty, we propose a possible alternative, DAMDO, which integrate 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 model 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 X*. Optimization of truss structures were used to validate the DAMDO approach. The empirical results show that the truss optimization problems can be solved by the DAMDO approach, which employs neural networks to integrate the structural analysis package and optimization package without requiring direct integration of the two packages. This approach is promising in many engineering optimization domains which need to couple an analysis package and an optimization one to obtain the optimum solutions.
    Relation: Neural Computing and Applications ,pp.1-13
    DOI: 10.1007/s00521-015-1878-z
    Appears in Collections:[土木工程學系暨研究所] 期刊論文

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