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논문 기본 정보

자료유형
학술저널
저자정보
Shahbazi, Yaser (Architecture and Urbanism Department, Tabriz Islamic Art University) Delavari, Ehsan (Department of Civil Engineering, Sahand University of Technology) Chenaghlou, Mohammad Reza (Department of Civil Engineering, Sahand University of Technology)
저널정보
테크노프레스 Smart structures and systems Smart structures and systems 제13권 제1호
발행연도
2014.1
수록면
81 - 98 (18page)

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This paper presents the elastic buckling of smart lightweight column structures integrated with a pair of surface piezoelectric layers using artificial intelligence. The finite element modeling of Smart lightweight columns is found using $ANSYS^{(R)}$ software. Then, the first buckling load of the structure is calculated using eigenvalue buckling analysis. To determine the accuracy of the present finite element analysis, a compression study is carried out with literature. Later, parametric studies for length variations, width, and thickness of the elastic core and of the piezoelectric outer layers are performed and the associated buckling load data sets for artificial intelligence are gathered. Finally, the application of soft computing-based methods including artificial neural network (ANN), fuzzy inference system (FIS), and adaptive neuro fuzzy inference system (ANFIS) were carried out. A comparative study is then made between the mentioned soft computing methods and the performance of the models is evaluated using statistic measurements. The comparison of the results reveal that, the ANFIS model with Gaussian membership function provides high accuracy on the prediction of the buckling load in smart lightweight columns, providing better predictions compared to other methods. However, the results obtained from the ANN model using the feed-forward algorithm are also accurate and reliable.

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