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RISS 인기검색어
- 검색키워드
- 저자 : Rajeev Chandak (검색결과 2 건)
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Shear lag prediction in symmetrical laminated composite box beams using artificial neural network
Chandak, Rajeev,Upadhyay, Akhil,Bhargava, Pradeep Techno-Press 2008 Structural Engineering and Mechanics, An Int'l Jou Vol.29 No.1
Presence of high degree of orthotropy enhances shear lag phenomenon in laminated composite box-beams and it persists till failure. In this paper three key parameters governing shear lag behavior of laminated composite box beams are identified and defined by simple expressions. Uniqueness of the identified key parameters is proved with the help of finite element method (FEM) based studies. In addition to this, for the sake of generalization of prediction of shear lag effect in symmetrical laminated composite box beams a feed forward back propagation neural network (BPNN) model is developed. The network is trained and tested using the data base generated by extensive FEM studies carried out for various b/D, b/tF, tF/tW and laminate configurations. An optimum network architecture has been established which can effectively learn the pattern. Computational efficiency of the developed ANN makes it suitable for use in optimum design of laminated composite box-beams.
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Shear lag prediction in symmetrical laminated composite box beams using artificial neural network
Rajeev Chandak,Akhil Upadhyay,Pradeep Bh 국제구조공학회 2008 Structural Engineering and Mechanics, An Int'l Jou Vol.29 No.1
Presence of high degree of orthotropy enhances shear lag phenomenon in laminated composite box-beams and it persists till failure. In this paper three key parameters governing shear lag behavior of laminated composite box beams are identified and defined by simple expressions. Uniqueness of the identified key parameters is proved with the help of finite element method (FEM) based studies. In addition to this, for the sake of generalization of prediction of shear lag effect in symmetrical laminated composite box beams a feed forward back propagation neural network (BPNN) model is developed. The network is trained and tested using the data base generated by extensive FEM studies carried out for various b/D, b/tF, tF/tW and laminate configurations. An optimum network architecture has been established which can effectively learn the pattern. Computational efficiency of the developed ANN makes it suitable for use in optimum design of laminated composite box-beams.
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