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Information Journal Paper

Title

ASSESSMENT OF LONGITUDINAL SHEAR STRENGTH PARAMETERS OF COMPOSITE SLAB BY ARTIFICIAL NEURAL NETWORK

Pages

  287-300

Keywords

ARTIFICIAL NEURAL NETWORK (ANN) 

Abstract

 Longitudinal shear strength is considered as a major constraint in the design of composite slab and it can be assessed by expensive and time consuming experimental techniques. The objective of the present work is to provide a numerical tool to minimize hurdles in the design process and to reduce the dependency on expensive and time-consuming experiments. In this article, Artificial Neural Networks model has been developed for finding the m and k values for determination of the horizontal shear resistance. It is demonstrated that, with proper training of the neural network using the ratio of pitch length to width of top flange and depth of profile as input values, the proposed neural network model can generate the values of m and k quite accurately. Inherently the Artificial Neural Network is computationally efficient tool and hence the developed Artificial Neural Network will be very useful in optimization procedures of the composite slab.

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    Cite

    APA: Copy

    MOHAN GANESH, G., UPADHYAY, A., & KAUSHIK, S.K.. (2006). ASSESSMENT OF LONGITUDINAL SHEAR STRENGTH PARAMETERS OF COMPOSITE SLAB BY ARTIFICIAL NEURAL NETWORK. ASIAN JOURNAL OF CIVIL ENGINEERING (BUILDING AND HOUSING), 7(3), 287-300. SID. https://sid.ir/paper/298689/en

    Vancouver: Copy

    MOHAN GANESH G., UPADHYAY A., KAUSHIK S.K.. ASSESSMENT OF LONGITUDINAL SHEAR STRENGTH PARAMETERS OF COMPOSITE SLAB BY ARTIFICIAL NEURAL NETWORK. ASIAN JOURNAL OF CIVIL ENGINEERING (BUILDING AND HOUSING)[Internet]. 2006;7(3):287-300. Available from: https://sid.ir/paper/298689/en

    IEEE: Copy

    G. MOHAN GANESH, A. UPADHYAY, and S.K. KAUSHIK, “ASSESSMENT OF LONGITUDINAL SHEAR STRENGTH PARAMETERS OF COMPOSITE SLAB BY ARTIFICIAL NEURAL NETWORK,” ASIAN JOURNAL OF CIVIL ENGINEERING (BUILDING AND HOUSING), vol. 7, no. 3, pp. 287–300, 2006, [Online]. Available: https://sid.ir/paper/298689/en

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