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    Prediction of Buckling Load of Columns Using Artificial Neural Networks

    Source: Journal of Structural Engineering:;1996:;Volume ( 122 ):;issue: 011
    Author:
    A. Mukherjee
    ,
    J. M. Deshpande
    ,
    J. Anmala
    DOI: 10.1061/(ASCE)0733-9445(1996)122:11(1385)
    Publisher: American Society of Civil Engineers
    Abstract: A number of investigators have proposed semiempirical formulas for the critical buckling load of slender columns. The departure from the assumptions of the elastic-plastic theory makes the task of incorporating all the features of real-life columns into a single formula very difficult. As a result, semiempirical formulas, adopted for design specifications often follow a lower bound to experimental observations to include a variety of column types. Therefore, a significant portion of the actual column strength remains unutilized, when such a lower bound is adopted in the design of axially compressed members. This technical note reports development of a tool for the prediction of buckling load of columns, which requires minimum assumptions using neural computing techniques. This concept can be extended to include a variety of column types in a single model for the buckling load of columns. This concept can also be further extended for reliability analysis as the network can also predict the standard deviation in the column strength.
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      Prediction of Buckling Load of Columns Using Artificial Neural Networks

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    http://yetl.yabesh.ir/yetl1/handle/yetl/32374
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    • Journal of Structural Engineering

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    contributor authorA. Mukherjee
    contributor authorJ. M. Deshpande
    contributor authorJ. Anmala
    date accessioned2017-05-08T20:56:10Z
    date available2017-05-08T20:56:10Z
    date copyrightNovember 1996
    date issued1996
    identifier other%28asce%290733-9445%281996%29122%3A11%281385%29.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/32374
    description abstractA number of investigators have proposed semiempirical formulas for the critical buckling load of slender columns. The departure from the assumptions of the elastic-plastic theory makes the task of incorporating all the features of real-life columns into a single formula very difficult. As a result, semiempirical formulas, adopted for design specifications often follow a lower bound to experimental observations to include a variety of column types. Therefore, a significant portion of the actual column strength remains unutilized, when such a lower bound is adopted in the design of axially compressed members. This technical note reports development of a tool for the prediction of buckling load of columns, which requires minimum assumptions using neural computing techniques. This concept can be extended to include a variety of column types in a single model for the buckling load of columns. This concept can also be further extended for reliability analysis as the network can also predict the standard deviation in the column strength.
    publisherAmerican Society of Civil Engineers
    titlePrediction of Buckling Load of Columns Using Artificial Neural Networks
    typeJournal Paper
    journal volume122
    journal issue11
    journal titleJournal of Structural Engineering
    identifier doi10.1061/(ASCE)0733-9445(1996)122:11(1385)
    treeJournal of Structural Engineering:;1996:;Volume ( 122 ):;issue: 011
    contenttypeFulltext
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