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    Neuroform—Neural Network System for Vertical Formwork Selection

    Source: Journal of Computing in Civil Engineering:;1992:;Volume ( 006 ):;issue: 002
    Author:
    Sagar V. Kamarthi
    ,
    Victor E. Sanvido
    ,
    Soundar R. T. Kumara
    DOI: 10.1061/(ASCE)0887-3801(1992)6:2(178)
    Publisher: American Society of Civil Engineers
    Abstract: This paper presents a neural network approach for building Neuroform, a computer system that provides the selection of vertical formwork systems for a given building site. The reasons for choosing a neural network approach instead of a traditional expert system are discussed. The selection of an appropriate neural network model, its architecture, representation of the network training examples, and the network training procedure are described. The details of the user interaction with the trained neural network system are presented. The performance of Neuroform is validated comparing its recommendations with that of Wallform, a rule‐based expert system for vertical formwork selection. A statistical hypothesis test, conducted on the recommendations of Neuroform when partial inputs are given, demonstrates the system's fault‐tolerant and generalization properties.
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      Neuroform—Neural Network System for Vertical Formwork Selection

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    http://yetl.yabesh.ir/yetl1/handle/yetl/42719
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    contributor authorSagar V. Kamarthi
    contributor authorVictor E. Sanvido
    contributor authorSoundar R. T. Kumara
    date accessioned2017-05-08T21:12:24Z
    date available2017-05-08T21:12:24Z
    date copyrightApril 1992
    date issued1992
    identifier other%28asce%290887-3801%281992%296%3A2%28178%29.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/42719
    description abstractThis paper presents a neural network approach for building Neuroform, a computer system that provides the selection of vertical formwork systems for a given building site. The reasons for choosing a neural network approach instead of a traditional expert system are discussed. The selection of an appropriate neural network model, its architecture, representation of the network training examples, and the network training procedure are described. The details of the user interaction with the trained neural network system are presented. The performance of Neuroform is validated comparing its recommendations with that of Wallform, a rule‐based expert system for vertical formwork selection. A statistical hypothesis test, conducted on the recommendations of Neuroform when partial inputs are given, demonstrates the system's fault‐tolerant and generalization properties.
    publisherAmerican Society of Civil Engineers
    titleNeuroform—Neural Network System for Vertical Formwork Selection
    typeJournal Paper
    journal volume6
    journal issue2
    journal titleJournal of Computing in Civil Engineering
    identifier doi10.1061/(ASCE)0887-3801(1992)6:2(178)
    treeJournal of Computing in Civil Engineering:;1992:;Volume ( 006 ):;issue: 002
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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