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논문 기본 정보

자료유형
학술저널
저자정보
Kwatra, Naveen (Department of Civil Engineering, Thapar Institute of Engineering & Technology) Godbole, P.N. (Department of Civil Engineering, University of Roorkee) Krishna, Prem (Department of Civil Engineering, University of Roorkee)
저널정보
테크노프레스 Wind & structures Wind & structures 제5권 제1호
발행연도
2002.1
수록면
1 - 14 (14page)

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Artificial Neural Networks (ANN) have the capability to develop functional relationships between input-output patterns obtained from any source. Thus ANN can be conveniently used to develop a generalised relationship from limited and sometimes inconsistent data, and can therefore also be applied to tackle the data obtained from wind tunnel tests on building models with large number of variables. In this paper ANN model has been developed for predicting wind induced pressures in various zones of a Gable Building from limited test data. The procedure is also extended to a case wherein interference effects on a gable roof building by a similar building are studied. It is found that the Artificial Neural Network modelling is seen to predict successfully, the pressure coefficients for any roof slope that has not been covered by the experimental study. It is seen that ANN modelling can lead to a reduction of the wind tunnel testing effort for interference studies to almost half.

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