Frontiers in Built Environment | |
Predicting Roof Pressures on a Low-Rise Structure From Freestream Turbulence Using Artificial Neural Networks | |
n, Pedro L.1 Masters, Forrest J.2 Ferná2 ndez-Cabá3 | |
[1] Environment, Herbert Wertheim College of Engineering, University of Florida, United States;Department of Civil and Environmental Engineering, University of Maryland, United States;Engineering School of Sustainable Infrastructure & | |
关键词: Low-rise building; Roof pressures; upwind terrain; freestream turbulence; artificial neural network; Back - propagation (BP) neural network; | |
DOI : 10.3389/fbuil.2018.00068 | |
学科分类:建筑学 | |
来源: Frontiers | |
【 摘 要 】
This paper presents a generalized approach for predicting (i.e., interpolating) the magnitude and distribution of roof pressures near separated flow regions on a low-rise structure based on freestream turbulent flow conditions. A feed-forward multilayer artificial neural network (ANN) using a backpropagation (BP) training algorithm is employed to predict the mean, root-mean-square (RMS), and peak pressure coefficients on three geometrically scaled (1:50, 1:30, and 1:20) low-rise building models for a family of upwind approach flow conditions. A comprehensive dataset of recently published boundary layer wind tunnel (BLWT) pressure measurements was utilized for training, validation, and evaluation of the ANN model. On average, predicted ANN peak pressure coefficients for a group of pressure taps located near the roof corner were within 5.1, 6.9, and 7.7% of BLWT observations for the 1:50, 1:30, and 1:20 models, respectively. Further, very good agreement was found between predicted ANN mean and RMS pressure coefficients and BLWT data.
【 授权许可】
CC BY
【 预 览 】
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RO201904027119772ZK.pdf | 4585KB | download |