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Front. Built Environ. | doi: 10.3389/fbuil.2018.00068

Predicting roof pressures on a low-rise structure from freestream turbulence using artificial neural networks

  • 1University of Maryland, College Park, United States
  • 2University of Florida, United States

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.

Keywords: Low-rise building, Roof pressures, upwind terrain, freestream turbulence, artificial neural network, Back - propagation (BP) neural network

Received: 22 Jul 2018; Accepted: 05 Nov 2018.

Edited by:

Gregory A. Kopp, University of Western Ontario, Canada

Reviewed by:

Daniel C. Lander, Rensselaer Polytechnic Institute, United States
Eri Gavanski, Osaka City University, Japan  

Copyright: © 2018 Fernández-Cabán, Masters and Phillips. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Pedro L. Fernández-Cabán, University of Maryland, College Park, College Park, United States, plferndz@umd.edu