Predicting Roof Pressures on a Low-Rise Structure From Freestream Turbulence Using Artificial Neural Networks

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

Effects of Freestream Turbulence on the Pressure Acting on a Low-Rise Building Roof in the Separated Flow Region

This paper presents the experimental design and subsequent findings from a series of experiments in a large boundary layer wind tunnel to investigate the variation of surface pressures with increasing upwind terrain roughness on low-rise buildings. Geometrically scaled models of the Wind Engineering Research Field Laboratory experimental building were subjected to a wide range of turbulent boundary layer flows, through precise adjustment of a computer control terrain generator called the Terraformer. The study offers an in-depth examination of the effects of freestream turbulence on extreme pressures under the separation “bubble” for the case of the wind traveling perpendicular to wall surfaces, independently confirming previous findings that the spatial distribution of the peaks is heavily influenced by the mean reattachment length. Further, the study shows that the observed peak pressures collapse if data are normalized by the mean reattachment length and a non-Gaussian estimator for peak velocity pressure

Multi-Objective Optimal Design of a Building Envelope and Structural System Using Cyber-Physical Modeling in a Wind Tunnel

This paper explores the use of a cyber-physical systems (CPS) “loop-in-the-model” approach to optimally design the envelope and structural system of low-rise buildings subject to wind loads. Both the components and cladding (C&C) and the main wind force resisting system (MWFRS) are considered through multi-objective optimization. The CPS approach combines the physical accuracy of wind tunnel testing and efficiency of numerical optimization algorithms to obtain an optimal design. The approach is autonomous: experiments are executed in a boundary layer wind tunnel (BLWT), sensor feedback is monitored and analyzed by a computer, and optimization algorithms dictate physical changes to the structural model in the BLWT through actuators. To explore a CPS approach to multi-objective optimization, a low-rise building with a parapet wall of variable height is considered. In the BLWT, servo-motors are used to adjust the parapet to a particular height. Parapet walls alter the location of the roof corner vortices, reducing suction loads on the windward facing roof corners and edges, a C&C design load. At the same time, parapet walls increase the surface area of the building, leading to an increase in demand on the MWFRS. A combination of non-stochastic and stochastic optimization algorithms were implemented to minimize the magnitude of suction and positive pressures on the roof of a low-rise building model, followed by stochastic multi-objective optimization to simultaneously minimize the magnitude of suction pressures and base shear. Experiments were conducted at the University of Florida Experimental Facility (UFEF) of the National Science Foundation’s (NSF) Natural Hazard Engineering Research Infrastructure (NHERI) program