
CivilComp Proceedings ISSN 17593433
CCP: 86 PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING COMPUTING
Edited by: B.H.V. Topping
Paper 140 A ComputerAssisted Wind Load Evaluation System for the Design of Cladding of Spatial Structures
Y. Uematsu ^{1}, R. Tsuruishi ^{2}, T. Hongo ^{3} and H. Kikuchi ^{4}^{1}New Industry Creation Hatchery Centre,
^{2}Department of Architecture and Building Science
Tohoku University, Sendai, Japan
^{3}Kajima Technical Research Institute, Chofu, Japan
^{4}Institute of Technology, Shimizu Corporation, Tokyo, Japan
Full Bibliographic Reference for this paper
Y. Uematsu, R. Tsuruishi, T. Hongo, H. Kikuchi, "A ComputerAssisted Wind Load Evaluation System for the Design of Cladding of Spatial Structures", in B.H.V. Topping, (Editor), "Proceedings of the Eleventh International Conference on Civil, Structural and Environmental Engineering Computing", CivilComp Press, Stirlingshire, UK, Paper 140, 2007. doi:10.4203/ccp.86.140
Keywords: spatial structures, cladding, wind tunnel experiment, aerodynamic database, artificial neural network, timehistory simulation, load evaluation system.
Summary
The authors have proposed a computerassisted wind load evaluation system for the design of roof cladding of spatial structures, using an aerodynamic database, artificial neural network and timeseries simulation technique. Focus is on spherical domes and vaulted roofs in the present paper. The system consists of the following four procedures:
 (1)
 Database of wind pressures
A series of wind tunnel experiments are carried out in two kinds of turbulent boundary layers corresponding to opencountry and urban exposures. Wind pressures are measured simultaneously at several hundred points on spherical domes and vaulted roofs with various rise/span and eavesheight/span ratios. The statistics of wind pressures, e.g. mean, standard deviation, skewness and kurtosis are computed. The data are stored in a database, together with the coordinates of the measuring points. The power spectrum of pressure fluctuations is approximated by an exponential function of the reduced frequency. The values of the parameters included in the function are stored in the database.
 (2)
 Artificial neural network for predicting the statistics of wind pressures
Artificial neural networks based on a Cascade Correlation Learning Network are constructed for predicting the statistics of wind pressures at arbitrary points on the roof. The input vector consists of several parameters, including the geometric parameters of the roof, the coordinates of evaluating point and the turbulence intensity of approach flow at the mean roof height; the wind direction is also considered in the vaulted roof case. Similarly one output node is used, because the expected output vector has a single component. That is, each network is constructed for each of the statistics.
 (3)
 Time series simulation of wind pressures
The approach is based on an FFT Algorithm. The Fourier amplitude is constructed from the power spectrum of pressure fluctuations. The spike features inducing the nonGaussian character to the pressure fluctuations are achieved by preserving the target skewness and kurtosis of pressure fluctuations.
 (4)
 Application of the wind load evaluation system to wind resistant design
The wind load evaluation system can provide peak pressure coefficients according to a predetermined risk level by combining the extreme value analysis. Simulating the wind pressure time series many times, we can calculate the probability of nonexceedence for the peak pressure coefficients precisely. Furthermore, by introducing a load cycle counting method, e.g. the rainflow count method, the system can provide the wind load cycles for fatigue design.
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