A Computer-Assisted Wind Load Evaluation System for the Design of Cladding of Buildings: a Case Study of Spatial Structures

Abstract

Thin sheet metal and/or membrane are often used for roof cladding of spatial structures because of their strength and lightness (Noguchi et al., 2003). Being light and flexible, such roofing materials are vulnerable to dynamic wind actions. Since wind pressures acting on spatial structures vary spatially as well as in time, the design wind loads should be determined based on the dynamic characteristics of wind pressures. Fatigue of cladding elements, such as roofing material and its fixings, may play an important role in the wind resistant performance of cladding systems, although it is seldom considered in the design. Roof cladding is usually designed based on the worst peak pressure coefficients irrespective of wind direction. The conventional codification provides a single peak design pressure coefficient for each roof zone considering a nominal worst-case scenario. Neither the probability distribution of the peak pressure coefficients nor the peaks other than the largest one are considered. Hence, they are not suitable for fatigue and risk-consistent designs. Building design has recently shifted to a performance-oriented one. Therefore, it is hoped to develop a new methodology that provides the peak pressure coefficients according to predetermined risk levels and the loading sequence for estimating the fatigue damage to roof cladding and its fixings. Computer simulation of wind pressure time series may be useful for this purpose. Kumar and Stathopoulos (1998, 1999, 2001) proposed a novel simulating methodology that generates both Gaussian and non-Gaussian wind pressure fluctuations on low building roofs. Despite its simple procedure, the technique is successfully applied to fatigue analysis as well as to the evaluation of extreme pressures in a risk-consistent way. Therefore, this technology is used in this chapter and a simplification of this method is discussed. Gaussian and non-Gaussian pressure fluctuations can be simulated from the statistics of wind pressures, i.e. the mean, standard deviation, skewness, kurtosis and power spectrum. These statistical values change with location as well as with many factors related to the structure’s geometry and the turbulence characteristics of approach flow. For such a complicated phenomenon, in which a number of variables involve, artificial neural networks (simply neural networks or ANN’s) can be used effectively. Artificial neural networks can capture a complex, non-linear relationship via training with informative input-output example data pairs obtained from computations and/or experiments. Among a variety of artificial neural