Such roof structures usually have the characteristics of light mass, high flexibility, slight damping and low natural frequency. Consequently, these structures have become progressively more wind sensitive than most conventional roof structures, and wind loads generally control the design of these structures. In designing such large-span roof structures, it is usually necessary to conduct wind tunnel tests to determine wind loads on rigid models by taking pressure measurements.

In these wind tunnel experiments, it needs to install as more pressure taps as possible on model surfaces in order to capture the detailed characteristics of wind loads on the structures; since variations of wind-induced pressures at different locations on surfaces of a roof are quite large [1].

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Recent technological advances have made it possible to simultaneously measure surface pressures at more than locations on a building model [2], but such experimental arrangements may still not be able to cover the whole surfaces of a large-span roof structure. According to the requirement of wind tunnel tests, the density of arrangement in the pressure taps for long roof structures in the model test should be measured at a sufficient number of locations so that no significant aerodynamic events are missed.

For a long span roof structure this may involve measurements at some to or more locations, depending on the complexity of the exterior geometry. As for buildings with very complex shapes, or with many fine-scale features, such as long span roof with complex geometry, it is not possible to install enough pressure taps in all the required locations to accurately resolve the overall forces on the building.

Until further experience is accumulated, the requisite number of simultaneously measured pressure taps ought to be judged with particular care and on a case by case basis. Meanwhile a finite element model is often utilized for the numerical analysis of wind-induced response of large span roof structure.

Normally the number of element nodes in finite element model is very large than the number of pressure taps arranged in the wind tunnel test. In order to obtain the information of aerodynamic wind load for unresolved element nodes in finite element model, the numerical interpolation or extrapolation methods is normally adopted. However the approximation accuracy for the kind of method could not be guaranteed as the physical phenomena of wind load distribution could difficultly obtained by the simple numerical interpolation or extrapolation method.

Thus the wind-induced pressures on an entire large-span roof structure could be obtained from the pressure measurements with limited number of pressure taps in the wind tunnel test. There are several useful ways to predict or simulate time series of random data such as wind-induced pressure. However, the model was found to be inappropriate for generating time series of random data with non-Gaussian features.

Another standard approach, the Fourier transform method [40], shows good performance for autocorrelation and power spectrum simulation, but it is also limited to the case of a Gaussian probability distribution. Comparing with the above-mentioned techniques, Artificial Neural Networks ANNs are capable of capturing complex, nonlinear functional relationships via training with the informative input-output example data pairs which are computational or experimental results.

Actually Artificial Neural Networks ANNs have been successfully applied to solve a number of wind engineering problems. For example, Turkkan and Srivastava [3] used the neural network approach to predict the wind load distribution for air-supported structures. Khanduri et al. Sandri and Mehta [5] adopted neural networks for predicting wind-induced damage to buildings.

Chen et al. On the other hand, fuzzy system based on the pioneering work of Zadeh [9] in fuzzy set theory has been an active research area with wide applications in civil engineering such as earthquake intensity evaluation [10], and a fuzzy model for load combinations [11]. In practice, there is a very close relationship between neural networks and fuzzy systems, since they both work with degrees of imprecision in a space that is not defined by sharp deterministic boundaries.

On the other hand, the ESWL is also an extremely important aspect in the design of large-span roof structures.

## Wind loads and their reduction on mesh fabrics | JVE Journals

For design use, the ESWLs is generally expressed in a separated form in terms of the background component and the resonant component of structural modes [14]. The background load component can be treated as a quasi-static load, its result mainly be obtained from the influence function and the external wind load. The resonant load component follows the distribution of the inertial load and can be expressed in terms of the inertial load for each structural mode, which depends on the mass distribution and mode shape []. Once the background component and the resonant component for each structural mode have been determined, the corresponding peak background and resonant responses can be calculated from static analysis approach.

This approach provides a more efficient response prediction framework and a physically more meaningful description of load distribution [], and its successful application in evaluation of the ESWLs on bridges was presented by Davenport and King [24] with the section model test results.

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However, the correlations between the background and resonant components in this approach were neglected; this would result in considerable error in the response estimation. In fact, it is not necessary to separate the wind-induced responses into background and resonant components for evaluating ESWLs on buildings and structures [14]. Fu et al. This implies that the proposed methodology in Fu et al. This paper presents some selected results from a combined numerical simulation and ESWL study of wind effects on Guangzhou International Sports Arena, which mainly contains three parts.

Furthermore, the correlations between the background and resonant response components are discussed in detail, the numerical analyzed results show that neglecting the correlations between the two components would result in considerable error in the response estimation. Finally, the ESWL approach proposed by the authors [25] is used to estimate the wind-induced responses of the roof, which are compared with those obtained by the CQC approach to examine the effectiveness of the proposed ESWL approach in the design and analysis of large-span roof structures.

Designed by MANICA Architecture in partnership with the Guangzhou Design Institute, the GISA will not only host Asian Games events in , but will be home to a wide variety of world class events such as basketball, international ice events, and major music concerts and stage shows, etc. The basic plan form of the GISA roof is essentially elliptical with a mean roof height of The lengths of the two diameters of the ellipse are The roof of the GISA consists of steel trusses spanning the arena in the short direction, supported on concrete columns at the perimeter of the building.

## Introduction to Wind Engineering, Wind Structure, Wind-Building Interaction

These trusses consists of steel wide-flange shapes with the webs oriented horizontally and are supported by steel columns that extend to the mechanical level where these columns align with concrete columns below. These trusses support the scoreboard, rigging grid, lighting, speaker array, fall protection system, and hockey net support system, etc.

Integrated within the truss will be catwalks and spotlight platforms. The primary lateral force resisting systems will consist of steel braced frames at the roof level and then concrete shear walls from the mechanical level down to the foundation. On the other hand, Guangzhou is close to the most active typhoon generating area in the world; hence, the GISA may be susceptible to severe wind forces induced by strong typhoons.

This makes a detailed study of wind effects on the GISA of particular importance and necessity. Since the boundary layer wind tunnel has been a basic tool of wind engineering research on wind effects on buildings and structures, it would be quite useful to conduct wind tunnel investigation to evaluate wind effects on the GISA to provide valuable information for the design and construction of other large-span roof structures in the future.

A rigid model with a geometric length scale of was made. Spires and roughness elements were used to simulate a boundary layer wind flow of suburban terrain type stipulated in the Load Code of China [27] as exposure B category. Mean wind speed and turbulence intensity profiles. The measured mean wind speeds and turbulence intensities at various heights over the test section are illustrated in Fig. Meanwhile, the turbulence intensity profile specified in the Japanese Load Code [28] is also shown in Fig.

The spectrum of longitudinal wind speed at the height of 45 m 0. In fact, only the upper surface of the GISA roof is exposed to wind actions. The layout of the pressure taps on the upper roof surface is shown in Fig. Spectra of longitudinal wind velocity at the height of 45 m in full-scale. Layout of the pressure taps on the upper roof surface. In the wind tunnel tests, pressures were measured simultaneously from all the taps on the upper roof surface, and data sampling frequency was The pressure coefficient of the pressure tap i on the roof surface is defined as follows:.

The design wind speed with year return period for GISA is about Then, the sampling frequency and the record length T t o t a l for the tested building are 6. The period for evaluating the statistics of pressure fluctuations is specified as 10 min, which is usually used for the averaged time in wind speed measurements in China. The record time is divided into consecutive 10 min periods and the pressures at Tap i are analyzed as mean, rms, maximum and minimum pressure coefficients, C p i m e a n , C p i r m s , C p i m a x and C p i m i n for each period.

The wind tunnel experimental results indicate that the wind loads on the large-span roof structure are mainly dominant by relatively high negative wind pressures. Therefore, the peak factor, g , corresponding to the minimum pressure coefficient can be defined as follows:.

By taking the GISA roof as an example, the present work mainly focuses on the development of an effective tool to accurately estimate the time series of wind-induced pressures in the unresolved area of long-span roof structure by neural network prediction method. Irwin , P. Eng, F. Un Yong Jeong , Ph. George A.

Kelley Member. Kaustubh Vibhakar Khanvilkar , P.

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John Kilpatrick , Ph. Gregory Alan Kopp , P. Marc L.