where is the intensity of turbulence at height which is equivalent to 0.6h, but not less than for all building heights. h is the mean roof height. and should be considered as 3.4. The background response is given by: (11) where B is the horizontal dimension, and stands for the integral length scale of turbulence at 0.6h height. can be estimated by using the following formula: (12) where and are constants listed in Table 26.9-1 in ASCE 7-10. The parameters used to estimate gust factor are listed in Table 1. Finally, the gust factor was calculated as 0.89.
In order to comprehensively evaluate the procedure defined by ASCE 7-10, new subdivisions are defined for each zone on the roof surface as can be seen in Figure 18, and*…show more content…*

(13) After calculating the new time history, , the peak pressure values should be evaluated. The peak pressure is a random variable with a distribution and a mean value. As a result, it is not possible to get the same value for peak pressures in different records while having the same mean value. Therefore, it is essential to estimate the probability of the peak value and refer, in the comparisons, to a specified percentage point of the distribution (Simiu 2011). To do so, in this study, the peak values are extracted from the time series at each tap locations by using the approach introduced in Ref. (Sadek and Simiu 2002). A MATLAB function for computing of quantiles (i.e., values corresponding to specified probabilities of non-exceedance) of the maximum and minimum values of the input time series was developed by NIST which is accessible on the NIST website (Main 2011). The function of “maxminqnt” is called within MATLAB as follows: [max_qnt, min_qnt] = maxminqnt (X, dur_ratio, CDF_qnt) (14) This function was used to calculate 95% peak quantile of the maximum (max_qnt) and minimum (min_qnt) values of time series of pressure coefficients. As can be seen in equation (5-28), this function requires three input arguments. The first input argument, “X”, stands for the time history of pressure data, and the second input argument “dur_ratio”, defines the ratio of the duration for which peaks are required to the

(13) After calculating the new time history, , the peak pressure values should be evaluated. The peak pressure is a random variable with a distribution and a mean value. As a result, it is not possible to get the same value for peak pressures in different records while having the same mean value. Therefore, it is essential to estimate the probability of the peak value and refer, in the comparisons, to a specified percentage point of the distribution (Simiu 2011). To do so, in this study, the peak values are extracted from the time series at each tap locations by using the approach introduced in Ref. (Sadek and Simiu 2002). A MATLAB function for computing of quantiles (i.e., values corresponding to specified probabilities of non-exceedance) of the maximum and minimum values of the input time series was developed by NIST which is accessible on the NIST website (Main 2011). The function of “maxminqnt” is called within MATLAB as follows: [max_qnt, min_qnt] = maxminqnt (X, dur_ratio, CDF_qnt) (14) This function was used to calculate 95% peak quantile of the maximum (max_qnt) and minimum (min_qnt) values of time series of pressure coefficients. As can be seen in equation (5-28), this function requires three input arguments. The first input argument, “X”, stands for the time history of pressure data, and the second input argument “dur_ratio”, defines the ratio of the duration for which peaks are required to the

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