[雙語翻譯]外文翻譯--現(xiàn)存的大跨徑橋梁的風(fēng)的特性是以測量數(shù)據(jù)為基礎(chǔ)（英文）

1、KSCE Journal of Civil EngineeringVol. 9, No. 3 / May 2005pp. 219~224Structural EngineeringVol. 9, No. 3 / May 2005 ? 219 ?Wind Characteristics of Existing Long Span Bridge Based on Measured DataBy Byung Wan Jo*, Jong Chi

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8、;············ AbstractThis paper presents the wind characteristics of an existing long span bridge based on the measured wind data. The wind data obtained from

9、the two measuring stations on the Seohae bridge are processed and statistically analyzed. The Weibull distribution is used to model the wind speed on the bridge. The parameters of the Weibull distribution, k and c are es

10、timated using two methods, the regression method and the Chi-Square method. To examine the fits of two methods, the Kolmogorov-Smirnov test is used. It is found that the two methods give a good fit to the distribution of

11、 the measured wind speed data on the bridge. The dominant wind directions on the FCM bridge are northwest and east. The wind rose diagram shows that strong winds usually come from the northwest. From the Weibull distribu

12、tion for each direction, it can be known that the parameters of k and c are quite different according to the wind direction.Keywords: long span bridge, Weibull distribution, wind characteristics, wind rose diagram·&

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19、#183;····· 1. IntroductionRecently, many long span cable-supported bridges, such as the Seohae bridge (2000), the Youngjong bridge (2000) and the Kwangan bridge (2003), have been constructed in K

20、orea. These are equipped individually with integrated monitoring systems. For the Seohae bridge, many instruments were placed in the optimal sections and the bridge is being continuously monitored. In this paper, the win

21、d data measured from anemometers installed on the Seohae bridge are processed and analyzed statistically. Extreme winds are one of the most important factors in bridge engineering. Thus, wind characteristics can provide

22、the designer and the bridge manager with information on the extreme winds that might affect a structure during its lifetime. One of the probability distributions used broadly for wind engineering is the Weibull distribut

23、ion (Simiu and Scanlan, 1996). To find the wind characteristics of the bridge, the Weibull distribution is applied. Two mathematical models have been used to estimate the parameters k (shape parameter) and c (scale param

24、eter) of the Weibull distribution. This study presents the applicability of the assumed models, and discusses the variations of k and c according to seasons and wind directions.2. Methods for Estimating Parameters of Wei

25、bull DistributionImportant efforts have been made since the 1970’ s to construct an adequate statistical model for describing the wind speed frequency distribution (Rehman et al., 1994; Rohatgi et al., 1989; Stevens and

26、Smulders, 1979). The Weibull distribution provides a close approximation to the probability laws of manynatural phenomena. It has been used to represent wind speed distributions for application in wind load studies somet

27、imes. In recent years, a lot of attention has been focused on this method for wind energy applications, not only due to its greater flexibility and simplicity, but also because it can give a good fit to the experimental

28、data. This model is commonly used for wind engineering. The Weibull probability density function is given by(1)where v is the wind speed, k is a shape parameter, and c is a scale parameter. The corresponding cumulative d

29、istribution function is(2)where F represents the probability for the wind speed v lower than a certain wind speed v0. Hennessey (1977) and Justus et al. (1978) discussed several methods for estimating the parameters of t

30、he Weibull distribution, such as the moments method, the regression method, and the Chi- Square method. Dorvlo (2002) estimated the wind distributions by applying the above three methods to the actually obtained wind spe

31、ed data. In this paper, two models by using the regression method and the Chi-Square method are adopted to study the possible statistical distributions of the wind speed on the existing bridge.2.1. Regression Method The

32、cumulative distribution function, Eq. (2), can be written asf v ? ? k c - - ? ? ? ? v c - - ? ? ? ? k 1 – exp v c - - ? ? ? ? k – =F v v0 ? ? ? 1 v0 c - - - - ? ? ? ? k – exp – =*Member, Professor, Department of Civil En

33、gineering, Hanyang University, Korea (E-mail: joycon@hanmail.net) **Member, Ph.D. Candidate, Department of Civil Engineering, Hanyang University, and Highway & Transportation Technology Institute, Korea (Correspondin

34、g Author, E-mail: pjcseven@freeway.co.kr) ***Member, Ph.D. Candidate, Department of Civil Engineering, Hanyang University, Korea (E-mail: yam2kr@hanmail.net)Wind Characteristics of Existing Long Span Bridge Based on Meas

35、ured DataVol. 9, No. 3 / May 2005 ? 221 ?the mean sea level, the first measuring station (PY1 Top) at the top of the pylon has a height of 187 m, while the second measuring station (FCM) at the deck of the FCM type bridg

36、e has a height of 57 m. Each anemometer can measure wind speed and wind direction at the sampling rate of 100 Hz. At the end of each 10-minute sampling period, the statistical data such as the maximum, mean and minimum w

37、ind speeds are determined and stored in the database of the SSHMS. In this paper, the data obtained from two propeller anemometers are processed and analyzed statistically. Also, most of the data are in the form of hourl

38、y mean wind speeds and directions.3.3. Statistical Analysis of Wind Speed For the years 2002 ~ 2004, the measured wind speed data are plotted in Fig. 4. Frequencies and probability densities of the measured data are list

39、ed in Table 1. The second and fourth column “ Frequency”are the number of the wind speed data gathered at the measuring stations PY1 Top and FCM respectively. The third and fifth column “ Probability Density”are computed

40、 through the relationship, fi= ni/N, where N is the total of the wind speed data and ni is the number of the wind speed data within the range of (i) m/s ~ (i-1) m/s. Bar graphs of Fig. 5 show the probability density base

41、d on the measured wind speed data. It can be seen that the actual distribution of the wind speed is right-skewed as indicated by the histogram. It is because of the fact that extreme wind speeds are rare events. Due to t

42、he skewness of the distributions, the Gaussian model with symmetric shape is considered inadequate for describing the distribution of the wind speed (Lin et al., 1999). Then, the distributions can be modeled using the We

43、ibull distribution. The two methods described above, the regression method and the Chi-Square method, are used to estimate the parameters of the Weibull distribution, k and c. The estimated shape and scale parameters are

44、 listed in Table 2. At the two stations, it can be seen that the estimated parameters for the two methods are slightly different. The results of fitting the hourly mean wind speed with both methods are shown together in

45、the same figure with the measured probability density (Fig. 5). It can be seen that the two assumed models can approximate the actual distribution satisfactorily. To examine the fits of the two models in detail, the cum

46、ulative distribution densities are calculated and shown in Fig. 6. The Kolmogorov-Smirnov (K-S) test is a widely used goodness-of- fit test (Ang and Tang, 1975). The basic procedure involves theFig. 3. Propeller-Type Ane

47、mometer at Deck of FCM Bridge (FCM)Fig. 4. Measured Wind Speed DataTable 1. Frequencies and Probability Densities of Measured Wind Speed DataWind Speed, v (m/s)PY1 Top FCMFrequency, nProbability Density, fFrequency, nP

48、robability Density, f0 9 0.0004 0 0.00001 1469 0.0579 823 0.03272 3630 0.1431 4266 0.16943 3568 0.1406 4079 0.16204 3145 0.1240 3541 0.14065 2802 0.1104 2941 0.11686 2454 0.0967 2436 0.09677 2168 0.0855 1879 0.07468 1629

49、 0.0642 1403 0.05579 1215 0.0479 1072 0.042610 975 0.0384 786 0.031211 699 0.0276 564 0.022412 551 0.0217 373 0.014813 414 0.0163 301 0.012014 252 0.0099 252 0.010015 165 0.0065 196 0.007816 83 0.0033 121 0.004817 75 0.0