Study on Typhoon Characteristic Based on Bridge Health Monitoring System

Hindawi Publishing Corporation e Scientiﬁc World Journal Volume 2014, Article ID 204675, 11 pages http://dx.doi.org/10.1155/2014/204675

Research Article Study on Typhoon Characteristic Based on Bridge Health Monitoring System

Xu Wang,1 Bin Chen,2 Dezhang Sun,3 and Yinqiang Wu4

1 State Key Laboratory Breeding Base of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, China 2 College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China 3 Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China 4 Bureau of Public Works of Shenzhen Municipality, Shenzhen 518006, China

Correspondence should be addressed to Dezhang Sun; [email protected]

Received 25 March 2014; Revised 8 May 2014; Accepted 8 May 2014; Published 10 June 2014

Academic Editor: Ying Lei

Copyright © 2014 Xu Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Through the wind velocity and direction monitoring system installed on Jiubao Bridge of Qiantang River, Hangzhou city, Zhejiang province, China, a full range of wind velocity and direction data was collected during typhoon HAIKUI in 2012. Based on these data, it was found that, at higher observed elevation, turbulence intensity is lower, and the variation tendency of longitudinal and lateral turbulence intensities with mean wind speeds is basically the same. Gust factor goes higher with increasing mean wind speed, and the change rate obviously decreases as wind speed goes down and an inconspicuous increase occurs when wind speed is high. The change of peak factor is inconspicuous with increasing time and mean wind speed. The probability density function (PDF) of fluctuating wind speed follows Gaussian distribution. Turbulence integral scale increases with mean wind speed, and itsPDF does not follow Gaussian distribution. The power spectrum of observation fluctuating velocity is in accordance with Von Karman spectrum.

1. Introduction field-measured research of wind characteristics developed later but very fast; some valuable research results had been Typhoon disaster is a major disaster in China, which has achieved in recent years [7–12]. Currently, most researches caused serious property and casualty losses annually and on near ground wind characteristics during typhoon attack threatened sustainable development along the east coast of focus on buildings; few have been performed on bridges, but China. To have a better understanding of bridge destruction it is important for wind engineering research. reasons from typhoon, wind characteristics near ground In China, health monitoring systems have been installed during typhoon attack must be studied firstly. However, on many long span bridges [13–16] to capture health states of experimental simulation of typhoon is very difficult because bridges. Among all these systems, the health monitoring of of its particularity. This has led to field measurement that has wind load is of primary importance to bridge health mon- been recognized as the most effective research method and itoring systems because the health monitoring information long-term direction in wind-resistant research of structure gathered could be used in bridge design and construction. [1].Ahugemassofdatawasaccumulatedandpartsof However, current practices of strong wind characteristics research result had been used in model of conduct in using wind load monitoring system on bridge site are still developed countries, where the field-measured research in encountering shortage. strong wind characteristics developed earlier. Database for Based on the wind data measured from the anemometer wind characteristics had been built in some countries, where positioned on Jiubao Bridge at 6 m height, Hangzhou city, the research of wind engineering is developed [2–6]. China’s near-ground wind characteristics under typhoon HAIKUI in 2 The Scientific World Journal

Experiment site Mainland China

Typhoon track

Figure 1: Experiment site and track of typhoon HAIKUI.

Jiubao bridge

Windsonic

Figure 2: The location and photo of anemometers.

2012werediscussedinthispaper,whichincludewindspeed 80 km/h of speed limit and about 1855 meters in span and direction, gust factor, turbulence intensity, peak factor, length. The whole bridge health monitoring systems which turbulence integral scale, and power spectrum of wind speed. were comprised of vehicle system, wind speed and direction Obviously, such studies are useful for promotions of wind- measurement system, and fatigue detection system were resistant design of bridge in the future. installedanddistributedonthebridge.Themeteorological data of typhoon was detected by a two-dimensional sonic 2. Site Description and Measurement anemometer in the wind speed and direction measurement system (see Figure 2). The anemometer, produced by British Characterization Gill Company, was Windsonic and has a sampling frequency ∘ of 4 Hz. Wind direction was defined north as 0 along a 2.1. Introduction of Typhoon HAIKUI. In 2012, the 11th ∘ ∘ tropical storm “HAIKUI” (named by HAIKUI, number 1211 clockwise direction varying from 0 to 360 . Specifications tropical cyclone), named from a Chinese marine animal, and parameters of anemometer were considered with the headed to the coast of East China Sea at 17:00 on August records indicated in Table 1. 17, 2012, and landed at Zhejiang province in the morning of August 8. The location of the eye was 640 km from 3. Study of the Field-Measured Wind Data southwestofZhejiangprovince,whichwasveryclosetothe test bridge. The maximum 10 min mean wind speed at 10m 3.1. Mean Wind Speeds and Wind Directions. Figures 3 and height was found to be 28 m/s, and the lowest pressure was 4 show the time history of wind speeds and directions of 980 hPa. Figure 1 shows a satellite view of the path of typhoon typhoonHAIKUIfrom0:00onAugust8,2012,to0:00August HAIKUI and the experiment site. According to the statistical 9, 2012. Because typhoon HAIKUI did not directly impact the results of meteorological data from Chinese Ministry of Civil experimental site in lateral direction, an uphill and a downhill Affairs and Disaster Reduction Office, typhoon HAIKUI of wind speed were observed in the time history, indicating caused 6 casualties in Zhejiang, Shanghai, Jiangsu, and Anhui that a peak value was recorded. provinces and forced 2.173 million people to evacuate. The Before analyzing wind characteristics, the overall sample most damaged area was Zhejiang province, resulting in 7.001 needs to be separated into several segments by a specified million people involved, 1.546 million people were forced to time interval. They are different from regulations of time move in emergency, and almost 5100 houses were destroyed. interval of mean wind speed among standards in many Jiubao Bridge was located on Qiantang River, Hangzhou. countries, such as 3 s used in America [17]andIndia[18] The construction work began on December 18, 2009, and was and 10 min used in Japan [19], Europe [20], and China [21]. completedin2012.Thebridgewasdesignedtoastandard In this paper, according to Chinese standard, wind data was bidirectional and six traffic lanes freeway, combining with separated into 144 samples, and the maximum 10min mean The Scientific World Journal 3

Table 1: Specifications of anemometer.

Brand (type) Wind speed Wind direction Measurement and structure ∘ 0.25 Hz, 0.5 Hz, 1 Hz, 2 Hz Range 0–60 m/s (116 Knots) Range 0–359 Frequency or 4 Hz ∘ Gill Accuracy ±2%; 12 m/s Accuracy ±3 at 12 m/s Parameter U and V Windsonic ∘ Measurement m/s, knots, mph, kph, Resolution 0.01 m/s (0.02 Knots) Resolution 1 unit ft/min Reaction Reaction 0.25 s 0.25 s Size 142 mm × 160 mm time time Lowest 0.01 m/s / / Wight 0.5 Kg

20 100 18 16 80 14 12 60 10 8 40 direction

Wind speed Wind 6 4 min mean wind 20 2 10 0 ) ) ) ) ) ) ) ) ) ) 08 08 08 08 09 08 08 08 08 09 ------00 00 00 00 00 08 08 08 08 08 00 00 00 00 00 : : : : : 08 08 08 08 08 : : : : : ------12 00 06 00 18 12 00 06 00 18 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 ( ( ( ( ( ( ( ( ( ( Typhoon HAIKUI Typhoon HAIKUI

Figure 3: Instantaneous wind speed during typhoon HAIKUI. Figure 6: 10 min mean wind direction during typhoon HAIKUI.

350 0 300 14 330 30 250 200 12 150 10 300 60 100 8 Wind direction Wind 50 6 0 ) ) ) ) ) 4 09 08 08 08 08 - - - - - 2 270 90 00 00 00 00 00 08 08 08 08 08 : : : : : - - - - - 00 00 06 12 18 4 2012 2012 2012 2012 2012 ( ( ( ( ( 6 Typhoon HAIKUI 8 240 120 Figure 4: Instantaneous wind direction during typhoon HAIKUI. 10 12

14 14 210 150 12 180 10 8 Mean wind speed speed 6

min mean wind Figure 7: 10 min mean wind speed versus mean wind direction. 4 10 2 ) ) ) ) ) 08 08 08 08 09 - - - - - wind speed was 14.12 m/s. Figures 5 and 6 show time histories 00 00 00 00 00 08 08 08 08 08 : : : : : - - - - - of10minmeanwindspeedandhorizontalwinddirection. 12 00 00 06 18

2012 2012 2012 2012 2012 Variationsof10minmeanwindspeedwithhorizontalwind ( ( ( ( ( Typhoon HAIKUI direction were shown in Figure 7,whichmadethevariation trendofmeanwindspeedwithhorizontalwinddirection Figure 5: 10 min mean wind speed during typhoon HAIKUI. intuitive. 4 The Scientific World Journal

After breaking down to 10 min time interval, the wind 3.3. Gust Factor. Gust factor is defined as the ratio of speed vector was resolved into three components [22], two maximum gust wind speed over average gust wind speed. It of which are orthogonal, namely, 𝑢𝑥(𝑡) and 𝑢𝑦(𝑡),andcan can be expressed as be measured synchronously by a two-dimensional sonic anemometer, denoting directions on 𝑥-axis and 𝑦-axis, max (𝑢 (𝑡)) 𝐺 (𝑡) =1+ , respectively. The horizontal wind speed 𝑈 and main wind 𝑢 𝑈 direction 𝜙 are calculated by the following formulas: (4) max (V (𝑡)) 𝐺 (𝑡) = , √ 2 2 V 𝑈= 𝑢𝑥(𝑡) + 𝑢𝑦(𝑡) , 𝑈 (𝑢(𝑡)) (V(𝑡)) 𝑢 (𝑡) where max and max are the maximum gust wind 𝑢𝑥 (𝑡) 𝑦 cos (𝜙) = , sin (𝜙) = , (1) speed in the period of 𝑡 for longitudinal and lateral fluctuates, 𝑈 𝑈 respectively. 𝑢 (𝑡) Figure 10 shows the distribution of 3 s gust factor as a 𝜙= 𝑥 + (−𝑢 (𝑡)) ⋅ 180∘, arccos 𝑈 step 𝑦 function of 10 min mean wind speed both in longitudinal (Figure 10(a))andlateral(Figure 10(b))directions.Anobvi- where step(⋅) is step function, 𝑢𝑥(𝑡) isthemeanwindspeed ous decrease of gust factor under low wind speed can be seen,buttherateofreductiongoessmallerwhenwindspeed in 10 min time interval along 𝑋-direction, and 𝑢𝑦(𝑡) has the same meaning along 𝑌-direction. becomes high. The average ratio of longitudinal gust factor to The longitudinal fluctuating wind speed 𝑢(𝑡) and lateral lateral gust factor is 0.30. fluctuating wind speed V(𝑡) are obtained from 3.4. Peak Factor. There is a similarity in the definition of peak 𝑢 (𝑡) =𝑢 (𝑡) 𝜙 (𝑡) +𝑢 (𝑡) 𝜙 (𝑡) −𝑈, 𝑥 cos 𝑦 sin factor as of gust factor. The following expression for peak (2) V (𝑡) =−𝑢(𝑡) 𝜙 (𝑡) +𝑢 (𝑡) 𝜙 (𝑡) . factor which describes the intensity of fluctuating wind speed 𝑥 sin 𝑦 cos is employed: ̂ 3.2. Turbulence Intensity. Turbulence intensity is considered 𝑈𝑡 −𝑈 𝑔𝑢 = , (5) the relative index for intensity of turbulence and then deter- 𝜎𝑢 mined as ̂ 𝜎𝑖 where 𝑈𝑡 is the maximum value of 𝑡 min average wind speed 𝐼𝑖 = (𝑖=𝑢,V,𝑤) , (3) 𝑈 of the longitudinal component of fluctuating wind velocity record and 𝜎𝑢 is the corresponding standard deviation. where 𝜎𝑖 is the standard deviation of fluctuating wind speed The quantity of peak factor was determined from the for component 𝑖(𝑖=𝑢,V,𝑤). three-second averages, shown as a function of time during Similar diurnal patterns of 10 min fluctuating wind speed a one-day period coincident with the passage of typhoon distribution are shown in longitudinal (Figure 8(a))and HAIKUI (Figure 11)and10minmeanwindspeed(Figure 12). lateral (Figure 8(b)) directions. The changes in turbulence In each figure, the distribution of the data is in the range of intensities are slow between 00:00 and 12:00 on August 8, 0.6 and 2.5; the average and standard deviations are 1.52 and with average turbulence intensities being 0.47 and 0.40 and 0.34, respectively. A noticeable decrease was found from the maximum values being 0.71 and 0.67 in longitudinal and analysis of the average peak factor compared with Huang’s lateral directions, respectively. In both directions, a sharp results [12]. decrease is found in the period between 12:00 and 20:00 (the lowest average intensities being 0.16 and 0.13, resp.) before an increase in the period between 20:00 and 00:00 on August 9 3.5. Probability Density Distribution. The probability density (variation ranging from 0.1 to 0.7). function of wind speed fluctuations is customarily with Figure 9 shows both longitudinal and lateral turbulence Gauss hypothesis. However, a similar statistical description intensity as a function of 10 min mean wind speed. The ofthewindspeedfluctuationsisgenerallythelackof overall turbulence intensities reduce with decreased mean certainty when applied to strong typhoon. Analysis of the wind speed. It is clear from Figure 9 that an obvious decrease wind speed fluctuations in longitudinal (Figure 13)andlateral is found in turbulence intensities before mean wind speed (Figure 14) directions confirms a fairly good agreement with approaching 8 m/s, but the changes become small when mean a Gaussian distribution through moment estimation method. wind speed is greater than 8 m/s. Itisbelievedthewindspeedfluctuationsareexaminedas Table 2 shows the ratio of average longitudinal turbulence Gaussian processes in this region of wind speed. intensity to average lateral turbulence intensity in both domestic and international field research results. It can be 3.6. Turbulence Integral Scale. Turbulence is a three-dimen- seen from Table 2 that the ratio of the case is slightly larger sional spatial structure, and its nine parameters correspond than that in Peng et al. [12]andFuetal.’s[23]resultsbutquite to fluctuating wind speeds in longitudinal 𝑢,lateralV,and 𝑥 𝑦 close to the results from normal storm wind and typhoon by vertical 𝑤 directional components. For example, 𝐿𝑢, 𝐿𝑢,and 𝑧 Li et al. [9], Cao et al. [6],andShiauandChen[24–26]. 𝐿𝑢 are the average integral scales of longitudinal-dependent The Scientific World Journal 5

1.0 1.0 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 Turbulence intensity Turbulence Turbulence intensity Turbulence 0.2 0.2 0.1 0.1 0.0 0.0 ) ) ) ) ) ) ) ) ) ) 08 08 08 08 09 08 08 08 08 09 ------00 00 00 00 00 00 00 00 00 00 08 08 08 08 08 08 08 08 08 08 : : : : : : : : : : ------12 00 18 00 06 12 00 00 06 18 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 ( ( ( ( ( ( ( ( ( ( Longitudinal Lateral (a) Longitudinal (b) Lateral

Figure 8: Variation of turbulence intensity during typhoon HAIKUI.

0.8 0.8

0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 Turbulence intensity Turbulence intensity Turbulence 0.2

0.2 0.1

0.1 0.0 2 468 10 12 14 2 4 6 8 10 12 14 10 min mean wind speed 10 min mean wind speed Longitudinal Lateral (a) Longitudinal (b) Lateral

Figure 9: Variation of turbulence intensities with wind speed.

Table 2: Ratios of turbulence intensity among the turbulence components.

Research man Wind Height (m) 𝐼𝑢 : 𝐼V Sites Tieleman [27] Normal strong wind 5 1 : 0.80 Holland Cao et al. [6] Typhoon Maemi 10 1 : 0.83 Japan Typhoon Zeb [24] 1 : 0.98 Shiau Typhoon Babs [25]26 1 : 0.78 Taiwan, China Normal strong wind [26] 1 : 0.78 Fu et al. (2008) [23] Typhoon Sanvu 10 1 : 0.7 Guangzhou, China 10 1 : 0.66 Peng et al. [12] Typhoon Muifa 20 1 : 0.65 Shanghai, China 40 1 : 0.74 Present test Typhoon HAIKUI 6 (upper bridge) 1 : 0.85 Hangzhou, China 6 The Scientific World Journal

2.2 1.2

2.0 1.0

1.8 0.8

1.6 0.6 Gust factor Gust Gust factor Gust 1.4 0.4

1.2 0.2

1.0 0.0 2 4 6 8 10 12 14 2 4 6 8 10 12 14 10 min mean wind speed 10 min mean wind speed Longitudinal Lateral (a) Longitudinal (b) Lateral

Figure 10: Variation of gust factors with wind speed.

2.6 3.0 2.4 2.2 2.5 2.0 1.8 2.0 1.6 1.4

Peak factor Peak 1.5 Peak factor Peak 1.2 1.0 1.0 0.8 0.6 0.5 ) ) ) ) ) 2 4 6 8101214 07 07 07 07 08 - - - - - 00 00 00 00 00 10 08 08 08 08 08 : : : : : min mean wind speed - - - - - 00 06 12 18 00 Peak factor 2012 2012 2012 2012 2012 ( ( ( ( ( Peak factor Figure 12: Variation of peak factor with wind speed.

Figure 11: Variation of peak factor during typhoon HAIKUI. be calculated by autocorrelation function integral method depending on Taylor hypothesis and interpreted as turbulence component fluctuations in longitudinal, lateral, 𝑈 𝛼 𝐿𝑥 = ∫ 𝑅 (𝜏) 𝑑𝜏, 𝑖 =𝑢, V,𝑤, and vertical directions, respectively. Turbulence integral scale 𝑖 2 (7) 𝑥 𝜎𝑖 0 𝐿𝑖 is mathematically defined as where 𝑅(𝜏) is the autocorrelation function and 𝛼 is the 1 ∞ variable when autocorrelation coefficient drops to 0.054 [ ]. 𝐿𝑥 = ∫ 𝑅 (𝑥) 𝑑𝑥, 𝑖 =𝑢, V,𝑤, 𝑖 2 𝑖 𝑖 (6) In Figure 15,turbulenceintegralscalesoflongitudinaland 𝜎 0 1 2 𝑖 lateral components determined from 10 min average show a clear dependency on 10 min mean wind speed, and increasing 𝑅 (𝑥) where 𝑖1𝑖2 isthecovariancefunctionoffluctuating quantity is recorded by increased 10 min mean wind speed. components in two positions. For 10 min mean wind speed is greater than 8 m/s, turbulence In general, spatial correlation needs to be transferred integral scales drastically change and distributed in a large into time correlation by Taylor hypothesis due to the fact region between 100 and 250. From the statistical analysis that simultaneously measuring recorded points in space are of Figure 16 for the turbulence integral scales both in lon- complex and difficult. Thus, turbulence integral scale can gitudinal (Figure 16(a))andlateral(Figure 16(b))directions, The Scientific World Journal 7

U = 13.56 m/s U=7.88m/s

0.1 0.1 Probability density (s/m) density Probability Probability density (s/m) density Probability

0.01 0.01 −8 −6 −4 −202468 −10 −8 −6 −4 −2 0 2 4 6 8 10 u (m/s) u (m/s)

Measured Measured Gaussian Gaussian (a) (b)

Figure 13: Probability density function of longitudinal wind speed fluctuations.

U=6.48 m/s U = 12.42 m/s

0.1 0.1 Probability density (s/m) density Probability (s/m) density Probability

0.01 0.01 −8 −6 −4 −202468 −8 −6 −4 −2 0 2 4 6 8 (m/s) (m/s)

Measured Measured Gaussian Gaussian (a) (b)

Figure 14: Probability density function of lateral wind speed fluctuations. a similarity of probability density distribution is observed in Whereas 𝑋(𝑡) denotes a stationary random process, the each component; as it is obviously asymmetrical and differs correlation function can be derived as from a standardized Gaussian distribution, in particular at 𝑅 (𝜏) =𝐸[𝑋 (𝑡) 𝑋 (𝑡+𝜏)] , low turbulence integral scales which is between 20 and 40, the 𝑋𝑋 (9) probability is the largest with most data locate in this region. where 𝑅𝑋𝑋 is the autocorrelation function and 𝜏 is the delaying time. 3.7. Correlation. Autocorrelation describes the correlation of Indicating correlation intensity of wind speed fluctua- two dependent values of a time series at different moments. tions for different directions, cross-correlation coefficient can 𝑋(𝑡) is here defined as a time series, and the correlation be expressed as functioncanbederivedas 𝑅𝑖𝑗 (0) 𝐶𝑅(𝑖𝑗) = , √𝑅 (0)√𝑅 (0) (10) 𝑅𝑋𝑋 (𝑡1,𝑡2) =𝐸[𝑋 (𝑡1) 𝑋 (𝑡2)] . (8) 𝑖𝑖 𝑗𝑗 8 The Scientific World Journal

250 250

200 200

150 150

100 100 Turbulence integral scale integral Turbulence Turbulence integral scale integral Turbulence 50 50

0 0 4 8 12 4 8 12 U (m/s) U (m/s)

Longitudinal Lateral (a) Longitudinal (b) Lateral

Figure 15: Variation of turbulence integral scale with 10 min mean wind speed.

0.020

0.015 0.015

0.010 0.010

Probability density Probability 0.005 density Probability 0.005

0.000 0.000 0 50 100 150 200 0 50 100 150 200 Turbulence integral scale Turbulence integral scale

Longitudinal Lateral (a) Longitudinal (b) Lateral

Figure 16: Probability density function of turbulence integral scale.

where 𝐶𝑅(𝑖𝑗) is cross-correlation coefficient of wind speed 3.8. Power Spectra of Wind Speed Fluctuations. Turbulent fluctuations in 𝑖 and 𝑗 directions, 𝑅𝑖𝑗 is cross-correlation power spectrum is a describer of turbulent energy distri- function of wind speed fluctuations in 𝑖 and 𝑗 directions, bution in frequency domain and characteristics of wind and 𝑅𝑖𝑖 and 𝑅𝑗𝑗 are autocorrelation functions of wind speed fluctuation. It can be expressed as28 [ ] 𝑖 𝑗 fluctuations in and directions, respectively. 𝑆 (𝑛,) 𝑧 𝐴 𝑢 = , The autocorrelation coefficients for both longitudinal and 𝑈∗2 (1 + 𝐵𝑓𝛽)𝛾 (11) lateral wind speed fluctuations are plotted in Figure 17 at 0 the average level of 10 min time interval. It is found that a where 𝑛 is frequency; 𝑧 is the observed height; 𝑓 denotes noticeable similarity of tendency is observed from the two reduced frequency derived as 𝑓 = 𝑛𝑧/𝑈(𝑧);and𝐴, 𝐵, 𝛽,and plots; in particular with 𝜏 increases, the autocorrelation coef- 𝛾 are undetermined parameters. ficients for both directions decrease, whereas the longitudinal Functions of power spectra densities of wind speed fluc- autocorrelation coefficient is shown slightly larger than the tuations were expressed in accordance with the Kolmogorov lateral autocorrelation coefficient at the same delaying time principle as Davenport spectrum, Von Karman spectrum, 𝜏. Simiu spectrum, Kaimal spectrum, and Harris spectrum. The Scientific World Journal 9

1 shown at wind speed of 13.56 m/s and 7.88m/s, presenting a good agreement with the Karman empirical spectra for the whole set of data. However, the PSD data estimated by field measurement are slightly larger than that by empirical method when the reduced frequency 𝑛𝑧/𝑈(𝑧) is smaller than

coefficients 0.1 0.1 or larger than 0.1. Autocorrelation

1 10 100 4. Conclusions 𝜏 (s) Through a full-scale wind speed and direction monitoring Longitudinal system on Jiubao Bridge in Hangzhou city, this paper presents Lateral a reliable study of wind speed and direction characteristics Figure 17: Autocorrelation coefficients of longitudinal and lateral during typhoon HAIKUI, such as time histories of mean wind wind speed fluctuation component. speed and direction, turbulence intensity, peak factor, gust factor, probability distribution of fluctuating velocity component, correlation among fluctuating velocities, turbulence Basedonthecomparisonswithfieldmeasurementandwind integral scale, and the power spectrum of fluctuating velocity, tunnel test, Von Karman spectrum is believed as the best and the results are summarized as follows. fitted function for wind speed fluctuations. The longitudinal fluctuating component can be expressed as[25, 29] (1) Turbulence intensities decrease with increased anemometer elevation for both longitudinal and lat- 󸀠2 𝑥 2𝑢 𝐿𝑢 eral components of mean wind speed. A remark- 𝑆𝑢 (𝑛) = , 2 5/6 (12) able decrease of turbulence intensity is shown with 𝑈[1 + (2𝑐𝑛𝐿𝑥 /𝑈) ] 𝑢 increased mean wind speed, typically at low mean 𝑢<8 𝑢 wind speeds ( m/s), but an unknown tendency where 𝑈 is mean wind speed; 𝐿𝑥 is longitudinal turbulence when mean wind speed exceeds 8 m/s. The ratio of integral scale; 𝑢󸀠2 is standard deviation of the longitudinal turbulence intensities of longitudinal component to fluctuating component; and constant coefficient 𝑐 is 4.2065. lateral component is 0.85 in this paper, indicating a Power spectral densities of the longitudinal and lateral clear similarity with the results by Tieleman, Cao, fluctuating components are derived from the same character- and Bao-Shi, whereas a certain deviation from Huang istic of turbulence hypothesis: et al.’s results [11]. 1 𝑛𝑑𝑆 (𝑛) (2) Gust factors decrease with mean wind speed. An obvi- 𝑆 (𝑛) =𝑆 (𝑛) = [𝑆 (𝑛) − 𝑢 ]. V 𝑤 2 𝑢 𝑑𝑛 (13) ous decrease of the change rate is found at low wind speeds, but wind speed seems to have less impact on 󸀠2 󸀠2 󸀠2 𝑥 𝑦 𝑧 For isotropic turbulences, V = 𝑤 = 𝑢 , 𝐿𝑢 =𝐿V =𝐿𝑤, thechangeratewhenitbecomeshigh.Theaverage some equations are obtained from plugging (12)into(13): ratio for gust factor of longitudinal component to 2 lateral component is 0.30. The upper and lower peak 󸀠2 𝑦 𝑦 V 𝐿V [1 + (8/3) (𝑐𝑛𝐿V/𝑈) ] factors are recorded as 0.6 and 2.5; the average and 𝑆 (𝑛) = , V 11/6 standard deviation are 1.52 and 0.34, respectively. A 𝑦 2 𝑈[1 + (2𝑐𝑛𝐿V/𝑈) ] noticeable decrease is found from the analysis of the (14) average peak factor compared with Peng et al.’s results 2 󸀠2 𝑧 𝑧 𝑤 𝐿𝑤 [1 + (8/3) (𝑐𝑛𝐿𝑤/𝑈) ] [12]. 𝑆𝑤 (𝑛) = . 2 11/6 (3) Probability density distribution of wind speed fluc- 𝑈[1 + (2𝑐𝑛𝐿𝑧 /𝑈) ] 𝑤 tuations at different wind speeds agrees well with a Gaussian distribution, which means the full- 𝐿𝑥 =𝐿𝑥 = 0.5𝐿𝑥 If V 𝑤 𝑢 for isotropic turbulences, the measured recorded data of wind speed fluctuations expressions of Von Karman spectrum are derived from 𝑥 𝑥 𝑥 are in accordance with Gauss assumption. plugging 𝐿V =𝐿𝑤 = 0.5𝐿𝑢 into (14): (4) Increasing quantity of turbulence integral scales is 𝑛𝑆 (𝑛) 4𝑓 𝑢 = , recorded by increased 10 min mean wind speed. A 2 5/6 𝜎𝑢 (1 + 70.8𝑓2) similarity of probability distributions of turbulence (15) integral scales of the three components is that each 4𝑓 (1 + 755.2𝑓2) distribution is obviously asymmetrical and does not 𝑛𝑆𝑖 (𝑛) = ,𝑖=V,𝑤. follow a standardized Gaussian distribution. 2 2 11/6 𝜎𝑖 (1 + 283.2𝑓 ) (5) Power spectral densities of recorded wind speed In Figures 18 and 19, field-measured power spectral fluctuations are fairly good in agreement with Von densities of longitudinal and lateral fluctuate components are Karman spectrum. 10 The Scientific World Journal

1 1

U = 13.56 m/s U = 13.56 m/s

0.1 0.1 2 2 (n)/𝜎 (n)/𝜎 u nS nS 0.01 0.01

1E − 3 1E − 3 1E − 3 0.01 0.1 1101E − 3 0.01 0.1 110

nLu/U nL/U

Measured Measured Karman Karman (a) Longitudinal (b) Lateral

Figure 18: Power spectral density of fluctuation wind speed components𝑈 ( = 13.56 m/s).

1 1

U=7.88m/s U=7.88m/s

0.1 0.1 2 2 (n)/𝜎 (n)/𝜎 u nS nS 0.01 0.01

1E − 3 1E − 3 1E − 3 0.01 0.1 110 1E − 3 0.01 0.1 110

nLu/U nL/U

Measured Measured Karman Karman (a) Longitudinal (b) Lateral

Figure 19: Power spectral density of fluctuation wind speed components𝑈 ( = 7.88m/s).

Conflict of Interests Foundation (Q12E080026) which are gratefully acknowl- edged. The authors declare that there is no conflict of interests regarding the publication of this paper. References

[1] M. Gu, The Research Process and Basic Scientific Issues about Acknowledgments Civil Structure, Science Press, Beijing, China, 2006. ThisprojectissupportedbyOpenFundofStateKey [2] C. S. Durst, “Wind speeds over short periods of time,” The Laboratory Breeding Base of Mountain Bridge and Tun- Meteorological Magazine,vol.89,pp.181–186,1960. nel Engineering (CQSLBF-Y14-15), the National Program [3]N.Kato,T.Ohkuma,J.R.Kim,H.Marukawa,andY.Niihori, on Key Basic Research Project (973 Program) Grant no. “Fullscalemeasurementsofwindvelocityintwourbanareas 2012CB723305, the Chinese National Natural Science Foun- using an ultrasonic anemometer,” Journal of Wind Engineering dation (51308510), and Zhejiang Provincial Natural Science and Industrial Aerodynamics,vol.41,no.1–3,pp.67–78,1992. The Scientific World Journal 11

[4] R. G. J. Flay and D. C. Stevenson, “Integral length scales in [22] E. Simiu and R. H. Scanlan, Wind Effects on Structures: Funda- strong winds below 20 m,” Journal of Wind Engineering and mentals and Applications to Design,JohnWiley&Sons,New Industrial Aerodynamics, vol. 28, no. 1–3, pp. 21–30, 1988. York, NY, USA, 1996. [5] E. C. C. Choi and F. A. Hidayat, “Gust factors for thunderstorm [23]J.Y.Fu,Q.S.Li,J.R.Wu,Y.Q.Xiao,andL.L.Song, and non-thunderstorm winds,” Journal of Wind Engineering and “Field measurements of boundary layer wind characteristics Industrial Aerodynamics,vol.90,no.12–15,pp.1683–1696,2002. and wind-induced responses of super-tall buildings,” Journal of [6]S.Y.Cao,Y.Tamura,N.Kikuchi,M.Saito,I.Nakayama,andY. Wind Engineering and Industrial Aerodynamics,vol.96,no.8-9, Matsuzaki, “Wind characteristics of a strong typhoon,” Journal pp. 1332–1358, 2008. of Wind Engineering and Industrial Aerodynamics,vol.97,no.1, [24] B.-S. Shiau, “Velocity spectra and turbulence statistics at the pp. 11–21, 2009. northeastern coast of Taiwan under high-wind conditions,” [7]Q.S.Li,Y.Q.Xiao,J.Y.Fu,andZ.N.Li,“Full-scale Journal of Wind Engineering and Industrial Aerodynamics,vol. measurements of wind effects on the Jin Mao building,” Journal 88, no. 2-3, pp. 139–151, 2000. of Wind Engineering and Industrial Aerodynamics,vol.95,no.6, [25] B.-S. Shiau and Y.-B. Chen, “In situ measurement of strong wind pp.445–466,2007. velocity spectra and wind characteristics at Keelung coastal area [8]Q.S.Li,Y.Q.Xiao,andC.K.Wong,“Full-scalemonitoringof of Taiwan,” Atmospheric Research,vol.57,no.3,pp.171–185,2001. typhoon effects on super tall buildings,” Journal of Fluids and [26] B.-S. Shiau and Y.-B. Chen, “Observation on wind turbulence Structures,vol.20,no.5,pp.697–717,2005. characteristics and velocity spectra near the ground at the [9]Q.S.Li,Y.Q.Xiao,C.K.Wong,andA.P.Jeary,“Field coastal region,” Journal of Wind Engineering and Industrial measurements of typhoon effects on a super tall building,” Aerodynamics,vol.90,no.12–15,pp.1671–1681,2002. Engineering Structures,vol.26,no.2,pp.233–244,2004. [27] H. W. Tieleman, “Strong wind observations in the atmospheric [10] Q. S. Li, L. Zhi, and F. Hu, “Boundary layer wind structure from surface layer,” Journal of Wind Engineering and Industrial observations on a 325 m tower,” Journal of Wind Engineering and Aerodynamics,vol.96,no.1,pp.41–77,2008. Industrial Aerodynamics, vol. 98, no. 12, pp. 818–832, 2010. [28]P.J.Richards,R.P.Hoxey,andJ.L.Short,“Spectralmodels [11] P. Huang, X. Wang, and M. Gu, “Field experiments for wind for the neutral atmospheric surface layer,” Journal of Wind loads on a low-rise building with adjustable pitch,” International Engineering and Industrial Aerodynamics,vol.87,no.2-3,pp. Journal of Distributed Sensor Networks,vol.2012,ArticleID 167–185, 2000. 451879, 10 pages, 2012. [29] T. Karman, “Progress in the statistical theory of turbulence,” [12] H. Peng, X. Wang, and M. Gu, “Study on near-ground wind Proceedings of the National Academy of Sciences,vol.34,pp.530– characteristics of a strong typhoon-wind speed, turbulence 539, 1948. intensities, gust factors and peak factors,” Disaster Advances,vol. 6,no.5,pp.3–18,2013. [13]T.H.Yi,H.N.Li,andM.Gu,“Recentresearchandapplications of GPS based technology for bridge health monitoring,” Science China Technological Sciences,vol.53,no.10,pp.2597–2610,2010. [14] T.-H. Yi, H.-N. Li, and M. Gu, “Optimal sensor placement for structural health monitoring based on multiple optimization strategies,” The Structural Design of Tall and Special Buildings, vol.20,no.7,pp.881–900,2011. [15] T.-H. Yi, H.-N. Li, and M. Gu, “Wavelet based multi-step filtering method for bridge health monitoring using GPS and accelerometer,” Smart Structures and Systems,vol.11,no.4,pp. 331–348, 2013. [16] T.-H. Yi, H.-N. Li, and M. Gu, “Experimental assessment of high-rate GPS receivers for deformation monitoring of bridge,” Journal of the International Measurement Confederation,vol.46, no. 1, pp. 420–432, 2013. [17] American Society of Civil Engineers, ASCE/SEI 7–10 Minimum Design Loads for Buildings and Other Structures,American Society of Civil Engineers, Reston, Va, USA, 2010. [18] Department of Civil Engineering Indian Institute of Tech- nology Roorkee, IS:875(Part3): Wind Loads on Buildings and Structures-Proposed Draft & Commentary,IndianInstituteof Technology, Roorkee, India, 2003. [19] Architectural Institute of Japan, AIJ 2004 Recommendations for Loads on Buildings, Architectural Institute of Japan, Tokyo, Japan, 2004. [20] Technical Committee CEN/TC250 “Structural Eurocodes”, Eurocode 1: Actions on Structures-General Actions-Part 1-4: Wind Actions, British Standards Institution, London, UK, 2004. [21] GB50009-2001, Load Code for the Design of Building Structures, Architecture and Building Press, Beijing, China, 2002.