Research Article Long-Term Vibration Monitoring of

Hindawi Publishing Corporation International Journal of Distributed Sensor Networks Volume 2013, Article ID 804516, 9 pages http://dx.doi.org/10.1155/2013/804516

Research Article Long-Term Vibration Monitoring of Cable-Stayed Bridge Using Wireless Sensor Network Khac-Duy Nguyen,1 Jeong-Tae Kim,1 and Young-Hwan Park2 1 2

Department of Ocean Engineering, Pukyong National University, Busan 608-737, Republic of Korea Structural Engineering Division, Korea Institute of Construction Technology, Gyeonggi-do 411-472, Republic of Korea

Correspondence should be addressed to Jeong-Tae Kim; [email protected] Received 5 August 2013; Accepted 17 October 2013 Academic Editor: Hong-Nan Li Copyright © 2013 Khac-Duy Nguyen 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. Wireless sensor networks provide a lot of advantages for vibration monitoring of bridges. The installation time and implementation cost of the monitoring system are greatly reduced by the adoption of this innovative technology. This paper presents a long-term vibration monitoring of the Hwamyung cable-stayed bridge in Korea using an Imote2-platformed wireless sensor network. First, the wireless vibration monitoring system of the bridge is briefly described by outlining the test history, the design of wireless sensor system, and the sensor deployment. Next, the vibration behaviors of the bridge are experimentally examined with respect to the variation of temperature, the wind loads induced by typhoons, and the change of bridge deck mass caused by pavement.

1. Introduction Over the two past decades, the structural health monitoring has become increasingly important for the service life of civil infrastructures. In order to secure the structural integrity, many robust sensing technologies and SHM methods have been developed [1–3]. Recently, the interest in the safety assessment of cable-stayed bridges has been increasing [4]. For a cable-stayed bridge, critical damage may occur in main structural components such as a deck, cables, and pylons resulting from stiffness loss, crack growth, and concrete degradation. Critical damage in cable-anchorage subsystems may include cable force loss, anchorage damage, and anchorage force loss. The conventional monitoring system mainly requires a number of sensors, a huge amount of signal transmitting wires, data acquisition instruments, and centralized data storage servers. Therefore, the cost associated with installation and maintenance of the monitoring system is very high. Recently, low-cost, stand-alone smart sensors have been developed by many research groups. Straser and Kiremidjian [5] proposed the design of a low-cost wireless modular monitoring system. Since then, many researchers have

developed wireless sensors based on a variety of sensor platforms [6–9]. By adopting those smart sensors for monitoring in large structures, the costs are greatly reduced and the data processing and information management will be very effective by ways of sensing and onboard computation, wireless transmission, and green energy harvesting. However, there have been a few full-scale implementations of wireless sensors for bridge monitoring so far [4, 6, 10]. Also, only a few studies have been performed on long-term monitoring using wireless sensors, which should provide solid guidelines for the practical applications. This paper presents a long-term vibration monitoring of the Hwamyung cable-stayed bridge in Korea using an Imote2platformed wireless sensor network. First, the wireless vibration monitoring system of the bridge is briefly described by outlining the test history, the design of wireless sensor system, and the sensor deployment. Next, the vibration behaviors of the bridge are experimentally examined with respect to the variation of temperature, the wind loads induced by typhoons, and the change of bridge deck mass caused by pavement.

2

International Journal of Distributed Sensor Networks Table 1: Experiment history on the Hwamyung bridge.

Period

Work phase

May 2011

Description

Event

(i) Vibration sensor design

Wireless sensor system design

—

(ii) Operation software design June 2011

(i) Sensor placement design

Sensor system setup

—

(ii) Internet-based remote sensing setup (i) Wireless communication test June-July 2011

(ii) Solar power harvesting evaluation

Performance evaluation

Typhoon Meari (June 2011)

(iii) Response signal measurement

August 2011–June 2013

(iv) Modal analysis and system identification (i) Cable-anchorage monitoring under temperature variation

Long-term monitoring

(ii) Effect of dead load (iii) Bridge behavior under typhoons

N E

W S

(i) Asphalt pavement (February 2012) (ii) Typhoons Bolaven, Tembin, and Sanba (August-September 2012)

measurement, and the feasibility of modal analysis and system identification are examined during June to July 2011. For the long-term monitoring of the bridge, several tasks such as monitoring cable-anchorage motions under temperature variation and investigating bridge behaviors under several typhoon and deck-mass changes have been performed during August 2011 to June 2013. During the entire monitoring periods, the temperature has varied in a wide range of about −10∘ C to 45∘ C.

Figure 1: Hwamyung cable-stayed bridge.

2. Wireless Monitoring System of Hwamyung Cable-Stayed Bridge 2.1. Test History. Field tests were conducted on Hwamyung cable-stayed bridge, as shown in Figure 1, crossing Nakdong River between Busan and Gimhae, Korea. The bridge was constructed by Hyundai Engineering & Construction Co., Ltd., from December 2004 to July 2012. The bridge consists of three spans including a 270 m central main span between two pylons and two 115 m side spans connecting east and west approaches. The clearance of the deck is 14.7 m from the water level. The height of two pylons is 65 m from the deck level. The box girder is 27.8 m in width and 4 m in height. The bridge has a total 72 cables, positioning 36 cables at each pylon. The test history on the bridge is summarized in Table 1. The research project can be outlined in four work phases: design of wireless sensor system, sensor system setup, performance evaluation, and long-term vibration monitoring. For the design of wireless sensor system, the vibration sensor and operation software are designed during May 2011. For the sensor system setup, the location and orientation of sensor and the internet-based remote sensing schematic are designed during June 2011. For the performance evaluation, the functionality of wireless communication, the durability of solar power harvesting, the flexibility of response signal

2.2. Design of Wireless Sensor Nodes. A multiscale vibration sensor node on Imote2 platform was designed as schematized in Figure 2(a). The high performance sensor platform, Imote2, provided by Memsic Co. [11], was selected to control the operation of the sensor node. For vibration monitoring, SHM-A, SHM-AS, and SHM-H sensor boards were selected. The SHM-A and SHM-H sensor boards were developed for acceleration measurement by University of Illinois at Urbana-Champaign (UIUC) [8, 12]. The SHM-AS sensor board was modified from SHM-A sensor board in order to measure PZT’s dynamic strain signal. The solar-powered energy harvesting is implemented by employing solar panel and rechargeable battery. Figure 2(b) shows a prototype of the multiscale sensor node which consists of three layers as follows: (1) X-bow battery board, (2) Imote2 sensor platform, and (3) SHM-H board or SHM-A (AS) board. The Imote2 platform is built with 13–416 MHz PXA271 XScale processor. This processor integrates with 256 kB SRAM, 32 MB flash memory, and 32 MB SDRAM. A 2.4 GHz surface mount antenna is equipped for each Imote2 platform. For long-term operation of wireless sensor nodes, energy harvesting is employed. For powering by rechargeable battery, the hardware of X-bow battery board is modified so that a solar panel (e.g., SPE-350-6 with 9 V and 350 mA) can be integrated with the Imote2 sensor platform. For deck’s responses, SHM-H sensor board is adopted to employ a SD1221L-002 accelerometer for high-sensitivity channel, the input range ±2 g, the sensitivity 2 V/g, and

International Journal of Distributed Sensor Networks

3

Acceleration-PZT strain sensor boards SHM-H

SHM -H/SHM-A (AS) board

SHM-A(AS)

1.8 V JTAG connectors 3.2 V Imote2 sensor platform USB 4.6–10 V 115–1400 mA

Imote2 platform

3.2–4.5 V Battery board

Rechargeable battery and battery board

Solar panel

(a) Schematic of sensor node

(b) Sensor prototype

Figure 2: Design of wireless multiscale sensor node.

P1

Gimhae

Busan

BLC02 BLC07 Z

BLC17 C1

BRC08 C2

C3

C4

BRC17 C5

Y X

D1

D2

D3

D4

D5

Gateway node SHM-H SHM-AS

Figure 3: Field sensor layout on Hwamyung cable-stayed bridge.

the output noise 5 𝜇g/√Hz. For cable’s acceleration measurement, the SHM-A employs the triaxial LIS344ALH accelerometer whose sensitivity is relatively lower and output noise is quite higher than those of the SHM-H. In this study, a modified SHM-AS sensor board is designed to measure PZT’s dynamic strain signals. The dynamic strain signal from PZT sensor is passed through a signal conditioner circuit to produce an analog signal of 0∼3.3 V.

2.3. Sensor Deployment on the Bridge. As shown in Figure 3, wireless sensor networks were deployed on Hwamyung bridge. Two hardware configurations of Imote2-based smart sensor nodes which are gateway node and leaf node are designed to monitor the responses of the bridge. A gateway node consists of an Imote2 platform with a 2.4 GHz antenna and an IBB2400 interface board connected to the PC via a USB cable. A leaf node consists of an Imote2 platform with a 2.4 GHz antenna, a sensor board (e.g., SHM-AS and SHM-H), and an X-bow battery board powered by a Powerizer Li-ion polymer rechargeable battery with a solar panel. For vibration monitoring, twelve sensor nodes (Imote2/ SHM-AS/SHM-H) including 11 leaf nodes and 1 gateway node were installed. Among the leaf nodes, 6 Imote2/SHM-H sensors were placed at five locations of the deck and at the top

of the west pylon (i.e., D1∼D5 and P1), and 5 Imote2/SHMAS sensors were placed on five selected cables (i.e., C1∼ C5). For each sensor board (i.e., SHM-H or SHM-AS), three axes accelerations were measured. A spared channel in SHM-AS sensor board was used for measuring dynamic strain in cables. Also for dynamic strain measurement, five PZT patches were bonded on aluminum tubes covering the five selected cables. The vibration signals were measured in duration of 600 seconds with sampling rate 25 Hz. In summary, the total 33 channels of acceleration and 5 channels for dynamic strain were monitored in Hwamyung bridge. All sensor nodes and base stations are placed in plastic boxes which have waterproof rubber gaskets to prevent them from sun heating, being absorbed and other damage caused by harsh environmental conditions such as rain, wind, and dust at the field site. Solar panels were mounted on the sensor boxes to harvest the solar energy for recharging the Li-ion polymer batteries embedded to the sensor boards.

3. Experimental Modal Identification of the Bridge 3.1. Modal Identification Method. In order to extract experimental natural frequencies and mode shapes of the deck

4


10−2

Max: 0.3 mg

0.6

10−4 PSD

Acceleration (g)

×10−3 1.2

0 −0.6 −1.2

100

200

300 Time (s)

400

500

1.086 1.465

−8

10−10

600

0.720

10−6 10

0

0.440

0

(a) Acceleration response

0.5

1 Frequency (Hz)

1.5

2

(b) Power spectral density

Figure 4: Vibration responses of the bridge: deck D2.

100

Max: 14 mg

0.02

10−2

PSD

Acceleration (g)

0.04

0

4.639

10−4 10−6

−0.02 −0.04

3.113

1.563

0

100

200

300 Time (s)

400

500

600

(a) Acceleration response

10−8

0

1

2

3 4 Frequency (Hz)

5

6

(b) Power spectral density

Figure 5: Vibration responses of the bridge: cable C4.

and the pylon, the SSI method [13] was performed. The SSI method utilizes the singular value decomposition (SVD) of a block Hankel matrix with cross correlation matrix of responses as follows:

H = [U1 U2 ] [

𝑇 Σ1 0 V1 ] [ 𝑇 ] ≈ U1 Σ1 V𝑇1 , 0 0 V 2

(1)

where H is the Hankel matrix; U, V are the unitary matrices; and Σ1 is the singular value matrix. The modal parameters can be identified from a system matrix which is determined from the SVD algorithm. A stabilization chart is used to find a suitable system order with the criteria which classify a mode as stable mode, unstable mode, or noise mode [14]. Once the stable modes are detected, damping ratio (𝜉𝑖 ) of the 𝑖th mode is identified from the eigenvalue (𝜆 𝑖 ) as follows: 𝜉𝑖 =

− Re (𝜆 𝑖 ) 󵄨󵄨 󵄨󵄨 . 󵄨󵄨𝜆 𝑖 󵄨󵄨

(2)

3.2. Modal Identification of the Bridge. Responses of the bridge were measured in every two-hour interval under ambient vibration condition. Figures 4 and 5 show examples of acceleration responses and the corresponding power spectral densities of the deck (i.e., sensor node D2) and the cable (i.e., sensor node C4), respectively, under normal condition without vehicle traffic on the bridge. Nevertheless,

of this low excitation condition, the maximum acceleration amplitudes are relatively high, such as 0.3 mg for the deck D2 and 14 mg for the cable C4. Also, natural frequencies of the bridge components can be well determined, which guarantees a reliable modal identification. Using the SSI method, modal parameters (natural frequency, damping ratio, and mode shape) for three vertical and three lateral bending modes (i.e., V1∼V3 and L1∼L3) were extracted as shown in Figure 6 and as listed in Table 2.

4. Long-Term Vibration Monitoring of the Bridge 4.1. Cable Vibration under Temperature Variation 4.1.1. Temperature Effect Estimation. For a stay cable subjected to two different temperatures, the axial extension of the cable due to temperature variation (e.g., Δ𝐿 𝑇 = 𝛼𝑇 𝐿Δ𝑇) is equivalent to the deformation in the change in tension force at anchorage (e.g., Δ𝐹 = −𝐸𝐴Δ𝐿 𝑇 /𝐿 = −𝛼𝑇 𝐸𝐴Δ𝑇), where 𝛼𝑇 is the coefficient of linear thermal expansion of the cable, Δ𝑇 is the temperature variation, and 𝐸𝐴 is the axial rigidity of the cable. The change in tension force can be simplified in a reduced order as Δ𝐹 ≈

𝑔 (𝑚, 𝐿) Δ𝑓𝑘2 , 𝑘2

(3)

where Δ𝑓𝑘2 = 𝑓𝑘∗2 − 𝑓𝑘2 is the change in square of natural frequency; 𝑓𝑘 and 𝑓𝑘∗ are the 𝑘th natural frequencies of


5

Table 2: Identified natural frequencies and damping ratios. Vertical modes V2 0.720 0.009

V1 0.444 0.004

Nature frequency Damping ratio

V3 1.028 0.025

L1 0.454 0.012

Lateral modes L2 0.666 0.011

L3 1.169 0.026

L indicates lateral mode; V indicates vertical mode. Z

V1: 0.444 Hz

Y

L1: 0.454 Hz

X X

X

Pylon 2

Pylon 1 Z

V2: 0.720 Hz

L3: 0.666 Hz

Y

X

L3: 1.169 Hz

Z

V3: 1.028 Hz

Y

X

(a) Vertical modes

X X

X X

(b) Lateral modes

2.3

40 30

2.29

20 2.28 2.27

10 Aug 13

Aug 14

Aug 15

Aug 16

Aug 17

0

2.12

50 40

Temperature (∘ C)

50 2nd frequency (Hz)

2.31

Temperature (∘ C)

2nd frequency (Hz)

Figure 6: Modal parameters extracted by SSI method.

2.11

30 20

2.1

10 2.09

Sep 16

Sep 17

Sep 18

Sep 19

Sep 20

0

Natural frequency Temperature

Natural frequency Temperature (a) Cable C1

(b) Cable C5

Figure 7: Monitoring results for long cables C1 and C5 in 5 days: 2nd natural frequency.

the cable before and after tension force change, respectively; 𝑘 is the mode number; and 𝑔(𝑚, 𝐿) represents geometric and material quantities of the cable. Hence, the change in square of natural frequency due to temperature variation is expressed as follows: Δ𝑓𝑘2 ≈ −𝛽𝑘 Δ𝑇,

(4)

where 𝛽𝑘 is temperature correlation index which may be calculated as 𝛽𝑘 = 𝛼𝑇 𝐸𝐴𝑘2 /𝑔(𝑚, 𝐿). Since it is difficult to obtain the geometric and material quantities 𝑔(𝑚, 𝐿) theoretically, the value of 𝛽𝑘 can be estimated from field experiment by correlating the change in natural frequency and temperature variation. 4.1.2. Natural Frequency versus Temperature Variation. The first natural frequency was monitored for the short cables C2, C3, and C4, and the second natural frequency was monitored for the long cables C1 and C5. It is worth noting

that the first natural frequency can be utilized to estimate tension force of short cables, and the second one can be used to estimate tension force of long cables, according to the method proposed by Zui et al. [15]. Figures 7 and 8 show the changes in the natural frequencies (corresponding to the first or the second mode dependent on cables) versus temperature change in 5 days. It is obvious that the natural frequencies change oppositely with temperature variation. This implies that axial stress of the cable is partially released when temperature increases and vice versa. The data in a long period of one month from August to September 2011 was utilized to construct the relationship between the natural frequencies and temperature. Figures 9 and 10 illustrate the correlations between natural frequencies and temperature. It is found that the change in the cables’ natural frequencies seems to be linear with temperature variation. Those monitoring results are consistent with the analytical model of temperature variation effect on change in cables’ natural frequencies (4).

2.96

50

40

2.95

40

30 20 10 Aug 14

Aug 15

Aug 16

Aug 17

2.94

30

2.93 20

2.92

10

2.91 2.9

0

Aug 13


Aug 14

Aug 15

Aug 16

Aug 17

0


(a) Cable C2

(b) Cable C3

1st frequency (Hz)

1.6

50 40

1.59

30 20

1.58

10 1.57

Aug 13

Aug 14

Aug 15

Aug 16

Aug 17

Temperature (∘ C)

Aug 13

1st frequency (Hz)

50 Temperature (∘ C)

1.8 1.79 1.78 1.77 1.76 1.75 1.74 1.73

Temperature (∘ C)


1st frequency (Hz)

6

0

Natural frequency Temperature (c) Cable C4

Figure 8: Monitoring results for short cables C2, C3, and C4 in 5 days: 1st natural frequency. 4.46

5.4

y = −0.0039x + 5.3388

y = −0.0013x + 4.4331

4.44

R2 = 0.3033

5.3

4.42

5.25

4.4

f22

f22

5.35

5.2

4.38

5.15

4.36

5.1 25

35 Temperature (∘ C)

45

(a) Cable C1

4.34 20

R2 = 0.3548

25

30 35 Temperature (∘ C)

40

45

(b) Cable C5

Figure 9: Correlation of natural frequency and temperature: long cables C1 and C5.

4.2. Bridge Behaviors under Wind Loads Induced by Typhoons. Over 2-year monitoring, there were 4 typhoons passing the Korean peninsula and directly affected the site of Hwamyung bridge, including typhoons Meari (June 2011), Bolaven (August 2012), Tembin (August 2012), and Sanba (September 2012). During typhoon Sanba, the highest wind speed was recorded on-site as 17.9 m/s. Figure 11 shows timefrequency analysis of acceleration responses of deck D2 and cable C3 during typhoon Sanba. It is observed that the magnitudes of all vibration modes increase significantly during the attacks of the typhoon. This

implies that the typhoon gave an impact in a wide frequency range. As also shown in Figure 12, the maximum acceleration magnitude during the typhoon was much larger than that in normal wind condition. From those observations, to have a monitoring system for the internal forces of the bridge components during typhoons is found essential. 4.3. Effect of Deck Mass Change Caused by Pavement. The effect of bridge deck mass change on the change in natural frequency was examined after the asphalt pavement was laid on the bridge in February 2012. The difference in the


7 8.7

3.2 2

R2 = 0.051

8.6

R = 0.0778

3.1

8.5

3.05

f12

f12

y = −0.0026x + 8.5499

y = −0.0007x + 3.078

3.15

8.4 3 8.3

2.95 2.9

25

35 45 Temperature (∘ C)

8.2 25.0

55

35.0

45.0

55.0

Temperature (∘ C)

(a) Cable C2

(b) Cable C3

2.6

y = −0.0007x + 2.5174

R2 = 0.0778

2.55

f12

2.5

2.45

2.4

25

30

35

40

45

50

Temperature (∘ C)

(c) Cable C4

Before typhoon 2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0

During typhoon

After typhoon

Before typhoon 10

(b)

(a)

During typhoon

After typhoon 3rd

6th 5th 4th 2nd 1st

121416182022 0 2 4 6 8 101214161820220 2 4 6 8 1012

Time (hour) (a) Deck D2

8 Frequency (Hz)

Frequency (Hz)

Figure 10: Correlation of natural frequency and temperature: short cables C2, C3, and C4.

6

2nd

4 1st 2 0

121416182022 0 2 4 6 8 10121416182022 0 2 4 6 8 1012

Time (hour) (b) Cable C3

Figure 11: Time-frequency acceleration response of deck and cable during typhoon Sanba.

surface of the bridge deck can be clearly seen in Figures 13(a) and 13(b). For the fairness in weather condition, the data of two time periods (August 2011 and August 2012) with similar temperatures were selected as shown in Figure 13(c). Figure 14 shows the measured natural frequencies of the first mode of the deck and the five cables C1∼C5 at the two periods. It is found that the natural frequency of deck decreased after laying the pavement, whereas the natural frequencies of the five cables increased after laying the pavement. These

phenomena can be explained by the following reasons: the mass of deck was increased due to the pavement’s mass, and the tension force of the cable was also increased due to the additional load from the increased weight of the deck.

5. Summary and Conclusions A long-term vibration monitoring of the Hwamyung cablestayed bridge in Korea using an Imote2-platformed wireless


0.02 0.016 0.012 0.008 0.004 0

Before typhoon

During typhoon

After typhoon Acceleration (g)

Acceleration (g)

8

1214 16 18 20 22 0 2 4 6 8 10 12 1416 18 2022 0 2 4 6 8 10 12

Before typhoon 0.5

During typhoon

After typhoon

0.4 0.3 0.2 0.1 0

1214 16 18 20 22 0 2 4 6 8 10 1214 16 18 20 22 0 2 4 6 8 10 12

Time (hour)

Time (hour)

(a) Deck D2

(b) Cable C3

Figure 12: Maximum acceleration of deck and cable during typhoon Sanba.

(a) Bridge view before pavement

(b) Bridge view after pavement Pavement (Feb 2012)

Temperature (∘ C)

50

40

30

20 Aug 2011

Aug 2012

(c) Temperature change

Figure 13: Bridge conditions before and after asphalt pavement.

sensor network was presented in this study. First, the wireless vibration monitoring system of the bridge was briefly described by outlining the test history, the design of wireless sensor system, and the sensor deployment. Next, the vibration behaviors of the bridge were experimentally examined with respect to the variation of temperature, the wind loads induced by typhoons, and the change of bridge deck mass caused by asphalt pavement. The long-term vibration monitoring of the bridge has been operated successfully via the wireless sensor network. It was found that the cables’ natural frequencies varied linearly with temperature variation. Natural frequencies of the deck and the cables were found much influenced by the deck

mass change caused by the pavement, but in opposite trends. During a typhoon passing the bridge (typhoon Sanba), vibration amplitudes of the deck and the cables significantly increased, and all modal responses became more resonant. Future works remain on examining the effect of temperature variation on the deck’s modal parameters and wind effect on the deck’s and the cables’ modal parameters.

Acknowledgment This research was supported by a grant from a Strategic Research Project (Development of Smart Prestressing and Monitoring Technologies for Prestressed Concrete


9

1.25

0.46

1.85

0.44 0.43 0.42

1.2

Frequency (Hz)

Frequency (Hz)

Frequency (Hz)

0.45

1.15

0.41 0.4

Aug 2011

1.1

Aug 2012

1.75

1.7

Aug 2012

(b) Cable C1

1.11

3

1.69

1.09

2.9 2.85

Aug 2011 (d) Cable C3

Aug 2012

Frequency (Hz)

1.74

2.95

1.64 1.59 1.54

Aug 2011

Aug 2012

(e) Cable C4

Aug 2011

Aug 2012

(c) Cable C2

3.05

Frequency (Hz)

Frequency (Hz)

(a) Deck

Aug 2011

1.8

1.07 1.05 1.03 1.01

Aug 2011

Aug 2012

(f) Cable C5

Figure 14: Changes in 1st natural frequencies of deck and five cables before and after asphalt pavement.

Bridges) funded by the Korea Institute of Construction Technology.

References [1] S. W. Doebling, C. R. Farrar, and M. B. Prime, “A summary review of vibration-based damage identification methods,” Shock and Vibration Digest, vol. 30, no. 2, pp. 91–105, 1998. [2] H. Sohn, C. R. Farrar, F. M. Hemez, D. D. Shunk, D. W. Stinemates, and B. R. Nadler, A Review of Structural Health Monitoring Literature: 1996–2001, Los Alamos National Laboratory Report, Los Alamos, New Mexico, 2003. [3] J.-T. Kim, J.-H. Park, D.-S. Hong, and W.-S. Park, “Hybrid health monitoring of prestressed concrete girder bridges by sequential vibration-impedance approaches,” Engineering Structures, vol. 32, no. 1, pp. 115–128, 2010. [4] B. F. Spencer and S. Cho, “Wireless smart sensor technology for monitoring civil infrastructure: technological developments and full-scale applications,” in Proceeding of the World Congress on Advances in Structural Engineering and Mechanics (ASEM ’11), Seoul, Korea, 2011. [5] E. G. Straser and A. S. Kiremidjian, “A modular, wireless damage monitoring system for structure,” John A. Blume Earthquake Engineering Center, Stanford University, Palo Alto, Calif, USA, 1998. [6] J. P. Lynch, Y. Wang, K. J. Loh, J.-H. Yi, and C.-B. Yun, “Performance monitoring of the Geumdang Bridge using a dense network of high-resolution wireless sensors,” Smart Materials and Structures, vol. 15, no. 6, article 008, pp. 1561–1575, 2006.

[7] D. L. Mascarenas, M. D. Todd, G. Park, and C. R. Farrar, “Development of an impedance-based wireless sensor node for structural health monitoring,” Smart Materials and Structures, vol. 16, no. 6, pp. 2137–2145, 2007. [8] J. A. Rice, K. Mechitov, S.-H. Sim et al., “Flexible smart sensor framework for autonomous structural health monitoring,” Smart Structures and Systems, vol. 6, no. 5-6, pp. 423–438, 2010. [9] J.-T. Kim, J.-H. Park, D.-S. Hong, and D.-D. Ho, “Hybrid acceleration-impedance sensor nodes on Imote2-platform for damage monitoring in steel girderconnections,” Smart Structures and Systems, vol. 7, no. 5, pp. 393–416, 2011. [10] G. D. Zhou and T. H. Yi, “The node arrangement methodology of wireless sensor networks for long-span bridge health monitoring,” International Journal of Distributed Sensor Networks, vol. 2013, Article ID 865324, 8 pages, 2013. [11] Memsic Co, 2010, Datasheet of ISM400, http://www.memsic .com/. [12] H. Jo, J. A. Rice, B. F. Spencer Jr., and T. Nagayama, “Development of high-sensitivity accelerometer board for structural health monitoring,” in Proceedings of the Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, San Diego, Calif, USA, March 2010. [13] V. P. Overschee and B. De Moor, Subspace Identification For Linear System, Kluwer Academic Publisher, 1996. [14] J.-H. Yi and C.-B. Yun, “Comparative study on modal identification methods using output-only information,” Structural Engineering and Mechanics, vol. 17, no. 3-4, pp. 445–466, 2004. [15] H. Zui, T. Shinke, and Y. Namita, “Practical formulas for estimation of cable tension by vibration method,” Journal of Structural Engineering, vol. 122, no. 6, pp. 651–656, 1996.

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Volume 2013

ISRN Signal Processing Volume 2013



Volume 2013

ISRN Sensor Networks Volume 2013


Volume 2013