2003 imac oresund a4

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CONTINUOUS MONITORING OF THE ØRESUND BRIDGE:SYSTEM AND DATA ANALYSIS

B. Peeters(1), G. Couvreur (2), O. Razinkov(2), C. Kündig(2), H. Van der Auweraer (1), and G. De Roeck(3)

(1)LMS International

Interleuvenlaan 68B-3001 Heverlee, BELGIUME-mail: [email protected]

www.lmsintl.com

(2)GeoSIG

Europastrasse 11CH-8152 Glattbrugg, SWITZERLAND

www.geosig.com

(3)Department of Civil Engineering

Katholieke Universiteit LeuvenKasteelpark Arenberg 40

B-3001 Heverlee, BELGIUMwww.bwk.kuleuven.ac.be/bwm

ABSTRACT

The Øresund Bridge opened in July 2000. It is the moststriking part of the fixed link across the Øresund connectingCopenhagen (Denmark) and Malmø (Sweden), which furtherincludes a tunnel and an artificial island. The bridge isequipped with a PC-based continuous monitoring system,capable of measuring both static and dynamic quantitiessuch as temperatures, wind characteristics, air humidity,strains and accelerations. The challenges for the design ofthe monitoring system were the long distances between themonitoring points and the variety of sensors. This paperdescribes the bridge and the monitoring systemcomponents. Some typical measurement data arepresented. Finally, the modal parameters of the bridge are

extracted from the deck, cable and tower vibrations. Thisshows that the system does not only give information aboutsudden events that exceed a certain threshold, but can alsobe used as a health monitoring system by tracking theevolution of the modal parameters.

1 INTRODUCTION

The number of civil engineering structures that are equippedwith monitoring systems is rapidly increasing. Typicalexamples are long-span cable-stayed and suspensionbridges which represent a large capital investment andwhere the use of a permanent monitoring system is easily

justified and often recommended by insurance companies.

Such a monitoring system can serve several purposes:

• Design verification. It is verified whether the structuralstatic and dynamic response is not exceeding the designvalues.

• Event recording. Important load (wind, traffic) orresponse (strains, accelerations) quantities are recordedwhen preset thresholds are exceeded; for archivalreasons or to take decisions about the serviceablenessof a bridge. For instance at too large wind speeds, it maybe uncomfortable to use the bridge.

• Health monitoring. The recorded data can be used toderive experimental models. Information on the structural

health can be obtained by tracking the evolution of theseexperimental models or by confronting experimental datawith analytical models.

Numerous examples of permanently monitored bridges arereadily found in the proceedings of the SHM [1], IMAC [2]and SPIE [3] conferences of the last years.

This paper presents a state-of-the-art monitoring system thatwas installed on a state-of-the-art bridge. In Section 2 theØresund Bridge is introduced. Section 3 presents themonitoring system itself and its normal mode of operation. Insection 4 the vibration signals of the cables, deck and towersare analysed. These analyses of dynamic data were doneoffline, using data captured by the permanent system but are

not part of the standard analysis procedures of the system.

2 THE ØRESUND BRIDGE

Since July 2000, Sweden and Denmark are connectedthrough the Øresund fixed link consisting of 8 km of bridgeand 4 km of tunnel, joined by a 4 km long artificial island(Figure 1). The bridge has a quite unique two-level design,with a four-lane motorway placed above a two-track railway

Figure 1: The Øresund fixed link.

mailto:[email protected]


http://www.lmsintl.com/


http://www.geosig.com/


http://www.bwk.kuleuven.ac.be/bwm








(Figure 2). The bridge consists of 49 approach spans (7spans of 120 m, 42 spans of 140 m) and a cable-stayedcomponent with 2 side spans at each side (160 m and

141 m) and a main span of 490 m over the navigationalchannel (Figure 3).

10 Pairs of cables at each side are connecting the pylons ofthe two H-shaped towers with the bridge deck (Figure 4).

The tops of the pylons are at 204 m above sea level and theminimum headroom under the main span is 57 m. Themonitoring system discussed in next section is installed onthe cable-stayed part of the bridge.

3 THE CONTINUOUS MONITORING SYSTEM

The bridge owner was concerned about the stay cable

oscillations under heavy wind conditions, as well as thedeformation of the bridge when trains or heavy trucks arepassing. Therefore GeoSIG installed a new type ofmonitoring system (called CR-4 Central Recorder) that isable to acquire both dynamic and static data.

At the Øresund Bridge, 85 dynamic channels are installedwhich permanently record at a sample rate of 100 Hz signalsfrom 22 triaxial accelerometers and 19 strain gauges. Thestatic measurement channels are connected to 12temperature sensors at different points of the bridge and to 2weather stations, one at the top of a pylon and the other atroad level. Static information, such as minimum, maximumand mean values, is extracted from the dynamic channels forlong-term analysis. The CR-4 acquisition system is placed in

a technical room at one of the pylons. There is a telephoneconnection to the general control room of the Øresund link,3 km away from the pylon, allowing automatic data retrieval.

18039602

30.5 m

Figure 2: Cross section of the cable-stayed bridge spans.

Figure 3: Cable-stayed part of the Øresund Bridge. Figure 4: Pylons and stay cables.



3.1 CR-4 System description

The CR-4 system allows different data collection strategies:

• Dynamic acquisition with A/D conversion in the recorder,handles up to 33 dynamic channels with maximumsample rate of 1000 Hz.

• Dynamic acquisition with A/D conversion near thesensor, handles up to 132 dynamic channels with amaximum sample rate of 200 Hz.

• Static acquisition with A/D conversion near the sensor,handles up to 44 chains with each up to 26 static junctionboxes (see below) with each up to 6 channels. So asmuch as 6864 static channels are possible.

For the Øresund Bridge a combination of the second andthird option has been chosen. The acquisition near thesensor is done by means of a CM-500 junction box withacquisition modules. Modules differ as a function of the typeof sensor that will be connected. In this case, CM-501modules were used for the strain gauges, CM-502 modulesfor voltage inputs like accelerometer and weather stationsignals, CM-503 modules for powering the sensors and

generating the sensor test pulse output, and CM-504modules for Pt100 temperature sensors. Not used in thisproject but also available is the CM-505 module for LVDTdisplacement sensors. The junction box is connected to theCR-4 main cabinet (Figure 5). Power can be supplied locallyor in this case from the cabinet. The signals from the junctionbox are digitally transferred to the cabinet through a RS-485link to avoid any loss of power.

The block diagram of Figure 6 represents the architecture ofthe dynamic measurement chain: from the sensors at the leftto the computer at the right. The analog signal goes from thesensor to the CM-5xx acquisition module where it isconverted on request to a digital signal and transferred to theCM-500 module. The CM-500 module makes a packagefrom the collected samples and sends it to the CR4-30module through the CR4-31 protection board. The CR4-31 isa galvanic separation between the CR-4 with the computerand the external part of the system. The CR4-30 contains 4PIC-Boards, each PIC controlling one CM-500 in dynamicmode and up to 26 chained ones in static mode. The datapackage of each channel is stored in the PIC and will besent on request to the CR4-20 DSP board where the data is

stored in ring buffers. Finally the computer (CR-4 software)collects each second one-second packages of the DSP-Boards. The computer does signal analysis and treatment,and according to the user-specified trigger mode, the signalsare stored in an event file.

The static architecture is different in the way that several junction boxes can be connected to the same wire. This isnot possible in the dynamic mode because of the high baudrate that must be guaranteed. To avoid unnecessary powerloss and voltage drops in the powering cables, the CM-500only powers the sensor if a sample is requested. A start-uproutine assures constant values.

A 12V/115Ah battery assures power for 15 h in case of

power loss. This is sufficient for local conditions, as powerfailures do not often occur and, if they occur, it would only befor a short period. For accurate timing, a GPS receiver canbe connected to the CR-4 that updates the computer time.

3.2 Sensors

The Øresund installation contains 4 types of sensors. Theyare specified below together with the type of information that

Figure 5: The CR-4 (Central Recorder) main cabinet.

C M - 5 0 0

C M - 5 x x

C M - 5 x x

P o w e r CR4-31

GalvanicSeparation

CR4-30PIC-

Board

CR4-20DSP-Board

C M - 5 x x

C M - 5 x x

CR4-20DSP-Board

CR4-20DSP-Board

Computer

C M - 5 x x

C M - 5 x x

C M - 5 0 0

Sensor 3-axis

Sensor 1-axis

Sensor 1-axis

Sensor 1-axis

Figure 6: Dynamic data acquisition architecture.



is extracted from the quantities that they measure.

• 22 Triaxial force balance accelerometers AC-53, 2 g fullscale (Figure 7). Most of these accelerometers (16) aremounted on the stay cables to measure the cablevibrations. The 2 tops of the east pylons are alsoequipped with accelerometers, as well as 4 locationsalong the deck. These sensors allow monitoring thecable vibrations under heavy wind load and the bridge

response to railway and road traffic.

• 19 Strain gauges LV3400VS0. Twelve strain gauges aremounted on 3 steel outriggers of the cables, one on eachside. Two are mounted on the rail level in the concreteand five are mounted on the lower side of the bridge.These sensors are mainly observing torsions due toheavy wind and railway traffic.

• 12 Thermometers Pt100 are mounted on differentlocations, but mostly at the pylons. These sensors aremeasuring temperatures, which are correlated with thestrain gauge measurements.

• 2 Weather stations measuring wind speed, wind direction(1172T), air humidity and air temperature (RHA1). One ismounted on the top of a pylon, the other at road level(Figure 8). The wind measurements serve as a referencefor the stay cable vibrations. The air humidity andtemperature complete the meteorological information.

3.3 CR-4 Software

The Windows-based data acquisition and processingsoftware for the CR-4 system consists of the following threeparts:

• SEISLOG controls the CR-4 system and acquires datafrom the DSP boards.

• CENTRAL provides the interface for remote access to

the CR-4 systems.• CMS (Civil Monitoring System) processes the static data

acquired by the system.

The software used for data acquisition in the monitoringsystem for the Øresund Bridge is an extended version of thewell-known seismic program SEISLOG developed andsupported by the University of Bergen, Norway [4]. GeoSIGhas extended this standard version with the new functionalitysuch as remote control, alarm functions, data view andanalysis, drivers for the CR-4 system and other options.SEISLOG configures the system upon start up and then

acquires both dynamic and static data continuously from allconfigured channels. Dynamic data are analysed by thetriggering algorithm, which is set to declare seismic eventsand alarms if the signal of selected data channels exceedssome specified level. In such cases the data are logged tothe event file, which can be viewed and analysed afterwards.

At the same time the alarm signal is sent to the traffic controlcentre alerting responsible persons about strong vibrationsof the bridge. Static data are logged to another file

periodically. SEISLOG has a monitoring screen, whichindicates the operating status of all dynamic data channels,status of the trigger and other parameters. The waveformsignal of any data channel can be viewed on the screen innear real-time mode.

Every event file and every new portion of the static data isdelivered to the traffic control centre through a telephoneline. The CENTRAL software is responsible for receiving thisinformation. SEISLOG also reports to CENTRAL its state ofhealth (SOH) periodically. SOH, event files, static data andother information about the CR-4 system can also berequested from CENTRAL by an operator at any time.Basically the CENTRAL software is used to monitor and tocontrol remotely many CR-4 systems. The software can be

configured to send automatically emails informingresponsible persons about the status of monitored systemsas well as about new-recorded events. And the last but notleast task of CENTRAL is to deliver received static data tothe Civil Monitoring System (CMS).

CMS is designed for the continuous monitoring of largestructures. It accepts data from many static channels, storesall received data in the database, indicates the current statusof all monitored points graphically and prepares statisticalinformation. CMS has also an alarm system, which alertsoperators if any of monitored parameters is out of its allowed

Figure 7: GeoSIG triaxial accelerometer AC-53 mounted on a cable.

Figure 8: Weather station at the deck level of the bridge.



range. The program can display any set of stored datacollected within a specified time interval in text datasheetsand graphically. CMS of the Øresund monitoring systemhandles the data acquired by two weather stations,thermometers and the strain gauges. The average values ofall dynamic data channels are processed as well. The CMSapplication is running in the traffic control centre in the samecomputer as the CENTRAL communication software.

4 DYNAMIC DATA ANALYSIS

In this section, a 5-minute recording of the accelerationchannels of the CR-4 monitoring system is thoroughlyanalysed. Although the type of analysis performed here(operational modal analysis), is not part of the standardprocedures of the Øresund Bridge, it is instrumental to seewhat kind of information can be extracted from the cable,deck and tower vibrations. A comprehensive overview ofnumerical and experimental dynamics of cable-stayedbridges can be found in [5].

4.1 Operational modal analysis

The aim is to identify an experimental dynamic model of thebridge. In laboratory situations, such a model can beobtained by artificially exciting a structure and measuring theresponses. Measurement functions (so-called FrequencyResponse Functions) that relate the input to the output serveas input for modal analysis methods to achieve this goal.Obviously, the data recorded by the monitoring system is so-called operational data: bridge responses are measuredunder dynamic wind or traffic loading without being able tomeasure exactly all these forces. Nevertheless it is stillpossible to derive an experimental dynamic model of thestructure from only response measurements. Hereto, atechnique called operational modal analysis is used. A verypopular operational modal analysis method in civil

engineering is peak picking . The method is named after itskey step: the identification of the eigenfrequencies as thepeaks of power spectrum plots. However, there exist moreadvanced methods that better exploit the data and lead tohigher quality models. It is for instance possible to extendclassical modal parameter estimation methods, such as theLSCE (Least Squares Complex Exponential) method, so thatthey also work on output-only data [6]. Another option is touse a state-of-the-art method, such as stochastic subspaceidentification. In this method, a so-called stochastic state-

space model is identified from output correlations [7] ordirectly from measured output data [8]. The first applicationof stochastic subspace identification to bridge vibration datadates from 1995 [9]. It is such a stochastic subspaceidentification method that will be used in this paper [10]. Anextensive overview and detailed discussion on operationalmodal analysis methods can be found in [11][12].

4.2 Cable vibrations

The simplest and cheapest method to measure the cableforces is by measuring the eigenfrequencies of that cable.Evidently, this is an indirect measurement: the tension forceis derived from eigenfrequencies, which are derived fromaccelerations. Figures 9 and 10 show 5-minute recordings of

typical accelerations of two stay-cables. The top parts areshowing the time histories. The auto-correlations i R , shownin the middle, are estimated from the time data k y accordingto:

−

=

+=

1

0

1 N

k

k ik i y y N

R (1)

where N is the number of time samples used to computethe correlations. From the plots it is clear that thecorrelations have an impulse-response like behaviour. Thiscan also be theoretically proven provided that the structure isexcited by white noise. The auto-correlations of the cables

0.00 299.90s

-0.21

0.13

R e a l

( m / s 2 )

0.00 149.90s

-0.001

0.001

R e a l

( m 2 / s 4 )

0.00 4.99Hz

454e-9

173e-6

L o g

( m 2 / s 4 )

Figure 10: Out-of-plane acceleration of 3rd longest cable from East-

North pylon to side span. (Top) Time history; (Middle) Output

correlations; (Bottom) Half spectrum magnitude.

0.00 4.99Hz

135e-9

18.9e-6

L o g

( m 2 / s 4 )

0.00 149.90s

-0.001

0.002

R e a l

( m 2 / s 4 )

0.00 299.90s

-0.15

0.13

R e a l

( m / s 2 )

Figure 9: Out-of-plane acceleration of longest cable from East-

South pylon to mid span. (Top) Time history; (Middle) Output

correlations; (Bottom) Half spectrum magnitude.



are subsequently used as basic functions in stochasticsubspace identification to extract the eigenfrequencies. Thebottom part of Figures 9 and 10 are showing the magnitudesof the discrete Fourier transform of the positive correlationlags. These are estimates of the so-called half spectra,which are not used in the identification method, but are givenhere for reference reason as they clearly indicate thepresence of resonances. As any parametric estimationmethod, subspace identification enables the use of so-calledstabilisation diagrams that allows for an easy and objectiveselection of the cable eigenfrequencies. An example of sucha diagram is shown in Figure 11. By repeating this analysis anumber of times, eigenfrequencies are extracted from the

accelerations of 10 cables; the 5 longest cables connectingthe East-South (ES) pylon to the main span and the 5

longest cables connecting the East-North (EN) pylon to theside span. These frequencies are presented in Table 1.From Figures 9 and 10 and Table 1, the set of cablefrequencies seems to be composed of a fundamentalfrequency

1 f and its higher harmonics

1 f n f n = . A stay

cable is assumed to satisfy the taut string theory withfollowing relation between frequencies and cable tensionforces:

m

H

Ln f S n2

1= (2)

whereS

n f [Hz] is the nth

harmonic; L [m] is the cable

length; H [N] is the cable force; and m [kg/m] is the cablemass per unit length. In this paper, an estimate of thefundamental frequency is obtained from all harmonics byapplying Least Squares. Afterwards, the cable forces arecomputed according to (2) and shown in Table 1. Aninteresting alternative approach to find the fundamentalfrequency of a set of harmonics is explained in [13]; wherethe cepstrum is used to detect the periodicity in the spectraof vibrating stay cables. This approach is easy to automate,but its accuracy is limited by the resolution of the cepstrum.

However, the taut string theory does not exactly hold for staycables which, inevitably, have some bending stiffness whichbecomes more important as the cables are shorter. In [14], following equation is derived to compute the out-of-plane

eigenfrequencies EI n f of a stay cable:

2

22242

1ε

π++

ε

+=/n

f

f S

n

EI

n (3)

whereS

n f are the frequencies if the bending stiffness would

be zero (the taut string frequencies (2)) and ε is adimensionless parameter related to the bending stiffness EI [Nm

2]:

EI

H L=ε (4)

Figure 11: Stabilisation diagram obtained by applying stochastic

subspace identification to the acceleration data of Figure 9.

Table 1: Determination of cable tension forces from out-of-plane acceleration measurements. Not for all cables it was possible to extract the

same number of harmonics.

Pylon ES ES ES ES ES EN EN EN EN ENCable number 1 2 3 4 5 5 4 3 2 1 L [m] 262 239 216 192 169 169 192 216 239 262m [kg/m] 91.2 91.2 91.2 91.2 91.2 91.2 91.2 91.2 91.2 91.2

1 f 0.473 0.508 0.563 0.631 0.728 0.743 0.638 0.560 0.504 0.457

2 f 0.929 0.995 1.115 1.248 1.445 1.471 1.263 1.106 0.985 0.894

3 f 1.386 1.498 1.677 1.870 2.166 2.198 1.894 1.662 1.480 1.3504

f 1.851 1.990 2.201 2.496 2.878 2.928 2.528 2.218 1.966 1.789

5 f 2.323 2.493 2.772 3.583 2.784 2.499 2.261

6 f 2.779 2.991 3.325 3.741 4.302 4.386 3.792 3.307 2.712

7 f 3.242 3.494 3.930 4.351 5.005 4.394 3.868 3.126

8 f 3.706 3.974 4.457 4.956 5.727 4.999 4.414 3.919 3.586

9 f 4.171 4.466 4.962 5.547 5.621 4.960 4.408 4.069

Cable eigenfrequencies [Hz]

10 f 4.621 4.973 4.911

Least Squares estimate ofS f 1

[Hz] 0.463 0.498 0.555 0.620 0.717 0.732 0.627 0.552 0.491 0.450Cable force H [kN] based on LS frequency 5368 5157 5248 5169 5352 5581 5288 5194 5030 5069Dimensionless parameter ε [-] 309 315 283 331 284 208 316 293 312 208Equivalent taut string fundamental freq.

S f 1

[Hz] 0.458 0.493 0.550 0.615 0.710 0.723 0.621 0.547 0.486 0.443Cable force H [kN] (with bending stiffness) 5246 5059 5140 5102 5249 5444 5207 5091 4930 4911Bending stiffness EI [kNm

2] 3781 2920 3000 1727 1855 3596 1938 2770 2896 7786

Cable force difference [%] 2.3 1.9 2.1 1.3 2.0 2.5 1.6 2.0 2.0 3.2



In [15] it is shown how these equations can be incorporatedin software that estimates the cable forces and bendingstiffness from identified eigenfrequencies. The procedure isas follows:

• Apply non-linear least squares to estimate S f 1

and ε from the measured frequencies n f , which are assumedto behave like

EI

n f in (3). • From the cable length L and mass per unit length m ,

andS f 1

and ε estimated in previous step, it isstraightforward to estimate the cable force H (2) andbending stiffness EI (4).

The estimated quantities are shown in Table 1. One cableconsists of 70 tendons, each consisting of 7 wires of 5 mmdiameter. The mass per unit length is estimated at91.2 kg/m. From the positions of the anchor points on thetower and the bridge, the lengths of the cables are

calculated. As seen from Table 1, the dimensionless bendingstiffness parameter ε is about 200 to 300. This is within thelimits for normal stay cables: 60070 <ε< . The frequency

S f 1

, and so the cable force, is determined more accuratelythan the dimensionless parameter ε , and so the bendingstiffness. The resulting bending stiffnesses have acomparable magnitude, except for cable 1 of the East-Northpylon (last column of Table 1). Finally, Figure 12 comparesthe two cable force estimates of the different cables: one

estimate according to the taut string theory using a LeastSquares estimate of the fundamental frequency; the othertaking the bending stiffness into account. The differences ofthe force estimates for the same cable range from 1.3 to3.2%. The tension force differences between cables can goup to 11%, regardless of the estimation method.

The analysis presented in this section shows that it ispossible to monitor the cable forces from the accelerationsthat are recorded by the continuous monitoring system.

4.3 Deck and tower vibrations

Figures 13 and 14 show 5-minute recordings of a verticalacceleration at the main span deck and a transversal

acceleration at the top of the East-South pylon. Again, thetime histories, the auto-correlations and the half spectra areshown. The recordings contain the passage of train, whichcaused an increase of vibration levels as can be seen fromFigure 13. From the pylon accelerations (Figure 14) itbecomes difficult to observe the train passage because thepylon vibrations are mainly caused by the wind. The cablevibrations (Figure 9 and 10) are apparently not influenced bythe train loading. As in Section 4.2, stochastic subspaceidentification was applied to determine the deck and towermodal parameters. Due to the limited number ofaccelerometers at the deck and tower, no detailed modeshapes can be obtained. Nevertheless, it was possible to

4800

4900

5000

5100

5200

5300

5400

5500

5600

C a b l e F o r c e

[ k N ]

1 2 3 4 5 5 4 3 2 1

ES ES ES ES ES EN EN EN EN EN

Cable

Cable w ith Bending

Stiffness

Taut String

Figure 12: Cable forces estimated from vibration measurements.

0.00 299.90s

-0.10

0.13

R e a l

( m / s 2 )

0.00 149.90s

-21.3e-6

69.7e-6

R e a l

( m 2 / s 4 )

0.00 5.00Hz

28.1e-6

492e-6

L o g

( m 2 / s 4 )

Figure 13: Vertical acceleration at the main span deck. (Top) Time

history; (Middle) Output correlations; (Bottom) Half spectrum

magnitude.

0.00 299.90s

-0.02

0.02

R e a l

( m / s 2 )

0.00 149.90s

-6.70e-6

20.3e-6

R e a l

( m 2 / s 4 )

0.00 5.00Hz

9.86e-6

280e-6

L o g

( m 2 / s 4 )

Figure 14: Transversal acceleration at the top of the East-South

pylon. (Top) Time history; (Middle) Output correlations; (Bottom)

Half spectrum magnitude.



interpret the lowest modes. They are represented in Table 2.It is interesting to compare these results with the onesobtained from other bridge tests. For example, in Figure 15such comparison is made based on a plot of the 1

st vertical

deck bending eigenfrequency as a function of the main spanlength. The data for the other plotted cable-stayed andsuspension bridges were found in [1][2][3].

In a continuous monitoring and modal analysis process, thebridge eigenfrequencies could be used to assess the healthof the structure. This is for instance shown in [11] for theZ24-Bridge in Switzerland. In [16], it is demonstrated thatdetailed mode shapes of a cable-stayed bridge (in this case

the Vasco da Gama Bridge) can be obtained by usingambient vibration data and stochastic subspaceidentification. In this case, the accelerations at much moredeck and tower locations had been measured with non-permanent equipment.

5 CONCLUSIONS

In this paper, the continuous monitoring system of theØresund Bridge was presented. It was also shown howoperational modal analysis, applied to the dynamic datacaptured by the system, provide useful information about thehealth of the bridge. Measuring and analysing cablevibrations allow monitoring the cable tension. Measuring andanalysing deck and tower vibrations allow assessing thehealth of these structural parts.

ACKNOWLEDGEMENTS

The authors would like to thank Mr. Svensson fromØresundsbro Konsortiet (www.oeresundsbron.com) forgranting us the right to use monitoring data and photographsof the Øresund Bridge.

The present research is conducted in the framework of theEuropean Commission 5

th framework programme projects

SAMCO (www.samco.org) and IMAC (www.vce.at/imac.htm).

REFERENCES

[1] Proceedings of the 1st, 2

nd, 3

rd International Workshop on

Structural Health Monitoring, Stanford University, CA, USA,1997–1999–2001.

[2] Proceedings of IMAC 16–20 , USA, www.sem.org, 1998–2002.

[3] Proceedings of SPIE, Health Monitoring and Management ofCivil Infrastructure Systems, Volume 4337, Newport Beach,CA, USA, March 2001.

[4] N ATVIK Ø, H AVSKOV J., AND UTHEIM T. SEISLOG: a SeismicData Acquisition System for Windows9X/NT/2000/XP, Version

1.0.5. Manual, Institute of Solid Earth Physics, University ofBergen, Norway, 2002.

[5] C AETANO E. Dynamics of Cable-stayed Bridges. Experimental Assessment of Cable-Structure Interaction. PhD thesis,Department of Civil Engineering, University of Porto, Portugal,

2001.[6] HERMANS L. AND V AN DER AUWERAER H. Modal testing and

analysis of structures under operational conditions: industrialapplications. Mechanical Systems and Signal Processing ,13(2), pp. 193–216, 1999.

[7] AKAIKE H. Stochastic theory of minimal realization. IEEETransactions on Automatic Control , 19, pp. 667–674, 1974.

[8] V AN OVERSCHEE P. AND DE MOOR B. Subspace Identification forLinear Systems: Theory – Implementation – Applications.Kluwer Academic Publishers, Dordrecht, The Netherlands,ftp.esat.kuleuven.ac.be/pub/SISTA/nackaerts/other/alln.ps.gz,1996.

[9] PEETERS B., DE ROECK G., POLLET T., AND SCHUEREMANS L.Stochastic subspace techniques applied to parameteridentification of civil engineering structures. In Proceedings of

the International Conference MV2 on New Advances in ModalSynthesis of Large Structures, Non-Linear, Damped and Non-Deterministic Cases, pp. 151–162, Lyon, France, 1995.

[10] LMS CADA-X, Operational Modal Analysis, Revision 3.5.E .Manual, LMS International, Leuven, Belgium, www.lmsintl.com,2002.

[11] PEETERS B. System Identification and Damage Detection inCivil Engineering. PhD thesis, Department of Civil Engineering,K.U.Leuven, Belgium, www.bwk.kuleuven.ac.be/bwm ,December 2000.

[12] PEETERS B. AND DE ROECK G. Stochastic system identificationfor operational modal analysis: a review. ASME Journal ofDynamic Systems, Measurement, and Control , 123(4), pp.659–667, 2001.

[13] SMITH S.W. AND C AMPBELL J.E. Testing and model verificationof the Maysville Kentucky Bridge stay cables. In Proceedings of

IMAC 20 , pp. 1050–1056, Los Angeles, CA, USA, 2002.[14] MEHRABI A.B. AND T ABATABAI H. Unified finite difference

formulation for free vibration of cables. ASCE Journal ofstructural engineering , 124(11), pp. 1313–1322, 1998.

[15] V AN GYSEL E. AND DE ROECK G. Estimation of Cable Tensionfrom Vibrations: Influence of Bending Stiffness and BoundaryConditions on Eigenfrequencies and Eigenmodes. IMAC-project report, 2002.

[16] PEETERS B., DE ROECK G., C AETANO E. AND CUNHA A. Dynamicstudy of the Vasco da Gama Bridge. In Proceedings of ISMA2002, International Conference on Noise and VibrationEngineering , K.U.Leuven, Belgium, September 2002.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 500 1000 1500

Main Span Length [m]

F i r s t V e r

t i c a l E i g e n f r e q u e n c y

[ H z ]

Cable-stayed Bridges

Suspension Bridges

Øresund Bridge

Figure 15: First vertical deck bending eigenfrequency as a function

of main span length for cable-stayed and suspension bridges.

Table 2: Deck and tower modal parameters.

Description Frequency

[Hz]

Damping

ratios [%]

Deck + tower transversal 0.252 0.74

Tower transversal, pylons in-phase 0.294 1.83

Tower transversal, pylons out-of- phase 0.300 0.82

Deck 1st vertical bending + tower longitudinal 0.368 0.65

Tower longitudinal, pylons in-phase 0.540 0.40

http://www.oeresundsbron.com/


http://www.samco.org/


http://www.vce.at/imac.htm



http://www.sem.org/

http://www.sem.org/

ftp://ftp.esat.kuleuven.ac.be/pub/SISTA/nackaerts/other/alln.ps.gz









http://www.sem.org/





2003 imac oresund a4

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