Real-Time Wireless Vibration Monitoring for Operational ...web2.clarkson.edu/projects/liitt/pubs/ES - Real Time Wireless... · Whelan, M.J., Gangone, M.V, Janoyan, K.D., and Jha,

Whelan, M.J., Gangone, M.V, Janoyan, K.D., and Jha, R. (2009) “Real-Time wireless vibration monitoring for operational modal analysis of an integral abutment highway bridge,” Engineering Structures 31(10), 2224-2235.

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Real-Time Wireless Vibration Monitoring for Operational Modal Analysis of an Integral

Abutment Highway BridgeMatthew J. Whelan, Michael V. Gangone, Kerop D. Janoyan, Ratneshwar Jha

Abstract— Remote structural health monitoring systems

employing a sensor-based quantitative assessment of in-service demands and structural condition are perceived as the future in long-term bridge management programs. However, the data analysis techniques and, in particular, the technology conceived years ago that are necessary for accurately and efficiently extracting condition assessment measures from highway infrastructure have just recently begun maturation. In this study, a large-scale wireless sensor network is deployed for ambient vibration testing of a single span integral abutment bridge to derive in-service modal parameters. Dynamic behavior of the structure from ambient and traffic loads was measured with accelerometers for experimental determination of the natural frequencies, damping ratios, and mode shapes of the bridge. Real-time data collection from a 40-channel single network operating with a sampling rate of 128Hz per sensor was achieved with essentially lossless data transmission. Successful acquisition of high-rate, lossless data on the highway bridge validates the proprietary wireless network protocol within an actual service environment. Operational modal analysis is performed to demonstrate the capabilities of the acquisition hardware with additional correlation of the derived modal parameters to a Finite Element Analysis of a model developed using as-built drawings to check plausibility of the mode shapes. Results from this testing demonstrate that wireless sensor technology has matured to the degree that modal analysis of large civil structures with a distributed network is a currently feasible and a comparable alternative to cable-based measurement approaches.

Index Terms— Bridge dynamics, Modal analysis, Bridge inspection, Wireless sensor network, Structural health monitoring, Stochastic Subspace Identification (SSI)

I. INTRODUCTION IGHWAY administrators have been faced with the burden of managing a rapidly aging network of highway bridges

in which a significant portion have met or exceeded their design lifetime and service limits. As demonstrated in the aftermath of recent bridge collapses over the past several decades, current schedule-based visual inspections fall short of ensuring a safe operational model for highway bridge management with bridge closures preceding imminent failure. Following development of advanced diagnostic and prognostic approaches, in-service monitoring of highway bridges with sensor networks may serve to evaluate the operational health of a particular structure and estimate the remaining service life. Wireless sensor networks furthermore enable the rapid instrumentation of bridges for assessing the impact of construction-related activities and evaluating the effect of structural retrofitting. Additionally, inherent monitoring of environmental loads and influences, operational loads, and traffic patterns and densities can be used to collect a database of field measurements for providing feedback on bridge design practice.

M. J. Whelan is a graduate student at Clarkson University, Potsdam, NY 13699 USA (e-mail: [email protected]).

M. V. Gangone is a graduate student at Clarkson University, Potsdam, NY 13699 USA (e-mail: [email protected]).

K. D. Janoyan is with the Civil and Environmental Engineering Department, Clarkson University, Potsdam, NY 13699 USA (phone: 315-268-6506 fax: 315-268-7985 e-mail: [email protected]).

R. Jha is with the Mechanical and Aeronautical Engineering Department, Clarkson University, Potsdam, NY 13699 USA (email: [email protected])

A study of over 500 bridge failures conducted by Wardhana and Hadipriono [1] assessing events from 1989 to 2000 concluded that the majority of collapse instances occur due to a triggering event. In particular, short-term hydraulic events, long-term scour, collision, and overload were sighted for 73% of the documented collapse, while deterioration of structural members, design flaws, and construction-related issues resulted in nearly 12% of the failures. Collisions, scour, and structural deterioration significant enough to produce bridge collapse should produce detectable changes in the dynamic response of the structure. Feedback from a sensor-based monitoring system would preemptively signal such deterioration to permit a schedule of repair or closure prior to unsafe operation. The aforementioned study also noted that bridge failures due to overloading were the most devastating in terms of human casualties. Since the load carrying capability is not a static quantity over its service life, routinely assessing and posting the structural capacity of bridge structures is further essential for maintaining public safety.

Due to the reasonable limitation on the density of deployed sensors across large civil structures, nondestructive testing has generally focused on characterization of changes in global dynamic properties as a consequence of local damage. A limited library of vibration-based instrumentation studies exist that have measured modal parameters of a full-scale bridge prior to and after progressive induced damage scenarios [2-5]. However, a definitive method of deriving damage

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identification and localization has yet to be formulated. Consequent to the prohibitively high cost and oversight associated with permanent, continuous monitoring of a county or statewide distributed system of bridges, short-term monitoring, concurrent to existing visual inspections, has also been another, more utilized, approach. As an overwhelming number of bridges nationwide have approached or surpassed their service lifespan, it has become critical to allocate resources to the structures most in need of repair or replacement. Diagnostic testing through short-term strain measurements from known truck load patterns has been utilized to assess the condition of these bridges [6]. This approach generally merges the results of the experimental strain analysis with codified analytical load rating measures to determine revised operating loads and ratings for the in-service bridge. The hardware deployed in this study is unique in that it provides a wireless platform for both vibration response measurement as well as static load ratings through strain readings.

While traditional cable-based sensors can be utilized to assist the field engineer with schedule-based inspections, the excessive instrumentation costs and time of installation often limits their use to special cases. Recently, there has been much interest in the use of wireless transceivers for communication of sensor data to alleviate the burdens associated with widely-distributed cable-based sensors [7]. However, while the number of unique wireless sensor platforms has continued to rapidly expand, there has been limited success in replicating previous cable-based test programs in regard to the number of deployed sensors and data acquisition rates. A review of recent wireless sensor deployments for structural health monitoring of bridges [8-10] reveals that the networks have generally relied on either local data logging and post-sampling transmission of sensor data or on low sampling rates and/or limited numbers of sensors in order to address transceiver bandwidth limitations. Such concessions severely limit the versatility and capability of a structural health monitoring system in terms of sampling duration, data acquisition rates, and spatial resolution as well as quality of the derived mode shapes.

The wireless sensor network deployed in this study has achieved more than adequate sampling rates necessary for modal analysis of highway bridges, including short-span and stiff structures, while maintaining reliable communication within a large, dense array of sensors. Consequent to years of development and limited field success with high throughput networks, wireless sensor technology has often become viewed as a conceptually ideal solution to the problem of in-service structural condition assessment, although incapable of providing the instrumentation framework needed. The results of this paper aim to demonstrate that low-cost wireless technology has emerged to the point that it is now a feasible and comparable alternative to cable-based structural instrumentation systems.

II. TEST STRUCTURE The in-service bridge investigated consists of a 24.1 cm

(9.5 in) thick reinforced concrete slab supported by four integral abutment girders with a single span of 17.07 m (56 ft). Carrying Wright Road over Trout Brook in Potsdam, N.Y., the bridge was constructed in 2004 and is under the jurisdiction of the St. Lawrence County Department of Highways (NBI# 000000003231620). Four W36x135 steel beams are spaced at 2.74 m (9 ft) with MC8x20 end diaphragms and two equally spaced C15x33.9 intermediate diaphragms between all girders (Fig. 1). The abutments are a U-type integral design supported by nine HP10x42 piles with strong-axis orientation at the south abutment and weak-axis orientation at the north abutment. The bridge has an inventory load rating of 40.8 metric tons for the HS25.4 truck and an operational load rating of 68.9 metric tons for the HS42.4, as determined through the load factor (LF) method. The bridge was inspected by a Professional Engineer through the NYSDOT under the National Bridge Inspection Standards (NBIS) in the same month as the in-service monitoring. Given that the bridge was only two years old at the time of inspection, the deck, superstructure, and substructure were all assigned condition ratings of 8 on the NBIS 0-9 rating scale.

III. TEST SETUP, METHODOLOGY, AND INSTRUMENTATION A total of 20 data points were monitored with dual-axis accelerometers, as shown in Fig. 2. The instrumentation layout was chosen to balance the requirements of the modal testing outlined and a concurrent quasi-static investigation using strain transducers connected to the same wireless hardware. The general test setup consists of twenty dual-axis accelerometers deployed alongside eleven reusable strain transducers that were interfaced with twenty wireless sensor

Fig. 1. Concrete slab on steel girder integral abutment bridge


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nodes. All wireless sensor nodes communicated in a single-network star topology with a central coordinator node connected to a local microcomputer notebook. Acceleration responses of the bridge from ambient excitation were measured using low-cost Micro-Electro-Mechanical Systems (MEMS) accelerometers mounted directly to the web of each girder using a fast curing epoxy. All measurement data was wirelessly streamed to the microcomputer in real-time in an acceleration time history format. The acquisition of complete time histories, rather than preprocessed spectrums or extracted parameters, permits the application of multiple post-processing analysis methods including those necessitating multi-node time-series data or high computational overhead, such as stochastic subspace identification.

A. Test Methodology Individual tests consisted of time history responses concurrently streamed in real-time from all forty sensor channels distributed across twenty wireless sensor units. Durations of three minutes and six seconds (186 seconds) were specified with an effective sampling rate of 128Hz in an effort to replicate similar recent experimental programs utilizing wired instrumentation as documented in Wenzel and Pichler [11]. Excitation of the structure was provided only by ambient environmental loads and vehicular traffic. The bridge selected exhibits very low-level vibration from ambient loading due to its relatively short span length and the integral abutment design, thereby providing a challenging platform for structural response measurement and modal analysis.

The accelerometers were distributed along all of the girders in a pattern (Fig. 2) established to derive the operational mode shapes. Finite element analysis was performed in advance to ensure that data points did not correspond with nodes of zero displacement for the modes of interest. One central girder was instrumented with a denser array as a result of concurrent strain monitoring; this configuration resulted in increased spatial resolution along this girder. The vertical and longitudinal vibration responses were measured as a result of sensor orientation. Longitudinal responses were inadvertently measured due to the orientation of the sensor placement. Transmission of the longitudinal acceleration served solely to demonstrate the network throughput capability. Consequently, only the vertical vibration response was incorporated in the operational modal analysis.

Capacitance-based dual-axis MEMS accelerometers (STMicroelectronics LIS2L02AL) were selected for low-power consumption and ultra-low noise floor characteristics. These integrated circuit sensors were mounted on printed circuit boards and encased in a small external housing with potting epoxy to enable direct placement of the sensor on the structure for superior vibration transfer. The accelerometers feature +/-2g full-scale range, 600mVg-1 sensitivity at 3V supply, and an ultra-low 30µg/√Hz noise density. Signal amplification of 64V/V or 128V/V was applied to the accelerometer channels depending on sensor location to maximize the range and resolution of the conversion. This signal conditioning resulted in approximate sensitivities of 38Vg-1 and 76Vg-1, respectively, with analog-to-digital conversion resolution of 16µg and 8µg, accordingly.

Fig. 2. Vibration measurement locations - Dual-axis

Conversion of the raw signals is provided at each node by a 12-bit ADC (Analog-to-Digital Converter) that was programmed to over-sample each measurement channel at 512Hz. The data was then processed using a 56th-order digital low-pass filter to remove potential alias frequencies and then down-sampled to an effective rate of 128Hz. The oversampling approach implemented increases the effective resolution of the ADC, rejects alias frequencies from the transition band of the analog low-pass filter, and produces a near brick-wall filter response with less than 0.01dB of attenuation in the 0-50Hz bandwidth.

B. Wireless Sensor Network A wireless sensor is comprised of a traditional sensor and appropriate signal conditioning integrated with a transceiver unit for the digital conversion and transmission of the raw signal from a remote measurement location to a central acquisition station without cabling. The wireless sensor network deployed utilized a commercial transceiver platform

Fig. 3. Wireless Sensor Solution (WSS) Hardware with MEMS Accelerometer and BDI Strain Transducer


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(MoteIV Tmote Sky) incorporated with a custom software, signal conditioning hardware, and sensor design developed in-house to specifically address the requirements of bridge condition assessment through modal analysis and load rating. The commercial transceiver platform features a 4MHz microcontroller interfaced with a 2.4GHz chip transceiver with a maximum data rate of 250kbps. The device is compliant with U.S. and Canadian radio frequency regulations and is certified by the FCC and Industry Canada for unlicensed use in either country. A complete description of the wireless sensor network hardware and software as well as results of laboratory validation can be found in Whelan and Janoyan [12].

The Wireless Sensor Solution (WSS) is a multi-functional system developed in-house by the authors to address both dynamic response and strain monitoring for load rating of in-service highway bridges. Independent circuits accommodate signal conditioning hardware specific to vibration measurement of typical bridge spectrums and strain measurement from commercial transducers. The accelerometer conditioning section provides a two-channel interface featuring a low-noise 3V power supply, 14-stage digitally programmable signal amplification, auto-offset nulling, and a 5th order analog low pass filter. A complementary signal conditioning circuit for differential sensors, such as strain transducers, offers an application-specific integrated circuit (ASIC) tailored to high-resolution measurements of resistive-bridge sensors. The chip features programmable gain and offset of the differential signal, a 15-bit charge-balancing ADC, a temperature interface with a digital correction algorithm for compensating strain measurements, and nonvolatile memory for storing configuration settings during power cycling. Hardware shutdown of the individual signal conditioning circuits as well as the transceiver provides for power conservation during periods of inactivity.

Custom embedded software applications were written for the sensor nodes to accommodate in-network task triggering and large-scale concurrent node operation. Unique features of the software include digital filtering of sensor data using the hardware multiplier, in-network remote sensor configuration, control of component power supplies, and robust communication. An optimized radio transmission protocol that coordinates the sampling and transmission software operations with node-based scheduling is implemented in favor of the TinyOS radio stack typically used for this transceiver platform, which relies on concurrent processing of these tasks. Embedded software was also written for the microcontroller at the host transceiver; a high-speed transparent bridge between the chip transceiver and the virtual COM port operating at 262144 baud across the USB bus enables significantly higher data throughput. A user-friendly LabVIEW software application controls bidirectional communication through the base transceiver, configures the sensor nodes remotely, displays real-time sensor data, and logs time histories to the hard disk for subsequent analysis.

The radio transmission protocol developed enabled nearly 100% data delivery across the entire twenty node network.

Data packets from each node are scheduled for transmission based on the local address of the node to prevent packet collision. The built-in clear channel assessment (CCA) feature of the transceiver is also utilized to further prevent packet transmission when the channel is already active. Data packets request an acknowledgement message from the base transceiver upon reception of packets that pass the error checking algorithm. Data packets that fail to receive acknowledgements are placed in a transmission queue for retransmission during a second scheduled window. Packets are removed from the queue only upon successful receipt of their respective acknowledgement. The optimized network transmission protocol coupled with high-speed base node communication transfers between the chip transceiver and the host microcomputer are directly responsible for alleviating issues with data delivery, reduced sampling rates, and limited remote acquisition channels that have typically plagued wireless sensor networks.

IV. TEST RESULTS

A. Wireless Sensor Network Performance The average data success rate across all of the sensor nodes over ten 3 minute test cycles was 99.91%, with 184 of the 200 time histories reported with 100% data delivery success. The minimum data success rate over these tests was 98.0%, which corresponds to a loss of only 17 data packets of the 850 requested for the sampling duration specified. The small loss of data has been attributed to a software bug discovered in the portion of the embedded software code responsible for transmitting any residual packets in the transmission queue after completion of sampling. While correction of the software will likely improve the data success rates, the current level of data recovery is generally more than sufficient for structural health monitoring as the system identification analysis suffered from no noticeable adverse distortion. For the network size and sampling rate of the deployment in this study, the radio protocol during active sampling resulted in a transmission overhead in the range of 97kpbs to 126kbps depending on the packet success and retransmission rates. The degree of transmission reliability attained at the high data throughput rate prescribed in this testing demonstrates that wireless sensor networks are currently capable of performing large-scale, real-time structural health monitoring.

B. Dynamic Bridge Response A single-span integral abutment bridge provides an ideal

platform for testing the performance of a structural health monitoring system as the high stiffness of the bridge results in a demanding measurement scenario in that structural vibrations are of very low amplitude. Throughout the duration of testing, peak accelerations across the structure ranged from less than 2mg to only nearly 10mg. Despite this low excitation, the amplified sensor signals produced clear time-history representations of the traffic loading (Fig. 4) as well as distinct peaks in the frequency spectra. The WSS signal conditioning hardware provides an extensive range of programmable gain amplification and therefore can adapt to


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the measurement range of bridges of various span lengths and support conditions. Likewise, it can be reasonably assumed that the system will even provide improved measurement performance on longer-span structures with larger amplitude vibrations, as the signal-to-noise ratio will be increased. A particular concern with wireless sensor networks is that in using localized self-contained data acquisition nodes distributed across a structure, there is no longer a shared sample clock by which time synchronization of measurements can be strictly enforced. Conversely, all nodes operate with their own clocks that will have unique offset and drift characteristics affecting the relative timing of tasks among nodes in the network. Unlike many wireless sensor networks that use the main microcontroller oscillator to provide sample timing, the WSS system relies on an accurate 32.768 kHz crystal oscillator to provide a precise, stable clock to each of the nodes. Synchronized initiation of sampling is invoked by a single command broadcast to all nodes in the network. Since the radio packet is an electromagnetic wave, therefore travelling at the speed of light, the difference in reception time amongst the nodes in the network will be on the order of nanoseconds. Consequently, the accuracy and stability of the crystal oscillators dictates the time synchronization of the samples relative to each node throughout time. Investigation of the accelerations measured across the network during traffic events reveals that the wireless sensors maintain phase amongst themselves, which is imperative for accurate operational modal analysis (Fig. 5). This thereby substantiates

the assumption that an accurate crystal oscillator can provide time synchronization of the network for at least the sampling duration utilized.

Fig. 4. Typical Acceleration Time Histories

C. Operational Modal Analysis Analysis of in-service dynamic measurements of highway

bridges challenges conventional modal analysis techniques as it is often impractical to measure the input excitation. The use of impulse excitation (drop-weight) or a shaker to provide forced vibration to the bridge would require at least a partial closure of traffic and does not lead itself to long-term continuous or autonomous structural health monitoring. Furthermore, the presence of any nearby vibration sources, such as machinery or traffic, and the effect of dynamic environmental loads, such as wind, geological, and hydrodynamic forces, are additional system inputs that are not accounted for in the measurement of the controlled excitation input. Consequently, extraction of system modal parameters of civil structures from ambient excitation has recently emerged as a widely sought-after approach for in-service condition assessment. Traditional system identification methods requiring both input and output measurements, such as using the frequency response function (FRF), can not be applied to data yielded from ambient excitation, as there is no measure of the system input. Recent work in the development of ambient vibration methods has produced several approaches for output-only system identification to enable modal parameter extraction from ambient time histories [13,14]. In this study, output-only system identification from the accelerometer time histories was performed using the Modal Analysis on Civil Engineering Constructions (MACEC) software package [15]. This software analysis package permits the use of both the classical Fourier-based Frequency Domain Decomposition (FDD) with peak peaking as well as the stochastic subspace identification (SSI) method for deriving mode shapes, natural frequencies, and damping ratios.

During implementation of FDD modal analysis, a 4096-point average normalized power spectral density (ANPSD) was computed using the time histories from all test sequences and sensor locations (Fig. 6). Natural frequencies were selected from the modal peaks present in the power spectrum and the corresponding mode shapes were derived for each test sequence. In this approach, discrete mode shape data are determined using the relative magnitude and phase angle of the spectral peaks at each sensor location, corresponding to the eigenfrequency, with respect to a specified reference sensor. Since multiple time histories were available for analysis, the derived mode shapes were then normalized and averaged to provide the final mode shape estimates (Fig. 7). Averaging reduces the effects of noise and improves the resolution of the mode shapes. Due to the mass of passing vehicles, the mode shapes and natural frequencies of the unloaded bridge will be slightly affected by traffic loads and patterns. When using traffic excitation, averaging mode shapes from a large number of tests also alleviates the effect

Fig. 5. Time window overlay of accelerations during a traffic event 100 seconds into real-time collection cycle


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and/or these mode were damped sufficiently to prohibit visual identification. Furthermore, while the second longitudinal bending mode (4th mode) was identified and estimated, the peak was relatively small in the spectrum and, as evident in the mode shape estimate, the low signal-to-noise ratio resulted in a relatively coarse approximation.

While the Fourier-based peak picking method analyzes the frequency-spectrum to extract natural frequencies and mode shapes, stochastic subspace identification techniques use a time-domain driven approach to solving for modal parameters from a discrete-time stochastic state-space model. The derivation of this method is outside the scope of this paper; full presentation of the state-space equations and model development can be found elsewhere [14], [16]. In short, the method uses a state-space model developed from the linear, second-order differential equation for a multi-degree of freedom spring, mass, and damper system with input forces. To account for inherent sensor noise and fixed-width computational effects, the state-space model includes stochastic terms to represent measurement and process noise that are assumed to have zero-mean, white noise characteristics. The MEMS accelerometers utilized in this study do in fact exhibit a white noise spectrum according to electrical specifications, so spurious poles should not arise in the model as a result of violations of this assumption. Since the system inputs are unknown and immeasurable in the case of ambient excitation, the input terms are then implicitly modeled within the noise terms resulting in a purely stochastic system. It should be noted that by modeling the unknown

Fig. 6. Average Normalized Power Spectral Density – Vertical Direction

Fig. 7. Operational defleciton shapes extracted by Frequency DomainDecomposition (FDD) (Numbering corresponds to mode as identified insubsequent FEA analysis)

f traffic load bias on one lane of the bridge, which is more robable from the results of only a single test.

Application of the FDD method on the experimental data as found to produce smooth mode shapes that correlate well ith the FEA analysis. However, this method relies on

ufficient excitation of the eigenfrequencies to permit dentification of each modal peak in the average normalized ower spectrum. Additionally, the mode must be sufficiently nder-damped such that resonance at the natural frequency is epresented by a distinct peak. In fact, the frequency domain eak-picking method actually produces operational deflection hapes rather than mode shapes in that the shape constructed rom the spectral data is the naturally weighted combination of ll mode shapes that would arise if the structure was excited y a pure harmonic at the selected natural frequency [16]. ince only spectral content near the natural frequency ontribute noticeably to the constructed mode shape estimate, he operational deflection shapes are generally similar to the ode shapes. However, for structures with closely spaced odes, the estimated mode shapes will be a combination of ultiple modes and therefore may not produce accurate

esponse estimation. Fortunately, the structure tested has dequately spaced natural frequencies with sufficient damping o permit clear extraction of many of the dominant modes ithin the measured bandwidth. However, the traffic patterns

aptured in the field testing either did not adequately excite everal additional modes identified in the subsequent SSI nalysis to permit identification in the frequency spectra

excitation forces with the noise terms, the inputs are assumed to also exhibit zero-mean, white noise characteristics. Violation of this assumption may produce spurious poles in the state-space model that are not inherent to the dynamics of the structure but arise from spectral bias within the excitation force. Once the system inputs to the state-space model are reduced solely to the stochastic terms, numerical methods can be used to solve for the state-space matrices from the measurement data in order to produce a mathematical description of the system from which all the modal parameters, except the mode scaling factor, can be determined.

Although the mathematical formulation of the SSI method and subsequent numerical solution is rather rigorous, especially to civil engineers unfamiliar with systems and control modeling, the application of the method is facilitated through the MACEC software environment, which requires only a basic understanding of state-space models and general system identification methodology. Following calculation of

F S

ig. 8. Principal angles between subspaces and stabilization plot for typicalSI analysis ( - stable pole, - pole with partial pass of stability criteria)


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the system model from the measurement data, the principal angles between subspaces can be plotted to provide a means of estimating the model order (Fig. 8a). A gap in subsequent principal angles indicates the model order [15], which correlates to twice the number of system poles, or eigenfrequencies, captured in the measurement bandwidth. Unfortunately, real-world data rarely produces a single, distinct gap, as evidenced in the results for the Wright Road Bridge analysis in which gaps indicate several possible model orders ranging from as low as six and as high as 44. Given this all too common scenario, over-specifying the model order is recommended [15] and the suggested model order can be determined from the number of stable poles identified in the stabilization diagram (Fig. 8b). At this point, subjectivity is required to select the poles likely to be a result of structural dynamics rather than spurious poles resulting from the numerical process.

The previously outlined approach to extracting modal parameters from a SSI model, as suggested by the MACEC manual, can be treated as a relatively simple means of quickly extracting mode shapes, natural frequencies, and damping ratio estimates from experimental data. However, through experience with application of output-only system identification to in-service dynamic response measurements from highway bridges, the authors of this paper highly recommend developing a single state-space model from which all modal parameters are estimated. In this manner, subjectivity in identification of actual structural poles versus spurious poles can be greatly reduced and the derived modal parameters will reflect a more consistent estimate of the system response less affected by spurious poles. The appropriate model order can be coarsely predicted by doubling the number of spectral peaks evident in the average power spectrum and then more accurately estimated by finding a gap in the principal angle plot (Fig. 8a) greater than the coarse model order prediction. This process can be aided by comparing the spectral density estimate of state-space model to the power spectrum of the measured data. For the case study presented, a model order 44 was determined to be most appropriate; the discontinuity in the principal angle plot was found to be the last significant gap in the profile. The average spectral density of the 44th order state-space model (Fig. 9) correlates well with spectral content of the ANPSD computed from the measurement data (Fig. 6) and the majority of poles have been found to arise from the structural response.

Plotting the poles over the average spectral density of the state-space model provides an efficient means of identifying which poles are spurious and which relate to the structural dynamics of the structure. Structural poles, i.e. the natural frequencies of the bridge, coincide with distinct peaks in the average spectral density, whereas the spurious poles generally fall between peaks for a model of appropriate order.

Modal parameters were then extracted from the state-space model to estimate the natural frequencies and damping ratios of the in-service bridge. Twelve non-spurious poles were identified in the model permitting successful extraction of twelve mode shapes from the measurement data, thereby demonstrating significant improvement over the application of FDD system identification (Fig. 10). In addition to identifying mode shapes that are less pronounced in the frequency spectrum, the SSI analysis resulted in smoother mode shapes, alleviating signal-to-noise issues for poorly excited modes. Consequently, although the SSI technique requires substantially more understanding of system identification and state-space modeling, is computationally more complex, and necessitates a higher degree of subjectivity, the process effectively yields a greater number of modes with higher quality shape estimation than the FDD technique for output-only modal analysis. Given the insignificant number of spurious poles in the state-space model, particularly in the portion of the spectrum above 8Hz where the dynamic structural response occurs, the signal-to-noise ratio is low enough to permit reliable extraction of these twelve modes. It will be shown in the subsequent section that by over-specifying the model order, as suggested in the MACEC manual, the remaining two structural modes can be extracted from the in-service measurements.

Fig. 10. Operational Mode Shapes Present in SSI Model of 44th Order (3D surface plot and plan view with magnitude shading; Numbering corresponds to mode as identified in subsequent FEA analysis)

Fig. 9. Average Spectral Density of Computed SSI State-Space Model (Order 44, Poles Indicated with Star)


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VA solid msuperstrucdrafted inHighwaysmodel waby ALGOLoad Stdimensionof 20.32 cwhich resAbutmenmaterial tbuilt-in lwere assirespective

Modelinsubstantiafor a deinteractioinfluencethat the plausibiliassumptioprovidingthe abutmwingwalldistributiobackfill sThis soilfrequencion the deto model only for lwith load

Cooperative Highway Research Program (NCHRP) curves for earth pressure coefficient versus relative wall displacement, as presented by Civjan [18] were approximated with a linear fit to the initial slope of passive pressure development. Since the backfill soil properties are unknown, the linear slope approximations for loose and dense sand were averaged. Translational spring elements were placed on vertices across the abutment in both the longitudinal and transverse directions

Fig. 11. Fi

nite element model: a) mesh, b) loads and boundary constraints

. NUMERICAL MODEL OF TEST STRUCTURE odel of the bridge tested consisting of the steel

ture, concrete slab, abutments, and railings was AutoCAD 2004 from the as-built Department of drawings (Fig. 11). Finite element analysis of the s performed using the FEMPRO software package R Incorporated. Natural frequency (Modal) with

iffening analysis was performed using three-al solids consisting of brick elements. A mesh size m (8 in.) was specified in the auto-mesh generation, ulted in 33768 total solid elements across the model. t and slab properties were specified by assigning the ype to medium-strength concrete as defined by the ibrary, while the steel superstructure and railings gned the material properties of A588 and A500 steel, ly. g the behavior of an integral abutment bridge is lly more complex than developing a similar model ck supported by bearings, as the soil-structure n on the abutments and piles has a significant on the dynamics of the superstructure [17]. Given

analysis was performed simply to provide a ty check with the measured response, some ns and modeling simplifications were made in boundary constraints and loads on the surfaces of ents. Lateral soil pressure on the abutments and

s was provided using an assumed linear pressure n, a 1.922 Mg/m3 (120 lb/ft3) unit weight of the

oil, and a coefficient of lateral earth pressure of 0.5. pressure results in a slight decrease in natural es, due to the longitudinal compressive force applied ck. While nonlinear spring elements are often used soil-structure interaction, the FEA software allowed inear elastic spring elements in the natural frequency stiffening analysis. Consequently, the National

Fig. 12. Finite element analysis mode shape development

of the bridge deck. The elastic modulus of each spring element was assigned with a linear profile increasing with depth, as determined by the approximation to the NCHRP curve. Translational stiffness was applied to the base of the abutment to represent bearing pressure and frictional forces along this surface. A moderate 350.4 kN/m (31.25 lb/in/in2) spring stiffness was assigned to the lateral springs on these vertices; vertical springs were applied with 14.01 MN/m (1250 lb/in/in2) stiffness. The contribution from support piles was modeled with additional translational springs at the vertices corresponding to pile locations. Although the pile axis orientations differed at each abutment, the translational springs applied to represent the piles assumed equal stiffness in both lateral directions.

The superstructure-dominant mode shapes, i.e. those

Fig. speci poles

13. Remaining experimental mode shapes extracted through over-fication of model order in stochastic subspace identification (respective identified over average spectral density of 76th order model)


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measurable through the accelerometer placement on the girders, estimated within the measurement bandwidth through the FEA are presented in Fig. 12. While additional mode shapes were identified in FEA, these mode shapes were generally either dominated by slab or wingwall movement rather than motion at the girders or the result of rigid body translation and rotation on the boundary springs. Comparison of the analytical modal parameters with those identified in the experimental system identification techniques yielded excellent correlation in terms of both visual comparisons of mode shapes and relative estimation of natural frequencies (Table 1). There are some differences in the order of the natural frequencies between the FEA analysis and the experimentally measured modes, particularly at the higher end of the spectrum. Given the bending orders of the modes, it is natural to assume that, for instance, the third order longitudinal bending mode with first order bending in the lateral direction will occur at a lower frequency than the third order longitudinal bending mode with second order bending in the lateral direction despite the discrepancy in the results of the FEA analysis. These differences can be attributed to the assumptions, approximations, and simplifications made in the finite element model. Consequently, the mode shapes have been sequentially numbered according to the proper order as measured experimentally.

It should be noted that the four highest order modes have natural frequencies that reside in the portion of the bandwidth with potential aliasing. As a result, these natural frequency estimates may be incorrectly aliased from the 64-78Hz frequency range. However, due to the model order associated with the bending patterns, it is likely that all of the modes, aside from the last, can be assumed to be correctly associated with the 50-64Hz band, as the last mode is of the highest bending order. Furthermore, signal attenuation in the aliased region for these modes is also significant enough that identification of an aliased peak in the spectrum would be highly unlikely. Consequently, only the last mode has been identified with two possible natural frequency estimates for the experimental analysis. While some discrepancies exists among the analytical and experimentally derived modal parameters, the complexity of modeling an integral abutment bridge coupled with modeling assumptions could accounts for these slight differences as well as any inconsistencies between the bridge design and actual construction tolerances and material properties.

Table 1. Comparison of Experimental and FEA Natural Frequency Estimates

Given that the FEA identified two additional superstructure-dominant modes within the measured bandwidth, stochastic subspace identification was revisited to employ a higher model order to extract these missing modes. At a model order of 76, the remaining two modes became apparent in the state-space model, though the shapes are only approximate due to the low excitation and large damping ratios associated with these poles. Examining the spectral density of the state space model, it becomes apparent that these modes suffer from a lack of excitation as well as significant damping. Furthermore, after introducing the additional degrees of freedom necessary for the corresponding poles to arise in the model, there are significantly more spurious poles introduced. These spurious poles are not intrinsic to the structural response, but arise from noise and excitation violations of the zero-mean, white noise assumptions. Consequently, the over-specified model is not recommended for implementation in any further analysis, such as forward prediction with a Kalman filter. It is recommended to use the lower order model even though it does not include two of the fourteen modes known to be present in the bandwidth of interest.

VI. CONCLUSION AND DISCUSSION To field-test the performance of a wireless sensor network

for dynamic response assessment of in-service highway bridges, a single-span integral abutment bridge has been investigated through operational modal analysis using ambient vibration testing. As a consequence of the experimental program, the Wireless Sensor Solution (WSS) platform for structural health monitoring developed at Clarkson University has demonstrated the capability to replace cable-based instrumentation for the majority of in-service condition assessment routines. In addition, the alleviation of obstacles associated with cabling, such as the associated installation time and cost, introduces the potential to monitor the performance of an increased number of bridges in a more condensed time frame.


10

The primary objective of the study was to evaluate the

performance of the wireless sensor network and local data acquisition hardware in a typical field setup outside of the laboratory environment. Real-time streaming of 40 channels of measurement data sampled at an effective rate of 128Hz per sensor for ten test durations each exceeding three minutes was successfully achieved while maintaining nearly 100% data delivery across the network. Additionally, the signal conditioning and data acquisition interface provided an in-network reconfigurable platform that enabled sufficient resolution of the low amplitude vibration experienced to extract fourteen mode shapes of the relatively stiff single-span bridge using low-cost MEMS accelerometers. These results present a significant breakthrough in the use of wireless sensor networks for structural health monitoring. By essentially replicating a typical cable-based dynamic test routine in terms of not only sampling rates, number of deployed sensors, and test duration but also in the quality and breadth of extracted modal parameters, the developed wireless sensing platform has emerged as both a feasible and comparable alternative to wired instrumentation in structural health monitoring and in-service condition assessment.

In analyzing the ambient vibrations measured by the wireless sensor nodes, a complementary study was performed in which the effectiveness of applying two output-only system identification methods to real-world measurements was evaluated. The frequency domain decomposition technique was compared to stochastic subspace identification to contrast the ability of the two methods to extract modal parameters. Overall, FDD was capable of constructing mode shapes and estimating natural frequencies for well excited modes in good agreement with finite element analysis. However, the use of the SSI technique permitted the extraction of an additional four modes from the time histories in addition to damping ratio estimates for all vibration modes. Furthermore, the relative quality of the mode shapes derived through SSI was deemed to be higher than obtained from FDD and is likely a result of the inclusion of stochastic noise components in the mathematical formulation of the SSI state-space model. In general, the use of SSI techniques to estimate modal parameters from output-only experimental data has been found to be preferable to the FDD method despite the increased computational effort and subjectivity required to identify system poles. Lastly, the authors present an approach for estimating modal parameters using a single order state-space model developed through the SSI system identification, rather than selecting poles from a stabilization diagram.

ACKNOWLEDGEMENTS This research has been funded by the New York State

Energy Research and Development Authority (NYSERDA), in collaboration with the St. Lawrence Highway Department, and the New York State Department of Transportation (NYSDOT). The authors would also like to acknowledge the assistance of Kevin Cross and Dan Nyanjom during the field deployment and Michael Fuchs with system development. Any opinions, findings, and conclusions or recommendations

expressed in this paper are those of the authors and do not reflect the views of the agencies.

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