The use of strain gauges is foundational to structural health monitoring, allowing infrastructure to continuously observe strain, infer stress, and potentially detect fatigue/fracture cracks. However, traditional strain gauges have drawbacks. In addition to being costly, a single-element strain gauge will only detect strain in a single direction and must be mounted on smooth surfaces to ensure good adhesion. Soft Elastomeric Capacitors (SECs) have been proposed as a low-cost alternative to traditional strain gauges while allowing for a broader range of applications. They are flexible and can be modeled with different dimensions based on the monitored structure. Each SEC consists of three layers; the two outer layers act as electrodes and are made of a styrene-ethylene-butylene-styrene polymer in a matrix with carbon black. The inner (dielectric) layer comprises titanium oxide in a matrix with SEBS. The use of the SECs is not limited by the geometry of the surface being monitored, and it can, therefore, be adhered to a variety of surfaces as its flexibility allows it to conform to the irregularity and complexity of the monitored structure. The change experienced by a structure will correlate directly to the change in capacitance observed across the sensor, which can be used to predict the monitored structure’s state. While SECs have been studied for applications on various materials, experiments have been limited to adhering the sensor to smooth surfaces. However, concrete structures have various surface finishes that are not uniform, often deriving from an architect’s aesthetic desire. This work tests a corrugated SEC through compression tests on concrete samples with different surface finishing to investigate the effect of surface finishing on the SEC-measured strain. Each concrete sample is subjected to loading by a dynamic testing system, and the data collected from the SEC are compared to off-the-shelf resistive strain gauges. The results show that the performance of the cSEC on the different surfaces is not hindered by different concrete finishes, where a high signal-to-noise ratio of 21 dB and low mean absolute error of 22 μϵ is seen on the concrete specimen with a rough concrete surface. The strain metrics and surface effect on SEC performance are discussed.
This paper studies time-delayed simultaneous input and state estimation to enhance estimation accuracy for systems without direct feedthrough, such as earthquake-excited building structures, using absolute floor acceleration measurements. Rank matching, strong observability, and invertibility conditions are crucial for the stability and convergence of input and state estimation. Real-time approaches have achieved successful estimations when those conditions are satisfied. However, a dynamic system model often does not hold those conditions when using acceleration measurements, leading to significant errors in the estimations. To this end, the authors recently developed an optimal sensor placement algorithm to ensure the system model holds the above conditions to achieve accurate real-time estimation. However, accurate estimation in some cases remains challenging because of incomplete measurements, modeling error, and measurement noise. This paper proposes an extended time-delayed joint input and state estimation algorithm (ETDIS) based on the invertibility matrix. Specifically, by incorporating the prior knowledge of the input, the proposed ETDIS is designed from a Bayesian perspective, considering measurement noise to enhance estimation accuracy. In particular, the innovation is used to obtain the input estimation, which is interconnected with the state space equation for state estimation. ETDIS relaxes the rank-matching condition and is more robust against the lack of conditions. Accurate online input and state estimation with a delay and satisfactory computational cost is achieved by utilizing the proposed ETDIS and limited acceleration measurements. Numerical studies are presented to verify the effectiveness of the proposed method.
Surface strain sensors, such as linear variable differential transformers, fiber Bragg gratings, and resistive strain gauges, have seen significant use for monitoring concrete infrastructure. However, spatial monitoring of concrete structures using these sensor systems is limited by challenges in the surface coverage provided by a specific sensor or issues related to mounting and maintaining numerous mechanical sensors on the structure. A potential solution to this challenge is the deployment of large-area electronics in the form of a sensing skin to provide complete coverage of a monitored area while being simple to apply and maintain. Along this line of effort, networks constituted of soft elastomeric capacitors have been deployed to monitor strain on steel and composite structures. However, using soft elastomeric capacitors on concrete surfaces has been challenging due to the electrical coupling between the sensors and concrete, which amplifies transduced strain signals obtained from the soft elastomeric capacitors. In this work, the authors investigate the isolation of the soft elastomeric capacitors from the concrete by extending the styrene-block-ethylene-co-butylene-block-styrene matrix of the soft elastomeric capacitors to include a decoupling layer between the electrode and the concrete. Experimental investigations are carried out on concrete specimens for which the soft elastomeric capacitor is adhered to with a thin layer of off-the-shelf epoxy and then loaded on the dynamic testing system to monitor strain provoked on the concrete samples. The results presented here demonstrate the viability of the electrically isolated soft elastomeric capacitors for monitoring strain on concrete structures. Initial comparisons between un-isolated and electrically isolated soft elastomeric capacitors showed that the nominal capacitance of the soft elastomeric capacitor is significantly lowered by adding an isolation layer of SEBS. Furthermore, strain results for the soft elastomeric capacitors are compared to ones from a resistive strain gauge and digital image correlation. The data obtained is significant for modifying soft elastomeric capacitors with the anticipation for future use on concrete structures.
Fatigue cracks can develop in mechanical, aerospace, and civil engineering structures over time due to repetitive loads. Growing fatigue cracks could reduce the lifespan of the structure and lead to catastrophic collapse. Distortion-induced fatigue cracks are specifically concerning in steel bridges. Computer vision-based crack detection have shown great potential in crack detection for being robust and easy-to-deploy. A vision-based feature point tracking method measures the changes in surface motion to detect fatigue crack and performs well in the presence of other crack like features like corrosion marks, boundaries, etc. When the video is recorded using a moving camera like a handheld camera, unmanned aerial vehicle, and mixed reality headset worn by an inspector, feature point movement contains camera motion as well as the true object motion. To accurately detect cracks, feature point displacement needs to be free from camera motion. Distortion induced fatigue cracks occur in regions with complex geometries like web-gap regions in girder bridges. Due to parallax effects, a single geometric transformation is not enough to compensate the camera motion accurately in videos with such complex geometry. The bundled camera paths approach divides a video into multiple mesh grid cells and estimates motion in each cell individually. These camera paths are then optimized to remove camera jitters and rolling shutter effects producing stable video. However, the global camera motion is still present in the smoothed video. We have extended the bundled camera paths method to remove the global motion from the smoothed video. The proposed approach was successfully tested in a laboratory experiment to compensate camera motion and detect distortion induced fatigue cracks.
This paper investigates the mechanism behind the wind-induced vibration of high mast illumination pole (HMIP) structures using wireless smart sensors. Several video recordings revealed significant vibrations of an HMIP under wind loading in Kansas, resulting in large cyclic displacements. In this study, to facilitate the estimate for the main cause of the HMIP’s vibration, finite element modeling and video analysis are employed to evaluate the fundamental natural frequencies and the recorded vibration frequencies of the HMIP of interest. Meanwhile, a 100-foot-tall, galvanized steel HMIP with three LED luminaires is selected for long-term vibration monitoring. A wireless smart sensor network is designed to monitor the structure's acceleration response, wind speed, and wind direction to further investigate the primary cause of the excessive wind-induced vibrations.
Steel bridges are susceptible to fatigue damage under traffic loading, and many bridges operate with existing cracks. The discovery and long-term monitoring of those fatigue cracks are critical for safety evaluations. In previous studies, the ability of the soft elastomeric capacitor (SEC) sensor that measures large-area strain was validated for detecting and monitoring fatigue crack growth in a laboratory environment. In this study, the performance of the technology is evaluated for field applications, for which an approach for long-term monitoring of fatigue cracks is developed. The approach consists of an integrated system, termed the wireless large-area strain sensors (WLASS), for wireless data collection and storage and a signal processing algorithm for monitoring fatigue cracks with bridge response induced by traffic loading. In particular, the WLASS consists of soft elastomeric capacitors (SECs) combined with sensor boards to convert capacitance to a measurable change in voltage and a wireless sensing platform equipped with event-triggered sensing, wireless data collection, cloud storage, and remote data retrieval. A modified crack growth index (CGI) is developed through detection of peak-to-peak amplitudes of the wavelet transform. Using the measurements from the WLASS, the modified CGI is able to obtain the crack status under various loading events due to random traffic loads. The performance of the developed approach is validated using a steel highway bridge.
An accurate numerical model that is able to represent real structural behaviors and reproduce structural responses with high fidelity is critical to numerous engineering applications such as damage detection, diagnosis, and prognosis, and data assimilation. While a wide variety of methods have been developed in the past decades for finite element model updating, a widely adopted concept is to solve a constrained optimization problem to minimize the prediction errors, such as those formulated by modal properties, frequency response functions, and static and dynamic responses, among others. This paper considers model updating for earthquake-excited building structures using incomplete acceleration measurements. In this case, due to the transient nature and limited duration of earthquake responses, as well as incomplete instrumentation and measurement noise, identifying accurate and adequate number of modal parameters for model updating is challenging. Furthermore, in presence of static nonlinear functions, model updating is a nonlinear inverse problem solved using nonlinear optimization methods. This often faces divergences due to issue of nonconvexity, resulting in unreasonable parameter estimations. Therefore, identifying an accurate model of building structure under earthquake excitation is still a challenge. In this paper, we propose a maximum a posteriori (MAP)- based approach using measured earthquake response time histories, which renders model updating as a regularized nonlinear optimization problem. The prior knowledge of structural parameters is incorporated to constrain the estimation. One main advantage of the proposed approach is that it makes no assumption of the upper and lower bounds while ensuring the physical meaning of the structural parameters. The proposed approach is validated through a numerical example.
The effect of low energy impacts can seriously impair the operational life span of composites in the field. These low-energy impacts can induce a permanent loss in the toughness of the composite without any visible indication of the material’s compromise. The detection of this damage utilizing nondestructive inspection requires dense measurements over much of the surface and has been traditionally achieved by removing the part from service for advanced imaging techniques. While these methods can accurately diagnose the damage inflicted internally by the impacts, they accrue non-trivial opportunity costs while the structure is inspected. To enable the capabilities of in-service monitoring of the composite, the novel soft elastomeric capacitor was investigated as a sensing solution. The sensor is made of three layers comprised of a styrene-ethylene-butylene-styrene (SEBS) matrix, a commercially available elastomer. These layers consist of a titania filled center layer that forms the dielectric of the capacitor and two highly conductive outer layers doped with carbon black. This simple formation allows for a capacitor that has extremely robust mechanical properties. The soft elastomeric capacitor functions by taking up deformations on the surface of the composite that is transduced into a measurable change in capacitance. This study provides an electro-mechanical model for impact damage and experimentally investigates the efficacy of these sensors for use in damage detection given their promising characteristics; that being that the sensor geometry can be arbitrarily large allowing for much fewer sensors than traditional sensor networks employed for this task at a much lower cost than installing traditional in-situ sensing solutions. To investigate these properties a set of impact trials were undertaken on a drop tower using small samples of glass fiber reinforced plate, of random orient and short fiber, with a soft elastomeric capacitor mounted directly opposite the impact site. The impactor head was only allowed one contact with the sample before being intercepted. The testing range for the samples ranged from well below the yield strength of the glass fiber reinforced plate to the ultimate strength of the plate. Experimental results reported a square root relation between the impact energy given to the plate when inducing plastic deformations and the sensor’s measured change in capacitance.
Automatic fatigue crack detection using commercial sensing technologies is difficult due to the highly localized nature of crack monitoring sensors and the randomness of crack initiation and propagation. The authors have previously proposed and demonstrated a novel sensing skin capable of fatigue crack detection, localization, and quantification. The technology is based on soft elastomeric capacitors (SECs) that constitute thin-film flexible strain sensors transducing strain into a measurable change in capacitance. Deployed in an array configuration, the SECs mimic biological skin, where local damage can be diagnosed over large surfaces. Recently, the authors have proposed a significantly improved version of the SEC, whereby the top surface of the sensor is corrugated in diverse non-auxetic and auxetic patterns. Laboratory investigations of non-auxetic patterns have shown that the use of corrugation can increase the sensor’s gauge factor, linearity, and signal stability when compared to untextured sensors, while numerical analyses of auxetic patterns have shown their superiority over non-auxetic corrugations. In this paper, we experimentally study the use of corrugated SECs, in particular with grid, diagrid, reinforced diagrid, and re-entrant hexagonal honeycomb-type (auxetic) patterns as a significant improvement to the untextured SEC in monitoring fatigue cracks in steel specimens. Results show that the use of corrugation significantly improves sensing performance, with both the reinforced diagrid and auxetic patterns yielding best results in terms of signal linearity, sensitivity, and resolution, with the reinforced diagrid having the added advantage of a symmetric pattern that could facilitate field deployments.
This paper proposes online input, state, and response estimation based on Augmented Kalman filter for systems without direct feedthrough, such as earthquake-excited building structures with absolute floor acceleration measurements. Measurement noise, modelling error, and incomplete absolute acceleration measurement are considered. The system model in this case lacks direct feedthrough, resulting in weak observability of system input, for which a small uncertainty in the model and measurement data would lead to a drastic change in the estimation. The augmented state Kalman filter for system without direct feedthrough is proposed for earthquake-excited building structures, in which the input with known variance is augmented with states in order to estimate them together. Compared with unbiased minimum-variance input and state estimation methods that make no assumption of input, the proposed online approach is able to perform robust estimation of states, input, and responses at unmeasured locations successfully using only a limited number of absolute acceleration measurements.
Estimating both state and ground input for earthquake-excited building structures using a limited number of absolute acceleration measurements is critical to post-disaster damage assessment and structural evaluation. Input estimation in this case is particularly challenging due to the lack of direct feedthrough term, which renders the system weakly observable for its input. Hence, input estimation in this scenario is sensitive to modeling error and measurement noise. In this paper, a two-step strategy is proposed to estimate both state (displacement and velocity) and ground input using a limited number of absolute acceleration measurements for building structures. First, the ground input is estimated by solving a least squares problem with Tikhonov regularization and Bayesian inference. In the second step, floor states are estimated using Kalman filter with input obtained from the first step, the least squares with Tikhonov regularization and Bayesian inference. The proposed strategy was numerically evaluated based on a sheartype building structure.
Distortion-induced fatigue cracks caused by differential deflections between adjacent girders are common issues for steel girder bridges built prior to the mid-1980s in the United States. Monitoring these fatigue cracks is essential to ensure bridge structural integrity. Despite various level of success of crack monitoring methods over the past decades, monitoring distortion-induced fatigue cracks is still challenging due to the complex structural joint layout and unpredictable crack propagation paths. Previously, the authors proposed soft elastomeric capacitor (SEC), a large-size flexible capacitive strain sensor, for monitoring in-plane fatigue cracks. The crack growth can be robustly identified by extracting the crack growth index (CGI) from the measured capacitance signals. In this study, the SECs are investigated for monitoring distortion-induced fatigue cracks. A dense array of SECs is proposed to monitor a large structural surface with fatigue-susceptible details. The effectiveness of this strategy has been verified through a fatigue test of a large-scale bridge girder to cross-frame connection model. By extracting CGIs from the SEC arrays, distortion-induced fatigue crack growth can be successfully monitored.
A capacitance based large-area electronics strain sensor, termed soft elastomeric capacitor (SEC) has shown various advantages in infrastructure sensing. The ability to cover large area enables to reflect mesoscale structural deformation, highly stretchable, easy to fabricate and low-cost feature allow full-scale field application for civil structure. As continuing efforts to realize full-scale civil infrastructure monitoring, in this study, new sensor board has been developed to implement the capacitive strain sensing capability into wireless sensor networks. The SEC has extremely low-level capacitance changes as responses to structural deformation; hence it requires high-gain and low-noise performance. For these requirements, AC (alternating current) based Wheatstone bridge circuit has been developed in combination a bridge balancer, two-step amplifiers, AM-demodulation, and series of filtering circuits to convert low-level capacitance changes to readable analog voltages. The new sensor board has been designed to work with the wireless platform that uses Illinois Structural Health Monitoring Project (ISHMP) wireless sensing software Toolsuite and allow 16bit lownoise data acquisition. The performances of new wireless capacitive strain sensor have been validated series of laboratory calibration tests. An example application for fatigue crack monitoring is also presented.
Fatigue cracks developed in metallic materials are of critical safety concerns for mechanical, aerospace, and civil engineering structures. For fracture-critical structures, if not appropriately inspected, excessive growth of fatigue cracks can lead to catastrophic structural failures. Current crack detection technologies developed for nondestructive testing (NDT) or structural health monitoring (SHM) often require costly equipment, extensive human involvement, or complex signal processing algorithms. Recently, computer vision-based methods have shown great promise in damage detection for being contactless, low cost, and easy-to-deploy. In this paper, we propose a novel computer vision-based method for detecting fatigue cracks in a video stream. This method is based on tracking the surface motion of structural members under crack opening and closing, and identifying fatigue cracks by extracting discontinuities in the surface motion caused by cracking. The effectiveness of this method was validated through an experimental test of a steel compact, C(T), specimen. Results indicate that the proposed approach can robustly detect the fatigue crack under ambient lighting condition, despite the crack was surrounded by other crack-like edges, covered by complex surface textures, or invisible to human eyes under crack closure.
Bolted steel joints are one of the most common types of connections in steel structures. Due to significant loads carried over long-term operation, bolted steel joints are prone to structural damage. Monitoring bolted steel joints is critical to ensure their functionality and structural safety. Among all factors related with joint damage, bolt loosening has been reported as a main cause of the damage of bolted joints. Detecting bolt loosening is therefore critical for the heath assessment of bolted steel joints. Recently, computer vision-based structural health monitoring (SHM) methods have been proposed in many research fields due to the benefits of being low-cost, easy-to-deploy, and contactless. In this study, we propose an image-based feature tracking approach to detect bolt loosening in steel connections. The method relies on a feature tracking algorithm, through which densely distributed feature points can be automatically detected and tracked from multiple images taken at different times. A novel algorithm is established to rapidly search feature points and track the movement of these feature points between images. If the bolt is loosened, feature points associated with the loosened bolt would exhibit a unique rotational movement pattern. By highlighting these feature points, the loosened bolt can be successfully localized. The effectiveness of the proposed approach was verified by a laboratory test of a steel joint using a consumer-grade digital camera.
KEYWORDS: Monte Carlo methods, Probability theory, Performance modeling, Stochastic processes, Error analysis, Buildings, Statistical modeling, Shape analysis, Mathematical modeling, Process modeling, Statistical analysis, Systems modeling
In Bayesian model updating, the likelihood function is commonly formulated by stochastic embedding in which the maximum information entropy probability model of prediction error variances plays an important role and it is Gaussian distribution subject to the first two moments as constraints. The selection of prediction error variances can be formulated as a model class selection problem, which automatically involves a trade-off between the average data-fit of the model class and the information it extracts from the data. Therefore, it is critical for the robustness in the updating of the structural model especially in the presence of modeling errors. To date, three ways of considering prediction error variances have been seem in the literature: 1) setting constant values empirically, 2) estimating them based on the goodness-of-fit of the measured data, and 3) updating them as uncertain parameters by applying Bayes’ Theorem at the model class level. In this paper, the effect of different strategies to deal with the prediction error variances on the model updating performance is investigated explicitly. A six-story shear building model with six uncertain stiffness parameters is employed as an illustrative example. Transitional Markov Chain Monte Carlo is used to draw samples of the posterior probability density function of the structure model parameters as well as the uncertain prediction variances. The different levels of modeling uncertainty and complexity are modeled through three FE models, including a true model, a model with more complexity, and a model with modeling error. Bayesian updating is performed for the three FE models considering the three aforementioned treatments of the prediction error variances. The effect of number of measurements on the model updating performance is also examined in the study. The results are compared based on model class assessment and indicate that updating the prediction error variances as uncertain parameters at the model class level produces more robust results especially when the number of measurement is small.
A large-area electronics (LAE) strain sensor, termed soft elastomeric capacitor (SEC), has shown great promise in fatigue crack monitoring. The SEC is able to monitor strain changes over a mesoscale structural surface and endure large deformations without being damaged under cracking. Previous tests verified that the SEC is able to detect, localize, and monitor fatigue crack activities under low-cycle fatigue loading. In this paper, to examine the SEC’s capability of monitoring high-cycle fatigue cracks, a compact specimen is tested under cyclic tension, designed to ensure realistic crack opening sizes representative of those in real steel bridges. To overcome the difficulty of low signal amplitude and relatively high noise level under high-cycle fatigue loading, a robust signal processing method is proposed to convert the measured capacitance time history from the SEC sensor to power spectral densities (PSD) in the frequency domain, such that signal’s peak-to-peak amplitude can be extracted at the dominant loading frequency. A crack damage indicator is proposed as the ratio between the square root of the amplitude of PSD and load range. Results show that the crack damage indicator offers consistent indication of crack growth.
Understanding the dynamic behavior of complex structures such as long-span bridges requires dense deployment of
sensors. Traditional wired sensor systems are generally expensive and time-consuming to install due to cabling. With
wireless communication and on-board computation capabilities, wireless smart sensor networks have the advantages of
being low cost, easy to deploy and maintain and therefore facilitate dense instrumentation for structural health
monitoring. A long-term monitoring project was recently carried out for a cable-stayed bridge in South Korea with a
dense array of 113 smart sensors, which feature the world’s largest wireless smart sensor network for civil structural
monitoring. This paper presents a comprehensive statistical analysis of the modal properties including natural
frequencies, damping ratios and mode shapes of the monitored cable-stayed bridge. Data analyzed in this paper is
composed of structural vibration signals monitored during a 12-month period under ambient excitations. The correlation
between environmental temperature and the modal frequencies is also investigated. The results showed the long-term
statistical structural behavior of the bridge, which serves as the basis for Bayesian statistical updating for the numerical
model.
A newly-developed soft elastomeric capacitor (SEC) strain sensor has shown promise in fatigue crack monitoring. The SECs exhibit high levels of ductility and hence do not break under excessive strain when the substrate cracks due to slippage or de-bonding between the sensor and epoxy. The actual strain experienced by a SEC depends on the amount of slippage, which is difficult to simulate numerically, making it challenging to accurately predict the response of a SEC near a crack. In this paper, a two-step approach is proposed to simulate the capacitance response of a SEC. First, a finite element (FE) model of a steel compact tension specimen was analyzed under cyclic loading while the cracking process was simulated based on an element removal technique. Second, a rectangular boundary was defined near the crack region. The SEC outside the boundary was assumed to have perfect bond with the specimen, while that inside the boundary was assumed to deform freely due to slippage. A second FE model was then established to simulate the response of the SEC within the boundary subject to displacements at the boundary from the first FE model. The total simulated capacitance was computed from the model results by combining the computed capacitance inside and outside the boundary. The performance of the simulation incorporating slippage was evaluated by comparing the model results with the experimental data from the test performed on a compact tension specimen. The FE model considering slippage showed results that matched the experimental findings more closely than the FE model that did not consider slippage.
In bridge construction, geometry control is critical to ensure that the final constructed bridge has the consistent shape as design. A common method is by predicting the deflections of the bridge during each construction phase through the associated finite element models. Therefore, the cambers of the bridge during different construction phases can be determined beforehand. These finite element models are mostly based on the design drawings and nominal material properties. However, the accuracy of these bridge models can be large due to significant uncertainties of the actual properties of the materials used in construction. Therefore, the predicted cambers may not be accurate to ensure agreement of bridge geometry with design, especially for long-span bridges. In this paper, an improved geometry control method is described, which incorporates finite element (FE) model updating during the construction process based on measured bridge deflections. A method based on the Kriging model and Latin hypercube sampling is proposed to perform the FE model updating due to its simplicity and efficiency. The proposed method has been applied to a long-span continuous girder concrete bridge during its construction. Results show that the method is effective in reducing construction error and ensuring the accuracy of the geometry of the final constructed bridge.
Fatigue cracks have been one of the major factors for the deterioration of steel bridges. In order to maintain structural integrity, monitoring fatigue crack activities such as crack initiation and propagation is critical to prevent catastrophic failure of steel bridges due to the accumulation of fatigue damage. Measuring the strain change under cracking is an effective way of monitoring fatigue cracks. However, traditional strain sensors such as metal foil gauges are not able to capture crack development due to their small size, limited measurement range, and high failure rate under harsh environmental conditions. Recently, a newly developed soft elastomeric capacitive sensor has great promise to overcome these limitations. In this paper, crack detection capability of the capacitive sensor is demonstrated through Finite Element (FE) analysis. A nonlinear FE model of a standard ASTM compact tension specimen is created which is calibrated to experimental data to simulate its response under fatigue loading, with the goal to 1) depict the strain distribution of the specimen under the large area covered by the capacitive sensor due to cracking; 2) characterize the relationship between capacitance change and crack width; 3) quantify the minimum required resolution of data acquisition system for detecting the fatigue cracks. The minimum resolution serves as a basis for the development of a dedicated wireless data acquisition system for the capacitive strain sensor.
System identification of civil engineering structures are often formulated as Multiple-Input, Multiple-Output (MIMO)
problems due to the complexity of loading conditions such as differential ground motion, which is also multi-directional
in nature. Such MIMO system identification problems are challenging due to strong coupling between the contributions
of multiple ground motion inputs to each individual response. Compared with Single-Input, Multiple-Output (SIMO)
system identification, MIMO problems are often more computationally complex and error prone. In this paper, a new
system identification strategy is proposed in which a more complex MIMO problem is converted into a number of SIMO
problems by decoupling the contribution of multiple inputs to the outputs. A QR-factorization based approach is adopted
for the decoupling and its accuracy is investigated. The effectiveness of the proposed strategy is demonstrated through
applications to a two-span straight bridge and a four-span curved bridge, both are highway bridges.
KEYWORDS: Bridges, Sensors, Smart sensors, Sensor networks, Structural health monitoring, Antennas, Data processing, Solar cells, Connectors, Data transmission
Cables are critical load carrying members of cable-stayed bridges; monitoring tension forces of the cables provides valuable information for SHM of the cable-stayed bridges. Monitoring systems for the cable tension can be efficiently realized using wireless smart sensors in conjunction with vibration-based cable tension estimation approaches. This study develops an automated cable tension monitoring system using MEMSIC’s Imote2 smart sensors. An embedded data processing strategy is implemented on the Imote2-based wireless sensor network to calculate cable tensions using a vibration-based method, significantly reducing the wireless data transmission and associated power consumption. The autonomous operation of the monitoring system is achieved by AutoMonitor, a high-level coordinator application provided by the Illinois SHM Project Services Toolsuite. The monitoring system also features power harvesting enabled by solar panels attached to each sensor node and AutoMonitor for charging control. The proposed wireless system has been deployed on the Jindo Bridge, a cable-stayed bridge located in South Korea. Tension forces are autonomously monitored for 12 cables in the east, land side of the bridge, proving the validity and potential of the presented tension monitoring system for real-world applications.
KEYWORDS: Clocks, Structural health monitoring, Sensors, Data communications, Smart sensors, Temperature metrology, Telecommunications, Data modeling, Switches, Receivers
Wireless Smart Sensor Networks (WSSNs) have attracted great attention in recent years for Structural Health Monitoring
(SHM), enabling better understanding of the dynamic behavior of large scale civil infrastructures through dense
deployment of sensors. With a fraction of the deployment time and cost compared with wired SHM systems, WSSNs can
serve as ideal systems for campaign-type monitoring for (i) short-term, in-service performance evaluation, (ii) postdisaster
condition assessment, (iii) design optimization of long-term SHM system before permanent deployment, etc.
Efficient data collection is generally needed in campaign monitoring due to limited operation time. A number of
improvements have been made to the Illinois SHM Project (ISHMP) Services Toolsuite to facilitate efficient data
collection for campaign monitoring. A post-sensing time synchronization scheme is proposed to reduce the latency of
data collection while maintaining high accuracy of synchronization of collected data. A multi-hop bulk data transfer
approach using multiple RF channels is also implemented to achieve high data throughput.
Rapid advancement of sensor technology has been changing the paradigm of Structural Health Monitoring (SHM)
toward a wireless smart sensor network (WSSN). While smart sensors have the potential to be a breakthrough to current
SHM research and practice, the smart sensors also have several important issues to be resolved that may include robust
power supply, stable communication, sensing capability, and in-network data processing algorithms. This study is a
hybrid WSSN that addresses those issues to realize a full-scale SHM system for civil infrastructure monitoring. The
developed hybrid WSSN is deployed on the Jindo Bridge, a cable-stayed bridge located in South Korea as a continued
effort from the previous year's deployment. Unique features of the new deployment encompass: (1) the world's largest
WSSN for SHM to date, (2) power harvesting enabled for all sensor nodes, (3) an improved sensing application that
provides reliable data acquisition with optimized power consumption, (4) decentralized data aggregation that makes the
WSSN scalable to a large, densely deployed sensor network, (5) decentralized cable tension monitoring specially
designed for cable-stayed bridges, (6) environmental monitoring. The WSSN implementing all these features are
experimentally verified through a long-term monitoring of the Jindo Bridge.
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