a remote bridge experiment with vibration control
Researchers at the University of Utah have been studying the vibrational characteristics of Rainbow Bridge, a sandstone arch bridge that spans the Feather River in northern California. A network of infrasound sensors was used to detect natural modes of the bridge from 2.6 km away and these frequencies were validated with on-structure accelerometers.
IEPE Accelerometers
The IEPE accelerometers used in the remote bridge experiment were manufactured by Gulf Coast Data Concepts, a manufacturer of microelectromechanical system (MEMS)-based acceleration sensors. The IEPE accelerometers are capable of measuring accelerations up to 50 G and feature built-in shock and sine-wave filtering. The accelerometers are powered with a DC bias voltage that is separate from the dynamic AC signal proportional to acceleration. The DC bias voltage must be filtered out within the signal conditioner/data acquisition system, otherwise it will degrade the performance of the accelerometer at low frequencies.
The Michigan Tech team used a variety of techniques to collect and analyze bridge-condition data. Infrasound monitoring, a geophysical technique utilizing acoustics below 20 Hz, was used to detect natural modes of the remote control vibrator, and the detected frequencies were validated with on-structure measurements. Infrasound analysis also showed that these modes were detectable from a distance of over 2.6 km.
A far-field infrasound array was set up at one end of the bridge and two additional arrays at locations 20 and 27 km away. For each hour, a time series of infrasound detections was collected and analyzed using a causal three-pole 1- to 10-Hz bandpass Butterworth filter. The filters were centered on frequencies known to correspond to the first several modes of long-span bridges. The resulting data were then used to determine the back azimuth at which the most significant power was directed toward the bridge.
Each bridge-condition data point was correlated to one of the infrasound detections and assigned a corresponding back azimuth. This process was repeated for each of the 1-hour windows over the 2-year monitoring period. When multiple of the far-field infrasound detections matched the same back azimuth, that hour was identified as a potential detection time for further investigation.
Enhanced data collection and analysis methods allow transportation agency bridge assessment teams to assess bridge condition in a more cost-effective, timely manner. This can help them allocate limited resources in repair and maintenance, extending the service life of existing bridges and maximizing the value of their investment. This research also highlights the importance of new methods that enable noncontact and nonline-of-sight remote sensing for bridge inspection, particularly those based on acoustics below 20 Hz.
Sensor Arrays
Infrasound sensor arrays were placed on Br 18-0009 in November 2015 to detect vibrations produced by the bridge deck movement. The sensors are located in three locations on Span 21 and three locations on Span 22, as shown in the image below. The infrasound sensor arrays consist of 12 Gulf Coast Data Concepts (Waveland, Mississippi) multifunction extended life accelerometer data logger – x2 units, which combine a battery-powered Class C microelectromechanical system (MEMS)-based accelerometer with a digitizer. The units are designed for continuous monitoring of bridge vibrations over a 46-h time frame. Each unit consists of a sensor, an on-board data recorder, and a power supply with up to 32 GB storage capacity. The sensor data loggers process the collected acceleration signals in two passbands: 0.5 to 10 Hz, and 0.5-4 Hz. The amplitudes of the detected frequencies in each passband were evaluated for correlation with the frequency-wave number model. Time intervals with a high correlation were selected and noted as potential detections.
For comparison, an accelerometer was also deployed on each of the bridge spans to capture and measure low-mode structural vibration. The accelerometer data was analyzed to validate the frequencies observed by the infrasound sensor arrays.
An additional goal of the experiment was to evaluate the scalability of this noncontact, persistent monitoring technology by analyzing sensor data over multiple days. The results from this analysis demonstrated that the infrasound-based approach could be scaled up to monitor a large-scale network of bridges.
This study presents a novel design strategy for a printed sensor array based on DIW printing technology that optimizes the three issues of simplicity, adaptability, and scalability. The resulting sensor arrays were able to detect vibrations on a concrete bridge and compare the signals to ground truth data that captured various levels of damage and contamination. This information is then used to develop predictive models that can be applied to the design and operation of future DIW-based sensing systems. This work represents an important step towards developing a scalable, robust, and cost-effective sensor platform for noncontact, persistent monitoring of engineering structures.
Vibration Monitoring
With 611,000 bridges in the United States and a need to maintain them at high levels of service, it is important that all available resources be used wisely. Noncontact remote sensing technologies that offer the ability to persistently monitor a bridge without the need for direct access would be an effective complement to existing hand inspection processes.
The objective of this experiment was to test the capability of a simple, low-cost sensor array that could detect vibrations from traffic impacts on a bridge deck and provide a continuous record for future analysis. The results showed that this technology, when combined with on-structure modal measurement and validation, can provide valuable information about the condition of a bridge.
Array infrasound sensors were deployed on both ends of the bridge deck of Br 18-0009, a steel two-girder bridge located in northern California. The sensors were connected to a computer that recorded the signals with a 24-bit resolution system. Data was digitized and sampled at 300 Hz. A Welch power spectral estimate was then performed for each signal binned based on peak acceleration amplitudes associated with the wheels of passing vehicles to determine frequencies in the bridge response.
A spectrogram of the frequency response of the deck from the first expected mode at 0.26 Hz to the fourth expected mode at about 0.62 Hz was shown in Figure 7. The displacement amplitudes for all of the frequency components within this range are well below the force of gravity for the bridge, and it can be concluded that there is little structural vibration due to traffic impact.
These frequency responses can be compared to the modal damping of the bridge measured on-structure using an operational modal analysis (OCA) technique. This can inform the assessment of strain conditions within the bridge structure, and a comparison between these measurements can be made against the predictions of finite-element models to evaluate their validity.
The infrasound-based MCS approach has the advantage over other techniques for monitoring a bridge in that it provides a direct measure of displacement – the primary output of the MCS process. This can be used to directly compare against OCA results and, with the addition of strain gauges, to deduce bridge stress state.
Modeling
As bridges age and experience increasing traffic loads, it becomes necessary to monitor their condition in order to determine the need for maintenance and repairs. Current SHM methods require the placement of sensors directly on a bridge structure, which are costly and difficult to maintain over long periods. These sensor locations are also limited to specific control points due to topographic conditions and bridge types, resulting in monitoring data that is often restricted to local metrics. Therefore, the ability to acquire unique signatures of a bridge that are both global and local in nature is needed.
A new technique is being explored that uses acoustic signals to measure structural deflection without the need for direct contact with the structure. This method, called infrasound monitoring, utilizes geophysical principles to measure vibrations induced on the bridge by traffic-induced stress and environmental influences. This information is used to develop unique “signatures” of a bridge’s condition, which are then correlated with in-place sensors to obtain a more complete picture of a bridge’s health and the need for maintenance.
To evaluate the feasibility of this technique, an experimental series was conducted using a specialized camera that uses actively illuminated LED targets to measure vertical motions of a bridge. Unlike conventional video deflectometers that use infrared light to detect structural motion, the proposed system uses high-brightness monochromatic LED targets to avoid interference from ambient lighting. In addition, a real-time digital image processing algorithm is used to provide spatial and temporal data that are subsequently used to calculate a dynamic bridge deflection series.
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The first part of the experimental series focused on a steel through-truss railroad bridge in Ft. Leonard Wood, Missouri. The bridge consists of two parallel, continuous-wave girders that span the Little Piney River and connect two towns. The accelerometers were triggered by vehicle wheel load impacts, and Welch’s power spectral estimates of the vibration responses to traffic peak-acceleration levels were performed.
The second part of the experimental series involved a similar bridge in northern California, which carries Route 20. The same through-truss design as the first bridge was employed for this study, but infrasound data were collected on both the bridge itself and at distances of approximately 2 km. Analysis of the far-field infrasound data identified a number of natural frequencies on the bridge, which were confirmed by on-structure accelerometer measurements.