Using NI WSN and NI LabVIEW to Wirelessly Monitor Fatigue Damage on Highway Bridges

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"With NI wireless sensor networks and the new WSN strain node, we can easily monitor critical structures without excessive cables. We use WSN nodes that are programmable with LabVIEW for onboard analysis of strain data to achieve an in-depth understanding of structural response."

- Jeremiah Fasl, The University of Texas at Austin

The Challenge:
Developing an economical and reliable system for real-time structural monitoring and fatigue analysis of highway bridges.

The Solution:
Using a low-power wireless sensor network (WSN) system to capture dynamic traffic-induced strains and performing real-time data analysis with a standard rainflow counting algorithm.

Author(s):
Jeremiah Fasl - The University of Texas at Austin

The highway network in the United States includes more than 600,000 bridges, many of which have exceeded their intended service life. The primary method used to identify maintenance and safety issues is periodic visual inspections, which are required at least once every two years by the National Bridge Inspection Program. The ability to provide real-time structural data of fatigue-sensitive components, for example, can augment current inspection practices and provide quantitative data to transportation officials so that priorities can be set on inspection, retrofit, and/or replacement schedules.

With the goal of developing an economical and reliable monitoring system for highway bridges, researchers from the Department of Civil, Architectural and Environmental Engineering at The University of Texas at Austin collaborated with National Instruments and Wiss, Janney, Elstner Associates on a research project sponsored by the National Institute of Standards and Technology (NIST) through its Technology Innovation Program. The team developed and validated a complete wireless monitoring system based on the NI WSN platform and LabVIEW.

Wireless Sensor Networking for Bridge Monitoring and Testing

One of the main challenges of monitoring large structures, such as bridges, is the time and cost it takes to configure and install the required sensor wiring. Therefore, monitoring systems that effectively use WSN technology are attractive in the civil infrastructure industry. However, large steel and concrete structural components in bridges can prove to be a challenge to reliable wireless communications.

The research team at The University of Texas first evaluated the operation and performance of low-power wireless networks and antenna types in demanding bridge environments. The team studied multiple bridge types, focusing primarily on trapezoidal box girder and I-girder bridges (Figure 1). As reported in other publications, the team concluded that IEEE Standard 802.15.4 wireless technology provided reliable wireless communications in these bridge structures and the mesh networking feature helped us extend the network for longer wireless transmission.

Intelligent, Wireless Strain Data Acquisition

A key measurement for steel bridges is the live-load strain, or dynamic strain induced by traffic on the bridge. To meet the need for a low-power, wireless measurement device in bridge applications, NI worked with the research team to design the NI WSN-3214 strain gage node for the NI WSN platform. This node features a full set of strain gage signal conditioning, high-resolution digitization (up to 4 kS/s), and low-power operation.

A key requirement for this application was fatigue life estimation, which calls for continuous acquisition of dynamic strains, typically at least 50 S/s, from key bridge components (Figure 2). Generally, networks based on IEEE 802.15.4 have limited data bandwidth, and transmitting multiple streams of continuous live-load data could quickly deplete typical batteries.

Therefore, the team used the LabVIEW WSN Module to program the WSN-3214 node and perform data reduction by processing the data in real time using a standard rainflow counting algorithm (Figure 3). The program was developed to run embedded on the WSN-3214 node and can be notified by the controller to run in one of five modes of operation:

  1. Idle: Node acquisition is inactive; waiting for command
  2. Streaming: Periodically acquires and transmits strain waveforms
  3. Rainflow: Continuously acquires dynamic data, performs real-time rainflow counting, and transmits a histogram of the results at a configurable period (typically every 30 minutes)
  4. Trigger: Transmits time-series strain waveforms if a configurable trigger level is exceeded
  5. Rainflow + Trigger: Performs rainflow and trigger modes simultaneously

By only transmitting the histogram of the stress ranges, as determined by the rainflow analysis, instead of the raw strain data, the amount of data transmitted wirelessly is significantly reduced, which minimizes the power consumption of the nodes and extends the battery life.

Field-Testing at the US 290 Bridge

The WSN system was tested and validated at the direct connector between Interstate 35 North and US 290 East in Austin, Texas. This curved bridge features twin trapezoidal box girders, each 2 m deep, and has four spans, which range from 64 m to 70 m in length.

A WSN system consisting of four WSN-3214 strain gage nodes, two NI WSN-3212 thermocouple nodes, and an NI 9792 real-time WSN gateway was deployed on the bridge (Figure 4). By configuring the WSN-3214 nodes as network routers, the acquired strain and temperature data was wirelessly transmitted the entire length of the bridge through the box girders and the steel plate diaphragms located between each span. The WSN gateway was connected to a cellular gateway to wirelessly transmit the acquired data to the Internet and a data repository residing in the NI Technical Data Cloud, a cloud-based service for aggregating, storing, and sharing measurement data (Figure 5).

Conclusion

The low-power wireless monitoring system provides reliable data while avoiding many of the shortcomings of traditional dataloggers and wired bridge test systems.  The research team is currently researching the use of this system to predict the remaining fatigue life of steel bridges using probabilistic methods. This type of quantitative information can be very useful to bridge owners in developing economical maintenance strategies. 

Author Information:
Jeremiah Fasl
The University of Texas at Austin
jdfasl@utexas.edu

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