Real-Time Low-Cost Wireless Reference-Free Displacement Sensing of Railroad Bridges

  • Ali Ozdagli
  • Bideng Liu
  • Fernando Moreu
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)


The U.S. freight rail network moves about 40 tons of freight per person over 225,000 km (140,000 miles) of rail track every year. The railroad infrastructure contains more than 100,000 bridges, which correspond to one bridge for every 2.25 km (1.4 miles) of track. Railroad resources and funds are limited. Consequently, railroads’ maintenance, repair, and replacement (MRR) decisions should be optimized. An objective prioritization of MRR decisions requires quantitative data that informs the structural integrity. Lateral displacement measurement of bridges is an objective and quantitative performance indicator. Traditional wired displacement measurement systems are costly, labor-intensive, and are difficult to apply on bridges due to the need of stationary reference points. This paper proposes an Arduino-based low-cost wireless sensing system to estimate bridge displacements from acceleration data. The system uses a low-cost MMA8451 accelerometer and implements a FIR-filter to convert the measurements to displacement. The data is transmitted to the base station using a XBee Series 1 module in real-time. Each sensor platform is estimated to cost about $75. To evaluate the feasibility of the proposed system, a set of laboratory experiments are conducted by placing the sensor platform on a shake table and simulating bridge displacements measured on the field during train crossing events. The proposed measurement system can have impact on many applications that need real-time displacement information including, but not limited to aerospace engineering, mechanical engineering, and wind engineering.


Wireless sensing Low-cost sensing Real-time sensing Acceleration Reference-free displacement estimation 



The financial support of this research is provided in part by the Department of Civil Engineering at the University of New Mexico, the Center for Teaching and Learning of the University of New Mexico under Teaching Allocation Grant, New Mexico Space Grant Consortium under NASA Award Number NNX15AL51H, Transportation Consortium of South-Central States (TRANSET) and US Department of Transportation (USDOT) under Project Number 17STUNM02, New Mexico Consortium under grant Number 249-01, and Los Alamos County Project under UNM Grant 2RKB5, and National Natural Science Foundation of China under grant number 51208107. The authors of this paper thank the Canadian National Railway (CN) for the data collected on the field to inform this proposed method. The conclusions of this research solely represent those of the authors.


  1. 1.
    Federal Highway Administration (FHWA): Freight facts and figures 2006. (2006). Accessed 15 July 2017
  2. 2.
    Cambridge Systematics, Inc.: National Rail Freight Infrastructure Capacity and Investment Study. Cambridge Systematics, Cambridge (2007)Google Scholar
  3. 3.
    Moreu, F., Kim, R.E., Spencer Jr., B.F.: Railroad bridge monitoring using wireless smart sensors. Struct. Control. Health Monit. (2016).
  4. 4.
    American Railway Engineering and Maintenance-of-Way Association (AREMA): Practical guide to railway engineering. Lanham: MD, 2003 (2003)Google Scholar
  5. 5.
    Association of American Railroads (AAR): A short history of U.S. Freight Railroads. (2017d). Accessed 15 July 2017
  6. 6.
    Transportation Research Board (TRB): Maintenance and operations of transportation facilities. Transportation Research Circular, E-C092 (2006)Google Scholar
  7. 7.
    Moreu, F., Kim, R.E., Spencer, B.F.: Railroad bridge monitoring using wireless smart sensors. Struct. Control. Health Monit. 24(2), (2017)CrossRefGoogle Scholar
  8. 8.
    Otter, D., Joy, R., Jones, M.C., Maal L.: Needs for bridge monitoring systems based on railroad bridge service interruptions. In:Transportation Research Board 91st Annual Meeting Proceedings (2012)Google Scholar
  9. 9.
    Otter, D., Unsworth, J.F. and Carter, J.N. Jr.: A railroad perspective on bridge measurement and monitoring systems. Structures Congress 2017, pp. 302–313 (2017)Google Scholar
  10. 10.
    Moreu, F., LaFave, J.M.: Current Research Topics: Railroad Bridges and Structural Engineering. Newmark Structural Engineering Laboratory. University of Illinois at Urbana-Champaign, Champaign (2012)Google Scholar
  11. 11.
    Moreu, F., LaFave, J.: Survey of current research topics-railroad bridges and structural engineering. Railw. Track Struct. 107(9), 65–70 (2011)Google Scholar
  12. 12.
    Uppal, A.S., Rizkalla, S.H., Pinkney, R.B.: Response of timber bridges under train loading. Can. J. Civ. Eng. 17(6), 940–951 (1990). CrossRefGoogle Scholar
  13. 13.
    Moreu, F., Jo, H., Li, J., Kim, R., Cho, S., Kimmle, A., Scola, S., Le, H., Spencer Jr., B., LaFave, J.: Dynamic assessment of timber railroad bridges using displacements. J. Bridg. Eng. 04014114 (2014). CrossRefGoogle Scholar
  14. 14.
    Gavin, H.P., Rodrigo, M., Kathryn, R.: Drift-free integrators. Rev. Sci. Instrum. 69(5), 2171–2175 (1998)CrossRefGoogle Scholar
  15. 15.
    Lynch, J.P., Loh, K.J.: A summary review of wireless sensors and sensor networks for structural health monitoring. Shock Vib. Dig. 38(2), 91–130 (2006)CrossRefGoogle Scholar
  16. 16.
    Lynch, J.P.: An overview of wireless structural health monitoring for civil structures. Philos. Trans. R. Soc. Lond. A Math. Phys. Eng. Sci. 365(1851), 345–372 (2007)CrossRefGoogle Scholar
  17. 17.
    Celebi, M.: Seismic instrumentation of buildings (With Emphasis on Federal Buildings), Technical Report No. 0–7460-68170, United States Geological Survey, Menlo Park, CA (2002)Google Scholar
  18. 18.
    Farrar, C.R.: Historical overview of structural health monitoring. Lecture Notes on Structural Health Monitoring Using Statistical Pattern Recognition, Los Alamos Dynamics, Los Alamos, NM (2001)Google Scholar
  19. 19.
    Spencer, B.F., Ruiz-Sandoval, M.E., Kurata, N.: Smart sensing technology: opportunities and challenges. Struct. Control. Health Monit. 11(4), 349–368 (2004)CrossRefGoogle Scholar
  20. 20.
    Spencer Jr., B.F., Moreu, F., Kim, R.E.: Campaign Monitoring of Railroad Bridges in High-Speed Rail Shared Corridors Using Wireless Smart Sensors. Newmark Structural Engineering Laboratory. University of Illinois at Urbana-Champaign, Champaign (2015)Google Scholar
  21. 21.
    Chebrolu, K., Raman, B., Mishra, N., Valiveti, P.K. and Kumar, R.: Brimon: a sensor network system for railway bridge monitoring. In: Proceedings of the 6th International Conference on Mobile Systems, Applications, and Services, pp. 2–14. ACM (2008)Google Scholar
  22. 22.
    Polastre, J., Hill, J. and Culler, D.: Versatile low power media access for wireless sensor networks. In: Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems, pp. 95–107 (2004)Google Scholar
  23. 23.
    Polastre, J., Szewczyk, R. and Culler, D.: Telos: enabling ultra-low power wireless research. In: Proceedings of the 4th International Symposium on Information Processing in Sensor Networks, p. 48, IEEE Press (2005)Google Scholar
  24. 24.
    Bischoff R., Meyer J., Enochsson O., Feltrin G., Elfgren L. (2009). Event-based strain monitoring on a railway bridge with a wireless sensor network. In: Proceedings of the 4th International Conference on Structural Health Monitoring of Intelligent Infrastructure; Zurich, Switzerland, 22–24; pp. 74–82Google Scholar
  25. 25.
    Hay, T.R.: Wireless Remote Structural Integrity Monitoring for Railway Bridges (No. HSR-IDEA Project 54), High-Speed Rail IDEA Program. Transportation Research Board, Washington, DC (2007)Google Scholar
  26. 26.
    Flammini F., Gaglione A., Ottello F., Pappalardo A., Pragliola C., and Tedesco A.: Towards wireless sensor networks for railway infrastructure monitoring. Proceedings of Electrical Systems for Aircraft, Railway and Ship Propulsion (ESARS), Bologna, Italy, 19–21 (2010)Google Scholar
  27. 27.
    Cho, S., Giles, R.K., Spencer, B.F.: System identification of a historic swing truss bridge using a wireless sensor network employing orientation correction. Struct. Control. Health Monit. 22(2), 255–272 (2015)CrossRefGoogle Scholar
  28. 28.
    Illinois Structural Health Monitoring Project: Imote2 for Structural Health Monitoring: User’s Guide. University of Illinois, Urbana-Champaign (2011)Google Scholar
  29. 29.
    Moreu, F., Li, J., Jo, H., Kim, R., Scola, S., Spencer Jr., B., LaFave, J.: Reference-free displacements for condition assessment of timber railroad bridges. J. Bridg. Eng. 04015052 (2015). CrossRefGoogle Scholar
  30. 30.
    Moreu, F., Spencer Jr., B.F.: Framework for Consequence-Based Management and Safety of Railroad Bridge Infrastructure Using Wireless Smart Sensors (WSS). Newmark Structural Engineering Laboratory. University of Illinois at Urbana-Champaign, Champaign (2015)Google Scholar
  31. 31.
    Moreu, F.: Framework for Risk-Based Management and Safety of Railroad Bridge Infrastructure Using Wireless Smart Sensors (WSS). University of Illinois at Urbana-Champaign, Champaign (2015)Google Scholar
  32. 32.
    Kim, R.E., Moreu, F., Spencer, B.F.: System identification of an in-service railroad bridge using wireless smart sensors. Smart Struct. Syst. 15(3), 683–698 (2015)CrossRefGoogle Scholar
  33. 33.
    Yang, J., Li, J.B., Lin, G.: A simple approach to integration of acceleration data for dynamic soil–structure interaction analysis. Soil Dyn. Earthq. Eng. 26(8), 725–734 (2005)CrossRefGoogle Scholar
  34. 34.
    Gindy, M., Vaccaro, R., Nassif, H., et al.: A state-space approach for deriving bridge displacement from acceleration. Comput. Aided Civ. Inf. Eng. 23(4), 281–290 (2008)CrossRefGoogle Scholar
  35. 35.
    Lee, H.S., Hong, Y.H., Park, H.W.: Design of a FIR filter for the displacement reconstruction using measured acceleration in low-frequency dominant structures. Int. J. Numer. Methods Eng. 82(4), 403–434 (2010)zbMATHGoogle Scholar
  36. 36.
    Arduino: Arduino Uno Rev3. (2017). Accessed 15 July 2017
  37. 37.
    NXP Semiconductors: MMA8451Q, 3-axis, 14-bit/8-bit digital accelerometer. (2017). Accessed 15 July 2017
  38. 38.
    Adafruit Industries: Adafruit MMA8451 Accelerometer Breakout. New York City, NY. (2017). Accessed 15 July 2017
  39. 39.
    GitHub: Arduino library for the MMA8451 Accelerometer sensors. San Francisco, California. (2017). Accessed 15 July 2017
  40. 40.
    Digi International: XBee S1 802.15.4 RF Modules. (2016). Accessed 15 July 2017
  41. 41.
    IEEE Standards Associations: IEEE Standard for Low-Rate Wireless Networks. (2015). Accessed 15 July 2017
  42. 42.
    SparkFun: XBee Shield Hookup Guide. (2017). Accessed 15 July 2017
  43. 43.
    SparkFun: Exploring XBees and XCTU. (2017). Accessed 15 July 2017
  44. 44.
    Tera Term: Tera Term Home Page. (2017). Accessed 15 July 2017

Copyright information

© The Society for Experimental Mechanics, Inc. 2019

Authors and Affiliations

  • Ali Ozdagli
    • 1
  • Bideng Liu
    • 2
  • Fernando Moreu
    • 1
  1. 1.Department of Civil EngineeringUniversity of New MexicoAlbuquerqueUSA
  2. 2.Beijing Municipal Institute of Labour ProtectionBeijingP.R. China

Personalised recommendations