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Bridge frequency estimation strategies using smartphones

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Abstract

Bridges are susceptible to deterioration and damage as they age and should be routinely assessed to evaluate their integrity and safety for service. Traditionally, structural monitoring has comprised visual inspections, however this is both time and labor intensive. Researchers have shown that sensors on moving vehicles may provide insight into the dynamic behavior of bridges. Accelerometers within smartphones may serve as the sensors from which data is collected; thus, enabling massive data collection from a fleet of potential monitoring vehicles. This paper presents four postprocessing strategies for estimating bridge frequencies from smartphone acceleration data streams with no a priori information about the mass or stiffness of the bridge or vehicle. These techniques utilize the DFT and MUSIC algorithms to calculate vehicle acceleration frequency spectrums from which the fundamental bridge vibration frequency may be estimated. Both single-vehicle and crowdsourced postprocessing techniques are investigated. Utilizing the MUSIC algorithm within a crowdsourcing framework, the correct bridge frequency was identified in all analytical simulations within 4% error, representing a significant increase in performance over single-vehicle estimations made using MUSIC. The effect of user interaction with the smartphone is studied by including superimposed acceleration signals on 25–100% of analytical results; the superimposed user events included a dropped smartphone and talking on a smartphone. Increasing the percentage of noisy signals in the pool of evaluated accelerations generally reduces performance with the exception of crowdsourced estimations made using the MUSIC algorithm, which proved to be robust against user interaction with the smartphone.

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Acknowledgements

The authors would like to thank the undergraduate researchers Christopher Stenzel and Hussam Khresat in the SMU Structural Engineering Laboratory and the GISD Summer Research Fellows (Anthony Aiyedun, Joseline Contreras, Arath Dominguez, Michelle Mendoza, Sarina Mohanlal, Caleb Vinson) for assistance with vehicle customization and experimental design. This research is supported through a research and education partnership with Garland Independent School District.

Funding

Smart Infrastructure Innovation Initiative (S3i) Program with Garland ISD.

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Authors

Contributions

All authors contributed to this research and the writing of this article. BS and DR conceptualized and developed the method. JS developed the numerical models and experimental setup and performed all simulations and experiments.

Corresponding author

Correspondence to Brett A. Story.

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The authors declare that they have no conflict of interest.

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Appendix: User interaction signals

Appendix: User interaction signals

In an ideal diagnostic scenario, a smartphone would be stationary within a vehicle traversing a bridge. In reality, user interaction may occur. As a first-order approximation to including user interaction among vehicle signals, several experiments were performed to investigate superposition of two user events (i.e., Talk and Drop events) on vehicular vibration signals. For both user events, the following acceleration responses were recorded experimentally from a smartphone in a vehicle:

  1. 1.

    Driving along flat ground with no user events performed;

  2. 2.

    The user event while the car was stationary;

  3. 3.

    The user event while the car was driven along flat ground.

Figures 

Fig. 11
figure 11

Time history comparison for drop event

11 and

Fig. 12
figure 12

Time history comparison for talking event

12 compare the superposition of signal types 1 and 2 with signal type 3 for both Drop and Talk events. To a first-order approximation, the superposition of individual events upon vehicle signals resembles the response of the smartphone to a user event during vehicle movement.

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Sitton, J.D., Rajan, D. & Story, B.A. Bridge frequency estimation strategies using smartphones. J Civil Struct Health Monit 10, 513–526 (2020). https://doi.org/10.1007/s13349-020-00399-z

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