Researchers from the Senseable City Laboratory at MIT report that crowdsourced data from the smartphones of vehicle passengers crossing bridges may help monitor bridge structural integrity. An open-access paper on their demonstration is published in the Nature journal Communications Engineering.

Monitoring and managing the structural health of bridges requires expensive specialized sensor networks. In the past decade, researchers predicted that cheap ubiquitous mobile sensors would revolutionize infrastructure maintenance; yet extracting useful information in the field with sufficient precision remains challenging. Herein we report the accurate determination of critical physical properties, modal frequencies, of two real bridges from everyday vehicle trip data.

—Matarazzo et al.

The team collected smartphone data from controlled field experiments and uncontrolled Uber rides on The Golden Gate Bridge—a long-span suspension bridge—and developed an analytical method to recover modal properties accurately.

The researchers also successfully applied the method to partially-controlled crowdsourced data collected on a short-span highway bridge in Italy.

Illustration of controlled data collection and the spatial segmentation approach. a Sensor layout on the dashboard of the first vehicle (Nissan Sentra) which was used to collect first fifty trips. b Sensor layout on dashboard of the second vehicle (Ford Focus) which was used to collect fifty-two trips. For all vehicle trips over the Golden Gate Bridge, The smartphones were facing upward, such that one axis was well-aligned with gravity. Such an orientation is not strictly necessary; although, knowledge on the configuration of the sensors is helpful for data preprocessing. c Generic schematic of spatial segmentation of a bridge which is defined through two independent parameters: Δs and c, which remain uniform over the length of the bridge. The red circles represent the centers of each segment, while the light colored boxes show the segment widths. A close-up of three adjacent segments s_i−1, s_i, and s_i+1, is shown to detail the segmentation parameters: c is the length of each segment, c_o is the length of the overlap between segments, and Δs is the distance between the centers (red circles) of adjacent segments. Matarazzo et al.

Smartphones that contain dozens of sensors are carried by almost 50% of the population globally. Recent applications of smartphones in civil engineering have shown that smartphone accelerometers can sufficiently capture structural vibrations. Theoretical and experimental research on vehicle-bridge interaction relationships have established governing equations and influential parameters.

The researchers found that bridge vibration frequencies can be identified from smartphone-vehicle trip data in real-world conditions. Data from a single trip is insufficient; yet as few as 100 crowdsourced datasets can produce useful modal frequency estimates (below 6 % error) for both short-span and long-span bridges.

Collectively, the analyses of controlled and ridesourcing data (N = 174 total) produced accurate estimates of ten (seven unique) modal frequencies of the Golden Gate Bridge; five of which had an error of 0.000%. The number of trips considered in the primary study was less than 0.1% of the daily trips made on the Golden Gate Bridge; this shows that there is an enormous sensing potential represented by smartphones globally that contains valuable information about bridges and other important infrastructure. Furthermore, the accuracy of the most-probable modal frequencies (MPMFs) improved as the number of datasets increased.

—Matarazzo et al.

Further analysis projected that the inclusion of crowdsourced data in a maintenance plan for a new bridge could add more than fourteen years of service (30% increase) without additional costs.

Our results suggest that massive and inexpensive datasets collected by smartphones could play a role in monitoring the health of existing transportation infrastructure.

—Matarazzo et al.

Resources

• Matarazzo, T.J., Kondor, D., Milardo, S. et al. (2022) “Crowdsourcing bridge dynamic monitoring with smartphone vehicle trips.” Commun Eng 1, 29 doi: 10.1038/s44172-022-00025-4