Smartphones Could Help Evaluate Bridge Conditions


A study conducted by a team involving researchers from the Massachusetts Institute of Technology has suggested that mobile phones could potentially collect useful integrity data while crossing bridges. Equipped with special software, mobile devices can reportedly capture the same kind of information about bridge vibrations that stationary sensors compile.

“The core finding is that information about structural health of bridges can be extracted from smartphone-collected accelerometer data,” said Carlo Ratti, Director of the MIT Sensable City Laboratory and co-author of a new paper summarizing the study’s findings.

The study, “Crowdsourcing Bridge Vital Signs with Smartphone Vehicle Trips,” is expected to be published in the journal Communications Engineering.

About the Study

To study the “modal frequencies” of natural bridge vibrations in many directions, engineers can typically place sensors, such as accelerometers, on the structures. Changes in these frequencies over time may indicate changes in a bridge’s structural integrity.

The first part of research was reportedly conducted on the Golden Gate Bridge, using an Android-based mobile phone application to collect accelerometer data when the devices were placed in vehicles passing over the bridge. Researchers could then see how well those data matched up with data record by sensors on bridges themselves, to see if the mobile-phone method worked.

“In our work, we designed a methodology for extracting modal vibration frequencies from noisy data collected from smartphones,” Santi said. “As data from multiple trips over a bridge are recorded, noise generated by engine, suspension and traffic vibrations, [and] asphalt, tend to cancel out, while the underlying dominant frequencies emerge.”

Researchers drove over the Golden Gate Bridge 102 times with their devices running, and the team used 72 trips by Uber drivers with activated phones as well. The team then reportedly compared the resulting data to that from a group of 240 sensors that had been placed on the Golden Gate Bridge for three months.

As a result, data from the phones converged with the bridge’s sensors, with 10 particular types of low-frequency vibrations a close match and five cases showing no discrepancy between the methods at all. Researchers estimate that mobile-device monitoring could add from 15-30% more years to the structure’s lifespan depending on the age of the bridge.

“We were able to show that many of these frequencies correspond very accurately to the prominent modal frequencies of the bridge,” Santi said.

“These results suggest that massive and inexpensive datasets collected by smartphones could play an important role in monitoring the health of existing transportation infrastructure,” the authors write in their new paper.

However, because only 1% of bridges in the United States are suspension bridges, the researchers also wanted to examine the method in a smaller concrete span bridge setting, which makes up about 41% in the U.S. This required studying a bridge in Ciampino, Italy, comparing 280 vehicle trips over the bridge to six sensors that had been placed on the bridge for seven months. 

According to MIT, results found up to a 2.3% divergence between methods for certain modal frequencies over all 280 trips, and a 5.5% divergence over a smaller sample. The data suggests a larger volume of trips could yield more useful data.

“Vibrational signatures are emerging as a powerful tool to assess properties of large and complex systems, ranging from viral properties of pathogens to structural integrity of bridges as shown in this study. It’s a universal signal found widely in the natural and built environment that we’re just now beginning to explore as a diagnostic and generative tool in engineering,” observed Markus J. Buehler, the Jerry McAfee Professor in Engineering and professor of civil and environmental engineering and of mechanical engineering at MIT.

Additional research could be conducted to account for the effects of the smartphone mount in the vehicle, the influence of the vehicle type on the data and more, the team explains.

“We still have work to do, but we believe that our approach could be scaled up easily — all the way to the level of an entire country,” Ratti said. “It might not reach the accuracy that one can get using fixed sensors installed on a bridge, but it could become a very interesting early-warning system. Small anomalies could then suggest when to carry out further analyses.”

Authors of the study also include Thomas J. Matarazzo, an assistant professor of civil and mechanical engineering at the United States Military Academy at West Point; Daniel Kondor, a postdoc at the Complexity Science Hub in Vienna; Sebastiano Milardo, a researcher at the Senseable City Lab; Soheil S. Eshkevari, a senior research scientist at DiDi Labs and a former member of Senseable City Lab; and Paolo Santi, principal research scientist at the Senseable City Lab and research director at the Italian National Research Council; and Shamim N. Pakzad, a professor and chair of the Department of Civil and Environmental Engineering at Lehigh University.

The researchers received support from Anas S.p.A., Allianz, Brose, Cisco, Dover Corporation, Ford, the Amsterdam Institute for Advanced Metropolitan Solutions, the Fraunhofer Institute, the former Kuwait-MIT Center for Natural Resources and the Environment, Lab Campus, RATP, Singapore–MIT Alliance for Research and Technology (SMART), SNCF Gares & Connexions, UBER, and the U.S. Department of Defense High-Performance Computing Modernization Program.


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