Offering a potential alternative to more expensive sets of sensors, the novel application of mobile technology was explored by a team involving researchers from the Massachusetts Institute of Technology (MIT).
“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 Senseable City Laboratory and co-author of a new paper summarising the study’s findings.
The research was conducted, in part, on the Golden Gate Bridge in California. The study showed that mobile devices can capture the same kind of information about bridge vibrations that stationary sensors compile. The team also estimated that, depending on the age of a road bridge, mobile device monitoring could add 15-30% to the structure’s lifespan.
“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 wrote.
Bridges naturally vibrate, and to study the essential ‘modal frequencies’ of those vibrations in many directions, engineers typically place sensors – such as accelerometers – on bridges themselves. Changes in the modal frequencies over time may indicate changes in a bridge’s structural integrity.
To conduct the study, the researchers developed an Android-based mobile phone application to collect accelerometer data when the devices were placed in vehicles passing over the bridge. They could then see how well that data matched up with data recorded by sensors on bridges themselves, to see if the mobile phone method worked.
“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,” said Paolo Santi, principal research scientist at the Senseable City Lab.
In the case of the Golden Gate Bridge, the researchers drove over the bridge 102 times with their devices running, and the team used 72 trips by Uber drivers with activated phones as well. The team then compared the resulting data to that from a group of 240 sensors that had been placed on the bridge for three months.
The data from the phones converged with that from the bridge’s sensors, the team found – for 10 particular types of low-frequency vibrations there was a close match, and in five cases there was no discrepancy between the methods at all.
Only 1% of bridges in the US are suspension bridges, however. About 41% are much smaller concrete span bridges, so the researchers also examined how well their method would fare in that setting.
To do so, they studied 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. Here, the researchers were also encouraged by their findings, although they 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, suggesting that 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,” said Markus Buehler, professor of civil, environmental and mechanical engineering at MIT. “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.”
The research could be refined and expanded, Ratti said, including accounting for the effects of the smartphone mount in the vehicle, the influence of the vehicle type on the data, and more.
“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.”
The work was published in Nature Communications Engineering.
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Content published by Professional Engineering does not necessarily represent the views of the Institution of Mechanical Engineers.