There could be a smartphone app to find out if San Francisco’s Golden Gate Bridge, or any bridge for that matter, is holding up, according to a study.
The new study shows that mobile phones placed in vehicles, equipped with special software, can collect useful data on structural integrity when crossing bridges. In doing so, they could become a cheaper alternative to sensor arrays attached to the bridges themselves.
“The main finding is that information about the structural health of bridges can be extracted from accelerometer data collected by smartphone,” says Carlo Ratti, co-author of the study.
The research was conducted, in part, on the Golden Gate Bridge itself. The study, involving researchers from the Massachusetts Institute of Technology (MIT), in the United States, showed that mobile devices can capture the same type of information about bridge vibrations that stationary sensors compile.
The researchers also estimate that, depending on the age of a highway bridge, mobile device monitoring could add an additional 15 to 30 percent years to the life of the structure.
“These findings suggest that massive, low-cost datasets collected by smartphones could play an important role in monitoring the health of existing transportation infrastructure,” the authors write in their new paper, published in Nature Communications Engineering.
Bridges naturally vibrate, and to study the essential “modal frequencies” of these vibrations in many directions, engineers typically place sensors, such as accelerometers, on the bridges themselves.
Changes in modal frequencies over time can indicate changes in the structural integrity of a bridge.
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 the data recorded by the sensors on the bridges themselves, to see if the cellphone method worked.
“In our work, we designed a methodology to extract modal vibration frequencies from noisy data collected from smartphones,” said lead researcher Paolo Santi.
“As data from multiple trips across a bridge is recorded, noise generated by engine, suspension and traffic vibrations, (and) asphalt, tends to cancel out, while that the underlying dominant frequencies emerge. “In the case of the Golden Gate Bridge, researchers crossed the bridge 102 times with their devices turned on, and the team also used 72 Uber driver rides with activated phones, according to the study.
The team then compared the resulting data to that of a group of 240 sensors that had been placed on the Golden Gate Bridge for three months.
The result, according to the study, was that the data from the phones converged with that from the sensors on the bridge; for 10 particular types of low-frequency vibration measured by engineers on the bridge, there was a close match, and in five cases there was no difference between the methods.
“We were able to show that many of these frequencies correspond very precisely to the prominent modal frequencies of the bridge,” says Santi.
However, only one percent of all bridges in the United States are suspension bridges. About 41% are bridges with much smaller concrete spans. So the researchers also considered how well their method would work in this context.
To do this, they studied a bridge in Ciampino, Italy, comparing 280 vehicle trips across the bridge to six sensors placed on the bridge for seven months.
Again, the researchers were encouraged by the results, although they found up to 2.3% discrepancy between the methods for some modal frequencies across all 280 trips, and a discrepancy of 5.5% on a smaller sample. This suggests that a greater volume of travel could yield more useful data.
“Our initial results suggest that only a modest amount of trips over a period of a few weeks is sufficient to obtain useful information about the modal frequencies of bridges,” says Santi.
Looking at the method as a whole, MIT Professor Markus Buehler observes, “Vibrational signatures are emerging as a powerful tool for assessing the properties of large and complex systems, ranging from the viral properties of pathogens to the structural integrity of bridges, as this study shows,” Buehler said.
“This is a universal signal widely distributed in the natural and built environment that we are just beginning to explore as a diagnostic and generation tool in engineering,” Buehler said.
As Ratti acknowledges, there are ways to refine and expand the search, including considering the effects of in-vehicle smartphone support, the influence of vehicle type on data, and more.
“We still have work to do, but we think our approach could be scaled up easily – up to the level of an entire country,” Ratti said.
”It may not achieve the accuracy that can be achieved using fixed sensors installed on a bridge, but it could become a very interesting early warning system. Small anomalies could then suggest when to perform further analyses,” Ratti said.
(This story has not been edited by the Devdiscourse team and is auto-generated from a syndicated feed.)