Researchers and engineers are finding it possible to diagnose the condition of bridges non-invasively by “listening” to how the structure responds to a truck driving across.
The sheer number of bridges in any given country means it is important to be able to detect structural damage as early as possible to avoid disastrous outcomes. But detection of structural damage, which is often done manually, can be costly and in many cases is not effective.
The approach by the Clarkson University mathematicians, working with a civil engineer, involves installing accelerometer sensors at various locations along a bridge, then combining that data with data analytics in order to measure how each small part of the bridge is disturbed in response to a truck driving across.
This process, which is highly automated, allows for early detection of structural changes and damage before requiring human inspection. The solution is applicable not just for bridges, but also for other structures, including wind turbines, buildings and airplanes.
Additionally, the use of common accelerometer sensors means the method does not require expensive instrumentation.
According to Erik M. Bollt, a professor in the Department of Mathematics at Clarkson University, the sensors are able to detect signals that provide useful information about the forces and accelerations travelling through the structure.
“Signals from sensors near the truck loading are relevant, but so are signals far away because they react as the bridge structure flexes under its load and the entire structure oscillates like a guitar string, but obviously more complicated,” he explained.
“Signals travelling through the structure are expected to change if the bridge undergoes a change, such as a crack within the structure or if some of the bolts holding it together are loosened deliberately.”
The data from the sensors is just the start: they are subject to state-of-the-art statistical estimation routines allowing the engineers to search for signals that indicate the bridge’s structure has been altered due to damage or deformation, in order to diagnose the health status of the structure.
This combination of non-invasive and automated data collection, and data analytics, allows the researchers to use the data to detect the presence of structural changes within the bridge.
The researchers will continue to work on the solution. They are building a database of bridge models that can be easily simulated and tested via computers to calibrate parameters in the method, and also developing improved statistical estimators to produce more accurate results faster.
They are also collaborating with other labs to test the method on other structures, such as airplane wings.