A group of Clarkson University mathematicians and a civil engineer developed an approach to “listen” to a collection of signals from bridges and other mechanical structures to diagnose changes or damage.

The group reports in Chaos that their approach involves installing accelerometer sensors along a bridge to measure how each part of the bridge is disturbed in response to a truck driving across.

Researchers use sensors to listen for sounds of structural change.According to researchers, accelerations serve as a listening media to forces and accelerations travelling through the structure. 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.

Part of the group's analysis is a data processing technique called “optimal mutual information interaction,” which was developed to identify significant direct interactions between individual components within a system.

The group's work stands out because it brings together two aspects to detect damage within bridges or other mechanical structures: The first is the automated data collection process; the second is the data analytics tool developed by researchers, which, they say, can infer direct information flow and significant interactions. By combining them, the researchers are able to detect the presence of structural changes within the bridge.

Bridges are ubiquitous, so it's important to be able to detect structural damage as early as possible to avoid disastrous outcomes. But deteching structural damage is often done manually, and can be costly and ineffective.

The group's work combines modern sensing technology with state-of-the-art data analytics tools to automate the process. As a result, the researchers hope it can be used for early detection of structural changes and damage before requiring inspection by a human.