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Infrastructure is in need of a makeover—we see it as bridges, roadways, water systems, and railways crack and crumble. At Carnegie Mellon University, the work of researchers across many disciplines intersects to create technologies that will improve and extend the life of transportation infrastructure.

Researchers in the Department of Civil and Environmental Engineering (CEE) are leaders in indirect structural health monitoring, a low-cost and low-maintenance monitoring approach where moving vehicles use sensors to indirectly sense railways, roads, and bridges while traveling their normal routes.

“By instrumenting operational vehicles with sensors, we can conduct inspections and maintenance, without having to instrument the structures themselves. This reduces costs and won’t interfere with regular traffic. Further, more data can be collected from vehicles that routinely run on roads and railroad tracks,” says Hae Young Noh, a professor in CEE.

Modelling railways

Track geometry is the measurement of a track system in three dimensions. Currently, the researchers use a track geometry car that runs once or twice a year to collect data. Ideally, the car should run constantly, but that would be too expensive.

Jingxiao Liu, a CEE Ph.D. student advised by professors Noh and Mario Berges, uses ANSYS simulation software to model how a track geometry train car moves and collects data.

The simulation models track parameters for a light rail system to predict when the railway’s health is degrading and in need of repair, or when the geometry indicates a dangerous situation that could cause a derailment. The predictive model provides a complementary way to continuously monitor infrastructure systems.

Liu’s project simulates a bogie (or wheel and axle system) accelerating over a railway. Using previously recorded data, the model predicts railway health by allowing researchers to test physical aspects of the bogie-track system. While they cannot change the parameters on the actual physical track, they can change parameters on the model. Testing the effects of different parameters can indicate when there may be a problem on the track, and maintenance can be done before an incident occurs.

To simulate how the bogie-track system operates, researchers conduct finite element analysis. This helps them understand how the system behaves physically under different loads and situations. Once the finite element analysis is complete, the final step is to analyze the system as it’s in motion.

 

According to Noh, the ANSYS software provides insight on how vehicles behave on the track, it helps the team develop algorithms from their collected data and simulations, and then it helps them validate their results.

“The value of this software is that we can make our simulation approximately realistic to the real world,” said Liu.

Collaborators on this research include Noh, Berges, CEE University Professor Emeritus Jacobo Bielak, former Department Head of Electrical and Computer Engineering Jelena Kovačević, and Dean of the College of Engineering Jim Garrett.

This project is supported by the National University Transportation Center Mobility21 at Carnegie Mellon. Mobility21 is a member of the university’s Metro21: Smart Cities Institute, which seeks to research, develop, and deploy solutions to improve the quality of urban life.

By instrumenting operational vehicles with sensors, we can conduct inspections and maintenance, without having to instrument the structures themselves. This reduces costs and won’t interfere with regular traffic. Further, more data can be collected from vehicles that routinely run on roads and railroad tracks.

Hae Young Noh, Professor, Civil and Environmental Engineering, Carnegie Mellon University