Final Report (PDF)
Tech Brief (PDF)
| Project Title | Quantification of Railroad Ballast Performance Using Advanced Sensor Network & Big Data |
| University | Penn State Altoona University of Delaware |
| Principal Investigator(s) | PI: Hai Huang, Penn State Altoona Co-PI: Allan Zarembski, University of Delaware |
| Funding Source(s) and Amounts Provided (by each agency or organization) | Penn State Altoona Federal Share — $57,686 University of Delaware Federal Share — $53,314 Penn State Altoona Match Share — $57,851 University of Delaware Match Share — $53,316 |
| Total Project Cost | $222,002 |
| Start and End Dates | 02/01/2022 - 05/01/2024 |
| Brief Description of Research Project | While railroads are aware of the fact that ballast behavior has a profound influence on track performance and that ballast fouling leads to accelerated track-bed deterioration, the methods for determining ideal maintenance (e.g., tamping, ballast cleaning, etc.) have relied heavily on past experience, manual inspection, and often anecdotal information. A technology that will allow railroads to identify an objective threshold by which they can establish a window of opportunity for ideal track maintenance is in great need. This proposed research is expected to satisfy this need by implementing an advanced ballast performance monitoring program based on innovative wireless sensors and Big Data technologies. This project is anticipated to result in a real-time data collection and integrated analysis system which will allow railroad companies to identify the instantaneous condition of their ballast and track-bed more accurately and proactively assign maintenance windows to ensure safe and efficient train operation with the least amount of train delay due to maintenance outages. |