You can learn a lot about a railroad track by tapping it and measuring its vibration. That simple idea is at the heart of research that just earned the University of Pittsburgh’s Piervincenzo Rizzo and his team the 2026 Outstanding Paper Award from the American Society for Nondestructive Testing. The honor comes as Rizzo steps into a new leadership role in his field, named Managing Editor of Structural Health Monitoring, an International Journal, one of the field’s leading journals.
Together, the recognitions reflect both the impact of Rizzo’s research and his growing leadership in the structural health monitoring community. His research is paving the way for nondestructive inspection that could, when mature, allow inspectors to measure how much longitudinal force a track is carrying, information that it is important to prevent train derailments.
Structural Health Monitoring is an international, peer-reviewed journal that publishes exceptional research advancing “the body of knowledge and its application in the discipline of structural health monitoring.” Rizzo’s appointment followed a competitive search, and he began serving as Managing Editor on July 1, 2026.
“Structural Health Monitoring is a tremendous source of cutting-edge, multidisciplinary research that is advancing our field,” said Rizzo, professor in the Swanson School of Engineering’s Department of Civil and Environmental Engineering. “As a past contributing author, it’s a great honor to serve as its managing editor and help amplify how researchers are improving structural monitoring research to ultimately protect public safety.”
Along with his appointment to Managing Editor, Rizzo, with first author Alireza Enshaeian (PhD ECE ’24); Matthew Belding (BS ECE ’19, PhD ECE ’25); and Shayan Baktash, a PhD student in civil engineering, received the 2026 Outstanding Paper Award from the American Society for Nondestructive Testing. The award recognizes individuals whose research and papers make important contributions in nondestructive testing. Their paper, “Vibration Nondestructive Testing of Continuous Welded Rails: A Finite Element Analysis,” (DOI: 10.1080/09349847.2024.2433483) was published in Research in Nondestructive Evaluation.
Rizzo and his team investigated railroad tracks that use continuously welded rails (CWR) instead of those joined by bolts and gaps. Although welded rails produce smoother rides and require less upkeep, they are more susceptible to temperature fluctuations that can make them unsafe.
To improve how these rails are monitored, Rizzo and his team developed a “tap and listen approach,” in which they struck the CWR with an instrumented hammer, recorded the vibrations, and analyzed the frequencies of the vibrations. However, instead of recording vibrations across many sections of rail, they focused on a five-meter stretch and simulated scenarios at various temperatures. With this data, the team trained an AI model to predict stress conditions based on the vibration frequencies.
“We found that more than temperature, what affects the vibrations is how firmly the track is held down,” said Enshaeian. “We also discovered the limitations of using AI to predict extreme conditions, which would require significantly more data.”
“Our research shows the potential of generating synthetic data to train AI models at a time when collecting real-world data is incredibly slow and expensive,” Rizzo said. “We are excited to continue expanding the model beyond a limited rail profile.”
‘;