About

Mehdi Ahmadian is a J. Bernard Jones Professor in the Department of Mechanical Engineering at Virginia Tech, where he has been a faculty member since 2001. He is the founding director of the Virginia Tech Center for Vehicle Systems and Safety, the Virginia Tech Railway Technologies Laboratory, and the Virginia Institute for Performance Engineering and Research. His research interests include smart materials and systems, advanced materials for improving ground vehicle dynamic performance and control, energy harvesting, vehicle system dynamics and control, intelligent suspensions, magneto-rheological fluids, biodynamics, railroad systems, health monitoring systems for railroad applications, rail vehicle dynamics, and rolling stock mechanics analysis. Dr. Ahmadian has made significant contributions to the fields of vehicle safety, vehicle dynamics, and railroad engineering, and has been recognized with numerous awards including the SAE International Magnus Hendrickson Innovation Award in 2019, and fellowships in SAE, AIAA, and ASME. He holds a Ph.D. in Mechanical Engineering from the State University of New York at Buffalo and has held various academic and leadership roles at Virginia Tech, including founding director positions and professorships.

Research topics

• Engineering
• Artificial Intelligence
• Automotive engineering
• Computer Security
• Computer Science
• Mechanical engineering
• Physics
• Medicine
• Mathematics
• Reliability engineering
• Aerospace engineering
• Electrical engineering
• Simulation
• Statistics

Selected publications

• Wheel–rail-induced derailment analysis: a comprehensive literature review of experimental and simulation-based approaches

  Railway Engineering Science · 2026-03-16

  articleOpen access

  Abstract This paper offers a comprehensive review of the literature on the role of wheel–rail contact mechanics in train derailments. Using experimental and simulation methods, this review examines various research efforts that analyze how contact forces influence wheel climb and derailment dynamics. The related studies are summarized, and insights are provided on how they have contributed to understanding derailments and enhancing overall rolling stock safety. The review shows significant progress across different specialized areas within the broader topic of derailments. This includes advanced, state-of-the-art testing rigs, high-fidelity models that accurately replicate field conditions, materials that help prevent derailments, and wheel and rail profiles that reduce derailment risks. However, the accuracy of testing and modeling often requires more complex setups, sophisticated data analysis techniques, and greater resources. Despite these advances over the past few decades, further scientific research is necessary to understand better the root causes of events like wheel climb derailments under controlled and repeatable conditions.

  PublisherDOI

• Laser-induced fluorescence sensing of flange lubrication: track cart testing

  International Journal of Rail Transportation · 2026-03-16

  articleSenior authorCorresponding
  PublisherDOI

• Rollover analysis and prevention for commercial vehicles: literature survey

  Vehicle System Dynamics · 2025-06-01 · 6 citations

  articleSenior authorCorresponding
  PublisherDOI

• Actuation and Control of Railcar-Mounted Sensor Systems

  Preprints.org · 2025-05-14 · 2 citations

  preprintOpen accessSenior author

  This study provides the design, analysis, and prototype fabrication of a remotely controlled actuation system for railcar-mounted sensors. Frequent railway inspections are essential for detecting and preventing major defects that could lead to train derailments or accidents. Integrating supplemental automated inspection systems into existing trains can aid inspection crews without interfering with standard railway operations. However, many sensors and cameras require protection during transit, motivating the need for a deployable mounting assembly. The feasibility of a deployable sensor system was successfully assessed by creating and demonstrating a functional prototype mounting assembly that can be used with future automated inspection systems. Typical loads and accelerations experienced by a train were used to design a lead screw and stepper motor system capable of working within desired tolerances. Optimized inputs controlling this motion with an Arduino Uno were found through iterative testing of digital signals and direct port manipulation. Further research testing in a field-like environment is suggested.

  PublisherOA PDFDOI

• Design Evaluation of a Single Wheelset Roller Rig for Railroad Curving Dynamics and Creepage Studies

  Designs · 2025-08-20

  articleOpen accessSenior authorCorresponding

  This study presents a novel design for emulating wheelset curving dynamics by implementing a laterally constrained wheelset and two independently powered rollers. The new configuration extends the test capability of the existing Virginia Tech-Federal Railroad Administration (VT-FRA) roller rig from a single wheel to a wheelset (i.e., two wheels). The redesigned rig is intended for evaluating both the tangent track and curving dynamics of a wheelset on a railcar. Test data from earlier experiments with a single wheelset is analyzed to assess the control system’s ability to maintain the commanded roller speed. This evaluation determines whether the new system can accurately emulate curves. The study develops correction factors to account for the dissimilar contact patch sizes and longitudinal creep forces resulting from the dissimilar roller diameters. A novel force measurement method is proposed to resolve the creep forces at each contact patch independently. An assessment of the existing VT-FRA roller rig data indicates a maximum roller speed deviation of 0.37% from actual values, which is deemed to be within the intended accuracy for future tests with the redesigned rig. An analysis of the force measurements by a load platform demonstrates the feasibility of accurately determining the wheel–rail contact forces for the new design rig, identical to the original design. Despite the numerous challenges in integrating a new wheel and roller into the existing VT-FRA roller rig, the study demonstrates that such a redesign can be achieved within the space and kinematic constraints, while maintaining the intended measurement accuracy.

  PublisherOA PDFDOI

• Effect of drum and disc brakes on roll stability of double-trailer trucks during braking-in-turn manoeuvers

  Vehicle System Dynamics · 2025-02-11 · 1 citations

  articleSenior authorCorresponding
  PublisherDOI

• Actuation and Control of Railcar-Mounted Sensor Systems

  Actuators · 2025-06-13

  articleOpen accessSenior authorCorresponding

  This study provides the design, analysis, and prototype fabrication of a remotely controlled actuation system for railcar-mounted sensors. Frequent railway inspections are essential for detecting and preventing major defects that could lead to train derailments or accidents. Integrating supplemental automated inspection systems into existing trains can aid inspection crews without interfering with standard railway operations. However, many sensors and cameras require protection during transit, motivating the need for a deployable mounting assembly. The feasibility of a deployable sensor system was successfully assessed by creating and demonstrating a functional prototype mounting assembly that can be used with future automated inspection systems. Typical loads and accelerations experienced by a train were used to design a lead screw and stepper motor system capable of working within desired tolerances. Optimized inputs controlling this motion with an Arduino Uno were found through the iterative testing of digital signals and direct port manipulation. Further research testing in a field-like environment is suggested.

  PublisherOA PDFDOI

• Evaluation of Flange Grease on Revenue Service Tracks Using Laser-Based Systems and Machine Learning

  Infrastructures · 2025-03-31 · 1 citations

  articleOpen accessSenior authorCorresponding

  This study presents a machine learning approach for estimating the presence and extent of flange-face lubrication on a rail. It offers an alternative to the current empirical and subjective methods for lubrication assessment, in which track engineers’ periodic visual inspections are used to evaluate the condition of the rail. This alternative approach uses a laser-based optical sensing system developed by the Railway Technologies Laboratory (RTL) located at Virginia Tech in Blacksburg, VA, combined with a machine learning calibration model. The optical sensing system can capture the fluorescence emitted by the grease to identify its presence, while the machine learning model classifies the extent of grease present into four thickness indices (TIs), from 0 to 3, representing heavy (3), medium (2), light (1) and low/no (0) lubrication. Both laboratory and field tests are conducted, with the results demonstrating the ability of the system to differentiate lubrication levels and measure the presence or absence of grease and TI with an accuracy of 90%.

  PublisherOA PDFDOI

• Practical Application of Passive Air-Coupled Ultrasonic Acoustic Sensors for Wheel Crack Detection

  Sensors · 2025-10-03

  articleOpen accessSenior authorCorresponding

  Undetected cracks in railroad wheels pose significant safety and economic risks, while current inspection methods are limited by cost, coverage, or contact requirements. This study explores the use of passive, air-coupled ultrasonic acoustic (UA) sensors for detecting wheel damage on stationary or moving wheels. Two controlled datasets of wheelsets, one with clear damage and another with early, service-induced defects, were tested using hammer impacts. An automated system identified high-energy bursts and extracted features in both time and frequency domains, such as decay rate, spectral centroid, and entropy. The results demonstrate the effectiveness of UAE (ultrasonic acoustic emission) techniques through Kernel Density Estimation (KDE) visualization, hypothesis testing with effect sizes, and Receiver Operating Characteristic (ROC) analysis. The decay rate consistently proved to be the most effective discriminator, achieving near-perfect classification of severely damaged wheels and maintaining meaningful separation for early defects. Spectral features provided additional information but were less decisive. The frequency spectrum characteristics were effective across both axial and radial sensor orientations, with ultrasonic frequencies (20-80 kHz) offering higher spectral fidelity than sonic frequencies (1-20 kHz). This work establishes a validated "ground-truth" signature essential for developing a practical wayside detection system. The findings guide a targeted engineering approach to physically isolate this known signature from ambient noise and develop advanced models for reliable in-motion detection.

  PublisherOA PDFDOI

• Traction enhancement evaluation of Magnetite and Alumina

  International Journal of Rail Transportation · 2025-03-27 · 2 citations

  articleSenior authorCorresponding
  PublisherDOI

Frequent coauthors

• Steve C. Southward

  Virginia Tech

  41 shared

• Jeong‐Hoi Koo

  Miami University

  25 shared

• Xubin Song

  Shanxi Medical University

  21 shared

• Yang Chen

  Third Xiangya Hospital

  21 shared

• Michael Craft

  19 shared

• Mohammad Elahinia

  University of Toledo

  18 shared

• Ahmad Radmehr

  Virginia Tech

  16 shared

• Fernando D. Goncalves

  Instituto Federal de Educação, Ciência e Tecnologia do Pará

  16 shared

Awards & honors

• 2019 - SAE International/Magnus Hendrickson Innovation Award
• 2012-Present, Fellow, Society of Automotive Engineers (The S…
• 2010-Present, Associate Fellow, American Institute of Aerona…
• 1997-Present, Fellow, American Society of Mechanical Enginee…
• 2014, Society of Automotive Engineers (SAE) Buckendale Lectu…