Category: Safety

Traditionally, most existing approaches for track geometry inspection scheduling are largely done through time-or condition-based approaches. These approaches are limited as problems in some critical track sections may not be resolved and they may result in inefficient resource allocation by treating all track segments as possessing same level of risk. Such limitations can lead to increased risks of undetected track geometry defects and increased cost of maintenance activities. To address these limitations, this research develops a non-linear risk-based track geometry inspection framework for optimizing inspection intervals, incorporating imperfect detection reliability. Stochastic models will be used to characterize the non-linear evolution of the degradation of the track geometry parameters. The developed stochastic processes will be evaluated using repeat inspection data of various track geometry parameters and used to determine time-to-defects values. Detection reliability will be incorporated through developing a probability of detection model influenced by various factors such as inspection technology and defect magnitude. Variation in behavior along the railway track will be accounted for by dividing the track into multiple segments and independently evaluating the risk characteristics of each segment. Time to
defects obtained from the stochastic models will be used to develop a novel methodology for determining inspection intervals based on risk characteristics at various segments.

The developed framework will be extended to multiple track geometry parameters at various track segments. Outputs from the risk-based track geometry inspection framework will be employed to develop practical inspection planning charts analogous to that for rail inspection scheduling. These inspection charts will provide infrastructure managers and railway maintenance engineers with a decision support tool for track geometry inspection planning based on degradation behavior at various track segments, thereby bridging the gap between probabilistic theory and maintenance practice on operational railway networks. By developing this risk-based inspection framework, this study will contribute to a more realistic, adaptable and operationally applicable framework for planning track geometry inspection activities, leading to improved railway safety, maintenance efficiency and enhanced infrastructure reliability.

Manual walking inspections are a federally mandated and operationally crucial component of railway track safety system. Despite generating extensive longitudinal records of observed defects, the analytical use of walking inspection data for risk-informed scheduling decisions has been limited across both transit and freight rails. Our current work, conducted under prior CoE funding, developed a data-driven framework that transforms multi-year, segment-level walking inspection records into interpretable cumulative risk indicators and uses the slope of cumulative effective risk per mile to drive dynamic inspection scheduling. The framework has been validated across two structurally distinct case studies: Metro Rail Observation Data from an urban transit network and Freight rail’s FRA Defects Data. Both applications confirm that the slope-based scheduling logic produces inspection schedules consistent with observed risk accumulation behavior, ensures optimal resource allocation: reduces surveillance effort in low-activity periods, and concentrates inspection resources during documented periods of elevated defect activity. The framework helps mitigate risk in track with reduced inspection efforts.

The proposed Year 3 research extends this framework in two directions identified during the current study as the most consequential limitations and opportunities. First, the framework currently operates as a retrospective characterization tool: it identifies periods of elevated accumulation after the fact but does not probabilistically project how risk will evolve in subsequent months. Markov Chain Monte Carlo (MCMC) simulation will be used to model segment-level risk state transitions, enabling the framework to generate forward-looking inspection frequency recommendations grounded in both historical behavior and probabilistic deterioration forecasts. Second, the current framework relies exclusively on walking inspection records. Railway track safety assurance in practice involves multiple inspection modalities operating in parallel, including automated geometry measurement and ultrasonic testing. Integrating walking inspection risk indicators with automated inspection outputs will allow the framework to produce joint resource allocation recommendations that account for what each inspection modality observes and what it misses, moving toward a unified inspection planning model. Together, these extensions advance the framework from a descriptive retrospective tool toward a prospective, operationally deployable decision-support system with direct applicability across freight and transit contexts.

Thermally induced rail defects—including track buckling in extreme heat and pull-aparts in extreme cold—remain a critical safety concern for the U.S. rail network. These failures arise from thermally induced longitudinal forces in continuous welded rail (CWR), governed by rail neutral temperature (RNT), ambient temperature variation, and track restraint. Reliable estimation of internal rail stress, from which RNT can be inferred, is therefore essential for safe CWR management. However, existing methods for assessing RNT are intrusive, localized, labor-intensive, or dependent on uncertain installation records, limiting their scalability for network-level monitoring [2,3].

This project proposes a physics-informed digital twin framework for nondestructive estimation of internal rail stress using non-contact vibration measurements. The approach leverages the acoustoelastic sensitivity of low-frequency flexural waves in rails, where axial stress induces measurable shifts in phase velocity and wavenumber [1]. The field system consists of three integrated components: (1) synchronized dual actuators to generate controlled excitation, (2) Scanning Laser Doppler Vibrometry (SLDV) to capture spatially resolved wavefields along the rail head and web, and (3) a physics-informed digital twin that models the coupled effects of axial stress, track-support stiffness, and damping.

A key technical challenge is the reliable measurement of wave propagation characteristics, particularly phase velocity, in the low-frequency regime (<10 kHz). Conventional single-input methods (e.g., hammer impact or single-shaker excitation) produce mixed forward- and backward-propagating waves that are strongly influenced by boundary reflections, making direct phase-velocity estimation difficult. While prior studies have demonstrated stress-sensitive wavelength measurements using harmonic excitation and laser vibrometry, they remain limited by wave interference effects.

To overcome this limitation, this project introduces a synchronized dual-actuation strategy to generate steady-state traveling waves over a scanned rail segment. This approach suppresses reflection-induced interference and enables direct, spatially resolved estimation of phase velocity. The measured wavefields will be integrated with a reduced-order, physics-informed digital twin to infer internal rail stress without requiring baseline RNT information.

This work establishes a scalable, nondestructive framework for rail stress monitoring and advances wave-based sensing methodologies for distributed structural state estimation in transportation infrastructure.

[1]Malladi, Vijaya VN Sriram, Mohammad I. Albakri, Manu Krishnan, Serkan Gugercin, andPablo A. Tarazaga. “Estimating experimental dispersion curves from steady-state frequencyresponse measurements.” Mechanical Systems and Signal Processing 164 (2022): 108218.

[2]Sun, L. J., Li, Z. W., Zhu, W. F., He, Y. L., Fan, G. P., Fang, W. P., & Shao, W. (2021). A methodfor long-term on-line monitoring of temperature stress of continuously welded rail. Advances inMechanical Engineering, 13(8), 16878140211041432.

[3]Phillips, R., Bartoli, I., Coccia, S., Lanza di Scalea, F., Salamone, S., Nucera, C., … & Carr, G.(2011, June). Nonlinear guided waves in continuously welded rails for buckling prediction. In AIPConference Proceedings (Vol. 1335, No. 1, pp. 314-321). American Institute of Physics.

Hunting (self-excited oscillation of rolling stock) is a typical example of harmful vehicle-track interaction. Hunting gives rise to wheel-rail dynamic forces, increasing derailment and buckling risks and accelerating wear and degradation. Wayside hunting detection can identify railcars exhibiting poor stability, thereby improving safety and supporting maintenance decision-making. However, existing systems, including truck hunting detectors (THD), truck performance detectors (TPD), and wheel impact load detectors (WILD), rely on track-mounted instrumentation, such as strain gauges or near-rail optical sensors. These systems require installation, calibration, and
maintenance within the railroad right-of-way, limiting their deployability and relocatability.

This project explores an easy-to-deploy wayside hunting detection concept based on Laser Doppler Vibrometry (LDV). LDV enables non-contact measurement of dynamic responses at standoff distances of several meters or more, and its use for wayside inspection of rolling stock remains underexplored. This remote sensing capability eliminates the need for on-track instrumentation, enabling flexible deployment, rapid relocation, and expanded coverage. Wayside LDV can directly capture the dynamic response of passing railcars, offering a new and potentially more accurate pathway for assessing rolling stock stability.

Despite these advantages, several technical challenges must be addressed in the design and development of such an LDV-based hunting detection system, including:

1) Variability of hunting behaviors: hunting behavior varies significantly across railcar types, speeds, wheel profiles, and loading conditions, resulting in diverse dynamic signatures. Moreover, vehicle-track interaction introduces additional stochastic vibration behaviors.

2) Laser speckle noise: high-speed scanning of a coherent laser on railcar surfaces leads to drastic changes in laser interference patterns, generating so-called speckle noise. Speckle noise elevates the noise floor of LDV measurements and complicates feature extraction.

3) Uncertainties in detectability: high speed usually triggers hunting, but at the same time, shortens the observation windows per railcar, reducing the frequency analysis resolution. This, combined with stochastic vehicle vibrations and measurement noise, poses challenges to reliable hunting detection.

This project develops a detectability-driven framework that integrates vehicle dynamics with LDV measurement uncertainty to study the detectability of the proposed LDV-based wayside hunting detection concept.

According to the Federal Railroad Administration (FRA) incident-accident database, from 2012 to 2017 there were 101 train handling accidents recorded by the on the mainlines of the four largest Class I railroads in the United States; from 2018 to 2023 there were 177 such train handling accidents, an increase of 75%. These two periods have also marked an increase in the length of freight trains operated in North America, and a decrease in train crew seniority and operating experience in the years following the COVID-19 pandemic, leading both trends to be cited as potential causing factors leading to the observed increase in train handling derailments
over time. However, since correlation does not imply causation, there is a need to further investigate these train handling derailments for possible shifts in their circumstances and locations over time. The nature of these geospatial and circumstantial changes may provide insight into the reasons for the increase in the frequency of train handling derailments, and help inform and optimize industry efforts to mitigate these incidents.

To address this research need, this project will use a combined statistical and geospatial analysis to determine if the location and sizes of trains involved in train handling accidents has changed between the two study periods of 2012-2017 and 2018-2023. Analysis of accident narratives and Geographic Information Systems (GIS) tools will be used to identify how train handling derailment hot spots have shifted between the two periods. Further clues regarding the changing frequency of train handling accidents could be the railroad track topography in the vicinity of the accident locations. Track features such as steep grades, undulating territory, and reverse curves could increase the risk of a train handling accident for longer trains or inexperienced crews. The proposed research will GIS use to overlay the North American Class I freight rail network onto elevation source data to estimate the grade and curvature of track segments in the vicinity of train handling derailments using sinuosity. The distribution of derailments by track topography will be compared to the overall composition of the network to identify if critical conditions are observed to change between the two study periods. Finally, the Advanced Locomotive Technology and Rail Infrastructure Optimization System (ALTRIOS) will be used to conduct train performance simulations over the track topography at each incident location to determine the throttle and/or brake settings leading up the incidents. The magnitude and number of changes in
those settings leading up to the accident location could infer the train handling demands placed on the train crew, and how those demands have changed between the two study periods.

The results from this research can inform freight railroads as to how the locations and circumstances of train handling accidents have changed as trains have become longer, enabling them to more effectively and efficiently deploy mitigation strategies. The proposed spatial analysis methodology could also be adapted and applied to investigate other railroad incident causes to improve overall rail network safety.

Train derailments resulting in hazardous materials releases from tank cars can have serious health, environmental, and economic consequences. Given the chaotic and complex nature of train derailments, quantifying the release probability of derailed tank cars is challenging. Statistical quantification of the safety performance of different tank car designs and tank car components in derailments has been conducted using an extensive historical database. However, these statistical probabilities are not a robust metric for new or novel tank car designs or materials because of the lack of data. Instead, analytical or simulation-based approaches are an appropriate alternative for these cases. These require an understanding of the underlying physics of derailment impact events and the dynamic behavior of derailed cars. Information on the
probabilistic impact load environment can be obtained by simulating a series of derailment scenarios that replicate derailment conditions.

An advanced computer model has been developed and partially validated that simulates derailment kinematics enabling estimates of the release probability of tank car components exposed to derailment forces to be estimated. In particular, the model develops probabilistic estimates of the resistance and demand for tank cars and their key components (i.e., tank heads, tank shells, top fittings, and bottom fittings). These models characterize the structural behavior of each tank car component in an impact scenario. They incorporate key elements, such as the derailment-caused impact forces, impact types, impact event characteristics, and tank car properties, to determine the quantities of interest at the level of an impact type affecting a component of a tank car. This model will be further validated and tank car release probabilities estimated using a design of experiments approach for a representative series of derailment scenarios. The validation procedure involves a series of primary and derived metrics to quantify characteristic dynamic behaviors of the train and derailed railcars, such as the extent of the derailment, the spatial dispositions of derailed railcars, their longitudinal and lateral spread, and their misalignment with the track.

The goal of this research is to understand and quantify the effect of four-quadrant gates on the safety of highway-rail grade crossings. There are several questions including whether four-quad gates reduce the rate of collisions between highway vehicles and trains compared to other grade crossing warning systems and if so by how much. Specifically, the project will investigate and attempt to quantify the marginal benefit of four-quad versus two-quad gate crossings. Related questions include how do crossing characteristics such as Annual Average Daily Traffic (AADT) or daily train traffic volume interact to affect the efficacy of four-quad versus two-quad gates.

This research will use the FRA’s Highway-Rail Grade Crossing Incident Database (Form 57) and the FRA’s Annual Snapshots taken from the Crossing Inventory Database (Form 71). Use of these databases will provide extensive details about the aforementioned characteristics of grade crossings. Using the data from these sources, both serial and parallel comparative methods will be employed. The serial analysis uses negative binomial regression to compare incidents at a population of crossings before and after they were converted from two-quad to four-quad gates. The parallel analysis uses categorical data analysis to compare the incident rate in two populations of crossings in which we control for Annual Average Daily Traffic (AADT) and train frequency but that differ in their use of two-quad versus four-quad gates. Preliminary results
suggest that there is little difference in the incident rate of crossings equipped with four-quad gates compared to those with two-quad gates calling for more in-depth statistical analysis, and an investigation comparing motor vehicle operator behavior at crossings with these two types of warning systems.

Rail safety research faces challenges as driver decision behavior at highway-rail grade crossings (HRGCs) has changed in counterproductive ways (FRA, 2020). One example of such an undesired shift is the increased percentage of crashes that take place at HRGCs equipped with active warning devices. Instead of eliminating the crashes at such locations, motorists have shifted the type of risky behavior, such as proceeding around the gates. Emerging V2I technologies may improve rail safety by providing adaptive, in-vehicle alerts and applied cognitive research is needed to inform their designs and implementation.

Recent research suggests that some motorists are making these decision errors when under cognitive load or when HRGCs involve adjacent turns (Brabb, Vithani, & Martin, 2017). However, few rail experiments have examined either factor (Read et al., 2021). Our proposed project begins to bridge this gap by conducting three driving simulator experiments to quantify the impact of HRGC configuration (e.g., short storage) and cognitive load on driver attention and decision behavior.

This project builds on earlier research which concluded that HRGC configuration affected driver decision behavior and that motorists attended to different safety information at different HRGCs (c.f., Linja, Lautala, Nelson & Veinott,2020). In the current experiments, we will focus on the interaction between two factors, cognitive load and HRGC configuration, on driver decision behavior (e.g., speed, lane deviations) and attention management and test several new scenarios suggested by past HRGC work. With new in-vehicle intelligent warning capabilities, this research may inform future technology design, implementation or adoption of these technologies. It will also inform future research to evaluate the potential impact on motorist decision behavior under different HRGC conditions.

The long-term goal of this research is to develop a continuous, non-contact rail monitoring system for high-speed defect detection. Railroads play a vital role in the safety, prosperity, and well-being of our communities and businesses. However, due to increases in train speeds and axle loads that are being applied to aging and deteriorating railroad tracks, the occurrence of broken rails has become of increasing concern for owners, and regulators. Current inspection methods, including ultrasonic testing (UT), magnetic flux leakage, and eddy current testing, have limitations in speed, coverage, and detection accuracy. UT, for instance, requires fluid-coupled transducers and examines discrete rail cross-sections, limiting efficiency and real-time defect detection. The proposed technology consists of a non-contact laser Doppler vibrometers (LDVs) system mounted on a rail car. The system will continuously capture rail vibrations generated by wheel-rail interactions as the train travels at speeds between 30 and 80 mph. We hypothesize that these vibrations can reveal internal rail flaws through detectable changes in the structural response.

In this project we plan to conduct preliminary studies to investigate the feasibility of the proposed approach. Specifically, small scale laboratory tests will be carried out on rail samples with known defects. These specimens will be mounted on ties and ballast to replicate real-world conditions, and impact hammer tests will simulate wheel-rail contact excitations. Advanced signal processing techniques will be developed to improve accuracy and minimize false positives of the proposed approach.

Thermite welding is the most widely used technique for in-track welding or replacing a damaged part of a rail. This process produces a Welded Zone (WZ) and a Heat Affected Zone (HAZ) with inhomogeneous microstructures and hardness, resulting in lower mechanical, fracture and fatigue properties of the thermite weld compared with the parent rail. Tuskegee University developed (US Patent # 20140166766 A1) a new technology based on multi-pass Gas Metal Arc Weld (GMAW) for in-situ repair of railhead defects. In this technique, a defect is removed via machining a perpendicular slot or groove in the railhead leaving the web and base unaltered. Uniform preheating at a specified temperature is applied to the rail web under the slot before and during welding. GMAW passes are used to fill the slot using a weld material suitable for the parent steel rail to be welded. The weld heat inputs and other welding parameters are controlled to produce a weld with the desired microstructures. The proposed research focuses on investigating the metallurgical, mechanical, fracture and fatigue crack growth properties of the weld zone and the HAZ for both thermite welds and modified geometry and welding parameters of the Tuskegee GMAW repair technique, and to compare these properties with those of the parent rail. In addition, both conventional and inverse slow bend test on the modified GMAW and thermite weld repairs will be performed according to AREMA standard test methods. Comparison between the two welding techniques as far as rail safety is concerned will be made based on the fracture and fatigue damage tolerance associated with the weld zone and the heat affected zone. The fracture damage tolerance is defined as the resistance of the material (WZ and HAZ) to a crack under static loading, while fatigue damage tolerance is defined as the resistance of material (WZ and HAZ) to a crack under dynamic loading.