Preventing rail buckling is critical for maintaining railway safety, especially in high-temperature or fluctuating environmental conditions. Adequate lateral resistance is essential to avoid this issue, as thermal expansion in rails can lead to severe track misalignment and potential derailments. This project introduces a novel solution by employing temperature-responsive Architected Instability-based Metamaterials (AIMs) to reinforce ballast at critical temperature thresholds, effectively preventing track buckling. AIMs are an advanced class of metamaterials that can undergo large, reversible deformations and dissipate energy through geometric phase transformations in response to specific stress or temperature fields. The key innovation of AIMs lies in their ability to autonomously trigger these geometric transformations at precise temperatures when designed with optimized geometry, topology, and material composition. By embedding AIMs within the ballast, their reversible deformation mechanisms enhance the stiffness of the surrounding ballast, thus ensuring track stability even when temperatures rise beyond critical limits. This process allows the track to adapt dynamically to thermal stress, reducing the likelihood of rail buckling and maintaining structural integrity under fluctuating temperature conditions.
The project will build on the theoretical and numerical framework established in earlier phases by optimizing AIM geometries, exploring additional material combinations, and developing a preliminary design manual to support effective implementation in railway track beds. The updated framework will refine key parameters, including material properties, geometric configurations, and thermal activation thresholds, to improve performance and scalability. To evaluate the optimized designs, lab-scale experiments will test different material combinations to assess the behavior of AIMs embedded in ballast and quantify their effectiveness in enhancing lateral resistance. This innovative approach not only provides a sustainable solution, as AIMs are reusable and maintain performance across multiple thermal cycles, but also offers adaptability to various environmental conditions. By combining advanced material science, geometry optimization, and application-driven design manual, this versatile solution addresses a long-standing railway safety challenge, paving the way for improved resilience in railway infrastructure across diverse climates and conditions.
Category: Project Abstracts
Incorporating Recycled Asphalt and Tire Rubber into the Underlayment Layer for Railroad Ballast to Improve Track Performance
Railroad ballast fouling and degradation can reduce track support conditions, increase maintenance requirements, and negatively affect long-term track performance. Asphalt underlayment has been successfully used beneath ballast layers to improve structural support, reduce the migration of fine particles, extend maintenance intervals, and potentially improve safety of railroad tracks. However, the relatively high cost of asphalt materials has limited wider implementation of this technology across the rail industry. This project investigates the feasibility of incorporating recycled asphalt pavement (RAP) and ground tire rubber (GTR) into asphalt underlayment mixtures for railroad applications. The proposed approach seeks to improve the cost-effectiveness of railroad track infrastructure while maintaining adequate mechanical
performance. RAP can reduce the demand for virgin aggregates and asphalt binder, while GTR provides an opportunity to beneficially reuse end-of-life tire materials and potentially enhance mixture durability. The project consists of three integrated components. First, a comprehensive literature review will summarize current knowledge and practice regarding asphalt underlayment applications in railroads, recycled asphalt materials, and rubber-modified asphalt technologies. Second, laboratory mix design and performance testing will be conducted to evaluate asphalt underlayment mixtures containing varying RAP and GTR contents. Dynamic modulus and flow
number tests will be used to characterize stiffness and deformation, which are critical performance indicators for railroad underlayment materials. Finally, a life-cycle cost assessment (LCCA) will be performed to quantify the economic benefits of incorporating recycled materials relative to conventional asphalt underlayment mixtures. The expected outcome is the identification of practical mix designs that balance performance, cost, and other potential benefits. The project will provide guidance for the use of recycled materials in railroad asphalt underlayment applications and establish a foundation for future field implementation and performance evaluation.
Enhancing Accuracy of Ultrasound Imaging in Detecting Surface Cluster Cracks in Metal Using Contrast-enhanced Ultrasound
The railway industry faces significant challenges in the early detection and characterization of sub-millimeter surface-breaking rail fatigue cracks. Resolving crack width, depth, and complex geometries such as clustered and branched networks is critical, as these defects compromise safety and lead to costly maintenance. Conventional ultrasonic inspection methods have limited ability to distinguish closely spaced defects and near-surface cracks. Consequently, multiple cracks may be misinterpreted as a single flaw, or cracks may remain undetected before they reach a detectable size or when obscured within the same transducer field.
To address these limitations, this work proposes a contrast-enhanced ultrafast ultrasound (CEUUS) imaging framework designed to provide the high resolution necessary to distinguish individual cracks within a cluster. The CEUUS technique images ultrasound contrast agents (UCAs) infiltrated into cracks to create high-resolution images. However, both UCA characteristics and transducer frequency influence the apparent size of the UCA’s ultrasound image. Experimental measurements in steel at 5 MHz show that a target with a physical size of approximately 0.4 mm produces an apparent size much larger than its true dimensions. This severe signal broadening leads to overlaps between adjacent features, ultimately degrading the ability to distinguish true boundaries.
Accordingly, this study investigates the influence of UCA properties including type, size, and concentration in relation to excitation frequency to optimize the detectability and separability of sub-millimeter cracks. Based on this analysis, optimal UCA and transducer configurations are identified to minimize the UCA signal overlap. Furthermore, plane-wave ultrasound imaging and compounding strategies are explored to improve angular sensitivity and accuracy in complex crack configurations. Experiments on steel blocks and rail sections in laboratory and field settings are expected to confirm that optimizing these parameters enables more accurate crack severity
assessment, supporting earlier detection of critical defects and improving overall rail safety.
By systematically evaluating and optimizing UCA parameters and imaging strategies across different frequencies in steel, this work aims to enable more accurate crack severity assessment, support earlier detection of critical defects, and contribute to improving rail safety and maintenance strategies.
Development of Data-driven Fragility Models for Rail Track Components Subjected to Storm Surge and Wave
Historically, coastal railway system has suffered many incidents of damage and failure due to storm hazards. Such incidents include inundation of track structure that causes safety hazards, operational restrictions, and damages to rail tracks due to scour in the embankment or ballast and failure of tracks and ties. Considering the importance of the rail system in the freight and passenger transport and the safety concerns that failure in rail system incurs, there is a major need to first determine the vulnerabilities and second to devise mitigation solutions to improve the safety of coastal rail system. Determining track vulnerabilities requires fragility models. Fragility
models map the characteristics of the rail tracks and the severity of the storms to the probability of failure and are key components in risk analysis of infrastructure systems. The only rail track fragility model that is available is the model developed in Japan for flooding impact on railroads. This fragility model is not developed for coastal railroads subjected to storm surge and wave. Furthermore, as railroad configuration in the USA is significantly different from Japan, the fragility model does not provide a robust estimate of failure probabilities in coastal railroads in the USA. To address this limitation, we will develop a US specific data driven fragility model for rail embankments, ballast, track and ties when subjected to storm induced surge and wave loads. For this purpose, we have partnered with CN railway and have access to damage data (in coastal tracks in Gulf of America’s coast) from Hurricane Ida and Isaac. In this process, we first simulate Hurricane Ida and Isaac in ADCIRC and SWAN hydrodynamic platform to determine max surge and wave and duration of inundation for the coastal rail tracks and then merge them with the damage data obtained from CN railway to perform a logistic regression to determine fragility functions for rail tracks. Such fragility models can be used in risk informed decision making to improve the safety of coastal railroads and reduce the direct and indirect economic, operational, and disruption costs of the US rail system.
Phase 2: Modular Tunable Kirigami Truss for Temporary Railroad Bridge Systems
Extreme weather events, including severe storms, pose significant risks to the safety and resilience of railroad substructures and major railroad bridges. Flooding and storm surge events have contributed to bridge failures that disrupt rail operations and generate substantial economic impacts. A railway bridge collapsed in Seville, Illinois in 2013 sending multiple cars into the Spoon River. Between 1999 and 2010, 29 railroad bridge failures were documented in the United States (Joy et al., 2013, FRA Report), with 64% attributed to scour and hydraulic hazards. These failures highlight the persistent vulnerability of railroad infrastructure and the significant operational and economic risks associated with extreme weather-induced damage.
Origami-inspired deployable structures represent a promising field of research in structural engineering, offering innovative solutions for the design and development of versatile next generation structural systems. Incorporating bio-inspired design principles from nature provides a unique pathway toward realizing such deployable systems. While deployable origami structures offer geometric transformability, compact transportation, and reusability advantages, implementation remains limited due to absence of frameworks unifying structural reliability with quantitative environmental evaluation. This research establishes a reproducible framework integrating experimental structural performance analysis, machine learning-based damage detection, and comparative life cycle assessment for a reusable temporary railroad bridge system.
This project will continue from Phase 1 to leverage an experimental approach informed by a computational and experimental study to investigate a modular pill bug inspired kirigami temporary railroad bridge system, a plate-based modular deployable structure that leverages origami mechanics and is morphologically inspired by pill bugs. In addition, life cycle and damage sensitivity assessments will provide understanding into using modular origami-inspired structures for temporarily railway systems.
Assessment of Impulse Response to Characterize Rail Lateral Stiffness in the Field
A research team from UIUC will finalize development and deploy a nondestructive testing system, comprising measurement hardware, software and an analysis approach, to estimate lateral track strength in situ. The proposed research framework follows on a previous research project where concept development and verification development on an impulse response testing approach were established. The method will be deployed at field sites along with direct measurement methods such as STPT.
Extension of Risk-Based Track Geometry Inspection Interval Model
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.
Extension of Risk-Based Walking Inspection Scheduling Framework: Probabilistic Deterioration Modeling and Multi-Modal Inspection Integration
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.
Physics-Informed Digital Twin for Nondestructive Rail Stress Monitoring Using Scanned Vibration Wavefields
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.
Rail Economic Impact Analysis Linking Local Businesses
Moving goods on trains instead of trucks provides positive externality by reducing congestion and greenhouse gas emissions. Freight rail industry can increase economic competitiveness of regional and local economies. Access to rail network is often a critical component of a logistics center that serves as an anchor for the local economy. However, impacts of rail freight on the economy of local areas through which railroads move goods, operate terminals, manage operations, and carry out corporate business activities are not well understood beyond anecdotal and/or isolated cases. This study will examine impacts of railroad facilities and operations on local economy using cross-sectional and longitudinal analysis of large data. While the main focus of the study will be quantifying the impacts on local jobs and business establishments, selected study areas will be analyzed in detail to document the process by which the presence of freight rail affect the economy.