Category: Infrastructure

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.

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.

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.

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.

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.

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.

The present research seeks to compare the flexural behavior of different crosstie technologies with a goal of understanding the similarities and differences in flexural behavior. Commercially produced wood, composite, and concrete crossties are tested in center negative and rail-seat positive flexure as described in AREMA Chapter 40 and the relative flexural performances are compared. Unsurprisingly, wood and composite ties have lower bending stiffness and somewhat lower strength while demonstrating greater deflection capability. The observations obtained in this testing help to quantify the relative structural contribution of the differing tie technologies and may indicate that required flexural capacity for concrete ties should be assessed on the basis of deflection rather than load. Accordingly, we suggest further research into concrete tie design paradigms that prioritize deflection performance which we believe will lead to robust performance and long service life.

Modern railway inspection systems generate large volumes of high-resolution data describing the condition of individual track components, including rails, crossties, fasteners, ballast, joints, and track geometry. While these data provide detailed information about infrastructure condition, they are often analyzed using component-specific thresholds or independent defect assessments, limiting our understanding of how deficiencies evolve over time and interact across neighboring track sections.

This project proposes an explainable spatiotemporal machine learning framework to analyze railway inspection data and uncover temporal and spatial patterns of track deterioration. The research addresses two fundamental questions: (1) Can historical measurements of individual track deficiencies be used to predict their future condition? (2) Do spatial relationships and interactions among multiple deficiencies improve our understanding and prediction of deterioration compared
with analyzing defects independently?

The study will utilize a large-scale inspection dataset comprising consecutive track sections with repeated measurements of track geometry, rail wear, crosstie condition, ballast characteristics, fasteners, anchors, and other infrastructure components. Segment-level features will characterize defect severity, spatial continuity, local variability, and interactions among neighboring components. Statistical methods and interpretable machine learning models will be employed to identify significant spatiotemporal relationships and forecast future deterioration of measured track conditions. Explainable AI techniques will quantify the influence of individual variables and their interactions, while spatially blocked validation will ensure reliable model evaluation.

The proposed research will advance understanding of railway deterioration by treating track defects as an interconnected spatiotemporal process rather than isolated observations. Expected outcomes include predictive models for the evolution of individual deficiencies, quantitative characterization of spatial and temporal degradation patterns, and improved insight into relationships among track components. The resulting framework will provide transportation agencies with a scientifically grounded approach for extracting actionable information from large-scale inspection datasets, supporting more proactive inspection planning, maintenance scheduling, and long-term infrastructure management.

Railroad track deterioration is governed by the interaction between demand factors, such as accumulated traffic and axle loads, and capacity factors including track structure and maintenance practices. Tamping is one of the most widely used maintenance interventions for restoring track geometry, yet it is implemented using different strategies. Capital tamping—also referred to as production or line tamping—treats extended sections of track, while spot tamping targets localized defects in response to emerging geometry issues. These strategies differ in cost, operational impact, and potential influence on long-term track performance. However, the relative effectiveness of these approaches in altering subsequent track geometry deterioration remains insufficiently quantified.

This project proposes the development of a comparative analytical framework to evaluate how different tamping strategies influence track geometry deterioration rates. Using multi-year geometry inspection data collected by Autonomous Track Geometry Measurement Systems (ATGMS) alongside maintenance records from a Class I railroad corridor, the study will identify deterioration cycles and associated maintenance events. Geometry measurements will be aggregated into fixed track segments and analyzed using statistical and signal-processing methods to identify degradation cycles and quantify changes in track condition before and after tamping activities.

The primary focus of the work is to establish a repeatable methodology for quantifying deterioration rates surrounding tamping interventions and comparing the resulting performance trends across different maintenance strategies. The outcome will be a data-driven framework for evaluating tamping effectiveness using longitudinal geometry records.

Such a framework could subsequently be used to investigate how factors such as traffic loading, track structure, maintenance frequency, and intervention type influence deterioration behavior.

Ultimately, this capability supports improved maintenance planning, better alignment of tamping practices with asset management strategies, and more efficient allocation of track maintenance resources.

Railroad inspections are a critical part of safe railway operations. Modern technologies offer great potential for complimentary methods for the traditional, mainly manual inspections. We are proposing to continue the evaluation of uncrewed aerial systems (UAS or drones) potential for long-distance (beyond visual line of sight) applications to provide rapid, flexible inspection of rail infrastructure. Building from previous research for the Federal Rail Administration (FRA) developing the Crossing-i system, we are planning on focusing on using UAS-enabled sensing to identify ground hazards and assess the safety of rail grade crossings. We will be conducting research in collaboration with CN Railroad on their corridor located in northeastern Michigan (see attached letter of support). We will also work with the Office of Rail from Michigan Department
of Transportation (MDOT) to identify greatest use potentials for inspecting state-owned rail network.

New rules from the Federal Aviation Administration (FAA) under “Part 108” will make “beyond visual line of sight” (BVLOS) operations available on a standard basis in 2026. Our MTRI UAS team is part of a project lead by ANRA Technologies, and funded by the Michigan Economic Development Corporation (MEDC), which has started developing the Chippewa County Airport (KCIU) in the eastern U.P. into a leading center for BVLOS operations.

For this NURail project, we are planning to deploy UAS to demonstrate how rail grade crossing safety assessment and ground hazard detection can be completed more efficiently and safely with BVLOS flights. CN has extensive rail lines in the area, and is willing to provide test sites in the eastern U.P. close to KCIU where they are interested in our BVLOS UAS inspection demonstrations. We will use our previous project knowledge and methods to assess a BVLOS stretch of railway, chosen by CN, for automated detection of issues such as muddy areas, track alignment, rail breaks, and ballast levels. The identification of muddy areas, indicative of drainage problems, will be completed using automated image analysis algorithms that identify patterns in UAS imagery showing mud pumping, along with estimating moisture levels. Track alignment issues will be identified by applying linear feature analysis that identifies anomalies similar to misalignment. Image analysis will also be used to identify breaks, and 3D UAS data will be analyzed for identifying different ballast levels. We will also use close-range photogrammetry for image processing to create the 3D data necessary to calculate sight lines at a series of CN-chosen crossings. These crossings will also be evaluated for low-ground clearance (“humped crossings”) status using our automated Crossing-i algorithm. We are planning on at least 6 data collection trips.