Train Derailment Research Articles

A Case Study of the East Palestine, Ohio Train Derailment Disaster for Upper-Level Undergraduate Analytical Chemistry Courses

A Case Study of the East Palestine, Ohio Train Derailment Disaster for Upper-Level Undergraduate Analytical Chemistry Courses

Uplift derailment for near-fault high speed train-track-hybrid control cable-stayed bridge system

Seismic uplift of a high-speed rail (HSR) cable-stayed bridge’s bearing affects train safety. Existing technique lacks improved seismic isolation in the vertical direction due to the bearing’s vertical stiffness. Based on the on-site dynamic test and train derailment table experiments and different software, the train running safety of the semi-floating system of the cable-stayed bridge-rail-train system was analyzed using different damping and isolation control strategies. The larger vertical seismic component increases the danger of flange climb derailment when subjected to near-fault earthquakes. After installing lateral/or viscous fluid dampers (VFDs) and magnetorheological (MR) bearing (A = 3.0), the bridge has an obvious bearing uplift response. Still, it is not enough to threaten the structure, and the seismic damping capacity of the bridge structure is improved, making the train ride safe. This research presents discoveries on the impact of bearing uplift on train operational safety. Specifically, it reveals that the lifting of the end bearings on both sides of the main girder is not synchronized. This investigation presents a technical reference for comparable initiatives.

What drives household protective actions in an industrial crisis? Insights from the East Palestine train derailment

Complex industrial disasters illustrate the challenges of underdeveloped public warning systems. Unlike most natural disasters, quickly identifying hazardous materials and assessing their threats is crucial for developing protective action recommendations (PARs) that guide household response in industrial crises. The 2023 East Palestine, Ohio (USA) train derailment, chemical spill, and fires revealed that gaps in rapidly identifying hazardous materials, and the threats they present, can severely impact the public warning system. As the crisis unfolded, responding agencies left crucial questions unanswered, leaving community members uncertain about their safety, the extent of environmental contamination, and what protective actions to take. It is imperative to study the drivers of household protective actions in the absence of a developed warning system and well-established PARS. To achieve this, we conducted a community survey in Ohio, Pennsylvania, and West Virginia (n = 259) in response to the East Palestine crisis. We used multivariate logistic regressions to identify statistically significant explanatory factors that predict protective action response. Our findings reveal gaps in response, where challenges identifying and communicating hazards created environmental justice concerns. We provide policy recommendations to strengthen hazard identification and outline further work to include equity as a pillar of environmental disaster response.

Natural Attenuation Potential of Vinyl Chloride and Butyl Acrylate Released in the East Palestine, Ohio Train Derailment Accident.

The East Palestine, Ohio train derailment released toxic vinyl chloride (VC) and butyl acrylate (BA), which entered the watershed. Streambed sediment, surface water, and private well water samples were collected 128 and 276 days postaccident to assess the natural attenuation potential of VC and BA by quantifying biodegradation biomarker genes and conducting microcosm treatability studies. qPCR detected the aerobic VC degradation biomarkers etnC in ∼40% and etnE in ∼27% of sediments collected in both sampling campaigns in abundances reaching 105 gene copies g-1. The 16S rRNA genes of organohalide-respiring Dehalococcoides and Dehalogenimonas were, respectively, detected in 50 and 64% of sediment samples collected 128 days postaccident and in 63 and 88% of sediment samples collected 276 days postaccident, in abundances reaching 107 cells g-1. Elevated detection frequencies of VC degradation biomarker genes were measured immediately downstream of the accident site (i.e., Sulphur Run). Aerobic VC degradation occurred in all sediment microcosms and coincided with increases of etnC/etnE genes and Mycobacterium, a genus comprising aerobic VC degraders. The conversion of VC to ethene and an increased abundance of VC reductive dechlorination biomarker genes were observed in microcosms established with sediments collected from Sulphur Run. All anoxic microcosms rapidly degraded BA to innocuous products with intermediate formation of n-butanol and acrylate. The results indicate that microbiomes in the East Palestine watershed have natural attenuation capacity for VC and BA. Recommendations are made to improve first-response actions in future contaminant release accidents of this magnitude.

Fusing binocular vision and deep learning to detect dynamic wheel-rail displacement of high-speed trains

Large displacements between the wheel and rail lead to track damage or even train derailment. Detecting dynamic wheel-rail displacement (DWRD) can provide a technical means for discriminating the operational health status of rail vehicles and formulating active control strategies. In this paper, a new method, which fuses binocular vision (BV) and deep learning (DL), is proposed to detect the DWRD online, the method is abbreviated as BV-DL. First, a binocular camera is used to capture the dynamic wheel-rail contact video online, while two lasers are equipped to focus on the wheel-rail contact region to locate some keypoints in the wheel-rail contact images. Then, an ROI (region of interest) detection network is proposed to recognize the wheel-rail contact region automatically, and a keypoint detection network is proposed to detect the wheel-rail pixel displacement (WRPD). Finally, a 3D coordinate transformation (3DCT) model is built to calculate the wheel-rail world displacement in real-time, i.e., the DWRD between the keypoints. To test the performance of the method, a laser displacement sensor-based method is used to verify the reliability of the 3DCT model. Meanwhile, a traditional image processing method, a MobileNet V3-based method, and a YOLOX-s-based method are introduced to predict the WRPD. Experimental results show that compared with the four methods, the proposed BV-DL method can achieve optimal results in pixel coordinate calculation and 3D coordinate computation.

Running safety assessment method of trains under seismic conditions based on the derailment risk domain

The accurate assessment of running safety during earthquakes is of significant importance for ensuring the safety of railway lines. Currently, assessment methods based on a single index suffer from issues such as misjudgment of operational safety and difficulty in evaluating operational margin, making them unsuitable for assessing train safety during earthquakes. Therefore, in order to propose an effective evaluation method for the running safety of trains during earthquakes, this study employs three indexes, namely lateral displacement of the wheel–rail contact point, wheel unloading rate, and wheel lift, to describe the lateral and vertical contact states between the wheel and rail. The corresponding evolution characteristics of the wheel–rail contact states are determined, and the derailment forms under different frequency components of seismic motion are identified through dynamic numerical simulations of the train–track coupled system under sine excitation. The variations in the wheel–rail contact states during the transition from a safe state to the critical state of derailment are analyzed, thereby constructing the evolutionary path of train derailment and seismic derailment risk domain. Lastly, the wheel–rail contact and derailment states under seismic conditions are analyzed, thus verifying the effectiveness of the evaluation method for assessing running safety under earthquakes proposed in this study. The results indicate that the assessment method based on the derailment risk domain accurately and comprehensively reflects the wheel–rail contact states under seismic conditions. It successfully determines the forms of train derailment, the risk levels of derailment, and the evolutionary paths of derailment risk.

Modeling of impact of operations and maintenance on safety, availability, capacity, and cost of Railways-A System dynamics approach

the transport infrastructure, particularly the railway infrastructure plays a vital role in the delivery of freight and the transportation of people. The ability and reliability of the railway infrastructure to deliver goods and transport people are challenged by train derailments and collisions caused by infrastructure breakdowns. Lack of maintenance has been identified as one of the causes of infrastructure breakdowns leading to accidents. The current paper proposes that if the railway infrastructure safety, availability, capacity, and cost are modeled using system dynamics, the impact of infrastructure operation and maintenance on safety can be predicted more accurately. The paper follows systems thinking approach that aims to understand the railway infrastructure as a system, by defining the system structure, system component relationships, and system behavior. The impact on railway infrastructure is modeled using system dynamics by developing causal loop diagrams and stock and flow diagrams which define the system structure, and system component relationships, and models the system behavior of safety, availability, capacity, and cost.

An Approach for Real-Time Monitoring of Train Derailments: An Impact-Based Monitoring Method

Train derailments are severe incidents in rail transportation, posing significant risks to passenger safety and infrastructure integrity. However, the excessive lateral vibrations of the wheelset, which are a direct cause of derailments, often cannot be effectively monitored and analyzed using traditional methods. Therefore, it is crucial to develop real-time monitoring methods that can detect and prevent derailments, ensuring the safety of railway transportation.To address and prevent train derailments, this paper introduces an impact monitoring method focused on rail fasteners. This technique enables the rapid detection of single-wheel derailments from the track. By analyzing the behavior of rail fasteners under various load conditions, we have identified key parameters that can be utilized to detect potential derailment risks. The method primarily involves monitoring changes in lateral and longitudinal vibrational energy, along with the intervals between impacts on fasteners, and incorporating the vertical displacement of wheels to trigger a derailment alarm.Simulation and experimental results demonstrate that this approach can accurately identify derailment characteristics and issue timely warnings, effectively preventing and mitigating train derailments. This method offers a practical solution for enhancing rail transportation safety and contributes to the advancements in this field.

Natural rail fracture defect calibration using infrared thermography and edge detection

Rail breakage can cause train derailment that leads to catastrophic consequences, prevention of which is worthy of a significant amount of investment in finance and time. Non-destructive evaluation can provide qualitative and quantitative inspection of rail tracks in-situ to detect dangerous faults, even though these defects are primarily invisible or hardly noticed from the surface of the rail. To make it efficient and convenient, infrared thermography technique offers a remote operation without interfering with rail transportation. The image processing for edge detection can further quantify the defect deterioration by depicting the contour of the defect. Moreover, the optimal parameters for various edge operators provide reliable detection for natural fracture defects.

Widespread impacts to precipitation of the East Palestine Ohio train accident

On 3 February 2023, a Norfolk Southern train derailment occurred in East Palestine, Ohio. The accident and subsequent fire resulted in the emissions of large amounts of hazardous compounds to the ambient atmosphere over many days. We used precipitation chemistry measurements routinely collected by the National Atmospheric Deposition Program (NADP) to estimate the spatial extent and chemical compounds deposited as a result of the accident. Our measurements revealed a large areal impact from the Midwest through the Northeast and likely Canada, and perhaps as far south as North Carolina (portions of 16 states, 1.4 million km2). Observations showed the expected high chloride concentrations, but also unexpectedly high pH (basic) and exceptionally elevated levels of base cations exceeding 99th percentiles versus the historic record. These results were consistent with the meteorological conditions and atmospheric trajectories, and were not due to highly-concentrated low volume precipitation samples or wildfires. The robust measurements of the NADP network clearly show that the impacts of the fire were larger in scale and scope than the initial predictions, and likely due to the uplift from the fire itself entraining pollutants into the atmosphere. A more detailed evaluation of the accident and resulting fire could further refine the full impact of the atmospheric concentrations, dry and wet deposition, and the more specific extent of the spatial impact.

Running safety control performance of high-speed trains on bridges under seismic excitation utilizing tuned mass damper

The safety of high-speed trains on bridges during seismic conditions is of paramount concern, especially for high-pier bridges characterized by lower lateral stiffness. The risk of train derailment during earthquakes can increase due to heightened low-frequency excitation. While Tuned Mass Dampers (TMDs) have proven effective in mitigating structural responses, their influence on time-varying vehicle-bridge interactions under earthquakes remains an unexplored area of study. In this research, a 1:10 scale model of a high-speed train-track-bridge coupling system, incorporating a nonlinear wheel-rail interface, was subjected to testing using a four-shaking-table system. This system exhibited good control accuracy and synchronization rates. The experimental results were used to validate the accuracy of the numerical model for the train-track-bridge coupled system under various train speeds. Subsequently, the seismic control performance of TMDs on high-speed trains was assessed through a combination of experimental tests and numerical simulations. Both sets of results, from the experimental and numerical approaches, confirmed the effectiveness of TMDs in controlling acceleration and force-related parameters for both moving and stationary trains. Notably, the control performance is most pronounced when vibrations closely align with the TMD's operating frequency. Additionally, this study delved into the mechanism of train vibration control and conducted a comprehensive parametric investigation, considering factors like pier heights (24 m–50 m), train speeds (0–300 km/h), ground motion intensities (0.04 g–0.21 g), and TMD mass ratios (0.03–0.08). The findings revealed that TMDs have a more significant impact on enhancing the safety index of trains for high-pier bridges characterized by lower frequencies. For instance, in the case of a 50 m pier, the running train's derailment coefficient can be reduced by 34.94 %. However, the control action is narrowband, effectively mitigating train vibrations near the bridge's natural frequency but less effective outside the working frequency range and absolute base acceleration.

An Assessment of the Ocular Toxicity of Two Major Sources of Environmental Exposure.

The effect of airborne exposure on the eye surface is an area in need of exploration, particularly in light of the increasing number of incidents occurring in both civilian and military settings. In this study, in silico methods based on a platform comprising a portfolio of software applications and a technology ecosystem are used to test potential surface ocular toxicity in data presented from Iraqi burn pits and the East Palestine, Ohio, train derailment. The purpose of this analysis is to gain a better understanding of the long-term impact of such an exposure to the ocular surface and the manifestation of surface irritation, including dry eye disease. In silico methods were used to determine ocular irritation to chemical compounds. A list of such chemicals was introduced from a number of publicly available sources for burn pits and train derailment. The results demonstrated high ocular irritation scores for some chemicals present in these exposure events. Such an analysis is designed to provide guidance related to the needed ophthalmologic care and follow-up in individuals who have been in proximity to burn pits or the train derailment and those who will experience future toxic exposure.

Shaking table study on high‐speed train seismic derailment mechanism and verification of spectrum intensity derailment index

AbstractThe derailment of a high‐speed train is a complex and uncertain dynamic process, especially under running conditions where the derailment index lacks comprehensive experimental validation. This research focuses on elucidating the mechanism of an earthquake‐induced train derailment and validating the structural response‐based spectrum intensity derailment index. To achieve this, a multi‐array shaking table system was utilized to test the safety of a running train and to physically replicate train derailment process under severe earthquake impact. We investigated the vibration characteristics and derailment progression of trains operating at different speeds, exposed to earthquakes of varying frequencies and intensities. In stationary derailments, significant wheelset lift was observed due to lateral rolling vibrations. However, in running test cases, there was no separation between the wheels and rails. Instead, the wheels underwent a long period of climbing and descending on the rails, similar to damped single‐degree‐of‐freedom oscillations. As the wheel climbed the rail and reached a critical potential energy point, the wheel flange of the wheel could potentially fall due to gravity or come off the rail due to external disturbances. The critical potential energy could be represented by the spectrum intensity threshold, and the prediction results aligned well with the derailment test results for both stationary and running trains. Furthermore, the method of determining train derailment based on structural responses was consistent with the test results. The spectrum intensity index shows strong positive correlations with other wheel‐force‐related indicators, albeit adopting a conservative perspective, reinforcing its efficacy in assessing train safety.

Serum Dioxin Levels in a Subset of Participants of the East Palestine, Ohio Train Derailment Health Tracking Study

Serum Dioxin Levels in a Subset of Participants of the East Palestine, Ohio Train Derailment Health Tracking Study

Comprehensive risk assessment of Pakistan railway network: A semi-quantitative risk matrix approach

Pakistan Railway (PR) is vital to the country's transportation infrastructure, facilitating passenger and freight transportation. However, the growing number of accidents associated with PR has raised concerns about its overall safety. In addition, there have been limited research efforts to investigate PR accidents and their underlying causes The present study conducts a comprehensive risk assessment and safety management of PR using a semi-quantitative risk matrix approach. The study combines historical accident data and expert evaluations to assess the likelihood and consequences of different railway accident types and potential contributing factors. The descriptive statistics analysis has revealed varying degrees of severity for different types of railway accidents in Pakistan. For instance, accidents like passenger and goods train derailments and collisions at unmanned level crossings were identified as extreme and intolerable, whereas train fire accidents were categorized as high and undesirable. Moreover, accidents attributed to human error are classified as extreme and intolerable, while those caused by negligence of road users and track defects are classified as high and undesirable. The study utilized the risk matrix approach and identified critical risk areas that can help the decision-makers prioritize effective risk mitigation strategies. In light of the present study's findings, policy implications, such as investment in infrastructure to mitigate risks associated with aging or deteriorating tracks, bridges, and tunnels, and human resource development for railway personnel to improve their skills, decision-making abilities, and awareness of safety protocols, are recommended.

Corrugation and Squat Classification and Detection with VGG16 and YOLOv5 Neural Network Models

Railway track defects in Malaysia pose significant risks of train derailments and accidents, underscoring the urgency for early and accurate defect detection and classification. This study presents a novel approach utilizing deep learning models, VGG16 and YOLOv5, for detecting and classifying railway track defects, explicitly focusing on corrugation and squat defects. The research's uniqueness lies in its application of these specific models and the composition of a dataset collected from extensive field measurements and inspections across various railway tracks within the Track Network Maintenance Ampang Line in Malaysia. The results demonstrate that these models achieve high precision in defect classification and detection of defects by more than 80%. The proposed methodology provides the railway industry with a powerful tool to streamline maintenance planning and prioritize defect remediation efficiently. Early defect detection can prevent potential accidents and improve safety and operational efficiency. Future studies can expand on these findings by exploring the extension of the proposed techniques to address other types of rail defects. Incorporating a diverse range of scenarios and operating conditions in the dataset could further enhance the models' performance and generalization. Real-time deployment and integration with existing maintenance systems are crucial for practical adoption. This research has strengths but acknowledges limitations. Additional evaluation metrics and a diverse dataset are essential for model performance. Leveraging deep learning models offers a reliable solution for railway maintenance, enhancing safety and efficiency. Addressing these limitations will drive proactive defect management, ensuring safe and reliable railway networks.

Dynamic damage evolution of double-block ballastless track structure under train derailment impact

Train derailment is a rare event, which seriously threatens the safety of personnel and the service status of track structures along the line. Aiming at the impact damage of high-speed train on ballastless track structure, a nonlinear dynamic analysis model of vehicle-ballastless track-bridge was established based on the theory of concrete plastic damage and impact dynamics. Based on the energy conversion characteristics, the damage evolution and energy dissipation characteristics of ballastless track are studied by using the plastic damage and failure constitutive model. The results show that the contact force during sleeper collision is mainly composed of longitudinal resistance and vertical pressure. At this time, the tensile damage of ballastless track develops rapidly and the shoulder is almost destroyed. When the track bed is collided, the contact force is characterized by a single peak. At this time, the edge of the track bed is seriously damaged by tension, and the shoulder falls off due to tensile damage. At the peak position of the collision contact force, the damage of the sleeper and the track slab is the largest. Compared with the collision track bed, the ballastless track has a larger damage volume when colliding with the sleeper, and can dissipate the kinetic energy of the train faster, and the dissipated energy is 5.07 times that of the former. The research results can provide help for the repair and protection design of the early structure of the accident.

Study on Dynamic Damage of Crash Barrier under Impact Load of High-Speed Train

The derailment of a high-speed train in a tunnel will cause a very serious accident, but there are few research articles on anti-collision facilities in tunnels. In order to promote the sustainable development of high-speed trains and reduce the severity of accidents caused by derailment in tunnels of high-speed trains, this paper puts forward a crash barrier scheme in tunnels through the method of numerical simulation; the coupling finite element model of train–crash barrier–tunnel is established by using ABAQUS. The changes in lateral velocity and lateral displacement after the train hits the crash barrier without embedding steel bars are explored. We also explore the influence of different reinforcement amounts on the changes in the lateral speed and lateral displacement of trains under the condition of embedding steel bars. The results show that with the increase in stirrups and vertical reinforcement, the anti-impact and sustainable operation capability of the crash barrier are greater. It can also be seen from the lateral displacement of the train that the train shows the reverse movement trend, and the crash barrier plays a good role in intercepting the train. These research results can provide a reference for the sustainable development of transportation infrastructure construction.

123 Utilizing Project ECHO to mitigate environmental impacts on health through collaborative provider education

OBJECTIVES/GOALS: Launch a case-based learning collaborative on best practices that meet social, emotional and physical health needs of underserved communities as they relate to environmental toxins—specifically those related to the train derailment in OH. Topics discussed could also include disasters and spills, air quality, extreme heat, and water. METHODS/STUDY POPULATION: In response to a call for action delivered by PA’s Acting Secretary of Health, we established a partnership between Penn State CTSI, Project ECHO at Penn State, and Primary Health Network (PHN). PHN is the largest Federally Qualified Health Center in PA, making it uniquely qualified to reach rural providers diagnosing and treating patients impacted by environmental events. Utilizing the ECHO model, we are hosting monthly, 1-hour sessions on environmental determinants of health starting October 2023. Experts in pulmonology, toxicology, atmospheric science, and rural medicine (whom many participants would have limited access to outside of the ECHO platform) and participants have the opportunity to share and learn from their varied experiences exemplifying a culture of ‘all teach, all learn’. RESULTS/ANTICIPATED RESULTS: Project ECHO is an ideal model for upscaling workforce quickly, allowing participants to be responsive in the care of their community, regardless of location and access to specialty clinics. 74 participants across 26 PA counties registered for the series, ranging from PCPs, medical directors, and state officials. Upon registration, nearly half of our direct patient-care participants do not routinely conduct an environmental exposure history and almost 70% report receiving questions from their patients related to how the environment might impact their health. More than half of those providers reported feeling unprepared to answer patients’ questions related to the environment’s impact on their health. Evaluation data will be collected at enrollment, after each session, and post-series. DISCUSSION/SIGNIFICANCE: This series could result in: * Reduction of health disparities caused by environmental events (no cost, virtual learning) * Increased preparedness to quickly address health questions/symptoms related to environmental exposures * Increased awareness of the environmental impacts on health. * Improved testing/treatment for patients

Analysis of Bridge Dynamics and Response Characteristics Under The Influence of Axle Coupling Vibration

To ensure the safety and stability of high-speed rail lines and reduce external interference, it is essential to build a large number of elevated bridges. These elevated bridges account for a considerable proportion of the total length of high-speed rail lines. However, when high-speed rail lines pass through earthquake prone areas, the likelihood of earthquakes occurring when trains pass through bridges increases significantly. Therefore, it is necessary to study the dynamic response of bridge structures under earthquake action to ensure the safety of bridges during train operation and operation. The optimization scheme proposed in this article has undergone moderate impact tests, and the results show that the maximum lateral displacement of the bridge can reach 124 mm, while the maximum vertical acceleration is 5.16 m/s2, Exceeded the safety limit of 0.35 g. Through the analysis of train derailment coefficient, wheel load reduction rate, lateral sway force, lateral and vertical acceleration, and Spelling comfort index, we have come to the conclusion that bridges can ensure the safety of train operation in the absence of earthquakes and small earthquakes, and can also maintain certain stability under medium and large earthquakes. These research results have important guiding significance for the design and construction of high-speed rail lines. By optimizing the bridge structure and adopting relevant technical measures, the seismic disaster resistance of high-speed rail lines can be further improved, ensuring the safety and comfort of passengers during travel. At the same time, these research results also provide useful reference and inspiration for the construction and improvement of future high-speed rail lines.