A Review of Parameters Affecting Rail Break Gap Size Using Analytical Methods

Management of continuous welded rail (CWR) stress is critical to maintaining railroad safety. To successfully manage the stress-state of the rail, knowing the rail neutral temperature (RNT) is critical. RNT is defined as the temperature at which the net longitudinal force in the rail is zero. If the RNT is set too low/high then the rail would buckle/pull apart and create unsafe operation conditions. To reduce unsafe operating conditions, researchers have previously developed guidelines for managing RNT maintenance activities. However, there remains an opportunity to improve these guidelines given there have been 24 derailments caused by buckled track between 2009 and 2018. Therefore, a research program has been established to improve current guidelines. It is difficult to manage the stress of CWR because the RNT is difficult to quantify, and has been shown to change over time, tonnage, or as a result of maintenance (tamping, etc.). Further, rail breaks may lead to local changes in RNT, leading to the need for RNT readjustment. Current guidelines estimate prevalent RNT before a rail break/cut based on rail gap size. Therefore, as a part of a broader research program, this paper reviews an analytical method presented by Kerr that quantifies rail break gap length and identifies the roles of longitudinal track resistance and stiffness. Results indicate that plastic track displacements driven by longitudinal track resistance dominate, and the longitudinal track stiffness has limited influence. This paper also identifies limitations of this analytical approach and documents recommendations for improved models.

Analytical Method to Estimate Railroad Spike Fastener Stress

Previous research indicates that spike fastener fatigue failures have led to at least ten derailments since 2000. Given that railroads continue to install fastening systems that have experienced spike failures, methods to quantify the stress state of the spike must be developed. Common approaches to quantify the effect of key variables include laboratory experimentation, field instrumentation, or finite element model development. However, these approaches may be both time and cost prohibitive. An analytical method based on beam on elastic foundation mechanics, similar to the analysis of laterally loaded piles in deep foundation design, was developed to estimate the spike stresses. The outcome is a laboratory-validated analytical approach that generates estimates of spike stress. This analytical model was used to investigate key design criteria (timber modulus, spike cross-sectional area, and load applied) that could be changed to improve the resiliency of the fastening system to increase railroad safety. Another outcome of this study is the development of an instrumented spike that quantifies the spike demands when installed and loaded within a crosstie.

Damnable Conspiracies

This chapter traces anxieties over railroad safety and train wrecks in the South, which had the nation’s most dangerous railroads by the 1890s. As carnage piled up on the South’s rail lines, companies tried to shift blame to anonymous gangs of train wreckers as a strategy to avoid lawsuits and stave off attempts at state or federal regulation. The chapter uses two case studies of train wrecks – a wreck at Bostian Bridge in Statesville, NC and in Cahaba Creek in Alabama – to show how corporate lawyers and officials tried to perpetuate the myth of the train wrecker. The chapter gives quantitative data that shows how southern newspapers fuelled the panic over train wrecking. The chapter argues that this panic was racialized and many of the accused wreckers were African Americans that some of the same dynamics that led to lynchings. It closes with a discussion of train wreck ballads

Negotiating railroad safety in the late Ottoman Empire: The state, railroad companies, trainmen, and trespassers

This study examines different approaches taken in the late Ottoman Empire to deal with the risks and dangers posed by railroads. Like its counterparts in Europe and the United States, the Ottoman state actively sought to protect individuals against railroad risks. For this purpose, it mandated the use of certain devices meant to facilitate the safe flow of railroad traffic and introduced measures that aimed to discipline railroaders and pedestrians into behaving appropriately. However, the state was not the only actor that struggled to address railroad risks. Railroad companies, primarily to advance their economic interests, incorporated technologies that considerably reduced the risk of collisions. Yet economic concerns also sometimes hampered investments in railroad safety. For instance, the manner in which trespassing cases were handled by accident investigation committees and courts allowed the companies to avoid their obligations with respect to fencing around railroad tracks. As a result, it was easy for pedestrians to use tracks near their homes and workplaces as pathways. Finally, the article also shows that in performing their duties, trainmen enjoyed considerable freedom from control by railroad managers. This freedom was further reinforced by the shortage of experienced and skilled labor in the Ottoman railroad industry.

Video Analytics for Railroad Safety Research: An Artificial Intelligence Approach

The volume of video data in the railroad industry has increased significantly in recent years. Surveillance cameras are situated on nearly every part of the railroad system, such as inside the cab, along the track, at grade crossings, and in stations. These camera systems are manually monitored, either live or subsequently reviewed in an archive, which requires an immense amount of human resources. To make the video analysis much less labor-intensive, this paper develops a framework for utilizing artificial intelligence (AI) technologies for the extraction of useful information from these big video datasets. This framework has been implemented based on the video data from one grade crossing in New Jersey. The AI algorithm can automatically detect unsafe trespassing of railroad tracks (called near-miss events in this paper). To date, the AI algorithm has analyzed hours of video data and correctly detected all near-misses. This pilot study indicates the promise of using AI for automated analysis of railroad video big data, thereby supporting data-driven railroad safety research. For practical use, our AI algorithm has been packaged into a computer-aided decision support tool (named AI-Grade) that outputs near-miss video clips based on user-provided raw video data. This paper and its sequent studies aim to provide the railroad industry with next-generation big data analysis methods and tools for quickly and reliably processing large volumes of video data in order to better understand human factors in railroad safety research.