Service Life Assessment and Predictive Modeling for an Aging Critical Infrastructure Proceedings of a Workshop (2021) / Chapter Skim
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5 Railways Infrastructure
5 Railways Infrastructure
Pages 34-43
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From page 34... ...
He noted that industry embraces technologies only once they induce profit. Derailments have increased since the 1980s, and such technologies could help detect potential rail failures and wheel failures before they happen, which could prevent future derailments.
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From page 35... ...
Ahmadian highlighted positive train control as a new development to replace this process, which utilizes global positioning technology to control and locate trains. He added that rails are classified according to their weight per yard -- the 136 rail is the most common in the United States (i.e., each yard weighs 136 lb)
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From page 36... ...
Other causes include broken wheels, bearing failures, and issues with track geometry, for example. In addition to the reverse bending that is caused by moving loads, rail and wheel life are significantly affected by creep forces (i.e., spin ning wheels unable to get traction)
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From page 37... ...
Jesus de la Garza, Virginia Tech, asked whether there are any data that compare reverse bending in locomotive and nonlocomotive cars. Ahmadian said that the data reveal more exaggerated reverse bending under the locomotive cars than the rail cars.
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From page 38... ...
, spoke about the Army Corps of Engineers' work on service life predictions. His current project in the Sustainment Management Systems program collects and synthesizes information about various civil infrastructure and facility assets in order to understand their condition and performance as well as to plan maintenance, repair, and replacement of those facili ties.
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From page 39... ...
• Count state transition frequencies: Counting transitions for each condition state pair for each observation interval to develop transition frequency matrices. • Optimization of characteristic transition matrix: Employing an algorithm that uses the transition frequency matrix from each observation interval to determine the characteristic deterioration matrix, which shows how one expects a component, based on its classification, to progress through its condition states over time.
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From page 40... ...
In closing, Grussing summarized that the discrete Markov process is a ben eficial approach to modeling and predicting component deterioration, driven by information collected during inspections. This process supports the transition from a rules-based process to a data-driven process for risk-informed facility asset management.
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From page 41... ...
In 2011, a collaboration among the University of Alberta, Canadian Pacific, Canadian National, Transport Canada, and the Asso ciation of American Railroads-Transportation Technology Center emerged as the Canadian Rail Research Laboratory.4 A much broader program than the one in 2003, this work focused on using field data to explore engineering problems more prevalent in the Canadian climate (e.g., air break leaks, impacts from frost and cold weather, ballast fouling) and developing an associated risk tolerance strategy for the railroads.
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From page 42... ...
As a result of this work, the Transportation Safety Board amended its watch list. The team also developed a method of using vertical track deflection for the evaluation of transitions between stiff and soft sections, which tend to be where the greatest difficulties exist -- ∆VTDsub.
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From page 43... ...
The Canadian Rail Research Laboratory is also collaborating with the National Research Council of Canada to study vehicle dynamics and variations in load by utilizing an instrumented wheel set and working with the Canadian National Railway to develop a qualitative risk assessment tool and to eventually develop a quantitative risk assessment tool to better allocate resources and evaluate the effectiveness of risk mitigation measures. Both efforts are oriented toward preven tion and mitigation of potential consequences and are associated with qualitative analysis, hazard metrics, derailment causes, automation for practical implementa tion, and frameworks for future quantification and increased resolution.
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