Optimizing rail defect inspection frequency to reduce the risk of hazardous materials transportation by rail-Reference-Cited by-同舟云学术

Optimizing rail defect inspection frequency to reduce the risk of hazardous materials transportation by rail

Published:2017-07 Issue: Volume:48 Page:151-161
ISSN:0950-4230
Container-title:Journal of Loss Prevention in the Process Industries
language:en
Short-container-title:Journal of Loss Prevention in the Process Industries

Author:

Liu Xiang

Funder

Federal Railroad Administration

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Management Science and Operations Research,Energy Engineering and Power Technology,Safety, Risk, Reliability and Quality,General Chemical Engineering,Food Science,Control and Systems Engineering

Reference64 articles.

1. Fatigue crack growth analysis of a premium rail steel;Aglan;J. Mater. Sci.,2001

2. Quantitative Analysis of Factors Affecting Railroad Accident Probability and Severity;Anderson,2005

3. Comments on PHMSA-2012-0082 HM 251–548 Hazardous Materials: Enhanced Tank Car Standards and Operational Controls for High-hazard Flammable Trains;Association of American Railroad (AAR),2014

4. Railroads and Chemicals;Association of American Railroads (AAR),2015

5. Railroads Safely Deliver Hazardous Materials;Association of American Railroads (AAR),2015

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