@article{dindar_hierarchical_2022,
	title = {A hierarchical {Bayesian}-based model for hazard analysis of climate effect on failures of railway turnout components},
	volume = {218},
	issn = {0951-8320},
	url = {https://www.sciencedirect.com/science/article/pii/S0951832021006220},
	doi = {10.1016/j.ress.2021.108130},
	abstract = {There has been a considerable increase in derailment investigations, in particular at railway turnouts (RTs), as the majority of derailments lead to lengthy disruptions to the appropriate rail operation and catastrophic consequences, being potentially severely hazardous to human safety and health, as well as rail equipment. This paper investigates the impact of climates with different features across the US on the derailments to light up a scientific way for understanding importance of climatic impact. To achieve this, official derailment reports over the last five years are examined in detail. By means of geographic segmentation associated with spatial analysis, different exposure levels of various regions have been identified and implemented into a Bayesian hierarchical model using samples by the M–H algorithm. As a result, the paper reaches interesting scientific findings of climate behaviour on turnout-related component failures resulting in derailments. The findings show extreme climate patterns impact considerably the component failures of rail turnouts. Therefore, it is indicated that turnout-related failure estimates on a large-scale region with extreme cold and hot zones could be investigated when the suggested methodology of this paper is considered.},
	language = {en},
	urldate = {2021-11-29},
	journal = {Reliability Engineering \& System Safety},
	author = {Dindar, Serdar and Kaewunruen, Sakdirat and An, Min},
	month = feb,
	year = {2022},
	keywords = {Bayesian network, Climate effect, Derailment, Railway operation},
	pages = {108130},
}

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This paper investigates the impact of climates with different features across the US on the derailments to light up a scientific way for understanding importance of climatic impact. To achieve this, official derailment reports over the last five years are examined in detail. By means of geographic segmentation associated with spatial analysis, different exposure levels of various regions have been identified and implemented into a Bayesian hierarchical model using samples by the M–H algorithm. As a result, the paper reaches interesting scientific findings of climate behaviour on turnout-related component failures resulting in derailments. The findings show extreme climate patterns impact considerably the component failures of rail turnouts. Therefore, it is indicated that turnout-related failure estimates on a large-scale region with extreme cold and hot zones could be investigated when the suggested methodology of this paper is considered.","language":"en","urldate":"2021-11-29","journal":"Reliability Engineering & System Safety","author":[{"propositions":[],"lastnames":["Dindar"],"firstnames":["Serdar"],"suffixes":[]},{"propositions":[],"lastnames":["Kaewunruen"],"firstnames":["Sakdirat"],"suffixes":[]},{"propositions":[],"lastnames":["An"],"firstnames":["Min"],"suffixes":[]}],"month":"February","year":"2022","keywords":"Bayesian network, Climate effect, Derailment, Railway operation","pages":"108130","bibtex":"@article{dindar_hierarchical_2022,\n\ttitle = {A hierarchical {Bayesian}-based model for hazard analysis of climate effect on failures of railway turnout components},\n\tvolume = {218},\n\tissn = {0951-8320},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0951832021006220},\n\tdoi = {10.1016/j.ress.2021.108130},\n\tabstract = {There has been a considerable increase in derailment investigations, in particular at railway turnouts (RTs), as the majority of derailments lead to lengthy disruptions to the appropriate rail operation and catastrophic consequences, being potentially severely hazardous to human safety and health, as well as rail equipment. This paper investigates the impact of climates with different features across the US on the derailments to light up a scientific way for understanding importance of climatic impact. To achieve this, official derailment reports over the last five years are examined in detail. By means of geographic segmentation associated with spatial analysis, different exposure levels of various regions have been identified and implemented into a Bayesian hierarchical model using samples by the M–H algorithm. As a result, the paper reaches interesting scientific findings of climate behaviour on turnout-related component failures resulting in derailments. The findings show extreme climate patterns impact considerably the component failures of rail turnouts. Therefore, it is indicated that turnout-related failure estimates on a large-scale region with extreme cold and hot zones could be investigated when the suggested methodology of this paper is considered.},\n\tlanguage = {en},\n\turldate = {2021-11-29},\n\tjournal = {Reliability Engineering \\& System Safety},\n\tauthor = {Dindar, Serdar and Kaewunruen, Sakdirat and An, Min},\n\tmonth = feb,\n\tyear = {2022},\n\tkeywords = {Bayesian network, Climate effect, Derailment, Railway operation},\n\tpages = {108130},\n}\n\n\n\n","author_short":["Dindar, S.","Kaewunruen, S.","An, M."],"key":"dindar_hierarchical_2022","id":"dindar_hierarchical_2022","bibbaseid":"dindar-kaewunruen-an-ahierarchicalbayesianbasedmodelforhazardanalysisofclimateeffectonfailuresofrailwayturnoutcomponents-2022","role":"author","urls":{"Paper":"https://www.sciencedirect.com/science/article/pii/S0951832021006220"},"keyword":["Bayesian network","Climate effect","Derailment","Railway operation"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx"],"keywords":["bayesian network","climate effect","derailment","railway operation"],"search_terms":["hierarchical","bayesian","based","model","hazard","analysis","climate","effect","failures","railway","turnout","components","dindar","kaewunruen","an"],"title":"A hierarchical Bayesian-based model for hazard analysis of climate effect on failures of railway turnout components","year":2022}