TY - JOUR
T1 - A hierarchical Bayesian-based model for hazard analysis of climate effect on failures of railway turnout components
AU - Dindar, Serdar
AU - Kaewunruen, Sakdirat
AU - An, Min
PY - 2022/2
Y1 - 2022/2
N2 - 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.
AB - 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.
KW - bayesian network
KW - climate effect
KW - derailment
KW - railway operation
UR - http://www.scopus.com/inward/record.url?scp=85117865951&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2021.108130
DO - 10.1016/j.ress.2021.108130
M3 - Article
SN - 0951-8320
VL - 218
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
IS - A
M1 - 108130
ER -