Risk assessment of engineering diseases of embankment–bridge transition section for railway in permafrost regions

Abstract The embankment–bridge transition section (EBTS) is one of the zones where railway diseases occur frequently in permafrost regions. Disease risk assessment of EBTSs can provide guidance for maintenance. In this study, considering the engineering geological conditions, climate characteristics...

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Bibliographic Details
Published in:Permafrost and Periglacial Processes
Main Authors: Zhang, Saize, Niu, Fujun, Wang, Shi, Sun, Yongning, Wang, Jinchang, Dong, Tianchun
Other Authors: Ministry of Science and Technology of the People's Republic of China, Research and Development, Chinese Academy of Sciences
Format: Article in Journal/Newspaper
Language:English
Published: Wiley 2021
Subjects:
permafrost
Permafrost and Periglacial Processes
Online Access:http://dx.doi.org/10.1002/ppp.2135
https://onlinelibrary.wiley.com/doi/pdf/10.1002/ppp.2135
https://onlinelibrary.wiley.com/doi/full-xml/10.1002/ppp.2135
https://onlinelibrary.wiley.com/doi/am-pdf/10.1002/ppp.2135
Description
Summary:Abstract The embankment–bridge transition section (EBTS) is one of the zones where railway diseases occur frequently in permafrost regions. Disease risk assessment of EBTSs can provide guidance for maintenance. In this study, considering the engineering geological conditions, climate characteristics, and embankment structure types along the Qinghai–Tibet Railway (QTR) as well as based on the disease inventory of the QTR from 2010 to 2019, the logistic regression (LR), support vector machine (SVM), and combination‐weight‐based gay relation analysis (GRA) were used for disease risk assessment of the EBTSs along the QTR in permafrost regions. The results indicate that the LR and SVM models have a better capability for EBTS disease prediction than the GRA model, and the SVM model can select more disease samples in relatively larger regions than the LR model. Based on the SVM and LR models, the risk level of EBTSs is divided into four classes: low‐ (29.9%), moderate‐ (39.6%), high‐ (22.1%), and very high (8.4%) risk. Finally, we selected 272 EBTSs in high‐ and very‐high‐risk classes for key observation during the maintenance of the QTR in permafrost regions. This study provides a reference for the risk assessment of railways built in permafrost regions using data‐driven methods.

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