Climate change impacts assessment on railway infrastructure in urban environments

被引:10
|
作者
Kasraei, Ahmad [1 ,5 ]
Garmabaki, A. H. S. [1 ]
Odelius, Johan [1 ]
Famurewa, Stephen M. [1 ,2 ]
Chamkhorami, Khosro Soleimani [1 ,4 ]
Strandberg, Gustav [3 ]
机构
[1] Lulea Univ Technol, Dept Civil Environm & Nat Resources Engn, Div Operat Maintenance & Acoust, Lulea, Sweden
[2] Swedish Transport Adm, Lulea, Sweden
[3] SMHI, Swedish Meteorol & Hydrol Inst, Rossby Ctr, Norrkoping, Sweden
[4] Islamic Azad Univ, Fac Comp Engn, Dept Math, Najafabad Branch, Najafabad, Iran
[5] Lulea Univ Technol, Dept Civil, Div Operat Maintenance & Acoust, SE-97187 Lulea, Sweden
关键词
Climate change adaptation; Reliability analysis; Cox proportional hazard model; Railway infrastructure; MODELS;
D O I
10.1016/j.scs.2023.105084
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Climate change impacts can escalate the deteriorating rate of infrastructures and impact the infrastructure's functionality, safety, operation and maintenance (O&M). This research explores climate change's influence on urban railway infrastructure. Given the geographical diversity of Sweden, the railway network is divided into different climate zones utilizing the K-means algorithm. Reliability analysis using the Cox Proportional Hazard Model is proposed to integrate meteorological parameters and operational factors to predict the degree of impacts of different climatic parameters on railway infrastructure assets. The proposed methodology is validated by selecting a number of switches and crossings (S&Cs), which are critical components in railways for changing the route, located in different urban railway stations across various climate zones in Sweden. The study explores various databases and proposes a climatic feature to identify climate-related risks of S&C assets. Furthermore, different meteorological covariates are analyzed to understand better the dependency between asset health and meteorological parameters. Infrastructure asset managers can tailor suitable climate adaptation measures based on geographical location, asset age, and other life cycle parameters by identifying vulnerable assets and determining significant covariates. Sensitivity analysis of significant covariates at one of the urban railway stations shows precipitation increment reveal considerable variation in the asset reliability.
引用
收藏
页数:15
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