Reliability analysis of railway assets considering the impact of geographical and climatic properties

Abstract

Various factors, including climate change and geographical features, contribute to the deterioration of railway infrastructures over time. The impacts of climate change have caused significant damage to critical components, particularly switch and crossing (S&C) elements in the railway network. These components are sensitive to abnormal temperatures, snow and ice, and flooding, making them susceptible to failures. The consequences of S&C failures can have a detrimental effect on the reliability and safety of the entire railway network.
It is crucial to have a reliable clustering of railway infrastructure assets based on various climate zones to make informed decisions for railway network operation and maintenance in the face of current and future climate scenarios. This study employs machine learning models to categorize S&Cs; therefore, historical maintenance data, asset registry information, inspection data, and weather data are leveraged to identify patterns and cluster failures. The analysis reveals four distinct clusters based on climatic patterns. The effectiveness of the proposed model is validated using S&C data from the Swedish railway network.
By utilizing this clustering approach, the whole of Sweden railway network divided into 4 various groups. Utilizing this groups the development of model can associated with enhancing certainty of decision-making in railway operation and maintenance management. It provides a means to reduce uncertainty in model building, supporting robust and reliable decision-making. Additionally, this categorization supports infrastructure managers in implementing climate adaptation actions and maintenance activities management, ultimately contributing to developing a more resilient transport infrastructure.

Full text

Vol.:(0123456789)
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Int J Syst Assur Eng Manag
https://doi.org/10.1007/s13198-024-02397-6
ORIGINAL ARTICLE
Reliability analysis ofrailway assets considering theimpact
ofgeographical andclimatic properties
AhmadKasraei1 · A.H.S.Garmabaki1 
Received: 10 December 2023 / Revised: 12 April 2024 / Accepted: 14 June 2024
© The Author(s) 2024
reduce uncertainty in model building, supporting robust and
reliable decision-making. Additionally, this categorization
supports infrastructure managers in implementing climate
adaptation actions and maintenance activities management,
ultimately contributing to developing a more resilient trans-
port infrastructure.
Keywords Climate zones· Clustering algorithm·
Railway asset· Reliability analysis
1 Introduction
The railway network as a linear asset spread over a long
distance and is susceptible to a range of weather impacts,
including severe weather events, heatwaves, flooding, and
rising sea levels, which ultimately lead to reduced availabil-
ity, safety, and punctuality, as well as increased operation
and maintenance costs (Forzieri etal. 2018; Thaduri etal.
2021; Garmabaki etal. 2021, 2022; IPCC 2022; Miller and
Huntsinger 2023; Salimi and Al-Ghamdi 2020; Pour etal.
2020; Famurewa and Hoseinie 2016; Soleimani-Chamkho-
rami etal. 2024). Moreover, railway infrastructure plays a
crucial role in facilitating safe and efficient transportation
of people and goods to fulfil the future sustainable develop-
ment goals (SDG) (Sachs etal. 2019). A significant portion
of the railway network, including structures such as bridges,
tunnels, and stations, was constructed without adequate con-
sideration of future climate change impacts, such as rising
temperatures, increased precipitation, and more frequent
extreme weather events. As a result, it is a requirement to
consider the potential impacts of climate change on rail-
way infrastructure assets to mitigate the risks and ensure
the longevity and resilience of this critical transportation
system (OECD 2018). Therefore, proactive planning and
Abstract Various factors, including climate change and
geographical features, contribute to the deterioration of rail-
way infrastructures over time. The impacts of climate change
have caused significant damage to critical components, par-
ticularly switch and crossing (S&C) elements in the railway
network. These components are sensitive to abnormal tem-
peratures, snow and ice, and flooding, making them suscep-
tible to failures. The consequences of S&C failures can have
a detrimental effect on the reliability and safety of the entire
railway network.
It is crucial to have a reliable clustering of railway infra-
structure assets based on various climate zones to make
informed decisions for railway network operation and main-
tenance in the face of current and future climate scenarios.
This study employs machine learning models to categorize
S&Cs; therefore, historical maintenance data, asset registry
information, inspection data, and weather data are leveraged
to identify patterns and cluster failures. The analysis reveals
four distinct clusters based on climatic patterns. The effec-
tiveness of the proposed model is validated using S&C data
from the Swedish railway network.
By utilizing this clustering approach, the whole of Swe-
den railway network divided into 4 various groups. Utilizing
this groups the development of model can associated with
enhancing certainty of decision-making in railway opera-
tion and maintenance management. It provides a means to
* Ahmad Kasraei
Ahmad.kasraei@associated.ltu.se
A. H. S. Garmabaki
Amir.garmabaki@ltu.se
1 Division ofOperation andMaintenance, Department
ofCivil, Environmental, andNatural Resources Engineering,
Luleå University ofTechnology, 97187Luleå, Sweden

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investment in new technologies and infrastructure, such as
improved drainage systems, weather-resistant materials, and
more resilient tracks and rolling stock, as well as the devel-
opment of new maintenance and inspection protocols, are
necessary to ensure the safe and reliable operation of rail-
ways in the face of climate change. In general, two kinds of
actions can be taken into account, including climate change
mitigation and adaptation. Mitigation related to all activi-
ties leads to decreasing CO2 emissions; however, adaptation
aims to reduce climate risks and vulnerabilities of existing
infrastructures. Adaptation strategies for railway asset infra-
structures can be grouped into (i) protect, (ii) accommodate,
(iii) retreat, and (iv) avoid (Tyler 2015; IPCC., 2022; Kasraei
etal. 2024).
One of the critical challenges associated with this strat-
egy is the impact of changing weather patterns and climates
across different geographic regions. Since the geographical
and climatic conditions across a country such as Sweden
can differ significantly, the management of transport infra-
structures and assets can be impacted profoundly. To tackle
this challenge, one possible solution is to divide the targeted
area into various classes/groups that share significant simi-
larities. This approach may make it easier to manage the
effects of weather patterns and climatic consequences on
railway assets. By categorizing regions based on their cli-
mate and geography, it becomes feasible to tailor infrastruc-
ture and asset management strategies to specific conditions,
ultimately enhancing the overall resilience and efficiency of
the railway network.
The paper’s objective is to partition Sweden’s railway
network into distinct clusters with the greatest similarity,
using meteorological and geographical attributes as criteria.
Moreover, by employing this clustering approach, the paper
intends to facilitate strategic comparisons among these clus-
ters, thereby enabling the development of resource manage-
ment strategies tailored to the specific characteristics of each
cluster. This approach will help optimize resource allocation
and enhance the efficiency of railway operations across Swe-
den’s diverse geographical and meteorological conditions.
This paper includes various sections as follows: in Sect.2,
different climate zone classifications are presented; next, in
Sect.3, the effect of climate change in Sweden is discussed,
and the results for some cities are presented. Section4 pre-
sents climate change’s impact on railway infrastructure.
Thereafter, the research methodology and case study have
been discussed in Sect.5. Finally, Sect.6, presents the main
findings and future research directions.
2 Climate zone classification systems
There are some classification systems for climate zones
include:
2.1 Köppen‑Geiger system
The Köppen-Geiger system is a widely used climate clas-
sification method that identifies different climates based on
temperature and precipitation patterns. It groups climates
into five main categories, which are further divided into sub-
types. The categories are tropical (A), dry (B), temperate
(C), continental (D), and polar (E). The system assigns a
combination of letters describing each climate zone, with the
first letter indicating the major climate group and the second
indicating the subtype. For example, a humid subtropical
climate is classified as Cfa, where C represents temperate
climates, f represents humid subtropical subtype, and A rep-
resents hot summer (Peel etal. 2007).
2.2 Hornthwaite climate classification system
This system classifies climates based on the water balance
and potential evapotranspiration, it is often used in hydrol-
ogy and water resource management to evaluate water avail-
ability and demand in a given region (Thornthwaite 1948;
Feddema 2005).
2.3 Trewartha climate classification system
This system is based on the concept of seasonality and takes
into account temperature and precipitation patterns through-
out the year (Trewartha 1968).
2.4 Bergeron classification system
The Bergeron classification system is based on the relation-
ship between temperature and precipitation. This system
divides climates into four groups: polar, boreal, temperate,
and tropical. Each group is further divided into subgroups
based on temperature and precipitation characteristics(Chiu
2020).
2.5 Spatial synoptic classification (SSC)
The Spatial Synoptic Classification (SSC) system was
developed by Mark S. Yarnal in the 1990s and is based on
the synoptic (large-scale weather) patterns that produce
local weather conditions. This system uses a combination
of temperature, humidity, wind, and cloud cover to clas-
sify climates into six groups: dry tropical, moist tropical,
dry mid-latitude, moist mid-latitude, dry polar, and moist
polar(Cakmak etal. 2016).
2.6 Holdridge life zones
The Holdridge Life Zones system is another climate clas-
sification system that is based on the relationship between

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temperature, precipitation, and potential evapotranspiration.
The Holdridge system divides the world into life zones based
on three climatic variables: mean annual temperature, mean
annual precipitation, and potential evapotranspiration. These
variables are combined to create an index of biotemperature
and a measure of aridity, which are then used to classify
areas into one of 30 different life zones (Holdridge 1947).
3 Sweden’s climate change
Sweden is classified as a cold climate zone according to the
Köppen-Geiger climate map (See Fig.1), and its average
temperature has risen by almost 2 degrees Celsius in com-
parison to the temperature to the end of the nineteenth cen-
tury. In contrast, the global mean temperature has increased
by approximately one degree Celsius. Figure2 displays the
charts of average temperature with bars, where the red bars
represent temperatures higher than the average temperature
for the normal period of 1961–1990, while the blue bars
represent temperatures lower than the average. The chart
also includes a black line showing a running mean calcu-
lated over approximately ten years. This curve clearly shows
the continuous increase in temperature during the past two
decades.
There are several specific future climatic scenarios for
the assessment of temperature variation, three of which are
Representative Concentration Pathways (RCPs) scenarios,
including RCP2.6, RCP4.5, and RCP8.5. Based on RCP2.6,
greenhouse gas emissions will start to decline in 2020 and
reach zero at the end of the century. In RCP4.5, emissions
continue to around 2040 and decline afterward. In RCP8.5,
emissions continued to increase until the end of the century.
Compared to today, the estimated warming to the end of the
century is roughly 1°C, 2°C and 4°C for RCP2.6, RCP4.5
and RCP8.5, respectively (IPCC 2014). Figure3 shows the
projected temperature changes in summer for four cities
in Sweden (Luleå: northeast; Kiruna: northwest; Malmö:
Southeast; Stockholm: middle) according to three different
RCP scenarios. The warming signal is everywhere in all
scenarios, and warming until the end of the century is 2°C
(RCP2.6) to 4°C (RCP8.5). At the end of the century, Luleå
and Kiruna may have the same average summer temperature
as Stockholm.
Figure4 shows the projected annual precipitation
for four cities in Sweden (Luleå: northeast; Kiruna:
Fig. 1 World map of Köppen-Geiger climate classification updated with mean monthly CRU TS 2.1 temperature and VASClimO v1.1 precipita-
tion data for the period 1951–2000 on a regular 0.5degree latitude/longitude grid (Kottek etal. 2006)

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Fig. 2 Average temperature for different seasons from 1860 till 2020 (SMHI 2022)
Fig. 3 Projected summer temperature for four cities in Sweden

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northwest; Malmö: Southeast; Stockholm: middle)
according to three different RCP scenarios. The annual
precipitation is predicted to increase everywhere, but
the northern regions show larger changes and variability
(Kasraei etal. 2024).
The following conclusions have been made for Sweden
under different RCPs (SMHI):
• High temperatures (above + 25-degree) will increase,
especially in the south (most considerable difference
in the north),
• Zero-crossing will decrease in the south and increase
in the north during winter. The north will experience
more rain and more snow at temperatures close to
0°C,
• The amount of snow will decrease in general but
increase in the north
• Rain will increase within the whole country.
4 Impact ofclimate onrailway infrastructure
Weather conditions such as temperature and precipitation
can significantly impact railway infrastructure, causing
severe damage. For example, high temperatures can cause
the rails to buckle or expand, leading to structural damage.
The proxy for high temperatures is summer days, defined as
days with maximum temperature > 25°C. A consequence
of a warmer climate is more warm days. The number of
summer days is projected to increase with 10–20 until the
end of the century in southern Sweden, around twice the
amount of today. Similarly, heavy rainfall or snowfall can
cause infrastructure slope failures, track misalignment, and
bridge scour. Additionally, extreme weather conditions can
also cause damage to the catenary line and signal equip-
ment, making it difficult for trains to operate smoothly (Palin
etal. 2021; Stenström etal. 2012; Garmabaki etal. 2022).
Flooding may occur due to intense short-time precipitation
Fig. 4 Projected precipitation for four cities in Sweden (Kasraei etal. 2024)

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or large precipitation amounts over longer periods. Heavy
precipitation is projected to increase in the future. Extreme
showers are expected to increase by around 40% by the end
of the century. The number of days with heavy or extreme
precipitation are also projected to increase.
Extreme weather conditions, such as heavy rainfall,
snowfall, freezing temperatures, and high winds, can lead
to delays and even failures in railway infrastructure in north-
ern Europe. Research has indicated that adverse climate
conditions account for 5–10% of total failures and 60% of
delays in the railway system in this region (Garmabaki etal.
2021). To address this issue, rail operators and infrastructure
managers may need to invest in measures such as improved
drainage, better insulation, more resilient track materials,
and enhanced maintenance protocols. Additionally, contin-
gency plans and procedures may need to be developed to
respond effectively to weather-related disruptions. How-
ever, the specific impact of adverse climate conditions on
rail infrastructure may vary depending on various factors,
including geography, topography, and types of infrastructure
and equipment used in different regions. Therefore, further
research and analysis may be required to fully understand
the relationship between climate conditions and rail perfor-
mance. Figure5 illustrates two climate-related incidents that
occurred in Sweden in 2023, leading to injuries and disrup-
tions in the railway network.
5 Methodology
Given the potential impact of adverse climate conditions on
railway assets, it is crucial to consider the diverse climatic
zones in different regions and their effects on the railway
infrastructure. In this context, this paper seeks to catego-
rize the areas of Sweden into different sections, taking into
account various climatic parameters such as temperature and
the type of railway assets, including stations. To achieve
this goal, unsupervised machine learning techniques such as
K-Means will be utilized to cluster and group different areas
based on their similar climatic conditions and railway assets
(Kasraei and Ali Zakeri 2022; Kasraei etal. 2021). The use
of machine learning techniques in this study can provide
Fig. 5 a Washed away track and b flooded track

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valuable insights into the relationships between climatic
zones and railway infrastructure in Sweden. By analyzing
the data on temperature and railway assets, the study can
identify the areas most vulnerable to adverse weather con-
ditions and require more attention and investment in adap-
tation and resilience measures. Furthermore, using unsu-
pervised machine learning techniques such as K-Means can
identify patterns and trends that may not be easily identifi-
able through traditional statistical methods. This approach
can also help overcome the limitations of human judgment
and bias, providing a more objective and data-driven per-
spective on categorizing different climatic zones and rail-
way assets. This study focuses on railway S&Cs which are
distributed over the network. S&C is one critical asset in the
railway infrastructure networks as shown in Fig.6. When the
switching mechanism is initiated, the switchblade moves to
its opposite position in order to divert the train in another
direction. Many failures can cause S&C malfunction, and
black box approaches have been followed while performing
failure analyses.
In addition, based on knowledge created in our previous
projects and discussions with experts, temperature has been
selected as the main climatic factor with a high impact on
the railway infrastructure network.
This study includes the following subsections:
Railway asset specifications: This section involves gather-
ing information on the railway network map of Sweden and
selecting over forty railway stations located in different parts
of the country. The GPS specifications of these assets are
then determined and recorded for further analysis.
Meteorological data gathering and pre-processing: This
section outlines the process of selecting weather stations
based on the identified railway stations. The desired mete-
orological data is then gathered from open sources, and tem-
perature records from the past 20years are collected and
analyzed using data from VviS and SMHI.
Clustering algorithms: This section explains the use
of K-means clustering techniques to analyze the gathered
data. The clustering technique groups the data based on their
similarities, allowing for easier interpretation and identifica-
tion of patterns in the data.
Reliability analysis: This section involves performing
reliability analysis on the clustered data. After grouping the
data into different clusters, the reliability of each cluster is
assessed.
The framework of this paper is depicted in Fig.7. The
diagram illustrates the different components and their rela-
tionships in the proposed framework.
5.1 Data gathering andpre‑processing
The present study involves collecting and analyzing diverse
data sets about the railway network in Sweden. Specifically,
the study includes the selection of forty railway stations
from various parts of the network, and their locations have
been identified for further analysis. In addition, meteorologi-
cal data from weather stations close to the selected railway
stations have also been collected. This data includes tem-
perature readings, and records have been gathered over a
period of more than 20years. Including these diverse data
sets allows for a comprehensive analysis of the railway net-
work and its associated environmental factors. By examining
the temperature records over an extended period, the study
can identify any trends or patterns that may be present in
the data.
5.2 Clustering according toclimate zones
K-Means is an unsupervised machine learning algorithm
that clusters and groups data points. The algorithm identi-
fies the centroid of each cluster and then iteratively reassigns
data points to the nearest centroid until the clusters stabilize.
The “K” in K-Means refers to the number of clusters the
algorithm aims to identify. This approach consists of the
following steps:
• Select the number of clusters (K)
• Initialize the centroids of each cluster randomly
Fig. 6 Illustration of switches and crossings (Nissen 2009)

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• Assign each data point to the nearest centroid based on
the Euclidean distance between the data point and the
centroid
• Recalculate the centroid of each cluster as the mean of
all the data points assigned to that cluster.
• Repeat steps 3 and 4 until the centroids no longer
change or until a maximum number of iterations is
reached.
• The algorithm outputs the final clusters, each contain-
ing the data points assigned to the same centroid.
The number of clusters needs to be determined at the
beginning of the K-Means method. The Elbow method
is commonly used for this purpose. As shown in Fig.8,
the optimal number of clusters for the given dataset was
determined to be four. Using this parameter (K = 4), the
Asset select ion (railway
stat ions)
Meteorological data
(history of temperature
for selected assets)
Determining members of different
clusters
Step1: Data Gathering and pre-proces sing
Step 2: Clustering according climate zones aspect
Step 3: Trendanalysis
Step 4: Reliability analysis
Extracting failures history data for each
clusters
Is there trend in failures
data of cluster?
NHPP
(power low process)
HPP
(Weibull, Normal,
Lognormal, etc.)
Yes
No
Reliabil ity function
Expected number of failures
Sate llite data History of failures and
maintenances of assets
Optimal number of clusters
with Elbow approa ch
•
•
Fig. 7 Framework of the study (Kasraei etal. 2023)

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K-Means technique was implemented using the Spyder
software.
In the next step of the analysis, the dataset was clus-
tered based on several features. Specifically, time series of
temperature for each railway station were considered, and
various parameters were extracted from these time series.
These parameters included the mean temperature, standard
deviation (std), skewness, kurtosis, as well as the geographic
coordinates of the station (i.e., latitude, longitude) and its
height above sea level. This approach allowed for a more
comprehensive analysis of the temperature data, taking into
account the temporal patterns and spatial characteristics of
the data. By considering a range of features, the resulting
clusters were able to capture the underlying structure of the
data and identify groups of similar observations.
Furthermore, including geographic coordinates and
height above sea level in the clustering process may reveal
patterns related to topography and microclimate, which are
known to affect temperature variations. This information
could be useful for understanding the spatial distribution
of temperature patterns and potentially identifying areas
that are more susceptible to extreme temperature events.
By leveraging multiple features in the clustering process, a
more nuanced understanding of the temperature data can be
obtained, leading to insights that may not be apparent from a
single feature analysis. Figure9 displays the outcome of the
grouping process, which reveals the presence of four distinct
clusters encompassing a total of 40 railway stations located
throughout Sweden. The clusters shown in the figure are
divided by green lines, and each cluster’s members are indi-
cated by four different colours (green, red, blue, and yellow).
5.3 Reliability analysis anddiscussion
5.3.1. Trend analysis.
The statistical trend test technique is utilized to assess
the presence of any patterns or trends in cumulative fail-
ure times of a particular system over time. To evaluate
trends in cumulative failure time, various statistical tests
such as the Laplace trend test, Military Handbook test,
and Anderson–Darling test can be employed. These tests
analyze the data for a monotonic trend, which refers to a
consistent increase or decrease in the cumulative failure
time over time. If the test indicates a significant increase
in the cumulative failure time, this may suggest that the
system is becoming less reliable and may require mainte-
nance or redesign.
Figure 10 depicts the trend in failures occurrence
which, performed based on the clustering outcome given
in subSect.5.2.
Fig. 8 Using the Elbow method to determine the optimal number of
clusters for K-means algorithm
Fig. 9 Depicting selected railway stations on various clusters over
Sweden (Google earth)

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It can be observed that over time, there is a gradual
increase in the curve representing the reduction in the reli-
ability performance of the assets.
In trend tests, the null hypothesis is a statement of
trend-free or no significant pattern in the data being ana-
lyzed. Table1 shows the results of the statistical analysis
that in all statistical tests, the null hypothesis is rejected
for all cases (except the pooled Laplace’s for cluster 3, and
the nonhomogeneous Poisson process (NHPP) is utilized
for reliability analysis in the next step (Garmabaki etal.
2016).
The results of statistical analysis confirm the interpreta-
tion of the above curve, and P-value indicates that in all
statistical tests, the null hypothesis is rejected except the
pooled Laplace’s for cluster 3, and the cumulative failure
time shows the trend, and the NHPP will be utilized for reli-
ability analysis in next step.
5.3.1 Nonhomogeneous poisson process (NHPP)
The NHPP is associated with an intensity function
(Eq.(1)) that signifies the rate of failures.
The shape parameter (β) value determines whether the
system improves, deteriorates, or remains constant over
time. The value of β indicates an increasing failure rate,
meaning the system is deteriorating. Under the NHPP
case, the number of failures can be estimated through
the integration of
𝜆(t)
and the reliability function can be
approximated as given in Eq.(2):
(1)
𝜆
(t)=
𝛽
𝜃
∗(t
𝜃
)
𝛽−1
Fig. 10 The presentation of trend test results graphically

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The reliability function, denoted by R(t), and the intensity
function at time s, denoted by λ(s), are related through the
cumulative intensity function, represented as
t
∫
0
𝜆(s)
ds
.
According to the previous analysis, the shape and scale for
the failure time are presented in Table2.
(2)
m(t)=
t
∫
0
𝜆(s)ds
R(
t
)=exp (−
m
(
t
))
Using the Eq.(1), Eq.(2), and the result, which are pre-
sented in Table2, the reliability curves of four clusters are
presented in Fig.11.
Notably, the data set under consideration comprises
four distinct clusters, labelled Cluster1, Cluster2, Clus-
ter3, and Cluster4, and encompasses 2,002 individual
assets. These assets experienced a total of 24,738 failures
Table 1 Trend test results for
different clusters Number of cluster Assessment of
null hypothesis
MIL-Hdbk-189 Laplace’s Anderson–Darling
TTT-based Pooled TTT-based Pooled
Cluster 1 P-value 0.000 0.000 0.000 0.000 0.000
Result rejected rejected rejected rejected rejected
Cluster 2 P-value 0.000 0.000 0.000 0.000 0.000
Result rejected rejected rejected rejected rejected
Cluster 3 P-value 0.000 0.003 0.000 0.869 0.000
Result rejected rejected rejected accepted rejected
Cluster 4 P-value 0.000 0.000 0.000 0.000 0.000
Result rejected rejected rejected rejected rejected
Whole assets P-value 0.000 0.000 0.000 0.000 0.000
Result rejected rejected rejected rejected rejected
Table 2 Parameters estimation for different clusters
Parameter Cluster1 Cluster 2 Cluster 3 Cluster 4 Whole of
S&Cs
Shape 1.20 1.34 1.08 1.28 1.24
Scale 14,298 20,310 11,548 15,890 15,573
Fig. 11 Reliability curves for
selected assets over time under
influence of different climate
zones
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
05,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000
50,000
Reliability
Operaonal me (h)
Cluster1
Cluster2
Cluster3
Cluster4
Whole assets
Table 3 Distribution of assets and failures in different clusters
#Cluster #S&C % Of assets #Failures %Of fail-
ures
#Failures
per #S&C
1 124 6% 1340 5% 10.80
2 387 19% 4113 17% 10.63
3 373 19% 4620 19% 12.38
4 1118 56% 14,665 59% 13.12
Whole
assets
2002 100% 24,738 100% 12.36

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over the course of the 18-year observation period. Table3
reveals that Cluster 4, consisting of assets located in the
southern region of Sweden, accounted for over 56 per-
cent of all assets and nearly 60 percent of total failures.
The data presented in Table2 highlights the remarkable
similarity between Cluster 4 and the integrated scenario,
including all assets, as evidenced by the fact that Cluster
4 contributed to almost 60 percent of all observed fail-
ures. Furthermore, Table3 (last column) shows that the
distribution ratio of the number of failures per asset is
approximately the same for all the clusters and integrated
scenario. In addition, the ratio of assets per cluster and the
ratio of number of failures per cluster are approximately
the same (See columns three and five). Hence, a weighted
combination of cluster failure parameters (shape param-
eter) provides a reasonably accurate approximation of this
parameter for the reliability analyses of whole assets. This
will help the infrastructure manager with tactical and stra-
tegical decision-making utilizing the cluster’s combined
pattern, considering the ratio weighted for reliability, qual-
ity management, and maintenance planning.
In Fig.11, comparing the reliability behavior of the whole
assets (black curve) and other clusters is evident that the
clustering technique can lead to a reduction in uncertainty in
modeling, as demonstrated by the varying reliability behav-
iors of the different clusters. This finding is of great signifi-
cance in the field of machine learning for climate adaptation
measures and risk assessment, where accurate and reliable
models are crucial for effective decision-making. By cluster-
ing S&Cs based on their meteorological feature, the analysis
provides insight into the underlying patterns and behaviors
of these assets, which can assist in developing more robust
and accurate models. Specifically, the reliability analysis
conducted on the different clusters reveals that different
groups of assets exhibit different reliability behaviors, with
some being more reliable than others over time.
Figure12 illustrates the differences between the inte-
grated scenario and the clustering approach that divides
the assets into four different groups based on their meteoro-
logical features. The results demonstrate that the number of
failures at t = 40,000h for each of the four clusters and the
whole assets vary, with cluster 2 having the lowest (2.50)
and cluster 3 having the highest (4.00). These differences
highlight the importance of considering assets’ specific char-
acteristics and environmental conditions when developing
machine learning models for risk assessment and climate
adaptation measures. Furthermore, the similarities between
the number of failures for the whole assets (3.25) and clus-
ter 4 (3.28) suggest that this cluster is representative of the
overall behavior of the assets.
6 Conclusion
Machine learning is a practical tool to integrate climate
adaptation strategies and risk assessments due to the exten-
sive distribution of assets across diverse geographical areas,
the varied characteristics of the assets, and their exposure to
fluctuating meteorological conditions. This research focuses
on 2002 S&Cs installed in different railway stations in Swe-
den. Temperature, a crucial meteorological factor associ-
ated with the coordination and altitude of railway stations,
was chosen to categorize the railway assets into distinct cli-
matic zones using the K-means technique. This technique
resulted in four various clusters. Subsequently, reliability
analysis was carried out for these clusters and an integrated
scenario. The analysis revealed that each cluster exhibits
distinct behaviors, with assets in Cluster 2 demonstrating the
Fig. 12 Expected number of
failures curves for selected
assets over time under influence
of different climate zonesf
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
05,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000
50,000
Expected number of failures
Operaonal me (h)
Cluster1
Cluster2
Cluster3
Cluster4
Whole assets

Int J Syst Assur Eng Manag
1 3
highest reliability and those in Cluster 3 showing the lowest
over time. Our findings indicated similarities in reliability
parameters between cluster 4 and the integrated scenario
(whole assets).
The clustering approach employed in this study enables
infrastructure managers to incorporate climate parameters
into asset health performance assessments (reliability analy-
sis), facilitating a deeper understanding of asset behavior
under diverse meteorological and geographical conditions.
Looking ahead, our future research plans involve expand-
ing the proposed methodology to include additional influen-
tial climatic factors, such as humidity, snow depth, rainfall
amount, and wind speed, in combination with operational
features like the age of assets, track types, gross tonnage,
and speed.
Funding Open access funding provided by Lulea University of
Technology. Authors gratefully acknowledge the funding provided by
Formas, to the project titled “Climate Adaptation and Risk Mitigation
of Swedish Railway Infrastructure (AdaptRail Grant no. 2022-00835)”
and Kempe Foundation (Grant no. JCK-2215). The authors gratefully
acknowledge the in-kind support and collaboration of Trafikverket,
SMHI, WSP AB, InfraNord, and Luleå Railway Research Center
(JVTC). Kempestiftelserna, JCK-2215, A.H.S Garmabaki,Svenska
Forskningsrådet Formas, 2022-00835,A.H.S Garmabaki.
Declarations
Conflict of interest There are no conflicts of interest declared by any
of the authors.
Human participants and/or animals Human Participants and/or
Animals are not involved in this research.
Informed consent Informed consent was obtained from all indi-
vidual participants included in the study.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing, adap-
tation, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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