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.

Climate change impacts pose challenges to a dependable operation of railway infrastructure assets, thus necessitating understanding and mitigating its effects. This study proposes a machine learning framework to distinguish between climatic and non-climatic failures in railway infrastructure. The maintenance data of turnout assets from Sweden’s railway were collected and integrated with asset design, geographical and meteorological parameters. Various machine learning algorithms were employed to classify failures across multiple time horizons. The Random Forest model demonstrated a high accuracy of 0.827 and stable F1-scores across all time horizons. The study identified minimum-temperature and quantity of snow and rain prior to the event as the most influential factors. The 24-hour time horizon prior to failure emerged as the most effective time window for the classification. The practical implications and applications include enhancement of maintenance and renewal process, supporting more effective resource allocation, and implementing climate adaptation measures towards resilience railway infrastructure management.

Life cycle cost (LCC) analysis is an important tool for effective infrastructure management. It is an essential decision support methodology for selection, design, development, construction, maintenance and renewal of railway infrastructure system. Effective implementation of LCC analysis will assure cost-effective operation of railways from both investment and life-cycle perspectives. A major setback in the successful implementation of LCC analysis by infrastructure managers is the availability of relevant, reliable, and structured data. Different cost estimation methods and prediction models have been developed to deal with this challenge. However, there is a need to include condition degradation models as an integral part of LCC model to account for possible changes in the model variables. This article presents an approach for integrating degradation models with LCC model to study the impact of change in design speed on key decision criteria such as track possession time, service life of track system, and LCC. The methodology is applied to an ongoing railway investment project in Sweden to investigate and quantify the impact of design speed change from 250 to 320 km/h. The results of the studied degradation models show that the intended change in speed corresponds to correction factor values between 0.79 and 0.96. Using this correction factor to compensate for changes in design speed, the service life of ballasted track system is estimated to decrease by an average of 15%. Further, the expected value of LCC for the route under consideration will increase by 30%. The outcome of this study will be used to support the design and requirement specification of railway track system for the project under consideration.

Climate change impacts such as extreme temperatures, snow and ice, flooding, and sea level rise posed significant threats to railway infrastructure networks. One of the important questions that infrastructure managers need to answer is, “How will maintenance costs be affected due to climate change in different climate change scenarios?” This paper proposes an approach to estimate the implication of climate change on the life cycle cost (LCC) of railways infrastructure assets. The proportional hazard model is employed to capture the dynamic effects of climate change on reliability parameters and LCC of railway assets. A use-case from a railway in North Sweden is analyzed to validate the proposed process using data collected over 18 years. The results have shown that precipitation, temperature, and humidity are significant weather factors in selected use-case. Furthermore, our analyses show that LCC under future climate scenarios will be about 11 % higher than LCC without climate impacts.

This paper is devoted to applying the inverse Data Envelopment Analysis (InvDEA) in the simultaneous presence of desirable and undesirable factors. One of the most common ways to improve units' performance in the business environment is through activity synergies called units' merging. The present study models how to identify the inherited input/output from the units participating in the merger process to achieve the desired efficiency goal. The proposed models are established based on the InvDEA approach and multiple-objective programming tools. Sufficient conditions to estimate desirable and undesirable data are obtained using Pareto solutions to multi-objective programming problems. The theory extended in the study is explained by an application in the banking sector.

Inverse data envelopment analysis (DEA) represents a fascinating and applicable topic within the DEA field that provides a tool for decision-makers to set target efficiency levels and identify what needs to change to achieve them. One of the most prevalent strategies to boost the efficiency of units involves unit restructuring, a process capitalizing on the synergy of activities. The focus of this study is the application of inverse DEA to build both a theoretical and practical framework during the restructuring of units that handle both desirable and undesirable data. The framework proposed provides a method to identify the inherited inputs/outputs from units involved in the restructuring process, aiming to achieve optimal efficiency objectives amidst the coexistence of both desirable and undesirable factors. The construction of the framework relies on the principles of inverse DEA and the tool of multi-objective programming. Pareto solutions from multi-objective programming issues are utilized to determine a sufficient condition for estimating both desirable and undesirable data. The proposed approach is evaluated through a case study in the educational.

Due to the accumulated greenhouse gas (GHG) effect, climate change will affect infrastructure networks regardless of different climate mitigation strategies. Our current investigation reveals an apparent increasing trend in the number of climatic-based failures in the Swedish railway infrastructure from 2010 until 2020.

Switch and crossing (S&C) is a critical part of the railway infrastructure network, which plays a key role in adjusting the railway network capacity and dependability performance. Due to the structure of S&C, it can be affected more by extreme climate change impacts, e.g., abnormal temperature, ice and snow, and flooding. Clearly, the reliability and hazard function of infrastructures will be affected by age and environmental conditions. Therefore, it is essential to analyze the effect of different climate change features / explanatory variables called "covariates" on the reliability of S&Cs. The proportional hazard model (PHM) is a practical approach to assess and prioritize the impact of various environmental covariates on S&Cs' reliability.

This paper aims to integrate climate change data with infrastructure asset health. This integration can be developed by utilizing proportional hazard methodology to assess the effect of different covariates on the reliability function. The proposed methodology has been verified through a number of S&Cs located on the Swedish railway network. As a main result, this study has revealed that the operational environment covariates significantly influence the reliability of S&Cs and profoundly affect the availability and capacity of railway tracks. The study indicates the need for effective climate adaptation options to reduce climate change impacts and risks to achieve resilience and climate-neutral railway infrastructure asset.

Extreme weather conditions from climate change, including high or low temperatures, snow and ice, flooding,storms, sea level rise, low visibility, etc., can damage railway infrastructure. These incidents severely affect the reliability of the railway infrastructure and the acceptable service level. Due to the inherent complexity of the railway system, quantifying the impacts of climate change on railway infrastructure and associated expenses has been challenging. To address these challenges, railway infrastructure managers must adopt a climate-resilient approach that considers all cost components related to the life cycle of railway assets. This approach involves implementing climate adaptation measures to reduce the life cycle costs (LCC) of railway infrastructure while maintaining the reliability and safety of the network. Therefore, it is critical for infrastructure managers to predict, "How will maintenance costs be affected due to climate change in different RCP's scenarios?"The proposed model integrates operation and maintenance costs with reliability and availability parameters such as mean time to failure (MTTF) and mean time to repair (MTTR). The proportional hazard model (PHM) is used to reflect the dynamic effect of climate change by capturing the trend variation in MTTF and MTTR. A use case from a railway in North Sweden is studied and analyzed to validate the process. Data collected over a 20-year period is analyzed for the chosen use case. As a main result, this study has revealed that climate change may significantly influence the LCC of switch and crossing (S&C) and can help managers predict the required budget.