Railway Infrastructure Research Articles

Digitalization and Dynamic Criticality Analysis for Railway Asset Management

The primary aim of this paper is to support the optimization of asset management in railway infrastructure through digitalization and criticality analysis. It addresses the current challenges in railway infrastructure management, where data-driven decision making and automation are key for effective resource allocation. The paper presents a methodology that emphasizes the development of a robust data model for criticality analysis, along with the advantages of integrating advanced digital tools. A master table is designed to rank assets and automatically calculate criticality through a novel asset attribute characterization (AAC) process. Digitalization facilitates dynamic, on-demand criticality assessments, which are essential in managing complex networks. The study also underscores the importance of combining digital technology adoption with organizational change management. The data process and structure proposed can be viewed as an ontological framework adaptable to various contexts, enabling more informed and efficient asset ranking decisions. This methodology is derived from its application to a metropolitan railway network, where thousands of assets were evaluated, providing a practical approach for conducting criticality assessments in a digitized environment.

Research on digital twin technology and its application in intelligent operation and maintenance of high-speed railway infrastructure

PurposeThis paper analyzes the application of digital twin technology in the field of intelligent operation and maintenance of high-speed railway infrastructure from the perspective of top-level design.Design/methodology/approachThis paper provides a comprehensive overview of the definition, connotations, characteristics and key technologies of digital twin technology. It also conducts a thorough analysis of the current state of digital twin applications, with a particular focus on the overall requirements for intelligent operation and maintenance of high-speed railway infrastructure. Using the Jinan Yellow River Bridge on the Beijing–Shanghai high-speed railway as a case study, the paper details the construction process of the twin system from the perspectives of system architecture, theoretical definition, model construction and platform design.FindingsDigital twin technology can play an important role in the whole life cycle management, fault prediction and condition monitoring in the field of high-speed rail operation and maintenance. Digital twin technology is of great significance to improve the intelligent level of high-speed railway operation and management.Originality/valueThis paper systematically summarizes the main components of digital twin railway. The general framework of the digital twin bridge is given, and its application in the field of intelligent operation and maintenance is prospected.

Roadmap for implementing climate adaptation innovations in railway

AbstractThe railway is often perceived as an industry where new technology is not utilised to its full potential. However, the future of the railway and its ability to respond to future transportation demands lies in its ability to implement emerging technology. The aim of this research was to propose a system-level innovation roadmap for implementing innovations that can support climate adaptation of railway infrastructure. To achieve the aim of the research, barriers, and enablers for implementing innovations have been explored, identified, and analysed. Two interview studies and a workshop have been conducted. In total, 58 experts from a population with knowledge in railway have been interviewed. The innovation is a decision support system (DSS) enhancing knowledge regarding effects of climate change on railway infrastructure and suggesting climate adaptation measures. The barriers and enablers concerning the implementation of the innovation are related to technology, business models, procurement, infrastructure, regulations, policy, and culture, values, and behaviour. Procurement processes and business models are vital enablers for the introduction of new solutions for a more climate-change-resilient infrastructure. Tendering documentation must include a broader scope of climate-change risk management indicators. A conclusion is that climate adaptation action has a multi-dimension aspect which require various stakeholders to be engaged. This work proposed a holistic system-level innovation roadmap for implementing DSS for climate adaptation of railway infrastructure.

Framework of Scan to Building Information Modeling for Geometric Defect Localization in Railway Track Maintenance

Railway transportation plays a vital role in modern society, enabling the safe and efficient movement of people and goods over long distances. To ensure the longevity and safety of a railway infrastructure, the regular maintenance of tracks is crucial. Traditional track inspections, conducted manually to monitor geometric parameters and to identify defects, are time-consuming, labor-intensive, and prone to human error. Current Scan-to-BIM frameworks for railway maintenance also lack standardized methods for extracting geometric parameters that can be easily integrated into Building Information Modeling (BIM). Additionally, the Industry Foundation Classes (IFC) standard, used for BIM data exchange, does not support storing parameter values at specific chainage points along the track, limiting defect localization. A framework is proposed to address these challenges by standardizing the extraction of geometric parameters from point cloud data and ensuring seamless integration with BIM. The framework calculates parameters at station chainage points and generates additional chainage points along the track, associating the data with the corresponding chainage. A case study demonstrates the framework’s ability to enhance defect localization, using the EN 13848-5 European Standard to identify defects at specific chainages. Ultimately, this approach contributes to the more effective lifecycle management of railway tracks.

Machine Vision AI in Railroad Safety: Advanced Inspection Techniques

This article explores the transformative impact of machine vision AI in railroad safety, focusing on advanced inspection techniques. It examines the integration of technologies such as line scan cameras, LiDAR, and IoT sensors in enabling comprehensive 360-degree inspections of railway infrastructure and rolling stock. The article discusses key innovations in continuous monitoring, data processing, and AI-driven analysis, highlighting their potential to significantly enhance defect detection accuracy, reduce maintenance costs, and prevent accidents. Real-world applications in structural integrity assessment and overhead line inspection are presented, along with an analysis of current challenges and future developments in the field. The article underscores the paradigm shift from reactive to proactive maintenance strategies, promising a safer and more efficient future for rail transport.

Risk Analysis of Railway Infrastructure Asset using Hazard Identification and FMEA Analysis on Track Asset

Risk Analysis of Railway Infrastructure Asset using Hazard Identification and FMEA Analysis on Track Asset

Fabrication and Characterisation of Kenaf Fibre Reinforced Polyamide Biocomposites for Railway Sleeper Applications

Railway passing traffic, speed, and load have significantly increased over the years, prompting industry stakeholders and researchers to seek an alternative sleeper material that can demonstrate its ability to potentially possess higher in-service bending resistance and be environmentally friendly and durable. To address these needs and due to environmental concerns, kenaf-reinforced polyamide has become of great importance. However, they could not be applied as railway track components because of the non-availability of their performance in this regard. In bridging this gap, this paper focused on fabricating and characterising six different formulations of treated kenaf fibre (TKF, 0–50% at 10% loading interval) reinforced polyamide biocomposites for railway sleeper applications. The result showed that the incorporation of TKF influenced the behaviour of the polyamide with respect to its water absorption, load-carrying capacity, and thermal stability. The result further demonstrated that the load-bearing capacity peaked at TKF 40 wt.%, surpassing conventional wooden and concrete sleepers. However, its water absorption (64-days saturation) behaviour increased significantly between 11%–21% as TKF rose from 10–50 wt.%, as expected due to TKF hydrophilic characteristics. On the other hand, TKF thermal stability was hampered beyond approximately 220°C for all TKF percentages. Kenaf fibre-reinforced polyamide biocomposites have demonstrated their potential for railway sleeper applications as their load-bearing capacity exceeded the minimum recommended AREMA specifications. Despite the milestone achieved, water absorption of kenaf fibre remained high. The development of sustainable and effective materials to meet the changing needs of contemporary railway infrastructure is greatly aided by the insights gained from this study.

Analisis Tingkat Risiko Penyebab Rel Patah pada Jalur Kereta Api Wilayah Divre IV Tanjung Karang

The increase in the volume of passengers and goods in rail transportation in Indonesia has an impact on the condition of railway infrastructure, especially railway infrastructure. The main focus of this condition is the risk of rail damage, especially broken rails. So a holistic assessment approach is needed to identify the root causes and formulate appropriate treatment and improvement strategies. The aim of the research is to identify the factors causing the rail to break and analyze the causes. This research uses a risk level value analysis method by multiplying the results of probability and impact, thus producing a risk value. Risk variables such as extreme rain events, expansion in the alignment area, and mud pumping are high level risks. This is followed by medium, low and very low risks, according to the classification of each risk variable analyzed. Therefore, the priority scale for handling broken rail repairs is grouped based on the risk level value starting from the highest risk to the lowest risk. The conclusion is that risk variables such as extreme rain produce a risk value of IDR 296,881,200 with a probability value of 0.99% and an impact of IDR 29,988,000,000, expansion in the straight area produces a risk value of IDR 376,992,000 with a probability value of 1.10% and impact IDR 34,272,000,000, and mud pumping produces a risk value of IDR 394,128,000 with a probability value of 1.15% and impact IDR 34,272,000,000, these three risk variables are classified as high risk.

Estimation of sleeper-ballast contact pressure based on fretting marks: field measurements and FEM modeling

The contact conditions between components of railway infrastructure are complex. This research focuses on the contact interactions between the ballast and steel sleepers. Unlike the prismatic design of concrete and wooden sleepers, steel sleepers are characterized by a unique U-shaped configuration, further contributing to the complexity of the contact conditions. To address this issue, this study focused on analyzing the lower surfaces of steel sleepers commonly used in railroads, which had contact marks resulting from the ballast and train load due to fretting. To simulate these contact marks, virtual ballast geometries were created and simplified to match the observed contact areas. Specifically, these contact areas represented 6% of the bottom area of the steel sleepers. We compared the efficiency of this approach using Finite Element Method simulations.

Post-earthquake inspection of high-speed railway viaducts with multi-scale task interaction deep learning strategy

Automated computer vision-based inspections of railway infrastructures, such as component type, damaged status, and location, have been investigated actively by resorting to task-specific deep learning models. However, task-specific models that fulfill these separate inspection tasks encountered bottlenecks in improving inference accuracy, and bring huge computational costs. Multi-task deep learning, which can fulfill these inspection tasks concurrently, has yet to be fully investigated in the context of structural inspection. In this study, a multi-scale task interaction deep learning strategy is presented towards component recognition, damage segmentation, and depth estimation for a comprehensive post-earthquake inspection of high-speed railway viaducts. Three modules for multi-scale task interaction were proposed to modify the multi-task deep neural network, taking full advantage of task commonalities at multiple scales. The proposed method was validated with a large-scale image dataset of high-speed railway viaducts. Component recognition and depth estimation were incorporated to implement multi-task learning since they have higher pattern affinities at multiple scales. Results reported that mean Intersection over Unions of testing samples of component and damage tasks were 91.2% and 72.1%, RMSE of depth estimation was 1.54 m. Compared with single-task cases, training time, inference duration, and FLOPs of the multi-task model were reduced by 23%, 30%, 27% respectively. Results showed improvement in both inference accuracy and training efficiency, substantiating the superiority of the proposed strategy.

Study on the Effect of Root Density on the Shear Strength of Root-Soil Composite: A Case Study of Cynodon Dactylon

Since the Belt and Road Initiative was proposed, many slopes with poor stability have been formed in China's highway and railway infrastructure construction. In response to the national goals set during the 14th Five-Year Plan to strengthen the ecological security barrier and protect biodiversity, and in line with the philosophy that "lucid waters and lush mountains are invaluable assets," ecological protection technology has been widely applied in slope protection. However, current research and design have not considered the role of roots in retaining walls. This study uses an eco-friendly reinforced soil retaining wall project as an example to investigate the relationship between root density and the cohesion and internal friction angle of root-soil composites through direct shear tests. The results show that increasing root density significantly improves the cohesion and internal friction angle of root-soil composites. When the root density is 5%, the cohesion and internal friction angle increased by 40.57% and 51.41%, respectively, compared to the unreinforced soil sample, providing a theoretical reference for engineering design.

A perspective on solar railway potential for the UK

AbstractIn the face of climate change challenges, energy demand is still increasing, and renewable energy is becoming increasingly necessary as a sustainable resource. Solar energy stands out as a promising renewable resource, yet its widespread adoption faces challenges, notably the substantial land requirements for photovoltaic (PV) panels. This conflict intensifies as land could be more efficiently utilized for agriculture and development. Conversely, railway infrastructure occupies expansive tracts of land, presenting an opportunity to integrate PV panels without disrupting rail operations. However, a comprehensive assessment of the solar potential along railway tracks on a national scale is lacking for different countries and the UK is one of them. This study addresses this gap by evaluating the feasibility of installing PV panels on railway tracks throughout the UK to generate electricity. Utilizing mathematical models tailored to five distinct solar panel technologies, the study incorporates various factors, including solar radiation levels across different months. Assumptions were made where necessary, with values for certain variables averaged to facilitate calculations. The findings reveal that solar energy harnessed from railways could satisfy up to 8% of the UK's total electricity demand, with a minimum contribution of 0.3%. This study provides valuable insights from a UK‐centric perspective and offers a replicable framework for similar assessments in other countries with extensive railway networks, such as China and India. By leveraging existing infrastructure for renewable energy generation, such initiatives could contribute significantly to global sustainability efforts.

Rail relations: Aboriginal storywork and remaking Australia’s settler‐colonial infrastructure

AbstractAustralian railway histories are dominated by narratives of engineering triumphs, colonial expansion into empty land, and bringing civilisation and development through railway infrastructure. These settler‐colonial stories can be read back on themselves as histories and geographies of Aboriginal dispossession and colonial possession. Indeed, Aboriginal people, lands, waterways, and cultures have always been implicated in railway infrastructures, willingly or not. Aboriginal people’s entanglements with the New South Wales railways, to which we refer as “rail relations,” have involved dispossession, removal, employment, mobility, and travel, including the forced removal of children known as the Stolen Generations. These are relations of harm, loss, and grief but also of pride, connectivity, and survival. We argue in this paper that when Aboriginal communities engage in storying the New South Wales railways as Aboriginal they reassemble this infrastructure otherwise: not just as a tool of dispossession but also as life affirming. Indigenous storytelling can therefore overcome settler colonial erasure and the oversimplification of railway infrastructure hi/stories. Research about how Aboriginal lives have been interconnected with railways expansion and development is limited. While Aboriginal railway stories are continuously told within communities, they remain almost entirely silenced elsewhere. Overcoming the invisibility of Aboriginal rail relations is crucial as both truth‐telling of the past and to ensure more just infrastructural outcomes now and in the future.

A Machine Learning-Based Algorithm for the Prediction of Eigenfrequencies of Railway Bridges

As part of the development of advanced, data-driven methods for predictive maintenance of railway infrastructure, this paper analyzes and evaluates more realistic predictions of eigenfrequencies of railway bridges, also referred to as natural frequencies, based on a population of already assessed, measured existing bridges using regression techniques. For this purpose, Machine Learning (ML) techniques such as Polynomial Regression (PR), ANN and XGBoost are consistently evaluated and the application of the XGBoost algorithm is identified as the most suitable prediction model for these eigenfrequencies, usable for dynamic train-bridge interactions. The results of the post-processing are incorporated into the safety architecture for bridge verification (risk management). The presented data-based techniques are a steppingstone towards digitalization of structural health monitoring and offer safety and longevity of the railway bridges. Furthermore, the use of these methods can save costs that would be incurred by physical in-situ measurements. The types of bridges analyzed with ML are Filler Beam Bridges (FBE), which outnumber other construction types of bridges in Germany (DB InfraGO AG). This methodology is applicable to any bridge type as long as sufficient data are gathered for training, validation and testing.

Показатели и алгоритмы оценивания качества результатов идентификации состояния прилегающих территорий

Machine learning methods for identification of territories near railway infrastructure facilities are considered. The main sources of initial data are presented, as well as algorithms for assessing the quality of the results for landscape elements automated identification using the forest vegetation example. Forest vegetation state identification is carried out based on spectral-brightness characteristics determined using multispectral aerospace imaging materials. Quality indicators for the results of survey materials automated processing and algorithms for calculating the considered indicators are proposed. Examples are given to assess the results quality of using machine learning methods.

A model-based approach for joint estimation of track irregularities and track support modulus from trackside measurements in railway turnouts

ABSTRACT The paper proposes a model-based approach for the concomitant estimation of track modulus, track irregularity, and dynamic loading by combining an analytical model of a beam on an elastic foundation with vibration data collected by a trackside measurement system. The model is empirically extended to incorporate the effects of dynamic wheel/rail interaction forces when both short wavelength defects (in the rail surface) and medium-long wavelength track irregularities (e.g. hanging sleepers, differential ballast settlement) are present. The power spectral density of the rail vertical acceleration is then exploited in combination with the extended model to estimate track parameters using least squares. The proposed method is validated on vibration data collected in a turnout of the Danish railway infrastructure over a period of 2.5 years. The obtained results show that changes over time in track modulus and track irregularities, and consequently in dynamic wheel/rail contact forces, can be efficiently monitored from trackside measurements.

Sound event detection of passing trains using convolutional recurrent neural network (CRNN)

Acoustic measurements near railway infrastructure typically involve precise identification and labelling of train passages in time series of noise measurements. The labelling of events is done manually by an acoustician, by cross-referencing several sources of information, such as train timetable data, audio data, images and/or videos. In this paper, we present a method which automatically detects and labels the passage of trains in front of a microphone using a convolutional recurrent neural network (CRNN). The advantage of this type of model is that it can be trained using existing data (time series of LAeq,1s and 1/3 octave band with a good labelling of passing trains); which has been widely available for a number of years. The results are promising if limited to binary classification but would require further development if the aim included the recognition and categorisation of various train types.

Application of vibration signals in railway track diagnostics using a mobile railway platform

The article presents a comprehensive method for using vibration signals to diagnose railway tracks. The primary objective is to gather detailed information on track conditions through a passive experiment. This involves using mobile diagnostic tools and techniques to assess railway infrastructure. The article elaborates on the range of diagnostic activities conducted in accordance with detailed railway regulations and highlights the benefits and capabilities of mobile diagnostics in railway transport. The research includes mobile field measurements across the general railway manager’s network, employing vibration signals to detect and evaluate track conditions. The methodology section provides a thorough description of the mobile measurement rail platform, detailing the equipment used, the routes taken for measurements, and the processes of data acquisition and processing. The data obtained from these measurements is crucial for understanding the actual technical condition of the railway tracks. The method of obtaining and processing data is explained in relation to the real technical condition of the railway track. This involves using transducers with specific parameters and parametrically defined signal recording, along with dedicated analysis techniques in post-processing. Vibration signals serve as the primary carrier of information in this diagnostic method. The article details the step-by-step procedures for collecting and analyzing these signals to provide accurate assessments of track conditions. Based on the results from the mobile measurement rail platform, the article characterizes various areas of diagnostics where vibration signals are particularly effective for technical evaluation. These areas include identifying track defects, monitoring track surface and railway crossing and assessing the overall structural health of the railway infrastructure. The use of vibration signals offers a non-invasive and efficient means of track diagnostics, providing real-time data for maintenance and repair decisions. In conclusion, the article underscores the significance of mobile diagnostics in enhancing the safety and reliability of railway transport. By leveraging vibration signals and advanced data processing techniques, this method provides a framework for continuous monitoring and assessment of railway track conditions, ultimately contributing to improved maintenance strategies and operational efficiency.

Methodology for improving the assessment of the economic efficiency of transport infrastructure development

Transport infrastructure, especially railway infrastructure, plays a key role in economic development, ensuring efficient movement of goods and people. It is characterised by high capital intensity, long operational lifespans, and generation of socio-economic effects. Therefore, there is a need to improve the methodology for assessing the economic efficiency of creating and developing transport infrastructure objects. The traditional approach to evaluation is based on discounting cash flows. When applying this method, significant capital investments are compared with devalued effects in subsequent years due to discounting, during which the infrastructure object reaches the specified estimated load. Moreover, it does not consider the fact that the transport infrastructure benefits and holds value not only for the current generation but also for the future one. Additionally, it, as an economic good, cannot be seen as a one-timely consumed product: this transportation service cannot be accumulated. For a more accurate and comprehensive assessment of the efficiency, particularly in the case of railway transport, it is proposed to consider the results of long-term operation of the transport infrastructure at full (estimated) load not in the future but in the present (present сontinious). The economic assessment indicators include initially invested and adjusted capital, annual effect, efficiency ratio, and the value of the transport infrastructure object. The research results aim to provide an adequate assessment of project implementation efficiency in the field in question. In the future, the methodology can be enhanced by defining risks of not ensuring normal infrastructure load throughout the entire calculation period as well as by assessing the reduction in profitability or increase in transportation costs.

Energy redistribution in railway transition zones by geometric optimisation of a novel transition structure

Railway transition zones are critical regions in railway infrastructure that are subjected to excessive operation-driven degradation due to energy concentration within these zones. This work presents a heuristic approach to optimise the geometry of the transition structure and investigate its influence on the strain energy distribution in the railway transition zones (RTZs), with a specific focus on embankment-bridge transitions equipped with a newly proposed ’Safe Hull-Inspired Energy Limiting Design (SHIELD)’ transition structure. For this purpose, a number of three-dimensional finite element models are used to analyze different geometric profiles of SHIELD in a systematic manner. By altering SHIELD’s geometry across longitudinal, transversal, and vertical directions, the influence of the different geometric profiles on the total strain energy distribution across the trackbed layers (ballast, embankment, and subgrade) is studied in terms of spatial and temporal variations. The results establish the contribution of geometry to energy redistribution in all three directions and present an optimum geometry for the type of transition under study. It is found that among all the profiles, the longitudinal geometric profile of SHIELD has the most significant impact on the strain energy distribution, while the transversal profile primarily influences the ballast layer, and the alteration of vertical profiles enhance the local redistribution of strain energy in the vicinity of the transition interface. The preliminary optimisation (heuristic approach) presented in this work provides the starting point for full-scale optimisation to obtain tailored shapes of transition structures such that there is neither a concentration of energy nor an obstruction in the flow of energy in RTZs.