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Each day tens of turnout-related derailment occur across the world. Not only is the prediction of them quite complex and difficult, but this also requires a comprehensive range of applications, and managing a well-designed geographic information system. With the advent of Geographic Information Systems (GIS), and computers-aided solutions, the last two decades have witnessed considerable advances in the field of derailment prediction. Mathematical models with many assumptions and simulations based on fixed algorithms were also introduced to estimate derailment rates. While the former requires a costly investment of time and energy to try and find the most fitting mathematical solution, the latter is sometimes a high hurdle for analysists since the availability and accessibility of geospatial data are limited, in general. As train safety and risk analysis rely on accurate assessment of derailment likelihood, a guide for transportation research is needed to show how each technique can approximate the number of observed derailments. In this study, a new stochastic mathematical prediction model has been established on the basis of a hierarchical Bayesian model (HBM), which can better address unique exposure indicators in segmented large-scale regions. Integration of multiple specialized packages, namely, MATLAB for image processing, R for statistical analysis, and ArcGIS for displaying and manipulating geospatial data, are adopted to unleash complex solutions that will practically benefit the rail industry and transportation researchers. • All estimates seem to be incapable of calculating an estimate for a low number of derailments. • the assumptions seldomly yield a precise estimate of the derailment rates under any uncertainty • Some assumptions which relied on turnout counts, are observed to deviate from the observations • the assumptions regarding turnout counts are a weak spot even when being generated mathematically on the basis of a concrete belief. [ABSTRACT FROM AUTHOR]

Railway turnouts or so-called 'switches and crossings' are complex systems by nature of design and construction. Railway turnouts are used to change direction of trains from one to another. They require high-quality construction and maintenance, in order to minimise rapid degradation and component failures that could result in train derailments. Due to the complexity of railway turnouts, the efficiency and effectiveness of maintenance can be improved by integrating existing practice by Building Information Modelling (BIM). This research establishes and analyses the world's first 6D BIM for life cycle management of a railway turnout system. The BIM (Level 3) has integrated 6-dimensions of field data information based on Revit-2018 and Navisworks-2018 platforms. The digital twins of a railway turnout in 3D embrace time schedule, costs and sustainability across the whole life cycle. The use of BIM for railway turnout systems has the potential to improve the overall information flow of the turnout planning and design, manufacturing pre-assembly and logistic, construction and installation, operation and management and demolition, thereby achieving better project performance and quality. Based on integrated information of railway turnout system, the 6D BIM has the ability to assess on economic, management and sustainability, and achieve a balance among them. This is the word first to demonstrate that BIM can fully deliver its essential benefits by information sharing, easing technical communication, improving design quality, reducing of design errors, accelerating implementation, speeding up work, shortening construction duration, reducing construction costs, enhancing carbon efficiency, supporting project management, and providing its owners with higher operational efficiency over the railway turnout system life-cycle. The results reveal that embodied material emission is the main contributor towards carbon footprint, especially produced during the manufacturing stage. The reconstruction stage contributes the most expensive phase of life cycle. The insight will significantly benefit the co-value creation among engineers, project managers, technicians, and senior management team. • Digital twin or BIM (Level 3) of railway switches and crossings is established. • It is the first to highlight sustainability-based life cycle management of railway turnout systems. • The life cycle analyses consider both cost and environmental impacts using field data, design and case studies. • The outcomes highlight best practices for sustainable and cleaner solutions for life cycle management of turnout systems. • The insight will benefit the co-value creation among engineers, project managers, technicians, and senior management team. [ABSTRACT FROM AUTHOR]

Abstract Since multiple failure events associated with derailments could not be identified and derailment probability could not be reached quantitatively by event tree and fault tree analysis for safety assessment in railway systems, applications of Bayesian network (BN) were introduced over the last few years. The applications were often aimed at understanding safety and reliability of railway systems through various basic principles and unique inference algorithms focusing on particular railway infrastructures. One of the most critical engineering infrastructure, railway turnouts (RTs) have been investigated and analysed critically in order to develop a new BN-based model with unique algorithm. This unprecedented study reveals the causal relations between primary causes and the subsystem failures, resulting in derailment, as a result of extreme weather-related conditions. In addition, the model, which is designed for rare events, has been proposed to identify the probability and underlying root cause of derailment. Consequently, it is expected that various weather-related causes of derailment at RTs, one such undesirable event, which can result, albeit rarely, damaging rolling stock, railway infrastructure and disrupting service, and having the potential to cause casualties and even loss of life, are identified to allow for smooth railway operation by rail industry itself. The insight into this weather-derailment will help the industry to better manage railway operation under climate uncertainty. [ABSTRACT FROM AUTHOR]

The construction of railway turnout entails a complex geometry and multidisciplinary engineering science, which makes it one of the most critical railway infrastructures. As such, these characteristics pose various risks in rail operation. A considerable number of derailment incidents at the turnouts are reported annually worldwide. Not only do these incidents cause operational downtime and financial loss, they also give rise to casualties and sometimes loss of life. One of the fundamental reasons for this may well be the fact that the railway industry pays little attention to the risk elements of railway turnouts. The paper provides an integrated approach of how to deal with the many different risks arising from various sources in railway turnout systems by suitably identifying the multi-disciplinary risk analysis methods for the complex systems. In order to do so, available open literatures are critically analysed by virtue of comparison, industry experiences and deductions. As a result, various qualitative- and quantitative-based risk analysis methods are proposed to fully understand a number of technical phenomena, e.g. ageing, degradation and signalling faults, in a railway turnout system. [ABSTRACT FROM AUTHOR]

To enhance rail operational flexibility, railway turnouts are special track systems, which are designed to divert or change a train from a particular direction or a particular track onto other directions or other tracks. In reality, the railway turnout is commonly built on complex track geometry and graded terrain, which makes it one of the most unique and critical railway infrastructures. The physical constraints and complexity of turnout systems cause various risks and uncertainty in rail operations. This study critically analyses emerging geotechnical risks on turnout systems considering all aspects that can potentially result in impaired reliability, availability, maintainability and safety (RAMS) of the turnout systems. The annual derailment incidents have been evaluated to identify emerging risk factors. Not only do these incidents yield operational downtime and financial losses, but they also give rise to the casualties and sometimes the loss of lives across the world. In particular, the climate change risks on geotechnical aspects of the turnout systems have been highlighted. This paper thus presents how turnout components work as a system, the diversity of emerging risks considering natural hazards and global warming potential to the system. In addition, it highlights the climate change adaptation strategies for georisk mitigation of the railway turnout systems in order to improve RAMS of the railway turnouts and crossings, focusing on trackbed failures on the systems. [ABSTRACT FROM AUTHOR]

There has been a considerable increase in derailment investigations, in particular at railway turnouts (RTs), as the majority of derailments lead to lengthy disruptions to the appropriate rail operation and catastrophic consequences, being potentially severely hazardous to human safety and health, as well as rail equipment. This paper investigates the impact of climates with different features across the US on the derailments to light up a scientific way for understanding importance of climatic impact. To achieve this, official derailment reports over the last five years are examined in detail. By means of geographic segmentation associated with spatial analysis, different exposure levels of various regions have been identified and implemented into a Bayesian hierarchical model using samples by the M–H algorithm. As a result, the paper reaches interesting scientific findings of climate behaviour on turnout-related component failures resulting in derailments. The findings show extreme climate patterns impact considerably the component failures of rail turnouts. Therefore, it is indicated that turnout-related failure estimates on a large-scale region with extreme cold and hot zones could be investigated when the suggested methodology of this paper is considered. • The climate effects causing derailments at turnouts have been identified and classified. • A novel methodology dealing with the climate effects on derailments is proposed. • The relationship between turnout component failures and climate impact is assessed. • The suggested model is validated through actual derailment cases in the US. [ABSTRACT FROM AUTHOR]

• Human errors causing derailments at switches and crossings were identified and classified. • A novel methodology dealing with the errors was proposed. • A novel DAG (Directed Acyclic Graph) built through Bayesian network was proposed. • The risks of errors were identified and analysed using new mathematical expressions. • Risk is prioritised by a most-to-least-critical importance ranking of human errors. The knowledge acquired in relation to failures associated with components has made significant contributions to the development of components with increased reliability, as well as a reduction in the number of rail incidents caused by certain system defects. These new systems have led to innovative developments in both the operations and technology of rail networks. Hence, rail employees must now function in conditions that have high complexity that are hard to comprehend. The risk of failure caused by human error (such as by dispatchers, train crews and track engineers) has developed into a significant safety problem. This study is the world first to provide novel insights into better understanding human errors, which result in derailments at rail turnouts. A most- to-least-critical importance ranking of these errors is established throughout a novel risk management technique. Moreover, the new findings and recommendations of this research study have a strong potential for industry to improve the reliability of rail operation, and avoid safety concerns regarding train derailments at rail turnouts. [ABSTRACT FROM AUTHOR]

Railway transportation, comprising freight and passenger transport, is the lifeblood of the social economy of a country today, especially for developing countries. Despite over a decade of operations, derailment accidents are among the most frequent accidents for railway transportation and may cause fatally or severe injury to passengers, loss of property and damage to the railway track. Hence, this study focuses predominantly on the structural response and performance evaluation of composite rail track slabs through 3D finite element analysis using ABAQUS. The response and performance of composite track slab subjected to derailment actions has been observed. Material strain-rate properties and impact loads have been introduced to the numerical simulation in order to investigate impact behaviours of composite slabs subjected to derailment loading in explicit dynamic analysis. Based on obtained results, it was found that 45 km/h in the direction of gravity is the limit impact velocity for the designed composite rail track slab. The outcome of this study will improve the design standard and calculation of composite rail track slabs subjected to derailment actions. [ABSTRACT FROM AUTHOR]

• This the world's first to establish a probabilistic Bayesian model for railway operations. • Model verifications against the long-term FRA's data exhibit excellent accuracy >95%. • Both of decision tree and Petri-nets models are embedded for risk assessment. • Both DT and PT models can be practically used by any railway authorities. • Railway accident reduction and reliability can be achieved by model adoption. Not only has the railway accidental prevention been a prime focus, but it has also become a key challenge for the industry in recent years. For many decades, rail authorities have attempted to significantly improve rail safety, whilst facing various passengers' risks and uncertainties. The overarching goal of this study is to develop a new posterior probability model to quantify uncertainties for benchmarking. This is the world's first to establish new insights from the benchmarking of risk and safety across different rail networks. The insights will point out the advantages and practicability of launching safety policies and reducing railway accidents for other rail networks. The new model has been developed using unparalleled long-term accidental data sets, including 'a trailer an accident' and 'causes of the accident'. The investigation adopts a Bayesian approach (via Python) to codify the novel model. The new findings lead to the better understanding into the uncertainty of railway accidents. Five notable rail networks have been selected as case studies. This study has also compared the effectiveness of the decision tree and Petri-net models using the posterior probability and number of injuries and fatalities. Based on the benchmarking outcomes, Chinese and Japanese railway systems denote the lowest risk over other networks, followed by Spanish, French and South Korean rail networks. The study also demonstrates that the novel benchmarking criteria can effectively measure and compare any rail networks' risk and uncertainties. Its adoption will lead to performance improvement in terms of safety, reliability and maintenance policies of railway networks globally. [ABSTRACT FROM AUTHOR]

• 3D FE model coupled with multi-body simulation has been developed. • Vulnerability of railway turnout systems has been assessed. • This study is the first to assess train-turnout interactions exposed to flood damage conditions. • The new insights will help track engineers to define operational restrictions building resilience capability. Track twists used in modern railways indicate the warpage of a particular track plane to identify track quality. In some cases, a twist is intentionally introduced on tracks to facilitate motions in curves. Nevertheless, twists can exceed above certain thresholds resulting in twist faults, which impose direct risk to safety and a potential cause for derailments. Twist faults are commonly observed in ballasted tracks, which consists of crushed rock particles and have low endurance to resist against dynamic track forces. In general, the deterioration in ballast structure progresses slowly. However, in reality, there are some catalysts such as extreme events that can speed up the deterioration of the ballast bed. Extreme events have rare occurrences but a high potential to damage structures and environment in a short duration. Even though the term 'rare' is still used to define extreme events, a consensus among the environmental scientists on the increased frequency of extreme events could be found in open literatures. In this study, the impacts of flooding, one of the most common extreme events, on the dynamic phenomena of a turnout structure is investigated in terms of dynamic twists. The emphasis is placed on railway turnouts of which the asymmetrical structure is expected to amplify twist responses. A 3-dimensional finite element method (FEM) model has been developed and many hypothetical scenarios ranging from various materials to vehicle speeds have been tested in virtual environments. It should be noted that the developed model is the modified version of a previously validated model and therefore, validation of the model is done by a comparison with the parent model. This study is the world's first to demonstrate that the dynamic performance of composite and plastic bearers is relatively poor in comparison to concrete bearers when considering dynamic twist phenomena. Our new results also exhibit that partially damaged turnout structures in the case of flooding is the most critical and vulnerable situation, which could result in detrimental train derailments. This vulnerability cannot practically be inspected by naked and untrained eyes. On this ground, when exposed to flooding conditions, it is recommended to halt any railway operations over vulnerable switches and crossings, and to avoid the approach of 'reach the station first' in emergency cases. [ABSTRACT FROM AUTHOR]