Vulnerability assessment modelling for railway networks

Railway networks are prone to many different potential disruptive events such as technical failures (e.g. the failure of aging components), natural disasters (e.g. flooding) and intentional man-made disasters (e.g. trespass and suicide). Assessing the vulnerability of railway networks can help infrastructure managers to create the right preventive strategies to improve the robustness and the resilience of railway networks before the occurrence of disruptions. This study proposes a stochastic-vulnerability analysis model that enables the critical components of railway networks to be identified. The model is developed using a discrete event simulation technique. Its framework includes modules for assigning the disruption to the network components, predicting the network vulnerability, in terms of passenger delays and journey cancellations, and calculating the risk-based criticality of network components. Finally, an example application of the model is presented using a part of the East Midland railway network in UK

The Development of modelling tools for railway switches and crossings

Network Rail records indicate that approximately 24% of the total track maintenance and renewal budgets are spent on railway switches and crossings (S&C), which account for only 5% of the total main line track mileage. S&C complexities also introduce a degree of risk, which must be adequately managed to ensure a safe and reliable network. In recent years, risk mitigation fell short, resulting in some high profile incidents at S&C. A recent derailment investigation uncovered knowledge gaps within the UK rail industry, including the understanding of S&C degradation. This PhD research project was therefore initiated to investigating modelling tools for S&C wheel-rail interaction and degradation. A new wheel-rail contact detection routine has been developed and validated using existing software and a novel experimental technique using thermal imagery. Existing techniques were then integrated to enable the prediction of normal and tangential contact stresses whilst also simulating wear accumulation. To improve accuracy for long-term S&C damage, a combined tool for assessing non-Hertzian normal contact stresses and multiple modes of S&C degradation was sought. A novel 2.5D boundary element model capable of simulating wheel-rail contact detection, surface and sub-surface elastic and elastic-plastic stress analysis and dynamic material response is presented. Superior computational effort is also achieved, illustrating further the feasibility of such an approach. To conclude, a three-dimensional dynamic finite element model of a railway wheel passing through a cast manganese crossing has also been developed. For the first time, a tool capable of simulating both dynamic contact forces and corresponding plastic material response has been used to discover flaws within existing designs of UK cast manganese crossings. This approach has enabled immediate recommendations for asset improvement to be provided to Network Rail and gives the UK rail industry more scientific insight into the optimal design of railway crossings.Open Acces

Fatigue Damage Identification by a Global-Local Integrated Procedure for Truss-Like Steel Bridges

Civil steel structures and infrastructures, such as truss railway bridges, are often subject to potential damage, mainly due to fatigue phenomena and corrosion. Terefore, damage detection algorithms should be designed and appropriately implemented to increase their structural health. Today, the vast amount of information provided by data processing techniques and measurements coming from a monitoring system constitutes a possible tool for damage identifcation in terms of both detection and description. For this reason, the research activity aims to develop a methodology for a preliminary description of the damage in steel railway bridges induced by fatigue phenomena. Te proposed approach is developed through an integration of global and local pro cedures. At the global scale, vibration-based procedures will be applied to improve a forecast numerical model and, subsequently, to identify the zones most involved in fatigue problems. At the local scale, careful and refned local identifcation will be pursued via image processing techniques whose evidence will be analyzed and described through nonlinear numerical models. A case study of a historical railway bridge in Spain will illustrate the methodology’s performance, potentiality, and critical issue

Modeling and Monitoring of the Dynamic Response of Railroad Bridges using Wireless Smart Sensors

Railroad bridges form an integral part of railway infrastructure in the USA, carrying approximately 40 % of the ton-miles of freight. The US Department of Transportation (DOT) forecasts current rail tonnage to increase up to 88 % by 2035. Within the railway network, a bridge occurs every 1.4 miles of track, on average, making them critical elements. In an effort to accommodate safely the need for increased load carrying capacity, the Federal Railroad Association (FRA) announced a regulation in 2010 that the bridge owners must conduct and report annual inspection of all the bridges. The objective of this research is to develop appropriate modeling and monitoring techniques for railroad bridges toward understanding the dynamic responses under a moving train. To achieve the research objective, the following issues are considered specifically. For modeling, a simple, yet effective, model is developed to capture salient features of the bridge responses under a moving train. A new hybrid model is then proposed, which is a flexible and efficient tool for estimating bridge responses for arbitrary train configurations and speeds. For monitoring, measured field data is used to validate the performance of the numerical model. Further, interpretation of the proposed models showed that those models are efficient tools for predicting response of the bridge, such as fatigue and resonance. Finally, fundamental software, hardware, and algorithm components are developed for providing synchronized sensing for geographically distributed networks, as can be found in railroad bridges. The results of this research successfully demonstrate the potentials of using wirelessly measured data to perform model development and calibration that will lead to better understanding the dynamic responses of railroad bridges and to provide an effective tool for prediction of bridge response for arbitrary train configurations and speeds.National Science Foundation Grant No. CMS-0600433National Science Foundation Grant No. CMMI-0928886National Science Foundation Grant No. OISE-1107526National Science Foundation Grant No. CMMI- 0724172 (NEESR-SD)Federal Railroad Administration BAA 2010-1 projectOpe

A Network Approach to Interdependent Infrastructure Resilience Assessment for Natural Hazards

Natural disasters are increasingly costly to society as they disrupt basic infrastructure functions. Infrastructure managers face challenges from growing urbanization, climate change, and aging infrastructure. Infrastructure resilience is an emerging concept that has been suggested as a solution to this problem; however, it is not yet mature. This thesis proposes to extend an existing dynamic infrastructure resilience quantification methodology to include infrastructure growth capabilities while relaxing some of the original constraints. The methodology uses a complex networks approach to model infrastructure interdependencies that is applied to a case study in the City of Toronto using a newly developed web tool

The assessment of track deflection and rail joint performance

Track stiffness is the one of the most critical parameters of the track structure. Its evaluation is important to assess track quality, component performance, localised faults and optimise maintenance periods and activities. Keeping the track stiffness within acceptable range of values is connected with keeping the railway network in a satisfactorily performing condition, allowing thereby upgrade of its capacity (speed, load, intensity). Current railway standards are changing to define loading and stiffness requirements for improved ballasted and ballastless performance under high speed train traffic. In recent years various techniques have been used to measure track deflection which have been also used to validate numerical models to assess various problems within the railway network. Based on recent introduction of the Video Gauge for its application in the civil engineering industry this project provides the proof of effective applicability of this DIC (Digital image correlation) tool for the accurate assessment of track deflection and the calculation of track stiffness through its effective applicability in various track conditions for assessing the stiffness of various track forms including track irregularities where abrupt change in track stiffness occur such as transition zones and rail joints. Attention is given in validation of numerical modelling of the response of insulated rail joints under the passage of wheel load within the goal to improve track performance adjacent to rail joints and contribute to the sponsoring company s product offering. This project shows a means of improving the rail joint behaviour by using external structural reinforcement, and this is presented through numerical modelling validated by laboratory and field measurements. The structural response of insulated rail joints (IRJs) under the wheel vertical load passage is presented to enhance industry understanding of the effect of critical factors of IRJ response for various IRJ types that was served as a parametric FE model template for commercial studies for product optimisation

Using Differential Shear Strain Measurements to Monitor Crosstie Support Conditions in Railroad Tracks

This thesis details a comprehensive numerical analysis of load determination, and crosstie support assessment and monitoring using strain gauges to measure differential rail shear strain in ballasted railroad tracks due to applied railcar wheel loads. These differential shear strain measurements can be related to applied wheel loading and crosstie support reactions through the geometric and constitutive properties of a given rail section. The basic theory behind the measurement technique was reviewed and investigated using finite element models of varying complexity. The impact of field conditions such as differential ballast and subgrade support, track stiffness, crosstie spacing, gauge installation location, and circuit calibration methods were explored, as well as the nature of the interaction between vertical and lateral loads on accurate load determination. The results of this theoretical study indicate that differential shear strain measurements are a robust method for load and crosstie support assessment and monitoring and can be used for accurate measurement of both vertical and lateral loads