SAFETY4RAILS Information System platform demonstration at Madrid Metro simulation exercise
Abstract
SAFETY4RAILS is the acronym for the European Union Horizon 2020 co-funded innovation project entitled: "Data-based analysis for safety and security protection for detection, prevention, mitigation and response in trans-modal metro and railway networks" which started in October 2020. Its focus is to support the increase of security and resilience against combined cyber-physical threats including natural hazards to railway and metro systems. Its objectives target capabilities to support the characteristics of resilient systems; resilience represented by cycles containing phases of identification, protection, detection, response and recovery (Department of Communications 2019) (or similarly named phases). An ESREL paper in 2021 introduced the SAFETY4RAILS project and the SAFETY4RAILS Information System platform as well as some of the tools that are included in the platform. This paper will describe the architectural solution implemented for the platform in the last year and the demonstration of representative capabilities from the first simulation exercise with Madrid Metro at the beginning of 2022.
In the EU project SAFETY4RAILS, the project partners developed a collaborative toolkit that is able to assess and eventually improve the resilience of rail and metro transportation and its infrastructure against various cyber, physical and combined cyber-physical threats. In general, to improve a property of a system such as resilience, it is necessary to assess that property first. Therefore, in this paper, we focus on the aspect of assessing the resilience by the synergistic collaboration of two tools out of this toolkit: CuriX, which is a tool for monitoring and detecting abnormal behaviour of infrastructure in the presence of threats, and CaESAR, which can asses propagation of performance losses over distributed systems that reflects its resilience. We showcase a resilience assessment for an exemplary scenario of combined cyber-physical threats which is applied to a metro system. In this assessment, the main functionalities and results of both tools as well as their combined usage will be described to demonstrate how their collaboration can contribute to an improved resilience assessment.
Railway transportation dynamically performs under complex coherent systems and fail-safe interlocking conditions. Security and safety are the first priorities of this massive intermodal transportation. However, railway systems, like any other critical infrastructure, face many threats that can take not only the form of physical, but may also be cyber or combined cyber-physical threats, since the automated-and digital-based technologies in the rail operation may be vulnerable. Therefore, SAFETY4RAILS (S4R), a H2020 EU project, was initiated to strengthen the EU rail operations by increasing the resilience and improving the safety and security of railway and metro networks against these threat types through the further development and combination of a variety of state-of-the-art tools, most of which start with a Technology Readiness Level (TRL) of around 5. Within S4R, many tools will be utilized including a predictive risk and resilience assessment tool, an anomaly detection tool and an asset management tool. To achieve effective tool development and integration, the project requires huge collaboration from different expert and informative sources. This paper will include a discussion on risk and resilience assessment specific to rail systems, including a section on hardware-based countermeasures, before focusing on the risk assessment tool and how it will be implemented. The paper will also introduce a few other tools within the project and discuss the expected interactions the predictive risk assessment tool will have.
As terrorist attacks continue all over the world and, therefore, the alarming rate of countries governments as well as the awareness of people about danger is significantly increasing, in recent years high explosive airblast effects assessment and protection have become a fundamental concern for structural designers. In this article, the implementation of an original predictive tool based on the literature empirical data to develop analyses of blast loading consequent to a bomb explosion is described. The in-house developed model enables the fast calculation of the distribution of the main blast wave parameters around and over three-dimensional complex geometries virtually reproducing potential attractive targets for terrorists. Considering these capabilities, the use of the proposed novel methodology is desirable in the preparatory and retrofit design in view of evaluating blast-induced adverse consequences and, accordingly, to identify and effectively set up the most appropriate protective strategies and countermeasures.
Asset managers are facing the challenges of shrinking budgets from one side and the increasing frequency and impact of disturbances on their assets from the other side. To further equip asset managers to address these challenges, we propose an analytical framework for resilient asset management based on an extension to the established analytical framework of resilience. The core concept of this extension is to understand the significance of two types of couplings, including: (i) the coupled impact of different categories of disturbances, including shocks and stressors, and (ii) the chronological coupling of events in life cycle of an asset. A dynamic model of the proposed framework for integrating resilience into regular asset management is developed for building facilities and is integrated with mathematical modeling of stressors and randomly generated shocks. The model is then simulated as the results pointed to the need to offset the overestimation of the resilience of assets by integrating the coupled impact of shocks and stressors and the compounding impact of multiple potential disturbances in its life cycle. The proposed approach can extend the existing asset management models to future proof the plans for the life cycle of an asset and align the maintenance, repair, or rehabilitation actions with the actions associated with resilience enhancement retrofits.
The current management process for community buildings in Australia is mainly reactive. Data collected using regular inspections are used for maintenance decision making in the period between consecutive inspections, disregarding the future degradation of the assets and the resultant levels of service. Forecasting future conditions using historic data is difficult because of the uncertainty and stochastic nature of deterioration. A major gap in knowledge is the lack of methods for predicting this highly uncertain degradation process for components of community buildings to support a strategic decision-making process. This paper presents a Markov process-based method for deterioration prediction of building components using condition data collected by the City of Kingston in Australia. Markov transition matrices for building components have been derived using a modified method combining the genetic algorithm with Monte Carlo sampling called direct absolute value difference, which offers superior accuracy. The derived matrices are validated using a new data set collected in 2011. Fourteen transition matrices for building components are proposed. The paper presents a typical decision-making method based on the Markov process.
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