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GIS-BASED INFRASTRUCTURE MANAGEMENT SYSTEM FOR OPTIMIZED RESPONSE TO EXTREME EVENTS OF TERRESTRIAL TRANSPORT NETWORKS

Periodic Reporting for period 2 - SAFEWAY (GIS-BASED INFRASTRUCTURE MANAGEMENT SYSTEM FOR OPTIMIZED RESPONSE TO EXTREME EVENTS OF TERRESTRIAL TRANSPORT NETWORKS)

Reporting period: 2020-03-01 to 2021-02-28

SAFEWAY’s main aim is to design, validate and implement holistic methods, strategies, tools and technical interventions to significantly increase the resilience of inland transport infrastructure by reducing risk vulnerability and strengthening network systems to extreme events.
The key to achieve this aim is SAFEWAY ICT Platform capable of handling the three dimensions of the disaster management cycle:
1. “Preparation”: the pillar of the SAFEWAY approach is settled in a substantial improvement of prediction, monitoring and decision tools that will contribute to the anticipation, prevention and preparation of critical European transport infrastructures for the damaging impacts of extreme events.
2. “Response and Recovery”: the incorporation of SAFEWAY Big Data and Smart ICT into emergency plans, as well as the real-time optimised communication with operators and end users (via crowdsourcing and social media) will contribute to the recovery on a short-term scale; the solutions adopted in the pre- and peri-event have a direct and crucial effect in recovery on a long-term scale.
3. “Mitigation”: improving precision in the adoption of mitigation actions by impact analysis of the different scenarios together with new construction systems and smart materials that will contribute to the resistance & absorption of the damage impact.
Until M30, SAFEWAY project continued to develop a common strategy to improve the resilience of transport infrastructure and how each project component can contribute to this objective from the outset of the project, in addition to the implementation of the work programmes devoted to contributing to the dimensions of preparedness, namely anticipating and predicting the impact of major extreme events on European critical infrastructures. Each WP made the following progress:
WP 2 has identified risk factors affecting hazard and vulnerability and provided an integration of hazard inventories, databases and maps as well as tools for quantification of their impacts. Critical hazards (natural and human-made) and plausible failure modes have been identified, that may lead to the disruption of the terrestrial transportation network (railway and roadway). Guidance for assessment of the probability and severity of service disruption, by using structural and functional vulnerability functions, has also been provided.
In WP3, an analysis of remote sensing technologies that contribute to more efficient monitoring of critical assets. These include satellite technologies and terrestrial technologies to obtain geometrical and radiometric data from the studied environments with high accuracy. After having a clear vision of which specific technologies can contribute to the monitoring requirements of operators' owners, several monitoring scenarios have been defined and clear data acquisition protocols have been proposed.
Crowdsourcing methods for gathering data regarding vehicle parameters are under development in WP4. An initial approach was focused on vehicle acceleration data as road pavement condition indicator, either obtained from built-in sensors or external equipment, including the use of smartphone accelerometers. Interface definition was formulated on extracting floating car data for area wide road network monitoring. Also, a web service was designed for designating restriction zones by the traffic management operator in order to influence planned car routes so that such areas are avoided.
The development of performance predictive models for critical infrastructure assets was conducted in WP5. Deterministic models were used for predicting the deterioration of infrastructure components. Stochastic models were also used for predicting the future condition, and to overcome the lack of inspection records. Additionally, work was developed for the infrastructure risk-based models, consisting of the production of hotspot-maps for wildfires and floods situation and consideration of fragility curves for different assets. Impact assessment at regional scale, i.e. assessment of probability of failure modes leading to reduced mobility was also conducted considering a mesoscopic traffic model.
WP6 concentrated on developing of a robust, resilience-based decision support framework for terrestrial transportation. The first task was to establish a general value system regarding network resilience. The current asset management practice for quality assessment of road/rail infrastructure and the state-of-the-art research on the Key Performance indicators (KPIs) were investigated. The value system will account for demands and priorities of stakeholders of infrastructure and it is going to be based on monetized direct and indirect consequences of inadequate asset performance due to hazards and/or man-made events. The decision support framework is being implemented within a web-based Decision Support Tool that is going to be compatible with the SAFEWAY IT platform.
In WP8 three main aspects are being developed. The first one is the completion of an effective Emergency Plan Guideline for linear infrastructures. The second one is a wide identification of adaptation needs after evaluating the performance of each of the critical assets when facing each of the hazards that could have an impact of them and novel laboratory research, related to the characterization and study of Shape Memory Steels (SMS) and Optical Fiber Sensors. The third aspect is focussed on defining the legal and normative framework for these solutions.
A risk-based framework for assessment of the probability and severity of infrastructure malfunctioning due to extreme events has been established. The framework encompasses risk identification, assessment of hazard, exposure, vulnerability and impacts. The vulnerability assessment is proposed done using structural and functional vulnerability functions, associated with the failure mode and hazard type in the assessment. Recommendations of modelling variables and failure modes are provided for natural and human-made hazards as well as strategies for developing vulnerability functions and their application in consequence assessment.
The stochastic predictive model developed using collaborative Gaussian Process Regression represents a progress beyond the state of the art, as it overcomes the lack of enough inspection data. The predictive model can be applied to assets characterized by diversity such as bridges (i.e. different attributes and different environments). The model was applied on 80 bridge decks, showing that collaborative prognosis has the potential to predict the condition of different types of bridge elements, composing different types of bridges.
The concept of Building Information Modelling (BIM) relies on the continuous use of digital information across the entire lifecycle of a built asset. In the beginning, this term was used specifically for buildings. Nowadays, its use is broader and it can also be referred to any infrastructure and so it is sometimes called Infrastructure Information Modelling (IIM). To this concern, SAFEWAY is delivering tools to create information models of the transportation network and its assets. This is performed according to the latest Open Standards developed by the international community. In this project, IFC is the data format to be used, following the present and future schemas to be published by buildingSMART International (bSI).
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