GIS-based methodology for prioritization of preparedness interventions on road transport under wildfire events

Climate change is leading to a rise in the occurrence and intensity of wildfires, exacerbated by the growing encroachment of communities into the natural environment, posing challenges to our global capacity to respond to wildfires. During wildfire events, road transport infrastructure becomes crucial for the evacuation of people and accessibility to an emergency by first responders. Nevertheless, resilience management of transportation infrastructure affected by wildfires is poorly considered, despite its relevant role and high exposure to wildfires. Therefore, this study proposes a new methodology to estimate the priority level for wildfire preparation by combining exposure and criticality of road transportation infrastructure to wildfire hazards with consideration of different wildfire categories. The analysis is conducted at the system level considering interdependencies and redundancies among infrastructure components and using a geographic information system (GIS) to automate the modelling process and visualization of results. The proposed methodology is applied to a case study in the Leiria region of Portugal, demonstrating its utility in prioritizing economic resources and decision-making for areas requiring preparation. This approach can serve as a resilience-based tool for decision-making, supporting the implementation of effective adaptation strategies to enhance wildfire resilience.This work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020, and under the Associate Laboratory Advanced Production and Intelligent Systems ARISE under reference LA/P/0112/2020. This work is financed by national funds through the Foundation for Science and Technology (Fundaç˜ao para a ciˆencia e tecnologia, FCT, Portugal), under grant agreement 2020.05755.BD attributed to the first author

Dynamic thresholds for the resilience assessment of road traffic networks to wildfires

The severe effects of extreme wildfire events in recent years have shown that the fire suppression approach is not enough to solve the problem. An alternative to dealing with this issue is to accept the impossibility of eliminating wildfire hazards and focus on preparing systems to be more resilient. However, existing decisionmaking tools based on resilience present important drawbacks that make them inadequate for this task. This paper proposes a new approach and methodology for the resilience assessment of road traffic networks to wildfires that overcomes the main drawbacks, paying attention to the different functions of the system and the acceptance of a specific loss of performance. The latter is done through the introduction of dynamic thresholds that reflect the different requirements of the system under different wildfire conditions, including normal and extreme fires. The methodology is exemplified for five traffic networks. The results support the relevance of appropriate wildfire management through the adaptation of the natural and built environment to increase the capacity of the traffic networks to cope with wildfires.This work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020, and under the Associate Laboratory Advanced Production and Intelligent Systems ARISE under reference LA/P/0112/2020. This work is financed by national funds through FCT, Foundation for Science and Technology, under grant agreement 2020.05755.BD attributed to the first author

Policies towards the resilience of road-based transport networks to wildfire events: the Iberian case

Wildfires are becoming more intense and frequent. This problem has tested the knowledge, response capacity, and resilience achieved by society throughout history, making it clear that they are insufficient to face this new wildfire regime. The effectiveness of the related policies mainly focused on fire suppression rather than prevention is increasingly insufficient and questionable. Consequently, there is a clear lack of tools to assess the impact of wildfire preventive actions. Therefore, it is imperative to review wildfire management practices, policies, and the tools used to support decision-making in this regard. This study performs an analysis of wildfire policies applied in the Iberian Peninsula case (Portugal and Spain), including cross-border policies and the role of road transport networks. A novel simplified methodology is included to evaluate different normal and extreme forest fire management policies in road transport infrastructures. The methodology includes different parameters related to wildfires, such as sources of exposure, identification of natural and artificial barriers, and traffic conditions that capture the economic characteristics of the studied area. The information provided by the tool is useful for strategic investment planning, resource prioritization, and evacuation time management. In addition, due to its simplicity of application, it is a useful tool for cross-border areas.This work was partly financed by FCT / MCTES through national funds (PIDDAC) under the R\&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB / 04029/2020, and under the Associate Laboratory Advanced Production and Intelligent Systems ARISE under reference LA/P/0112/2020. This work is financed by national funds through FCT, Foundation for Science and Technology, under grant agreement 2020.05755.BD attributed to the first author

Risk assessment of road infrastructures as key for adaptability measures selection

Road infrastructures are one of the most important assets in the world due to the dependency on other critical infrastructures upon it. Society expects an uninterrupted availability of the road network, nevertheless it has become a difficult task as, in the last decades, climate change has significantly affected transport networks, especially due to the occurrence of extreme natural events leading to the disruption of the network. Those events include floods, wild fires, landslides and others, and all of them may increase both in frequency and intensity in the coming century. Therefore, there is a clear need for timely adaptation. Regarding those adaptability measures, an important step is needed to quantify how the transport network is directly and indirectly affected by extreme weather events, which can be obtained within a risk assessment. Nonetheless, there are many questions and variability about this topic such as uncertainties in projections of future climate, effects assessment, and how it can be an integration of all these aspects into the decision-making process. In that scope, this work de-scribes a risk assessment methodology having account the cause, effect, and consequence of extreme events in road networks to identify the major risks and therefore the assets that may be suitable to be analyzed within a selection of adaptation measures aiming at a holistic decision-making support tool.H2020 -Horizon 2020 Framework Programme(UIDB / 04029/2020

Reliability-based Bayesian updating using visual inspections of existing bridges

Structural reliability has become a widely accepted performance indicator for infrastructures over the past decade, providing valuable information about their structural condition. As a result, it has been assessed in combination with deterioration prediction, aiming at defining optimal maintenance, and rehabilitation strategies for bridge networks. In that case, reliability values need to be updated based on collected data. To this purpose, there has been a rapid development of advanced bridge condition assessment techniques, both in the fields of structural health monitoring as well as on non-destructive assessment techniques. Most of the sophisticated non-destructive methods are the preferred option but sometimes are not possible. Thus, visual inspection is still the predominant bridge condition assessment technique being adopted within the majority of Bridge Management Systems (BMS). However, there is a procedural gap when incorporating information obtained from visual inspections into a re-liability assessment. Therefore, this paper describes a methodology for a time-dependent reliability-based condition evaluation of existing bridges. The procedure is applied to a pre-stressed reinforced concrete railway bridge located in Portugal, in which prediction of reliability levels are calculated for different periods assuming corrosion initiation, causing a reduction in the cross-section area of the steel reinforcement and residual strength reduction, based on onsite inspection evidence. Finally, the updating is made through a Bayesian approach to compute the posterior bridge reliability based on inspection results. This approach may apply to other types of structures considering information obtained from visual inspection concerning the actual deterioration state in a quantitative way.This work was partly financed by FCT / MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Engineering Structures (ISISE), under reference UIDB / 04029/2020. The first author would like to thank FCT – Portuguese Scientific Foundation for the research grant 2020.05755.BD. The second author would like to thank FCT – Portuguese Scientific Foundation for the research grant SFRH/BD/144749/2019. This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 769255

Dynamic Risk-based Predictive Models (D5.1) - SAFEWAY: GIS-Based Infrastructure Management System for Optimized Response to Extreme Events of Terrestrial Transport Networks

[Excerpt] Executive Summary The scope of this deliverable is to provide a framework for dynamic risk-based predictive models for transportation networks. First, an overview of the most common predictive models for forecasting the future condition of transportation infrastructures is presented. These models were built upon performance indicators available in large databases. Then, the possibility of updating the predictive models based on new collected information from different sources of data was introduced into the framework through Bayesian inference procedures.[...]This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 769255