Hossein Nasrazadani

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Nasrazadani

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Hossein

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0000-0001-7620-0672

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Publications 1 - 10 of 18

Stress test framework for evaluating the resilience of transport systems
Adey, Bryan T.; Nasrazadani, Hossein; Chambers, Katherine; et al. (2024)
This document outlines a comprehensive framework for conducting stress tests and evaluating the resilience of transportation systems. It is targeted at stakeholders engaged in transportation planning, risk analysis, and decision-making processes. It includes policymakers, transport authorities, engineers, and consultants, providing them with a standardized procedure to conduct stress tests and estimate the resilience of their system using both qualitative and quantitative approaches. Moreover, the framework emphasizes the importance of addressing uncertainties and offers guidance on identifying critical system components, potential interventions, and areas for further analysis. By following this framework, transportation stakeholders can enhance their understanding of system vulnerabilities, make informed decisions, and develop effective strategies to improve the overall resilience of transport networks. This document should be connected with other standards and guidelines on risk/resilience assessment and adaptation of transportation systems to climate change, including ISO 14090 (ISO 2019), ISO14091 (ISO 2021), BS 8631 (BSI 2021), UIC1 RailAdapt (UIC 2017), PIARC’s International climate change adaptation framework for road infrastructure (PIARC 2015), and PIANC’s climate change adaptation planning for ports and inland waterways (EnviCom WG 178).
Simulation-Based Stress Tests: Integrated Simulation Approach for Assessing Resilience of Transport Systems under Multiple Stressors
Nasrazadani, Hossein; Adey, Bryan T.; Dorren, Luuk (2024)
Journal of Infrastructure Systems
A transportation system stress test is a set of one or more hypothetical scenarios designed to help determine if a transport system can continue to provide an acceptable level of service when subjected to one or more potentially disruptive events. Stress tests, if done well, enable policy makers, regulators, and network operators to assess the ability of transport systems to withstand and recover from potentially disruptive events, and modify the systems if necessary. Although work has been conducted on how to conduct high-level preliminary transportation stress tests, there has not yet been work conducted on how to conduct detailed quantitative simulation-based stress tests on transportation systems. This paper fills this gap by presenting both an approach to conduct simulation-based stress tests and example simulation-based stress tests for a transportation system subject to potentially disruptive hydrometeorological events. Three types of stress tests are conducted for the transportation system in the region of Chur, Switzerland, which is subject to heavy rainfalls that may result in floods and landslides. These include the stress caused by climate change leading to increasing the intensity of extreme rainfall events in the future, stress caused by uncertain future mobility behavior of people leading to excessive travel demand on the network, and stress caused by poor planning of the required resources for restoration interventions leading to having less resources for restoration than planned. It is argued that conducting such stress tests makes it clear to decision makers whether or not a transportation system is sufficiently resilient against various stressors, and if not, gives clear indications as to how it could be improved.
Stress Testing and Climate Adaptation: Using Simulation to Assess the Resilience of Transport Systems and Evaluate Potential Improvements
Nasrazadani, Hossein (2025)
Transport systems play a vital role in ensuring societal well-being and economic stability, yet they are susceptible to disruptions caused by natural hazards. Such disruptions can severely impact the services they provide and lead to significant economic and socio-economic consequences. For climate-induced hazards, such as extreme rainfall, the accelerating pace of climate change is expected to increase both their frequency and intensity in many locations, placing additional stress on these systems. To address these challenges, infrastructure managers must not only assess the resilience of their systems but also implement interventions that enhance their capacity to withstand and recover from such events. Given the frequency and severity of the adverse effects of climatic hazard events and the increasing complexity of infrastructure systems, advanced methods are needed to accurately assess resilience and plan resilience enhancing interventions. Quantitative simulation-based methods have emerged as effective tools for capturing such complexities and their associated uncertainties, offering a more comprehensive assessment than previously possible, thereby providing an improved basis for decision-making. The primary objective of this doctoral research was to advance the state-of-the-art in the use of simulation-based methodologies for assessing the resilience of transport systems and evaluating possible resilience enhancing interventions. The research developed methods that not only contributed to scientific knowledge but are also applicable in real-world scenarios. In particular, the developed methods improve the understanding of how various parts of transport systems, including the physical infrastructure, its environment, and the responsible organization, interact and evolve over time under scenarios of stress and improvement. This enables infrastructure managers to better understand and identify system vulnerabilities, evaluate the dynamic impacts of interventions, and plan to improve the resilience of their systems. The first major contribution of this research is the development of a simulation-based methodology for defining, setting up, and evaluating resilience-enhancing interventions. This methodology addresses key requirements of an effective simulation-based decision-support tool. The first requirement is capturing the diversity of intervention types, including those that can be done on the physical infrastructure, e.g., retrofitting vulnerable assets, those on the environment, e.g., building a flood protection wall, and those related to the organization, e.g., hiring more crews to expedite restoration. Other requirements include incorporating the spatial and temporal characteristics of interventions, modeling the interactions between interventions when combined into portfolios, and accounting for the uncertainties that affect their performance. By addressing these aspects, the methodology provides infrastructure managers with a more comprehensive understanding of how different interventions or combinations thereof can enhance system resilience. The second contribution focuses on the assessment of system resilience through stress testing. Stress tests represent hypothetical scenarios designed to help determine if a transport system can continue to provide an acceptable level of service when subjected to one or more potentially disruptive events. The proposed stress testing methodology systematically defines, sets up, and executes various types of stress tests across different parts of the system. This includes not only hazard-based stress tests but also those that examine the performance of assets, user behavior, and organizational aspects, such as recovery management. By introducing a comprehensive stress testing method, this research offers a more nuanced analysis of vulnerabilities in transport systems that can inform the planning of resilience-enhancing interventions. One significant challenge in resilience assessment is the vast number of potential stress tests that can be conducted. To address this, the third major contribution of this dissertation presents a novel computation-free methodology that assesses resilience under each stress test without the need for simulations. A stress test ranking measure was then introduced, indicating the potential impact of each stress test on resilience. This enables transport infrastructure managers to rank and prioritize stress tests accordingly and select a subset of them for more detailed assessment. By doing so, it ensures efficient use of computational resources while concentrating on scenarios with the highest potential to result in lowest resilience levels. All proposed methodologies were demonstrated through real-world applications, with case studies involving a transportation system of roads and bridges in Switzerland exposed to extreme rainfall scenarios resulting in flooding, and landslides. The case studies illustrated the practical utility of the developed methods by assessing the resilience of the investigated transport system, together with modeling and evaluating multiple interventions and intervention portfolios. Several stress tests were defined and executed, and the proposed prioritization methodology was applied to rank candidate stress tests for further investigation.
Stress Testing Transport Systems
Nasrazadani, Hossein; Adey, Bryan T. (2024)
NSL Newsletter ~ Mobility and Transport Infrastructure
Anwendung von Referenzklassenprognosen für die Kostenvoranschläge der ETH Immobilien
Zani, David; Nasrazadani, Hossein; Adey, Bryan T. (2024)
Simulation-Based Evaluation of Resilience-Enhancing Measures for Transportation Systems Subject to Hydrometeorological Hazard Events
Nasrazadani, Hossein; Adey, Bryan T.; Moghtadernejad, Saviz; et al. (2024)
Journal of Infrastructure Systems
This paper identifies the essential requirements for simulation-based approaches such that these approaches serve as effective decision support tools for evaluating the effectiveness of climate-adaptation measures that enhance the resilience of transport systems against hydrometeorological events. These requirements include the ability to capture the effect of different types of measures, the spatial and temporal possibilities of their execution, their aggregate effect when executed together, and the effect of uncertainties in their evaluation. A novel simulation-based approach that meets the identified requirements is presented, and its application in a case study is showcased. The presented approach uses a set of interacting probabilistic models to generate numerous scenarios, each representing chains of cascading events from the occurrence of a possible hazard event, the impact on the assets and the network, restoration of the infrastructure, and the temporal evolution of its service. The models enable capturing the effect of resilience-enhancing measures on the intensity of hazard events and their ensuing consequences. The case study includes a road system in Switzerland comprising 605 km of roads and 121 bridges and subject to rainfall events leading to flooding and landslide. Twenty-one portfolios of measures combining four specific types are considered, and their effect on resilience was evaluated. Those include flood protection walls, stormwater retention basins, raising road embankments, and temporary flood barriers. The proposed approach enables infrastructure managers to engage in an appropriate quantitative evaluation to better devise and plan measures with the aim of cost efficiently improving resilience.
Evaluating resilience enhancing interventions for transport networks subjected to floods using simulations: example of flood protection wall
Nasrazadani, Hossein; Adey, Bryan T.; Heitzler, Magnus; et al. (2022)
Proceedings of the 3rd International Conference on Natural Hazards and Infrastructure
This paper presents a high-resolution simulation approach for evaluating the effects of interventions to improve the resilience of transport infrastructure against climate-related hazards. This simulation approach facilitates modeling and evaluation of a broad range of interventions, such as constructing floodwalls to reduce the exposure of road sections to inundation or retrofitting bridge piers to decrease scouring vulnerability. This is achieved through a risk simulation tool which comprises a library of interacting probabilistic models that simulate the: (1) initiation of source events, e.g., heavy rainfall; (2) formation of hazard events, e.g., floods and landslides; (3) physical and functional impacts on individual infrastructure components, as well as their collective performance as a network; and (4) restoration of infrastructure components. The risk is quantified in terms of the ensuing economic consequences, such as restoration costs, and socio-economic consequences, such as costs of increased travel time and loss of connectivity. Finally, through a host of simulated scenarios, resilience improvement interventions are evaluated based on their contribution to reducing the considered risk measures. The probabilistic modeling capability of this tool captures the spatiotemporal uncertainty in source events and its propagation throughout the entire risk estimation, which is unprecedented in literature. This approach is showcased through an application to a transport network located in Switzerland subject to heavy rainfall, flooding, and landslides. The application demonstrates the evaluation of three flood protection wall interventions through multiple scenarios of hazard events. The proposed approach can serve as a decision support tool for infrastructure managers by providing them with a thorough and quantitative evaluation of interventions to better plan and allocate resources.
Stress test framework
Adey, Bryan T.; Nasrazadani, Hossein; Chambers, Katheine; et al. (2023)
This document outlines a comprehensive framework for conducting stress tests and evaluating the resilience of transportation systems. It was prepared at the request of the Group of Experts on Assessment of Climate Change Impacts and Adaptation for Inland Transport (group of experts).
Project-Level Optimization of Repair Activities for the Recovery of Transportation Assets after Inland Flood Events
Lichty, Benjamin; Zhang, Ning; Nasrazadani, Hossein; et al. (2025)
Journal of Infrastructure Systems
Postdisaster reconstruction poses a major challenge as it requires a trade-off between rapid restoration and a more prolonged, cost-conscious approach. Quick restoration reduces community disruptions but comes at a higher cost due to expedited resource acquisition, increased labor, and equipment mobilization. Conversely, a slower, budget-conscious approach, while saving on immediate costs, might amplify indirect socioeconomic losses due to prolonged disruptions such as driver delays, reduced accessibility to critical facilities, supply chain interruptions, or impacts on vulnerable populations. Attempts to optimize between these approaches are further complicated by a lack of data on the response to different damages. Additionally, the ambiguity of time and costs for restoration depends on available resources, severity of events and associated damages, and accessibility. This paper addresses these gaps by proposing a framework to optimize restoration schedules for various damage scenarios involving different types of damage to roads or bridges and by collecting data on tasks, costs, and times required to restore different damages. The optimization uses a simulation-based approach, generating multiple schedule solutions for each scenario using probabilistic distributions of tasks and their precedence relations to find the optimal schedule. Data on damages and associated costs and times are based on 15 years of data from Iowa, United States, which were reported as part of the detailed damage inspection reports submitted to receive emergency relief funds by Iowa Department of Transportation. It covers 10 bridge and six road damage scenarios. By evaluating these scenarios, the framework provides a realistic understanding of the required steps to restore each damaged bridge and a holistic view of possible restoration strategies, allowing decision-makers to select the best course of action to minimize duration, cost, or balance both.
Prioritizing simulation-based stress tests to assess the resilience of transport systems: a computation-free methodology
Nasrazadani, Hossein; Nogal, Maria; Adey, Bryan T.; et al. (2025)
Journal of Infrastructure Preservation and Resilience
This paper introduces a computation-free method for evaluating and prioritizing simulation-based stress tests for resilience assessment of transport systems. It enables infrastructure managers to efficiently screen and rank stress tests, optimizing the selection process to maximize insights into system resilience while minimizing computational demands. Stress tests have been proven to be a practical tool for understanding and mitigating the impact of disruptive events, yet conducting all possible tests using simulations, particularly for complex systems including plausible scenarios to account for climate change and other stressors, is computationally impractical, thus discouraging their use in practice. To address this, the paper suggests a methodology to estimate the impact of stress tests on risks at no computation cost and rank them accordingly to be selected for more detailed assessment. It uses the results of an initial risk assessment and, through a novel implementation of importance sampling and Bootstrapping resampling, selects subsets of the initial results to mimic specific stress test conditions, estimating their impact on risks. The methodology was validated through application to a Swiss road network facing flooding, demonstrating its practical effectiveness in identifying stress tests with significant potential impact on risks, hence having higher priority for more detailed assessment. In the presented case study, the proposed method enabled instant screening of 80 stress test scenarios, saving approximately 56 weeks of computation.

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