Hossein Nasrazadani

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Nasrazadani

First Name

Hossein

ORCID

0000-0001-7620-0672

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Publications1 - 10 of 18

A simulation-based methodology to assess resilience enhancing interventions for transport systems: A retention basin example
Nasrazadani, Hossein; Adey, Bryan T.; Moghtadernejad, Saviz; et al. (2022)
Proceedings of the 32nd European Safety and Reliability Conference (ESREL 2022)
This paper proposes a simulation-based methodology to evaluate the resilience of infrastructure systems considering multiple intervention scenarios. The proposed methodology features probabilistic models that are used to simulate the: 1) spatiotemporal formation of hazard events, e.g., heavy rainfall causing flooding, 2) physical and functional impacts on individual infrastructure components, followed by their performance as a system, and lastly, 3) the implementation of response and restoration measures. It also features models that characterize interventions and simulate their effects on models mentioned above. The output of the simulations is a list of metrics, e.g., the reduction in direct and indirect consequences, that can be used to evaluate the effects of interventions. The proposed methodology takes into account the uncertainties related to hazard occurrence and their impact on infrastructure systems in the evaluation of interventions, which is a major advancement over existing studies that use static hazard maps. The proposed methodology is demonstrated by using it to evaluate the benefits of three candidate storm water retention basins on enhancing the resilience of a road network in Switzerland subject to heavy rainfall, flooding, and landslides. The example provides insight into the data required to conduct such a comprehensive analysis with the presented level of detail. The proposed methodology serves as a decision support tool to facilitate the assessment and hence, planning of resilience enhancing interventions.
Probabilistic Framework for Evaluating Community Resilience: Integration of Risk Models and Agent-Based Simulation
Nasrazadani, Hossein; Mahsuli, Mojtaba (2020)
Journal of Structural Engineering
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.
Probabilistic quantification of community resilience using discrete event simulation
Nasrazadani, Hossein; Mahsuli, Mojtaba (2017)
COMPDYN 2017: Computational Methods in Structural Dynamics and Earthquake Engineering. Proceedings of the 6th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering
This paper puts forward a comprehensive framework for probabilistic quantification of community resilience considering multiple interdependent infrastructure systems. The proposed framework integrates various dimensions of resilience including technical, organizational, social, and economic. To this end, first the post-hazard status of the components of the community, e.g., infrastructure systems, is determined through casualty and damage models. Next, discrete events simulation is employed to quantify the recovery of the community, and the infrastructures thereof. For this purpose, the community restoration capacity, comprising workforce, material, and equipment, is assigned to the damaged components, which produces repair events. Once a component restored, the status of all components is updated considering interdependencies. At this point, the framework quantifies the costs incurred by the community comprising direct costs, i.e., restoration and relocation costs, and indirect costs, i.e., business interruption and socioeconomic costs due to absence of services, during the pre-repair period. Thereafter, the released restoration capacity is reassigned to another unrestored component, producing another event. This process continues until all components reach the intended functionality. The total community cost, which is the accumulated cost over the entire recovery period, is regarded as an indicator of the community resilience. The functionality of different infrastructure systems as well as different dimensions of resilience is incorporated in this single global indicator. This, in turn, provides the ability to determine the importance of each component based on the extent of contribution to this indicator. Therefore, the proposed framework provides decision makers with a decision support tool to identify the optimal resource allocation strategy to achieve a resilient community. The proposed framework is showcased by an application to a community with a building portfolio, commercial units, transportation network, healthcare facilities, and a power distribution network.
Probabilistic modeling framework for prediction of seismic retrofit cost of buildings
Nasrazadani, Hossein; Mahsuli, Mojtaba; Talebiyan, Hesam; et al. (2017)
Journal of Construction Engineering and Management
This study presents a framework that utilizes Bayesian regression to create probabilistic cost models for retrofit actions. Performance improvement is the key parameter introduced in the proposed framework. The incorporation of this novel feature facilitates the characterization of retrofit cost as a continuous function of the desired performance improvement. Accounting for the performance gained from retrofit enables the use of the models in determining the optimal level of retrofit. Furthermore, accounting for the model uncertainty facilitates the use of the models in risk and reliability analyses. The proposed framework is applied to create seismic retrofit cost models for masonry school buildings in Iran. A cost database of 167 masonry retrofit projects was compiled and used to create cost models for three retrofit actions, namely, Shotcrete, fiber-reinforced polymer, and steel belt. The proposed framework identifies the most influential variables that govern building retrofit cost. Practitioners can use the proposed framework to create cost models for various retrofit actions to decide whether to retrofit a building and to identify the least costly retrofit action. © 2017 American Society of Civil Engineers.
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).
A methodology for prioritizing simulation-based stress tests for transportation systems
Nasrazadani, Hossein; Adey, Bryan T.; Nogal, Maria; et al. (2024)
Book of Abstracts EMI/PMC 2024
Transportation systems are integral to the economic development and prosperity of communities. Their service, however, is affected by disruptive events causing economic and socio-economic consequences. To ensure these consequences are acceptably minimal under various disruptive scenarios, stress testing, as a diagnostic approach, has shown promise. A stress test, in this context, represents a stressed situation, where at least one variable describing the system, e.g., hazard intensity or performance of assets, is significantly worse than expected. Nasrazadani et al. (Nasrazadani et al. 2024) proposed a method to define and conduct simulation-based stress tests on transportation systems. Their approach entails an initial reference risk assessment, where the system is in its baseline condition, followed by conducting stress tests, which represent stressed situations. Although simulation-based stress tests offer detailed insights into system's resilience, their computational demand makes it impractical to conduct all potential tests in real applications. The challenge remains in determining which stress tests, among numerous possibilities, are more critical to be conducted. To address this gap, this research proposes a novel computationally efficient method to prioritize candidate stress tests. The proposed methodology features a novel implementation of importance sampling approach that utilizes only the results of the reference assessment to identify which stress tests, if were to be conducted explicitly using simulations, would potentially have a higher impact on elevating the risks. For each stress test, a resampled subset of the results of the reference assessment is selected such that it would realize the specific conditions of that stress test, and thus its potential impact on risks. Those stress tests that are shown to lead to higher increase in risks in this approach are expected to do so as well if conducted explicitly, and hence deemed more critical. The methodology is applied to a road network in Switzerland subjected to extreme scenarios of rainfall flooding and landslides. The proposed methodology allows infrastructure managers to conduct a screening analysis of candidate stress tests, identify the critical ones without explicitly conducting them, and allocate resources to conduct only the most critical ones. This reduces the number of potential scenarios while maximizing insights into the system’s resilience. Additionally, it identifies which stress tests, if improved by taking risk-reducing measures, can contribute to higher improvement in system’s resilience.
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.
Stress Testing Transport Systems
Nasrazadani, Hossein; Adey, Bryan T. (2024)
NSL Newsletter ~ Mobility and Transport Infrastructure
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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