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Hiba Baroud
Vanderbilt University, 2201 West End Ave, Nashville, TN, 37235, USA

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Journal article
Published: 17 August 2021 in International Journal of Disaster Risk Reduction
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A greater dependence on post-disaster temporary housing has emerged following an increase in the severity and frequency of disasters and their corresponding social and economic impacts on communities. Effective operations management of temporary housing before, during, and after a disaster is critical to the recovery of communities. On one hand, lack of inventory stock increases the cost associated with emergency purchases made during disasters. On the other hand, increasing inventory stock comes at an initial investment cost before a disaster but can potentially avoid large financial losses incurred from emergency purchases, temporary housing demands, and prolonged community recovery. This study addresses the challenge of balancing temporary housing allocation before and during a disaster. The proposed approach adopts a simulation-based demand forecasting method and an inventory optimization model to identify the most cost-effective stocking inventory for temporary housing. Following the proposed approach, the final Newsvendor optimized inventory reduces expected losses from post-disaster temporary housing units by nearly $936 million when compared to previous United States baseline stocking inventories.

ACS Style

Daniel V. Perrucci; Hiba Baroud. Temporary housing operations: A simulation-based inventory management approach using the newsvendor model. International Journal of Disaster Risk Reduction 2021, 102512 .

AMA Style

Daniel V. Perrucci, Hiba Baroud. Temporary housing operations: A simulation-based inventory management approach using the newsvendor model. International Journal of Disaster Risk Reduction. 2021; ():102512.

Chicago/Turabian Style

Daniel V. Perrucci; Hiba Baroud. 2021. "Temporary housing operations: A simulation-based inventory management approach using the newsvendor model." International Journal of Disaster Risk Reduction , no. : 102512.

Journal article
Published: 09 June 2021 in Reliability Engineering & System Safety
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Measuring the performance of infrastructure networks is critical to the allocation of resources before, during, and after a system’s disruption. However, the lack of data often hinders the ability to accurately estimate infrastructure performance, resulting in uncertainty in its evaluation which can lead to biased estimates. To address this challenge, this study develops a Bayesian approach to measure the performance of the infrastructure network at the component level and incorporate it in the evaluation of the system-level serviceability. Component fragility metrics are estimated using a hierarchical Bayesian model and then integrated into the system serviceability assessment using Monte Carlo simulation and a shortest-path algorithm. These performance measures can be dynamically updated as more data becomes available. A case study of the water distribution system of Shelby County in Tennessee subject to earthquake and flood hazards is presented to illustrate the proposed approach. Results show that system topology is more important in determining component functionality under seismic hazard while vulnerability is the dominant factor in the case of flood hazard.

ACS Style

Jin-Zhu Yu; Mackenzie Whitman; Amirhassan Kermanshah; Hiba Baroud. A hierarchical Bayesian approach for assessing infrastructure networks serviceability under uncertainty: A case study of water distribution systems. Reliability Engineering & System Safety 2021, 215, 107735 .

AMA Style

Jin-Zhu Yu, Mackenzie Whitman, Amirhassan Kermanshah, Hiba Baroud. A hierarchical Bayesian approach for assessing infrastructure networks serviceability under uncertainty: A case study of water distribution systems. Reliability Engineering & System Safety. 2021; 215 ():107735.

Chicago/Turabian Style

Jin-Zhu Yu; Mackenzie Whitman; Amirhassan Kermanshah; Hiba Baroud. 2021. "A hierarchical Bayesian approach for assessing infrastructure networks serviceability under uncertainty: A case study of water distribution systems." Reliability Engineering & System Safety 215, no. : 107735.

Journal article
Published: 17 May 2021 in Reliability Engineering & System Safety
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While significant modeling advances have unpacked the complexities of interdependent infrastructure, post-disaster reconnaissance consistently demonstrates a wide variability of outcomes and how much is still to be learned. With that in mind, one might expect the treatment of uncertainty to be quite advanced in interdependent infrastructure models, but we find that to not be the case. In this work, we identify, define, and describe two key classes of uncertainty: system uncertainty and modeling uncertainty. System uncertainty is inherent in all complex infrastructure systems and possesses several subclasses (e.g., physical uncertainty and operational uncertainty). Modeling uncertainty occurs when researchers downscale a complex system to a mathematical or other symbolic representation. It too has several subclasses (e.g., parameter uncertainty and completeness uncertainty). We then identify how the literature to date treats uncertainty with respect to each type of uncertainty. While some work has investigated the implications of physical and temporal uncertainty, by and large, most types of uncertainty have had minimal exploration, suggesting significant knowledge gaps. Finally, we suggest a path forward for treatment and discussion of uncertainty, including what can be learned from other fields involving complex interdependent systems.

ACS Style

Allison C. Reilly; Hiba Baroud; Roger Flage; Michael D. Gerst. Sources of uncertainty in interdependent infrastructure and their implications. Reliability Engineering & System Safety 2021, 213, 107756 .

AMA Style

Allison C. Reilly, Hiba Baroud, Roger Flage, Michael D. Gerst. Sources of uncertainty in interdependent infrastructure and their implications. Reliability Engineering & System Safety. 2021; 213 ():107756.

Chicago/Turabian Style

Allison C. Reilly; Hiba Baroud; Roger Flage; Michael D. Gerst. 2021. "Sources of uncertainty in interdependent infrastructure and their implications." Reliability Engineering & System Safety 213, no. : 107756.

Conference paper
Published: 18 December 2020 in Communications in Computer and Information Science
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Understanding human activities and urban mobility patterns is key to solving many urban issues such as congestion and emissions. With the abundant data sets available at different levels of fidelity, one of the main challenges is the sparsity and heterogeneity of data sources. The integration of such data sources is essential to better inform system design and community-level strategies. In this paper, we incorporate a variety of data sources including land use, vehicle emissions and building footprint to comprehensively visualize and analyze traffic patterns in the Chicago Loop area. We first implement and compare three different nearest-neighbor-search algorithms to determine building occupancy assignment, and then perform a spatial-temporal correlation analysis of vehicle emissions focusing on factors such as land use, public transit and demographic. Lastly, we discuss the traffic characteristics from data analysis, such as traffic congestion formation and rush hours etc.

ACS Style

Ao Qu; Yu Wang; Yue Hu; Yanbing Wang; Hiba Baroud. A Data-Integration Analysis on Road Emissions and Traffic Patterns. Communications in Computer and Information Science 2020, 503 -517.

AMA Style

Ao Qu, Yu Wang, Yue Hu, Yanbing Wang, Hiba Baroud. A Data-Integration Analysis on Road Emissions and Traffic Patterns. Communications in Computer and Information Science. 2020; ():503-517.

Chicago/Turabian Style

Ao Qu; Yu Wang; Yue Hu; Yanbing Wang; Hiba Baroud. 2020. "A Data-Integration Analysis on Road Emissions and Traffic Patterns." Communications in Computer and Information Science , no. : 503-517.

Review
Published: 11 December 2020 in Sustainability
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Temporary housing plays a critical role in disaster response and recovery by providing a temporary home for displaced people before they return to their permanent residence. In recent years, temporary housing has faced three primary dilemmas related to design type, site selection, and cost. Significant contributions have been made in research and in practice to improve temporary housing management. However, gaps still exist in resolving the dilemmas, and a critical review and evaluation of current methods is needed to determine the path forward and identify priorities of future research. This paper presents a comprehensive overview of prior methods developed and applied towards temporary housing management and identifies future pathways for success in temporary housing research and implementation. The literature review reveals that temporary housing requires further research in proactive management, storage, sustainability, and community resilience to effectively enhance post-disaster temporary housing. This study finds that programs such as the Leadership in Energy and Environmental Design (LEED) and the Sheltering and Temporary Essential Power (STEP) program provide methodologies which can benefit temporary housing implementation, designs, and modeling. In addition, circular economy thinking can enable the recyclability of temporary housing to reduce economic and environmental impacts.

ACS Style

Daniel Perrucci; Hiba Baroud. A Review of Temporary Housing Management Modeling: Trends in Design Strategies, Optimization Models, and Decision-Making Methods. Sustainability 2020, 12, 10388 .

AMA Style

Daniel Perrucci, Hiba Baroud. A Review of Temporary Housing Management Modeling: Trends in Design Strategies, Optimization Models, and Decision-Making Methods. Sustainability. 2020; 12 (24):10388.

Chicago/Turabian Style

Daniel Perrucci; Hiba Baroud. 2020. "A Review of Temporary Housing Management Modeling: Trends in Design Strategies, Optimization Models, and Decision-Making Methods." Sustainability 12, no. 24: 10388.

Original research article
Published: 24 June 2020 in Risk Analysis
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The concepts of vulnerability and resilience help explain why natural hazards of similar type and magnitude can have disparate impacts on varying communities. Numerous frameworks have been developed to measure these concepts, but a clear and consistent method of comparing them is lacking. Here, we develop a data‐driven approach for reconciling a popular class of frameworks known as vulnerability and resilience indices. In particular, we conduct an exploratory factor analysis on a comprehensive set of variables from established indices measuring community vulnerability and resilience at the U.S. county level. The resulting factor model suggests that 50 of the 130 analyzed variables effectively load onto five dimensions: wealth, poverty, agencies per capita, elderly populations, and non–English‐speaking populations. Additionally, the factor structure establishes an objective and intuitive schema for relating the constituent elements of vulnerability and resilience indices, in turn affording researchers a flexible yet robust baseline for validating and expanding upon current approaches.

ACS Style

Paul M. Johnson; Corey E. Brady; Craig Philip; Hiba Baroud; Janey V. Camp; Mark Abkowitz. A Factor Analysis Approach Toward Reconciling Community Vulnerability and Resilience Indices for Natural Hazards. Risk Analysis 2020, 40, 1795 -1810.

AMA Style

Paul M. Johnson, Corey E. Brady, Craig Philip, Hiba Baroud, Janey V. Camp, Mark Abkowitz. A Factor Analysis Approach Toward Reconciling Community Vulnerability and Resilience Indices for Natural Hazards. Risk Analysis. 2020; 40 (9):1795-1810.

Chicago/Turabian Style

Paul M. Johnson; Corey E. Brady; Craig Philip; Hiba Baroud; Janey V. Camp; Mark Abkowitz. 2020. "A Factor Analysis Approach Toward Reconciling Community Vulnerability and Resilience Indices for Natural Hazards." Risk Analysis 40, no. 9: 1795-1810.

Journal article
Published: 27 March 2020 in ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
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Modeling the resilience of interdependent critical infrastructure (ICI) requires a careful assessment of interdependencies as these systems are becoming increasingly interconnected. The interdependent connections across ICIs are often subject to uncertainty due to the lack of relevant data. Yet, this uncertainty has not been properly characterized. This paper develops an approach to model the resilience of ICIs founded in probabilistic graphical models. The uncertainty of interdependency links between ICIs is modeled using stochastic block models (SBMs). Specifically, the approach estimates the probability of links between individual systems considered as blocks in the SBM. The proposed model employs several attributes as predictors. Two recovery strategies based on static and dynamic component importance ranking are developed and compared. The proposed approach is illustrated with a case study of the interdependent water and power networks in Shelby County, TN. Results show that the probability of interdependency links varies depending on the predictors considered in the estimation. Accounting for the uncertainty in interdependency links allows for a dynamic recovery process. A recovery strategy based on dynamically updated component importance ranking accelerates recovery, thereby improving the resilience of ICIs.

ACS Style

Jin-Zhu Yu; Hiba Baroud. Modeling Uncertain and Dynamic Interdependencies of Infrastructure Systems Using Stochastic Block Models. ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg 2020, 6, 1 .

AMA Style

Jin-Zhu Yu, Hiba Baroud. Modeling Uncertain and Dynamic Interdependencies of Infrastructure Systems Using Stochastic Block Models. ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg. 2020; 6 (2):1.

Chicago/Turabian Style

Jin-Zhu Yu; Hiba Baroud. 2020. "Modeling Uncertain and Dynamic Interdependencies of Infrastructure Systems Using Stochastic Block Models." ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg 6, no. 2: 1.

Journal article
Published: 08 January 2020 in Sustainable Cities and Society
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With the growing rate of urbanization, freight transportation systems have become critical in supporting economic and social needs in the United States. In order to achieve efficient and effective operations, freight transportation systems are recognizing the value of using Cyber-Physical (CP) technologies. This research provides a scenario-based assessment of CP technology penetration to improve sustainability in freight transportation systems with a focus on the use of smart GPS technologies by the trucking industry in Tennessee. Based on the case study, CP technologies can result in significant benefits to the economy, environment, and society. A cost-benefit analysis shows that benefits can be more than 7 times the costs in terms of present values. Social benefits can amount to more than $77M in the period of study when only 3 % of the truck population uses smart GPS technologies. Environmental benefits of 1 % CP technologies penetration in the truck industry are equivalent to GHG emissions savings from 46,930 passenger vehicles driven for one year. Benefits become more significant with higher penetration rates. For example, at 5 % usage rate of GPS technologies in the trucking industry in Tennessee, CO2 emissions will decrease by more than 1 million metric tons per year.

ACS Style

Amirhassan Kermanshah; Hiba Baroud; Mark Abkowitz. Cyber-Physical technologies in freight operations and sustainability: A case study of smart GPS technology in trucking. Sustainable Cities and Society 2020, 55, 102017 .

AMA Style

Amirhassan Kermanshah, Hiba Baroud, Mark Abkowitz. Cyber-Physical technologies in freight operations and sustainability: A case study of smart GPS technology in trucking. Sustainable Cities and Society. 2020; 55 ():102017.

Chicago/Turabian Style

Amirhassan Kermanshah; Hiba Baroud; Mark Abkowitz. 2020. "Cyber-Physical technologies in freight operations and sustainability: A case study of smart GPS technology in trucking." Sustainable Cities and Society 55, no. : 102017.

Journal article
Published: 01 September 2019 in Journal of Water Resources Planning and Management
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Water resource management is a complex process involving multiple stakeholders with different interests and different expertise. Typically, planning and management of water resources incorporate the analysis of social, economic, and environmental impacts as well as the consideration of hydrology and energy aspects. This study demonstrated the applicability of multicriteria decision analysis (MCDA) methods for the planning and management of a system of multipurpose reservoirs given competing development goals. We applied the methodology to the Mahaweli multipurpose project of Sri Lanka. The economic, social, and environmental planning goals were evaluated using multicriteria decision analysis techniques with multiple attributes measured in several units and trade-off weights elicited from multiple decision makers. Results showed that decision makers tend to prioritize objectives according to their institutional affiliations; however, they also demonstrate an understanding of the importance of other, less preferred objectives. The best alternative chosen by the models performed well across all objectives, decision makers, and MCDA methods. Sensitivity analyses confirmed the robustness of the best alternative under varying input parameters.

ACS Style

Thushara De Silva Manikkuwahandi; George M. Hornberger; Hiba Baroud. Decision Analysis for Expansion of Mahaweli Multipurpose Reservoir System in Sri Lanka. Journal of Water Resources Planning and Management 2019, 145, 05019013 .

AMA Style

Thushara De Silva Manikkuwahandi, George M. Hornberger, Hiba Baroud. Decision Analysis for Expansion of Mahaweli Multipurpose Reservoir System in Sri Lanka. Journal of Water Resources Planning and Management. 2019; 145 (9):05019013.

Chicago/Turabian Style

Thushara De Silva Manikkuwahandi; George M. Hornberger; Hiba Baroud. 2019. "Decision Analysis for Expansion of Mahaweli Multipurpose Reservoir System in Sri Lanka." Journal of Water Resources Planning and Management 145, no. 9: 05019013.

Original research article
Published: 09 July 2019 in Risk Analysis
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The ability to accurately measure recovery rate of infrastructure systems and communities impacted by disasters is vital to ensure effective response and resource allocation before, during, and after a disruption. However, a challenge in quantifying such measures resides in the lack of data as community recovery information is seldom recorded. To provide accurate community recovery measures, a hierarchical Bayesian kernel model (HBKM) is developed to predict the recovery rate of communities experiencing power outages during storms. The performance of the proposed method is evaluated using cross‐validation and compared with two models, the hierarchical Bayesian regression model and the Poisson generalized linear model. A case study focusing on the recovery of communities in Shelby County, Tennessee after severe storms between 2007 and 2017 is presented to illustrate the proposed approach. The predictive accuracy of the models is evaluated using the log‐likelihood and root mean squared error. The HBKM yields on average the highest out‐of‐sample predictive accuracy. This approach can help assess the recoverability of a community when data are scarce and inform decision making in the aftermath of a disaster. An illustrative example is presented demonstrating how accurate measures of community resilience can help reduce the cost of infrastructure restoration.

ACS Style

Jin‐Zhu Yu; Hiba Baroud. Quantifying Community Resilience Using Hierarchical Bayesian Kernel Methods: A Case Study on Recovery from Power Outages. Risk Analysis 2019, 39, 1930 -1948.

AMA Style

Jin‐Zhu Yu, Hiba Baroud. Quantifying Community Resilience Using Hierarchical Bayesian Kernel Methods: A Case Study on Recovery from Power Outages. Risk Analysis. 2019; 39 (9):1930-1948.

Chicago/Turabian Style

Jin‐Zhu Yu; Hiba Baroud. 2019. "Quantifying Community Resilience Using Hierarchical Bayesian Kernel Methods: A Case Study on Recovery from Power Outages." Risk Analysis 39, no. 9: 1930-1948.

Original research article
Published: 09 July 2019 in Risk Analysis
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Communities are complex systems subject to a variety of hazards that can result in significant disruption to critical functions. Community resilience assessment is rapidly gaining popularity as a means to help communities better prepare for, respond to, and recover from disruption. Sustainable resilience, a recently developed concept, requires communities to assess system‐wide capability to maintain desired performance levels while simultaneously evaluating impacts to resilience due to changes in hazards and vulnerability over extended periods of time. To enable assessment of community sustainable resilience, we review current literature, consolidate available indicators and metrics, and develop a classification scheme and organizational structure to aid in identification, selection, and application of indicators within a dynamic assessment framework. A nonduplicative set of community sustainable resilience indicators and metrics is provided that can be tailored to a community's needs, thereby enhancing the ability to operationalize the assessment process.

ACS Style

Leslie Gillespie‐Marthaler; Katherine Nelson; Hiba Baroud; Mark Abkowitz. Selecting Indicators for Assessing Community Sustainable Resilience. Risk Analysis 2019, 39, 2479 -2498.

AMA Style

Leslie Gillespie‐Marthaler, Katherine Nelson, Hiba Baroud, Mark Abkowitz. Selecting Indicators for Assessing Community Sustainable Resilience. Risk Analysis. 2019; 39 (11):2479-2498.

Chicago/Turabian Style

Leslie Gillespie‐Marthaler; Katherine Nelson; Hiba Baroud; Mark Abkowitz. 2019. "Selecting Indicators for Assessing Community Sustainable Resilience." Risk Analysis 39, no. 11: 2479-2498.

Original research article
Published: 07 June 2019 in Risk Analysis
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Managing risk in infrastructure systems implies dealing with interdependent physical networks and their relationships with the natural and societal contexts. Computational tools are often used to support operational decisions aimed at improving resilience, whereas economics‐related tools tend to be used to address broader societal and policy issues in infrastructure management. We propose an optimization‐based framework for infrastructure resilience analysis that incorporates organizational and socioeconomic aspects into operational problems, allowing to understand relationships between decisions at the policy level (e.g., regulation) and the technical level (e.g., optimal infrastructure restoration). We focus on three issues that arise when integrating such levels. First, optimal restoration strategies driven by financial and operational factors evolve differently compared to those driven by socioeconomic and humanitarian factors. Second, regulatory aspects have a significant impact on recovery dynamics (e.g., effective recovery is most challenging in societies with weak institutions and regulation, where individual interests may compromise societal well‐being). And third, the decision space (i.e., available actions) in postdisaster phases is strongly determined by predisaster decisions (e.g., resource allocation). The proposed optimization framework addresses these issues by using: (1) parametric analyses to test the influence of operational and socioeconomic factors on optimization outcomes, (2) regulatory constraints to model and assess the cost and benefit (for a variety of actors) of enforcing specific policy‐related conditions for the recovery process, and (3) sensitivity analyses to capture the effect of predisaster decisions on recovery. We illustrate our methodology with an example regarding the recovery of interdependent water, power, and gas networks in Shelby County, TN (USA), with exposure to natural hazards.

ACS Style

Camilo Gomez; Andrés D. González; Hiba Baroud; Claudia D. Bedoya‐Motta. Integrating Operational and Organizational Aspects in Interdependent Infrastructure Network Recovery. Risk Analysis 2019, 39, 1913 -1929.

AMA Style

Camilo Gomez, Andrés D. González, Hiba Baroud, Claudia D. Bedoya‐Motta. Integrating Operational and Organizational Aspects in Interdependent Infrastructure Network Recovery. Risk Analysis. 2019; 39 (9):1913-1929.

Chicago/Turabian Style

Camilo Gomez; Andrés D. González; Hiba Baroud; Claudia D. Bedoya‐Motta. 2019. "Integrating Operational and Organizational Aspects in Interdependent Infrastructure Network Recovery." Risk Analysis 39, no. 9: 1913-1929.

Articles
Published: 18 March 2019 in The Engineering Economist
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Managing risks to critical infrastructure systems requires decision makers to account for impacts of disruptions that render these systems inoperable. This article evaluates dock-specific resource allocation strategies to improve port preparedness by integrating a dynamic risk-based interdependency model with weighted multicriteria decision analysis techniques. A weighted decision analysis technique allows for decision makers to balance widespread impacts due to cascading inoperability with certain industries that are important to the local economy. Further analysis of the relationship between inoperability and expected economic losses is explored per commodity flowing through the port, which allows an understanding of cascading impacts through interdependent industries. Uncertainty is accounted for through the use of probability distributions of total expected loss per industry that encompass the uncertainty of the length of disruption and severity of the impact that is mitigated by alternative strategies. A set of discrete allocations options of preparedness plans is analyzed in a study of the Port of Catoosa in Oklahoma along the Mississippi River Navigation System. The economic loss analysis showed that the integration of multicriteria decision analysis helps in prioritizing strategies according to several criteria such as gross domestic product (GDP) and decision maker risk aversion that are not typically addressed when strategies are prioritized according to the average interdependent economic losses alone.

ACS Style

Mackenzie Whitman; Hiba Baroud; Kash Barker. Multicriteria risk analysis of commodity-specific dock investments at an inland waterway port. The Engineering Economist 2019, 64, 346 -367.

AMA Style

Mackenzie Whitman, Hiba Baroud, Kash Barker. Multicriteria risk analysis of commodity-specific dock investments at an inland waterway port. The Engineering Economist. 2019; 64 (4):346-367.

Chicago/Turabian Style

Mackenzie Whitman; Hiba Baroud; Kash Barker. 2019. "Multicriteria risk analysis of commodity-specific dock investments at an inland waterway port." The Engineering Economist 64, no. 4: 346-367.

Journal article
Published: 04 March 2019 in Sustainable and Resilient Infrastructure
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ACS Style

Katherine Nelson; Leslie Gillespie-Marthaler; Hiba Baroud; Mark Abkowitz; David Kosson. An integrated and dynamic framework for assessing sustainable resilience in complex adaptive systems. Sustainable and Resilient Infrastructure 2019, 5, 311 -329.

AMA Style

Katherine Nelson, Leslie Gillespie-Marthaler, Hiba Baroud, Mark Abkowitz, David Kosson. An integrated and dynamic framework for assessing sustainable resilience in complex adaptive systems. Sustainable and Resilient Infrastructure. 2019; 5 (5):311-329.

Chicago/Turabian Style

Katherine Nelson; Leslie Gillespie-Marthaler; Hiba Baroud; Mark Abkowitz; David Kosson. 2019. "An integrated and dynamic framework for assessing sustainable resilience in complex adaptive systems." Sustainable and Resilient Infrastructure 5, no. 5: 311-329.

Conference paper
Published: 01 January 2019 in Proceedings of the 29th European Safety and Reliability Conference (ESREL)
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ACS Style

Jin-Zhu Yu; Hiba Baroud. A Probabilistic Approach for Modeling the Resilience of Interdependent Power and Water Infrastructure Networks. Proceedings of the 29th European Safety and Reliability Conference (ESREL) 2019, 1 .

AMA Style

Jin-Zhu Yu, Hiba Baroud. A Probabilistic Approach for Modeling the Resilience of Interdependent Power and Water Infrastructure Networks. Proceedings of the 29th European Safety and Reliability Conference (ESREL). 2019; ():1.

Chicago/Turabian Style

Jin-Zhu Yu; Hiba Baroud. 2019. "A Probabilistic Approach for Modeling the Resilience of Interdependent Power and Water Infrastructure Networks." Proceedings of the 29th European Safety and Reliability Conference (ESREL) , no. : 1.

Journal article
Published: 31 May 2018 in Sustainable and Resilient Infrastructure
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Vulnerability, resilience, and sustainability are three concepts commonly used in assessing the quality of a variety of systems. While each can be applied independently when performing risk analysis, there is growing interest across multiple disciplines in understanding how these concepts can be integrated when considering complex adaptive systems, such as communities. In this paper, we identify issues related to the use of these respective concepts in assessing complex adaptive systems, and describe how these issues may produce imbalanced results and maladaptive outcomes. We identify five critical areas where alignment and integration across concepts can lead to improved system assessment. As a result, we introduce a new paradigm, sustainable resilience, in which these concepts are integrated to enable alignment of adaptation and transformation strategies with desired resilience outcomes. This work provides the foundation for the development of an integrated assessment framework to help guide informed risk-based decisionmaking for sustainable and resilient systems.

ACS Style

Leslie Gillespie-Marthaler; Katherine S. Nelson; Hiba Baroud; David S. Kosson; Mark Abkowitz. An integrative approach to conceptualizing sustainable resilience. Sustainable and Resilient Infrastructure 2018, 4, 66 -81.

AMA Style

Leslie Gillespie-Marthaler, Katherine S. Nelson, Hiba Baroud, David S. Kosson, Mark Abkowitz. An integrative approach to conceptualizing sustainable resilience. Sustainable and Resilient Infrastructure. 2018; 4 (2):66-81.

Chicago/Turabian Style

Leslie Gillespie-Marthaler; Katherine S. Nelson; Hiba Baroud; David S. Kosson; Mark Abkowitz. 2018. "An integrative approach to conceptualizing sustainable resilience." Sustainable and Resilient Infrastructure 4, no. 2: 66-81.

Journal article
Published: 01 April 2014 in Reliability Engineering & System Safety
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ACS Style

Kash Barker; Hiba Baroud. Proportional hazards models of infrastructure system recovery. Reliability Engineering & System Safety 2014, 124, 201 -206.

AMA Style

Kash Barker, Hiba Baroud. Proportional hazards models of infrastructure system recovery. Reliability Engineering & System Safety. 2014; 124 ():201-206.

Chicago/Turabian Style

Kash Barker; Hiba Baroud. 2014. "Proportional hazards models of infrastructure system recovery." Reliability Engineering & System Safety 124, no. : 201-206.