Probabilistic Hurricane Wind-Induced Loss Model for Risk Assessment on a Regional Scale

Khajwal, Asim Bashir; Noshadravan, Arash

doi:10.1061/ajrua6.0001062

Cited by 11 publications

(9 citation statements)

References 27 publications

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“…22,23,28,29 For other types of hazards, risk analysis is also based on a similar combination of models. Examples found in the literature include wind storms, 30,31 floods 32,33 and volcanoes. 34,35 In Figure 1, we present a generic framework for risk analysis of infrastructure systems under natural hazards.…”

Section: General Frameworkmentioning

confidence: 99%

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Selection of representative natural hazard scenarios for engineering systems

Rosero‐Velásquez

Straub

2022

Earthq Engng Struct Dyn

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Representative hazard scenarios are essential for many tasks in risk management, such as preparedness and emergency response planning. However, criteria and methods for systematically selecting such scenarios for natural hazards are lacking. From a risk perspective, such scenarios should be selected considering the losses they incur. Hence, we propose to define a scenario that is representative for a certain degree of loss, for example, the 100‐year loss, as the most likely one among all possible scenarios leading to this loss. Taking basis in a generic model of natural hazards and their impact on engineering systems, we formally introduce the representative scenarios. We then develop algorithms that enable an efficient evaluation of these scenarios. The method and algorithms are demonstrated on a hypothetical example considering a spatially distributed infrastructure system subjected to earthquakes.

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Section: General Frameworkmentioning

confidence: 99%

“…For other types of hazards, risk analysis is also based on a similar combination of models. Examples found in the literature include wind storms, 30,31 floods 32,33 and volcanoes 34,35 …”

Section: Probabilistic Hazard and Risk Analysismentioning

confidence: 99%

Selection of representative natural hazard scenarios for engineering systems

Rosero‐Velásquez

Straub

2022

Earthq Engng Struct Dyn

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“…There have been numerous ANN techniques developed to date, each of which may befit a specific application (e.g., self-organizing maps, recurrent neural networks, and feed-forward back-propagation neural networks). However, ANN is more commonly employed in predictive algorithms [54,56,57] and pattern recognition applications [23,36,55,58]. For the study presented herein, SOM was utilized using the Deep Learning Toolbox in MATLAB, where the Kohonen rule was adopted [55,59].…”

Section: Unsupervised Learning: Clusteringmentioning

confidence: 99%

Data-Driven Community Flood Resilience Prediction

Abdel-Mooty

El‐Dakhakhni

Coulibaly

2022

Water

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Climate change and the development of urban centers within flood-prone areas have significantly increased flood-related disasters worldwide. However, most flood risk categorization and prediction efforts have been focused on the hydrologic features of flood hazards, often not considering subsequent long-term losses and recovery trajectories (i.e., community’s flood resilience). In this study, a two-stage Machine Learning (ML)-based framework is developed to accurately categorize and predict communities’ flood resilience and their response to future flood hazards. This framework is a step towards developing comprehensive, proactive flood disaster management planning to further ensure functioning urban centers and mitigate the risk of future catastrophic flood events. In this framework, resilience indices are synthesized considering resilience goals (i.e., robustness and rapidity) using unsupervised ML, coupled with climate information, to develop a supervised ML prediction algorithm. To showcase the utility of the framework, it was applied on historical flood disaster records collected by the US National Weather Services. These disaster records were subsequently used to develop the resilience indices, which were then coupled with the associated historical climate data, resulting in high-accuracy predictions and, thus, utility in flood resilience management studies. To further demonstrate the utilization of the framework, a spatial analysis was developed to quantify communities’ flood resilience and vulnerability across the selected spatial domain. The framework presented in this study is employable in climate studies and patio-temporal vulnerability identification. Such a framework can also empower decision makers to develop effective data-driven climate resilience strategies.

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“…Wind damage assessment is usually done using a number of wind vulnerability models that have been developed to assess damage/loss for buildings and infrastructure which have been reviewed (Pita et al, 2015). This review showed that different types of wind vulnerability functions have been developed over the last 2 decades including deterministic (e.g., Emanuel et al, 2006;Pinelli et al, 2011;Pita et al, 2012) and probabilistic models (e.g., Mishra et al, 2017;Khajwal and Noshadravan 2020). Fragility-based wind vulnerability models were the focus of the literature over the last 2 decades since they allow uncertainty propagation through the damage assessment process (Li and Ellingwood, 2006;Massarra et al, 2020;Nofal, 2020;Wang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

BIM-GIS integration approach for high-fidelity wind hazard modeling at the community-level

Nofal

Lind

Zakzouk

2022

Front. Built Environ.

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Wind hazards often result in significant damage to the built environment cascading into impacts on the socio-economic systems within a community. The increasing frequency and intensity of hurricane hazards highlight the importance of developing high-resolution wind hazard models to better predict the consequences. Although previous studies have investigated hurricane-induced wind hazards in terms of hazard modeling and the subsequent vulnerability of buildings and infrastructure, these studies have not yet investigated applications of computational fluid dynamics (CFD) at the community-level. Therefore, in this study, a novel approach was developed to generate CFD models at the community-level by integrating building information modeling (BIM) and geographical information systems (GIS) to automate the generation of a high-resolution 3-D community model to be used as an input for a digital wind tunnel. This was done by harnessing the current advances in BIM and GIS applications and maximizing their capabilities by developing an algorithm that automates the 3-D geometry generation of communities with a detailed discretization of each building within the community. The 3-D community model was developed using the GIS shapefile of the buildings’ footprint and a parametric BIM model that uses a number of building parameters such as footprint dimensions, roof shape, foundation type, and the number of stories. Then, an algorithm was developed to automate the creation of the BIM model for each building within the community based on the prescribed building’s characteristics. The developed community model was used as an input for a numerical wind tunnel that uses CFD to account for the detailed wind pressure at each building after including the impacts of aerodynamics interference at the community-level. This novel BIM-GIS integration approach provides, for the first time, the next generation of high-resolution community-level CFD wind hazard modeling which aims to shift the current practice of wind hazard simulation at the community-level.

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Probabilistic Hurricane Wind-Induced Loss Model for Risk Assessment on a Regional Scale

Cited by 11 publications

References 27 publications

Selection of representative natural hazard scenarios for engineering systems

Selection of representative natural hazard scenarios for engineering systems

Data-Driven Community Flood Resilience Prediction

BIM-GIS integration approach for high-fidelity wind hazard modeling at the community-level

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