Physics-based fragility functions

Nocera, Fabrizio; Tabandeh, Armin; Guidotti, Roberto; Boakye, Jessica; Gardoni, Paolo

doi:10.4324/9781315142074-13

Cited by 7 publications

(2 citation statements)

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“…Physics-based or other fragility functions could be included to more accurately represent components and conditions in the region (Nocera et al 2018;Pitilakis, Crowley, and Kaynia 2014). Effects from other hazards can also be considered to identify similarities in damage that would further support investment in risk reduction measures.…”

Section: -74 Modelingmentioning

confidence: 99%

Methods for representing regional disaster recovery estimates: modeling approaches and assessment tools for improving emergency planning and preparedness

Deelstra

Bristow

2023

Nat Hazards

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The complexity and interconnected nature of critical infrastructure systems across metropolitan regions presents a unique challenge for communities to understand how they may respond and recover in the face of a major disruption. Disaster recovery modeling facilitates coordination and planning among stakeholders, but detailed system models are often complex and require significant technical skill to construct and interpret. The first part of this work presents the development and assessment of a simplified seismic recovery model for water, wastewater, and power systems in the Metro Vancouver region of British Columbia, Canada. The model considers important geospatial and interdependent characteristics of multi-infrastructure systems without requiring access to complete operational models.The model is expanded in the second part of this work to consider the effectiveness of disaster risk reduction measures on infrastructure service recovery to the population after the earthquake. Finally, a detailed hydraulic water system analysis is compared to the simplified modeling approach for a seismic hazard scenario to consider how results from each compare given various restoration strategies. Results from the three sections of this work demonstrate the utility of a simplified multi-infrastructure modeling approach for assessing recovery at a regional scale, the potential benefits of investing in disaster risk reduction measures to improve recovery outcomes for residents, and aspects of modeling approaches that provide an understanding of their use and benefits for disaster management purposes.

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Section: -74 Modelingmentioning

confidence: 99%

Methods for representing regional disaster recovery estimates: modeling approaches and assessment tools for improving emergency planning and preparedness

Deelstra

Bristow

2023

Nat Hazards

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“…The modeled infrastructure and the type of analysis typically shape the definition of Q(t). For example, in the case of transportation infrastructure, we can estimate Q(t) from topology-based approaches (e.g., Nocera et al 2019), whereas in the case of potable water infrastructure, we can estimate Q(t) from flow-based approaches (e.g., Iannacone et al 2022). Next, we divide the region of interest into n a service areas and obtain Q(t) by mapping…”

Section: Modeling the Time-varying Performance Of Infrastructurementioning

confidence: 99%

Digital Twins or Equivalent Infrastructure Models? The Role of Modeling Granularity in Regional Risk Analysis of Infrastructure

Nocera,

Gardoni

2023

Proceeding of the 33rd European Safety and Reliability Conference

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Accurate regional risk analysis requires representative mathematical models of infrastructure. One of the main challenges in developing mathematical models of infrastructure is defining their modeling granularity, i.e., the level of detail in the topology of the model. Different modeling granularities affect our ability to capture the spatial variability of the impact arising from the changes in the capacities of infrastructure and service demands. A recent trend in infrastructure modeling is to develop detailed digital twins to mimic all aspects of the real infrastructure. However, detailed digital twins might require data not readily available, and their analyses often have prohibitive computational costs, making digital twins not always the most suitable option to model the performance of infrastructure. The goal of selecting the optimal modeling granularity is to allocate computational resources to the model that best delivers the desired information with the desired accuracy level. This paper presents a mathematical formulation to systematically select the appropriate modeling granularity of infrastructure. The formulation adaptively increases the granularity starting from a low-granularity infrastructure model until we reach the desired tradeoff among accuracy, simplicity, and computational efficiency. To define the tradeoff, we introduce metrics that measure the level of agreement between estimates of the quantities of interest computed using different levels of granularity. Such metrics include global measures that assess if a model is insufficiently detailed to capture the quantities of interest and local measures that identify specific regions of the model that may require further refinement. As an example, we apply the illustrated formulation to select the granularity of the potable water infrastructure model in Seaside, Oregon, to quantify its performance following a seismic event.

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Resilience-based Recovery Scheduling of Transportation Network in Mixed Traffic Environment: A Deep-Ensemble-Assisted Active Learning Approach

Zou

Chen

2021

Reliability Engineering & System Safety

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Physics-based fragility functions

Cited by 7 publications

References 0 publications

Methods for representing regional disaster recovery estimates: modeling approaches and assessment tools for improving emergency planning and preparedness

Methods for representing regional disaster recovery estimates: modeling approaches and assessment tools for improving emergency planning and preparedness

Digital Twins or Equivalent Infrastructure Models? The Role of Modeling Granularity in Regional Risk Analysis of Infrastructure

Resilience-based Recovery Scheduling of Transportation Network in Mixed Traffic Environment: A Deep-Ensemble-Assisted Active Learning Approach

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