Methodology for Interdependent Population–Building–Infrastructure Posthazard Functionality Assessment for CommunitiesSource: Journal of Structural Engineering:;2025:;Volume ( 151 ):;issue: 005::page 04025048-1Author:Omar M. Nofal
,
Nathanael Rosenheim
,
Sabarethinam Kameshwar
,
Jayant Patil
,
Xiangnan Zhou
,
John W. van de Lindt
,
Leonardo Duenas-Osorio
,
Eun Jeong Cha
,
Amin Endrami
,
Elaina Sutley
,
Harvey Cutler
,
Hwayoung Jeon
,
Tao Lu
,
Chen Wang
DOI: 10.1061/JSENDH.STENG-13222Publisher: American Society of Civil Engineers
Abstract: Modeling and improving community resilience to natural hazards has gained substantial interest over the past two decades, in part, due to the increased level of coupled risk resulting from climate change and urbanization. Evidence suggests that climate change increases both the frequency and intensity of climatic hazards, such as hurricanes, tornadoes, and floods. Further, urbanization in hazard-prone areas increases exposure and the vulnerability of communities. Although significant progress has been made in resilience research, a model that can quantify the posthazard functionality of buildings by considering the state of nonphysical factors has not yet been fully explored. This is due to the complexity of coupling physics-based and data-driven models, which include population demographics, buildings, and distributed infrastructure along with their physical, social, and economic interdependencies. Therefore, in this paper, a novel probabilistic formulation is developed to model the interdependent population-buildings-infrastructure relationship and quantify their role in community resilience, with a focus on immediate posthazard functionality. This is accomplished by developing a new posthazard functionality method for a computational environment (IN-CORE) that allows an analyst to perform comprehensive community-level analysis at building-level and household-level resolution. The methodology is developed such that it quantifies the probabilistic functionality of each subsystem separately (e.g., buildings, utilities, social institutions, etc.), and then combines their functionality in a functionality matrix that has the exceedance probability for a prescribed functionality state. This probabilistic functionality matrix is then converted into a deterministic functionality vector that has the total functionality ratio for each subsystem using the contribution of each functionality state corresponding to each subsystem to the total building functionality. The novel contribution of this approach is the ability to systematically quantify across scales posthazard functionality of communities by combining physical and nonphysical systems and their components after including interdependencies and uncertainties within these systems.
|
Collections
Show full item record
contributor author | Omar M. Nofal | |
contributor author | Nathanael Rosenheim | |
contributor author | Sabarethinam Kameshwar | |
contributor author | Jayant Patil | |
contributor author | Xiangnan Zhou | |
contributor author | John W. van de Lindt | |
contributor author | Leonardo Duenas-Osorio | |
contributor author | Eun Jeong Cha | |
contributor author | Amin Endrami | |
contributor author | Elaina Sutley | |
contributor author | Harvey Cutler | |
contributor author | Hwayoung Jeon | |
contributor author | Tao Lu | |
contributor author | Chen Wang | |
date accessioned | 2025-08-17T22:14:56Z | |
date available | 2025-08-17T22:14:56Z | |
date copyright | 5/1/2025 12:00:00 AM | |
date issued | 2025 | |
identifier other | JSENDH.STENG-13222.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4306662 | |
description abstract | Modeling and improving community resilience to natural hazards has gained substantial interest over the past two decades, in part, due to the increased level of coupled risk resulting from climate change and urbanization. Evidence suggests that climate change increases both the frequency and intensity of climatic hazards, such as hurricanes, tornadoes, and floods. Further, urbanization in hazard-prone areas increases exposure and the vulnerability of communities. Although significant progress has been made in resilience research, a model that can quantify the posthazard functionality of buildings by considering the state of nonphysical factors has not yet been fully explored. This is due to the complexity of coupling physics-based and data-driven models, which include population demographics, buildings, and distributed infrastructure along with their physical, social, and economic interdependencies. Therefore, in this paper, a novel probabilistic formulation is developed to model the interdependent population-buildings-infrastructure relationship and quantify their role in community resilience, with a focus on immediate posthazard functionality. This is accomplished by developing a new posthazard functionality method for a computational environment (IN-CORE) that allows an analyst to perform comprehensive community-level analysis at building-level and household-level resolution. The methodology is developed such that it quantifies the probabilistic functionality of each subsystem separately (e.g., buildings, utilities, social institutions, etc.), and then combines their functionality in a functionality matrix that has the exceedance probability for a prescribed functionality state. This probabilistic functionality matrix is then converted into a deterministic functionality vector that has the total functionality ratio for each subsystem using the contribution of each functionality state corresponding to each subsystem to the total building functionality. The novel contribution of this approach is the ability to systematically quantify across scales posthazard functionality of communities by combining physical and nonphysical systems and their components after including interdependencies and uncertainties within these systems. | |
publisher | American Society of Civil Engineers | |
title | Methodology for Interdependent Population–Building–Infrastructure Posthazard Functionality Assessment for Communities | |
type | Journal Article | |
journal volume | 151 | |
journal issue | 5 | |
journal title | Journal of Structural Engineering | |
identifier doi | 10.1061/JSENDH.STENG-13222 | |
journal fristpage | 04025048-1 | |
journal lastpage | 04025048-14 | |
page | 14 | |
tree | Journal of Structural Engineering:;2025:;Volume ( 151 ):;issue: 005 | |
contenttype | Fulltext |