John W van de Lindt
Biographic Data
| ID | 8367085 |
|---|---|
| NAME | John W van de Lindt |
| GIVEN NAMES | John W |
| FAMILY NAME | van de Lindt |
| SIGNATURE | VAN DE LINDT J W |
| AFFILIATIONS | Colorado State University |
| ORCID | 0000-0001-6386-4509 |
| VERIFIED | Yes |
| TOTAL WORKS | 22 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 22 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Integrated decision-support framework for enhancing coastal community resilience against hurricane-induced hazards
Coastal communities face escalating threats from hurricanes, necessitating a shift from descriptive risk assessment to prescriptive, optimized portfolio planning to enhance resilience. This study introduces an integrated decision-support framework that couples high-fidelity, multihazard socio-physical system modeling with a multi-objective optimization model. The framework quantifies the cascading impacts of hurricane-induced wind, storm surge, a…
Integrated data imputation of national inventories for bridging information gaps and outcome risk uncertainty in community resilience modeling
Reliable natural hazard resilience modeling requires the systematic synthesis of building inventories; however, data scarcity and incomplete records often render public datasets unsuitable for quantitative risk analysis. This paper addresses these limitations by establishing a link between data gaps and uncertainty in risk metrics. We propose a data imputation approach that uses stratified Monte Carlo sampling to impute missing exposure attribute…
Modeling Wind-Induced Community Resilience Impacted by Broad Policy Levers
A well-designed resilience policy should be based on extensive risk assessment as well as a city’s experience with natural hazards. These policy decisions can significantly influence a community’s resilience to future hazards. An appropriate policy strategy can improve community resilience from a multidimensional perspective, which includes stability of the population, economy, and the social and physical systems. However, policies that can be im…
Enhanced risk mapping for hurricane surge losses using synthesized probabilistic and machine learning models
Quantifiable community resilience solutions for coastal communities over long time horizons
Hurricanes pose some of the most severe threats to coastal communities, with global warming exacerbating their destructive impacts as sea levels continue to rise. As the pace of climate change accelerates, the need for effective climate adaptation strategies is more urgent than ever, particularly to support long-term community resilience. Quantifying this resilience is complex, but cumulative population outmigration can serve as a valuable indica…
Mainshock-aftershock fragility surfaces
This study presents a machine learning (ML)-based methodology for developing fragility surfaces under mainshock–aftershock (MS–AS) seismic sequences, extending a previously validated framework originally developed for earthquake–tsunami (EQ–TS) scenarios. Using a two-phase nonlinear time history analysis approach and a dataset of 30 real MS–AS ground motion pairs, fragility surfaces are developed for ductile and non-ductile reinforced concrete (R…
Climate Adaptation Matrix Concept for Multidisciplinary Community Resilience in a Changing Climate
Coastal structures are currently built based on standards that rely on stationary loading assumptions and do not account for the changes in hurricanes due to climate change. As a result, buildings and infrastructure are susceptible to future loadings in excess of what they were originally designed. The resulting structural damage would lead to a lack of stability for a community by causing substantial losses and subsequent outmigration of residen…
Machine Learning-Based Prediction of Optimal Building-Level Flood Mitigation Strategies in At-Risk Communities
Flood-prone communities face severe economic and structural losses, necessitating strategic building retrofitting to enhance resilience. However, selecting the most effective mitigation strategies remains challenging due to financial constraints and the varying characteristics of at-risk buildings. While previous studies have explored optimization and machine learning separately for flood mitigation, there is a need for a more integrated approach…
A genetic algorithm framework for seismic retrofit of building portfolios to enhance community resilience
Bonds for disaster resilience
Commercial Building Recovery Methodology for Use in Community Resilience Modeling
Commercial businesses in an area affected by a natural hazard can have significant interruption and disruption because of damage and impeding factors including financing delays. The recovery of commercial buildings is a necessary but not solely sufficient condition for owners to reopen their businesses. At the community-level modeling scale for planning, the ability to model commercial recovery across the entire community, including short-term bu…
A Discrete-Event Simulation Model of Hospital Patient Flow Following Major Earthquakes
Barriers and Possibilities for Interdisciplinary Disaster Science Research
The strength and speed of modeling software has increased drastically in recent years. As it does so, researchers across a variety of fields work to determine how most effectively to utilize this strength and speed. Many of them have turned to interdisciplinarity as a means of creating more representative models. This is the case for disaster research as this area of interest involves many intricately interdependent systems. In working on interdi…
Incorporating Socioeconomic Metrics in Civil Engineering Projects
This paper explores the role of socioeconomic metrics in the design, planning and operations of civil engineering projects within the specific context of resilience. The inclusion of resilience analysis and design is consistent with the goals of the ASCE Infrastructure Resilience Division (IRD) and related emerging standards and duties of civil engineers. This paper focuses on the practices in design, planning, construction, and operation of civi…
High-resolution flood risk approach to quantify the impact of policy change on flood losses at community-level
Communities are changing the way they manage flood risk as a result of climate change which increases both the flood hazard intensity and frequency and therefore results in more flood exposure for the built environment. Quantifying the feasibility of policy changes requires a standardized approach that can allow a risk-informed decision. The current body of flood risk mitigation research is populated with a host of qualitative approaches to reduc…
Quantitative modeling of residential building disaster recovery and effects of pre- and post-event policies
Understanding the process of community recovery impacted by various post-disaster decisions such as dynamic policies can effectively guide the recovery process and expedite recovery, thereby establishing a more resilient community. This paper proposes a methodology based on a multi-layer Monte Carlo simulation to model a two-stage recovery process for residential buildings: functional downtime due to delay and functional downtime due to repair. T…
State of the research in community resilience
Community resilience has been addressed across multiple disciplines including environmental sciences, engineering, sociology, psychology, and economics. Interest in community resilience gained momentum following several key natural and human-caused hazards in the United States and worldwide. To date, a comprehensive community resilience model that encompasses the performance of all the physical and socio-economic components from immediate impact …
High-resolution approach to quantify the impact of building-level flood risk mitigation and adaptation measures on flood losses at the community-level
Validation of Time-Dependent Repair Recovery of the Building Stock Following the 2011 Joplin Tornado
This paper seeks to calibrate an existing analytical framework to develop building repair fragility models using a restoration dataset collected through a longitudinal field study in the city of Joplin, Missouri, after the catastrophic 2011 Enhanced Fujita 5 tornado. First, an existing recovery data set from Joplin is documented and the main findings pertaining to the observed recovery trajectory of the buildings are highlighted. In the next step…
Discrete-Outcome Analysis of Tornado Damage Following the 2011 Tuscaloosa, Alabama, Tornado
Ground-truth measurements of damage caused by natural disasters are routinely used to determine which factors of the natural, built, and social environment contribute to losses from these events. Process-based models, which are based on structural design and probability theory, are often used in engineering risk analysis to predict damage and subsequent losses from these events, but empirical modeling can effectively aid in capturing the partial …
Development of Building Restoration Functions for Use in Community Recovery Planning to Tornadoes
Postdisaster community scale recovery and resilience assessments have gained interest in the United States and around the world following natural and human-made disasters. Tornadoes are one of the most devastating natural disasters that occur in the United States every year with an annual average of more than 1,200 causing an impactful number of deaths as well as economic losses. The postdisaster recovery planning and process of communities can b…
Community Resilience-Focused Technical Investigation of the 2016 Lumberton, North Carolina, Flood
In early October 2016, Hurricane Matthew crossed North Carolina as a Category 1 storm, with some areas receiving 0.38–0.46 m (15–18 in.) of rainfall on already saturated soil. The NIST-funded Center for Risk-Based Community Resilience Planning teamed with researchers from NIST’s Engineering Laboratory (Disaster and Failure Studies Program, Community Resilience Group, and the Applied Economics Office) to conduct a field study focused on the impact…
No prominent works on this page.
State of the research in community resilience
Community resilience has been addressed across multiple disciplines including environmental sciences, engineering, sociology, psychology, and economics. Interest in community resilience gained momentum following several key natural and human-caused hazards in the United States and worldwide. To date, a comprehensive community resilience model that encompasses the performance of all the physical and socio-economic components from immediate impact …
High-resolution approach to quantify the impact of building-level flood risk mitigation and adaptation measures on flood losses at the community-level
Validation of Time-Dependent Repair Recovery of the Building Stock Following the 2011 Joplin Tornado
This paper seeks to calibrate an existing analytical framework to develop building repair fragility models using a restoration dataset collected through a longitudinal field study in the city of Joplin, Missouri, after the catastrophic 2011 Enhanced Fujita 5 tornado. First, an existing recovery data set from Joplin is documented and the main findings pertaining to the observed recovery trajectory of the buildings are highlighted. In the next step…
Discrete-Outcome Analysis of Tornado Damage Following the 2011 Tuscaloosa, Alabama, Tornado
Ground-truth measurements of damage caused by natural disasters are routinely used to determine which factors of the natural, built, and social environment contribute to losses from these events. Process-based models, which are based on structural design and probability theory, are often used in engineering risk analysis to predict damage and subsequent losses from these events, but empirical modeling can effectively aid in capturing the partial …
Development of Building Restoration Functions for Use in Community Recovery Planning to Tornadoes
Postdisaster community scale recovery and resilience assessments have gained interest in the United States and around the world following natural and human-made disasters. Tornadoes are one of the most devastating natural disasters that occur in the United States every year with an annual average of more than 1,200 causing an impactful number of deaths as well as economic losses. The postdisaster recovery planning and process of communities can b…
Community Resilience-Focused Technical Investigation of the 2016 Lumberton, North Carolina, Flood
In early October 2016, Hurricane Matthew crossed North Carolina as a Category 1 storm, with some areas receiving 0.38–0.46 m (15–18 in.) of rainfall on already saturated soil. The NIST-funded Center for Risk-Based Community Resilience Planning teamed with researchers from NIST’s Engineering Laboratory (Disaster and Failure Studies Program, Community Resilience Group, and the Applied Economics Office) to conduct a field study focused on the impact…
High-resolution flood risk approach to quantify the impact of policy change on flood losses at community-level
Communities are changing the way they manage flood risk as a result of climate change which increases both the flood hazard intensity and frequency and therefore results in more flood exposure for the built environment. Quantifying the feasibility of policy changes requires a standardized approach that can allow a risk-informed decision. The current body of flood risk mitigation research is populated with a host of qualitative approaches to reduc…
Quantitative modeling of residential building disaster recovery and effects of pre- and post-event policies
Understanding the process of community recovery impacted by various post-disaster decisions such as dynamic policies can effectively guide the recovery process and expedite recovery, thereby establishing a more resilient community. This paper proposes a methodology based on a multi-layer Monte Carlo simulation to model a two-stage recovery process for residential buildings: functional downtime due to delay and functional downtime due to repair. T…
A Discrete-Event Simulation Model of Hospital Patient Flow Following Major Earthquakes
Barriers and Possibilities for Interdisciplinary Disaster Science Research
The strength and speed of modeling software has increased drastically in recent years. As it does so, researchers across a variety of fields work to determine how most effectively to utilize this strength and speed. Many of them have turned to interdisciplinarity as a means of creating more representative models. This is the case for disaster research as this area of interest involves many intricately interdependent systems. In working on interdi…
Incorporating Socioeconomic Metrics in Civil Engineering Projects
This paper explores the role of socioeconomic metrics in the design, planning and operations of civil engineering projects within the specific context of resilience. The inclusion of resilience analysis and design is consistent with the goals of the ASCE Infrastructure Resilience Division (IRD) and related emerging standards and duties of civil engineers. This paper focuses on the practices in design, planning, construction, and operation of civi…
Commercial Building Recovery Methodology for Use in Community Resilience Modeling
Commercial businesses in an area affected by a natural hazard can have significant interruption and disruption because of damage and impeding factors including financing delays. The recovery of commercial buildings is a necessary but not solely sufficient condition for owners to reopen their businesses. At the community-level modeling scale for planning, the ability to model commercial recovery across the entire community, including short-term bu…
A genetic algorithm framework for seismic retrofit of building portfolios to enhance community resilience
Bonds for disaster resilience
Enhanced risk mapping for hurricane surge losses using synthesized probabilistic and machine learning models
Quantifiable community resilience solutions for coastal communities over long time horizons
Hurricanes pose some of the most severe threats to coastal communities, with global warming exacerbating their destructive impacts as sea levels continue to rise. As the pace of climate change accelerates, the need for effective climate adaptation strategies is more urgent than ever, particularly to support long-term community resilience. Quantifying this resilience is complex, but cumulative population outmigration can serve as a valuable indica…
Mainshock-aftershock fragility surfaces
This study presents a machine learning (ML)-based methodology for developing fragility surfaces under mainshock–aftershock (MS–AS) seismic sequences, extending a previously validated framework originally developed for earthquake–tsunami (EQ–TS) scenarios. Using a two-phase nonlinear time history analysis approach and a dataset of 30 real MS–AS ground motion pairs, fragility surfaces are developed for ductile and non-ductile reinforced concrete (R…
Climate Adaptation Matrix Concept for Multidisciplinary Community Resilience in a Changing Climate
Coastal structures are currently built based on standards that rely on stationary loading assumptions and do not account for the changes in hurricanes due to climate change. As a result, buildings and infrastructure are susceptible to future loadings in excess of what they were originally designed. The resulting structural damage would lead to a lack of stability for a community by causing substantial losses and subsequent outmigration of residen…
Machine Learning-Based Prediction of Optimal Building-Level Flood Mitigation Strategies in At-Risk Communities
Flood-prone communities face severe economic and structural losses, necessitating strategic building retrofitting to enhance resilience. However, selecting the most effective mitigation strategies remains challenging due to financial constraints and the varying characteristics of at-risk buildings. While previous studies have explored optimization and machine learning separately for flood mitigation, there is a need for a more integrated approach…
Integrated decision-support framework for enhancing coastal community resilience against hurricane-induced hazards
Coastal communities face escalating threats from hurricanes, necessitating a shift from descriptive risk assessment to prescriptive, optimized portfolio planning to enhance resilience. This study introduces an integrated decision-support framework that couples high-fidelity, multihazard socio-physical system modeling with a multi-objective optimization model. The framework quantifies the cascading impacts of hurricane-induced wind, storm surge, a…
Integrated data imputation of national inventories for bridging information gaps and outcome risk uncertainty in community resilience modeling
Reliable natural hazard resilience modeling requires the systematic synthesis of building inventories; however, data scarcity and incomplete records often render public datasets unsuitable for quantitative risk analysis. This paper addresses these limitations by establishing a link between data gaps and uncertainty in risk metrics. We propose a data imputation approach that uses stratified Monte Carlo sampling to impute missing exposure attribute…
Modeling Wind-Induced Community Resilience Impacted by Broad Policy Levers
A well-designed resilience policy should be based on extensive risk assessment as well as a city’s experience with natural hazards. These policy decisions can significantly influence a community’s resilience to future hazards. An appropriate policy strategy can improve community resilience from a multidimensional perspective, which includes stability of the population, economy, and the social and physical systems. However, policies that can be im…
Computer Science (13 works) · Disaster Management and Resilience (13 works) · Engineering (13 works) · Environmental Science (11 works) · Flood Risk Assessment and Management (11 works) · Business (9 works) · Community resilience (9 works) · Environmental resource management (9 works) · Infrastructure Resilience and Vulnerability Analysis (9 works) · Tropical and Extratropical Cyclones Research (9 works)