Dylan Sanderson
Biographic Data
| ID | 9958791 |
|---|---|
| NAME | Dylan Sanderson |
| GIVEN NAMES | Dylan |
| FAMILY NAME | Sanderson |
| SIGNATURE | SANDERSON D |
| AFFILIATIONS | National Institute of Standards and Technology |
| ORCID | 0000-0002-4443-7074 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
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…
A decision-support framework for fair building retrofit allocation under multi-hazard risk
Hazard mitigation planning often misses distributional effects, producing unequal outcomes for vulnerable groups. We present a building-level, multi-objective optimization framework that treats equity as a first-class objective alongside efficiency. Equity is measured with the decomposable Theil index, which summarizes how unevenly mitigation resources are distributed across demographic and housing categories. Efficiency objectives are expected e…
Quantifying future local impacts of sea level rise on buildings and infrastructure
Community resilience planners need tools to prepare for anticipated hazards and adapt to conditions associated with a changing climate. This paper presents a refined method for assessing the consequences of sea level rise on coastal communities by quantifying future impacts to buildings and infrastructure at a local level. While community resilience models typically address acute hazards, this work considers sea level rise and tides as a chronic …
Simulating future household adaptation to sea level rise using agent-based modeling and reinforcement learning
This paper presents a novel decision support tool for community planners that simulates household adaptation to the future impacts of sea level rise. Local sea level rise scenarios are combined with tide predictions to determine impacts on building exposure, electric power outages, and increases in travel times. Reinforcement learning is then used to train heterogeneous agents – each representing one household – how to respond to these impacts ba…
Multi-objective optimization of mitigation strategies for buildings subject to multiple hazards
Comparison of national and local building inventories for damage and loss modeling of seismic and tsunami hazards
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Comparison of national and local building inventories for damage and loss modeling of seismic and tsunami hazards
Multi-objective optimization of mitigation strategies for buildings subject to multiple hazards
Quantifying future local impacts of sea level rise on buildings and infrastructure
Community resilience planners need tools to prepare for anticipated hazards and adapt to conditions associated with a changing climate. This paper presents a refined method for assessing the consequences of sea level rise on coastal communities by quantifying future impacts to buildings and infrastructure at a local level. While community resilience models typically address acute hazards, this work considers sea level rise and tides as a chronic …
Simulating future household adaptation to sea level rise using agent-based modeling and reinforcement learning
This paper presents a novel decision support tool for community planners that simulates household adaptation to the future impacts of sea level rise. Local sea level rise scenarios are combined with tide predictions to determine impacts on building exposure, electric power outages, and increases in travel times. Reinforcement learning is then used to train heterogeneous agents – each representing one household – how to respond to these impacts ba…
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…
A decision-support framework for fair building retrofit allocation under multi-hazard risk
Hazard mitigation planning often misses distributional effects, producing unequal outcomes for vulnerable groups. We present a building-level, multi-objective optimization framework that treats equity as a first-class objective alongside efficiency. Equity is measured with the decomposable Theil index, which summarizes how unevenly mitigation resources are distributed across demographic and housing categories. Efficiency objectives are expected e…
Environmental Science (4 works) · Flood Risk Assessment and Management (4 works) · Computer Science (3 works) · Engineering (3 works) · Disaster Management and Resilience (2 works) · Environmental planning (2 works) · Environmental resource management (2 works) · Geology (2 works) · Infrastructure Resilience and Vulnerability Analysis (2 works) · Agent-based model (1 work)