Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Abbreviated Injury Scale (1)
- Black-box Variational Inference (1)
- Building Contents (1)
- Disaster Preparedness for tropical cyclones (1)
- Displaced Short-term and Long-term shelter needs (1)
-
- Economic loss and damage states (1)
- Emergency Management (1)
- FEMA Individual assistance and direct housing (1)
- FEMA P-58 (1)
- Fragility Function (1)
- Gaussian Process and Surrogate Model (1)
- Generalization and Robustness (1)
- Geometric Complexity-Minimum Description Length (1)
- Hurricane Housing Demand Forecasting (1)
- Hurricane hazard quantification (1)
- Insurance Risk Analysis (1)
- Inverse problem and Uncertainty Quantification (1)
- Long Duration Earthquake (1)
- Loss Function (1)
- Machine learning-based Data and model driven (1)
- Mitigation (1)
- Performance Based Earthquake Engineering (1)
- Probabilistic Injury Model (1)
- Restrainers (1)
- Seismic Casualty Assessment (1)
- Subduction Zones (1)
- US government support during hurricanes (1)
- Publication
Articles 1 - 3 of 3
Full-Text Articles in Risk Analysis
Quantifying Hurricane Effects On Housing: Evaluating Damage, Loss, And Shelter Demands Using Historical And Simulated Storm Tracks, Adish Deep Shakya
Quantifying Hurricane Effects On Housing: Evaluating Damage, Loss, And Shelter Demands Using Historical And Simulated Storm Tracks, Adish Deep Shakya
All Theses
This research introduces an advanced framework which employs parametric wind field models for peak wind speeds, and building fragility curves, loss functions, and demographic data to estimate for estimating housing damage and loss. The uninhabitable units immediate displaced households, short-term and long-term shelter need households are determined. with a particular focus on those eligible for FEMA assistance. The framework's validity is reinforced by a high correlation in the analysis of recent hurricane events between estimated numbers of displaced households and actual FEMA aid recipients, where FEMA aids about 20-60% of the predicted long-term displaced households. A novel application of the …
Machine Learning-Based Data And Model Driven Bayesian Uncertanity Quantification Of Inverse Problems For Suspended Non-Structural System, Zhiyuan Qin
All Dissertations
Inverse problems involve extracting the internal structure of a physical system from noisy measurement data. In many fields, the Bayesian inference is used to address the ill-conditioned nature of the inverse problem by incorporating prior information through an initial distribution. In the nonparametric Bayesian framework, surrogate models such as Gaussian Processes or Deep Neural Networks are used as flexible and effective probabilistic modeling tools to overcome the high-dimensional curse and reduce computational costs. In practical systems and computer models, uncertainties can be addressed through parameter calibration, sensitivity analysis, and uncertainty quantification, leading to improved reliability and robustness of decision and …
Seismic Performance Assessment Of Building Contents: Monetary Losses And Injuries, Sereen Majdalaweyh
Seismic Performance Assessment Of Building Contents: Monetary Losses And Injuries, Sereen Majdalaweyh
All Dissertations
Building contents include all the components that are not attached to the building which the owners place after the construction phase, such as furniture, electrical equipment, glassware, and other personal items. Loss and damage assessment of building contents proved to be challenging in performance-based earthquake engineering frameworks because of the data sparsity. Damages to building contents during an earthquake not only cause monetary losses; tumbling and over-toppling of heavy building contents could result in injuries and even deaths of occupants. While major advancements have been made in performance-based earthquake engineering; however, the focus is mainly on damages and collapse risk …