- Overview of what’s included in paper including key results

- Overview of the 2014 South Napa Earthquake
- Description of damage to built environment
- Buildings and lifelines

- Socio-economic impacts

- Previous research on Post-Disaster recovery simulation
- Overview of what is covered in this paper

- Description of study region
- Residential communities
- Business districts

- Inventory of damaged buildings
- Data sources
- Field surveys
- Permit data from Building Department
- Census Data
- Real estate data

- Construction types
- Occupancy Types
- Description of damage

- Stochastic Simulation Approach
- Each building has two recovery states (recovered/not recovered)
- Two possibilities
- We have recovery data for all damaged buildings
- Simulate recovery at the building scale and then aggregate to census block or study region scales

- We have recovery data for some of the damaged buildings
- Use bootstrap to sample buildings and model recovery at the census block and study-region scales

- State transition modeled using Poisson distribution
- Time-based model
- Regression performed on data from field survey and building department using the recovery time as the dependent variable. Various explanatory/independent variables (census data, building damage, building value, building age etc.)
- Different types of statistical models used for regression including machine learning algorithms

- Use inverse method and monte carlo simulation to generate the recovery time

- State-based model
- Regression performed on data from field survey and building department using the rate parameter from the exponential distribution as the dependent variable. Various explanatory/independent variables (census data, building damage, building value, building age etc.)
- Different types of statistical models used for regression including machine learning algorithms

- At any given point in time, we can compute the probability of recovery given the rate parameter from the exponential distribution
- Use monte carlo to simulate the state of the building at any given point in time

- Statistical Model
- Each building has two recovery states (recovered/not recovered)
- Two possibilities
- We have recovery data for all damaged buildings
- Simulate recovery at the building scale and then aggregate to census block or study region scales

- We have recovery data for some of the damaged buildings
- Use bootstrap to sample buildings and model recovery at the census block and study-region scales

- Logistic regression performed on data from field survey and building department using the probability of recovery the dependent variable. Various explanatory/independent variables (time, census data, building damage, building value, building age etc.)
- Different types of statistical models used for regression including machine learning algorithms
- Use inverse method and monte carlo simulation to generate the recovery time

- Use monte carlo to simulate the state of the building at any given point in time

Hua Kangalmost 3 years ago · PublicShould we specify “recovery of damaged building” since this is our main focus i.e. we do not consider lifelines, socio-economic recovery etc.