Outline of Simulating Recovery Following the 2014 South Napa Earthquake

1. Abstract
  • Overview of what’s included in paper including key results
2. Introduction
  • 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
3. Desripition of Study Region and Inventory of Damaged Buildings
  • 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
4. Simulating Post-Earthquake Recovery of Impacted Buildings
  • 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
5. Summary and Conclusion

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