Residential building recovery

Residential building recovery#

Description

This analysis computes the recovery time needed for each residential building from any damage states to achieve full restoration. Currently, supported hazards are tornadoes.

The methodology incorporates a multi-layer Monte Carlo simulation approach and determines the two-step recovery time which includes both the delay period and repair period. The delay model was modified based on the REDi framework and calculated the end-result outcomes resulting from delay impeding factors such as post-disaster inspection, insurance claims, and building permit issuance. The repair model followed the FEMA P-58 approach and ultimately utilized fragility functions from Koliou and van de Lindt (2020).

The outputs of this analysis is a CSV file with time-stepping recovery probabilities at the building level.

Contributors

Science: Wanting Lisa Wang, John W. van de Lindt
Implementation: Wanting Lisa Wang, Gowtham Naraharisetty, and NCSA IN-CORE Dev Team

Related publications

Wang, Wanting Lisa, and John W. van de Lindt. “Quantitative Modeling of Residential Building Disaster Recovery and Effects of Pre-and Post-event Policies.” International Journal of Disaster Risk Reduction (2021): 102259.
Koliou, M. and J.W. van de Lindt. (2020). “Development of Building Restoration Functions for use in Community Recovery Planning to Tornadoes.”, ASCE Natural Hazards Review, Vol 21 (2) doi.org10.1061/(ASCE)NH.1527-6996.0000361.

Input parameters

key name	type	name	description
`result_name` ^*	`str`	Result name	Name of the result dataset.
`num_samples` ^*	`int`	Samples number	Number of sample scenarios.
`seed`	`int`	Seed	Initial seed for the probabilistic model.
`repair_key`	`str`	Repair key	A repair key to use in mapping dataset.

Input datasets

key name	type	name	description
`buildings` ^*	`ergo:buildingInventoryVer4` `ergo:buildingInventoryVer5` `ergo:buildingInventoryVer6` `ergo:buildingInventoryVer7`	Building dataset	A building dataset.
`dfr3_mapping_set` ^*	`incore:dfr3MappingSet`	DFR3 Mapping Set	DFR3 Mapping Set.
`sample_damage_states` ^*	`incore:sampleDamageState`	Damage states	Sample damage states.
`socio_demographic_data` ^*	`incore:socioDemograhicData`	Socio demographic	Socio-demographic data with household income group predictions.
`financial_resources` ^*	`incore:householdFinancialResources`	Financial resources	Financial resources by household income groups.
`delay_factors` ^*	`incore:buildingRecoveryFactors`	Delay factors	Delay impeding factors such as post-disaster inspection, insurance claim, and government permit based on building’s damage state. Provided by REDi framework.

Output datasets

key name	type	parent key	name	description
`time_stepping_recovery` ^*	`incore:buildingRecovery`	Results	Time Stepping Recovery	A dataset containing results (format: CSV) with percentages of residential building recovery.
`total_delay` ^*	`incore:buildingRecoveryDelay`	Results	Total Delay	A dataset containing results (format: CSV) with delay time of residential building recovery.
`recovery` ^*	`incore:buildingRecoveryTime`	Results	Recovery	A dataset containing results (format: CSV) with delay time of residential building recovery.

(* required)

Execution

code snippet:

    # Create Residential building recovery instance
    res_recovery = ResidentialBuildingRecovery(client)
    
    # Load input building infrastructure dataset
    res_recovery.load_remote_input_dataset("buildings", buildings)

    # Load repair mapping
    repair_service = RepairService(client)
    mapping_set = MappingSet(repair_service.get_mapping(mapping_id))
    res_recovery.set_input_dataset('dfr3_mapping_set', mapping_set)
    
    # Load input datasets  
    res_recovery.load_remote_input_dataset("sample_damage_states", sample_damage_states)
    res_recovery.load_remote_input_dataset("socio_demographic_data", socio_demographic_data)
    res_recovery.load_remote_input_dataset("financial_resources", financial_resources)
    res_recovery.load_remote_input_dataset("delay_factors", delay_factors)

    # Specify the result name
    result_name = "joplin_recovery"

    # Set analysis parameters
    res_recovery.set_parameter("result_name", result_name)
    res_recovery.set_parameter("seed", seed)
    res_recovery.set_parameter("num_samples", 10)

    # Run residential recovery analysis
    res_recovery.run_analysis()

full analysis: residential_building_recovery.ipynb