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
residential_building_recovery *	incore:buildingRecovery	Results	A dataset containing results (format: CSV) with percentages 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