Commercial building recovery

Commercial building recovery#

Description

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

The methodology incorporates the multi-layer Monte Carlo simulation approach and determines the two-step recovery time that includes delay and repair. The delay model was modified based on the REDi framework and calculated the end-result outcomes resulted from delay impeding factors such as post-disaster inspection, insurance claim, financing and government permit. The repair model followed the FEMA P-58 approach and was controlled by fragility functions.

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 and NCSA IN-CORE Dev Team

Related publications

Wang, W.L., Watson, M., van de Lindt, J.W. and Xiao, Y., 2023. Commercial Building Recovery Methodology for Use in Community Resilience Modeling. Natural Hazards Review, 24(4), p.04023031.

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.
`mcs_failure` ^*	`incore:failureProbability`	MCS failure	mcs_failure.
`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.
`building_dmg` ^*	`ergo:buildingDamageVer6`	Building Damage	Damage result that has damage intervals.

Output datasets

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

(* required)

Execution

code snippet:

    # Create Commercial building recovery instance
    comm_recovery = CommercialBuildingRecovery(client)
    
    # Load input building infrastructure dataset
    comm_recovery.load_remote_input_dataset("buildings", buildings)

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

    # Specify the result name
    result_name = "joplin_recovery"

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

    # Run commercial recovery analysis
    comm_recovery.run_analysis()

full analysis: commercial_building_recovery.ipynb