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