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* 260 update notebook using restoration utils (#268) * update restoration util construction * changelog entry * 274 update indp documentation (#275) * add parameter and also citation * changelog * update the markdown link * 272 local hazard tutorial (#276) * Added multiple hazards from local * added description * removed unused packages * added zip * updated zip * Update CHANGELOG.md * added tutorials.md * added files * Update _toc.yml * Update _toc.yml * Commercial buildings markdown and notebook added (#273) * Commercial buildings added * Adding changelog and references * Corrections * Update commercial_building_recovery.md * Update commercial_building_recovery.md * changelog --------- Co-authored-by: YONG WOOK KIM <[email protected]> Co-authored-by: Ya-Lan Yang <[email protected]> Co-authored-by: Vismayak Mohanarajan <[email protected]>
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# Commercial building recovery | ||
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**Description** | ||
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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. | ||
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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. | ||
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The outputs of this analysis is a CSV file with time-stepping recovery probabilities at the building level. | ||
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**Contributors** | ||
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- Science: Wanting Lisa Wang, John W. van de Lindt | ||
- Implementation: Wanting Lisa Wang and NCSA IN-CORE Dev Team | ||
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**Related publications** | ||
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- 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. | ||
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**Input parameters** | ||
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key name | type | name | description | ||
--- | --- | --- | --- | ||
`result_name` <sup>*</sup> | `str` | Result name | Name of the result dataset. | ||
`num_samples` <sup>*</sup> | `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. | ||
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**Input datasets** | ||
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key name | type | name | description | ||
--- | --- | --- | --- | ||
`buildings` <sup>*</sup> | `ergo:buildingInventoryVer4`<br>`ergo:buildingInventoryVer5`<br>`ergo:buildingInventoryVer6`<br>`ergo:buildingInventoryVer7` | Building dataset | A building dataset. | ||
`dfr3_mapping_set` <sup>*</sup> | `incore:dfr3MappingSet` | DFR3 Mapping Set | DFR3 Mapping Set. | ||
`sample_damage_states` <sup>*</sup> | `incore:sampleDamageState` | Damage states | Sample damage states. | ||
`mcs_failure` <sup>*</sup> | `incore:failureProbability` | MCS failure | mcs_failure. | ||
`delay_factors` <sup>*</sup> | `incore:buildingRecoveryFactors` | Delay factors | Delay impeding factors such as post-disaster inspection, insurance claim,<br>and government permit based on building's damage state. Provided by REDi framework. | ||
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**Output datasets** | ||
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key name | type | parent key | name | description | ||
--- | --- |-------------------------| --- | --- | ||
`time_stepping_recovery` <sup>*</sup> | `incore:buildingRecovery` | `buildings` | Results | A dataset containing results (format: CSV)<br>with percentages of commercial building recovery. | A dataset containing results (format: CSV)<br>with percentages of commercial building recovery. | ||
`recovery` <sup>*</sup> | `incore:buildingRecoveryTime` | `buildings` | Building Recovery Time | A dataset containing results (format: CSV)<br>with commercial building recovery time. | ||
`total_delay` <sup>*</sup> | `incore:buildingRecoveryDelay` | `buildings` | Building Recovery Delay | A dataset containing results (format: CSV)<br>with commercial building delay time. | ||
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<small>(* required)</small> | ||
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**Execution** | ||
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code snippet: | ||
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``` | ||
# 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() | ||
``` | ||
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full analysis: [commercial_building_recovery.ipynb](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/commercial_building_recovery.ipynb) |
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