Nonstructural building damage

Description

This analysis computes the non-structural damage to buildings based on a particular hazard. Currently, supported hazard is: earthquake.

The process is similar to evaluating other structural damages. The probabilities for building damage state are obtained using fragility curves and a hazard definition, each building site will have a specific PGA (Peak Ground Acceleration), a measurement of an earthquake hazard for each scenario. Liquefaction effect, which is defined as a change in stress condition, in which material that is ordinarily a solid behaves like a liquid can be considered as well. The LMF (Liquefaction Modification Factor) values are implemented as multiplication factors to the median fragility values and they must be present in the dataset. This analysis uses two types of fragility curves assigned to the building; acceleration-sensitive (AS) and drift-sensitive (DS).

The code covers Normal and LogNormal fragilities with 3 limit states for AS and DS and creates an output CSV file with corresponding damage states. The second output file is a JSON with information about hazard and fragilities.

Input Parameters

key name	type	name	description
result_name *	str	Result name	Name of the result dataset.
hazard_type	str	Hazard type	Hazard type (earthquake).
hazard_id	str	Hazard id	ID of the hazard from the Hazard service.
fragility_key_as	str	AS fragility	Acceleration-sensitive fragility key used in mapping dataset.
fragility_key_ds	str	DS fragility	Drift-sensitive fragility key used in mapping dataset.
use_liquefation	bool	Liquefaction	Use liquefaction, if applicable to the hazard. Default False.
liq_geology_dataset_id	str	Liquefaction id	A liquefaction susceptibility dataset.
use_hazard_uncertainty	bool	Uncertainty	Use hazard uncertainty. Default is False.
num_cpu	int	Number of CPUs	Number of CPUs used for parallel computations. Default 1.

Input Hazards

key name	type	name	description
hazard	earthquake	Hazard	Supported hazard object for using local and remote hazards.

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.

Output datasets

key name	type	parent key	name	description
result *	ergo:nsBuildingInventoryDamageVer2	buildings	Results	A dataset containing results with both, acceleration sensitivy (AS) related and drift sensitivity (DS) related damage states (format: CSV).
damage_result *	incore:nsBuildingInventoryDamageSupplement	buildings	Results	Information about applied hazard value and fragility (format: JSON).

(* required)

Execution

code snippet:

    # Create an instance
    non_structural_building_dmg = NonStructBuildingDamage(client)

    # Load input datasets
    non_structural_building_dmg.load_remote_input_dataset("buildings", building_dataset_id)

    # Load fragility mapping
    fragility_service = FragilityService(client)
    mapping_set = MappingSet(fragility_service.get_mapping(mapping_id))
    non_structural_building_dmg.set_input_dataset('dfr3_mapping_set', mapping_set)

    # Specify the result name
    result_name = "non_structural_building_dmg_result"

    # Set analysis parameters
    non_structural_building_dmg.set_parameter("result_name", result_name)
    non_structural_building_dmg.set_parameter("hazard_type", hazard_type)
    non_structural_building_dmg.set_parameter("hazard_id", hazard_id)
    non_structural_building_dmg.set_parameter("num_cpu", 4)

    # Shelby County Liquefaction Susceptibility
    use_liquefaction = True
    liq_geology_dataset_id = "5a284f55c7d30d13bc0824ba"

    non_structural_building_dmg.set_parameter("use_liquefaction", use_liquefaction)
    non_structural_building_dmg.set_parameter("liq_geology_dataset_id", liq_geology_dataset_id)

    non_structural_building_dmg.run_analysis()

full analysis: non_structural_building_dmg.ipynb