Monte Carlo failure probability

Description

This analysis calculates a probability of failure using a stochastic process. Failure probability and Failure state are derived using the dictionary of failed damage states in the input infrastructure dataset. Failure probability is calculated from all stochastic runs, failure state shows all infrastructure standings as a string of failed (0) and not failed (1) states of each individual run.

The output of this analysis are two CSV files; a failure proability base_name_failure_probability.csv with allocated house units and base_name_failure_state.csv.

Input Parameters

key name	type	name	description
result_name *	str	Result name	Name of the result dataset.
num_samples *	int	Samples	Number of Monte Carlo samples.
damage_interval_keys *	List[str]	Damage keys	Column names of the damage intervals.
failure_state_keys *	List[str]	Failure keys	Column names of damage intervals.
num_cpu	int	Number of CPUs	Number of CPUs used for parallel computations. Default 1.
seed	int	Seed	Initial seed for the probabilistic model to ensure reproducibility.

Input Datasets

key name	type	name	description
damage *	ergo:buildingDamageVer4 ergo:buildingDamageVer5 ergo:bridgeDamage ergo:bridgeDamageVer2 incore:epfDamage incore:epfDamageVer2 ergo:nsBuildingInventoryDamage ergo:nsBuildingInventoryDamageVer2 incore:pipelineDamage incore:pipelineDamageVer2 ergo:roadDamage ergo:roadDamageVer2 ergo:waterFacilityDamageVer4 ergo:waterFacilityDamageVer5	Infrastructure damage	A file with infrastructure damage intervals.

Output Datasets

key name	type	name	description
failure_probability *	incore:failureProbability	Results	A dataset containing failure probability results (format: CSV).
sample_failure_state *	incore:sampleFailureState	Results	A dataset containing failure state for each sample (format: CSV).

(* required)

Execution

code snippet:

    # Create instance
    mc = MonteCarloFailureProbability(client)

    # Load remote datasets
    mc.load_remote_input_dataset("damage", damage_id)

    # Set analysis parameters
    mc.set_parameter("result_name", "mc_failure_probability")
    mc.set_parameter("num_cpu", 8)
    mc.set_parameter("num_samples", 10)
    mc.set_parameter("damage_interval_keys", ["DS_0", "DS_1", "DS_2", "DS_3"])
    mc.set_parameter("failure_state_keys", ["DS_1", "DS_2", "DS_3"])
    mc.set_parameter("seed", 1111)

    # Run Monte Carlo failure
    mc.run_analysis()

full analysis: mc_failure_prob.ipynb