Pipeline restoration

This analysis computes water pipeline restoration time after a particular hazard. The analysis itself is hazard agnostic, as it works with the output of Pipeline damage with repair rate analysis and restoration curves.

The restoration curves are obtained based on the hazard type and diameter-specific class of the pipeline. Based on the restoration curve applicable, we can obtain the time it takes to repair each of the pipelines. It considers the leak rate, break rate, pipe length and the number of available workers to calculate the repair times. One of the inputs of this analysis is the output of the Pipeline damage with repair rate analysis.

The output of this analysis is a CSV file with expected time taken to repair for each pipeline.

Input Parameters

key name	type	name	description
result_name *	str	Result name	Name of the result dataset.
num_available_workers *	str	Number of available workers	Number of available workers to work on the repairs.
restoration_key *	str	Restoration Key	Restoration key to use in mapping dataset.
num_cpu	int	Number of CPUs	Number of CPUs used for parallel computations. Default 1.

Input Datasets

key name	type	name	description
pipeline *	ergo:buriedPipelineTopology ergo:pipeline	Pipeline dataset	A pipeline dataset.
pipeline_damage *	ergo:pipelineDamageVer2 ergo:pipelineDamageVer3	Pipeline damage with repair rates dataset	A pipeline damage with repair rates dataset.
dfr3_mapping_set *	incore:dfr3MappingSet	DFR3 Mapping Set	DFR3 Mapping Set.

Output Datasets

key name	type	parent key	name	description
pipeline_restoration *	incore:pipelineRestorationVer1	pipeline	Results	Repair time for each pipeline (format: CSV).

(* required)

Execution

code snippet:

    # Create pipeline restoration
    pipeline_restoration = PipelineRestoration(client)

    # shelby county pipelines
    pipeline_restoration.load_remote_input_dataset("pipeline", pipeline_dataset_id)
    pipeline_restoration.load_remote_input_dataset("pipeline_damage", pipeline_dmg_dataset_id)

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

    pipeline_restoration.set_parameter("result_name", "pipeline_restoration_times")

    pipeline_restoration.set_parameter("restoration_key", "Restoration ID Code")
    pipeline_restoration.set_parameter("num_available_workers", 4)
    pipeline_restoration.set_parameter("num_cpu", 4)

    # Run pipeline restoration analysis
    pipeline_restoration.run_analysis()

full analysis: pipeline_restoration.ipynb