Pipeline restoration

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