Mean damage#
Description
The process for computing the structural damage uses mean damage and standard deviation values from damage ratios tables. The four damage state probabilities are multiplied by the mean damage and aggregated to get the Mean damage for each individual structure (building, bridge, waterfacility etc.).
The output of this analysis is a CSV file with probabilities of damage.
Input parameters
key name |
type |
name |
description |
---|---|---|---|
|
|
Result name |
Name of the result dataset. |
|
|
Damage ratio |
Names of the four damage intervals. |
|
|
Number of CPUs |
Number of CPUs used for parallel computation. |
Input datasets
key name |
type |
name |
description |
---|---|---|---|
|
|
Infrastructure dataset |
An infrastructure dataset. |
|
|
Damage ratios |
A damage ratios dataset. |
Output datasets
key name |
type |
name |
description |
---|---|---|---|
|
Results |
A dataset containing results |
(* required)
Execution
code snippet:
# Create mean damage instance
md = MeanDamage(client)
# Load input datasets
md.set_input_dataset("damage", bridge_damage_result)
md.load_remote_input_dataset("dmg_ratios", dmg_ratios_id)
# Specify the result name
result_name = "result_name"
# Set analysis parameters
md.set_parameter("result_name", "bridge_mean_damage")
md.set_parameter("damage_interval_keys",["DS_0", "DS_1", "DS_2", "DS_3", "DS_4"])
md.set_parameter("num_cpu", 1)
# Run mean damage analysis
md.run_analysis()
full analysis: mean_dmg.ipynb