Monte Carlo Limit State Probability#
Description
This analysis calculates a probability of limit state using a stochastic process. Limit state probability and damage state are derived using the dictionary of failed damage states in the input infrastructure dataset. Limit state probability is calculated from all stochastic runs, limit 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 limit state 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 
Name of the result dataset. 


Samples 
Number of Monte Carlo samples. 


Damage keys 
Column names of the damage intervals. 


Failure keys 
Column names of damage intervals. 


Number of CPUs 
Number of CPUs used for parallel computations. 


Seed 
Initial seed for the probabilistic model to ensure reproducibility. 
Input Datasets
key name 
type 
name 
description 



Infrastructure damage 
A file with infrastructure damage intervals. 
Output Datasets
key name 
type 
name 
description 


Results 
A dataset containing failure probability results 


Results 
A dataset containing failure state for each sample 


Results 
A dataset containing damage state for each sample 
(* required)
Execution
code snippet:
# Create instance
mc = MonteCarloLimitStateProbability(client)
# Load remote datasets
mc.load_remote_input_dataset("damage", damage_id)
# Set analysis parameters
mc.set_parameter("result_name", "mc_limit_state_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 limit state analysis
mc.run_analysis()
full analysis: mc_limit_state_prob.ipynb