Probability of repairable damage

[rezvan]

Hi Adam,

I have another question regarding the probability of repairable damage.
I was wondering whether the collapsed cases are also considered to get this probability or only the excessive RID damage state is considered to calculate it.

Thank you so much,

[adamzs]

Hi Pooya,

Could you provide more context for this question?

I suspect that you meant to ask about irreparable damage. Please confirm that and I am happy to explain how irreparable damage is assessed in the FEMA P58 example.

Adam

[rezvan]

Thank you, Adam

I am asking about the following line of codes which are located in the last part of the example you provided in the Jupyyter notebook:

# get only the results that describe repairable damage
agg_DF_ = agg_DF.loc[agg_DF['repair_cost'] < 2.0e7]

print('\nProbability of experiencing repairable damage:', agg_DF_.shape[0] / agg_DF.shape[0])

 I guess :

1-The probability of "repairable damage" is defined as the number of realizations whose repair cost is less than (2.0E+7) over the total number of realizations. Is that right?

2- In the realization that the building experiences collapse or excessive drift, the repair cost is taken equal to the replacement cost which is more than 2.0E+07, therefore, they will not be considered repairable cases. Is that right?

3- How did you define 2.0E+07? Is it almost half of the replacement cost? Meaning that if the repair cost is more than 50% of the replacement cost, it is not reasonable to repair the building?

Again, I really appreciate your endless support.

Pooya,

[adamzs]

Hi Pooya,

I see, thank you for the clarification.

Your understanding is correct; my answer is yes to your first and second questions.

As for your third question, I picked 2e7 because the replacement cost was set to 2.16e7 (see the additional_consequences table in the Consequence data section) and I was focusing on removing total losses from the results.

The 50% you suggest I assume comes from your literature review - FEMA P58 indeed suggests using 0.4-0.5 of the replacement cost as the threshold beyond which an owner would not choose to repair. I did not consider that in the Jupyter notebook because my objective with this filter was to create a nice plot that focuses on the distribution of repair costs and times. You can think about those 'repairable' cases as damages that can be repaired but might not be economical to repair. So, the actual proportion of repaired realization is less than or equal to the repairable ones.

I hope this makes sense. Let me know if you have any follow-up questions.

Adam