The document discusses quantifying risks from hazards like hurricanes, dust explosions, and meteorites that could threaten the 100 residents of a building. It explains that to quantify the risks, the likelihood (annual frequency) and consequences (potential fatalities) of each event need to be estimated. Once quantified, the risks can be displayed on a risk matrix based on their expected value (frequency multiplied by fatalities), or plotted on a frequency-number (FN) curve showing the relationship between event frequency and potential fatalities. An example risk matrix is given showing the estimated annual frequency and potential fatalities for hurricanes, dust explosions, and meteorites to demonstrate how these risks can be quantified and compared.

1. QRA math explained
Sanjeev Saraf

2. 100 residents in the building face threats from
hurricane, dust explosion from nearby grain storage
and meteorite.
④
METEORITE
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AGE .
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TT
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1.00 residents .
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3. To quantify risks from these events, we need likelihood
(annualized frequency, f) and consequence severity
(fatalities) for each event.
Once quantified, the risks can be presented on risk
matrix, as expected value (fn) or plotted as FN curve.
METEORITE
f
-
-
I in million years
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14=100
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¥:*:¥:
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ii.
¥¥.
Return period
f -
-
Yiooo Yi Hoo years
N
-
-
IO N = 4 .
-

4. RISK = likelihood x
consequence
f
N
annualized fatalities
frequency Gyri
FATALITIES HEAR
f N fxN-
Hurricane 10-2 4 '
04 or $125
Dust explosion 10-3 10 -
01 or 11100
Meteorite 10-6 100 10--4 or
1/10000
-
↳ Enpected value E. 0501
OR
I
fatality 120 years
MAP ) Risk MATRIX
onto
p
-
matters Likelihood
#-
SEVERITY
-7

5. FN Culver
(
cumulative
frequency
fhlphlolmoeefatalities
Meteorite 10-6 100 10-6
Enpeosion
10-3 10 10-3+10-6=10-3
- Z -
2
Hurricane
10 4 10-710-3+10-6=1.1×10
F
frequency n
of Hormone
fatalities
i
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.
- '
Tinian.in?i::::in.eoriie÷
,
10-5 .
105
.
.
10-6 -
0 Meteorite
I n1 to Yoo .
Fatalities