ce 5603 seismic hazard assessment lecture 3: a review...
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CE 5603 SEISMIC HAZARD ASSESSMENT
LECTURE 3: A REVIEW ON PROBABILITY CONCEPTS
By : Prof. Dr. K. Önder Çetin
Middle East Technical University
Civil Engineering Department
Partial Descriptors of a Random Variable
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A random variable is completely defined by its PMF or PDF. However, often it is
useful to partially characterize a random variable by providing overall features of
its distribution such as the central location, breadth, skewness and other
measures of shape. The mean of x, denoted E(x) or μx is defined as the first
moment of its PMF or PDF, i.e;
Another central measure of a random variable is the median. Denoted X0.5, the
median is such that 50% of outcomes lie below it and 50% above it. For a given
random variable, the median is obtained by solving the equation
Partial Descriptors of a Random Variable
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A third central measure is the mode. Denoted the mode is the outcome that
has the highest probability or probability density. It is obtained by maximizing p(x)
or f(x).
Partial Descriptors of a Random Variable
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Partial Descriptors of a Random Variable
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Partial Descriptors of a Random Variable
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Total Probability Over a Random Variable
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Let peak ground acceleration (PGA) be considered as a continuous random
variable with a median and standard deviation. Also assume that the probability of
occurence of an event is dependent on the peak ground acceleration expected at
a site. Then the proper notation for the event can be expressed as:
Total Probability Over a Random Variable
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Total Probability Over a Random Variable
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Example:
Two reinforced concrete buildings A and B are located in a seismic region. It is
estimated that an impending EQ in the region might be strong (S), moderate (M),
or weak (W) with probabilities P(S)=0.02, P(M)=0.20, P(W)=0.78. The probabilities
of failure of each building if these earthquakes occur are 0.20, 0.05 and 0.01
respectively.
a) What is the probability of failure of building A if the impending earthquakes
occur ?
b) If the building A fails, what is the probability that EQ was of moderate strength ?
c) Due to similar procedures used in the design and construction of the two
buildings, it is estimated that if building A fails, the probability that B also fail is 0.5,
0.15 and 0.02 for 3 types of earthquakes. Determine the probability that both
buildings will fail in the impending earthquake.
d) If building A has failed and building B has survived, what is the probability that
EQ was not strong ?
Total Probability Over a Random Variable
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Example:
Two reinforced concrete buildings A and B are located in a seismic region. It is
estimated that an impending EQ in the region might be strong (S), moderate (S),
or weak (W) with probabilities P(S)=0.02, P(M)=0.20, P(W)=0.78. The probabilities
of failure of each building if these earthquakes occur are 0.20, 0.05 and 0.01
respectively.
a) What is the probability of failure of building A if the impending earthquakes
occur ?
Total Probability Over a Random Variable
11
Example:
Two reinforced concrete buildings A and B are located in a seismic region. It is
estimated that an impending EQ in the region might be strong (S), moderate (S),
or weak (W) with probabilities P(S)=0.02, P(M)=0.20, P(W)=0.78. The probabilities
of failure of each building if these earthquakes occur are 0.20, 0.05 and 0.01
respectively.
b) If the building A fails, what is the probability that EQ was of moderate strength ?
Total Probability Over a Random Variable
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Example:
Two reinforced concrete buildings A and B are located in a seismic region. It is
estimated that an impending EQ in the region might be strong (S), moderate (S),
or weak (W) with probabilities P(S)=0.02, P(M)=0.20, P(W)=0.78. The probabilities
of failure of each building if these earthquakes occur are 0.20, 0.05 and 0.01
respectively.
c) Due to similar procedures used in the design and construction of the two
buildings, it is estimated that if building A fails, the probability that B also fail is 0.5,
0.15 and 0.02 for 3 types of earthquakes. Determine the probability that both
buildings will fail in the impending earthquake.
Total Probability Over a Random Variable
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Example:
d) If building A has failed and building B has survived, what is the probability that
EQ was not strong ?
Poisson Process and Poisson Distribution
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1. An event can occur at random and at any time or any point in space.
2. The occurrence(s) of an event in a given time (or space) interval is independent
of that in any other non-overlapping intervals.
Useful Probability Models
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Useful Probability Models
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Useful Probability Models
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Useful Probability Models
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Widely Used Continuous Distributions
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Widely Used Continuous Distributions
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Aleatory Variability and Epistemic
Uncertainty
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Aleatory variability is defined as the randomness in the phenomena itself. There's
no control of uncertainty in the phenomenon and the process is purely random.
Aleatory variability can not be reduced with more or better observations. One
solution to decrease aleatory variability inherent in the model may be to completely
change the model (i.e. switch from empirical observations to a physical model).
Natural randomness is modeled as a probability density function for the specific
problem being handled.
Epistemic uncertainty is due to lack of information in how the properties of the
process changes. With more data and enhanced models, epistemic uncertainty
can be reduced. Attenuations by proposed by different researchers using the same
raw sample set, and different best function fits to sample points with small number
of samples will vary the epistemic uncertainty for the problem. Figures 8-10 show
the seismic source characterizations used by various researchers in nationwide
seismic hazard mapping studies, forming a typical example for epistemic
uncertainty definition.
Aleatory Variability and Epistemic
Uncertainty
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Aleatory Variability and Epistemic
Uncertainty
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Aleatory Variability and Epistemic
Uncertainty
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