Ramin_Shamshiri_Risk_Analysis_Lecture.ppt

7/30/2019 Ramin_Shamshiri_Risk_Analysis_Lecture.ppt

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Risk Analysis in Engineering

A lecture on

Reliability Engineering and System SafetyOn how to define, understand and describe riskAuthor: Terje Aven

University of Stavanger, Norway

By: Ramin Shamshiri


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1. Introduction

2. Risk definitions and descriptions

3. Examples I, II, III

4. Discussion

5. Conclusion and final remarks


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1. IntroductionIn Engineering, Risk is often linked to the expected loss.

Risk is:

1. A measure of the probability and severity of adverse effects

2. Risk is the combination of probability of an event and its consequences

3. Risk is equal to the triplet (si, pi, ci),

Common in all definitions:

Risk =(A,C,P)

Other definitions of risk with taking into account the uncertainties beyond the probabilities

4. Risk refers to uncertainty of outcome, of actions and events

5. Risk is a situation or event where something ofhuman value (including humans themselves)

is at stake and where the outcome is uncertain

6. Risk is an uncertain consequence of an event or an activity with respect to something that

humans value

7. Risk is equal to the two-dimensional combination of events/ consequences and associated

uncertainties

8. Risk is uncertainty about and severity of the consequences (or outcomes) of an activity withrespect to something that humans value

si is the ith scenario,

pi is the probability of that scenario

ci is the consequence of the ith scenario, i=1,2,.., N

A: events, i.e., gas leakage or terrorist attack

C: the consequences of A, i.e., the number of causalities due to leakages, terrorist attacks, etc

P: the associated probabilities


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2. Risk definitions and descriptionsi. Frequency based perspective Risk =( A,C,Pf ) Where Pf is a relative frequency-interpreted probability

ii. Alternative perspective Risk =( A,C,U ) Where U is the uncertainly about A and C

2.1. Risk description for the relative frequency case:

i. According to the probability of frequency approach= (A, C, Pf, P(Pf), K) where Know is the

background knowledge that the estimate Pf* and the probability distribution P is based on.

ii. According to the pure traditional statistical approach= (A, C, Pf, C(Pf), K) where C is a traditionalconfidence interval for Pf:

2.2. Risk description for the alternative approach, Risk description = ( A, C, U, P, K )

where P is a subjective probability expressing U based on the background knowledge K.

2.3. Comparison of the definitions (4-8) and the alternative approach According to (4) risk refers to uncertainty of outcome, of actions and events

According to the definition (5), risk is a situation or event where something of human value (including

humans themselves) is at stake and where the outcome is uncertain

The same conclusion is made for the definition (6), which says that risk is an uncertain consequence of an

event or an activity with respect to something that human value

The definitions(7)and(8) are consistent with the(A,C,U) definition, although(8) introduces the term

severity which refers to intensity, size, extension, scope and other potential measures of magnitude, andaffects something that humans value(lives, the environment, money, etc).


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EXAMPLE 1: Offshore Diving Activities

-Divers working in support of the exploration and

production sector of the O&G industry

- Risk: long-term effect-health problems ( hearing problem, heart

problem, brain/forgetfulness, etc)

-How to manage the risk

- safety management, apply caution andprecaution


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Example 2 : Security Example

1.Risk : Exposure to the children in thekindergarten and other neighbourscaused by possible robberies theresidents feel that the NOKAS facility

must be moved2.Example of such factors are:

Possible trends within the robbery environment.

The scenario development in the case of an attack.

3. How to manage risk

Extended risk description would have given a moreinformative decision basis and led to a stronger involvement ofthe politicians as the bureaucrats would not have been able toconclude.


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EXAMPLE 3: MARKET PRICE RISK Price is just a number, considered as

historical data

The estimation or probabilistic analysismaybe done to predict the price for the

next year or future months

Prediction prices might not 100% true

Become a risk


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RISK: PREDICTION PRICES MIGHT

WRONG

The prices jump up or down, not 100% exactly

as prediction

Suddenly surprise occur (which might beaffect/change something)

HOW TO MANAGE THE RISK: Identify the uncertainties beyond the

probabilistic analysis to give informative risk

description to the decision-makers


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IMPORTANT!!

By doing PB can improve prediction result

BUT may end up with over-interpretting

By doing PB can analyse the possible

factors which affect the prices BUT difficult

to identify which is the most important

factors

PB can solve the numeric problem BUTcant solve hindsight problems

Prepared by: Raja Manisa bt Raja Mamat (GS32199)


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Probabilistic analysis requires strong simplifications and assumptions and as a result important

factors could be ignored or hidden in the background knowledge

The Frequentists and Bayesian schools of thought have collided over the definition of probability.

And attempts to mend these two perspectives have led to the probability of frequency approach.

relative frequency-interpreted probability is obvious when performing controlled

experiment, but in complex situation is not obvious

Anti-frequentists would conclude that the relative frequency-interpreted does not exist and in a

way constructs uncertainties.

How can we then estimate and assess uncertainties in a meaningful way?

The different probabilities can be combined for decision-making purposes as if all uncertaintieswere of the same nature.

Classical decision theorists believe that the distinction between aleatory and epistemic

uncertainties is unnecessary.


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From the perspective of Pat-cornell , rationality can be viewed as

more complex than the simple maximization of expected utility

Indeed, we should consider uncertainty as a main component of a risk

description, and also probability is a just tool used to express the

uncertainties.

For conclusion:We believe a more open qualitative approach for revealing such

uncertainties is better.

It is common to define and describe risk using probabilities, and assigned

probabilities depend on the background knowledge. ++

For problems with large uncertainties ,risk assessments could support

decision-making ,but other principles, measures and instruments are also

required.++

Alone in considering probabilities, important uncertainty aspects are easily

truncated.it means that potential suprises could be left unconsidered.


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To put a whole view in the nutshell, we

should mention that, the uncertainty issue inrisk assessments is so challenging

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Ramin_Shamshiri_Risk_Analysis_Lecture.ppt