Departments' graduate courses
Course start and periodicity may vary. Please see details for each course for up-to-date information. The courses are managed and administered by the respective departments. For more information about the courses, how to sign up, and other practical issues, please contact the examiner or course contact to be found in the course information.
Probabilistic Risk Analysis (PRA)
- Course code: FBOM095
- Course higher education credits: 5.0
- Department: ARCHITECTURE AND CIVIL ENGINEERING
- Graduate school: Civil and Environmental Engineering
- Course is normally given: Concact Lars Rosén, email@example.com
- Language: The course will be given in English
Risk assessment has become an increasingly important part of civil and
environmental engineering. It has a wide range of applications, e.g. in
transportation, remediation of contaminated land, protection of drinking water
resources, ground stability, and tunneling. Risk assessment not only includes
the estimation of risk levels, but also provides a basis for decisions
regarding what are the most relevant actions to be taken to reduce and control
risks. In some cases, risk assessment is also part of the regulatory framework
and required as a basis for design, construction and performance of technical
Risk assessment is an inherently complex and multi-dimensional
concept, since it involves the combination of probability and consequence of
unwanted events as well as the evaluation of risk with respect to risk
tolerability and alternative actions to reduce and control risks. Probabilistic
risk assessment (PRA) is a quantitative concept and thus involves
quantification of probabilities and consequences. A major issue in PRA is the
understanding of uncertainty and how it can be modeled mathematically using
In this doctoral course we will:
The course is a mixture of
lectures by Tommy Norberg and Lars Rosén, seminars, and a project assignment.
The students are encouraged to work on their own projects.
- Present the basic concepts of PRA and uncertainty modeling.
- Present Bayesian and classical statistical inference methods.
- Apply probabilistic methods for modeling uncertainty.
- Apply logic tree models (fault trees, event trees, decision trees).
- Apply Monte Carlo simulation.
- Present methods for expert judgments.
- Work on project assignments.
- Present our findings at a seminar.
Bedford, T., Cooke, R. 2001. Probabilistic Risk
Analysis. Cambridge University Press.
Senior Lecturer Tommy Norberg, former on the department of Mathematical Science,
Senior Lecturer Jenny Norrman, division of GeoEngineering,
Professor Lars Rosén, division of GeoEngineering,
Contact Lars Rosén, firstname.lastname@example.org