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EG551D: ADVANCED METHODS FOR RISK AND RELIABILITY ASSESSMENT (2020-2021)

Last modified: 05 Aug 2021 13:04


Course Overview

The aim of this course is to understand and be able to carry out probabilistic modelling of uncertainty in engineering components and systems. Students will be able to obtain a good knowledge and understanding on random variables in probabilistic analysis and be able to carry out approximation and numerical schemes on components and systems.

Course Details

Study Type Postgraduate Level 5
Session Second Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus Aberdeen Sustained Study No
Co-ordinators
  • Dr Srinivas Sriramula

What courses & programmes must have been taken before this course?

What other courses must be taken with this course?

None.

Are there a limited number of places available?

No

Course Description

The course focuses on advanced modelling of random variables. All 3 levels of modelling are discussed with

emphasis on FORM and simulations. Extensive tutorials are provided to help students to obtain a good understanding of the subject.

The following modules will be covered in the course:

1. Convolution integral

2. First-order reliability methods (FORM).

3. FORM for non-normal variables.

4. Correlated variables.

5. Monte Carlo simulation techniques.

6. Safety factors in engineering design.

7. Introduction to systems analysis.


In light of Covid-19 and the move to blended learning delivery the assessment information advertised for second half-session courses may be subject to change. All updates for second-half session courses will be actioned in advance of the second half-session teaching starting. Please check back regularly for updates.

Summative Assessments

Assessment 1: Open Book exam (50%)

Assessment 2: Software based assessment (20%)

Assessment 3: Open Book exam (30%)

 

Resit:
Re-sit of only the failed assessment component(s)

Formative Assessment

There are no assessments for this course.

Course Learning Outcomes

Knowledge LevelThinking SkillOutcome
FactualRememberILO’s for this course are available in the course guide.

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