Subject Datasheet
Completion requirements
Subject Datasheet
Download PDFI. Subject Specification
1. Basic Data
1.1 Title
Engineering Risk Assessment
1.2 Code
BMEEOHSMSFST05-00
1.3 Type
Module with associated contact hours
1.4 Contact hours
| Type | Hours/week / (days) |
| Lecture | 1 |
| Seminar | 1 |
1.5 Evaluation
Midterm grade
1.6 Credits
3
1.7 Coordinator
| name | Dr. Vigh László Gergely |
| academic rank | Associate professor |
| vigh.laszlo.gergely@emk.bme.hu |
1.8 Department
Department of Structural Engineering
1.9 Website
1.10 Language of instruction
hungarian
1.11 Curriculum requirements
Compulsory in the Structural Engineering (MSc) programme
1.12 Prerequisites
1.13 Effective date
1 September 2025
2. Objectives and learning outcomes
2.1 Objectives
The objective of the course is that the student shall understand and be aware of the principles and basis of practical methods of engineering risk assessment and analysis, and their application especially to extreme actions (earthquake, fire, extreme snow, blast load, tornado, etc.) It includes the fields of extreme effects, statistics, probability theory, reliability analysis, numerical methods, risk analysis and optimization. It also serves as the basis of the subsequent MSc subjects on modelling, design and programming.
The aim is that during the semester, students will acquire a complex knowledge of engineering risk assessment at a level that will allow them to present this competence as an element of their portfolio.
2.2 Learning outcomes
Upon successful completion of this subject, the student:
A. Knowledge
1. is aware of the principles and basic terms of statistics and probability theory, knows the basic statistical analysis and assessment methods,
2. is aware of the uncertainties in engineering problems, the distribution functions that are typical in civil engineering problems, and the model development methods,
3. is aware of the terms of failure probability and reliability index, the principles of basic reliability analysis methods (FORM, SORM and Monte Carlo analysis),
4. is aware of the definition of risk, principles of risk analysis and decision making analysis,
5. understands the objective function of optimization, can distinguish local and global optimum, and is aware of the principles of the most important classic optimization techniques,
6. is aware of extreme actions and the corresponding design methods,
7. is aware of the risk assessment methods applicable to extreme effects,
8. knows reliability background of Eurocode standards, and their provisions and design methods.
B. Skills
1. applies the statistical and analysis methods for assessment of measuring results,
2. is able to develop models,
3. solves simple reliability problems by FORM and Monte Carlo methods using specific softwares,
4. computes risk on the basis simple logic tree,
5. applies provisions and design methods of Eurocode standards,
6. is able to complete optimization in practice,
7. applies risk analysis for extreme effects,
8. is able to present his/her results in proper written form,
C. Attitudes
1. follows the lectures, makes effort to understand the study material,
2. collaborates with the teacher in gaining knowledge,
3. is continuously gaining knowledge,
4. is open to the use of IT tools and equipments,
5. aims accuracy in his/her calculations/solutions,
D. Autonomy and Responsibility
1. is independent in problem statements and solutions,
2. aims understanding the complexity, comprehensiveness of the problems and recognizing the synergies.
2.3 Methods
Theoretical lectures and practical seminars are basically not separated, but are held in hybrid way. Theoretical parts emphasize the principles; rigorous mathematical derivation is not addressed. Practical parts illustrate the practical application of the methods, incorporating the use of specific practical tools. Active involvement in and communication during the lectures are expected, helping the understanding of the study material. Homeworks help strenghtening the skills, while control tests support in deepen the knowledge. The subject uses elements if Research Based Learning methods.
2.4 Course outline
1. Introduction.
2. Problem statement in engineering, model development.
3. Uncertainties in engineering problems.
4. Mechanical model, numerical analysis methods.
5-6. Basis of statistics and probability theory. Statistical analysis in practice.
7. Summary.
7-8. Methods of reliability analysis: practical use of FORM, SORM, Monte Carlo analysis.
9. Optimization. Basics of linear programming and gradient method.
10. Acceptable risk. Risk assessment, decision making.
11. Risk assessment methods.
12. Eurocode. Code evolution. Reliability background of Eurocodes.
13. Extreme effects. Earthquake. Extreme snow. Fire.
14. Extreme effects. Tornado. Blast load. Summary.
The above programme is tentative and subject to changes due to calendar variations and other reasons specific to the actual semester. Consult the effective detailed course schedule of the course on the subject website.
The above programme is tentative and subject to changes due to calendar variations and other reasons specific to the actual semester. Consult the effective detailed course schedule of the course on the subject website.
2.5 Study materials
a) Textbooks, literature:
Wilcox: Numerical methods for PDEs. Unit 2, 16.90 Computational Methods in Aerospace Engineering, MITOpenCourseware.
Hoffman – Frankel: Numerical methods for engineers and scientists. CRC Press, 2001.
Faber: Risk and safety in civil, environmental and geomatic engineering
Sorensen: Structural reliability theory and risk analysis
Lyons , R.G.: Understanding Digital Signal Processing . Prentice Hall, 2001.
Rao, S.R.: Engineering optimization – Theory and practice. Fourth Edition. Wiley, 2009.
b) Online materials:: materials uploaded to the web site of the subject, e.g.:
Lecture notes, electronic lecture notes,
slides of lectures and practices,
solved problems
midterm test samples with solution
2.6 Other information
0
2.7 Consultation
The instructors are available for consultation during their office hours, as advertised on the information system. Special appointments can be requested via e-mail. Consultation during lecture breaks is also available.
This Subject Datasheet is valid for:
2025/2026 semester II
II. Subject requirements
Assessment and evaluation of the learning outcomes
3.1 General rules
The achievements of this course will enable the student to demonstrate mastery of the structural engineering profession on completion of the full course, if the final mark is excellent (5).
The assessment of the learning outcomes specified in clause 2.2. above and the evaluation of student performance occurs via midterm stests, homework assignments and continuous performance assessments.
3.2 Assessment methods
| Assessment Name (Type) | Code | Assessed Learning Outcomes |
|---|---|---|
| Control Test #1 | of the progress presentations are accepted by the supervisor. | CT1 |
| Control Test #2 | CT2 | |
| Homework* | HW | |
| continuous performance assessment | A | |
| *Progress presentations are assigned to the homework; the actual schedule is announced on the web site of the subject. Criterion for final submission of the homework is that at least 50 |
The dates of deadlines of assignments/homework can be found in the detailed course schedule on the subject’s website.
3.3 Evaluation system
| Code | Weight |
|---|---|
| CT1 | 20% |
| CT2 | 20% |
| HW | 40% |
| A | 20% |
| Total | 100% |
3.4 Requirements and validity of signature
No signature can be obtained.
3.5 Grading system
| Grade | Score (P) |
|---|---|
| excellent (5) | 85≤P |
| good (4) | 70≤P<85% |
| satisfactory (3) | 55≤P<70% |
| pass (2) | 40≤P<55% |
| fail (1) | P<40% |
3.6 Retake and repeat
1. Late submission of homeworks – with penalty fee applied – is normally possible two weeks after the normal deadline. In case the normal deadline of a homework falls on the last week of the study period, the late submission deadline is the last day of the supplementary week, at 12:00. Schedule and details on the homework submissions can be found on the web site of the subject.
2. Each CT can be repeated (2nd attempt) during the supplementary week; the exact date and time of the repetition is announced on the web site of the subject. The new result overwrites the result of the 1st attempt.
3. “Continuous performance assessment” A cannot be repeated, cannot be substituted with other forms of activity.
3.7 Estimated workload
| Activity | Hours/Semester |
|---|---|
| contact hours | 14×2=28 |
| preparation for the lectures and | 12×0,5+2×8=22 |
| for continuous performance assessments | |
| preparation for the tests | 5 |
| homework | 35 |
3.8 Effective date
1 September 2025
This Subject Datasheet is valid for:
2025/2026 semester II