Course content

Safety is a principle requirement for engineering structures. This course aims to provide students the basic understanding, skills and tools for the evaluation of safety in structural and civil engineering and contains the following main topics: Practical application of probability theory – probabilistic representation of relevant engineering phenomena as loads and material resistances. Integration of new information in probabilistic modelling – Rule of Bayes (probability updating). Introduction to classical and structural reliability theory and its application to practical engineering problems. System Reliability. Structural Code Concepts, Code Calibration. Re-evaluation of the safety of existing structures. Aspects of quality control. Teaching and learning in the course will be aligned to practical structural and civil engineering problems in order to illustrate the theoretical content and demonstrate its relevance and applicability.

Learning outcome

General competence:

The course provides a fundamental understanding of safety and reliability in structural and civil engineering. The students will develop the ability to recognize and evaluate uncertainties in engineering problems and understand when such uncertainties must be explicitly considered. When simplified deterministic procedures are applied, students will learn to critically reflect on the implications and limitations of these simplifications. With this foundation, they will be able to ask the right questions in more advanced contexts and know when to consult experts or use specialized tools.

Knowledge:

Students will gain insight into the general safety requirements that apply to structures and infrastructure and learn how to assess whether these requirements are met in practice. The course provides the theoretical basis for understanding risk-based design principles and introduces the probabilistic concepts that underpin modern standards and regulations.

Skills:

Students will learn to:

• Formulate and apply simple probabilistic models representing relevant engineering phenomena.
• Extract and process new information from measurements and data, integrating it into existing probabilistic frameworks.
• Define adverse events (such as failure or malfunction) in terms of limit states and assess their probabilities.
• Analyse system effects and dependencies in structural reliability.
• Perform reliability-based calibration of design codes and evaluate safety margins.
• Approach advanced and exceptional engineering problems with a structured and quantitative mindset.

Digital competence:

Through hands-on exercises, students will apply programming tools (primarily Python) for solving practical reliability problems. They will gain experience with optimization, Monte Carlo simulation, iterative solution strategies, and basic object-oriented approaches. Emphasis is placed on using digital tools not only for computation but also for developing insight and visualizing uncertainty in engineering decision-making.

Sustainability competence:

A key outcome of the course is to enable students to design structures that are both safe and resource-efficient. Understanding the relationship between safety, economy, and environmental impact provides a foundation for making balanced, evidence-based decisions in line with sustainable development principles. By integrating risk-informed design and probabilistic thinking, students learn how safety and sustainability can reinforce each other in modern engineering practice.

Learning methods and activities

The course consists of lectures, exercises, and guided exercise sessions. The learning activities are closely linked to real problems in structural and civil engineering, for example through the discussion of safety aspects in typical mechanical problems previously encountered in courses such as Mechanics 1 and 2. Examples from real consulting projects will also be presented and discussed during lectures and exercises.

Several exercises build directly on topics from earlier mechanics courses and demonstrate how uncertainty and reliability can be addressed in practical engineering analysis. The exercises will introduce the use of Python as a tool for solving more demanding problems related to reliability and safety assessment.

Approved exercises are a prerequisite for participation in the final written examination.

Compulsory assignments

• Exercises

Recommended previous knowledge

TMA4245 Statistikk, TKT4116 Mekanikk 1, TKT4122 Mekanikk 2, TBA4125 Prosjektering, TKT4240 Bygningsmaterialer.

Course materials

Text book will be decided at the start of the course. Lecture notes, compendium.