Module manager: Dr Amirul Khan
Email: A.Khan@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2026/27
Admission to MSc programmes in the School of Civil Engineering
This module is not approved as an Elective
In this module, students learn about optimisation techniques and how to apply them to engineering problems and, in particular, structures and structural systems. The module focuses on formulating and solving optimisation problems using modern numerical methods and software tools. Topics include constrained and unconstrained optimisation, multi-objective problems, genetic algorithms, design sensitivity analysis, and robust design.
The objectives of this module are:
- To acquire a comprehensive understanding of the scientific principles behind design optimisation.
- To learn to formulate design optimisation problems as systematic design improvements.
- To be able to select and apply appropriate optimisation methods and software tools.
- To understand, compare and evaluate different optimisation techniques and their limitations.
- To learn how to interpret and evaluate optimisation results.
On successful completion of the module, students will have demonstrated the following learning outcomes (AHEP4 learning outcomes between brackets):
1. Apply a comprehensive knowledge of mathematics, natural science and engineering principles to the solution of optimisation problems in structural engineering. Much of the knowledge will be at the forefront of structural design optimisation and informed by an awareness of new developments and the wider context of engineering (M1).
2. Formulate and analyse complex problems to reach substantiated conclusions. This will involve evaluating available data and using engineering judgment to work with information that may be uncertain or incomplete, discussing the limitations of the techniques employed (M2).
3. Select and apply appropriate computational and analytical techniques to model complex problems of structural design optimisation, discussing the limitations of the techniques employed (M3).
On successful completion of the module students will be able to:
Academic
a. The ability to plan time, prioritise tasks and organise academic and personal commitments effectively
b. An ability to extract and evaluate pertinent data and to apply engineering analysis techniques in the solution of optimisation problems.
Digital
c. The ability to find, evaluate, organise and share information across a variety of formats, ensuring the reliability and integrity both of the sources used.
d. The ability to use digital technology and techniques to create digital items and the willingness to engage with new practices and perspectives to solve problems, make decisions and answer questions.
Enterprise
e. The ability to search for, evaluate and use appropriate and relevant information sources to help strengthen the quality of academic work and independent research.
Sustainability Skills
f. Recognises and understands relationships; analyses complex systems; considers how systems are embedded within different domains and scales; deals with uncertainty; uses analytical thinking
g. Understands and evaluates multiple outcomes; their own visions for the future; applies the precautionary principle; assesses the consequences of actions; deals with risks and changes; uses scenario planning
Work ready
h. The ability to prioritise, work efficiently and productively and to manage your time well in order to meet deadlines.
i. The ability to take a logical approach to solving problems; resolving issues by tackling from different angles, using numerical skills. The ability to understand, interpret, analyse and manipulate numerical data.
j. The ability to take a logical approach to solving problems; resolving issues by tackling from different angles. The ability to understand, interpret, analyse and manipulate numerical data.
k. The ability to gather information from a range of sources, analyse, and interpret data to aid understanding and anticipate problems. To use reasoning and judgement to identify needs, make decisions, solve problems, and respond with actions.
- Formulation of optimisation problems as nonlinear mathematical programming problems. Choice of design variables and the objective function. Formulation of typical constraints.
- Classification of design optimisation problems. Constrained and unconstrained problems. Global and local optima. Multi-objective problems.
- Numerical optimisation techniques. Local and global one-dimensional optimisation. Penalty methods. Linear programming. General constrained optimisation techniques. Random search, genetic algorithms.
- Approximation techniques.
- Design sensitivity analysis based on the finite element modelling of structural behaviour. Analytical, semi-analytical, and finite difference techniques.
- The relationships between fully-stressed and minimum weight structures. Topology, shape and sizing optimisation.
- Structural identification problems: finite element model identification, material parameter identification, structural damage recognition.
Methods of assessment
The assessment details for this module will be provided at the start of the academic year
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lectures | 11 | 2 | 22 |
| Tutorials | 11 | 1 | 11 |
| Private study hours | 117 | ||
| Total Contact hours | 33 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Progress will be monitored in the tutorial periods and feedback returned to students on their work during the tutorials. The return of the graded problem sheets and solutions will also include feedback.
Check the module area in Minerva for your reading list
Last updated: 30/04/2026
Errors, omissions, failed links etc should be notified to the Catalogue Team