| Unit name | Artificial Intelligence Applications in Structures and Materials |
|---|---|
| Unit code | CADEM0019 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Dr. Elsaied |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
CADE30001 Advanced Structures and Materials or equivalent Working knowledge of programming |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Civil, Aerospace and Design Engineering |
| Faculty | Faculty of Engineering |
Why is this unit important?
Artificial Intelligence (AI) applications are pivotal in modern engineering, bridging the gap between advanced computational techniques and the design of novel structures and materials. As industries increasingly seek innovative solutions for designing, analysing, and optimizing complex structures and materials, the challenges associated with characterisation of experiments and simulations increase substantially. AI driven methods can be an effective approach to tackling these challenges.
How does this unit fit into your programme of study?
This unit builds on the students’ knowledge of material and structures and equips them with the knowledge to leverage AI algorithms, machine learning models, and data-driven approaches to enhance predictive accuracy and streamline material discovery. Skills gained in this unit are valuable for research and for industrial applications.
An overview of content
This unit provides a comprehensive introduction to machine learning (ML) algorithms, focusing on the strengths, weaknesses, and appropriate use cases of different approaches. Students will develop the ability to evaluate the accuracy and generalization of these algorithms. The course emphasizes hands-on implementation of both supervised and unsupervised ML algorithms using industry-relevant software libraries. Additionally, students will learn to develop surrogate models for structures and materials using real-world datasets, as well as the analysis of experimental data to extract insights into material and structural performance. The unit also introduces optimization algorithms and generative AI, demonstrating their applications in material and structural design.
How will students, personally, be different as a result of the unit
Upon completing this unit, the student will be able to tackle material and structure problems using data driven methods. Students will be able to develop a suitable modelling and/or analysis strategy, choose appropriate AI/ML technology, implement and evaluate the model’s performance based on accuracy and generalisation.
Learning Outcomes
On the successful completion of this unit, students will be able to:
The unit will be taught using a blend of synchronous and asynchronous activities, including lectures, guided computer labs, drop-in sessions, and self-directed exercises. A critical aspect of the unit's success is the practical experience gained in developing machine learning (ML) algorithms using Python, alongside the presentation and discussion of these algorithms during lectures.
Tasks which help you learn and prepare you for summative tasks (formative):
Weekly practical computer lab session on applied machine learning will be held to complement the taught lectures. Students will keep a portfolio of the tasks completed during the lab sessions, which will contribute towards the summative individual portfolio. Regular feedback will be given on the students’ portfolios throughout the term.
Tasks which count towards your unit mark (summative):
[100%] – individual portfolio (ILO 1, 2, 3, 4)
This portfolio will consist of the tasks completed during the lab sessions, and an individual unit project technical report on the application of machine learning techniques to structures and materials.
When assessment does not go to plan
The coursework and lab work are unchanged for re-assessment; students will revise and resubmit their work.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. CADEM0019).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
