Course syllabus
ACE405 Design and performance optimization in architecture Study Period 3, Spring 25 (10 ECTS)
Course is offered by the department of Architecture and Civil Engineering.
NB: if there are any discrepancies between this Syllabus page and the Course PM document, the Course PM document takes precedence.
“Modern, timber-clad preschool” as imagined by Generative AI (Adobe Firefly)
Contact details
Examiner
- Alexander Hollberg (AH), Associate Professor in Computational sustainable design, ACE, alexander.hollberg@chalmers.se, 073 856 77 59
Teacher
- Toivo Säwén (TS), PhD Candidate, SBE research area, ACE, sawen@chalmers.se
- Xinyue Wang (XW), PhD Candidate, SBE research area, ACE, xinyue.wang@chalmers.se
External tutors and lecturers
- Markus Gustafsson (MG), Architect, Kaminsky Arkitektur, markus@kaminsky.se
- Maja Lindborg (ML), Architect, Kaminsky Arkitektur, maja.l@kaminsky.se
- Anna Ulvehed (GPE), Project manager, Partillebo
Course aim
The aim of the course is to deepen the skills in using simulation tools for the integrated performance optimisation of an architectural design project. Performance criteria can include energy demand, daylight, and embodied carbon, among others. The skills are developed through a case study provided by the teachers. The design optimisation process will be based on and driven by knowledge gained using the simulation tools.
Schedule
NB: The schedule is subject to minor changes.
Course literature
Building Optimization, Max Tillberg, Klas Moberg, Chalmers School of Architecture, ARK415 Building Design Lab, 2018
Course design
The course is divided into two phases: Phase 1, Analysis methods and tools, and Phase 2, Design.
Phase 1: Analysis methods and tools
In Phase 1, students work in groups of three-four to investigate specific building performance phenomena and their impact on design. Each week is assigned to a specific analysis topic:
- Week 1: Holistic sustainability analysis and optimisation
- Week 2: Daylight and environment
- Week 3: Life cycle assessment
- Week 4: Energy and power
- Week 5: Multi-criteria decision making
After the introductory week with the course start, group formation, and study visit, Phase 1 follows a regular schedule each week (note the different schedule week 3 because of CHARM fair).
- Wednesday morning: introduction of the analysis method
- Wednesday afternoon: group work emphasising collecting literature, data and resources
- Thursday morning: computer tutorial using provided Heath analysis tool
- Thursday afternoon: group work emphasising analysis
- Friday morning: group work emphasising design implications
- Friday afternoon: digital pin-up and presentation (mandatory)
Phase 1 is concluded with a mid-critique when the analysis outputs are collected and presented during a 30 minute presentation as a framework for evaluating the design developed in Phase 2.
The assignments each week are described in the appendix to the "Course PM" document in Canvas.
Phase 2: Design
Students work in groups of three to four to develop a design proposal for the case study provided in the course. Each week follows the following approximate structure:
- Tuesday (all day): group work
- Wednesday (all day): group work
- Thursday morning: tutorial with TS and MG (30-45 mins per group)
- Thursday afternoon: group work
- Friday morning: group work
- Friday afternoon: group work/physical pin-up and presentation (mandatory, week 7)
Phase 2 is concluded with the final critique which includes a 45 minute presentation also including peer critique of one specific group.
Detailed instructions for what to present are provided in the appendix to the "Course PM" document in Canvas.
For any questions on the course design, please contact Toivo Säwén (sawen@chalmers.se). For questions regarding examination/mandatory elements, please contact Alexander Hollberg (alexander.hollberg@chalmers.se).
Changes made since the last occasion
- Removed option to use a previous project
- Combined course PM and design brief
- Groups organised by course
- Removed reading instructions for study week 1
- Introduction of individual design journal
- Removed written final hand-in
Learning objectives and syllabus
Learning objectives:
After completion of ACE405, students should be able to:
Knowledge and understanding
- demonstrate knowledge and understanding of the underlying methods for life cycle building performance assessment and multi-criteria design optimisation approaches
Competence and skills
- apply life cycle building performance simulation software in the early phases of the design process to answer specific design questions with a sustainability perspective
- structure and document their multi-criteria optimisation process of the design solution
- describe and evaluate design choices and their outcomes in terms of quantitative and qualitative criteria through several design iterations, using results from the simulation tools and simple architectural visualisations to support the argumentation
- present their proposal in a series of digital and physical hand-ins
Judgement and approach
- describe, argue for, evaluate, and discuss their own and others proposals during a final critique together with university faculty and external reviewers
Link to the syllabus on Studieportalen: Study plan
Examination form
To achieve a passing grade in the course, active participation in the following course elements (or, with a valid excuse, a written complementary assignment as agreed with AH) is required:
- Phase 1 weekly digital pin-ups
- Phase 1 mid-crit
- Phase 2 physical pin-up
- Phase 2 final critique
In case of a passing grade, the course participation is graded 3-5. The grading is carried out based on the following three assessment criteria:
- Multi-criteria design evaluation strategy presented in mid-critique (33%)
- Demonstrated understanding of links between evaluation criteria and design parameters in mid-critique and final critique (33%)
- Quality of design proposal in relation to the developed design evaluation framework (33%)
Use of Generative AI
The use of Generative AI (such as ChatGPT or Dall-E) is permitted during all elements of the course, and critical application of novel technologies is encouraged. However, any use of Generative AI technology, whether it is used to generate text, code, geometry, visual material, or any other application, is to be clearly indicated adjacent to the generated material, including the technology used and the extent of the application. Any use of Generative AI which does not comply with this requirement will be reported to the Chalmers’ Disciplinary Committee.