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Unit outline_

BADP2001: Algorithmic Architecture

Semester 2, 2024 [Normal day] - Camperdown/Darlington, Sydney

This unit introduces a set of principles and skills in algorithmic architecture. Through a series of parametric design exercises, modelling is construed as an explicit formulation of architectural design problem and opportunities. This includes defining design logic and parameter as well as converting data into meaningful information for design analysis and synthesis. The parametric model's performance will be contested by how well it delivers the design intentions and ventures new design opportunities. Students will be exposed to various computational design methods to develop their understanding of the basic principles in architectural computing.

Unit details and rules

Academic unit Architecture
Credit points 6
Prerequisites
? 
None
Corequisites
? 
BAEN2002
Prohibitions
? 
None
Assumed knowledge
? 

Basic skills in 3D modelling

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Rizal Muslimin, rizal.muslimin@sydney.edu.au
Lecturer(s) Rizal Muslimin, rizal.muslimin@sydney.edu.au
Tutor(s) Sarah Yap, sarah.yap@sydney.edu.au
Victor Martinez Contreras, victor.martinezcontreras@sydney.edu.au
Anirudha Agara, anirudha.agara@sydney.edu.au
Michelle Lee, michelle.lee1@sydney.edu.au
The census date for this unit availability is 2 September 2024
Type Description Weight Due Length
Assignment Initial exercises
Report
10% Week 04
Due date: 22 Aug 2024 at 09:00
3 weeks
Outcomes assessed: LO1 LO2
Assignment Parametric Modeling - Preliminary
Report and script file
40% Week 09
Due date: 26 Sep 2024 at 09:00
4 weeks
Outcomes assessed: LO1 LO3 LO2
Presentation Parametric Modeling - Final
Final presentation
50% Week 13
Due date: 31 Oct 2024 at 09:00
3 hours
Outcomes assessed: LO1 LO4 LO3 LO2

Assessment summary

1. Initial exercises: Students will submit early exercises on parametric modeling.

2. Parametric modeling – Preliminary: Students will submit a parametric model of their case building and its parametric variations with a clear schema and coherent presentation.

3. Parametric modeling – Final: Students will present a refined version of their parametric modeling script to analyze data input and generate a specific building component in more detail, with a well-structured presentation and data visualization.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

Work of outstanding quality, demonstrating mastery of the learning outcomes assessed. The work shows significant innovation, experimentation, critical analysis, synthesis, insight, creativity, and/or exceptional skill.

Distinction

75 - 84

Work of excellent quality, demonstrating a sound grasp of the learning outcomes assessed. The work shows innovation, experimentation, critical analysis, synthesis, insight, creativity, and/or superior skill.

Credit

65 - 74

Work of good quality, demonstrating more than satisfactory achievement of the learning outcomes assessed, or work of excellent quality for a majority of the learning outcomes assessed.

Pass

50 - 64

Work demonstrating satisfactory achievement of the learning outcomes assessed.

Fail

0 - 49

Work that does not demonstrate satisfactory achievement of one or more of the learning outcomes assessed.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

WK Topic Learning activity Learning outcomes
Week 01 Introduction to algorithmic architecture Lecture (1 hr) LO1
Introduction to parametric modeling Tutorial (2 hr) LO2
Week 02 Design versioning Lecture (1 hr) LO1 LO2
List management Tutorial (2 hr) LO1 LO2
Week 03 Design Conditioning 1 Lecture (1 hr) LO1 LO2
Conditional Statement 2 Tutorial (2 hr) LO1 LO2
Week 04 Design Conditioning 2 Lecture (1 hr) LO1 LO2
Conditional Statement 2 Tutorial (2 hr) LO1 LO2
Week 05 Parametric Model Planning Lecture (1 hr) LO1 LO2
Optimization Tutorial (2 hr) LO1 LO2
Week 06 Design Data 1 Lecture (1 hr) LO3
Data Structure 1 Tutorial (2 hr) LO3
Week 07 Design Data 2 Lecture (1 hr) LO3
Data Structure 2 Tutorial (2 hr) LO3
Week 08 Project Proposal Lecture (1 hr) LO2 LO3
Parametric Modeling Review Tutorial (2 hr) LO1 LO2 LO3
Week 09 Performative Design 1 Lecture (1 hr) LO2 LO3
Building performance analysis Tutorial (2 hr) LO2 LO3
Week 10 Performative Design 2 Lecture (1 hr) LO2 LO3
Progress Review Tutorial (2 hr) LO2 LO3
Week 11 Performative Design 3 Lecture (1 hr) LO3
Progress Review Tutorial (2 hr) LO4
Week 12 Review Lecture (1 hr) LO1 LO4
Progress Review Tutorial (2 hr) LO1 LO2
Week 13 Final Presentation Presentation (3 hr) LO1 LO2 LO3 LO4

Attendance and class requirements

Attendance: Students are expected to attend a minimum of 90% of
timetabled activities on time, unless granted exemption by the Head of School and Dean, Associate Dean Education or relevant Unit Coordinator.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

Please refers to the references on Canvas page.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. formulate architectural design logics and express them through parametric modelling techniques
  • LO2. develop a parametric model to simulate variations and the relationship between design variables
  • LO3. make effective use of quantitative data in the parametric design
  • LO4. communicate design ideas or problems algorithmically through parametric visualisation strategies.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9
National Standard of Competency for Architects -
Competency code Taught, Practiced or Assessed Competency standard
3.5 T P A Exploration and application of ordering, sequencing and modelling of three-dimensional form and spatial content.
3.8 T P A Application of manual and digital graphic techniques and modelling to describe three-dimensional form and spatial relationships.
4.1 T P A Evaluation of design options in relation to project requirements.
4.2 T P A Evaluation of design options against values of physical, environmental and cultural contexts.

This section outlines changes made to this unit following staff and student reviews.

The outline has been adjusted to the sem 2 - 2024 schedule.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.