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Unit of study_

BADP2001: Algorithmic Architecture

Semester 2, 2020 [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

Unit code BADP2001
Academic unit Architecture
Credit points 6
Prohibitions
? 
None
Prerequisites
? 
None
Corequisites
? 
BAEN2002 and (BDES2013 or BADP2004)
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
Type Description Weight Due Length
Assignment Parametric Modeling (Progress)
Online Presentation
5% Week 03
Due date: 10 Sep 2020 at 09:00
2 weeks
Outcomes assessed: LO1
Assignment Parametric modelling (Final)
Report, script file, and online presentation
35% Week 06
Due date: 01 Oct 2020 at 09:00
5 weeks
Outcomes assessed: LO1 LO2
Assignment Design Tooling (Proposal)
Online Presentation
10% Week 09
Due date: 29 Oct 2020 at 09:00
2 weeks
Outcomes assessed: LO3 LO4
Assignment Design tooling (Final)
Report, script file, and online presentation
50% Week 12
Due date: 26 Nov 2020 at 09:00
5 weeks
Outcomes assessed: LO1 LO2 LO3 LO4

Assessment summary

1. Parametric modeling (Progress): Students will present an existing building as a case study and a step-by-step diagram to model the building parametrically.

2. Parametric modeling (Final): Students will present a final parametric model of their case study and its parametric variations with explicit schema and coherent presentation.

3. Design tooling (Proposal): Students will present a specific design problem with its significance and propose an idea for a parametric design tool to solve such a problem. The proposal will include a precedent analysis and modeling diagram of how the tool solves the problem with a given data.

4. Design tooling (Final): Students will demonstrate how their parametric design tool analyzes design data and improves a specific design problem with well-structured presentation and data visualization strategy.

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 sydney.edu.au/students/guide-to-grades.

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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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.

WK Topic Learning activity Learning outcomes
Week 01 Introduction to algorithmic architecture Lecture (1 hr)  
Introduction to parametric modeling Tutorial (2 hr)  
Week 02 Design versioning Lecture (1 hr)  
List management Tutorial (2 hr)  
Week 03 Progress Review Presentation (3 hr) LO1
Week 04 Design Conditioning Lecture (1 hr)  
Conditional Statement Tutorial (2 hr)  
Week 05 Data Structure Lecture (1 hr)  
Data Tree Tutorial (2 hr)  
Week 06 Parametric Modeling Presentation (3 hr) LO1 LO2
Week 07 Review and project proposal Lecture (1 hr)  
Environmental Analysis Tutorial (2 hr)  
Week 08 Performative Design Lecture (1 hr)  
Environmental Synthesis Tutorial (2 hr)  
Week 09 Proposal Review Presentation (3 hr) LO3 LO4
Week 10 Algorithmic architecture in practice 1 Lecture (1 hr)  
Progress Review 1 Tutorial (2 hr)  
Week 11 Algorithmic architecture in practice 2 Lecture (1 hr)  
Progress review 2 Tutorial (3 hr)  
Week 12 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.

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

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

To manage students' workload distribution, the weight for assessment 1 has been reduced, and the assessment 3 submission has been split into two: (a) final presentation in week 13; and (b) portfolio submission in week 14.

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.