CIVL5266: Semester 1, 2022

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Units CIVL5266 Semester 1 2022 [Normal day]

Unit outline_

CIVL5266: Steel Structures - Stability

Semester 1, 2022 [Normal day] - Camperdown/Darlington, Sydney

Overview

This unit aims to: provide fundamental understanding at advanced level of the behaviour and design of hot-rolled/fabricated and cold-formed steel members; to provide fundamental understanding of newly developed Direct Design Method (DDM) for analysis and design of structural systems; and to develop an understanding of the behaviour and design of steel connections in open and hollow sections. It is anticipated that at the end of this unit of study students should be familiar with the behaviour of steel structures at advanced level in selected areas, including design for local buckling and design for flexural-torsional buckling of columns and beams; have a sound knowledge of AS 4100 in the areas of section capacity determination of slender cross-sections, and flexural-torsional buckling of beams; have a sound knowledge of AS/NZS 4600 in the areas of section capacity determination of slender cross-sections, and flexural-torsional buckling of columns and beams; have knowledge of the use of FEM software in the design of structural systems; have the skills to assess the behaviour of specific connections; have an appreciation of some practical aspects of economical steel connection design. This unit will examine stability theory, Stability design to AS4100 and AS/NZS4600, Direct Design Method, Steel connection design.

Unit details and rules

Academic unit	Civil Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	There are no prerequisites for this unit of study but it is assumed that students are competent in the content covered in Structural Mechanics, Steel Structures, and Structural Analysis
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Cao Hung Pham, caohung.pham@sydney.edu.au
Guest lecturer(s)	John Hewitt, john.hewitt@sydney.edu.au
Lecturer(s)	Cao Hung Pham, caohung.pham@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Take-home short release)	Final exam (Take-home short release) 180 minutes plus 10 minutes reading time. Buffer & upload time not included	55%	Formal exam period	3 hours
Outcomes assessed:
Assignment	Assignment Design assignment	20%	Multiple weeks	n/a
Outcomes assessed:
Assignment	Design project Design project	25%	Multiple weeks	n/a
Outcomes assessed:
= Type D final exam ?

Assessment summary

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
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

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.

Learning support

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	1. Introduction - flexural-torsional buckling; 2. Small and moderately large displacement theory for beams	Lecture and tutorial (4 hr)
Week 02	1. Bifurcation, Euler columns; 2. Flexural-torsional buckling theory, buckling of columns	Lecture and tutorial (4 hr)
Week 03	1. Flexural-torsional buckling of beams; 2. Design of columns and beams, AS4100 and AS/NZS4600	Lecture and tutorial (4 hr)
Week 04	1. Design examples for AS4100 and AS/NZS4600; 2. Revision - Flexural-torsional buckling	Lecture and tutorial (4 hr)
Week 05	1. Steel structures in fire; 2. Connections, revision of basic individual connector capacities	Lecture and tutorial (4 hr)
Week 06	1. Bolt group analysis; 2. Weld group analysis	Lecture and tutorial (4 hr)
Week 07	1. Web side plate connection; 2.Rigid bolted moment end plate connection	Lecture and tutorial (4 hr)
Week 08	1. Structural steel tubular and cold-formed connections; 2. Case study: steel gridshell design	Lecture and tutorial (4 hr)
Week 09	1. Direct design method, material nonlinear effects; 2. Design examples for advanced analysis	Lecture and tutorial (4 hr)
Week 10	1. Computer modelling of frames - elastic analysis; 2. Computer modelling of frames - 2nd order elastic analysis	Lecture and tutorial (4 hr)
Week 11	1. 2nd order, inelastic analysis - 1 (structural modelling); 2. 2nd order, inelastic analysis - 2 (specification)	Lecture and tutorial (4 hr)
Week 12	1. Design examples for advanced analysis	Lecture and tutorial (4 hr)
Week 13	1. Case studies and DDM project; 2. Revision	Lecture and tutorial (4 hr)

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

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.

Outcomes
Graduate qualities

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

LO1. communicate solutions of complex problems related to the advanced analysis and design of steel structures through well-prepared and well-documented reports
LO2. apply sound knowledge of AS 4100 in the areas of section capacity determination of slender cross-sections, and flexural-torsional buckling of beams
LO3. apply sound knowledge of AS/NZS 4600 in the areas of section capacity determination of slender cross-sections, and flexural-torsional buckling of columns and beams
LO4. understand the behaviour and design of steel connections in open and hollow sections
LO5. demonstrate familiarity with the behaviour of steel structures at advanced level in selected areas, including design for flexural-torsional buckling of columns and beams
LO6. demonstrate knowledge of the use of FEM software in the design of structural systems
LO7. understand the theory for determining structural stability.

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
Learning outcomes

Responding to student feedback

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

No changes have been made since this unit was last offered.

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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

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