Outline

Overview

For a practicing engineer, the ability to solve real-world problem relies not only on fundamental understanding of theory but also proficient skills to measure and analyse data. The measurement of data often introduces new complications and limitations that must be first understood before properly analysing and interpreting any result. In this unit, students will learn the essential knowledge and skills to measure, analyse, model, and visualize data for applications in civil and environmental engineering. This unit comprises lectures and related hands-on practices to learn and exercise skills in measurement, programming, and digital tools, with case studies tackling data analysis problems in civil and environmental engineering.

Unit details and rules

Academic unit	Civil Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Basic fundamental concepts common to any undergraduate civil and environmental engineering degree program. Familiarity with a programming language like Python, R, or MATLAB is preferred but not required.
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Michael Heisel, michael.heisel@sydney.edu.au
Lecturer(s)	Michael Heisel, michael.heisel@sydney.edu.au
Lecturer(s)	Jiaying Li, jiaying.li@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2025

Details
Criteria

Type	Description	Weight	Due	Length
Tutorial quiz	Mini quiz #1 in-class assessment	5%	Week 04	15 minutes
Outcomes assessed:
Tutorial quiz	Mini quiz #2 in-class assessment	5%	Week 07	15 minutes
Outcomes assessed:
Assignment	Lab report #1 Written report on wind tunnel lab practical.	25%	Week 08 Due date: 18 Apr 2025 at 23:59	multiple weeks
Outcomes assessed:
Tutorial quiz	Mini quiz #3 in-class assessment	5%	Week 09	15 minutes
Outcomes assessed:
Assignment	Lab report #2 Written report on field measurement practical.	25%	Week 11 Due date: 16 May 2025 at 23:59	multiple weeks
Outcomes assessed:
Presentation	Final project oral presentation Presentation to report methods and findings of data analysis project	35%	Week 13	15 minutes
Outcomes assessed:
= group assignment ? = AI allowed ?

Assessment summary

Tutorial quiz: Consisting of 5 multiple choice questions, these quizzes will test your understanding of material covered in that week's lecture. These quizzes need to be completed in-person at the beginning of the tutorial session.
Lab report: This assignment will require you to use the concepts learned in lectures and tutorials to prepare a written report based on the lab practicals.
Final presentation: The group presentation during the final week will require you to perform an analysis using the skills developed in the unit and communicate the results to your peers.

Assessment criteria

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.

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

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator.

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions.

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit.

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply.

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site.

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Late submission of the quizzes and presentation is prohibited. Late submission of lab reports will be deducted 10% of the maximum mark for each calendar day after the due date.

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.

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.

Support for students

The Support for Students Policy 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. 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

Weekly schedule

WK	Topic	Learning activity
Week 01	Unit introduction. Weekly lecture topics are tentative and subject to change.	Lecture and tutorial (4 hr)
Week 02	Fundamentals of physical measurements.	Lecture and tutorial (4 hr)
Week 03	Understand your measurements: sensor properties and sources of uncertainty.	Lecture and tutorial (4 hr)
Week 04	Understand your measurements: analog to digital conversion of signals.	Lecture and tutorial (4 hr)
Week 05	Analyse your measurements: signals in Fourier space.	Lecture and tutorial (4 hr)
Week 05	Laboratory practical acquiring force measurements in a wind tunnel. Sessions will take place in weeks 5 and 6.	Practical (2 hr)
Week 06	Analyse your measurements: signal processing and filtering.	Lecture and tutorial (4 hr)
Week 07	Describe your data: samples, variables, and statistics.	Lecture and tutorial (4 hr)
Week 08	Understand your data: sample distribution, probability and uncertainty.	Lecture and tutorial (4 hr)
Week 08	Individual self-guided practical to acquire simple measurements in Sydney’s public spaces. Can be completed any time between weeks 1 and 8.	Individual study (3 hr)
Week 09	Correlation and multi-variable analysis.	Lecture and tutorial (4 hr)
Week 10	Modelling: linear regression model and machine learning.	Lecture and tutorial (4 hr)
Week 11	Spatial data analysis and visualization.	Lecture and tutorial (4 hr)
Week 12	Qualitative data acquisition and analysis.	Lecture and tutorial (4 hr)
Week 13	Final project oral presentations delivered in-class during lecture and tutorial periods.	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.

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

LO1. engage with common methods of measurement and data acquisition in the context of civil and environmental engineering.
LO2. apply digital signal processing tools to evaluate complex measurements which involve uncertainty, noise, and error.
LO3. develop and fluently apply advanced statistical analyses using programming software.
LO4. perform basic skills in correlation analysis and modelling.
LO5. conduct data visualisation for statistics, modelling, and spatial variation to interpret results and formulate reliable conclusions.
LO6. communicate effectively to an engineering audience through data visualisation, technical writing, and oral presentation.

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

Alignment with Competency standards

Outcomes	Competency standards
	Engineers Australia Curriculum Performance Indicators - 2.3. Meaningful engagement with current technical and professional practices and issues in the designated field. 5.4. Skills in the selection and application of appropriate engineering resources tools and techniques, appreciation of accuracy and limitations;. 5.6. Skills in the design and conduct of experiments and measurements.
	Engineers Australia Curriculum Performance Indicators - 5.4. Skills in the selection and application of appropriate engineering resources tools and techniques, appreciation of accuracy and limitations;. 5.8. Skills in recognising unsuccessful outcomes, sources of error, diagnosis, fault-finding and re-engineering. 5.9. Skills in documenting results, analysing credibility of outcomes, critical reflection, developing robust conclusions, reporting outcomes.
	Engineers Australia Curriculum Performance Indicators - 1.1. Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice. 5.4. Skills in the selection and application of appropriate engineering resources tools and techniques, appreciation of accuracy and limitations;.
	Engineers Australia Curriculum Performance Indicators - 1.1. Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice. 2.4. Advanced knowledge and capability development in one or more specialist areas through engagement with: (a) specific body of knowledge and emerging developments and (b) problems and situations of significant technical complexity. 5.5. Skills in the development and application of mathematical, physical and conceptual models, understanding of applicability and shortcomings.
	Engineers Australia Curriculum Performance Indicators - 2.3. Meaningful engagement with current technical and professional practices and issues in the designated field. 3.2. Information literacy and the ability to manage information and documentation. 5.9. Skills in documenting results, analysing credibility of outcomes, critical reflection, developing robust conclusions, reporting outcomes.
	Engineers Australia Curriculum Performance Indicators - 3.1. An ability to communicate with the engineering team and the community at large. 3.2. Information literacy and the ability to manage information and documentation. 3.6. An ability to function as an individual and as a team leader and member in multi-disciplinary and multi-cultural teams.

Engineers Australia Curriculum Performance Indicators -

Competency code	Taught, Practiced or Assessed	Competency standard
1.1		Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice.
2.3		Meaningful engagement with current technical and professional practices and issues in the designated field.
2.4		Advanced knowledge and capability development in one or more specialist areas through engagement with: (a) specific body of knowledge and emerging developments and (b) problems and situations of significant technical complexity.
3.1		An ability to communicate with the engineering team and the community at large.
3.2		Information literacy and the ability to manage information and documentation.
3.6		An ability to function as an individual and as a team leader and member in multi-disciplinary and multi-cultural teams.
5.4		Skills in the selection and application of appropriate engineering resources tools and techniques, appreciation of accuracy and limitations;.
5.5		Skills in the development and application of mathematical, physical and conceptual models, understanding of applicability and shortcomings.
5.6		Skills in the design and conduct of experiments and measurements.
5.7		Proficiency in appropriate laboratory procedures; the use of test rigs, instrumentation and test equipment.
5.8		Skills in recognising unsuccessful outcomes, sources of error, diagnosis, fault-finding and re-engineering.
5.9		Skills in documenting results, analysing credibility of outcomes, critical reflection, developing robust conclusions, reporting outcomes.

Responding to student feedback

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

This is the first time this unit has been 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

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

CIVL6002: Measurement and Data in Civil Engineering

Semester 1, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links