ENE5044 AI applications for civil and environmental engineers
Faculty of Engineering
ENE5044 AI applications for civil and environmental engineers is a level 5, 6-credit-point, postgraduate unit from the Faculty of Engineering, offered in 2027 in Semester 2 at Clayton. It has no prerequisites.
- Credit points
- 6
- Offered in 2027
- Semester 2
- Clayton
- Assessment
- No exam
- 4 tasks
- Workload
- 144 hours
- per semester
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Requisites
Before ENE5044
Prohibitions
You can't enrol if you have passed any of these.
After ENE5044
No unit lists ENE5044 as a prerequisite in the 2027 handbook.
Enrolment rules
You must be currently enrolled in a master’s course in engineering or related STEM and in a related discipline. The unit assumes you have the necessary foundational knowledge.
Equivalent units
The same content under another code. Only one of them counts.
Overview
This unit explores integrating artificial intelligence (AI) and machine learning (ML) technologies within engineering, including the civil and environmental sectors, focusing on their application for big data management and harnessing the power of industry-collected data. It will focus on leveraging cutting-edge technology to address current challenges, safeguard future sustainability, unravel potential risks, and develop intelligent systems to navigate the complexities of tomorrow's environmental landscape.
The unit aims to equip you with the necessary industry-applicable skills and knowledge to apply AI and ML in addressing critical issues within the civil and environmental business significantly enhancing your job-market readiness and employability in the era of AI. The blend of theoretical understanding, practical application, and ethical considerations ensures a comprehensive learning experience. You will apply your skills to real-world challenges and data brought to you by industry mentors who will guide you in exploring the entire process of delivering an industry-relevant project.
Offerings in 2027
| Teaching period | Campus | Mode |
|---|---|---|
| Second semester | Clayton | On campus |
Assessment
- QuizzesQuiz / Test20%
- AssignmentWritten40%
- Final project reportExercise20%
- Oral presentationExercise20%
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Learning outcomes
When you finish this unit, you should be able to:
- 1
Define and justify the principles and methodologies of AI and ML for civil and environmental sectors.
- 2
Apply AI techniques and work with ML algorithms to manage big data and explore solutions for real-world problems in these sectors.
- 3
Evaluate social biases, risks and ethical considerations associated with AI implementations.
- 4
Critically reflect on digital innovation, staff engagement, reliable instrumentation, training and business cases.
- 5
Conduct independent research on adoption barriers and present findings on AI applications in engineering businesses.
Workload and teaching
- Practical activities24 hours
- Workshops24 hours
- Teaching approachActive learning
The minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of 3-6 hours of scheduled learning activities and 6-9 hours of independent study per week. Scheduled activities may include a combination of teacher-directed learning, peer-directed learning and online engagement. Independent study may include associated readings, assessment and preparation for scheduled activities.
Contacts
- Unit Coordinators
- Dr Arash Zamyadi
- Chief Examiners
- Dr Arash Zamyadi
Common questions
What are the prerequisites for ENE5044?
ENE5044 has no prerequisites, but enrolment rules apply.
When is ENE5044 offered?
In 2027, ENE5044 runs in Semester 2 at Clayton.
How much work is ENE5044?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does ENE5044 have an exam?
No. ENE5044 has 4 assessment tasks and no exam.