ENE5044 · AI applications for civil and environmental engineers

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.

Areas of study: E6014 Master of Engineering - Specialisation: Civil engineering E6017 Master of Advanced Engineering - Specialisation: Smart Manufacturing Engineering

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Continuous assessment: 100% The assessments of this unit are designed to demonstrate the achievement of the advanced learning outcomes and standards expected of Master’s level coursework.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

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

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.

Student experience, not official rules. Nothing here changes what the Handbook says.