Quantity Surveying, Artificial Intelligence and Machine Learning

Unit code: NCM6003 | Study level: Postgraduate
12
(Generally, 1 credit = 10 hours of classes and independent study.)
Footscray Park
N/A
Overview
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Overview

This unit of study explores advanced methodologies in quantity surveying, with a specific focus on Artificial Intelligence (AI) and Machine Learning (ML) technologies. It addresses the critical role of quantity surveyors in measuring and estimating costs in building and civil engineering projects while advocating for and implementing sustainable practices to reduce embodied emissions. The curriculum integrates theoretical knowledge with work-integrated learning (WIL) of practical AI applications to optimise construction processes, enhance decision-making, and promote sustainability in the construction sector. Students will learn to apply AI and ML techniques to analyse large datasets, predict project costs, and optimise resource allocation. The unit helps prepares future construction managers to lead innovative and efficient projects, leveraging advanced technologies for improved accuracy, efficiency, and sustainability in construction management.

Learning Outcomes

On successful completion of this unit, students will be able to:

  1. Analyse large datasets with AI and ML techniques to monitor and predict project costs, optimise resource allocation, and improve decision-making processes in complex construction projects;
  2. Implement AI and ML solutions to achieve environmental, social, and governance (ESG) goals in construction projects;
  3. Collaborate effectively using AI and ML insights to improve project workflows and decision-making within construction teams; and
  4. Articulate the implications and benefits of AI and ML strategies clearly to a range of stakeholders, promoting understanding and support for innovative and sustainable construction practices.

Assessment

For Melbourne campuses

Assessment type: Assignment
|
Grade: 20%
Oncost planning and forecasting using parametric modelling and AI (Individual)
Assessment type: Report
|
Grade: 20%
ESG and application of AI and ML (Individual)
Assessment type: Case Study
|
Grade: 40%
Building/civil engineering emissions assessment and value engineering (Group)
Assessment type: Presentation
|
Grade: 20%
Presentation of Case Study findings (Group)

Required reading

Required readings will be made available on VU Collaborate.

As part of a course

This unit is studied as part of the following course(s):

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