Module Details

Module Code: INTR9021
Title: IT in AEC Industry 4.0
Long Title: IT in AEC Industry 4.0
NFQ Level: Expert
Valid From: Semester 2 - 2020/21 ( January 2021 )
Duration: 1 Semester
Credits: 10
Field of Study: 5213 - Interdisciplinary Engineering
Module Delivered in: 2 programme(s)
Module Description: AEC has evolved to incorporate significant capabilities from other disciplines such as ICT. This module will extend learners' knowledge in the field of new technologies and methods (e.g. The Internet of Things, Big Data, Immersive Technology, AR, VR, concepts of AI and Digital Twins, Data Security). Learners will use digital technologies such as Object Oriented Programming (OOP), software engineering using Unified Modelling Language (UML) and VR to leverage data toward higher performance through automatisation and the improvement of workflows in project environments.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Discuss key terms and definitions associated with Big Data, Internet of Things, Digital Twins, Augmented and Virtual Reality, and Data Security in the AEC sector appraising main benefits.
LO2 Formulate and assess cases utilising appropriate data sets, maths and logic.
LO3 Execute and appraise basic workflows in OOP through the use of existing variables, functions, code blocks and available libraries.
LO4 Execute and appraise individually and as a team application requirements to a problem using Unified Modelling Languages and software engineering methodologies.
LO5 Apply and evaluate new and immersive technologies in leveraging BIM data relevant to visualising the built environment and associated assets.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Artificial Intelligence
Concepts of AI in engineering including : Machine Learning, Deep Learning, Neural Networks and applications in AEC sector.
Big Data
Concepts of structured, semi-structured and unstructured data. Principles of Data Mining. Predictive modelling and other advanced analytics applications.
Immersive Technology; Augmented Reality, Virtual Reality.
Concepts and case studies for applications of AR and VR in the AEC sector.
Internet of Things (IoT) in AEC
The Internet of Things (IoT) - use of unique identifiers for physical assets enabling connections to networks for data transfer to and from relevant assets. Three different visions of IoT are addressed :things oriented vision, internet-oriented vision, semantic oriented vision.
Digital Twins
Digital Twin Technology and concepts for the AEC industry. Focusing on connectivity, homogenization, reprogrammable and smart, digital traces and modularity.
Data Security
Concepts and standards for Data Security relevant to AEC projects and organisations.
Software Engineering
Concepts of software engineering and Unified Modelling Language, requirements elicitation, system architecture design, requirements traceability, development, test, field trials, software development methodologies, relevant standards and practices.
Object Oriented Programming (OOP)
OOP using Dynamo interface to Python, nodes, libraries, code blocks, wires, data, strings and logic.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Presentation % of Total Mark 20
Timing Week 5 Learning Outcomes 1,5
Assessment Description
Technical presentation discussing and evaluating immersive technologies and industry case studies leveraging BIM data relevant to visualising built environments and assets.
Assessment Type Essay % of Total Mark 30
Timing Week 8 Learning Outcomes 1,5
Assessment Description
Students will develop a technical report addressing an application of AEC industry 4.0 technologies such as Big Data, Internet of Things, Digital Twins, Augmented and Virtual Reality, and Data Security. An evaluation and critique of positive and negative impacts on organisations and projects will also be developed by the student.
Assessment Type Project % of Total Mark 50
Timing Week 13 Learning Outcomes 2,3,4,5
Assessment Description
Develop, test and evaluate, individually and as a team, existing/potential future applications for problem solving using digital technology with respect to: Software Engineering; Object Oriented Programming, VR and AR.
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Delivery of module content Every Week 2.00 2
Lab Contact Computer Laboratory Every Second Week 1.00 2
Lecturer-Supervised Learning (Contact) Contact Collaborative group activity Every Second Week 1.00 2
Independent Learning Non Contact Revision of lecture material; Self directed learning; Completion of assignments. Every Week 10.00 10
Total Hours 16.00
Total Weekly Learner Workload 14.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Delivery of module content. Every Week 2.00 2
Lab Contact Computer Laboratory Every Second Week 1.00 2
Independent Learning Non Contact Revision of lecture material; Self directed learning; Completion of assignments. Every Week 10.00 10
Lecturer-Supervised Learning (Contact) Contact Collaborative group activites Every Second Week 1.00 2
Total Hours 16.00
Total Weekly Learner Workload 14.00
Total Weekly Contact Hours 4.00
 
Module Resources
Recommended Book Resources
  • Gebrail Bekda, Sinan Melih Nigdeli, Melda Yücel. (2019), Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering, IGI Global, [ISBN: 1799803015].
  • Ajibade A. Aibinu, Fernando Koch, S. Thomas Ng. (2019), Data analytics and big data in construction project and asset management, Emerald Publishing, [ISBN: 1839093080].
  • Steve Aukstakalnis. (2016), Practical Augmented Reality: A Guide to the Technologies, Applications, and Human Factors for AR and VR, Addison-Wesley Professional, [ISBN: 0134094239].
  • Uttam Ghosh, Danda B Rawat, Raja Datta, Al-Sakib Khan Pathan. (2021), Internet of Things and Secure Smart Environments: Successes and Pitfalls, Chapman and Hall/CRC, [ISBN: 978-036726639].
  • Dan Raker. (2020), Infrastructure Digital Twins: A Leadership Short Course 1: Getting to know iTwins, Bentley, [ISBN: ASIN: B08DF7D].
  • D. Jeya Mala. (2019), Integrating the Internet of Things Into Software Engineering Practices, IGI Global, [ISBN: 1522586210].
Recommended Article/Paper Resources
  • International Organization for Standardization. (2018), Organization and digitization of information about buildings and civil engineering works, including building information modelling (BIM) — Information management using building information modelling — Part 1,2,3,5.
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_CBIMD_9 Master of Science in Building Information Modelling and Digital AEC 1 Mandatory
CR_CABIM_9 Postgraduate Diploma in Science in Applied Building Information Modelling and Digital AEC 1 Mandatory