Digital Construction Analytics & BIM

The area of Digital Construction and Building Information Modelling (BIM) is expanding in the construction industry, and new roles have been created recently which need special skills. The key special feature of this course refers to its structure as an integrated Digital Construction and Data Science course, enabling you to obtain advanced knowledge and analysis skills. Additionally, you will learn to apply and manage the technologies and the new business practices in the field of built environment for effective and data-driven decision making, especially from a sustainability perspective.

Students will develop skills in several areas including:
• Building Information Modelling
• Digital Construction
• Data Science Foundations
• Business Intelligence
• Machine Learning and Data Modelling
• Research analysis and data interpretation techniques

The course is ideal for graduates of any Built Environment related subject.

For further course details please see "Course Web Page" below.

Year one
Digital Construction: Technology, Strategy & Management
This module explores the area of Digital Construction, both in general, and as a means of helping to address pertinent AECFM sector problems or processes identified as requiring advancement. Learners will study contemporary practice and undertake their own research for an identified discipline specific task. Additionally, there will be a focus on strategic and management capabilities. This module will provide rich connections to other related subject fields for learners to develop a holistic view of the opportunities resulting from the integration of digital construction processes and technologies. The module aims to close the gap in the curricula for supporting the AECFM sector in embracing the Industry 4.0 vision.

Building Information Modelling
The aim of this module is to provide students with insight into the main concepts and principles of Building Information Modelling/Management, vis-à-vis processes, protocols, and enabling technologies. The module is mainly concerned with recent paradigm shift within the AECFM industries worldwide to implement BIM in projects. This module is also heavily inspired by the prospective of UK BIM Framework (formerly BIM level 3) and the relevant BIM BS ISO standards. The module will cover strengths and weaknesses, opportunities and threats associated with adopting BIM in AECFM projects and its impact on working procedures as well as business models of AECFM industry. This will be further elaborated in lights of real-life national and international scenarios and case studies.

Industry Project
This module is industry focused where students will work collaboratively in teams to develop solutions for specific projects normally within an industrial or practice-based test bed, and/or in association with an appropriate design or industrial organisation. Field trips and regular progress reports are an important aspect of the module. The module is designed to cover topics that address key industry challenges within the themes of Digital Construction. Students are expected to apply the obtained Data Science knowledge in the field of Digital Construction; for example, to develop solutions for challenges of: Lean Construction to reduce waste, Generative Design to achieve Net-Zero emission, product innovation, whole life asset performance, evaluating new relevant technologies (e.g. off-site manufacturing), and achieving energy efficient construction.

Business Intelligence and Analytics
This module aims to contextualise the role of Business Intelligence and Business Analytics and why we need them. A particular focus will be on how to turn already stored data into valuable information and why this is important. For instance, vast amounts of data regarding company's customers and operations is routinely collected and stored in large corporate data warehouses. This data can be of immense value if properly analysed. Students will explore techniques and tools for data analysis, and presentation of the results to non-technical and managerial staff, in alignment with business strategies. Business intelligence and analytics however, are open to certain ethical and consent issues along with risks. These will be analysed, reviewed and evaluated.

Deep Learning and Natural Language Processing
Deep Learning(DL) and Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate most everything in language: web search, advertisement, emails, customer service, language translation, radiology reports, etc. There are a large variety of underlying tasks and machine learning models powering NLP applications. Recently, deep learning approaches have obtained very high performance across many different NLP tasks. These models can often be trained with a single end-to-end model and do not require traditional, task-specific feature engineering. In this course students will learn to implement, train, debug, visualize and invent their own neural network models. The course provides a deep excursion into cutting-edge research in deep learning applied to NLP. The final project will involve training a complex neural network and applying it to a large scale NLP problem. On the model side we will cover word vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, convolutional neural networks as well as some very novel models involving a memory component. Through lectures and programming assignments students will learn the necessary engineering tricks for making neural networks work on practical problems.

Data Science Foundations
The focus of this module is to present an understanding of key data science concepts, tools and programming techniques. Within the arena of data science, the theory behind the approaches of statistics, modelling and machine learning will be introduced emphasising their importance and application to data analysis. The notion of investigative and research skills will also be introduced through a number of problem-solving exercises. The material covered will be contextualised by providing examples of the latest research within the area. Students will also be introduced to programming with Python. They will learn the basics of syntax, and how to configure their development environment for the implementation and testing of algorithms related to data science.

Research Methods and Project
This module provides students with a broad understanding of research methods and techniques, and how these can be used to investigate a research problem in any context. Students will be provided with the necessary theoretical foundations in statistical research, including the ability to plan research ethically and conduct a literature review. Students will also be able to record, analyse, interpret and present qualitative and quantitative data appropriately.

Year two
Applied Research (Digital Construction)
This module is optional
The research dissertation module integrates and further develops the knowledge and skills acquired within the taught element of the programme. The module specifically allows the students to apply knowledge and skills acquired to undertake a research dissertation investigating a topic relevant to Digital Construction in the AECFM industry. The Digital Construction topic will vary depending on the interests of the student. Students will be required to demonstrate their knowledge in the chosen subject area as well as critical analysis and investigative skills in both written and oral format.

Minimum 2:2 Honours degree (or equivalent) in Architecture, Engineering, Construction or other Built Environment related subject. Previous computer programming skills are not essential requirement. Applicants from other relevant backgrounds (e.g. Data Science and Computer Game) can be considered. Applicants must also provide evidence of English Qualification to the level that meets the University requirements.

English Language Requirements
If English is not your first language this course requires
a minimum English level of IELTS (academic) 6.0 with no band
score less than 5.5, or equivalent.

This course is open to international (non-EU) students (full-time only).

For full entry requirements please see "Course Web Page" below.

Your Application
Application is through the University's online application system (see "Application Weblink" below).

Career options
The area of Digital Construction and BIM is expanding in the market and new posts have been created recently in the industry (e.g. BIM manager, Information manager) which need special skills that the course will help learners to develop. These special skills become also essential for career progression. In addition to the construction related knowledge and skills obtained by studying this course, the Data Science knowledge and skills will reinforce the graduates' abilities and capabilities; hence support their employability in relation to Information Management roles that become more mainstream in the industry. The course offers a unique opportunity for all Built Environment related graduates to study at Masters level and to develop skills applicable to their employment locally or internationally. The course strategy is designed to provide a balance between theory and practice with hands-on approach on solving real industry problems in collaboration with industry partners. This strategy will provide graduates with key critical thinking and analysis skills that are transferrable and applicable across a wide range of industry sectors. Graduates will be also equipped with the essential skills to pursue a PhD route on graduation if they wish to.