Module Catalogue 2026/27

Pre Requisite Comment

Students enrolling should have some pre-experience with using GIS and Python programming and be aware of concepts including machine learning, BIM and digital twins.

Co Requisite Comment

N/A

Aims

This module aims to equip students with the knowledge and skills required to manage construction projects across their full lifecycle, from early feasibility and planning through delivery, handover, and operation. It treats construction project management as a structured yet context-dependent decision-making discipline, integrating qualitative and quantitative approaches to feasibility, risk, and stakeholder management with project planning, monitoring, and control. The module integrates digital engineering analytics by showcasing how the use of data, digital models, and programming-based analytical tools to represent and analyse the built environment, interrogate project and engineering datasets, and support evidence-based planning, coordination, performance evaluation, and decision-making can be done using the latest technologies.

Outline Of Syllabus

The following syllabus outline is a guide only. Actual topics may slightly differ:
•Project lifecycle and project feasibility and appraisal studies (financial & economic)
•Project risk, uncertainty, and value management
•Stakeholder management, collaboration, and communication in projects
•Project planning, monitoring and control, including the use of digital data to support performance management
•Procurement strategies, cost estimating, bidding, and contractor selection
•Managing project and asset information in the digital age, including data standards, interoperability, and information flows across the lifecycle
•Digital representation of the built and urban environment, including GIS-based modelling and spatial data
•Data modelling concepts and the structuring of engineering and project datasets
•Data analytics and data-science methods for engineering, including the use of Python for analysis
•Digital data visualisation and communication of analytical insights for project decision-making
•Application of digital tools and analytical approaches in contemporary engineering practice
•Emerging themes in project management and digital transformation, including the evolving role of data, automation, and decision support.

Intended Knowledge Outcomes

On successful completion of this module, students will be able to demonstrate knowledge and understanding of:
•The influence of stakeholders, organisational contexts, and communication processes on construction project definition, governance, and success (M17).
•Established qualitative and quantitative approaches to project feasibility appraisal and value engineering at early project stages, including economic, financial, and strategic considerations, and their role in improving project outcomes (M4, M7).
•The principles, characteristics, advantages, and limitations of different procurement systems and contracting strategies, and their implications for risk allocation, value, and project performance (M5).
•The nature and sources of uncertainty and risk in engineering projects, including systematic approaches to risk identification, assessment, and response, and their role in improving project outcomes (M9).
•The role of digital technologies, data, and information systems in supporting project and asset manegement across the project lifecycle, including how digital innovation enhances information quality, coordination, transparency, and decision-making (M3, M15).
•How digital methods are employed to represent, analyse, and interpret the built environment in engineering contexts, including principles of data modelling, analytics, and good practice in the use of digital data resources (M3, M4).

Intended Skill Outcomes

On successful completion of this module, students will be able to:
•Analyse and articulate project management concepts, processes, and terminology to investigate complex project situations (M6, M15).
•Apply project management knowledge to analyse complex, multifaceted project problems, evaluating feasible solutions that consider technical, commercial, economic, and stakeholder factors (M4, M6).
•Use programming-based analytical environments (e.g. Python) to analyse and interpret complex engineering and project datasets, demonstrating awareness of how advanced data-driven techniques, including how AI and machine learning, may be applied (M2, M3).
•Represent and analyse the built environment using digital engineering tools (e.g. BIM and GIS), integrating spatial, project, and engineering data to support analysis and project decision-making (M3, M13).
•Design and evaluate project and engineering solutions from conceptualisation through to implementation, considering digital alternatives, data-driven approaches, and relevant ethical, legal, and professional constraints (M8, M13, M15).
•Critically reflect on individual and group performance in project-based contexts, demonstrating self-awareness, effective teamwork, and a commitment to lifelong learning and professional development (M16, M17).

Teaching Rationale And Relationship

Knowledge-based learning outcomes are primarily delivered through lectures and seminars, which introduce core project management principles alongside digital engineering and data-driven concepts, including emerging technologies. These sessions are supported by structured learning materials to underpin independent study.
Skills-based learning outcomes are developed through computer-based practical sessions and a gamified project management simulation. Practical sessions enable students to apply digital tools and analytical methods to realistic engineering scenarios, while the simulation provides experiential learning within an authentic project context, exposing students to uncertainty, stakeholder dynamics, and project trade-offs.
Together, these methods support the integration of project management judgement and digital analytics, and the development of analytical, collaborative, and reflective capabilities aligned with the module learning outcomes.

Assessment Rationale And Relationship

The in-class, closed-book written examination assesses learning outcomes related to project management, with emphasis on critical understanding and applied judgement across feasibility appraisal, procurement strategies, risk and stakeholder management, and planning and control. Scenario-based questions require students to interpret project contexts, justify decisions, and critically evaluate alternatives.
The written report is used to assess the digital engineering analytics component, allowing students to demonstrate a knowledge and awareness through critical evaluation of the role and ability to which digital tools can be used in a data-driven digital engineering future.
The oral presentation, delivered through a tutorial, discussion, or authentic workshop-style activity, assesses integrative learning outcomes related to professional communication and critical reflection. It enables students to articulate and defend project and digital decisions, respond to questioning, and demonstrate reflective insight appropriate to advanced professional practice.

General Notes

N/A