Abstract
This research aims to develop a data-driven conceptual framework for enhancing decision-making across the lifecycle of built assets in the Architecture, Engineering, Construction, and Operations (AECO) sector, with a specific focus on design and operations. The study presents a comprehensive approach that integrates Big Data technologies, Internet of Things-based data collection, and Off-Site Manufacturing methods within a lifecycle-oriented framework. Designed around the Royal Institute of British Architects (RIBA) Plan of Work, the framework enables well-defined data strategies and feedback loops, connecting operational performance data to design decisions. Hence, this research addresses the rising need for a method that successfully applies operational data.The research examines how asset data can be analysed to uncover actionable insights to support continuous improvement in their operation and design. A three-route survey and interview approach were used to validate the value of predictive maintenance and performance-informed design, as well as to confirm the need for closed-loop feedback systems. While the study presented uneven digital maturity among professional groups, there was wide agreement that IoT and Big Data analytics are valuable for real-time monitoring, predictive analytics, and informed strategic planning.
The novel contribution to the field is the development and validation of an innovative integration framework that integrates technological and methodological advancements into the current industry workflows. It introduces concepts such as smart design and smart maintenance by combining quantitative sensor data and qualitative user insights. In practical terms, the framework offers a tested solution compliant with industry best practices and global standards, supporting digital transformation initiatives.
The research highlights that evidence-based, Interdisciplinary strategies can significantly improve the digital performance of the built environment. The findings confirm that a structured, collaborative approach to data management facilitates more effective and streamlined asset management and design, positioning the study as a key contribution to both academic literature and professional practice in digital construction.
| Date of Award | 9 Jan 2026 |
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| Original language | English |
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| Supervisor | Catherine Teeling (Supervisor), Mark Danso (Supervisor) & Hamidreza Khaleghzadeh (Supervisor) |