A BIM-Integrated Process Framework and Decision Support Tool for Precast Concrete Deconstruction

Deconstruction of precast concrete buildings is increasingly promoted within circular economy agendas, yet current UK practice remains predominantly demolition-led. This could be largely attributed to the fragmented as-built and as-is information, structural uncertainty in existing buildings, and limited use of Building Information Modelling (BIM) beyond basic visualisation and measurement tasks. As a result, planning-stage decisions for building end-of-life are often made with incomplete or unreliable information, limiting the feasibility of systematic deconstruction. This research addresses the gap between policy ambition and operational reality by reframing BIM as a decision-support mechanism for deconstruction planning in contexts where key parameters such as in-situ structural condition, connection detailing, and material recovery potential remain uncertain or only partially verifiable prior to intervention. A mixed-methods research design was adopted to capture both conceptual and operational dimensions of precast concrete deconstruction. The study commenced with a critical literature review combining bibliometric mapping, systematic review of academic publications, analysis of BIM-enabled reuse research projects, and examination of documented industry case studies to establish the state of knowledge and identify persistent implementation gaps. This was complemented by a structured review of industry guidance and policy documents to contextualise regulatory expectations and prevailing professional practices. Primary empirical evidence was then gathered through a nationwide practitioner questionnaire examining deconstruction decision-making, BIM adoption, and material recovery challenges, followed by field observations during live demolition works to capture real-world sequencing, risk management, and information constraints. Questionnaire responses reveal low but gradually increasing uptake of BIM for end-of-life applications, with practitioners highlighting the need for integrated decision-support that can jointly evaluate cost, programme duration, and material recoverability. Field observations further demonstrate that deconstruction is inherently verification-driven, requiring sequencing decisions to be continuously adapted as in-situ conditions are exposed. Collectively, these insights emphasise the need for workflows that incorporate vii early digital data capture, iterative validation, and scenario-based evaluation to support reliable planning under uncertainty. The research produced two main outcomes: a BIM-integrated process framework that embeds structured decision gates, sequencing logic, and information verification stages within deconstruction planning, and a BIM-based Decision support tool that operationalises the proposed framework through scenario-based evaluation of cost, time, and material recovery implications. Overall, the contributions of this study demonstrate that BIM’s primary value in deconstruction lies in supporting transparent, evidence-based decision-making under uncertainty, rather than fully automating optimisation processes. From an academic perspective, the study advances understanding of how digital modelling can move beyond representation towards decision-support in circular construction and building end-of-life management. In practical terms, the study provides industry with a robust and auditable methodology for risk-aware deconstruction planning of precast concrete buildings. This study also offers policy-relevant insights by demonstrating how existing circular economy ambitions can be more effectively realised through digitally enabled, evidence-based planning approaches under conditions of uncertainty.