Integrating industry foundation classes and knowledge graphs for automated deconstruction planning
Bibliographic record
Abstract
Decarbonizing the built environment and urbanization have driven a surge in demolitions, resulting in significant waste generation. While deconstruction offers a more sustainable alternative by enabling material reuse, demolition remains the dominant practice due to its short-term benefits. Facilitating the shift from demolition to deconstruction requires rigorous planning and the elimination of key barriers that hinder implementation, including challenges related to information, knowledge, execution, and automation. To this end, the present study aims to integrate Industry Foundation Classes (IFC) and Knowledge Graphs (KGs) to enhance deconstruction planning and support its broader adoption. The framework comprises three modules, i.e., (i) ‘IFCDecon’, which automates the extraction of IFC data and generates deconstruction-related information; (ii) ‘DeconKG’, which processes this information and creates a deconstruction knowledge graph (DKG); and (iii) ‘DeconPlanner’, which generates deconstruction schedules and offers a verification step through 4D simulations. The framework was tested on two benchmark case studies and demonstrated significant potential for advancing automated deconstruction planning., Additionally, the framework's functionalities were evaluated against industry-driven barriers to ensure practical applicability. The proposed framework offers a practical solution for deconstruction contractors by automating deconstruction planning through IFC data and providing 4D simulations. Its potential for rapid market adoption is driven by increasing demand for deconstruction, fueled by rising home renovations and the global push for eco-friendly building practices. Furthermore, integrating the framework with retrofit assessment tools could enhance decision-making by offering a holistic view of both deconstruction and post-renovation performance, ultimately improving project lifecycle management .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".