Aligning building information modeling and prefabricated construction with sustainable development goals: A framework for sustainable urbanization
Bibliographic record
Abstract
Given its large environmental footprint and the urgent need for more efficient and sustainable practices, sustainability in the construction industry has never been more urgent. This paper explores the integration of building Information Modelling (BIM) and prefabricated building (PC) as a transformative approach to aligning the construction sector with the United Nations Sustainable Development Goals (SDGS), Special attention is given to SDG 9 (industry, innovation and infrastructure), SDG 11 (Sustainable cities and communities), SDG 12 (responsible consumption and production), SDG 13 (climate action) and SDG 7 (affordable clean energy). Through the lens of BIM and PC, the study illustrates how these technologies can significantly reduce waste, optimize resource use, and contribute to the construction of energy-efficient buildings, thereby mitigating the environmental impact of urban development. Enhanced stakeholder collaboration and information sharing facilitated by BIM was highlighted as a key factor in integrating community needs and environmental considerations into project planning and execution. This paper argues that the strategic adoption of BIM and PC not only promises to revolutionize construction practices by increasing efficiency and reducing costs, but also plays a vital role in the global pursuit of sustainability. Through a comprehensive analysis of the benefits and challenges associated with BIM and PC integration, this study highlights the importance of continuous technological progress, adaptation to different global environments, and alignment with the Sustainable Development Goals as important steps towards achieving sustainable urbanization.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".