Industry 4.0 and the circular economy: using design-stage digital technology to reduce construction waste
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This study examines how applying innovative I4.0 technologies at the design stage can help reduce construction waste and improve the recovery, reuse, and recycling of construction materials. Design/methodology/approach The study adopts a three-stage sequential mixed methods approach, involving a thorough review of current literature, interviews with six experts in digital construction, and a survey of 75 experienced industry practitioners. Findings The study identifies and discusses how ten specific digital technologies can improve design stage processes leading to improved circularity in construction, namely, (1) additive and robotic manufacturing; (2) artificial intelligence; (3) big data analytics; (4) blockchain technology; (5) building information modelling; (6) digital platforms; (7) digital twins; (8) geographic information systems; (9) material passports and databases; and (10) Internet of things. It demonstrates that by using these technologies to support circular design concepts within the sector, material recycling rates can be improved and unnecessary construction waste reduced. Practical implications This research provides researchers and practitioners with improved understanding of the potential of digital technology to recycle construction waste at the design stage, and may be used to create an implementation roadmap to assist designers in finding tools and identifying them. Originality/value Little consideration has been given to how digital technology can support design stage measures to reduce construction waste. This study fills a gap in knowledge of a fast-moving topic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it