Exploring the Development Status and Prospects of 3D Printed Construction Technology
Bibliographic record
Abstract
Environmental, economic, and social pressures on the construction industry make it paradigm-shifting. The traditional construction methods are undergoing mounting problems in terms of resource scarcity, excessive energy use, and the generation of waste, which are demanding solutions. The history and future of 3D printed construction technology (C3DP) are of interest to this paper, as the technology has become a disruptive technology that can transform the art of architecture and construction. The technical concepts, case studies, and the difficulties of regulation are reviewed through the qualitative literature analysis. As evidenced by two case studies, the Milestone Project of the Netherlands and the Chicon House of the United States, C3DP can cause housing to be cheaper, faster, and more sustainable, and the issue of partial automation, long-term resistance, and code standardization has been identified. C3DP has been discovered to have colossal advantages concerning planability, productivity of projects, as well as environmental friendliness, and its extensive use requires homogeneity of materials, regulatory flexibility, and economic sustainability. Of course, at present, 3D printed buildings are still in the transitional stages. Nevertheless, it incorporates digital technologies, materials with low carbon, and global housing programs, which will be an opportunity for a more sustainable and innovative future of the construction industry.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".