Significance of 3D Printing Risks in Construction Projects
Bibliographic record
Abstract
There is a big shift towards 3D printed construction projects in the United Arab Emirates.Although these projects encourage innovation and digital transformation, they are usually riskier than traditional construction projects.The purpose of this paper is to identify and assess the risks in 3D printed construction projects in the UAE.A total of thirty risks were identified from literature.These were then grouped into six categories: 3D printing material, 3D printing equipment, 3D printing design risks, construction site and environment risks, management risks, regulatory and economic risks.A survey was then distributed to construction professionals in the UAE to evaluate the probability of occurrence and impact of each risk, sixty-six responses were collected.the severity of each risk was calculated by multiplying the probability with the impact and relative importance index was used to rank the risks accordingly.The results revealed that the top five severe risks were lack of codes for 3D printing in construction, delays in government approvals, shortage in labour skilled in 3D printed construction, lack of knowledge and information of 3D printed design concepts, changes in 3D construction codes and regulations.This research allows for proper guidance for risk response planning and control in 3D printed construction projects.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".