Construction 4.0: A comparative analysis of research and practice
Bibliographic record
Abstract
This paper presents an overview of the existing literature on Construction 4.0 technologies over the past decade and their most common applications in both research and practice, aimed at achieving three objectives. First, the search for the most relevant articles on Construction 4.0, published in the scientific literature, and small firms that are developing and delivering 4.0 technologies in the construction industry allows to identify the numerous applications associated with Construction 4.0. Second, the applications found in the scientific literature and those identified in practice are classified and compared based on a framework consisting of three distinct axes. Third, the classification framework highlights current research trends and potential areas for future research, which can be summarized as follows: (i) development of hybrid digital solutions; (ii) alignment with effective collection of more structured data, smart interactive web technologies, robotics, autonomous systems, and intelligent built assets; and (iii) strengthen the capacities of artificial intelligence and machine learning.
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.
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 | 0.036 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.045 | 0.060 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".