Gap Analysis of Digitalization Levels in Construction and Manufacturing: A Comparative Study of Construction 4.0 and Industry 4.0
Bibliographic record
Abstract
The digital transformation of the construction industry has been much slower than that of manufacturing. The gaps and causes require in-depth study, and the findings contribute to future directions. Therefore, this study conducts extensive research on the gaps and differences between Construction 4.0 and Industry 4.0. This study utilizes hybrid approaches of brainstorming, bibliometric analysis, literature review, the Delphi method, and the Bayesian network to benchmark digital technologies and application scenarios. The results of this study show that there are only half the number of digital construction academic records than there are of digital manufacturing; the build-phase substantially lags behind other phases in the lifecycle; cyber-physical systems and cybersecurity are less applied in construction; scenarios such as operational efficiency and process optimization, equipment and facility management, supply chain, and cybersecurity show fewer applications in construction; and data openness and worker digital skill are the most contributive and changeable causes of construction digitalization.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.023 | 0.032 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".