Monitoring the Construction Industry Towards a Construction Revolution 4.0
Bibliographic record
Abstract
The key concept of the Industrial Revolution IR 4.0 has been conceptualized as the new wave of digitalization, robotization, and broader usage of information and communication technology. However, the construction industry is complicated, which has led to its slow industrial evolution. The construction industry still follows traditional labor-intensive industry practices, with high energy consumption, environmental pollution, and low productivity in project delivery. Moreover, the recent cataclysmic COVID 19 pandemic has opened the vision of the construction industry towards IR 4.0 due to the human movement restriction. This paper aims to investigate the adoption of indispensable monitoring technology in the construction industry as effective visual communication of data towards the IR 4.0. This research closes the gap and gives an intensive literature investigation to acquire insights into Construction 4.0 and a case study to showcase the developed monitoring dashboard. Adoption of IR 4.0 technologies will achieve sustainable construction development, lower costs and fast construction with the highest quality. The critical literature review of previous studies with content analysis to demonstrate the recent research in this area. The monitoring dashboard brings the construction performance assessment data to real life and provides key performance indicators required for construction management and support decisions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".