Blockchain Technology toward Smart Construction: Review and Future Directions
Bibliographic record
Abstract
The construction industry has been criticized for low productivity, lack of collaboration and information sharing, poor contract administration, and the like due to its decentralized and fragmented structure as well as sequential and chain-resembling nature. Recently, blockchain technology and its benefits have received wide attention and interest. This research synthesizes the research trends and needs of this growing area by means of a bibliometric-qualitative review method. Scopus and Web of Science were selected as the literature databases to retrieve relevant academic publications. Through a systematic literature search and screening, 181 related articles were identified for bibliometric analysis, and 149 publications were critically discussed in a qualitative review. The bibliometric results indicated the recent research regarding blockchain in construction is primarily directed into several clusters, such as “smart contract,” “Building Information Modeling (BIM),” “supply chain management,” “construction contract,” “construction and project management,” “digital twin,” and “smart city.” These clusters were further synthesized for a qualitative review revealing deep insight into research challenges and gaps. Both quantitative and qualitative review results were then mapped to the future directions. It was noted that future research needs to focus on (1) quantifying the cost-benefits of the blockchain applications in construction, e.g., return on investment, practitioners training, and improving industry readiness, (2) integration of blockchain with different project delivery systems, and (3) technology fusion with blockchain for construction management.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".