AIoT-powered drones in the construction industry: a review
Bibliographic record
Abstract
The digital revolution in construction is driving the nexus of emerging technologies. Technologies, such as the Artificial Intelligence of Things (AIoT), are transforming the sector by enhancing efficiency, promoting sustainability, and fostering innovation in construction projects. However, extant literature has failed to document integrated applications of Artificial Intelligence (AI) and Internet of Things (IoT). The interface of AIoT with drones and their applications in the construction industry is underreported. To address this gap, the current study systematically reviewed literature from the Scopus and Web of Science repositories to uncover the applications of AIoT-enabled drones. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocols were applied to review 33 highly relevant articles. The results show applications of AIoT-powered drones in various construction sectors such as land surveying and site selection, site layout, analytics and logistical planning, quality and progress monitoring, safety management, regulatory compliance, facility and asset management, and disaster management. Theoretical and practical implications, challenges, and future directions for research and industry to adopt AIoT-powered drones are reported. This study is a pioneering effort investigating the applications of AIoT-powered drones in the construction industry with equal benefits for researchers, academics, and industry practitioners
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".