Towards the smart construction site: improving productivity and safety of construction projects using multi-agent systems, real-time simulation and automated machine control
Bibliographic record
Abstract
With the increasing complexity of construction projects grows the challenge of securing safety and achieving desirable productivity as the chief priorities of the construction industry. Addressing these issues requires robust mechanisms for on-site real-time data capturing, information processing and decision-making. The current research aims to further investigate the concept of the Smart Construction Site where workers, equipment, and materials are continuously tracked, and the collected information is processed in near real time to update the design model and the simulation of upcoming tasks, and to provide navigation guidance and safety warnings in case of potential collisions. The objective of our research is to improve the productivity and safety of heavy construction projects by integrating 3D design models (e.g. highway models) with the managerial and operational processes of heavy construction using advanced agent technology and multi-agent systems, real-time simulation, and automated machine control. 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".