Modeling and categorizing standardized artifacts for scheduling occupancy on building construction sites
Bibliographic record
Abstract
The new trend in planning building projects involves incorporating space management on the construction site. Failing to consider the management of site operations can lead to either relaxed or congested construction sites. This is because labor, tools, equipment, and materials all require space. It is important to plan for the circulation and temporary storage of materials, including recycling, reuse, and disposal. Temporary installations and finished products also take up space and can impact traffic flow. For example, partitions can impair traffic flow, while finished flooring can restrict it. The occupation of spaces also changes as the work progresses, so it is important to monitor and accommodate these changes. Therefore, space management needs to be coordinated throughout the construction phase. Traditional planning methods do not consider the analysis of critical spaces or their evolution over time. As a result, they produce schedules that do not reflect the reality of the construction site. Dynamic space-time planning means modelling the operational flow in different sectors of the construction site to optimize the construction process. Efficient space occupation allows for more effective utilization of available resources. This paper aims to analyze the spatial requirements of various construction operations and develop standardized artifacts that integrate spatial information into the schedule. To achieve this objective, the methodology aims to: (i) Conduct site surveys to collect data and analyze construction operations and their spatial needs; (ii) Define the necessary parameters for determining the occupancy rate (OR); (iii) Develop the needed artifacts for representing the static models based on the survey results and the developed occupancy parameters to visually depict and compute various types of occupancy and operations; (iv) Conduct workshops with the professionals in the construction industry. The participants were asked to provide feedback on whether the artifacts effectively captured the various aspects of construction work, such as equipment, tools, materials, and safety protocols. The aim is to validate that the artifacts as reliable representations of real-life construction scenarios. The feedback and input provided by the professionals helped to ensure that the artifacts were accurate, informative, and valuable for training purposes in the construction industry.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".