Dynamic Spatiotemporal Scheduling for Construction Building Projects
Bibliographic record
Abstract
For building projects, the manager is responsible for coordinating the work of subcontractors at the construction site. This includes operations, material flows, and storage. In summary, one of their main roles is to ensure smooth team rotation, maintain fluid circulation, and avoid congestion or relaxation on the site. However, traditional tools lack the ability to consider the planning and management of worksite spaces when calculating the execution schedule and critical path. Consequently, three-week planning is usually carried out separately on independent plans, often using spreadsheets. In addition, a construction site is highly dynamic and mobile in nature, and the positioning of resources and workers can change daily. This makes the management of available space even more complex, and effective space management becomes an imperative. To address this challenge, this paper develops visual dynamic artifacts that present different operation types. The methodology and the conceptual framework facilitate the calculation of the Occupancy Rate (OR) that enables construction project managers to create simple yet dynamic spatiotemporal models of the construction schedule. By incorporating factors such as crew turnover and occupancy evolution, managers can simplify the calculation process and effectively optimize construction work by utilizing site occupancy rates. In summary, this paper presents the Dynamic Model of the Occupancy Rate Schedule (DMORS), a methodology developed through design science. This model utilizes created artifacts representing various operation types to ensure accurate calculations of dynamic occupancy by floor and sector in a site. Consequently, it enables the construction of a more realistic schedule based on critical space ideologies. The DMORS enables managers to use the OR for different floors and sectors of a site, allowing for better space management. A proof of concept demonstrates that this tool can enhance the efficiency and productivity of construction projects by optimizing crew schedules and resource allocation based on site OR.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".