Physical Distancing Analytics for Construction Planning Using 4D BIM
Bibliographic record
Abstract
The COVID-19 pandemic has impacted how the construction industry operates around the world. To fight the risk of transmission, new health, safety, and environmental (HSE) protocols have been put in place. Among these protocols are social distancing and limiting the number of workers per area, where social distancing acts as a so-called protective bubble for each worker. Contractors are now required to attempt to achieve (and be prepared to keep) social distancing among their workers whenever needed and possible. Otherwise, they could be forced to halt operations due to having an unsafe environment. Accordingly, construction plans, and corresponding workspace assignments, should be revised in a four-dimensional (4D) environment to ensure fulfillment. Even after the end of this pandemic, the new HSE awareness achieved during this experiment is expected to reshape the so-called new normal of construction. Therefore, this paper presents a novel workspace simulation and management solution comprising a theoretical framework and a semiautomated tool to incorporate physical distancing during 4D planning. The semiautomated tool creates a 4D building information model, loaded with workspaces and social distance bubbles as stochastic variables, and utilizes Monte Carlo simulation to model uncertainties occurring onsite. The uncertainties considered are both temporal and spatial, i.e., changes in productivity and workspace sizes, respectively. This tool surpasses existing workspace management solutions in that (1) it has a schedule generation module to recompute schedule projections based on temporal uncertainties, (2) its workspace generation module can automatically create physical distance buffers around selected workspaces, as per site conditions, (3) its 4D simulation can realistically mimic the work progress on the site, and (4) its 4D clash detection module can smartly detect and report both soft and hard operational clashes. Additionally, the proposed analytics target three levels of clash resolution: site, workspace, and activity level. The framework and developed tool were tested against a residential building case study. Over the course of 155 days, 26 activities with 257 workspace assignments were examined. The proposed solution was able to capture the critical schedule duration (21 out of 155 days), the impactful 4D clashes (44 out of 2,900), and the activities involved in the most sever clashes (5 out of 26). Hence, the proposed method and the developed software tool will help planners/construction managers understand the space requirements for construction operations considering social distancing and other required safety buffering, identify critical spatiotemporal zones, and suggest resolution strategies for the resulting clashes based on the analytics.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".