Physical Distancing Analytics for Construction Planning Using 4D BIM
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it