BIM-Integrated Simulation of Construction Operations for Lean Production Management
Bibliographic record
Abstract
As construction projects become larger and more complex, traditional construction planning and control practice which relies on historical data and heuristic adjustment can no longer produce a plan that incorporates all the managerial details such as productivity dynamics. In addition, the plan, more often than not, does not synchronize with the procurement schedule; as a result, the whole supply chain has failed to accomplish expected level of efficiency. In these regards, this paper presents a simulation framework that can not only predict productivity dynamics by considering factors affecting on productivity at the operational level, but also automatically generate a procurement plan harmonizing with the simulation results for reliable production management. We developed APIs for the framework 1) enabling a BIM model to produce input data for the construction operation simulation; 2) composing construction simulation in operational level; 3) facilitating the productivity prediction by providing BIMintegrated construction simulation models. The simulation framework had tested with structural steel erection cases. The results show that we can expect significant improvement of efficiency along supply chain including optimized resource allocation, schedule reliability increase, storage cost saving, and material loss reduction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".