Framework for BIM-Based Simulation of Construction Operations Implemented in a Game Engine
Bibliographic record
Abstract
Construction simulation has been widely used in academia for research purposes. However, it has been neglected by the industry for various reasons, including the amount of data, skills, effort, and time required to develop complex simulation models, the difficulty of model reuse, and the abstract and confusing way in which simulation results are usually presented. This article demonstrates how BIM can be employed to facilitate the development of a construction simulation model that considers constraints related to resource allocation and task interdependencies. Furthermore, it shows how a game engine can be used as a platform to implement the proposed framework for the integration of BIM and construction simulation and to produce animations from simulation results. The feasibility of the framework is demonstrated through a case study on masonry construction. Results of implementing the framework reveal that BIM-based simulations can reduce the skills, effort, and time required to develop simulation models and enable model reuse. The integration of simulation-based animations provides a model verification and validation mechanism and a means to communicate model results to stakeholders unfamiliar with simulation.
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.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".