Physics-Based Simulation for Construction Activity Sequence Planning
Bibliographic record
Abstract
Construction activity sequence planning ensures the structured execution of construction processes by defining task order, dependencies, and constraints.While traditional planning methods rely on predefined templates, fragnets, or automated approaches using large language models, machine learning, and 4D building information modeling (BIM), these methods often struggle to address site-specific and real-time constructability challenges.To address these challenges, this study introduces a physicsbased simulation approach that automates sequencing by evaluating structural dependencies and spatial constraints.In this study, a 3D BIM model is transformed into a physics-simulated environment using universal robot description format files, where a brute-force search iteratively refines sequences based on stability and path clearance constraints.To confirm the method's ability to dynamically generate feasible sequences that can subsequently be structured into construction schedules, testing is conducted on an industrial module comprising steel frames, pipe spools, and cable trays.Notably, as structural complexity increases, computational demands grow exponentially, demonstrating the limitations of brute-force search.The results indicate that physics-based simulation effectively validates constructability but requires optimization for scalability.Therefore, future advancements in this area should focus on AI-driven sequence optimization, improved BIM data integration, and distributed computing to enhance efficiency.By bridging the gap between digital models and real-world constraints, this study offers a novel method for advancing automated construction sequencing, making it more practical for industry-wide adoption.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".