Fabrication drawings are essential for design evaluation, lean manufacturing, and quality detection of precast concrete (PC) components. Due to the complicated shape of PC components, the fabrication drawing needs to be customized to determine manufacturing dimensions and relevant assembly connections. However, the traditional manual drawing method is time-consuming, labor-intensive, and error-prone. This paper presents a BIMbased framework to automatically generate the readable drawing of PC components using building information modeling (BIM) and multi-agent reinforcement learning (MARL). Firstly, an automated generation method is developed to transform BIM model to view block. Secondly, a graph-based representation method is used to create the relationship between blocks, and a reward mechanism is established according to the drawing readability criterion. Subsequently, the block layout is modeled as a layout optimization problem, and the internal spacing and position of functional category blocks are regarded as agents.
| Authors | Zhang, Chao and Zhou, Xuhong and Xu, Chengran and Wu, Zhou and Liu, Jiepeng and Qi, Hongtuo |
|---|---|
| Year | 2025 |
| Venue | Buildings (2075-5309) |
| DOI | 10.3390/buildings15020284 |
| Source Database | EBSCO (Applied Science & Technology) |
| Bridge-to-GNN | Category Borderline |
| GNN Architecture | GNN (heuristic — verify) |
| Graph Encoding | BIM Element Topology Graph (inferred from title) |
| AEC Task | Not yet extracted from PDF |
| Cohort | Mature Applications (2024–2026) |
| Dataset Size | 28 buildings; 28 models (heuristic — verify) |
|---|---|
| Key Hyperparameters | lr 0.0015; optimizer=Adam optimizer (heuristic — verify) |
| Primary Metric | Accuracy (heuristic — verify) |
| Key Finding | BIMbased framework to automatically generate the readable drawing of PC components using building information modeling (BIM) and multi-agent reinforcement learning (MARL) (heuristic — verify) |
| Quality Assessment | 4/6 · rigor: Medium · Code/Data: ✓Benchmark: ✓Baseline: ✓Reproducible: ✓ (heuristic — verify) |
| GNN architectures detected | Knowledge Graph Embedding (TransE/RotatE/DistMult) |
|---|---|
| Benchmark datasets referenced | PubMed |
| Hyperparameters (regex-detected) | learning_rate=0.0015 · optimizer=Adam optimizer |
| Dataset stats found | 28 models · 28 buildings |
| Quality (heuristic, 6-flag) | 1/6 · rigor: Low Code/Data: —Benchmark: ✓Baseline: —CV/Split: —Ablation: —Reproducible: — |
Part of the GML/GNN in AEC Systematic Review (PRISMA 2020) — 112 papers, 2020–2026