# BIGNet: Pretrained Graph Neural Network for Embedding Semantic, Spatial, and Topological Data in BIM Models

arXiv: https://arxiv.org/abs/2509.11104 · HTML: https://mlsanity.com/paper/2509.11104/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 45 references that are in the map.
- First posted on arXiv 14 Sep 2025.

## Builds on (most cited first, 12 of 12)

- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [GraphMAE: Self-Supervised Masked Graph Autoencoders](https://mlsanity.com/paper/2205.10803/) (2022)
- [GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner](https://mlsanity.com/paper/2304.04779/) (2023)
- [Adaptive Graph Encoder for Attributed Graph Embedding](https://mlsanity.com/paper/2007.01594/) (2020)
- [Learning Graph Representations with Embedding Propagation](https://mlsanity.com/paper/1710.03059/) (2017)
- [Fine-tuning vision foundation model for crack segmentation in civil infrastructures](https://mlsanity.com/paper/2312.04233/) (2023)
- [Pretrained Domain-Specific Language Model for General Information Retrieval Tasks in the AEC Domain](https://mlsanity.com/paper/2203.04729/) (2022)
- [Generative AIBIM: An automatic and intelligent structural design pipeline integrating BIM and generative AI](https://mlsanity.com/paper/2311.04052/) (2023)
- [A Text Classification-Based Approach for Evaluating and Enhancing the Machine Interpretability of Building Codes](https://mlsanity.com/paper/2309.14374/) (2023)
- [QuakeBERT: Accurate Classification of Social Media Texts for Rapid Earthquake Impact Assessment](https://mlsanity.com/paper/2405.06684/) (2024)
- [A natural-language-based approach to intelligent data retrieval and representation for cloud BIM](https://mlsanity.com/paper/2411.09951/) (2024)
- [CBIM: A Graph-based Approach to Enhance Interoperability Using Semantic Enrichment](https://mlsanity.com/paper/2304.11672/) (2023)

## Built on by (most cited first, 2 of 2)

- [Learning and Simulating Building Evacuation Patterns for Enhanced Safety Design Using Generative Models](https://mlsanity.com/paper/2510.19623/) (2025)
- [BEST-KAG: Enhancing Question Answering of Building Engineering Standards with Multimodal Knowledge Graph Modeling and Large Language Model](https://mlsanity.com/paper/2608.11244/) (2026)

## Abstract (arXiv)

Large Foundation Models (LFMs) have demonstrated significant advantages in civil engineering, but they primarily focus on textual and visual data, overlooking the rich semantic, spatial, and topological features in BIM (Building Information Modelling) models. Therefore, this study develops the first large-scale graph neural network (GNN), BIGNet, to learn, and reuse multidimensional design features embedded in BIM models. Firstly, a scalable graph representation is introduced to encode the "semantic-spatial-topological" features of BIM components, and a dataset with nearly 1 million nodes and 3.5 million edges is created. Subsequently, BIGNet is proposed by introducing a new message-passing mechanism to GraphMAE2 and further pretrained with a node masking strategy. Finally, BIGNet is evaluated in various transfer learning tasks for BIM-based design checking. Results show that: 1) homogeneous graph representation outperforms heterogeneous graph in learning design features, 2) considering local spatial relationships in a 30 cm radius enhances performance, and 3) BIGNet with GAT (Graph Attention Network)-based feature extraction achieves the best transfer learning results. This innovation leads to a 72.7% improvement in Average F1-score over non-pretrained models, demonstrating its effectiveness in learning and transferring BIM design features and facilitating their automated application in future design and lifecycle management.