用生成模型快速学习建筑疏散模式，提升安全设计效率
Learning and Simulating Building Evacuation Patterns for Enhanced Safety Design Using Generative Models
- 基于扩散模型从热力图学习疏散模式，解耦布局与人流特征
- 相比传统方法，模拟速度提升16倍，精度提升超37%且耗时仅2分钟
- 适合早期建筑设计快速迭代，支持大规模方案探索
疏散模拟对建筑安全设计至关重要，但传统方法依赖精细建模和大量参数，难以在设计初期快速迭代。为此，本文提出DiffEvac，一种基于生成模型（GMs）学习建筑疏散模式的新方法，实现高效模拟与安全优化。首先构建包含399种功能布局及对应疏散热力图的数据集；其次提出解耦特征表示，将布局、人员密度等物理特征嵌入生成模型；最后采用基于图像提示的扩散模型，从仿真热力图中学习疏散模式。相较于使用RGB表示的条件生成对抗网络（Conditional GAN），DiffEvac在结构相似性（SSIM）上提升37.6%，峰值信噪比（PSNR）提高142%，模拟速度加快16倍，单次模拟仅需2分钟。案例研究显示，该方法显著加速设计迭代，为智能建筑安全优化提供新路径。研究意义在于降低建模负担，支持大规模‘假设分析’，并可集成至多目标设计工具。
原文摘要 · Abstract (English)
Evacuation simulation is essential for building safety design, ensuring properly planned evacuation routes. However, traditional evacuation simulation relies heavily on refined modeling with extensive parameters, making it challenging to adopt such methods in a rapid iteration process in early design stages. Thus, this study proposes DiffEvac, a novel method to learn building evacuation patterns based on Generative Models (GMs), for efficient evacuation simulation and enhanced safety design. Initially, a dataset of 399 diverse functional layouts and corresponding evacuation heatmaps of buildings was established. Then, a decoupled feature representation is proposed to embed physical features like layouts and occupant density for GMs. Finally, a diffusion model based on image prompts is proposed to learn evacuation patterns from simulated evacuation heatmaps. Compared to existing research using Conditional GANs with RGB representation, DiffEvac achieves up to a 37.6% improvement in SSIM, 142% in PSNR, and delivers results 16 times faster, thereby cutting simulation time to 2 minutes. Case studies further demonstrate that the proposed method not only significantly enhances the rapid design iteration and adjustment process with efficient evacuation simulation but also offers new insights and technical pathways for future safety optimization in intelligent building design. The research implication is that the approach lowers the modeling burden, enables large-scale what-if exploration, and facilitates coupling with multi-objective design tools.
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