找建筑最易因火灾倒塌的位置，用AI加速安全评估。
Prediction of the Most Fire-Sensitive Point in Building Structures with Differentiable Agents for Thermal Simulators
- 用图神经网络预测火灾下最大层间位移比，指导找最敏感点。
- 在大规模仿真数据上验证，能高效识别最危险起火位置。
- 适合结构设计与消防评估人员，可显著减少模拟成本。
火灾安全对保障建筑结构稳定至关重要，但评估结构是否满足防火要求极具挑战。火灾可能在建筑内任意位置发生，逐一模拟所有潜在火情既昂贵又耗时。为此，本文提出“最火敏感点”（MFSP）概念及高效机器学习框架以识别该点。MFSP定义为若在此处起火，将对建筑稳定性造成最严重破坏的位置，即最坏情况的火灾场景。框架中，图神经网络（GNN）作为可微分代理，接入传统有限元分析（FEA）模拟器，预测火灾下的最大层间位移比（MIDR），并据此训练与评估MFSP预测器。此外，引入新型边更新机制与基于迁移学习的训练方案。在大规模仿真数据集上的评估表明，该框架在识别MFSP方面表现优异，为结构设计中的火灾安全优化提供了变革性工具。所有数据集与代码均已开源。
原文摘要 · Abstract (English)
Fire safety is crucial for ensuring the stability of building structures, yet evaluating whether a structure meets fire safety requirement is challenging. Fires can originate at any point within a structure, and simulating every potential fire scenario is both expensive and time-consuming. To address this challenge, we propose the concept of the Most Fire-Sensitive Point (MFSP) and an efficient machine learning framework for its identification. The MFSP is defined as the location at which a fire, if initiated, would cause the most severe detrimental impact on the building's stability, effectively representing the worst-case fire scenario. In our framework, a Graph Neural Network (GNN) serves as an efficient and differentiable agent for conventional Finite Element Analysis (FEA) simulators by predicting the Maximum Interstory Drift Ratio (MIDR) under fire, which then guides the training and evaluation of the MFSP predictor. Additionally, we enhance our framework with a novel edge update mechanism and a transfer learning-based training scheme. Evaluations on a large-scale simulation dataset demonstrate the good performance of the proposed framework in identifying the MFSP, offering a transformative tool for optimizing fire safety assessments in structural design. All developed datasets and codes are open-sourced online.
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