主管：中华人民共和国应急管理部
主办：应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU
消防科学与技术

消防科学与技术 ›› 2026, Vol. 45 ›› Issue (4): 27-34.

• • 上一篇    下一篇

基于机器学习的古代木廊桥火灾致灾因子危险性评估

刘杰1,2, 魏诗霖2, 潘柏瑀1,2   

  1. （1.上海交通大学 设计学院，上海 200240； 2.上海交通大学木建筑设计与研究中心，上海 200050）
  • 收稿日期:2026-02-04 修回日期:2026-03-12 出版日期:2026-04-15 发布日期:2026-04-15
  • 作者简介:刘 杰，上海交通大学设计学院建筑学系，教授，主要从事木构建筑技术史及廊桥方面的研究，上海市闵行区东川路800号，200240，jackliu@sjtu.edu.cn。
  • 基金资助:
    国家重点研发计划项目（2023YFF0906103）

Machine-learning-based hazard assessment of fire-inducing factors in ancient timber covered bridges

Liu Jie1,2, Wei Shilin2, Pan Boyu1,2   

  1. (1. School of Design, Shanghai Jiao Tong University, Shanghai 200240, China; 2. Timber Architecture Research and Design Center, Shanghai Jiao Tong University, Shanghai 200050, China)
  • Received:2026-02-04 Revised:2026-03-12 Online:2026-04-15 Published:2026-04-15

摘要： 为定量识别古代木廊桥火灾中不同致灾因子对火灾发生的相对危险性，构建可用于宏观筛查与分级管控的量化指标体系。通过整合地方志文献、火灾调查报告、新闻记录与现状调查资料，建立包含226座古代木廊桥、285次不同灾害事件的正负样本数据库，以“是否发生火灾”为响应变量；选取祭祀用火、爆竹燃放、电气设施、易燃物堆积、旅游开发、居民生活、雷击高发区及重要交通节点8类致灾因子，均以二元变量表征其存在与否。采用随机森林（RF）、支持向量机（SVM）、XGBoost 与 LightGBM 进行二分类对比建模，使用准确率（ACC）与 ROC 曲线下面积（AUC）评估模型性能，选取综合表现最优的RF模型；基于RF特征重要性（MDI/Gini）归一化获得致灾因子危险性权重，并进一步构建木廊桥“火灾致灾因子危险性指数”（FFHI）与四级分级规则。RF模型输出的权重显示：易燃物堆积（25.50%）与祭祀用火（21.20%）为危险性最高的两类因子；居民生活（17.80%）与电气设施（10.30%）次之；爆竹燃放（8.30%）与重要交通节点（8.20%）占比接近；旅游开发（5.00%）与雷击高发区（3.70%）权重相对较低。基于权重构建的FFHI取值范围为0~1，可用于对木廊桥火灾致灾条件进行快速量化，并据此划分四级危险性等级。木廊桥火灾防控应优先聚焦易燃物治理与祭祀用火可控化，并同步规范居民生活与电气设施风险；对爆竹燃放、交通节点与旅游高峰等情境型因素实施分时段强化管控。FFHI与四级分级方法可支撑跨区域宏观筛查、巡查资源配置与整治优先顺序制定，并具备随数据补充而滚动更新的应用潜力。

关键词: 古代木廊桥, 火灾致灾因子, 火灾危险性评估, 机器学习, 文物建筑防火

Abstract: This study aims to quantitatively identify the relative hazard of different fire-inducing factors and to establish a metric framework applicable to large-scale screening and graded fire-risk management. Multi-source data including local gazetteers, official fire investigation reports, news records, and field surveys were integrated to construct a positive and negative sample database covering 226 ancient timber covered bridges and 285 disaster events. Fire occurrence (yes/no) was defined as the response variable, while eight fire-inducing factors—ritual fire use, fireworks setting off, electrical installations, combustible accumulation, tourism development, residential activities, lightning-prone areas, and important transportation nodes—were encoded as binary variables indicating presence or absence. Random Forest (RF), Support Vector Machine (SVM), XGBoost, and LightGBM classifiers were developed and compared, with model performance evaluated using accuracy (ACC) and the area under the ROC curve (AUC). The RF model exhibited the most balanced performance and was selected for subsequent analysis. Based on normalized feature importance derived from the RF model (MDI/Gini), hazard weights of fire-inducing factors were quantified, and a Fire-inducing Factor Hazard Index (FFHI) with a four-level classification scheme was further established. The results indicate that combustible accumulation (25.50%) and ritual fire use (21.20%) are the most hazardous factors, followed by residential activities (17.80%) and electrical installations (10.30%). Fireworks (8.30%) and important transportation nodes (8.20%) show comparable contributions, while tourism development (5.00%) and lightning-prone areas (3.70%) exhibit relatively lower weights. The FFHI ranges from 0 to 1 and enables rapid quantification of fire-inducing conditions at the individual bridge level, supporting classification into four hazard grades. The findings suggest that fire prevention for timber covered bridges should prioritize the control of combustible materials and ritual fire use, while simultaneously regulating residential activities and electrical installations. Context-dependent factors such as fireworks setting off, transportation nodes, and tourism peaks require strengthened time-specific management. The proposed FFHI and grading approach provide a quantitative basis for regional-scale screening, inspection prioritization, and intervention sequencing, and can be dynamically updated as new data become available.

Key words: ancient timber covered bridges, fire-inducing factors, fire hazard assessment, machine learning, fire protection of heritage buildings