本研究以港珠澳大桥珠海公路口岸钢网架屋盖结构的温度、应变实测数据为实际工程背景，基于反向传播 (BP) 神经网络模拟温度和应变之间的非线性关系实现大跨度空间钢结构温度效应预测; 并将温度荷载及风荷载作用下的结构监测应变分离，实现杆件荷载的主导工况自主判定; 最后开发基于数据库与 Matlab 接口的神经网络程序，以插件的形式嵌入建筑信息模型 (BIM) 软件中，并将监测数据解析后的信息集成于BIM 三维模型中，从而指导监测人员对结构进行检修。本研究构造的神经网络模型较好地拟合了大跨度空间钢结构温度与应变的非线性关系，基于 BIM 技术的二次开发，实现了温度效应的预测与风荷载识别，进一步开拓了监测数据解析的研究，对同类型的工程有一定的借鉴作用。

Temperature and strain measured data of the steel grid roof structure at Zhuhai port of Hong Kong-Zhu-hai-Macao bridge were taken as the actual engineering background，and the nonlinear relationship between tempera-ture and strain was simulated by back propagation (BP) neural network to realize temperature effect prediction of long-span spatial steel structure． The strain monitored of the structure under the action of temperature load and wind load was separated to realize the independent determination of the dominant working condition of the loads of the member． Finally，a neural network program based on database and Matlab interface was developed，and it was fur-ther embedded into building information model (BIM) software in the form of a plug-in． The information obtained from the analysis of the monitoring data was integrated into the BIM three-dimensional model，so as to guide the monitoring personnel to conduct maintenance on the structure． The neural network model constructed in this study can well fit the non-linear relationship between temperature and strain of long-span spatial steel structure． Based on the secondary development of BIM technology，the prediction of temperature effect and the identification of wind load were realized，and the research on the analysis of monitoring data was further developed，which can provide reference for similar projects．