西安建筑科技大学
国家自然科学基金项目（61373112，51878536）;西安建筑科技大学基础研究基金(RC1716)
Xi`an University of Architecture and Technology
The National Natural Science Foundation of China(61373112，51878536),and Basic Research Fund for Xi 'an Architecture and Technology University (RC1716).
近年来，BIM模型在建筑行业中发挥了核心作用，成为了智能建筑和智慧城市的活跃研究方向。房间是BIM模型中重要组成部分，房间数据的完善性对提高合规性检查、空间分析等BIM应用的自动化程度、效率、准确率具有十分重要的意义。针对于BIM模型中房间信息不足导致识别房间困难的问题，提出融合模拟退火和SVM的房间功能类型识别方法，并在住宅模型集上进行验证。首先，基于Revit平台二次开发技术，先获取房间形状特征参数，通过拓展空间句法变量如连接度、控制度、平均深度、集成度、智能度得到房间的空间拓扑特征，获取房间特征数据集；然后，使用模拟退火算法，优化SVM模型的主要参数。最后，在验证集上验证优化后的SVM模型的准确率。实验结果表明，与其他同类文献提出的算法比较，该算法准确率可达95.59%，优于同类算法，在4个UCI数据集上的实验验证了分类模型的稳定性和有效性。
BIM models have played a central role in the construction industry in recent years and become an active research direction for intelligent buildings and intelligent cities. Room is an important part of BIM model, and the perfection of room data is of great significance to improve the automation, efficiency and accuracy of BIM applications such as compliance inspection and spatial analysis. to the problem that insufficient room information in the BIM model leads to the difficulty of identifying the room, a room function type identification method combining simulated annealing and SVM is proposed and verified on the residential model set. first, based on the Revit platform secondary development technology, the room shape feature parameters are obtained first, and the main parameters of the SVM model are optimized by expanding the spatial syntactic variables such as connectivity, control, average depth, integration, intelligence. finally, the accuracy of the optimized SVM model is verified on the validation set. experimental results show that compared with the algorithms proposed in other similar literatures, the accuracy of the proposed algorithm can reach 95.59%, which is better than that of similar algorithms. experiments on four UCI datasets verify the stability and effectiveness of the classification model.
