基于IFC的建筑信息模型(BIM)语义检索技术研究

项目名称： 基于IFC的建筑信息模型(BIM)语义检索技术研究

项目编号： No.61472202

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 计算机科学学科

项目作者： 刘玉身

作者单位： 清华大学

项目金额： 83万元

中文摘要： 三维模型语义检索是当前研究热点，语义检索不仅要考虑检索对象的几何形状特征，还应关注应用领域的语义特征。近年来，在建筑工程领域随着BIM（Building Information Modeling）技术的快速发展和普及，互联网上三维BIM模型正迅猛增长，如何有效地检索这些模型资源变得日益迫切。传统的三维模型检索大多基于关键字匹配、几何形状相似匹配方法，而缺乏对建筑工程领域语义信息的提取、表示和匹配，不能很好地满足用户查询，难以有效地支持设计复用。本课题针对互联网上建筑产品库开展BIM模型语义检索技术研究，并开发一个针对BIM模型的垂直搜索引擎原型，有助于建筑设计的快速查找和有效复用。主要研究内容包括：基于IFC标准的BIM领域本体构建和学习，基于几何与语义混合特征的BIM模型自动标引，基于Tversky模型与信息量的BIM模型语义相似性度量，基于SPARQL与LCA剪枝的本体扩展查询方法。

中文关键词： 三维模型检索；建筑信息模型(BIM)；工业基础类(IFC)；语义检索；领域本体

英文摘要： Semantic-based 3D model retrieval is the current research hot topic, which should not only consider the geometric features of 3D models, but also focus on the domain-specific semantic features. With the rapid popularity of Building Information Modeling (BIM) technologies in AEC field, BIM model resources such as building product libraries are growing rapidly on the Web. Meanwhile, how to efficiently search such BIM models becomes increasingly urgent. However, the conventional methods for 3D model retrieval are mostly based on keyword matching or geometric shape matching, which cannot provide effective support for design reuse applications due to the lack of extraction, representation and matching of semantic information in AEC field. This proposal presents a new methodology for semantic-based BIM model retrieval, especially for the Web building product libraries. We also develop a vertical search engine for BIM model retrieval, which will help users to quickly find the 3D models related to design reuse. Our work consists of several parts as follows. Firstly, based on Industry Foundation Classes (IFC) schema that is a major data exchange standard for BIM, we construct a domain-specific ontology for encoding the domain knowledge and extend the ontology through machine learning techniques. Secondly, an annotation and indexing method for BIM models is presented based on the geometric and semantic features. Thirdly, we propose a semantic similarity measure for BIM models based on Tversky measure and information content techniques. Finally, an ontology query expansion method is presented based on SPARQL and LCA-based concept tree pruning techniques.

英文关键词： 3D model retrieval;Building Information Modeling (BIM);Industry Foundation Classes (IFC);Semantic-based retrieval;Domain ontology