期刊文献+

深港数字化工程合作的BIM应用平台建设 被引量:2

The Development of BIM Application Platform for Digital Construction Cooperation Between Shenzhen and Hong Kong
下载PDF
导出
摘要 香港的工程项目建设与内地相比,有着鲜明的特色。本文根据深港两地合作的工程项目经验,阐述了项目中BIM数据信息管理要求,特别突出了香港项目建模要求的关键点,总结了香港模式的优点和可借鉴之处。根据香港的项目实践经验,基于严格的建模需求,可以实现一致地用于竣工交付的模型。未来深港两地的合作应当以此为基础,通过平台进一步挖掘工程数据的潜力。文中指出,依靠现有的信息化技术,可以建立一套结合多种类数据的架构体系。通过将模型数据提取到独立的平台中,可以逐步实现工程项目全过程BIM应用的目标。这一平台基于香港的标准化BIM体系,结合深圳方面的工程数字化技术能力,在粤港澳大湾区建设的政策推动下,具备广阔的发展前景。 The construction projects in Hongkong are quite different from those of the mainland.In this paper we describe the BIM data management requirements based on our Shenzhen-Hong Kong project cooperation experience.We focus on the key points of BIM modeling requirements in project of Hong Kong and summarize the advantages and lessons that the mainland can learn from.According the Hongkong experience,with strict modeling standards and cooperation guidelines,it is possible to realize consistent as-built BIM models.We believe future cooperation between Shenzhen and Hong Kong in construction will follow this approach,and a project BIM platform can exploit the full potential of engineering data.With state-of-art information technology,a comprehensive platform architecture that collects various forms of data is proposed in this paper.The BIM data can be retrieved to an independent database to gradually realize BIM application in the full process of construction projects.With the support of"Guangdong-Hong Kong-Macao Greater Bay Area"development policy,a construction project BIM platform can combine the advantages of Hongkong and Shenzhen and has vast potential for future development.
作者 潘多忠 程嘉 吴鼎政 Pan Duozhong;Cheng Jia;Wu Dingzheng(CHN-HK Construction Project Management Research Centre,HongKong,China;Shenzhen Sogar Engineering Consultants Co.,Ltd.,Shenzhen 518054,China)
出处 《土木建筑工程信息技术》 2021年第3期15-23,共9页 Journal of Information Technology in Civil Engineering and Architecture
关键词 BIM IFC 全过程工程咨询 智能建造 大数据 BIM IFC Full-Process Engineering Consulting Intelligent Construction Big Data
  • 相关文献

参考文献2

二级参考文献13

共引文献51

同被引文献19

引证文献2

二级引证文献4

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部