Zhenzhong Hu

    He received both his BE and PhD degree in the Department of Civil Engineering at Tsinghua University, China. He was a visiting researcher in Carnegie Mellon University.
    He is now the associate professor in Shenzhen International Graduate School, Tsinghua University, and also the secretary general of the BIM Specialty Committee of the China Graphics Society.
    His research interests include information technologies in civil and marine engineering, building information modeling (BIM) and digital disaster prevention and mitigation.
  • 2018-11-21

    The research team had academic discussions with Professor Jacob Beetz, from the Architecture College of German RWTH Aachen University in the afternoon of November 21st, 2018. Students Yaqi Xiao and Shuang Yuan, presented the research achievements on "Automatically generating MEP logic relationships from building information models with identification rules" and "Geometric Optimization of Building Information Models in MEP Projects: Algorithms and Techniques for Improving Storage, Transmission and Display" to Prof. Beetz. Prof. Beetz also presented his relevant research on BIM Server and IFC.

  • 2018-11-21

    The progress report of Glodon Software Company Limited-Tsinghua University Building Information Modeling Joint Research Centre was held in Glodon Building Phase II in the morning of November 21st, 2018. The directors and undertakers of the project respectively reported the recent achievements of 9 scientific research projects in last year. The research team presented the achievements on "Acquisition and Analysis Techniques of Dynamic Information based on BIM". In addition, the second batch of research topics were permitted and the proposed research on "Knowledge Graph and Intelligent Search Technology in MEP Field" was approved in this meeting.

  • 2018-11-16

    Our new article entitled "A Review of BIM-Based Artificial Intelligence Methods" has been published in "Journal of Graphics". Based on an extensive literature review, the paper summarizes previous studies on BIM-based AI from the perspective of technology, system and application, and introduces commonly used AI technologies, typical development platforms and methods, as well as the successful applications in different construction stages such as design, construction, operation and maintenance. In this light, the problems and challenges in the field are analyzed, the causes of the problems are summarized, and the future direction of development are anticipated.

  • 2018-11-15

    I was invited to give a keynote speech in "The Conference on Application and Achievements for Lifecycle in Engineering Projects based on BIM". The topic was "Research on Key Technologies of BIM Application in MEP Projects". Aming at MEP Projects, the presentation introduced the definitions, processes and optimizations of the information model, the development of application system in the design, construction, operation and maintenance phases, as well as the application of specific engineering projects, and the application status and development trances of BIM-based technology in MEP projects. The Conference was sponsored by Smart Building Network and co-sponsored by the China Construction First Bureau Co., Ltd.

  • 2018-11-02

    The research team reported the progress of the sub-project "Standardized Big Data Extraction Technology of Building and MEP system based on Energy Consumption Monitoring system", which belongs to the project "Key technologies for Database and Data integrated of Building and MEP system", in the third quarter symposium in 2018 for the 13th Five-Year Key R&D Project "Research and Demonstration on Big Data Management Technologies for Green Buildings within the Building Lifecycle". A software was developed by our research team, which mainly established the building energy information model for dynamic monitoring. We also proposed the integration methods of building energy information model and dynamic monitoring data, and formed the standardized extraction technology for static and dynamic data.