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.
  • 2021-12-25

    2021-12-25

    The 7th National BIM Academic Conference and the 8th Annual Meeting of the Professional Committee of the Building Information Modeling (BIM) of the China Graphics Society were successfully held in Chongqing on December 25, 2021.

    With the theme of "Innovation Leads Digital Empowerment", this conference focuses on the research and application of BIM technology and related technologies in intelligent construction and life-cycle project management. The total number of offline and online participants and viewers exceeded 10,000. I chaired the fourth session.

    At the annual meeting, a total of 100 members of the Special Committee attended the meeting, and the relevant matters of the development committee were discussed at the meeting. After discussion among the members, the 2022 BIM Committee will continue to be organized The National BIM Academic Conference, BIM Lecture Hall, BIM Academic Forum and other series of activities continue to support the "Longtu Cup" National BIM Competition and BIM Skill Level Examination, and strengthen links with the BIM Student Forum.

  • 2021-12-14

    2021-12-14

    Data Integration and Lightweight Technology for Rural Housing has been published in the Journal of Information Technology in Civil Engineering and Architecture.

    This paper focuses on integration and lightweight method of building information. It is one of the series research results in the field of information exchange in building engineering. Rural houses usually take the form of villages and are greatly affected by the geographical environment. Therefore, in the process of planning, design and construction, factors such as building information, village information and geographical environment need to be considered at the same time. In order to integrate GIS and BIM multi-source data into one platform, this study proposes an integration method and a lightweight method. It puts forward an automatic integration method based on FBX SDK. After reviewing the existing lightweight methods, it puts forward an improved QEM algorithm for rural housing, which uses vertex importance and model characteristic factor to control the simplification process. Above methods improve an existing rural housing lightweight integration platform from two aspects: expanding the application scenarios of the platform and improving the model processing ability of the platform. Taking a village in Xuzhou City, Jiangsu Province as an example, the methods prove to be practical. This study promotes the research and application of information technology in rural housing design and construction.

    Note: The research results were funded by the National Key R&D Program of China and the National Natural Science Foundation of China.

  • 2021-12-13

    2021-12-13

    2021 is the 110th anniversary of the founding of Tsinghua University and the 20th anniversary of Tsinghua University's postgraduate education in Shenzhen!

    Fast forward 20 years, the Nanguo campus has developed from Shenzhen Graduate School to Shenzhen International Graduate School, from borrowing the 518 office of the research institute building, to settling in the beautiful university town and building a new campus, which is a good illustration of the extraordinary leapfrog development of higher education! In the past 20 years, the International Graduate School has built 6 research institutes, with a total of more than 14,600 graduate students and more than 180 full-time teachers, and the international graduate school with increasing international reputation and influence has become an academic and scientific research bridgehead connecting Shenzhen to the world!

    20 years of deep love, the peach and plum trees as fragrant as possible. Wind and rain in the same boat, the Ming Dynasty is more brilliant! I believe that the celebration of the 20th anniversary of the Tsinghua Shenzhen International Graduate School will become a new starting point for inheriting the past, inheriting the past and opening up the future, and creating brilliant achievements again!

  • 2021-11-16

    2021-11-16

    A Data Integration and Simplification Framework for Improving Site Planning and Building Design has been published in the journal IEEE Access.

    This paper is the latest research results of the task force in the field of information exchange in building engineering. In the whole life of building, site planning and building design are two closely related stages. However, there are still some obstacles to data exchange in the current two phases. Site planning information is typically based on GIS management. The building design results are generally presented in BIM. Data incontextability makes it difficult for project participants to evaluate planning and design results on a unified platform. In view of this problem, this paper puts forward a cross-stage data integration and simplification framework for site planning and building design. By developing multi-scale data integration model, this study achieves multi-source, multi-scale, multi-format BIM-GIS data integration. Subsequently, this paper develops a geometric optimization algorithm for building models to realize the simplified building models generation of different LODs, to support the model display requirements at different scales. Finally, this paper develops a Web-side visualization platform to support multi-scale planning and design results fusion browsing. This study provides a feasible method for integrating site planning and building design results, which is expected to improve the efficiency of collaborative design process.

    Note: The IEEE Access is an important journal in the field of engineering and technology and belongs to the SCI journal. The research results was supported in part by the National Natural Science Foundation of China.

  • 2021-11-10

    2021-11-10

    Deep Learning-Based Instance Segmentation for Indoor Fire Load Recognition has been published in the journal IEEE Access.

    This article uses deep learning-based instance segmentation to automatically detect indoor fire loads. Accurate fire load (combustible objects) information is crucial for safety design and resilience assessment of buildings. Traditional fire load acquisition methods are relatively time consuming,  tedious, and error-prone, failed to adapt to dynamic changed indoor scenes. Thus, this research proposes a computer vision-based method to automatically detect indoor fire loads using deep learning-based instance segmentation. First, indoor elements are classified into different categories according to their material composition. Next, an image dataset of indoor scenes with instance annotations is developed. Finally, a deep learning model, based on Mask R-CNN, is developed  to detect fire loads in images. Experimental results show that our model achieves promising accuracy and proves the method has high performance characteristics. This research contributes to the body of knowledge 1) a novel method of high accuracy and efficiency for automated fire load recognition in indoor environments based on instance segmentation; 2) training techniques for a deep learning model in a relatively small dataset of indoor images; 3) an image dataset with annotations of indoor fire loads. Although instance segmentation has been applied for several years, this is a pioneering research on using it for automated indoor fire load recognition, which paves the foundation for automatic fire load estimation and resilience assessment for the built environment.

    Note: The IEEE Access is an important journal in the field of engineering and technology and belongs to the SCI journal. The research results was supported in part by the National Natural Science Foundation of China.