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.
  • 2025-04-12

    2025-04-12

    Thesis AI-based prediction of seismic time-history responses of RC frame structures considering varied structural parameters has been published in the Journal of Building Engineering.

    In this paper, an end-to-end framework for Intelligent Seismic Response Prediction, ISRPnet, is introduced. ISRPnet comprises a structural parameter module for discretizing reinforced concrete frame structures into a series of static features and an encoder-decoder architecture for encoding seismic loads and autoregressively predicting seismic responses. The model is trained on a data set of 16,544 cases generated through validated fibre-based finite element models. ISRPnet achieves promising performance on both frequent and rare earthquakes. ISRPnet rapidly and highly precisely predicts temporal responses for frequent earthquakes. The peak displacement predictions remain accurate for rare earthquakes. The superiority of the physical loss and the advantages of gated recurrent unit over long short-term memory are analysed in comparative experiments. Verification with unseen seismic waves beyond the training data shows the robust generalization and extrapolation capabilities of the framework. The proposed model accomplishes efficient surrogate computation of the full-process seismic response for a class of RC frame structures.

    Note: Journal of Building Engineering, a journal in the field of engineering and technology, belongs to the SCI journal in Q1 region, with an impact factor of 6.7 in 2024. The first author is Ge Kang,  a master's student in 2022, and the corresponding author is Wang Chen, an assistant professor in the Department of Civil Engineering at Tsinghua University. The research results were supported by the National Natural Science Foundation of China and the Cross-disciplinary Research and Innovation Fund Research Plan of Tsinghua Shenzhen International Graduate School.

  • 2025-03-31

    2025-03-31

    "Development of CAE courses for civil and architectural engineering to cultivate strong cross-disciplinary talents in the new era" has been first published on the China National Knowledge Network in the Journal of Architectural Education in Institutions of Higher Learning.

    In the current rapidly developing economic and technological environment, the civil and architectural engineering industry is facing new challenges and opportunities. To meet the demands of the new era, cultivating talents with interdisciplinary knowledge and comprehensive abilities has become an important direction for education reform in this industry. Based on an analysis of the demand for talents in the new era of civil engineering and architecture, this article explores how to cultivate strong cross-disciplinary talents through the construction of computer-aided engineering (CAE) courses and summarizes the basic experience of CAE course for civil and architectural engineering construction and reform.

    Note: I am the first author of the paper. The authors include Zhu Shiyi, a member of the research team, and Lin Jiarui, an associate researcher at the Institute of Disaster Prevention and Mitigation, Department of Civil Engineering, Tsinghua University.

  • 2025-03-30

    2025-03-30

    The citation count of "A self-learning dynamic path planning method for evacuation in large public buildings based on neural networks" on Google Scholar is 100. 

    Evacuation path planning is of significant importance to safely and efficiently evacuate occupants inside public buildings. Current computer simulation methods carry out evacuation analysis and then provide emergency education and management with a vivid virtual environment. However, efficient evacuation path planning approaches for evacuation guidance still meet the challenges of generating the analysis models, and lacking of real-time analysis methods under dynamic circumstances. In this study, a dynamic path planning approach based on neural networks is proposed for evacuation planning in large public buildings. First, an automatic process to develop the evacuation analysis model with simplified but sufficient information is presented. Then a path generation algorithm is proposed, together with an evaluation process, to generate a number of training sets for policy neural networks. When the primary policy neural network is preliminarily trained, it falls into a self-learning iteration process. Finally, the approach embeds a dynamic algorithm to simulate the mutual influences among all occupants in the building. The neural network was trained according to a real large public building and then the approach managed to provide rapid and feasible evacuation guidance for both occupants to escape in multiple scenarios and managers to design the evacuation strategy. Test results showed that the proposed approach runs 8–10 times faster than existing software and traditional search algorithms.

    Note: Neurocomputing is a journal of engineering technology in Q2 area. I am the corresponding author of the paper.

  • 2025-03-21

    2025-03-21

    A Multi-Factor-Fusion Framework for Efficient Prediction of Pedestrian-level Wind Environment Based on Deep Learning Published in IEEE Access.

    Efficient and accurate assessment of the Pedestrian-Level Wind Environment is essential to maintain a healthy and safe urban living environment. Numerical simulations, such as computational fluid dynamics and multi-scale modeling techniques, are commonly used for wind environment analysis. However, they are computationally intensive and time-consuming, particularly when dealing with the complexities of urban landscapes. This study proposes a novel Multi-Factor-Fusion (MFF) framework that leverages deep learning techniques. This framework integrates Graph Convolutional Networks and Long Short-Term Memory networks to extract and fuse multiple factors and create an end-to-end neural network model capable of directly predicting wind fields. By avoiding the need for grid division and iterative calculations, the framework significantly enhances the efficiency of wind environment analysis. Furthermore, multi-scale simulation data is used to train the model and correct the predictive results, ensuring the accuracy of the final results. This innovative approach has the potential to revolutionize the Pedestrian-Level Wind Environment prediction by achieving a trade-off between efficiency and accuracy.

    Note: IEEE Access is an important SCI journal in the field of engineering technology. I am the first author and corresponding author of the paper. The research results were supported by the National Key Research and Development Program of China, and the Guangdong Basic and Applied Basic Research Foundation and the National Natural Science Foundation of China. 

  • 2025-03-18

    2025-03-18

    The paper Intelligent BIM Searching via Deep Embedding of Geometric, Semantic, and Topological Features has been published in the journal Buildings.

    As a digital representation of buildings, building information models (BIMs) encapsulate geometric, semantic, and topological features (GSTFs), to express the visual and functional characteristics of building components and their connections to create building systems. However, searching for BIMs pays much attention to semantic features, while overlooking geometric and topological features, making it difficult to find and reuse rich knowledge in BIMs. Thus, this study proposes a novel approach to intelligent BIM searching by embedding GSTFs via deep learning (DL). First, algorithms for extracting GSTFs from BIMs and identifying required GSTFs from search queries are developed. Then, different GSTFs are embedded via DL models, creating vector-based representations of BIMs or search queries. Finally, similarity-based ranking is adopted to find BIMs highly related to the queries. Experiments show that the proposed approach demonstrates an efficiency of 780 times greater than manual retrieval methods and 4–6% more efficient than traditional methods. This study advances the field of BIM searching by providing a more comprehensive, accurate, and efficient method for finding and reusing rich knowledge in BIMs, ultimately contributing to better building design and knowledge management.

    Note: Buildings, a journal in the field of Engineering and technology, is a Q2 SCI journal with an impact factor of 3.1 for 2024. The first author of the paper is Huang Pinhao, a master's student in the Department of Civil Engineering of Tsinghua University, and the corresponding author is Lin Jiarui, a civil engineering student of Tsinghua University. The research results have been funded by the financial support received from the National Key R&D Program of China and the National Natural Science Foundation of China.