TOP-1

The research group will recruit several PHD and master students and 2 postdocs.

There are three requirements for doctoral and master students  enrollment: (1) Applicants should have an engineering background and have a strong interest in information technology. Applicants should have obtained a relevant bachelor's or master's degree; (2) Strong technical background, including but not limited to research experiences in BIM/GIS, Internet, digital twin, artificial intelligence, etc. Candidates with research or practical experiences in algorithms, and the development of large-scale software systems or Web/App will be preferred; (3) Highly self-motivated, good written and oral English communication skills, and independent working ability.

Postdoctoral recruitments should also meet the following two points: (1) The applicant should be under the age of 35 and have obtained a doctoral degree no more 3 years; (2) The research directions are civil engineering information technology, Marine environmental information modeling and application, data-driven knowledge discovery and application, etc. (Note: postdoctoral candidates are required to present a half-hour academic presentation, including the main research works during PHD period and future postdoctoral work plans).

If you are interested, please send your resume, transcripts and work plan to the email: hu.zhenzhong@sz.tsinghua.edu.cn. For details, please see: PHD Master Recruitment and Postdoctoral Recruitment.

TOP-2

26 November 2021, Discharge of treated Fukushima nuclear accident contaminated water: macroscopic and microscopic simulations has been published on National Science Review, which is a full affirmation of the students and teachers of the subject group who are generous and rigorous in their learning! NSR officer micro-push high-quality and efficient, reflecting China's outstanding leading journals of the super-class level! Thanks to the director of singhua University's Institute for Ocean Engineering (IOE), Zhang Jianmin's guidance and support, thanks to the editorial department and reviewers for their high evaluation!

The results of this study are of great significance for the prediction of long-term spread of pollutants, the rational response of nuclear wastewater discharge plans and the monitoring of subsequent radioactive material concentrations. In the future, we will continue to deepen our research, Further explore the long-term impact of the discharge of nuclear waste water on the whole ocean and mankind, and provide important decision support for the country and the world to deal with the nuclear wastewater crisis!

Note: National Science Review , whose impact factor in 2021 is 17.275, is the top journal in the multi-discipline domain. For more information, please see the introduction video.

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

"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

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.