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.

2026-04-28

"The method and system for monitoring the structural health of offshore wind turbines" has been granted an invention patent certificate (Patent Number: ZL202610248475.5). 

The present invention discloses a method and system for structural health monitoring of offshore wind turbines. The method comprises: in the offline stage, constructing a strain snapshot matrix through finite element simulation, extracting a low-dimensional feature subspace basis matrix by using proper orthogonal decomposition, and mapping the structural grid to a graph; Simultaneously, the Fourier feature mapping of node coordinates is carried out, and after concatenating with the reconstructed strain features, a spectral graph neural network based on Chebyshev graph convolution is trained. In the online monitoring stage, based on the real-time collected sparse strain data on one side, the modal coefficients are quickly solved through the mask matrix and the pre-stored basis matrix, and the full-field strain is preliminarily reconstructed. Then, it is input into the trained network model to real-time infer the high-fidelity full-field displacement and stress fields. This invention can effectively adapt to any changes in sensor layout and achieve real-time and accurate reconstruction of the full-field physical response of complex structures under the harsh condition of sparse measurement on one side, solving the problem of traditional techniques' dependence on fixed measurement points and bilateral measurement.

Note: The inventors of this patent also include doctoral student Jiang Chunhao and postdoctoral fellow Li Yihong.

2026-04-16

The paper "ST-SRNet: A deep learning framework for seismic response prediction of subsea tunnels" has been published in Tunnelling and Underground Space Technology.

As key infrastructure in nearshore and offshore areas, subsea tunnels face extremely high risks from earthquake disasters. Conventional numerical methods are computationally intensive, making them inadequate for real-time prediction demands under complex marine environments. To address this, this study proposes a rapid prediction method integrating high-fidelity finite element (FE) simulation and deep learning technology. A two-dimensional FE model incorporating bidirectional (horizontal and vertical) ground motions and hydrodynamic pressure effects is established to construct a comprehensive seismic response database. On this basis, the Subsea Tunnel Seismic Response Network (ST-SRNet) is developed, combining one-dimensional convolution, attention mechanisms, long short-term memory (LSTM), and a Feature-wise Linear Modulation (FiLM) module to achieve efficient and accurate prediction of seismic responses at multiple monitoring points. Results show that the model achieves high prediction accuracy, with the coefficient of determination values exceeding 0.95 and peak relative errors controlled below 10%, while also exhibiting strong generalization. An engineering case study further validates the robustness and applicability of the proposed method, providing effective technical support for disaster assessment, structural monitoring, and real-time early warning of subsea tunnels.

Note: Tunnelling and Underground Space Technology is a Q1 top journal in the field of engineering and technology, with an impact factor of 7.4 in 2025. The first author of the paper is Master’s student Yimu Chen, and Prof. Yutao Guo is the corresponding author. This research was supported by the Shenzhen Science and Technology Program.

2026-03-26

The paper "Typhoon-Induced Risk Evolution in Wind Farms: From Disaster-Inducing Factors Identification to Domino Effect Assessment" has been published in the journal Reliability Engineering & System Safety. 

In response to the technical accidents caused by typhoons in the complex waters of the South China Sea, this study proposes a Disaster-Inducing and Risk Evaluation (DIRE) framework. This framework integrates two modules: the Disaster Inducing Factor Extraction Model (DIFEM), which identifies the key environmental driving factors that cause damage to wind turbines through a hybrid physical-data fusion analysis; and the Hierarchical Analytical Domino Evaluation System (HADES), which realizes systematic hazard classification, hierarchical structuring, probability assessment, and consequence assessment. Through the analysis of five typhoon-induced disaster events in the South China Sea, this study not only identified the common and specific disaster-causing factors that cause damage to wind turbines, but also quantified the cascading disaster risks. The findings of this study provide reliable data and decision-making support for disaster prevention and mitigation on wind farms.

Note: "Reliability Engineering & System Safety" is a top journal in the field of engineering technology in the Q1 zone, with an impact factor of 11.0 in 2025. The first author of the paper is doctoral student Li Yilin and postdoctoral researcher Li Yihong, and I am the corresponding author. The research results were supported by the National Key R&D Program of China, the Guangdong Basic and Applied Basic Research Foundation, the Shenzhen Science and Technology Program, and the Shuimu Tsinghua Scholar Program.