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.

2026-02-20

The paper "Multi-task deep learning-based failure diagnosis for tubular joints: Automated identification and description generation" was published in the journal Advanced Engineering Informatics.

This study addresses the critical yet vulnerable tubular joints in offshore platforms and bridges, proposing a multi-task deep learning model for simultaneous failure identification and description generation. Traditional failure inspection methods are time-consuming and labor-intensive, while existing intelligent methods often focus on single-task or single-modal designs, which fail to fully leverage multimodal data and struggle to capture coexisting failure modes, limiting comprehensive diagnostic assessments. To overcome these challenges, a multimodal database is constructed from 141 experimental programs, including 409 failed joints with images, textual descriptions, and component-level labels, further augmented to 1,227 images and 3,681 sentences. The proposed model employs a pretrained encoder for image feature extraction, a multi-label classification decoder for failure identification, and an attention-based image captioning decoder for failure description generation. Experimental results demonstrate that the model achieves a BLEU-4 score of 76.70 and an mAP of 0.9467, outperforming single-task baselines. Validation on real engineering images further shows its preliminary transferability, highlighting the potential of the multi-task learning framework for automated failure diagnosis.

Note: Advanced Engineering Informatics is a top-tier Q1 journal in the field of engineering technology, with an impact factor of 9.9 in 2025. The first author of the paper is Zhang Wenhao, a PhD student at Southern University of Science and Technology, with Professor Hou Chao as the corresponding author. The research was funded by the National Natural Science Foundation of China (NSFC) , the National Key Research and Development Program of China, and Shenzhen Science and Technology Program.

2026-02-12

The paper titled “Deep learning-based real-time multiload response prediction and inverse analysis of offshore bridges” has been published in Engineering Structures.

Offshore bridges operate in complex ocean environments, making structural analysis, design, and monitoring more challenging. Conventional time-history analysis and nonlinear model updating based on finite-element methods are computationally intensive and time-consuming, which limits their use in many scenarios. To develop a more efficient analytical tool, this study proposes a deep learning-based offshore bridge predictor, DeepOBP. By integrating structural characteristics with coupled dynamic loads in ocean environments, DeepOBP enables millisecond-level, high-precision nonlinear dynamic offshore bridge response predictions. Building on this surrogate model, a differentiable structural inverse framework (Inverse DeepOBP) is further developed, coupling the predictor with gradient-based optimization to support rapid damage identification and model calibration for structural health monitoring.

Experimental results show that DeepOBP achieves high accuracy under both normal operating conditions and multi-hazard coupled conditions, with R² = 0.93 and 0.92, respectively. Inverse DeepOBP delivers more than a 10-fold speed-up over surrogate-based model updating using a heuristic algorithm, and more than a 10⁴-fold speed-up over nonlinear finite-element model updating, while maintaining relative errors below 7% for each identified parameter. These results demonstrate the potential of the proposed framework for efficient structural analysis and real-time monitoring of offshore bridges.

Note: Engineering Structures is a Q1 journal in the Engineering and Technology field, with a 2025 impact factor of 6.4. The first author of the paper is Master’s student Zhuyu Sun, and Prof. Yutao Guo is the corresponding author. This work was supported by the National Key Research and Development Program of China and the Shenzhen Science and Technology Program.

2026-02-11

The paper entitled “ROM-PINN: A physics-informed neural network with reduced-order modelling for nonlinear structural response prediction” has been published in the journal Structures.

To address the high computational cost of conventional finite element time-history analysis for nonlinear structural responses and the lack of physical consistency in purely data-driven neural networks, this study proposes a Reduced-Order Model Physics-Informed Neural Network (ROM-PINN). The proposed method embeds the nonlinear reduced-order dynamic equilibrium as a physics constraint in network training, while learning nonlinear effects through a virtual-force representation. By integrating reduced-order projection with a sensor-aware deployment strategy, ROM-PINN enables efficient and physically consistent response prediction for high-dimensional structural systems from limited observations. A case study on an offshore bridge demonstrates that ROM-PINN achieves accurate multi-sensor response prediction and provides clear quantitative improvements over a purely data-driven baseline: the mean squared error (MSE) decreases by 10.0%, the physics-consistency metric (PR) increases by 83.7%, and under 10% measurement noise the MSE is further reduced by 23.3%. These results highlight the strong potential of ROM-PINN for practical engineering applications such as rapid analysis and online monitoring of large-scale nonlinear structures.

Note: Structures is a Q1 journal in the Engineering and Technology category, with an Impact Factor of 4.3 (2025). The first author of the paper is Master’s student Zhu-Yu Sun, and Dr. Yu-Tao Guo served as the corresponding author. This work was supported by the National Key Research and Development Program of China and the Shenzhen Science and Technology Program.