TOP

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 offshore engineering structural analysis, marine environment numerical simulation and prediction, marine digital twin, etc. Candidates with research or practical experiences in numerical algorithms, industrial software development, or high-performance computing 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.

2026-08-17

Recently, the journal "Ocean" (published by Tsinghua University) has been officially included in the EuroPub database (the European Academic Publishing Center Database). The journal is edited by Professor Torgeir Moan from the Norwegian University of Science and Technology, Academician Wang Zhonglin from the Chinese Academy of Sciences, and Academician Zhang Jianmin from Tsinghua University.

The EuroPub database (https://europub.co.uk/) is a well-known comprehensive and multidisciplinary open-access academic journal index database covering various fields such as natural sciences, engineering technology, and social sciences. Currently, it includes over 27,000 active and authoritative journal index records and more than 700,000 articles. Most of these journals are also indexed by Web of Science and Scopus. This inclusion in the EuroPub database marks a solid step forward for the Ocean journal in terms of international dissemination and academic influence, and also builds a broader international academic exchange platform for global marine research results.

Ocean focuses on the latest research progress in marine science, marine engineering, and marine technology, covering areas such as marine biology, marine earth science, marine automation and robotics technology, and underwater engineering. We sincerely invite submissions of articles with leading-edge, innovative, and practical content related to the marine field, to jointly promote the development of marine disciplines and technological innovation!

Submission link: https://mc03.manuscriptcentral.com/ocean.

2026-08-12

The paper "Nonlinear Vortex-Induced Vibrations of Fluid-Conveying Pipes with Gravity-Induced Slight Initial Curvature" has been published in the journal Materials.

Vortex-induced vibration (VIV) is one of the primary causes of fatigue failure in subsea pipelines and has attracted significant attention from researchers in recent years. Most existing studies have focused on idealized straight pipes, while the effect of slight bending caused by gravity in free-span fluid-conveying pipes has been less explored. This paper establishes a theoretical model incorporating axial tension effects and gravity-induced initial slight curvature. The governing equations were derived based on Hamilton's principle, and the fluid-structure interaction was modeled using the Van der Pol equation. The vibration response was solved using the Galerkin method combined with the Runge-Kutta method. The accuracy of the model was verified by comparing response curves and bifurcation diagrams with those from previous literature. The study revealed that gravity-induced slight curvature reduces the VIV response mode; the static deformation of the pipe decreases with increasing axial tension but increases with higher internal flow velocity. At the same external flow velocity, the initial deformation lowers the dominant frequency and transforms the vibration response from quasi-periodic to periodic motion.

Note: Materials is a Q2 zone journal in the field of engineering and technology, with an impact factor of 3.7 in 2026. The first author is Zhang Bin from Offshore Oil Engineering Co., Ltd., and Professor Li Sunwei is the corresponding author. This research was supported by grants from the Guangdong Basic and Applied Basic Research Foundation and Shenzhen Pengrui Young Faculty Program of Shenzhen Pengrui Foundation.

2026-07-10

Recently, the editorial department of the engineering technology journal "Engineering Structures" announced the winning papers of the Editor's Featured Paper for the first issue of 2026. Among them, the paper "Real-time multiload response prediction and inverse analysis of offshore bridges based on deep learning" published in Volume 353-C of this journal in 2026 won the award. The authors include Master's student Zhuyu Sun, Associate Professor Yu-Tao Guo, Doctoral student Kang Ge, and Associate Professor Chao Hou from the Southern University of Science and Technology, alongside Professor Zhen-Zhong Hu.

Offshore bridges operate in complex ocean environments, making structural time history analysis and nonlinear model updating based on finite-element methods computationally intensive and time consuming, which limits their usage in scenarios requiring real-time analysis. To address the challenge of balancing computational efficiency with prediction accuracy inherent in existing methods, the research team proposed a deep learning-based offshore bridge predictor that integrates structural characteristics and coupled dynamic loads in ocean environments, enabling millisecond-level, high-precision nonlinear dynamic response predictions. Building upon this, a differentiable structural inverse framework was further developed, coupling the surrogate model with gradient-based optimization to enable rapid damage identification and model calibration for structural health monitoring. This research delivers an intelligent technical approach for efficient structural analysis and real-time monitoring of offshore bridges, advancing the application of deep learning in marine structural engineering.

Note: Engineering Structures, an authoritative journal in structural engineering published by Elsevier, was founded in 1978 and is ranked as a CAS Zone 1 TOP journal, with a 2026 impact factor of 7.6. The Editor's Featured Paper Award is conferred by the journal's Editor-in-Chief and Associate Editors based on a comprehensive evaluation of content innovation, research applicability, and writing quality. The selection covered Volumes 350 through 353-C, published between March and April 2026, across the Asia-Pacific, European, and Americas & Africa regions.

2026-07-02

The research article "Intelligent failure mode diagnosis of tubular joints using multimodal visual question answering" has been published online in the Journal of Building Structures.

Addressing the challenge of diagnosing failure modes of tubular joints in marine environments under coupled multi-loading conditions, this paper proposes a diagnostic method based on multimodal visual question answering (VQA). A multimodal database was constructed, comprising 409 failure images and 6,510 question-answer pairs, covering semantic information such as failure modes, components, locations, and quantities, and expanded to 1,227 images using image augmentation techniques. ResNet152 and LSTM were adopted to extract visual and semantic features, respectively, and the model was optimized by combining visual Feature-wise Linear Modulation, attention mechanisms, and multimodal fusion strategies. Results demonstrate that the improved model achieves an overall accuracy of 77.6%, with a 14.4% improvement in accuracy for failure location-related questions. Attention visualization confirms that the model effectively focuses on key failure regions, exhibiting strong interpretability.

Note: The first author is Zhang Wenhao, a PhD student in the Department of Ocean Science and Engineering at Southern University of Science and Technology, and the corresponding author is Associate Professor Hou Chao from Southern University of Science and Technology. This research was supported by the National Natural Science Foundation of China and the Shenzhen Science and Technology Program.