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.

2026-10-09

The invention titled "Method and System for Structural Damage Identification of Floating Offshore Wind Turbines" has been granted a patent certificate (Patent No.: ZL202611188223.4).

This invention discloses a method and system for identifying structural damage in floating offshore wind turbines. The method includes: establishing a joint state space incorporating discrete damage states and continuous operational conditions; acquiring multi-source dynamic response data and performing window-based feature extraction; using a temporal neural network to generate proposals for discrete damage states, and employing a neural posterior estimation model combined with a uniform distribution to generate proposals for continuous parameters; calculating observation likelihood based on a structural response prediction model; correcting particle weights via sequential Monte Carlo by integrating constraints on irreversible damage evolution, transition relationships among continuous parameters, and statistical patterns of activation rates; and recursively updating and applying backward smoothing to obtain posterior distributions of damage states, continuous parameters, damage activation rates, and the first activation time, which are then outputted. By unifying external operational variations and damage evolution within a sequential inference framework, this approach avoids misinterpreting environmental-induced modal drift as structural damage, delivering probabilistic identification results that support online safety assessment and maintenance decision-making for floating offshore wind turbines.

Note: The inventors of this patent also include doctoral student Jiang Chunhao and postdoctoral researcher Liu Yi.

2026-09-24

According to the latest data released by Clarivate’s Essential Science Indicators (ESI) database in September this year, the paper entitled "Digital disaster prevention for ocean engineering: Current progress and future directions", published in *Ocean Engineering* (a Q1 Top journal, IF = 6.3), has been selected as an ESI Highly Cited Paper.

This study addresses the risks of natural hazard-triggered technological accidents (Natech) intensified by the increasing frequency of typhoons and other extreme climate events, and systematically synthesizes recent progress in digital disaster prevention for ocean engineering under climate extremes. Focusing on three core areas—disaster-inducing factor identification, disaster mechanism modeling, and structural safety assessment—the paper summarizes the integration of physics-based numerical modeling, data-driven simulation, and system dynamics, and reviews the applications of digital twins, machine learning, and deep learning in scenario-based risk analysis, safety evaluation, and early-warning systems, with particular emphasis on offshore wind farms. Building upon this framework, the study proposes a forward-looking digital technology system that integrates environmental sensing, interpretable modeling, intelligent prediction and warning, and resilience-oriented decision support, paving the way toward more adaptive, predictive, and resilient safety management in ocean engineering.

Note: ESI Highly Cited Papers refer to papers published within the last ten years that rank in the top 1% globally by citation frequency within their respective subject fields. ESI Highly Cited Papers have now become one of the important indicators for measuring the academic influence of a university.

2026-09-18

The paper "Fine-tuning vision foundation model for crack segmentation in civil infrastructures" has received over 100 citations on Google Scholar. It was published in May 2024 and achieved over 100 citations within just over two year, which is the fastest rate of citation breakthrough to date.

The rapid growth in citations closely reflects the broader trend of intelligent transformation in civil engineering. Traditional vision-based detection models trained on small datasets commonly suffer from limited generalization and poor adaptability to complex working conditions, which has constrained the large-scale application of intelligent inspection technologies in engineering practice. This study introduced a vision foundation model for crack segmentation and adapted it through parameter-efficient fine-tuning, effectively addressing these bottlenecks and providing a technical paradigm that can be directly adopted by related research.

The techniques adopted in the paper can also be extended to segment other types of structural defects, such as earthquake damage segmentation, rebar corrosion segmentation, and structural water seepage identification, gradually covering various inspection needs in structural health monitoring (SHM). Building on this technical framework, the team will carry out further exploration to advance the deployment of AI technologies in front-line engineering inspection and maintenance, providing strong support for the intelligent management of civil infrastructure.

2026-09-11

The paper "Beyond annual dose: Assessing lifetime cancer risk from dietary radiocesium exposure and public risk-perception gaps in Japan" has been published in Environment International.

Radiocesium in food poses an ongoing challenge in environmental health risk assessment. Annual dose compliance (1 mSv/year) is essential for radiological protection but may not reflect cumulative lifetime cancer risk (LCR) under chronic exposure or corresponding public risk perceptions. This study developed an integrated radiological risk assessment framework. Using a nationwide survey in Japan together with food-monitoring data on radiocesium concentrations, prefecture-level LCR and risk-perception gaps were estimated through multilevel regression with poststratification. The annual dietary radiation dose was well below the 1 mSv/year benchmark, while modeled mortality and morbidity LCRs remained above 10⁻⁵, ranging from 1.84×10⁻⁵ to 1.37×10⁻⁴ and from 2.70×10⁻⁵ to 2.02×10⁻⁴, respectively. The high-risk prefectures identified in spatial analyses, particularly in Tohoku, warrant prioritized monitoring. Agricultural products were the dominant exposure pathway, accounting for approximately 55.0–64.5% of the estimated LCR. Estimated risk tended to exceed perceived risk in northern Japan, indicating a need for proactive risk control, whereas perceived risk tended to exceed estimated risk in central Japan, suggesting a greater need for targeted communication. By considering the LCR alongside annual dose metrics, this framework supports region-specific monitoring and risk communication.

Note: Environment International is a Q1-ranked TOP journal in environmental sciences and ecology, with a 2026 impact factor of 10.2. Professor Mao Liang and Professor Huang Lei from Nanjing University are the corresponding authors. This research was supported by the National Natural Science Foundation of China.