Zhenzhong Hu

    He received both his BE and PhD degree in the Department of Civil Engineering at Tsinghua University, China. He was a visiting researcher in Carnegie Mellon University.
    He is now the associate professor in Shenzhen International Graduate School, Tsinghua University, and also the secretary general of the BIM Specialty Committee of the China Graphics Society.
    His research interests include information technologies in civil and marine engineering, building information modeling (BIM) and digital disaster prevention and mitigation.
  • 2026-05-29

    2026-05-29

    The "Deep Water Structure Health Monitoring Method and System" has been granted an invention patent certificate (Patent No.: ZL202610409127.1).

    This invention discloses a deep-water structural health monitoring method and system. The method includes: establishing a discrete model and constructing an element strain matrix; collecting single-sided surface strain data and establishing a mapping relationship between theoretical surface strain and node displacements; formulating a single-sided inverse finite element data fitting term that incorporates a surface strain fitting component and a transverse shear strain penalty term; constructing a geometry-guided mixed regularization term composed of membrane and bending regularization components; assembling the global equations to solve for full-field displacements; recovering full-field strains and stresses, and outputting monitoring results. The system comprises a data acquisition module, a preprocessing and inverse reconstruction module, a stabilization solving module, and a visualization output module. Based on a full-field response perception scheme combining single-sided inverse finite element analysis with geometry-guided mixed regularization, this invention enables stable and high-precision inversion of full-field displacements, strains, and stresses using only sparse single-sided sensor data, effectively overcoming the dependency on bilateral sensor configurations in traditional methods. 

    Note: The inventors of this patent also include Ph.D. student Jiang Chunhao, Ph.D. student Min Yantao, Associate Professor Guo Yutao, and Associate Professor Li Binbin.

  • 2026-05-22

    2026-05-22

    The paper "Assessing coastal groundwater risks from nuclear-contaminated water discharge under extreme drought and sustainable recharge solutions" has been published in Journal of Contaminant Hydrology.

    The discharge of nuclear-contaminated water from the Fukushima Daiichi Nuclear Power Plant in Japan has raised concerns about seawater quality and potential environmental risks to coastal groundwater systems, particularly in densely populated metropolitan areas. In response to this pressing challenge, this study developed a radionuclide decay and transport model tailored to the hydrogeological conditions of Shanghai, aiming to comprehensively assess the combined impacts of nuclear discharge, extreme drought, and Managed Aquifer Recharge (MAR) on groundwater quality. The results show that under extreme drought conditions, declining groundwater levels may exacerbate seawater intrusion and intensify seawater–groundwater interactions, resulting in higher radionuclide concentrations than those observed under normal scenarios. Model simulations indicate that, under the assumed input scenario, after 30 years of continuous discharge, the concentrations of ¹³⁷Cs and ⁹⁰Sr at a depth of 160 m in Shanghai's aquifer increase by 33.93% and 46.40%, respectively. Meanwhile, MAR demonstrates significant potential for risk mitigation, reducing the concentrations of ¹³⁷Cs and ⁹⁰Sr by up to 99.58% and 99.10%, respectively. These findings highlight the critical role of proactive, nature-based interventions such as MAR in mitigating nuclear contamination risks and provide important scientific support for enhancing the resilience and sustainability of coastal groundwater resources.

    Note: Journal of Contaminant Hydrology is a Q1 journal in the field of Environmental Sciences and Ecology, with a 2025 impact factor of 4.4. The corresponding authors of the paper include Prof. Yaqiang Wei from Shanghai University, Prof. Xinde Cao from Shanghai Jiao Tong University, and Prof. Hui Li from Shanghai University. This research was supported by the National Natural Science Foundation of China.

  • 2026-05-06

    2026-05-06

    The paper "A Differentiable Optimization Framework for Automated Design of Offshore Jacket Structures under Varied Scenarios" has been published in Computer-Aided Civil and Infrastructure Engineering.

    To address the long-standing issue that conventional offshore jacket platform design relies heavily on engineers’ experience and often leads to overly conservative solutions, this study proposes a beam element-based topology optimization method for offshore jacket structures. By integrating multidisciplinary knowledge, the proposed method simultaneously optimizes nodal coordinates and cross-sectional sizes, with the objective of minimizing the weighted sum of structural volume and compliance. Considering that marine loads are dependent on structural configuration, a differentiable formulation of Morison’s equation is developed, while pile-soil interaction is incorporated through a hybrid sensitivity scheme. Displacement constraints, diameter-to-thickness ratio constraints, and symmetry constraints are also considered. In addition, a Gumbel-Softmax-based strategy is employed to enable differentiable optimization of discrete standard sections, thereby improving manufacturability. A member removal strategy is further proposed to generate diversified design alternatives. The method is applicable to various multi-leg spatial frame configurations, can complete the design of a four-leg jacket within 3–5 minutes, and has been validated through a real engineering case.

    Note: Computer-Aided Civil and Infrastructure Engineering is a top-tier journal in the field of engineering and technology, with a 2025 Impact Factor of 9.1. The first author of the paper is doctoral student Kang Ge, and Prof. Yutao Guo is the corresponding author. This research was supported by the Shenzhen Science and Technology Program.

  • 2026-05-01

    2026-05-01

    The paper "Real-time virtual sensing for offshore wind turbines with arbitrary sensor configurations using subspace-based spectral graph networks" has been published in Ocean Engineering.

    To address the challenges faced by offshore wind turbines under harsh marine environments, including unstandardized sensor layouts and the limitations of sparse, single-sided measurements, this study proposes a subspace-based spectral graph network for virtual sensing. The proposed method overcomes the dependence of conventional structural health monitoring techniques on fixed sensor configurations. Specifically, sparse and single-sided strain measurements from arbitrary sensor layouts are first reconstructed in a proper orthogonal decomposition subspace. The reconstructed features are then combined with Fourier-enhanced spatial coordinates and mapped to full-field structural responses through a Chebyshev spectral graph network. Experimental results demonstrate that the model maintains robust reconstruction accuracy even under highly non-uniform sensor distributions, significantly outperforms baseline models, and achieves real-time inference within 0.01 s. In addition, theoretical guarantees on sensor count and error bounds are derived and numerically validated. This work effectively overcomes practical sensing constraints in engineering applications and enables continuous, autonomous integrity management to support the long-term resilience of critical offshore energy infrastructure.

    Note: Ocean Engineering is a Q1 Top journal in the field of engineering and technology, with a 2025 impact factor of 5.5. The first authors of the paper are PhD student Chunhao Jiang and postdoctoral researcher Yihong Li, and I serve as the corresponding author. This research was supported by the Guangdong Basic and Applied Basic Research Foundation.

  • 2026-04-28

    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.