2026-03-25

The paper "Digital disaster prevention for ocean engineering: Current progress and future directions" has been published in the journal Ocean Engineering. 

The increasing frequency of typhoons and other extreme climate events has intensified the risks of natural hazard-triggered technological accidents (Natech). Traditional disaster-prevention approaches, largely dependent on static analyses, are no longer sufficient for real-time risk identification or proactive mitigation in ocean engineering. This review synthesizes recent progress in digital disaster prevention, focusing on three core areas: disaster-inducing factor identification, disaster mechanism modeling, and structural safety assessment. The paper integrates physics-based numerical modeling, data-driven simulation, and system dynamics to analyze hazard triggers and cascading failures. It highlights applications of digital twins and deep learning in scenario-based risk analysis and early-warning systems, particularly for offshore wind farms. Furthermore, it proposes a forward-looking digital technology system that integrates environmental sensing, interpretable modelling, and resilience-oriented decision support. Overall, digital disaster prevention provides a significant pathway toward more adaptive, predictive, and resilient safety management in ocean engineering.

Note: Ocean Engineering is a top journal in the field of engineering technology, classified in Q1, with an expected impact factor of 5.5 in 2025. I am the first author and corresponding author of the paper. The research was funded by the Shenzhen Science and Technology Program and the Guangdong Basic and Applied Basic Research Foundation.

2026-03-05

The paper titled “Dynamics-Based Adaptive Scheduling for Large-Scale Edge Computing Networks in Smart Cities” has been published in  IEEE Internet of Things Journal.

Addressing the critical challenge of real-time and efficient task scheduling in large-scale distributed edge computing networks caused by the exponential growth of task requests from the rapid proliferation of IoT devices in smart cities, this paper proposes a novel Dynamics-based Adaptive Scheduling (DAS) method. By innovatively transforming the complex task scheduling problem into a two-dimensional geometric model and leveraging the principles of forward dynamics simulation, DAS achieves efficient and non-iterative task assignment, successfully reducing computational complexity to O(n log n). Simulation results demonstrate that the DAS method significantly outperforms existing benchmark strategies: it reduces service response time by 9% to 15%, improves system load balance by 93% to 98%, and decreases overall energy consumption by 2% to 12%. This solution effectively resolves scheduling efficiency and reliability issues in large-scale edge computing environments.

Note: The IEEE Internet of Things Journal is a top journal in the field of engineering and technology, ranked in the Q1 zone, with an impact factor of 8.9 in 2025. The first author of the paper is Master's student Feng Yixiao, and Professor Ren Zhengru from Shanghai Jiao Tong University serves as the corresponding author. The research was supported by the National Key R&D Program of China.

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.