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Marine soil-structure interfaces are commonly encountered in marine engineering, where they are inevitably subjected to temperature variations and complex stress conditions, including static, dynamic, and creep loads. However, limited studies have addressed the temperature-dependent mechanical behavior of marine soil-structure interfaces under various loading scenarios. This study introduces a self-developed multifunctional large-scale shear apparatus that enables temperature-controlled testing of marine soil interfaces with various structural materials, including concrete, polymer grids, and polymer layers. The apparatus supports static, dynamic, and creep shear testing under precisely controlled thermal conditions. A series of shear tests were conducted on marine soil-concrete, marine soil-polymer grid, and marine soil-polymer layer interfaces to verify the device's performance. The test results demonstrate that the apparatus can accurately and reliably capture the mechanical responses of marine soil-structure interfaces under different temperatures and loading modes. Furthermore, the results highlight the significant influence of temperature on the shear behavior of these interfaces, emphasizing the necessity of developing such equipment. The findings offer essential insights for the design, evaluation, and long-term stability of marine engineering structures, supporting the development of practical ocean solutions.

In marine engineering, polymer layers are anti-seepage barrier materials. The mechanical interaction between marine sand and polymer layer significantly affects overall structural stability. In this study, direct shear tests at different temperatures in the marine environment are simulated to evaluate the shear behavior of marine sand and polymer layer interface, and a database is developed. Based on the experimental data, the study employs the Back propagation Neural Network (BPNN), Genetic Algorithm and Particle Swarm Optimization BPNN, and convolutional neural network (CNN) models, which are trained and tested. The findings show that the CNN algorithm significantly outperforms other models in terms of prediction accuracy and efficiency. Sensitivity analysis shows that temperature, shear displacement, normal stress, and particle size have influence on interfacial shear strength, and the impact of normal stress is the greatest. In addition, an empirical formulation is proposed to provide tools for those without machine learning. Based on the research results, the deep learning CNN model developed in the study can accurately predict the shear strength of the interface between marine sand and the polymer layer, which provides an effective tool for the design and optimization of marine engineering.

Aging bridges worldwide face growing safety challenges due to extended service life and environmental stressors. However, most existing studies lack a systemic perspective and mainly rely on fragmented, expert-driven assessments. Such approaches fail to capture the interplay of risk factors. This gap in understanding the interactions and propagation of risks limits the development of effective safety strategies for bridge operation. To address this gap, this study aims to identify and structure key risk factors affecting bridge safety in operational contexts by adopting a data-driven hierarchical model. Utilizing 132 officially documented accident reports from national safety databases in China (2007–2024), text mining techniques are applied to extract lexical risk items, which are subsequently refined through expert workshops and association rule mining to capture factor relationships. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, integrated with Adversarial Interpretive Structural Modeling (AISM), is applied to construct a multi-level causal hierarchy of safety risks. The findings reveal 19 distinct risk factors, structured into seven levels with 20 transmission pathways. Notably, insufficient informatization management and unqualified managerial competence are identified as foundational factors, while overweight vehicle passage, inadequate inspection and maintenance, and geological and meteorological hazards emerge as direct triggers of safety incidents. The constructed hierarchy demonstrates a clear propagation chain from latent management deficiencies to observable surface-level hazards. Theoretically, the study advances the understanding of risk interaction mechanisms by integrating quantitative data analysis with expert interpretation. Practically, it provides infrastructure safety managers with a structured roadmap for targeted interventions, emphasizing the importance of enhancing digital management systems, traffic load regulation, and emergency preparedness in bridge operation contexts.

Public health emergencies (PHEs) pose significant challenges to global urban governance systems, necessitating the establishment of more efficient and dynamically adaptive response mechanisms. Numerous cases indicate that current urban governance still faces the risk of systemic failure under PHE shocks, leading to severe socio-economic consequences. Existing studies, based on theories such as resilience, emergency management, and risk management, primarily employ traditional statistical modeling or single-discipline approaches to explore improvement pathways. However, they fall short in cross-system and multi-agent coordination mechanisms, as well as data-driven intelligent optimization. Therefore, this project draws inspiration from the principles of the human immune system, introduces the concept of urban immunity to characterize the level of urban governance under PHEs, and follows the approach of “feature decoding → mechanism analysis → spatiotemporal measurement → trend prediction → model optimization → decision output.” It refines the theoretical framework of urban immunity, analyzes urban immune response mechanisms, develops an immunity indicator system, assesses the spatiotemporal patterns of urban immunity, and builds a decision-making model using intelligent optimization methods to generate optimized solutions for different scenarios. Ultimately, the project aims to establish a data-driven, evidence-based decision-making approach. This project seeks to provide a more systematic and operational theoretical framework for urban public health governance while promoting the digital and intelligent transformation of public health management, thereby enhancing PHE prevention and control capabilities.

To comprehensively examine the dynamic shear characteristics of the marine silica sand–geogrid interface under representative marine environmental conditions, a series of cyclic direct shear tests with controlled temperature were carried out using a custom-designed experimental apparatus. The interface between marine silica sand (particle size 0.075–2 mm) and a biaxial geogrid was examined across a wide temperature range (−5°C–80°C) and under varying normal stresses (50, 150, and 250 kPa). The coupled effects of temperature and normal stress on the interfacial cyclic shear response were systematically analyzed. The results demonstrate that the interfacial shear behavior is markedly influenced by the combined effects of temperature and normal stress. Under a normal stress of 50 kPa, the peak shear stress increases progressively with the number of loading cycles, indicating shear hardening behavior. At normal stress of 150 kPa, the peak shear stress gradually stabilizes, indicating a movement toward mechanical equilibrium. In contrast, at a normal stress of 250 kPa, the shear stress increases during the initial cycles but then declines, demonstrating a shift toward shear softening behavior. Additionally, as the temperature increases from −5 °C to 20 °C, both the interfacial strength and stiffness show noticeable improvement. However, further heating to 80 °C results in a significant deterioration of these mechanical properties. Notably, the interface behavior under 250 kPa exhibits the highest sensitivity to temperature variation. Furthermore, the maximum dynamic shear stiffness increases with temperature up to 20 °C and subsequently declines, whereas the damping ratio is highest during the initial cycle and gradually stabilizes with continued cyclic loading. The results emphasize the significant and interconnected impacts of temperature and normal stress on the dynamic behavior of the interface between marine silica sand and geogrid. These findings provide valuable insights for the design, improvement, and long-term assessment of geosynthetic-reinforced systems in marine engineering applications.

To enhance the estimation of dredged soil shear strength in marine settings, this research conducted 1,600 direct shear tests under varying thermal conditions and multiple drying–wetting cycles. Drawing from the test data, a structured database was assembled, and a new learning framework was developed by combining the Logical Development Algorithm (LDA), Adaptive Boosting (BA), and Artificial Neural Networks (ANN). The motivation behind this hybridization lies in the need to effectively capture nonlinear interactions and latent logical patterns among influencing factors, which are often overlooked by traditional single-algorithm models. This approach marks a pioneering use of such a hybridized model for strength evaluation in dredged soils. For performance verification, four alternative predictive models were established, including LDA–ANN, support vector machines (SVM), Particle Swarm Optimization (PSO), and a GA-tuned BA–ANN. Comparative analysis demonstrated that the LDA–BA–ANN configuration delivered the highest prediction precision and computational speed over traditional models. Moreover, sensitivity studies revealed that normal stress, temperature, and initial density were the dominant influencing parameters, whereas moisture cycling and shear rate had relatively minor effects. An empirical equation was further extracted from the optimized model, offering a user-friendly solution for practical engineering applications without requiring machine learning proficiency.

Marine coral sand-clay mixtures (MCCM) are widely used in marine engineering, with their mechanical behavior strongly influenced by clay content. This study investigates the effects of 3D-printed triaxial geogrid reinforcement on MCCM through triaxial testing. Based on the experimental results, a dataset was established, while a novel machine learning model named GP-BPNN was proposed, integrating genetic algorithm (GA), particle swarm optimization (PSO), and backpropagation neural network (BPNN). This model was applied for the first time to predict the strength of MCCM. Results show that lower clay content, more reinforcement layers, and higher confining pressure significantly enhance the strength and cohesion of MCCM, with little effect on the internal friction angle. The strength first decreases, then increases, and finally decreases again with increasing water content. Particle breakage is influenced by clay content and water content; moreover, fractal analysis reveals a linear relationship between the breakage rate and the fractal dimension. SEM images reveal the interaction between MCCM and the geogrid. Additional stress and matrix suction analyses highlight the effects of reinforcement layers and water content on the strength. These findings offer insight into triaxial geogrid-reinforced MCCM behavior and provide guidance for marine engineering construction.

Introduction: Estimation of forest carbon (C) storage is essential for understanding the global C cycle, mitigating climate change, and developing carbon markets. However, systematic research on forest C storage estimation needs improving.

Methods: Herein, a bibliometric and content review of literature published between 2008 and 2025 was conducted to synthesize temporal and spatial trends and to identify methodological advances and gaps in forest C-storage estimation.

Results: The results revealed that environmental sciences accounted for the largest share of publications (n = 718). The most productive institution and country were the Chinese Academy of Sciences (n = 208) and the United States (n = 691), respectively. Research progress in the field was categorized into three distinct stages since 2008. The early stage (2008–2012) was dominated by eddy covariance, satellite remote sensing, and airborne radar. The middle stage (2013–2017) was characterized by greater use of process-based and statistical simulation models. In the later stage (2018–2025), techniques such as random forest (RF), machine learning and biomass mapping became more widely used. Over this period, model performance improved substantially, especially the coefficient of determination (R2) increased from 0.62 to 0.97 for the TRIPLEX-Flux C-exchange model and from 0.63 to 0.97 for RF models.

Discussion: Spatially, most studies addressed local-to-regional scales, whereas large-scale or global assessments remain limited. This synthesis clarifies methodological trajectories and persistent gaps that can guide the development and wider deployment of forest C-storage estimation approaches and support evidence-based climate policy and C-market design.

Introduction: Urban communities, as the basic unit of urban governance, play a crucial role in responding to public health emergencies (PHEs). This study aims to investigate the resilience measurement and optimization strategies of urban communities in responding to PHEs in order to improve their resilience.

Methods: The study constructed a resilience assessment framework and identified 31 key influencing factors to measure the resilience of case communities in Nanjing. Through sensitivity analysis, static optimization strategies were proposed from social, environmental, and economic levels. Dynamic Bayesian network inference simulation and importance analysis were used to propose dynamic optimization strategies from pre, during, and long-term perspectives.

Results: Through the combination of dynamic and static strategies, community managers promote resilience building from both short-term and long-term perspectives.

Discussion: The study provides a valuable reference for comprehensively improving the emergency management system.

Marine coral sand-clay mixtures (MCCM) are widely used as fill materials in offshore engineering, where their strength characteristics are critical to structural stability and safety. This study conducted a series of triaxial shear tests under varying conditions of clay content, reinforcement layers, confining pressure, water content, and strain to establish a comprehensive strength database for MCCM. Based on this dataset, multiple predictive models were developed, including Backpropagation Neural Network (BPNN), Genetic Algorithm optimized BPNN (GA-BPNN), Particle Swarm Optimization enhanced BPNN (PSO-BPNN), and a Logical Development Algorithm preprocessed BPNN model (LDA-BPNN). Among them, the LDA-BPNN model demonstrated superior accuracy and generalization capabilities compared to traditional optimization algorithms. Sensitivity analysis identified water content, clay content, and confining pressure as the primary factors influencing MCCM strength. Furthermore, an explicit empirical formula derived from the LDA-BPNN model was proposed, offering a practical and efficient tool for engineers without specialized machine learning expertise. These findings provide valuable technical support for the optimized design and safety assessment of MCCM materials in marine geotechnical engineering applications.