Helical piles are utilized worldwide as foundations for onshore infrastructure, providing fast installation and high capacity. Their unique advantages have led to their potential as an alternative to monopile foundations for offshore wind turbines. One challenge in their service life is dealing with combined loading caused by wind, waves, current, and the weight of the structure itself. While research has focused on helical piles’ capacity for uniaxial tensile loads, there is limited knowledge regarding their performance under combined loads. This study used FEM analysis to investigate the impact of aspects such as helix-to-shaft diameter ratios and helix position on the capacity and failure mechanisms of hollow shaft single-plate helical piles in clay. With 561 analysis cases under both uniaxial and combined loading, failure envelopes were evaluated for various helix-to-shaft diameter ratios and positions. The study revealed a linear positive correlation between helix-to-shaft diameter aspect ratios and load-bearing ability, while the effect of helix positioning on failure envelopes was more complex and nonlinear. The outcomes from this comprehensive analysis enabled the development of a formula to predict the bearing capacity of helical anchors under combined loading.

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The erosion of the seabed around offshore structures has emerged as a critical factor impeding the operational safety of offshore engineering facilities. Prompt and precise identification and monitoring of the water–soil interface hold significant importance in mitigating the seabed erosion challenges facing offshore structures. To tackle this issue, a monitoring framework for the water–soil interface is proposed, grounded in heat transport theory. This framework exploits the thermodynamic variances between seawater and the seabed soil to examine the temperature changes in linear heat sources in water and soil under a constant power. In this study, a typical metallic material—iron (Fe)—and non-metallic material—polyvinyl chloride (PVC)—are considered the linear heat sources, and their temperature variations are analyzed within this framework. The findings reveal that the temperature of the linear heat sources rapidly stabilizes, with the ultimate temperature exhibiting a logarithmic correlation with the convective heat transfer coefficient. To further test the practicability of the framework, an indoor test is conducted. The errors between the theoretical calculation results and the experimental results are less than 14% in water and 19% in soil. The results of the framework and the indoor test have a high degree of coincidence. This framework has proved that it can be used in practical engineering.

In this study, a novel rigid–flexible coupled computational model for floating offshore wind turbines (FOWTs) is developed using the vector-form intrinsic finite element (VFIFE) method, named VFIFE-FOWT. This framework integrates multi-body dynamics and the VFIFE method to establish a comprehensive dynamic model for FOWTs, enabling high-fidelity simulations of FOWT systems. Validation of the VFIFE-FOWT model is conducted through comparisons with results from the industry-standard software OpenFAST, together with experimental data from a 1:80 scale model test of a shallow-draft stepped Spar platform equipped with a NREL 5MW wind turbine. The results demonstrate good agreement, verifying the accuracy and reliability of the proposed VFIFE-FOWT framework for predicting the dynamic behavior of FOWTs.

Urban flood information enables managers to rapidly synthesize comprehensive flood event profiles, serving as critical evidence for flood control decision making. Compared with traditional methods, public data offer unprecedented spatiotemporal granularity due to its high volume, multidimensionality, and real-time nature. In this paper, we investigated public data’s usefulness and generalizability of spatial feature differences using multi-source social media data as an entry point. We selected rainstorm events that occurred in three cities located in the North China Plain, the Southeast Coastal Region, and the Western Region of China, with vastly different developmental statuses in 2023. Then, multi-platform data from the events were collected and analyzed through crawling and topic mining. The results indicate that: (1) social media data from different sources are complementary to each other and can collectively extract plenty of neglected waterlogging points to supplement official data, with a supplementary rate reaching 171% on average; and (2) social media data has significant value in spatial characterization, which means that its availability remains constant despite geographical differences and can self-adapt to local geography, inhabitant profiles and social development levels. To address the issues of limited available data and essential information lacking during the analysis process, we propose recommendations for data processing and city managers to enhance the scientific value of social media data utilized in practice.

The scouring and sedimentation of wharf bank slopes significantly impact port safety and efficiency. To overcome the limitations of existing monitoring technologies in real-time capability, adaptability, and precision, this study introduces an innovative device based on distributed fiber optic sensing technology. By analyzing changes in the temperature gradient at the water–soil interface, the device enables dynamic monitoring of the results of scouring and sedimentation processes. It employs a modular design, integrating a linear heat source with fiber optic temperature sensing to capture high-resolution changes. Laboratory experiments evaluated variables such as heating duration, pipe material, pipe diameter, and fiber winding pitch. Results show optimal performance with a 20-min heating duration, with PVC sensors offering higher sensitivity and steel sensors providing greater stability. This study presents a high-precision, real-time solution for monitoring wharf bank slopes, offering insights for equipment optimization and engineering applications.