50 results about "Storm tide" patented technology

The invention relates to the technical field of storm surge disaster monitoring and intelligent forecasting, and provides a regional collaborative storm surge water increase forecasting method of a coupling graph attention and gated circulation network, which comprises the following steps of: obtaining storm surge water increase residual field data based on a mixed wind field driving two-dimensional hydrodynamic model formed by typhoon parameterization and reanalysis data fusion, registering the water-increasing residual field data and the observation time sequence in time and space, and taking the registered data as physical prior input; constructing a dynamic edge weight graph structure based on a space-time causal correlation analysis method, and adaptively updating the dependency relationship among multiple observation stations; physical priori and key features are used as node input, a graph attention mechanism of GATv2 and a time sequence feature extraction capability of GRU are fused, and time-space coupling modeling is carried out on storm surge water increase; and carrying out multi-station joint prediction through regional cooperation, carrying out model evaluation and result optimization, and outputting a storm surge water increase prediction sequence with 15-minute time steps and 15-4-hour advance.

The invention discloses an estuary coast embankment risk early warning evaluation system and method under composite disaster-inducing factors, and relates to the technical field of hydraulic engineering safety monitoring and risk evaluation. Comprising a multi-source data acquisition module, a data preprocessing module, a dynamic index system construction module, an adaptive weight determination module, a risk assessment module, a refined early warning grade division module and a visual decision support module. Real-time sensing, quantitative evaluation and graded early warning of the dike safety state are achieved, the method can be directly applied to coastal city disaster prevention and reduction, dike project full-life-cycle operation and maintenance, major storm surge disaster emergency response and other scenes, the technical bottlenecks of single disaster-inducing factor, static weight and insufficient early warning precision in a traditional evaluation method are effectively broken through, and the method has a wide application prospect. And a precise technical support is provided for the safety protection of the estuary coast embankment.

The invention discloses a grading early warning method and system for a storm surge submerging area, and relates to the technical field of data processing. Comprising the following steps: S1, multi-source data preprocessing: a standardized data set is obtained through multi-source heterogeneous data acquisition, space-time reference unification and quality grading processing, and a quality grading result is used for weight distribution of subsequent feature extraction; s2, dynamic feature extraction; s3, performing initial submerging prediction; s4, deviation self-adaptive correction is carried out; s5, grading threshold value adaptation is carried out; and S6, issuing and feeding back graded early warning. According to the grading early warning method, effective integration of multi-source data is realized through sub-steps of multi-source heterogeneous data acquisition, space-time reference unification and quality grading processing. The uniform space-time reference ensures the consistency of the data, the three-level quality grading standard defines the use priority of the data, the utilization rate and reliability of the data are improved, and a solid foundation is laid for subsequent feature extraction and model prediction.

The invention discloses a storm surge artificial intelligence forecasting method based on physical equation constraints, and relates to the technical field of artificial intelligence storm surge forecasting. Comprising the following steps: step 1, collecting multi-source data; step 2, assimilating and preprocessing the data; step 3, realizing a machine learning calculation method of physical loss; step 4, constructing a physical and data co-constrained artificial intelligence model; 5, performing hyper-parameter adjustment by using a random search algorithm, and further optimizing the model; and step 6, carrying out interpretability analysis on the forecasting process and result. According to the invention, three control equations for storm surge calculation are introduced into the machine learning model, so that the dependence on the training data volume is greatly reduced compared with a traditional data-driven intelligent forecasting model; and analyzing the change trend of the physical variables in the forecasting process, carrying out visual display and uncertainty analysis, and solving the problem of uninterpretability of an artificial intelligence forecasting model.

The invention discloses a multi-algorithm fusion cyclone similarity identification method and a storm surge forecasting method based on weight optimization, relates to the field of cyclone similarity identification, and establishes a similarity calculation function by constructing a multi-dimensional time sequence containing a plurality of key factors and combining a weighted dynamic time warping algorithm. In this way, multi-dimensional comprehensive characterization of the cyclonic space-time evolution process is achieved, and the identification deviation caused by the fact that a traditional method only depends on a single feature is overcome. On this basis, a genetic algorithm, a particle swarm optimization algorithm and a differential evolution algorithm are respectively adopted to carry out iterative optimization on the weight vectors, minimization of a deviation coefficient of a candidate similar cyclone set is taken as a target, and a global optimal weight vector corresponding to each algorithm is searched; and based on a preset fusion strategy, determining a target optimal weight vector from the plurality of globally optimal solutions. According to the closed-loop recognition process, multi-dimensional feature modeling and scientific weight optimization are effectively fused, and objectivity and accuracy of cyclone similarity recognition are remarkably improved.

The invention discloses an ocean water pollutant monitoring device, belongs to the technical field of seawater detection, and aims to solve the problems that a maintainer needs to regularly and repeatedly calibrate and maintain detection probes on a buoy, the number of the detection probes on a traditional buoy is limited, only one sample can be detected each time, and the real-time sampling rate of data is low. A liquid storage tank is embedded in the bottom face of a buoy body, a mounting shell is fixedly arranged on the bottom face of the buoy body, a power assembly is fixedly arranged in the buoy body, a stirring assembly is fixedly connected to an output shaft of the power assembly, and a connecting assembly is arranged at the bottom of the stirring assembly; a sliding assembly, a compression assembly and a plugging assembly are separately arranged in the mounting shell, the high-frequency detection can be performed under the conditions of marine pollution accidents, red tide / algae bloom outbreak and typhoon / storm surge by changing the shape of the shifting assembly, and the automatic calibration can be realized after the detection probe is used every time.

The application discloses a nearshore disaster-bearing body disaster-causing path tracing method based on digital-analog experiments, relates to the technical field of ocean dynamic disaster simulation, determines the ocean dynamic disaster characteristics of a nearshore research area, integrates multi-source historical disaster data, real-time monitoring data and disaster-bearing body background data, and constructs a time-space correlation database; based on a three-dimensional ocean and a third-generation sea wave model, a nearshore storm surge-sea wave coupling numerical model is established and verified. Through the construction of the storm surge-sea wave coupling numerical model, the present application comprehensively considers the nonlinear interaction of multiple disasters, introduces an energy conservation equation to quantify the generation, transmission and dissipation process of disaster energy, solves the limitations of traditional single-disaster linear models, and combines the real-time feedback of the three-dimensional dynamic model of the disaster-bearing body to form a closed energy-damage correlation mechanism, significantly improves the tracing accuracy of the disaster chain propagation path, and ensures that the identification of key nodes and dominant paths is more consistent with physical reality.

The application discloses a storm surge intelligent early warning method and system based on multi-model coupling and a medium, and relates to the technical field of disaster early warning.The method comprises the following steps: establishing a basic data set; acquiring real-time meteorological data and marine data, performing fuzzy path fitting, and establishing a fitting result; performing regional grid segmentation by using the basic data set and the fitting result, and establishing a grid segmentation result; inputting the real-time data and the basic data into a simulation unit, performing simulation operation, and establishing node simulation data; acquiring a node preset safety response measure, performing joint early warning identification according to the response measure and the simulation data, and reporting an early warning signal, so as to solve the technical problems that the existing storm surge early warning method has strong subjectivity in experience prediction, inherent errors of a single model lead to insufficient simulation accuracy, and early warning timeliness and refinement degree are insufficient, and achieve the technical effect that the accuracy, timeliness and refinement degree of storm surge early warning are improved through fuzzy path fitting and multi-model coupling simulation operation.

The present application relates to the technical field of marine disaster risk assessment, and particularly relates to a storm surge risk assessment method for a seawater photovoltaic field area, comprising the following steps: S1: identifying a critical region in the field area where a bearing stable state is converted into a bearing attenuation state, and generating a corresponding support state transition unit; S2: determining the transmission direction and the expansion boundary of the support state transition unit between array regions, and generating a corresponding imbalance transmission unit; S3: dividing the overall risk level based on the transmission range and the expansion degree, and generating a corresponding storm surge risk assessment result. According to the present application, by combining support state transition identification, imbalance transmission path analysis and risk space partitioning, the storm surge risk of the seawater photovoltaic field area is changed from static determination to dynamic evolution analysis, thereby improving the accuracy and spatial directivity of risk assessment.

The present application relates to a kind of simple calculation method of shallow foundation and clay seabed interface failure time under extreme storm surge action.The technical scheme is: measuring the roughness R of shallow foundation a , making structure;Collecting seabed clay at shallow foundation, carrying out dynamic simple shear test, test structure and the initial undrained shear strength S of clay interface u ;Select and set 0.4S u , 0.6S u , 0.8S u And 1S u As the typical amplitude of extreme storm surge load;Measured structure and the corresponding period number N of clay interface failure;Fitting extreme storm surge load amplitude and period number N logarithmic curve;According to wave force calculation formula, load amplitude is calculated;The amplitude is substituted into logarithmic curve and the corresponding period number N of interface failure and failure time are obtained.The present application can calculate the failure time of shallow foundation and seabed interface under extreme storm surge, provide the time length of shallow foundation against storm surge, determine the stability of shallow foundation, provide feedback for whether the shallow foundation of engineering site needs to be reinforced to resist extreme storm surge.

This invention provides a method and apparatus for recommending emergency response plans for marine storm surges. The method includes: constructing a "scenario-response" disaster management system and a "scenario-response" visualized knowledge graph that integrates event chains and emergency plans. Through the constructed marine storm surge "scenario-response" disaster management system, "scenario-response" knowledge graph, and knowledge reasoning model, it can not only complete "scenario-response" matching of historical disaster events to achieve pre-disaster rehearsals and post-disaster analysis, but also perform "response" knowledge base text matching based on real-time generated disaster "scenario" text to generate emergency response plans and visualize the graph recommendation results, thereby achieving in-disaster response. This improves the entire process of disaster prevention and mitigation chain for marine storm surge disasters, enhances the generalization ability of knowledge reasoning methods, and provides important support for further improving the intelligent handling capabilities of marine storm surge disasters.

This invention discloses a method for determining the bottom deck elevation of an offshore platform, mainly comprising the following steps: collecting and processing relevant data such as wave and tide levels in the engineering area; selecting appropriate samples; considering the joint probability distribution of storm surge and wave-related marine hydrological environmental elements; generating an environmental envelope; and then obtaining the joint return period design parameters of relevant marine hydrological environmental elements from the environmental envelope, which are then used to calculate and determine the bottom deck elevation of the offshore platform. This invention reasonably considers the characteristic laws of important marine hydrological environmental elements in setting the bottom deck elevation of an offshore platform, and uses the idea of ​​statistical modeling of the joint distribution of marine hydrological environmental elements to construct environmental envelopes under different return periods. It can conveniently select the joint return period design parameters of marine hydrological environmental elements and obtain appropriate combinations of environmental parameter return values. The scheme is simple and easy to implement, with clear and reasonable engineering significance, and can effectively guide the design of actual engineering structures.

This invention relates to the field of storm surge disaster monitoring and intelligent forecasting technology, and provides a regional collaborative storm surge rise forecasting method using coupled graph attention and gated cyclic networks. The method includes: using a hybrid wind field-driven two-dimensional hydrodynamic model formed by fusing typhoon parameterization and reanalysis data to obtain storm surge rise residual field data; registering the rise residual field data with the observation time series in time and space, and using it as a physical prior input; constructing a dynamic edge-weighted graph structure based on a spatiotemporal causal correlation analysis method to adaptively update the dependencies between multiple observation stations; using the physical prior and key features as node inputs, fusing the graph attention mechanism of GATv2 and the temporal feature extraction capability of GRU to perform spatiotemporal coupled modeling of storm surge rise; conducting multi-site joint prediction through regional collaboration, performing model evaluation and result optimization, and outputting storm surge rise forecast sequences with a 15-minute time step and a lead time of 15 minutes to 4 hours.

The invention relates to the technical field of disaster early warning, in particular to a marine disaster risk prevention and control early warning system. Comprising a data fusion module used for collecting data from multiple channels in real time and carrying out standardized preprocessing and fusion; the risk prediction module is used for predicting the storm surge risk by utilizing a space-time diagram neural network component, a physical information neural network component and a layered deep neural network component which are included in the risk prediction module based on the fused data; the risk assessment module receives the generated storm surge risk prediction data, assesses the disaster risk severity degree in combination with the vulnerability information of the disaster-bearing body, and determines the corresponding risk level; and the early warning issuing module is used for generating corresponding early warning information based on the determined risk level and issuing the early warning information through multiple channels. According to the system, a high-fidelity prediction result is converted into operable risk assessment, storm surge early warning is automatically generated and issued, and the limitations of storm surge prediction precision, physical authenticity and long-term stability in the prior art are overcome.

The invention provides an R-KGCN emergency disposal method for the silt danger of a floating wing suspension door in the storm surge period of a super-huge tide gate. According to the method, a system framework covering knowledge modeling, reasoning definition and reasoning optimization is constructed on the basis of a relationship-enhanced knowledge graph convolutional network, and structured expression and intelligent reasoning of sediment disaster emergency disposal knowledge are realized. The method comprises the following main technical links: (1) constructing a knowledge graph mode layer and a data layer oriented to a sediment disaster scene, and establishing a'feature-event-disposal 'ternary model and a hierarchical relationship; (2) proposing a structured reasoning problem definition framework of an emergency processing task, and supporting modeling requirements of multi-source, multi-scale and multi-relation semantics; (3) designing an R-KGCN inference algorithm integrated with a semantic relationship enhancement module, and improving the accuracy and interpretability of inference; and (4) forming a knowledge reasoning and intelligent recommendation method oriented to sediment dangerous cases, and providing decision support and emergency response guarantee for operation scheduling of the oversize tide gate.

The application discloses a storm surge artificial intelligence prediction method based on physical equation constraints and relates to the technical field of artificial intelligence storm surge prediction, and comprises the following steps: step 1, collecting multi-source data; step 2, data assimilation and preprocessing; step 3, realizing a machine learning calculation method of physical loss; step 4, constructing an artificial intelligence model jointly constrained by physics and data; step 5, using a random search algorithm to adjust hyperparameters, and further optimizing the model; and step 6, performing explainability analysis on a prediction process and results. The three control equations used for storm surge calculation are introduced into the machine learning model, the dependence on the training data volume is greatly reduced compared with a traditional data-driven intelligent prediction model, the trend of the change of the physical variables in the prediction process is analyzed, visual display and uncertainty analysis are performed, and the unexplainability problem of the artificial intelligence prediction model is solved.

This invention provides a method and apparatus for assigning rescue vessels based on a combinatorial optimization algorithm in storm surge scenarios. The method includes: acquiring and updating information on stranded personnel and vessel status in real time; ensuring that vessels temporarily withdraw from task allocation once they reach their maximum capacity, and rejoin after completing the current rescue; dynamically determining the rescue weight of each stranded person by combining their personal attributes and location relationships; and using an improved Hungarian algorithm to dynamically generate an optimal vessel allocation scheme based on the real-time rescue weight of the stranded personnel, while fully considering vessel capacity limitations. Each generated allocation scheme is calculated in real time based on the latest rescue weights and vessel availability, ensuring the effectiveness and timeliness of the scheme. As stranded personnel are gradually rescued, the above process is repeated until all stranded personnel are rescued. This invention ensures the effectiveness, comprehensive coverage, and timeliness of the rescue operation.

The invention relates to the field of disaster risk assessment, in particular to a storm surge risk assessment system based on deep learning, which comprises an initial assessment module used for determining disaster exposure range parameters based on disaster-causing assessment indexes so as to determine a disaster analysis area; the area analysis module is used for determining the area toughness index of each disaster analysis area, and determining diffusion risk analysis or centralized trend analysis according to the reference area toughness index and the area toughness difference index; the diffusion analysis module is used for determining a diffusion interference area according to the difference conflict index or the difference amplitude index, and determining whether to optimize range setting for each disaster analysis area according to diffusion interference distribution parameters; and the centralized analysis module is used for determining whether to optimize the range setting for each disaster analysis area according to the centralized risk coefficient. According to the method, the effectiveness of the determination result of the influence range of the storm surge is ensured, and the risk assessment quality of the storm surge is further improved.

The application discloses a storm surge inundation area grading early warning method and system, and relates to the technical field of data processing. The method comprises the following steps: S1, multi-source data preprocessing: through multi-source heterogeneous data collection, space-time reference unification and quality grading processing, a standardized data set is obtained, wherein the quality grading result is used for weight distribution of subsequent feature extraction; S2, dynamic feature extraction; S3, initial inundation prediction; S4, deviation self-adaptive correction; S5, grading threshold adaptation; S6, grading early warning release and feedback. Through the sub-steps of multi-source heterogeneous data collection, space-time reference unification and quality grading processing, the application realizes effective integration of multi-source data. The unified space-time reference ensures the consistency of data, the three-level quality grading standard clearly defines the use priority of data, improves the utilization rate and reliability of data, and lays a solid foundation for subsequent feature extraction and model prediction.

The invention provides a mechanism digital twinning construction method and system for marine disaster safety analysis, and the method comprises the steps: constructing a two-dimensional and three-dimensional space-time digital twinning base, and forming a two-dimensional and three-dimensional space-time data set; determining a target area and influence factors causing the storm surge of the target area; based on an ADCIRC and SWAN coupling model, constructing a storm surge disaster prediction mechanism model matched with the target area features so as to predict water level and flow velocity data of each grid point; constructing a virtual meteorological model by combining the terrain and meteorological data of the target area; key airflow change points are extracted based on the virtual meteorological model, an adjustment coefficient corresponding to the current position area is calculated according to airflow change characteristics of the key airflow change points, and water level and flow velocity data of each grid point at the corresponding position are optimized and regulated based on the adjustment coefficient corresponding to each key airflow change point; the prediction mechanism model is embedded into a two-dimensional and three-dimensional space-time digital twinning base, and mechanism digital twinning of marine disaster safety analysis is achieved.

The utility model provides an anti-storm-surge integrated monitoring marker structure, which belongs to the technical field of anti-storm-surge monitoring markers and comprises an electro-galvanized steel pipe, a foundation structure, a heat dissipation case, a communication component and a rotatable support, the electro-galvanized steel pipe is fixed at the top of the foundation structure, the heat dissipation case is fixed at the bottom of the foundation structure, and the rotatable support is fixed at the bottom of the foundation structure. The foundation structure is fixed in an existing foundation; the rotatable bracket is fixed on the electro-galvanized steel pipe through a bidirectional hoop and is used for keeping stable under the action of external force; the heat dissipation case is fixed on the electro-galvanized steel pipe, the outer layer of the heat dissipation case adopts a shutter structure and is used for preventing water, ventilating and balancing internal and external air pressure, and the interior of the heat dissipation case adopts a separated cabin isolation design; the utility model provides an anti-storm surge integrated monitoring marker structure which can solve the problems that an existing monitoring marker is prone to loss and short in service cycle, and parts are fixedly arranged, replaced and maintained difficultly.

The invention discloses a storm surge inundation range identification method and system based on a multi-source remote sensing image, and particularly relates to the field of image analysis, and the method comprises the steps: obtaining a pre-disaster and post-disaster image and digital elevation model data in a target region, carrying out the feature extraction and multi-source image fusion, generating a broad-spectrum suspected inundation region image, and carrying out the recognition of a storm surge inundation range; according to the pre-disaster optical image, a complex underlying surface area is identified from the broad-spectrum suspected submerged area image, and a corresponding binary mask image is obtained; for each complex underlying surface area, texture feature analysis and pixel-level classification are respectively carried out, and an actual water body area is identified; and according to the actual water body area of each complex underlying surface area, generating a preliminary submerging range map, and performing terrain connectivity analysis on the preliminary submerging range map to generate a final storm surge submerging range map. According to the invention, based on the pre-disaster and post-disaster images of the storm surge, texture interferences of different complex underlying surfaces in the images can be filtered in a targeted manner, so that the accuracy and reliability of storm surge range identification can be improved.

The invention relates to the field of storm forecasting, and discloses a parallel calculation storm surge set numerical forecasting method and device which are used for improving the efficiency, accuracy and robustness of storm surge set numerical forecasting. Through a dynamic resource allocation strategy, computing nodes are migrated in real time according to historical variance priority signals, computing resource utilization is optimized, a double-assembly-line scheduling mechanism is constructed, prediction and correction tasks are alternately executed, assembly line waiting is eliminated in combination with hardware-level synchronization signals, and generation of a random disturbance field is accelerated through an FPGA coprocessor. And the physical rationality of a disturbance field is ensured, an entropy comparator is introduced to detect abnormal data in real time, calculation is rapidly recovered through a process thermal migration technology, a high-precision storm surge water level field time sequence is generated in combination with an MPI parallel framework, and a forecast result is output. According to the method, high efficiency, accuracy and robustness of storm surge forecasting are realized through technical innovation, and powerful technical support is provided for disaster prevention and reduction.

The utility model discloses an ultra-large span tide gate with a self-stabilizing structure, which comprises gate foundations arranged on two sides of a riverway estuary, and the gate foundations are provided with arc-shaped guide grooves and spherical trunnions; the spherical trunnions are connected with one ends of the water retaining gates, the other ends of the water retaining gates make contact with each other to be in a closed state when storm surge comes, and the closing point of the water retaining gates is located on the open sea side of the connecting line of the two spherical trunnions. An arc-shaped stability maintaining piece matched with the arc-shaped guide groove is arranged in the arc-shaped guide groove, one end of the arc-shaped stability maintaining piece is fixedly connected with the water retaining gate, and the other end of the arc-shaped stability maintaining piece is connected with the hoist. The device has the advantages of being stable in structure, flexible to open and close, safe in operation, convenient and fast to overhaul and the like, navigation and the hydrological river regime of an original river channel are not affected in the processes of structural design, manufacturing, transportation, installation, overhaul and maintenance and the like, and the influence on the original river channel can be reduced to the maximum extent.

An eco-friendly seawall comprises a seawall open caisson foundation, a stepped revetment, riprap protection feet, a seawall front wavebreak forest, a seawall rear windbreak forest and a seawall top footpath. A stepped revetment is arranged above the seawall open caisson foundation, and the stepped revetment is composed of a stepped concrete revetment and modularized prefabricated concrete steps; a plurality of drainage pipes are arranged at the bottom of the stepped concrete protection slope; drainage ports of the drainage pipes are arranged in the breakwater front wave-proof forest; the prefabricated concrete step comprises a viewing platform on the uppermost layer and a plurality of concrete planting pools arranged below the viewing platform in a stepped manner; riprap protection feet are arranged on the outer side of the stepped revetment, and the ripraps are covered with sandy soil for planting a breakwater wavebreak forest; and the inner side of the stepped revetment is sequentially provided with an embankment rear windbreak forest and an embankment top footpath. The seawall integrates ecological restoration, landscaping, moisture prevention and disaster reduction, and the function of coping with storm surge is reserved while the seawall is landscaped.

The invention relates to the technical field of data processing, and discloses a wind turbine generator self-adaptive protection method based on high-precision storm surge numerical forecasting. The method comprises the steps that a dynamic resolution forecasting system is established by improving a spherical coordinate system Arakawa C-type grid, high-precision storm surge forecasting is generated by fusing data of a Jason-3 satellite and a tide station and adopting an ensemble Kalman filtering algorithm, a storm surge intensity-fan action mapping table is established based on forecasting data, edge computing nodes execute millisecond-level protection control, and a high-precision storm surge model is established. The multi-parameter sensor evaluates the protection effect, and protection strategy parameters are adaptively optimized according to the evaluation result. The technical problems that in an existing wind turbine generator protection technology, storm surge forecasting precision is insufficient, a protection decision lacks dynamic adaptability, and a protection strategy cannot be optimized autonomously are solved.

A system for lifting a structure above flood water during storm surges and other severe weather events wherein the structure has a reinforced support frame that is slidably attached to multiple vertical poles positioned around the structure. The structure may be raised up and down the poles using an automated lifting mechanism or by one or more floating pontoons secured below the structure that rise and fall with any flood waters.

The invention discloses an RBF network short-time storm surge prediction method and system based on LM back propagation optimization, belongs to the technical field of storm surge prediction, and solves the problems that a traditional RBF network is difficult to process non-static data and is insufficient in peak capture capability. A K-means algorithm is adopted to determine a center parameter of the RBF network, a trainlm function is adopted to perform back propagation optimization on the RBF network, the RBF network is executed, and a storm surge short-term water increase prediction result is output; according to the invention, the RBF network combines multi-feature and multi-time sequence information and a reasonable pre-processing and optimization training strategy, and the RBF network adopts a trainlm training function to carry out back propagation optimization on network parameters, so that the problem that a traditional RBF network is insufficient in capturing a storm surge peak value is solved. Compared with a traditional RBF model, the method can better cope with complex meteorological time sequence data, and has higher accuracy and generalization ability.

This product offers excellent drainage, prevents the accumulation of sand and debris, and is highly user-friendly. Furthermore, it provides a high-tide protection vehicle stopper that can withstand storm surges. [Solution] A high tide countermeasure vehicle stopper is provided by attaching a sheet holding member (11) to the lower end of the land side (1a) of a rectangular steel pipe (1) which is installed floating above the ground surface (G) so as to form a gap between it and the ground surface (G). The sheet holding member (11) has a sheet member (2) that closes the gap and has its tip wrapped towards the sea side (S) to contact the ground surface (G), a comb-shaped holding part (11a) that holds the tip of the sheet member (2) so as not to be pushed towards the land side by seawater, a plurality of openings (11b) provided adjacent to the holding part (11a) that do not obstruct rainwater from the land side from flowing to the sea side S, and three L-shaped fixing parts (11c) that are fixed to the ground surface (G) on the land side.