Computer-implemented method for designing artificial reefs

Abstract

The present invention relates to a computer-implemented method for designing artificial reefs (2), comprising the steps of: i) analysing geographical data (3) about a selected site (1); ii) generating a 3D model of the terrain (5) of the selected site (1) based on the geographical data (3); iii) defining constraints (41) in the 3D model of the terrain (5); iv) defining an optimised area (42) in the 3D model of the terrain (5); v) defining an optimised design (43) of the landing points (51) in the 3D model of the terrain (5), specifically intended for a system or method developed as a self-learning algorithm configuration, which allows same to perform and evolve in an open environment at sea.

Description

[0001] DESCRIPTION

[0002] COMPUTER-IMPLEMENTED METHOD FOR DESIGNING ARTIFICIAL REEFS

[0003] TECHNICAL FIELD OF THE INVENTION

[0004] A computer-implemented method for designing artificial reefs, comprising the steps of analyzing geographic data of a selected site, generating a 3D model of the terrain, determining its constraints, determining an optimized area, and an optimized design of the landing points, according to claims 1 to 15, incorporating notable innovations and advantages over the technical solutions used to date.

[0005] BACKGROUND OF THE INVENTION

[0006] It is now known that an artificial reef is a man-made structure created for various purposes, such as promoting marine life, controlling erosion, protecting coastal areas, blocking the passage of ships, blocking the use of trawling nets, supporting reef restoration, improving aquaculture, or enhancing diving and surfing.

[0007] A conventional artificial reef uses materials such as concrete, which can be molded into specialized shapes. Alternatively, artificial reefs can incorporate renewable and organic materials, such as plant fibers and seashells, to improve sustainability and reduce energy consumption, pollution, and greenhouse gas emissions.

[0008] Artificial reefs typically provide hard surfaces where algae and invertebrates such as barnacles, corals, and oysters attach themselves, and spaces where fish of varying sizes can hide. The accumulation of attached marine life, in turn, provides intricate structures and food for the fish communities. The ecological impact of an artificial reef depends on multiple factors, such as its location, construction method, and the types of marine species involved.

[0009] A related patent is CN117481058, which describes a special tetrahedral-type artificial fish reef body for accumulation. The body is a tetrahedral frame structure enclosed by six support rods. Each of the four surfaces of the tetrahedral frame structure is triangular, and four intersection points of the tetrahedral frame structure are each equipped with an extension rod. The extension rods arranged at the four intersection points of the tetrahedral frame structure and the three intersecting support rods are integrally formed. This improves the stability of the individual fish reef bodies and enhances the overall stability of the artificial fish reef accumulation body. The height of the stacked body can be effectively increased, and the stacked body offers the advantages of greater firmness and a higher stacking height.

[0010] Another patent related to the present invention is CN220402772, which describes an artificial fish reef comprising a main semicircular component. Auxiliary semicircular components are arranged on either side of the main semicircular component. The main semicircular component and the auxiliary semicircular components are fixedly connected by reinforced concrete cross slabs. A plurality of preformed holes are formed in the main semicircular component, and the main semicircular component and the auxiliary semicircular components are fixedly connected by the reinforced concrete cross slabs.

[0011] However, there are no prior art disclosures specifically directed at a system or method developed as a self-learning algorithm configuration, enabling it to function and evolve in an open-sea environment.

[0012] DESCRIPTION OF THE INVENTION

[0013] The present invention relates to a system, and in particular a computer-implemented method, for designing artificial reefs capable of creating a significant positive impact on the marine environment and the local economy. This system acts as a platform where the engineering, marine biology, architectural, and economic requirements of any site can be analyzed, thereby providing evolutionary and iterative design solutions.

[0014] The present invention integrates the ability to extract and process GIS (Geographic Information Systems) data using custom algorithms designed by marine biologists, engineers, architects, and economists for the intelligent design of artificial reefs. The system is based on a multi-layered design thinking approach, which has generated flexible platforms within the system for experts from diverse fields to engage and has enabled the creation of a strategic and symbiotic environment for knowledge sharing.The invention comprises a design library, which is a collection of artificial reef designs developed through collaborations with biologists, engineers, artists, architects, and computational designers. Based on provided parameters related to specific site conditions, the system's algorithms can create a set of customized, evolving reef designs and their deployment patterns. The library consists of designs at various scales and typologies, ranging from simple polyhedra to complex organic structures. Each design has been studied and calibrated for its biological impact, user experience, materiality, fabrication, assembly, and implementation. The development and evolution of these designs is a collaborative approach with an evolutionary and adaptive vision; therefore, the algorithms used operate using a self-learning methodology.In addition to intelligent evolutionary reef designs and their distribution strategies, the system is also capable of creating systems such as modular reefs with stacking potential, interlocking reefs that respond to avoid erosion, and distribution strategies for gamification.

[0015] GIS data on the location of existing natural reefs is translated by algorithms from numerical to raster and vector formats. Raster data is read and translated from pixels to a coordinate system, while the remaining vector-based data is read as is. These images are read within the algorithm as pixels and converted into a coordinate system, which is overlaid on the terrain geometry and used as filters to optimize the selection of areas for new interventions. Depending on the programmatic and site requirements, the algorithms can be adapted to focus on either the scientific or restorative purpose of the project.

[0016] Regarding coastal hydrodynamics, data on current velocity at various depths is analyzed to determine the height of the interventions. The intervention height is also optimized for structural stability based on the wavelength and depth of the seabed. Furthermore, the formal morphology of the intervention can be adapted to the site's biological requirements, resulting in varying degrees of current resistance. This data is presented in graphs and tables for further analysis during monitoring.

[0017] Regarding sedimentology and sediment dynamics, a Computational Fluid Dynamics (CFD) analysis is used to optimize voxel placement, ensuring high nutrient retention to feed sessile species. This analysis also optimizes placement to avoid excessively high sediment retention rates that could bury the voxels, rendering them useless. As for slope requirements, since slopes exceeding 15° are considered unsuitable for artificial reef installation due to their low stability and high risk of displacement, the terrain is analyzed and filtered to select areas with slopes less than 15°.

[0018] Regarding light availability, it's worth noting that it's heavily influenced by the depth at which the voxels are located. Turbidity in the area will affect the amount of light received by a given area within a voxel. The raster map of satellite images is interpreted by the algorithms as pixels, and the RGB values ​​are translated into vectors to determine and optimize the morphology and distribution of the artificial reefs.

[0019] More specifically, the computer-implemented method for designing artificial reefs comprises the steps of: i) analyzing geographic data of a selected site; ii) generating a 3D terrain model of the selected site from the geographic data; iii) determining constraints on the 3D terrain model; iv) determining an optimized area within the 3D terrain model; and v) determining an optimized layout of the landing points within the 3D terrain model. This enables the implementation of marine restoration strategies by configuring self-learning algorithms. It is a design protocol algorithm for analyzing, designing, implementing, evaluating, and monitoring marine restoration strategies. The algorithm is also used to design artificial reefs, which will play a key role in most marine restoration strategies.It also allows for greater complexity when designing marine restoration strategies.

[0020] It is worth mentioning that the geographical data includes abiotic and / or biotic and / or terrain data and / or project logistics data, so that the design of the artificial reef can be adapted very precisely to the marine environment.

[0021] More specifically, abiotic data comprise any of the groups of topography, current speed, current direction, wave height, wind speed, wind direction, wave direction, turbidity, seabed substrate type, sediment deposition height, average depth, seabed accumulation rate, speed for species, lighting, and protected areas, so that the design of the artificial reef can be very precisely tailored to the specific physical conditions of the marine environment.

[0022] In addition, biotic data includes any of the following groups: larval dispersal extent, larval dispersal rate, coral habitats, species type, proximity of existing species, and protected species, so that the design of the artificial reef can be very precisely tailored to the specific biological conditions of the marine environment.

[0023] On the other hand, the terrain data includes any of the existing port groups and diving areas, so the design of the artificial reef can be very precisely tailored to the specific needs of visitors to the area, and meet their expectations.

[0024] According to another aspect of the invention, the 3D model of the selected site's terrain is a mesh model or NURBS model. Specifically, a 3D mesh model is a 3D representation of an object consisting of a collection of faces, edges, and vertices that define the object's shape and structure in a realistic manner. Each edge connects two adjacent vertices, which are coordinates or points in 3D space. Furthermore, a non-uniform rational basis spline (NURBS) is a mathematical model that uses base splines (B-splines) commonly used in computer graphics to represent curves and surfaces. It offers great flexibility and accuracy for handling both analytical shapes, defined by common mathematical formulas, and modeled shapes.

[0025] Additionally, the constraints in the 3D terrain model include any of the following: required depth, substrate type, protected areas, presence of outfalls, and seabed slope. This allows the artificial reef design to be precisely adapted to the marine environment. In the field of sanitary engineering, a submarine outfall is a conduit through which wastewater, after primary treatment, is pumped to a location some distance offshore. At the end of the pipe, a perforated section called a diffuser is installed to facilitate the diffusion of the wastewater into the receiving body of water.

[0026] In a preferred embodiment of the invention, determining an optimized area comprises aligning the landing points along the ocean current in a parallel direction, so that the artificial reefs do not oppose the ocean current and, therefore, interference with the natural environment is minimal, and also, the durability of the artificial reefs is greater.

[0027] Advantageously, determining an optimized area involves placing landing points according to their distance from the shore and / or the type of seabed, ensuring that the artificial reef landing points are located at a suitable distance to be accessible by boat from the coast. The consistency of the seabed must also be considered for the proper settlement of the artificial reef. Furthermore, determining the optimal area includes determining the number of landing points based on the number and / or size of vessels to be moored, so that the artificial reef landing points meet the expectations of visitors.

[0028] According to another aspect of the invention, determining an optimized area involves avoiding at least one topographic boundary and / or at least one intersection event. Topographic boundaries are defined by size and are calculated using the optimization methods described above. Intersection events are triggered when cells developed for hard distribution transplantation are located outside or on the topographic boundary and / or in the discarded topographic area, i.e., on the rocky seabed within the topographic boundary.

[0029] Furthermore, determining an optimized area involves maintaining a distance between landing points, within minimum and maximum limits, to facilitate access and safety for vessels.

[0030] More specifically, the determination of an optimized design of the landing points comprises the steps of i) selecting a first cell; ii) analyzing a plurality of adjacent second cells; iii) calculating the fractal dimension of each combination of the first cell with the plurality of adjacent second cells, using a box-counting method; iv) selecting a second cell that creates a higher fractal dimension, thus arriving at an optimal distribution of the landing points, according to precise mathematical calculations.

[0031] In summary, the fractal dimension is calculated using a box-counting method. This involves establishing a combination of cell A and each of its adjacent cells, thus obtaining the fractal dimension of cells A and B, cells A and C, and cells A and D. A specific algorithm chooses the maximum fractal dimension between a cell and the combination of each of its adjacent cells, using a box-counting method that comprises a fixed grid, no rotation, and an exponential decrease in the size of the boxes. The algorithm skips each time a cell has fewer than two free adjacent cells and stops completely when it reaches a predefined maximum area.

[0032] Regarding the fractal dimension calculator, the definition begins by dividing the entire selected surface into a Voronoi diagram. The initial area is then uniformly filled with various random points. The point density determines the average size of each cell. In the current definition, the average size of each cell is set to a number of square meters. A simple Voronoi cell algorithm is then applied, using the random points as input within the perimeter of the initial area.

[0033] Regarding the selection of the first point, the first cell from which the fractal calculator will start is chosen randomly, or a feature is implemented that selects the cell closest to the first landing point. The first landing point is the first one followed by the vector of the prevailing current, serving as the initial cell from which to start the fractal calculator.

[0034] Furthermore, according to the fractal dimension calculator, the only possible candidates for the initial cell are those adjacent to it. In this example, cell A will be the initial cell. Cells B, C, and D are the only cells adjacent to cell A. The box-counting method will be used to calculate the fractal dimension due to its ease of use and compatibility with a preferred computer program. This method employs fixed grid scans, without rotation or overlap, and exponential decrease in box size. The next step is to calculate the fractal dimension, using the box-counting method, of the combination of cell A and each of its adjacent cells (fractal dimension of cells A and B, cells A and C, and cells A and D). The combination with the highest fractal dimension is selected, and the algorithm is restarted.If, for example, the combination of cells A and D has the highest fractal dimension, cell D becomes the new starting point. Cells E, F, and G are the next adjacent cells. The algorithm then calculates the fractal dimension of the combinations ADE, ADF, and ADG. The combination with the highest fractal dimension is chosen, and the last cell added becomes the next starting point. The algorithm continues until, for any given combination, fewer than two adjacent cells are available. The calculator stops, the definition jumps to a new random cell, and the fractal calculator algorithm starts again. The entire definition stops once it reaches a predefined cumulative area.

[0035] Additionally, a function can be added to stop the fractal calculator once a specific combination of cells reaches a predefined size. A function can also be added to provide more control over the next randomly selected point at which the fractal calculator will restart (for example, a certain distance in the direction of the current, or a certain distance to the next landing point). This adds the aforementioned feature of having more control over the first cell that will initiate the fractal dimension calculator. According to a preferred embodiment of the invention, the above steps are repeated until the entire extent of the optimized area is covered, so that an optimal design is iteratively achieved.The algorithms are designed to perform rule-based iterations by analyzing the environmental and economic impact of the design on-site, thereby optimizing it to maximize the positive potential of the designs. This achieves a highly efficient machine learning configuration, aligning it with the intended purpose of being a self-evolving system. Therefore, the present invention is a system that not only provides context-based designs with topographic, biological, and economic requirements, but also functions as a system where designs can be studied and analyzed over an extended period, thus compiling and providing a data structure for exploring the future possibilities of underwater design ecologies.

[0036] Its iterative nature allows for a self-learning and evolving system that refines the overall bionomic design process and primary categories based on the analysis of the site's behavior and ecosystem. It functions as a multi-layered, reflective system capable of adapting to programmatic requirements at both simple and complex levels.

[0037] It is worth noting that the cell comprises either a structural reef or a plant, thus providing greater flexibility in the design of the artificial reef. The cell is a defined region for a structural reef or a collection of reefs, or a region for the transplantation of soft substrate, particularly a plant.

[0038] The present invention also relates to an artificial reef designed by a computer-implemented method for designing artificial reefs, as described above.

[0039] The accompanying drawings show, by way of non-limiting example, a computer-implemented method for the design of artificial reefs, as set forth in the invention. Other features and advantages of said computer-implemented method for designing artificial reefs, the subject of the present invention, will become apparent from the description of a preferred, but not exclusive, embodiment, which is illustrated by way of non-limiting example in the accompanying drawings, in which: BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1.- View of a selected geographical site suitable for the design of an artificial reef, according to the present invention.

[0041] Figure 2A.- It is a diagram that includes geographical data of the selected site, according to the present invention.

[0042] Figure 2B.- It is a diagram that includes geographical data and a first 3D model of the selected site, according to the present invention.

[0043] Figure 2C.- It is a diagram that includes geographical data and a second 3D model of the selected site, according to the present invention.

[0044] Figure 2D.- It is a diagram that includes geographic data and a third 3D model of the selected site, according to the present invention.

[0045] Figure 3A.- It is a diagram that includes geographical data of the selected site including the ocean current, according to the present invention.

[0046] Figure 3B.- It is a diagram that includes geographical data of the selected site including some landing points, according to the present invention.

[0047] Figure 3C.- It is a diagram that includes geographical data of the selected site including some landing points, according to the present invention.

[0048] Figure 3D.- It is a diagram that includes geographic data of the selected site including an optimized area, according to the present invention.

[0049] Figure 3E.- It is a diagram that includes geographical data of the selected site including an optimized area, according to the present invention.

[0050] Figure 4.- It is a diagram that includes geographical data of the selected site including an optimized area, according to the present invention.

[0051] Figure 5.- It is a diagram that includes an optimized design with cells, according to the present invention.

[0052] Figure 6A.- It is a diagram of the process for determining a first optimized design, according to the present invention.

[0053] Figure 6B.- It is a diagram of the process for determining a second optimized design, according to the present invention.

[0054] Figure 7.- It is a diagram of an optimized layout with artificial reefs, according to the present invention.

[0055] Figure 8A.- It is a diagram of a first optimized layout with structural reefs and plants, according to the present invention.

[0056] Figure 8B.- It is a diagram of a second optimized layout with structural reefs and plants, according to the present invention.

[0057] Figure 9.- This is a perspective view of an optimized layout with structural reefs and plants, according to the present invention. DESCRIPTION OF A PREFERRED EMBODIMENT

[0058] In view of the aforementioned figures and in accordance with the numbering adopted, a preferred embodiment of the invention can be seen, comprising the parts and elements indicated and described in detail below.

[0059] Figure 1 illustrates a view of a selected geographical site (1) suitable for the design of an artificial reef (2). The location of the coast (51 c) and the seabed (51 d) can be observed.

[0060] Figure 2A illustrates a diagram that includes geographic data (3) of the selected site (1), geographic data (3) comprising abiotic data (31), biotic data (32), terrain data (33) and logistic data (34).

[0061] Figure 2B illustrates a diagram that includes geographic data (3) and a first 3D model (5) of the selected site (1).

[0062] Figure 2C illustrates a diagram that includes geographic data (3) and a second 3D model (5) of the selected site (1).

[0063] Figure 2D illustrates a diagram that includes geographic data (3) and a third 3D model (5) of the selected site (1).

[0064] Figure 3A illustrates a diagram that includes geographical data (3) of the selected site (1), including the ocean current (51 b).

[0065] Figure 3B illustrates a diagram that includes geographical data (3) of the selected site (1) including some landing points (51), following the alignment criterion of the arrangement of said landing points (51) along the marine current (51 b)

[0066] Figure 3C illustrates a diagram including geographic data (3) of the selected site (1), including some landing points (51), according to the present invention, following the criteria of identifying and avoiding topographic boundaries (61) and intersection events (62). Figure 3D illustrates a diagram including geographic data (3) of the selected site (1), including an optimized area (42), following the criterion of compliance with a condition or restriction (41) to maintain a certain distance between the landing point (51) within the minimum and maximum limits.

[0067] Figure 3E illustrates a diagram that includes geographical data (3) of the selected site (1) including an optimized area (42), following the criteria of maintaining a minimum distance around the artificial reef (2) to facilitate access and safety, which also represents an increase in the area of ​​the plaza.

[0068] Figure 4 illustrates a diagram that includes geographical data (3) of the selected site (1) which includes an optimized zone (42), comprising the landing points (51), as a berthing place for ships (51a).

[0069] Figure 5 illustrates a diagram that includes an optimized design (43) with cells (43a, 43b)

[0070] Figure 6A illustrates a diagram of the process for determining a first optimized design (43) with a first cell (43a) and a second cell (43b).

[0071] Figure 6B illustrates a diagram of the process for determining a second optimized design (43) with a first cell (43a) and a second cell (43b).

[0072] Figure 7 illustrates a diagram of an optimized design (43) with artificial reefs (2) including structural reefs (21) and plants (22).

[0073] Figure 8A illustrates a diagram of a third optimized design (43) with artificial reefs (2) including structural reefs (21) and plants (22).

[0074] Figure 8B illustrates a diagram of an optimized fourth design (43) with artificial reefs (2) including structural reefs (21) and plants (22).

[0075] Figure 9 illustrates an optimized design perspective (43) of a site (1) with artificial reefs (2) including structural reefs (21) and plants (22).

[0076] More particularly, according to Figures 2A and 4, the computer-implemented method for designing artificial reefs (2) comprises the steps of i) analysis of geographic data (3) of a selected site (1); ii) generation of a 3D terrain model (5) of the selected site (1) from the geographic data (3); iii) determination of constraints (41) in the 3D terrain model (5); iv) determination of an optimized area (42) in the 3D terrain model (5); v) determination of an optimized layout (43) of the landing points (51) in the 3D terrain model (5), wherein the geographic data (3) comprises referenced information, which helps to determine the position of the landing points (51). Furthermore, the position of the landing points (51) is determined by the logistics of the project, also depending on the number and size of the vessels (51a), so that the landing points (51) can be increased or reduced.The position of the landing points (51) can also be determined by the distance from the coast (51c), the area of ​​filtered topography based on the type of seabed (51d) and the direction of the ocean current (51b).

[0077] Furthermore, according to Figure 2A, geographic data (3) comprises abiotic data (31), biotic data (32), terrain data (33), and / or project logistics data (34). This information is collected through various channels, such as open-source platforms / websites, self-directed data collection by the UGI team, services provided by consultants, and / or documentation of the location of files at local centers.

[0078] More precisely, according to Figure 2A, abiotic data (31) comprise any of the groups of topography, current speed, current direction, wave height, wind speed, wind direction, wave direction, turbidity, seabed substrate type (51 d), sediment deposition height, average depth, seabed accumulation rate (51 d), species speed, lighting, and protected areas.

[0079] Furthermore, according to Figure 2A, the biotic data (32) comprise any of the groups of larval dispersal extent, larval dispersal rate, coral habitats, species type, proximity of existing species, and protected species.

[0080] Furthermore, according to Figure 2A, the terrain data (33) comprise any of the existing port groups and diving areas.

[0081] According to another aspect of the invention, and according to Figures 2C and 2D, the 3D terrain model (5) of the selected site (1) is a mesh model or NURBS model format. It should be noted that, according to Figures 1 and 3D, the constraints (41) on the 3D terrain model (5) are any of the following: required depth, substrate type, protected areas, presence of outfalls, and seabed slope (51d).

[0082] Additionally, according to Figures 3A and 3B, determining an optimized area (42) involves aligning the landing points (51) along the marine current (51b). The marine current (51b) influences the definition of the landing points (51) and the placement of the hard and soft elements, based on the gamification aspect of the project. Since flowing with the marine current (51b) is easier, and divers can perform longer and easier dives while diving with the current, the landing points (51) should be positioned accordingly. The placement of the hard and soft elements is also influenced by the flow of the marine current (51b) with respect to bionomic connectivity, as the transmission of biotic data is more efficient along the current flow.

[0083] More specifically, according to Figure 3D, the determination of an optimized zone (42) comprises the placement of landing points (51) based on the distance from the coast (51c) and / or the type of seabed (51d).

[0084] In a preferred embodiment of the invention, according to Figures 3E and 4, the determination of an optimized area (42) comprises the determination of the number of landing points (51) based on the number and / or size of the ships (51a) to be moored.

[0085] Advantageously, according to Figure 3C, the determination of an optimized area (42) includes avoiding at least one topographic boundary (61) and / or at least one intersection event (62). The determination of an optimized area (42) also includes maintaining a distance between landing points (51) within the minimum and maximum limits to facilitate access and safety.

[0086] Furthermore, according to Figures 5 and 6A, determining an optimized layout (43) of the landing points (51) comprises the steps of: i) selecting a first cell (43a); ii) analyzing a plurality of adjacent second cells (43b); iii) calculating the fractal dimension of each combination of the first cell (43a) with the plurality of adjacent second cells (43b), using a box-counting method; and iv) selecting a second cell (43b) that creates a higher fractal dimension. One criterion for identifying the first cell (43a) for automatic selection is that the first cell (43a) be chosen randomly from the filtered area. Additional parameters will be selected based on their relationship to the landing points (51). It should be noted that fractal dimension is a term used in mathematics to characterize fractals and measure their spatial distribution.Some studies have found a correlation between high fractal dimension in natural patterns (savannah, forest patches, etc.) and greater biodiversity. The method of the present invention starts with a given first cell (43a), analyzes adjacent cells, calculating the fractal dimension of each combination, and selects the second cell (43b) that creates the highest data point. The process is repeated until the desired total area is reached.

[0087] According to a preferred embodiment of the invention, and according to Figures 6B and 7, the steps of claim 12 are repeated until the entire extent of the optimized area (42) is covered.

[0088] More precisely, according to Figures 8A and 8B, the cell (43a, 43b) comprises either a structural reef (21) or a plant (22). One criterion for choosing the type of element to be installed in the selected cell (43a, 43b), whether structural reef (21) or plants (22), is to distribute them based on factors such as: a) financial aspects of the artificial reef production project (2); b) plant (22) transplant area requirements provided by the interested scientist or biologist involved in the project's transplant process; c) bionomic requirements developed from the site (1) ecosystem requirements.

[0089] The present invention also relates, according to Figure 9, to an artificial reef (2) designed by a computer-implemented method for designing artificial reefs (2).

[0090] The details, shapes, dimensions and other accessory elements, as well as the components used in the computer-implemented artificial reef design method, may be conveniently replaced by others that are technically equivalent, and that do not depart from the essential nature of the invention or the scope defined by the claims included in the following list.

[0091] List of numerical references:

[0092] 1 site

[0093] 2 artificial reef

[0094] 21 Structural reef

[0095] 22nd floor

[0096] 3 Geographical data

[0097] 31 Abiotic data

[0098] 32 Biotic data

[0099] 33 Terrain data

[0100] 34 Logistics data

[0101] 41 Restrictions

[0102] 42 Optimized Area

[0103] 43 Optimized design

[0104] 43 bis First cell

[0105] 43b Second cell

[0106] 5 3D Model

[0107] 51 Landing Point

[0108] 51 bis boat

[0109] 51 b Ocean current

[0110] 51 quater ribera

[0111] 51 quinquies seabed

[0112] 61 Topographic boundary

[0113] 62 Intersection Event

Claims

CLAIMS 1 - Computer-implemented method for the design of artificial reefs (2), comprising the steps of: i) analysis of geographic data (3) of a selected site (1); i) generation of a 3D terrain model (5) of the selected site (1) from the geographic data (3); iii) determination of constraints (41) in the 3D terrain model (5); iv) determination of an optimized area (42) in the 3D terrain model (5); v) determination of an optimized layout (43) of the landing points (51) in the 3D terrain model (5). 2- Computer-implemented method for designing artificial reefs (2), according to claim 1, characterized in that the geographical data (3) comprises abiotic data (31) and / or biotic data (32) and / or terrain data (33) and / or project logistics data (34). 3- A computer-implemented method for designing artificial reefs (2), according to claim 2, characterized in that the abiotic data (31) comprise any of the group of topography, current speed, current direction, wave height, wind speed, wind direction, wave direction, turbidity, seabed substrate type (51d), sediment deposition height, average depth, seabed accumulation rate (51d), speed for species, lighting, and protected areas. 4- Computer-implemented method for the design of artificial reefs (2), according to claim 2, characterized in that the biotic data (32) comprise any of the groups of larval dispersal extent, larval dispersal rate, coral habitats, species type, proximity of existing species and protected species. 5- Computer-implemented method for the design of artificial reefs (2), according to claim 2, characterized in that the terrain data (33) comprise any of the group of existing ports and diving areas. 6- Computer-implemented method for the design of artificial reefs (2), according to any of the preceding claims, characterized in that the 3D terrain model (5) of the selected site (1) is a mesh model format or NURBS model. 7- Computer-implemented method for the design of artificial reefs (2), according to any of the preceding claims, characterized in that the constraints (41) in the 3D terrain model (5) are any of the group of required depth, substrate type, protected areas, presence of outfalls and seabed slope (51 d). 8- Computer-implemented method for the design of artificial reefs (2), according to any of the preceding claims, characterized in that the determination of an optimized area (42) comprises the alignment of the landing points (51) along the ocean current (51 b). 9- Computer-implemented method for the design of artificial reefs (2), according to any of the preceding claims, characterized in that the determination of an optimized area (42) comprises the positioning of landing points (51) according to the distance from the coast (51c) and / or the type of seabed (51d). 10- Computer-implemented method for the design of artificial reefs (2), according to any of the preceding claims, characterized in that the determination of an optimized area (42) comprises the determination of the number of landing points (51) based on the number and / or size of the vessels (51a) to be moored. 11 - Computer-implemented method for the design of artificial reefs (2), according to any of the preceding claims, characterized in that the determination of an optimized area (42) comprises avoiding at least one topographic boundary (61) and / or at least one intersection event (62). 12- Computer-implemented method for the design of artificial reefs (2), according to any of the preceding claims, characterized in that the determination of an optimized layout (43) of the landing points (51) comprises the steps of: i) selecting a first cell (43a); i) analyzing a plurality of adjacent second cells (43b); iii) calculating the fractal dimension of each combination of the first cell (43a) with the plurality of adjacent second cells (43b), using a box-counting method; iv) selecting a second cell (43b) that creates a higher fractal dimension.

13. A computer-implemented method for designing artificial reefs (2) according to claim 12, characterized in that the steps of claim 12 are repeated until the entire extent of the optimized area (42) is covered.

14. A computer-implemented method for designing artificial reefs (2) according to any of claims 12 to 13, characterized in that the cell (43a, 43b) comprises a structural reef (21) or a plant (22). 15- Artificial reef (2) designed by a computer-implemented method for designing artificial reefs (2), in accordance with any of the preceding claims.

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