Aqueduct structure optimization design method based on BIM technology

Abstract

The invention relates to the technical field of structure optimization, in particular to an aqueduct structure optimization design method based on a BIM technology, and solves the technical problem of low accuracy of aqueduct structure optimization design in the prior art. The method comprises the following steps: acquiring a BIM model of an aqueduct structure; performing finite element model conversion and mesh generation on the BIM model to obtain a finite element model; performing various dynamic load simulations on the finite element model, and determining a first refined region which needs to be refined based on the load; identifying a joint area of the aqueduct structure according to geometric features of the BIM model, and determining a second refined area needing to be refined based on a joint; performing mesh refinement on the first refinement area and the second refinement area, and updating the BIM model according to a refinement result; and optimizing the aqueduct structure based on the updated BIM model.

Description

Technical Field

[0001] This invention relates to the field of structural optimization technology, and specifically to a method for optimizing the design of aqueduct structures based on BIM technology. Background Technology

[0002] Aqueducts are key water conveyance structures in hydraulic engineering, used to cross valleys and transportation routes to achieve long-distance water transport. Currently, the design of aqueduct structures typically begins with creating a 3D BIM model of the aqueduct, which is then converted into a finite element model for mechanical performance analysis. The analysis results are then used to optimize design parameters. However, this existing analysis method relies heavily on engineers' experience or simple stress contour plots to determine the finite element mesh refinement region. This results in a lack of sufficient scientific basis for mesh refinement, insufficient accuracy of the finite element analysis, and consequently, low accuracy in the optimized design of the aqueduct structure. Summary of the Invention

[0003] To address the issue of low accuracy in the optimization design of aqueduct structures in existing technologies, the present invention aims to provide a method for optimizing the design of aqueduct structures based on BIM technology. The specific technical solution adopted is as follows: Obtain the BIM model of the aqueduct structure; The BIM model is converted into a finite element model and meshed to obtain a finite element model. Multiple dynamic load simulations were performed on the finite element model to determine the first refinement region that needs to be refined based on the load; Identify the joint areas of the aqueduct structure based on the geometric features of the BIM model, and determine the second refinement area that needs to be refined based on the joints; The first and second refinement regions are meshed, and the BIM model is updated based on the refinement results. Optimize the aqueduct structure based on the updated BIM model.

[0004] In one possible implementation, the method further includes: acquiring stress sequences of each mesh element in the finite element model under various load simulations; calculating the stress influence index of each mesh element based on the stress sequence, the stress influence index being used to characterize the stress response characteristics of the mesh element under load; calculating the stress concentration of each mesh element based on the stress influence index, the stress concentration being used to characterize the stress state of the mesh element under various loads; and determining the mesh elements with stress concentration greater than or equal to a first preset threshold as the first refinement region.

[0005] In one possible implementation, the method further includes: determining a local element set of the target mesh element, the local element set including a predetermined number of mesh elements spatially adjacent to the target mesh element; the target mesh element being any one of the mesh elements; calculating, based on the stress sequence, the first stress ratio between the average stress of the target mesh element and the local element set and the average stress of the finite element model; calculating the average stress ratio of the first stress ratio at all load simulation times; calculating the dispersion of the stress sequence of the target mesh element to obtain the stress discrete value; and determining the average stress ratio and the stress discrete value as stress influence indices.

[0006] In one possible implementation, the method further includes: calculating the Euclidean distance between the centroid of the target mesh cell and the centroids of other mesh cells; and selecting a preset number of mesh cells that are closest to the target mesh cell based on the Euclidean distance to determine a local cell set.

[0007] In one possible implementation, the method further includes: extracting all grid cells of the bottom region from the BIM model; identifying the boundary joint cells in the bottom region that intersect with the two retaining walls; identifying the planar joint cells inside the bottom region, where planar joint cells refer to grid cells located at the structural boundary in the same plane; and using the boundary joint cells and planar joint cells as a second refinement region.

[0008] In one possible implementation, the method further includes: obtaining the coordinates of the vertices of all grid cells in the BIM model; clustering each vertex based on the height coordinates of each vertex to obtain multiple clusters; determining the cluster containing the most vertices as the bottom vertex cluster; and using the grid cells corresponding to the vertices belonging to the bottom vertex cluster as the grid cells of the bottom region.

[0009] In one possible implementation, the method further includes: determining a set of associated cells; the set of associated cells includes mesh cells that share a common vertex in the bottom region; calculating the normal vector of each mesh cell in the set of associated cells; calculating the angle between the normal vectors of each mesh cell, and taking the maximum value of the angle corresponding to a vertex as the anisotropy parameter; determining vertices whose anisotropy parameter is greater than or equal to a second preset threshold, and the mesh cell to which they belong is the boundary seam cell.

[0010] In one possible implementation, the method further includes: projecting the vertices of the bottom region onto a two-dimensional plane; fitting the vertex projections to the two-dimensional plane using a preset straight line fitting algorithm to obtain multiple candidate straight lines; calculating the directional difference and length difference between each candidate straight line; calculating the seam difference index of each candidate straight line based on the directional difference and length difference, the seam difference index being used to evaluate the degree to which the straight line conforms to the characteristics of a real seam; determining straight lines with a seam difference index less than or equal to a third preset threshold as seam straight lines; and performing a secondary check on straight lines with a seam difference index greater than the third preset threshold to identify irregular seam straight lines; the irregular seam straight lines being seam straight lines in a geometrically discontinuous region of the structure; and determining the mesh cells corresponding to the vertices on the seam straight lines as planar seam cells.

[0011] In one possible implementation, the method further includes: for any two candidate lines, calculating the absolute value of the angle between the direction vectors of the two candidate lines to obtain the direction difference degree, and calculating the absolute value of the length difference between the two candidate lines to obtain the length difference degree.

[0012] In one possible implementation, the method further includes: using an adaptive mesh refinement algorithm to locally refine the mesh cells in the first and second refinement regions; performing finite element analysis again on the locally refined mesh to obtain the analysis results; and correcting the structural design parameters of the BIM model based on the analysis results.

[0013] The present invention has the following beneficial effects: After constructing a BIM model and converting it into a finite element model, this application identifies key stress areas through dynamic load simulation, extracts structurally weak areas based on geometric features, and refines both the key stress areas and structurally weak areas. This allows for targeted refinement of specific areas in the BIM model, resulting in more comprehensive and reasonable mesh refinement. Therefore, the BIM-based aqueduct structure optimization design method provided in this application can improve the accuracy of finite element analysis, thereby improving the accuracy of aqueduct structure optimization design. Attached Figure Description

[0014] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A method flow for optimizing the design of an aqueduct structure based on BIM technology, provided in one embodiment of the present invention. Figure 1 ; Figure 2 This is a schematic diagram of an aqueduct structure provided in one embodiment of the present invention; Figure 3 A method flow for optimizing the design of an aqueduct structure based on BIM technology, provided in one embodiment of the present invention. Figure 2 ; Figure 4 A method flow for optimizing the design of an aqueduct structure based on BIM technology, provided in one embodiment of the present invention. Figure 3 . Detailed Implementation

[0016] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a BIM-based aqueduct structure optimization design method proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0018] First, the technical terms used in this application will be explained in detail: The following description, in conjunction with the accompanying drawings, details a specific scheme for an aqueduct structure optimization design method based on BIM technology provided by this invention.

[0019] Please see Figure 1 The diagram illustrates a flowchart of an aqueduct structure optimization design method based on BIM technology, as provided in an embodiment of the present invention. Figure 1 As shown, the method includes the following steps: Step 101: Obtain the Building Information Modeling (BIM) model of the aqueduct structure.

[0020] BIM (Building Information Modeling) is a digital model used in engineering design, construction, and management. By digitally representing the physical and functional characteristics of building components, BIM models construct three-dimensional information models containing rich parameters such as geometric information, material properties, and spatial relationships. In this application, the BIM model refers to a three-dimensional digital model of the aqueduct structure, which not only visually displays the aqueduct's geometry but also serves as a data source and platform for subsequent finite element analysis and structural optimization.

[0021] As one implementation method, this application can construct a 3D BIM model of a rectangular aqueduct based on architectural model processing software (such as Revit). The specific method of constructing the BIM model can refer to existing technologies, and this application does not limit it in this regard.

[0022] As an example, such as Figure 2 The diagram shown is a schematic diagram of an aqueduct structure provided in this application.

[0023] Step 102: Perform finite element model conversion and mesh generation on the BIM model to obtain the finite element model.

[0024] The finite element model (FEM) is an analytical model that approximates the simulation of complex continuum structures using numerical computation methods. Its basic principle is to discretize a complex solid structure into a large number of simple, finite-sized small elements (such as triangular or quadrilateral elements), and then simulate the mechanical behavior of the original structure by connecting these elements at nodes. In this application, the FEM model is converted from a BIM model and used to perform structural mechanics analysis. By solving for the stress and deformation of each element, the overall performance and local response of the aqueduct structure under external loads are evaluated.

[0025] As one implementation method, this application imports the 3D BIM model into finite element modeling software to obtain the initial finite element model of the aqueduct structure. It should be noted that the finite element modeling software is not limited to ABAQUS, ANSYS, or MSC; this application uses ABAQUS for finite element model conversion as an example for illustration.

[0026] After obtaining the initial finite element model, a mesh generation algorithm is used in ABAQUS software to generate a new finite element model. Following this, the coordinates of the vertices of each triangular mesh in three-dimensional space, as well as the centroid coordinates of each triangular mesh, are obtained. It should be noted that the mesh generation in this application can be triangular, quadrilateral, or other shapes; this application does not limit the type of mesh. This application primarily uses triangular mesh generation as an example. The mesh generation algorithms used in this application include, but are not limited to, the Medial Axis algorithm and the Advancing Front algorithm.

[0027] It should be noted that the above-mentioned finite element model conversion and mesh generation process can refer to existing technologies, and this application does not limit it in this regard.

[0028] In addition, to eliminate the influence of dimensions between data, all data are normalized, such as by Z-score, maximum value normalization, maximum and minimum value normalization, etc., to eliminate data dimensions.

[0029] Step 103: Perform various dynamic load simulations on the finite element model to determine the first refinement region that needs to be refined based on the load.

[0030] Since aqueducts are affected not only by water flow loads during water transport in natural environments, but also by external dynamic loads such as wind and earthquakes, we can first use ABAQUS software to apply external loads to the three-dimensional model of the aqueduct to simulate the structural changes of the aqueduct under water transport conditions. Then, by analyzing the changes in element stress on each triangular facet under the aqueduct transport conditions, we can reflect the stress influence on each triangular facet and thus determine whether further refinement of the triangular facets is needed.

[0031] Dynamic load simulation refers to simulating the response of a structure to loads whose magnitude, direction, or point of application varies over time in a computer environment using numerical calculation techniques. Unlike static loads, dynamic loads more realistically reflect the complex force environment a structure experiences in actual working conditions. In this application, dynamic load simulation involves sequentially applying seismic loads, wind loads, and water pressure loads to the aqueduct structure, collecting complete data sequences of stress changes over time, and determining the stress conditions in different regions of the aqueduct within the real natural environment based on these data sequences, with further refinement based on the stress conditions in different regions.

[0032] Optionally, the first refinement region refers to structural parts exhibiting high stress or severe stress fluctuations under external dynamic loads, such as windward surfaces or areas directly impacted by water flow. Refining the first refinement region can accurately capture the structural mechanical behavior caused by external loads, thereby improving the calculation accuracy of finite element analysis in these critical areas.

[0033] Step 104: Identify the joint areas of the aqueduct structure based on the geometric features of the BIM model, and determine the second refinement area that needs to be refined based on the joints.

[0034] Optionally, the second refinement region may contain inherent structural weaknesses due to manufacturing, construction, or thermal expansion and contraction. These regions may not experience high stress in conventional load analysis, but they are highly susceptible to cracking or leakage due to stress concentration during long-term use. Refining the second refinement region can prevent cracking or leakage caused by stress concentration during long-term use, thus compensating for potential omissions in simple stress analysis and ensuring that the model has sufficient analytical capabilities for inherently weak points.

[0035] It should be noted that some areas may only be affected by a single type of load during external load simulation. For example, the joint area at the bottom of the aqueduct usually bears mainly the pressure of the water flow at the bottom of the aqueduct and its own weight, and is less affected by wind and seismic loads. Since the joint area at the bottom of the aqueduct is only affected by a single load, its stress concentration is low and may be missed during the refinement process. Therefore, in addition to the load analysis according to step 103 above, this application can further analyze based on the structural characteristics of the aqueduct itself. For example, due to seasonal temperature changes in the natural environment, the aqueduct structure may experience significant thermal expansion and contraction effects: when the aqueduct heats up, it causes joint compression, and when it cools down, it causes joint stretching. Therefore, during long-term use, this can lead to cracks or leaks in the joint area. Therefore, this application can further analyze whether the area where the triangular facet is located may be a joint area to verify whether refinement is needed to avoid missing key areas.

[0036] Step 105: Refine the mesh of the first and second refinement regions, and update the BIM model based on the refinement results.

[0037] As one possible implementation, this step can be specifically implemented as follows: using an adaptive mesh refinement algorithm to locally refine the mesh elements in the first refinement region and the second refinement region; re-performing finite element analysis on the locally refined mesh to obtain the analysis results; and correcting the structural design parameters of the BIM model based on the analysis results.

[0038] Step 106: Optimize the aqueduct structure based on the updated BIM model.

[0039] Optionally, this application can further refine all identified refined triangular faces and joint triangular faces using an adaptive mesh refinement algorithm (AMR) from the existing field of artificial intelligence, resulting in a further refined aqueduct structure model. Based on the refined aqueduct structure model, a more accurate finite element analysis is performed, and the BIM model is updated and optimized according to the analysis results, achieving targeted optimization of the aqueduct structure and comprehensively improving its safety and durability during long-term use.

[0040] Based on the above technical solution, after constructing a BIM model and converting it into a finite element model, this application identifies key stress areas through dynamic load simulation, extracts structurally weak areas based on geometric features, and refines both the key stress areas and structurally weak areas. This allows for targeted refinement of specific areas in the BIM model, resulting in more comprehensive and reasonable mesh refinement. Therefore, the BIM-based aqueduct structure optimization design method provided in this application can improve the accuracy of finite element analysis, thereby improving the accuracy of aqueduct structure optimization design.

[0041] like Figure 3 As shown, in one possible implementation, the process of determining the first refined region in step 103 above can be specifically implemented through the following steps: Step 301: Collect the stress sequence of each mesh element in the finite element model under various load simulations.

[0042] Optionally, the various load simulations in this application include, but are not limited to, at least one of the following: seismic load, wind load, and water flow load. In other words, this application uses ABAQUS software to apply certain seismic loads, wind loads, and water flow loads to the finite element model, and the three dynamic loads are simulated sequentially; the element stress data of each triangular mesh in the finite element model are collected as a function of time during the seismic / wind / water flow simulation, and sorted according to the time sequence of data acquisition to construct the seismic stress sequence, wind stress sequence, and water stress sequence of each triangular facet, thereby characterizing the dynamic stress changes of each triangular facet during the seismic / wind / water flow simulation.

[0043] It should be noted that in this step, in order to eliminate the influence of dimensions between data, the seismic stress sequence, wind stress sequence, and water stress sequence are normalized to eliminate the data dimensions.

[0044] Step 302: Calculate the stress influence index of each mesh element based on the stress sequence.

[0045] Among them, the stress influence index is used to characterize the stress response characteristics of mesh elements under load.

[0046] As one possible implementation, this step can be achieved through the following process: determining the local element set of the target mesh element, which includes a predetermined number of mesh elements spatially adjacent to the target mesh element; the target mesh element being any one of these mesh elements; calculating the first stress ratio between the average stress of the target mesh element and the local element set, and the average stress of the finite element model, based on the stress sequence; calculating the average stress ratio of the first stress ratio at all load simulation times; calculating the dispersion of the stress sequence of the target mesh element to obtain the stress dispersion value; and determining the average stress ratio and the stress dispersion value as stress influence indices. Based on this, this application can effectively identify local stress concentration phenomena by comparing the average stress of the local element set with the average stress of the overall model; simultaneously, by combining the analysis of the dispersion of the stress sequence, it can capture stress fluctuation characteristics. This multi-faceted stress influence assessment method provides sufficient basis for accurately judging the necessity of mesh element refinement, ensuring the scientific nature and representativeness of the stress influence indices.

[0047] Optionally, the process of determining the local element set is as follows: calculate the Euclidean distance between the centroid of the target mesh element and the centroids of other mesh elements; based on the Euclidean distance, select a preset number of mesh elements that are closest to the target mesh element to determine the local element set.

[0048] It should be noted that the above-mentioned stress influence indicators include the average stress ratio and the stress dispersion value. The determination process of these two values ​​is explained below: For the average stress ratio, firstly, define the local region of the target triangular face, that is: taking the centroid of the i-th triangular face as the reference, select the N triangular faces with the closest Euclidean distance (N ranges from 9 to 12) as its local triangular face set.

[0049] Subsequently, the ratio of local to overall stress levels is calculated. Specifically, for time a in the wind simulation, the average wind stress s1 of the i-th triangular facet and its local triangular facet set, and the average wind stress s2 of all triangular faces in the entire aqueduct model at the same time are calculated. The ratio of s1 to s2 is recorded as the first ratio at time a. This ratio reflects the relative intensity of the wind load borne by the target area at a specific time.

[0050] Finally, the average value over the entire simulation period is calculated: the arithmetic mean of the first ratios at all times during the entire wind simulation process is taken to obtain the average stress ratio. This index characterizes the average stress concentration in the target area under continuous wind load.

[0051] To address the stress dispersion value, the wind stress sequence of the target triangular facet is directly analyzed throughout the wind simulation process. The dispersion of this sequence (such as standard deviation or other coefficients of variation) is calculated and used as the stress dispersion value. This value reflects the severity of stress changes in the target area under wind load; a larger dispersion value indicates a more unstable stress state in the area.

[0052] Step 303: Calculate the stress concentration of each grid element based on the stress influence index.

[0053] Stress concentration is used to characterize the stress state of mesh elements under various loads; specifically, it is used to quantitatively evaluate the stress state of local areas of the structure. It is calculated by fusing the average stress ratio and the discrete stress value under multiple load conditions. Based on stress concentration, the average stress level and its fluctuation severity of a specific mesh element under multiple dynamic loads can be comprehensively evaluated. A higher stress concentration value indicates a greater overall stress impact on that area during aqueduct operation, making it more likely to become a weak point in the structure, and therefore giving it higher priority in mesh refinement.

[0054] As an example, the stress concentration of the i-th triangular facet of the aqueduct Satisfy the following formula: In the formula, T is the number of types of loads applied for simulation. In this application, T can be 3, which represents the simulation of earthquake, wind, and water flow loads, respectively. The average stress ratio of the i-th triangular face in the t-th simulation process; Let be the discrete stress value of the i-th triangular facet in the t-th simulation process. and These are weighting coefficients. + =1. This can be set based on practical engineering experience or analysis of different aqueduct structure failure modes. and The value can be set if more attention is paid to the strength under long-term static load. Larger (e.g., 0.7). A smaller value (e.g., 0.3). If you are more concerned about fatigue or dynamic response, you can set a higher value. Larger (e.g., 0.6). Smaller (e.g., 0.4). If both are considered equally important, then let... = =0.5, at which point the formula degenerates into a simple addition of equal weights.

[0055] Step 304: Determine the mesh cells with stress concentration greater than or equal to the first preset threshold as the first refinement region.

[0056] As one possible implementation, the first preset threshold in this embodiment is a threshold determined based on the Otsu thresholding method. In other words, after determining the stress concentration of each mesh element according to the method described in the above steps, this application uses these stress concentrations as input to the Otsu thresholding method and uses the segmentation threshold output by the Otsu thresholding method as the first preset threshold. Mesh elements with stress concentrations greater than or equal to the first preset threshold are designated as the first refined region.

[0057] Based on the above technical solution, this application can accurately reflect the comprehensive stress state of grid cells under complex load conditions through the fusion analysis of multi-dimensional stress characteristics, providing reliable data support for identifying key areas and effectively improving the accuracy and comprehensiveness of load response area identification.

[0058] like Figure 4 As shown, in one possible implementation, the process of determining the second refined region in step 104 above can be specifically implemented through the following steps: Step 401: Extract all grid cells of the bottom region from the BIM model.

[0059] As one possible implementation, this step can be achieved through the following process: obtaining the coordinates of the vertices of all grid cells in the BIM model; clustering each vertex based on its height coordinates to obtain multiple clusters; identifying the cluster containing the most vertices as the bottom vertex cluster; and using the grid cells corresponding to the vertices belonging to the bottom vertex cluster as the grid cells of the bottom region.

[0060] In other words, when analyzing whether a triangular facet might be located in the seam area at the bottom of the aqueduct, it is necessary to extract the triangular facets located at the bottom of the aqueduct from the 3D aqueduct model. Since the height coordinates of the triangular facets at the bottom of the aqueduct are consistent and the bottom area is the largest, the z-coordinates (i.e., the coordinates representing the height) of all triangular facet vertices can be used as input for a clustering algorithm. The clustering algorithm is not limited to k-means, DPC, or DBSCAN. This embodiment uses the k-means algorithm for clustering, and obtains the optimal number of clusters through the elbow rule. The cluster with the most internal elements among all clusters is recorded as the bottom cluster (because the bottom area is the largest, and correspondingly, the number of triangular facets and vertices is also the largest). In this way, the vertex coordinates located in the bottom region of the aqueduct can be extracted, and the corresponding bottom triangular facets can be located using the vertex coordinates.

[0061] Step 402: Identify the junction joint units in the bottom area that intersect with the two side retaining walls.

[0062] As one possible implementation, this step can be implemented through the following process: determining the associated unit set; the associated unit set includes mesh units that share a common vertex in the bottom region; calculating the normal vector of each mesh unit in the associated unit set; calculating the angle between the normal vectors of each mesh unit, and taking the maximum value of the angle corresponding to a vertex as the anisotropy parameter; determining the vertex whose anisotropy parameter is greater than or equal to a second preset threshold, and the mesh unit to which it belongs is the boundary seam unit.

[0063] In other words, since a vertex may be the vertex of multiple triangles simultaneously, let's take the coordinates of the u-th vertex in the bottom cluster as an example. In the 3D model of the aqueduct, the triangles with the u-th vertex as their vertices are all denoted as the connecting faces of the u-th vertex.

[0064] Because the bottom of the rectangular aqueduct and the left and right retaining walls are geometrically non-coplanar, typically intersecting at right or obtuse angles, if the u-th vertex is located at the junction of the bottom and the left and right retaining walls, the connecting surfaces of the u-th vertex will be clearly distributed on two different planes. Therefore, this application can obtain the normal vectors of each connecting surface of the u-th vertex and the maximum value of the angle between each pair of normal vectors of each connecting surface, denoted as the skewness parameter of the u-th vertex. The skewness parameter reflects the degree of abrupt change in the geometric shape of the connecting surfaces of the u-th vertex; the larger the skewness parameter value, the more likely the connecting surfaces of the u-th vertex are located on different planes, and the more likely the u-th vertex is located at the junction of the bottom and the left and right retaining walls.

[0065] Based on the above method, the skewness of all vertices in the bottom cluster is calculated and used as input to the Otsu thresholding method. Vertices with skewness greater than or equal to the segmentation threshold output by the Otsu thresholding method (i.e., the second preset threshold mentioned above) are recorded as the two boundary points, and vertices with skewness less than the segmentation threshold are recorded as planar vertices. The cell mesh to which the two boundary points belong is recorded as the boundary seam cell.

[0066] It should be noted that the anisotropy parameter is a geometric characteristic defined in this application to characterize the geometric discontinuity of a structure. Specifically, in a 3D model, it refers to the maximum angle between any pair of normal vectors of all triangular mesh elements sharing a common vertex. This parameter effectively quantifies the degree of abrupt change in geometry at that vertex; a larger anisotropy parameter value indicates a higher probability that the local region where the vertex is located is formed by the intersection of planes in different directions, typically corresponding to the junction between the bottom and different structural components such as retaining walls.

[0067] Step 403: Identify the planar seam units inside the bottom area.

[0068] Among them, planar joint elements refer to mesh elements located at the structural boundaries within the same plane.

[0069] As one possible implementation, this step can be achieved through the following process: Projecting the vertices of the bottom region onto a two-dimensional plane; fitting the vertex projections to the two-dimensional plane using a preset straight line fitting algorithm to obtain multiple candidate straight lines; calculating the directional and length differences between each candidate straight line; calculating the seam difference index for each candidate straight line based on the directional and length differences, the seam difference index is used to evaluate the degree to which the straight line conforms to the characteristics of a real seam; determining straight lines with a seam difference index less than or equal to a third preset threshold as seam straight lines; to further ensure the completeness of identification, a secondary verification is performed on the remaining candidate straight lines with a seam difference index greater than the third preset threshold to avoid missing key irregular seams due to special geometric features. The secondary verification evaluates the degree of local geometric discontinuity (or "abrupt change") in the region where the straight line is located by analyzing the abrupt changes in the normal vectors of adjacent grid cells at the vertices of each remaining candidate straight line. Specifically, for a candidate straight line, the maximum value of the angle between the normal vectors of adjacent grid cells at each vertex is calculated, and the average of this maximum value for all vertices on the line is taken to quantify its overall geometric abrupt change level. If the level of geometric abrupt change exceeds the set fourth preset threshold, the straight line is determined to be an irregular joint line representing a region of geometric discontinuity in the structure. The mesh element corresponding to the vertex on the joint line is determined as a planar joint element.

[0070] Optionally, for any two candidate lines, the absolute value of the angle between the direction vectors of the two candidate lines is calculated to obtain the direction difference, and the absolute value of the length difference between the two candidate lines is calculated to obtain the length difference.

[0071] A more specific implementation process is as follows: Since the seams within the bottom region of the aqueduct are not randomly distributed but rather arranged in a regular, linear pattern, vertices located on the same seam exhibit a clear linear distribution when projected onto a two-dimensional coordinate system. Based on this characteristic, the coordinates of all planar vertices in the bottom region are first projected into a two-dimensional Cartesian coordinate system, and the Z-coordinate component is filtered out to simplify the analysis. Subsequently, the resulting two-dimensional coordinate dataset is used as input to a pre-defined straight-line fitting algorithm to fit multiple candidate straight lines.

[0072] It should be noted that the candidate lines obtained by the fitting algorithm may contain random linear arrangement of vertices in non-joint regions. Therefore, it is necessary to screen and remove abnormal lines based on the structural characteristics of the aqueduct. Considering the regular design characteristics of the aqueduct structure, its bottom joints follow a specific design rule in spatial distribution: the joints not only maintain a parallel relationship (i.e., consistent slope), but also maintain a consistent length.

[0073] The specific screening process is as follows: Taking any two candidate lines (denoted as line v and line w) as an example, firstly, the absolute value of the angle between the direction vectors of the two lines is calculated as the direction difference degree. This value reflects the degree of parallelism between the lines; the smaller the value, the closer the directions are to parallelism. Simultaneously, the absolute value of the difference in length between the two lines is calculated as the length difference degree. This value reflects the dimensional consistency between the lines; the smaller the value, the closer the lengths are to each other. Next, the normalized value of the direction difference degree is added to the normalized value of the length difference degree to obtain the comprehensive line difference value. This index comprehensively evaluates the similarity between the two lines from both direction and size dimensions. The smaller the value, the greater the likelihood that the two lines are both true seams. The above normalized value is used to eliminate differences in data scale and to eliminate dimensions.

[0074] Furthermore, for each candidate line, the overall line difference between it and every other candidate line is first calculated. Then, the K smallest differences are selected from these differences, where K is a preset positive integer. Finally, the arithmetic mean of these K smallest differences is used as the seam difference index of the current candidate line. In this way, the seam difference index can more accurately reflect the degree of consistency between the current line and its most similar local line group. The smaller the index value, the more the line conforms to the characteristics of regular seams in the local area.

[0075] Finally, the Otsu thresholding method is used to process the joint difference index of all candidate lines. Lines with an index value less than or equal to the segmentation threshold output by the Otsu thresholding method (i.e., the aforementioned third preset threshold) are identified as joint lines. Lines with a joint difference index greater than the third preset threshold are subjected to a secondary test to identify irregular joint lines that characterize geometrically discontinuous areas of the structure, thereby accurately extracting the joint areas in the bottom plane of the aqueduct. This method enables precise positioning of key areas, providing a basis for subsequent high-density mesh generation, thereby improving the accuracy of finite element analysis and the quality of BIM model optimization, ultimately ensuring the safety performance of the aqueduct structure.

[0076] Step 404: Use the boundary joint unit and the planar joint unit as the second refinement area.

[0077] Based on the above technical solution, this application identifies joints from two dimensions: the junction of the bottom and the retaining wall, and the interior of the bottom plane, fully considering the different types of joint characteristics of the aqueduct structure. This strategy of classification, identification, and unified processing can systematically capture various structural joints, ensuring that no key areas are missed, and providing a complete target area for mesh refinement based on structural features.

[0078] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0079] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for optimizing the design of aqueduct structures based on BIM technology, characterized in that, The method includes: Obtain the BIM model of the aqueduct structure; The BIM model is converted into a finite element model and meshed to obtain a finite element model; The finite element model is subjected to various dynamic load simulations to determine the first refinement region that needs to be refined based on the load. Identify the joint areas of the aqueduct structure based on the geometric features of the BIM model, and determine the second refinement area that needs to be refined based on the joints; The first and second refined regions are refined into meshes, and the BIM model is updated based on the refinement results. Optimize the aqueduct structure based on the updated BIM model.

2. The aqueduct structure optimization design method based on BIM technology according to claim 1, characterized in that, The process of performing various dynamic load simulations on the finite element model to determine the first refinement region that needs to be refined based on the load includes: The stress sequence of each mesh element in the finite element model under various load simulations was collected. Based on the stress sequence, the stress influence index of each grid cell is calculated, and the stress influence index is used to characterize the stress response characteristics of the grid cell under load. Based on the stress influence index, the stress concentration of each grid cell is calculated. The stress concentration is used to characterize the stress state of the grid cell under various loads. The mesh cells with stress concentration greater than or equal to a first preset threshold are identified as the first refined region.

3. The aqueduct structure optimization design method based on BIM technology according to claim 2, characterized in that, The step of calculating the stress influence index of each mesh element based on the stress sequence includes: A local set of cells for a target grid cell is determined, the local set of cells including a predetermined number of grid cells that are spatially adjacent to the target grid cell; the target grid cell is any one of the grid cells. Based on the stress sequence, calculate the average stress of the target mesh element and the local element set, and the first stress ratio of the average stress of the finite element model; Calculate the average stress ratio of the first stress ratio at all load simulation times; The degree of dispersion of the stress sequence of the target mesh element is calculated to obtain the stress discrete value; The average value of the stress ratio and the discrete value of the stress are determined as the stress influence index.

4. The aqueduct structure optimization design method based on BIM technology according to claim 3, characterized in that, The local cell set for determining the target mesh cell includes: Calculate the Euclidean distance between the centroid of the target mesh cell and the centroids of other mesh cells; Based on the Euclidean distance, a preset number of grid cells that are closest to the target grid cell are selected to determine the local cell set.

5. The aqueduct structure optimization design method based on BIM technology according to claim 1, characterized in that, The step of identifying the joint areas of the aqueduct structure based on the geometric features of the BIM model, and determining the second refinement area that needs to be refined based on the joints, includes: Extract all grid cells from the bottom region of the BIM model; Identify the junction joint units in the bottom region that intersect with the two side retaining walls; Identify the planar seam unit inside the bottom region, where the planar seam unit refers to the grid unit located at the structural boundary in the same plane; The boundary seam unit and the planar seam unit are used as the second refined region.

6. The aqueduct structure optimization design method based on BIM technology according to claim 5, characterized in that, The extraction of all grid cells for the bottom region from the BIM model includes: Obtain the coordinates of the vertices of all mesh elements in the BIM model; Based on the height coordinates of each vertex, the vertices are clustered to obtain multiple clusters. The cluster containing the most vertices is identified as the bottom vertex cluster; The mesh cells corresponding to the vertices belonging to the bottom vertex cluster are used as the mesh cells of the bottom region.

7. The aqueduct structure optimization design method based on BIM technology according to claim 5, characterized in that, The method for identifying the junction joint unit in the bottom region where it intersects with the two side retaining walls includes: Determine the associated unit set; the associated unit set includes grid units that share a common vertex in the bottom region; Calculate the normal vector of each grid cell in the associated cell set; Calculate the angle between the normal vectors of each mesh element, and take the maximum value of the angle corresponding to a vertex as the anisotropy parameter; Vertices whose anisotropy parameter is greater than or equal to the second preset threshold are identified as the boundary seam cells.

8. The aqueduct structure optimization design method based on BIM technology according to claim 5, characterized in that, The unit for identifying the planar seam within the bottom region includes: Project the vertices of the bottom region onto a two-dimensional plane; In the two-dimensional plane, a preset straight line fitting algorithm is used to fit the vertex projection to obtain multiple candidate straight lines; Calculate the directional difference and length difference between each of the candidate lines; Based on the directional difference degree and length difference degree, the seam difference index of each candidate straight line is calculated. The seam difference index is used to evaluate the degree to which the straight line conforms to the characteristics of a real seam. The lines with a seam difference index less than or equal to a third preset threshold are identified as seam lines; and the lines with a seam difference index greater than the third preset threshold are subjected to a second test to identify irregular seam lines; the irregular seam lines are seam lines in the geometrically discontinuous regions of the structure. The mesh element corresponding to the vertex on the seam line is defined as the planar seam element.

9. The method for optimizing the design of aqueduct structures based on BIM technology according to claim 8, characterized in that, The calculation of the directional difference and length difference between each of the candidate lines includes: For any two candidate lines, the absolute value of the angle between the direction vectors of the two candidate lines is calculated to obtain the direction difference degree, and the absolute value of the length difference between the two candidate lines is calculated to obtain the length difference degree.

10. The method for optimizing the design of aqueduct structures based on BIM technology according to claim 1, characterized in that, The mesh refinement of the first and second refined regions includes: An adaptive mesh refinement algorithm is used to locally refine the mesh cells in the first and second refinement regions; The finite element analysis was performed again on the locally refined mesh to obtain the analysis results; Based on the analysis results, the structural design parameters of the BIM model are corrected.

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