Bridge component intelligent modeling and checking method and system based on dynamo

Abstract

The application discloses a bridge component intelligent modeling and checking method and system based on Dynamo, belongs to the field of bridge engineering information modeling and structural performance visual analysis, and comprises the following steps: presetting component family sketches and parameter control rules in Revit; constructing a three-dimensional modeling process based on geometric lofting in Dynamo; realizing two-way synchronization between parameters and geometry by using a Python script; calling a bridge component standard database to compare and replace model configurations; structuring nested family parameters and outputting XML format information; connecting a structural analysis platform through a model-checking interface and automatically establishing a finite element model; executing multi-working-condition checking and returning structural performance indexes; embedding visual results in the form of a heat map in Dynamo; realizing closed-loop feedback and real-time updating of structural design and checking; and constructing a whole-process parameter-driven bridge component modeling and performance analysis integrated system.

Description

Technical Field

[0001] The present invention relates to the field of bridge engineering information modeling and structural performance visualization analysis, and more specifically, to a Dynamo-based bridge component intelligent modeling and verification method and system. Background Art

[0002] Traditional bridge component modeling and verification methods rely heavily on manual modeling, decentralized calculations, and result aggregation, resulting in low modeling efficiency, disconnected geometry and parameters, and unintuitive performance expression. With the development of BIM technology, the Revit platform enables the visual expression of building information, and Dynamo, as its parametric extension tool, enables intelligent and data-driven component modeling. Existing technologies have gradually explored the integration of Dynamo with structural analysis software, but the following shortcomings remain:

[0003] The lack of high-precision path normal control leads to distorted cross-section layouts; structural verification results and model properties are not effectively integrated, making performance visualization difficult; thermal maps are crudely expressed, making it impossible to distinguish performance levels in detail; and the one-way control between parameters and geometry limits modeling flexibility. To address these issues, a Dynamo-based intelligent bridge component modeling and verification method was proposed. By introducing tangent vector field smoothing, automatic mapping of verification results, color-segmented thermal maps, and a bidirectional synchronization mechanism between parameters and geometry, it comprehensively improves the intelligence, visualization, and efficiency of bridge modeling. Summary of the Invention

[0004] The present invention aims to provide a Dynamo-based intelligent modeling and verification method and system for bridge components to address the following issues raised in the aforementioned background technology: the lack of high-precision path normal control, which leads to distorted cross-section layout; the ineffective integration of structural verification results and model properties, making performance visualization difficult; the crude expression of thermal maps, which cannot distinguish performance levels in detail; and the one-way control between parameters and geometry, which lacks modeling flexibility. To address these issues, the proposed Dynamo-based intelligent modeling and verification method for bridge components comprehensively enhances the intelligence, visualization, and efficiency of bridge modeling by introducing tangent vector field smoothing, automatic mapping of verification results, and a color-segmented thermal map and parameter-geometry bidirectional synchronization mechanism.

[0005] Technical solution: The Dynamo-based intelligent modeling and verification method for bridge components includes the following steps:

[0006] S1. Preset the geometric logic relationships and parameter control rules of bridge components in the Revit platform, and create family style sketches and geometric prototypes for Dynamo calls;

[0007] S2. Build a node-driven bridge component generation framework in the Dynamo platform, generating 3D models of structural components by combining geometric lofting paths and control sections.

[0008] S3. Utilize Python script nodes and built-in function libraries to establish a synchronous execution mechanism for component geometry generation and parameter callbacks, and utilize a bidirectional modeling logic synchronous feedback mechanism to achieve cross-platform bidirectional control of modeling logic.

[0009] Parameter changes trigger geometry calculations:

[0010] If the component geometry is determined by parameters P1, P2, ..., P n control:

[0011] ;

[0012] G represents the output geometric entity, and f is the modeling function or mapping logic used to generate the geometry.

[0013] Geometry feedback parameters:

[0014] Return parameters through geometric measurement, such as component length L:

[0015] ;

[0016] And write it back to the parameter table as feedback update.

[0017] S4. Construct a multi-parameter standard database for bridge components, including various bridge construction parameter sets for span levels, load standards, prestressing configurations, reinforcement configurations, and flange expansion factors, for use in configuration comparison and template replacement when generating a three-dimensional model of the structural component using the Dynamo platform;

[0018] Configuration comparison in the S4 component standard database,

[0019] A similarity function can be defined for component matching:

[0020] .

[0021] Where: C i : Current component parameter vector; T j : jth standard template; W k : The weight of the kth parameter; : A very small number that prevents division by zero errors.

[0022] Output component physical properties, usage level, construction stage and material status data;

[0023] S6. Use the model-verification interface program to automatically call the structural analysis platform, import the 3D geometry and attribute model in IFC format, and construct the finite element model;

[0024] S7. Perform multi-condition verification calculations for bearing capacity, stability, and crack control within the structural analysis platform, and output component response indicators and performance evaluation results;

[0025] S8. The analysis result data is automatically transferred back to the Dynamo platform environment, and the verification results are displayed in the three-dimensional model of the structural component by component ID mapping, and the structural performance is expressed by a graded thermal map;

[0026] S9. Implement a visual feedback loop in the Dynamo platform interface, allowing designers to instantly obtain verification performance and adjust structural design parameters within the Dynamo platform environment. At the same time, all configuration changes will automatically trigger bidirectional updates of geometry and parameters, forming an integrated structural modeling-verification process with dynamic linkage capabilities.

[0027] Preferably, the construction of the geometric lofting path in S2 includes the following steps:

[0028] S2-1. Sample the main path of the bridge layout line, discretize the nodes and generate an interpolation point set;

[0029] S2-2. Use the spline interpolation function to smoothly reconstruct the path node set to generate a central lofting curve with a smoothness constraint;

[0030] S2-3. Arrange control section nodes along the center lofting curve, and set control variables for generating a cross-section of a component having a changing trend at the section node;

[0031] S2-4. The control variables include section elevation, flange width, bevel angle, and longitudinal slope correction factor, all of which are set as adjustable parameter nodes in Dynamo.

[0032] Preferably, a component topology recognition network is constructed through the Dynamo platform and a bridge component parameter set is input to perform a multi-factor matching analysis with a standard bridge type database;

[0033] The matching bridge type is identified through the structural topology priority graph inference algorithm, and the corresponding bridge type logic control network is dynamically loaded to realize the component geometry reconstruction;

[0034] A structural component reclassification mechanism is introduced during the loading process of the bridge-type logic control network, so that the heterogeneous parameter components are reassigned attribute structures under the new bridge-type template.

[0035] Preferably, the process of generating a smooth curve by spline interpolation in S2-2 further includes the following steps:

[0036] S2-2-1. Tangential vector field construction is performed on the initial path nodes, and the rate of change of the inter-node tangential included angle is used as the fairness evaluation index;

[0037] Tangential vector field construction: the tangential vector of the adjacent nodes is calculated:

[0038]

[0039] wherein, is the coordinate of the i-th node on the path; represents the tangential vector of the i-th node; and represent the coordinate vectors of the i+1-th and i-1-th nodes, respectively; represents the modulus (i.e., the Euclidean norm) of the vector.

[0040] Rate of change of the tangential included angle (used as the fairness index):

[0041]

[0042] S2-2-2. Bezier curve segments are used for transition between nodes, and a normal regulation function is introduced to maintain the cross-sectional orthogonality;

[0043] Bezier curve transition: if each path segment is fitted as a cubic Bezier curve, the general formula is:

[0044]

[0045] wherein B(t) represents a point on the curve corresponding to any t; P0, P1, P2, P3 are four control points (in vector form) that determine the starting point, the ending point, and the tangential direction and shape of the curve; t is the Bezier parameter (varying from 0 to 1) that controls the interpolation position of the curve between the control points.

[0046] S2-2-3. A judgment function is established based on tangential continuity and curvature variation to automatically insert secondary nodes to refine the geometric configuration.

[0047] Preferably, the tangential vector field construction in S2-2-1 further comprises the following steps:

[0048] S2-2-1-1. Calculate the vector included angle between each path node and the two adjacent nodes of the path node, and construct a local tangential vector;

[0049] S2-2-1-2. Perform vector smoothing operation on all the local tangential vectors, and use a weighted median filter to remove fluctuation abnormal points;

[0050] ​​​S2-2-1-3. The vector field after smoothing guides the normal arrangement direction of the control section in the subsequent geometric lofting, realizing the orthogonality constraint of the entire section.

[0051] Preferably, the verification result file exported by the structural analysis platform is read by a middleware script and converted into a parameter mapping dictionary in the Dynamo platform, and is automatically bound to the component family in the three-dimensional model of the structural component according to the component ID and the structural parameter type, and graphic coding expressions of different performance levels are achieved by setting color segmentation rules. The graphic coding is embedded in the component display properties through the RevitAPI interface.

[0052] Preferably, the performance mapping process of the heat map in S8 includes the following steps:

[0053] S8-1. Set thresholds for the bearing capacity margin and displacement limit ratio performance indicators in the structural verification results;

[0054] S8-2 uses the color mapping function to map each type of performance indicator value to a color value, and set a five-level classification color segment;

[0055] S8-3. Write the color field into the Revit component properties according to the component ID and render it in the 3D view to form a visual structural heat map.

[0056] Preferably, the color mapping function generation process in S8-2 includes the following steps:

[0057] S8-2-1. Establish a normalization function for performance indicators, unifying the range of indicators under different dimensions into the interval [0,1];

[0058] Performance index normalization: For any performance index X, set the normalization function:

[0059] ;

[0060] S8-2-2. Construct a five-segment linear interpolation mapping table and divide the normalized value interval into five levels: excellent, good, medium, poor, and over-limit;

[0061] Five-segment linear interpolation mapping (color segments):

[0062] Assume that the normalized interval is [0, 1], divided into five segments (each segment length 0.2):

[0063] ;

[0064] Where H(x′) is the hue interpolation function, for example:

[0065] ;

[0066] Used to map performance from "good" to "poor" as a color sequence from green to red.

[0067] S8-2-3. Map each gear to the HSB color space and set a boundary fuzzy buffer for each gear to reduce critical jumps.

[0068] Preferably, the bidirectional control mechanism of the modeling logic in S3 further includes establishing a control dependency path between the parameter node and the geometry node through the Python interface during the geometric modeling process, and triggering the recalculation of the geometry node if the parameter value changes;

[0069] At the same time, after the geometric node update is completed, the geometric changes are read in reverse and sent back to the parameter control list, forming a recursive synchronous feedback between parameters and geometry;

[0070] The bidirectional control mechanism of the modeling logic completes the logic write-back without interrupting the modeling process.

[0071] A Dynamo-based intelligent modeling and verification system for bridge components is designed according to a Dynamo-based intelligent modeling and verification method for bridge components.

[0072] Compared with the prior art, the advantages of the present invention are:

[0073] (1) Vector smoothing and fairing processing are introduced in the construction of line vector fields to improve the accuracy of path normal control and achieve the continuity and stability of section orthogonality constraints.

[0074] (2) The verification results are mapped to the three-dimensional component model through the middleware script, realizing the automatic binding and graphic coding integration of structural performance parameters and component family properties, thereby improving visualization efficiency.

[0075] (3) The heat map mapping process supports performance threshold setting and segmented color expression, enhancing the spatial expression capability of structural performance differences.

[0076] (4) Normalization and five-segment interpolation mapping function are used for color coding to improve the universality and readability between different performance indicators and reduce visual jump errors.

[0077] (5) The bidirectional modeling logic realizes recursive synchronous feedback of parameters and geometry, supports parameter-driven geometry update and reverse parameter synchronization of geometry changes, and enhances the closed-loop control capability of the modeling logic.

[0078] (6) The entire process is based on the Dynamo platform embedded in the Revit environment, realizing integrated operations from modeling, analysis to visualization, and improving modeling efficiency and collaborative consistency. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 The figure is a schematic diagram of the overall process of the intelligent modeling and verification method of bridge components based on Dynamo of the present invention. DETAILED DESCRIPTION

[0080] For examples, see Figure 1 ,The intelligent modeling and verification method of bridge components based on Dynamo includes the following steps:

[0081] S1. Preset the geometric logic relationships and parameter control rules of bridge components in the Revit platform, and create family style sketches and geometric prototypes for Dynamo calls;

[0082] S2. Build a node-driven bridge component generation framework in the Dynamo platform, generating 3D models of structural components by combining geometric lofting paths and control sections.

[0083] S3. Using Python script nodes and built-in function libraries, establish a synchronous execution mechanism for component geometry generation and parameter callbacks, enabling cross-platform bidirectional control of modeling logic.

[0084] S4. Build a multi-parameter standard database for bridge components. This database includes various bridge construction parameter sets for span levels, load standards, prestressing configurations, reinforcement configurations, and flange expansion factors. This database is used for configuration comparison and template replacement when generating 3D models of structural components using the Dynamo platform.

[0085] S5. Based on the family parameter structuring rules, each component parameter is nested and bound to the BIM model, and component physical properties, usage level, construction stage, and material status data are output in an XML structure;

[0086] S6. Use the model-verification interface program to automatically call the structural analysis platform, import the 3D geometry and attribute model in IFC format, and construct the finite element model;

[0087] S7. Perform multi-condition verification calculations for bearing capacity, stability, and crack control within the structural analysis platform, and output component response indicators and performance evaluation results;

[0088] S8. Automatically transfer the analysis result data back to the Dynamo platform environment, display the verification results by component ID mapping in the 3D structural component model, and express the structural performance through thermal maps;

[0089] S9. Implement a visual feedback loop in the Dynamo platform interface, allowing designers to instantly obtain verification performance and adjust structural design parameters within the Dynamo platform environment. At the same time, all configuration changes will automatically trigger bidirectional updates of geometry and parameters, forming an integrated structural modeling-verification process with dynamic linkage capabilities.

[0090] Specifically, the implementation environment is configured as follows: Use Dynamo 2.13 and above, and run based on Revit 2023 or later. Plug-ins that need to be pre-installed include Data-Shapes, Clockwork, BimorphNodes, Rhythm, etc. If using structural analysis platforms such as MIDASCivil, SOFiSTiK, ETABS, etc., the corresponding API or plug-in interface should be used. PythonScript nodes can be used for custom logic implementation, and Python version 3.8 is recommended. Recommended hardware includes: CPU with 8 cores or above, 32GB of memory, NVIDIA RTX series GPU, and adaptation to large-scale modeling. Input parameters are imported through Excel spreadsheets (such as fields including "component type", "span", "support form", and "load combination"), and structured reading is achieved through nodes such as "Excel.ReadFromFile" and passed into the component geometry generation node. During the modeling process, the Dynamo graphical interface is mainly divided into three parts: "geometry generation area", "parameter control area", and "data export area". Model components such as T-beams and box girders are implemented using the "Point.ByCoordinates→PolyCurve.ByPoints→Solid.ByLoft" combination. If the geometric path includes transition curves, the "Math.Spline" and "Curve.ByPoints" nodes are introduced to smooth the curves. The final model parameters are output in formats such as XML, IFC, and JSON. Structural analysis uses Excel or JSON for parameter transfer. Verification results are then mapped to the model components after being transmitted back. Thermal map visualization is achieved through "OverrideGraphicSettings." Structural performance level annotation and feedback linkage are also supported.

[0091] The construction of the geometric lofting path in S2 includes the following steps:

[0092] S2-1. Sample the main path of the bridge layout line, discretize the nodes and generate an interpolation point set;

[0093] S2-2. Use the spline interpolation function to smoothly reconstruct the path node set to generate a central lofting curve with a smoothness constraint;

[0094] S2-3. Arrange control section nodes along the center loft curve, and set control variables at the section nodes to generate a component cross section with a changing trend;

[0095] S2-4. The control variables include section elevation, flange width, bevel angle, and longitudinal slope correction factor, all of which are set as adjustable parameter nodes in Dynamo.

[0096] Specifically, the path sampling node is implemented using the "Curve.PointAtParameter" node. The interpolation node uses "NurbsCurve.ByPoints" to perform smooth curve fitting, while "Vector.TangentAtParameter" is used to calculate tangent directions to ensure cross-section orthogonality. Cross-sectional geometry control variables (such as beam height and flange width) can be interpolated along the path using "List.Transpose" and "List.Map" to achieve dynamic geometric adaptation. The lofted path serves as the main trajectory for subsequent Solid.ByLoft component generation.

[0097] Build a component topology identification network through the Dynamo platform and input the bridge component parameter set to conduct multi-factor matching analysis with the standard bridge type database;

[0098] The matching bridge type is identified through the structural topology priority graph inference algorithm, and the corresponding bridge type logic control network is dynamically loaded to realize the component geometry reconstruction;

[0099] A structural component reclassification mechanism is introduced during the loading process of the bridge-type logic control network, so that heterogeneous parameter components can be reassigned attribute structures under the new bridge-type template.

[0100] Specifically, the bridge parameter set is input as an Excel file, which includes fields such as bridge type code, bridge type, standard span range, flange development ratio, and prestressing arrangement method. A custom Python script is combined with "List.FilterByBoolMask" to filter out standard bridge types that match the input parameters. "Object.Type" and logical judgment nodes are used to classify and call component topology reconstruction paths. For example, a "Dictionary" structure is used to bind the bridge type number to the Dynamo sub-module mapping and dynamically load the modeling process of each bridge type. Component reclassification uses a "Tag" tagging system to unify the classification of beam segments of different configurations and assign unified logical structural parameters.

[0101] The process of generating a smooth curve by spline interpolation in S2-2 further includes the following steps:

[0102] S2-2-1. Construct the tangent vector field for the initial path nodes and use the rate of change of the tangent angle between nodes as the smoothness evaluation index;

[0103] S2-2-2. Use Bezier curve segments to transition between nodes and introduce normal control functions to maintain cross-section orthogonality.

[0104] S2-2-3. Establish a judgment function based on tangent continuity and curvature change, and automatically insert secondary nodes to refine the geometric configuration.

[0105] Specifically, the "Vector.AngleBetween" node is used to determine the curvature continuity of each segment, and Bezier logic is introduced to construct inter-segment transitions ("BezierCurve.ByControlPoints") to maintain continuity and differentiability. "CoordinateSystem.ByOriginVectors" is used at each cross-section interpolation node to ensure that the cross-section normal direction is orthogonal to the path. When necessary, secondary nodes are automatically inserted through "List.InsertItemAtIndex" to improve geometric smoothness and numerical stability.

[0106] The construction of the tangent vector field in S2-2-1 also includes the following steps:

[0107] S2-2-1-1. Calculate the vector angle between each path node and the two adjacent nodes of each path node to construct a local tangent vector;

[0108] S2-2-1-2. Perform vector smoothing on all local tangential vectors and use a weighted median filter to remove abnormal fluctuation points.

[0109] S2-2-1-3. The vector field after smoothing guides the normal arrangement direction of the control section in the subsequent geometric lofting, realizing the orthogonality constraint of the entire section.

[0110] Specifically, by taking an adjacent point before and after the path node, constructing the angle between the two vectors and calculating the unit tangent vector, the neighborhood weighting factor is used to weight and smooth multiple tangent vectors, and the abnormal direction jumps caused by local path jitter are filtered out. Finally, a continuous and stable tangent vector field is generated to guide the section lofting process, so that each control section maintains normal orthogonality and geometric continuity on the overall path, effectively improving the spatial adaptability and constructibility of the geometric model.

[0111] The verification result file exported by the structural analysis platform is read by the middleware script and converted into a parameter mapping dictionary in the Dynamo platform. It is automatically bound to the component family in the 3D model of the structural component according to the component ID and structural parameter type. By setting color segmentation rules, graphic coding expressions of different performance levels are achieved. The graphic coding is embedded in the component display properties through the Revit API interface.

[0112] Specifically, the results output by the structural analysis platform include the component ID and its corresponding performance indicators in CSV or JSON format. The middleware script calls the parsing function through Python or DesignScript language to read the data, converts it into a parameter mapping dictionary with a key-value pair structure, establishes a mapping relationship between the component ID and the parameter category, and automatically matches it to the corresponding component family parameter field in Revit. Then, the color value code is generated through the custom color grading logic, and the fill color in the component display properties is modified with the help of the RevitAPI interface to achieve automatic embedding and real-time feedback of structural performance visualization, thereby improving the integration level of structural verification and visual modeling.

[0113] The performance mapping process of the heat map in S8 includes the following steps:

[0114] S8-1. Set thresholds for the bearing capacity margin and displacement limit ratio performance indicators in the structural verification results;

[0115] S8-2 uses the color mapping function to map each type of performance indicator value to a color value, and set a five-level classification color segment;

[0116] S8-3. Write the color field into the Revit component properties according to the component ID and render it in the 3D view to form a visual structural heat map.

[0117] Specifically, by presetting reasonable evaluation intervals for different performance indicators (such as bearing capacity margin is [0,1], displacement ratio is [0,2]), setting threshold intervals for five levels: excellent, good, medium, poor, and over-limit, and using color mapping functions (such as linear or hyperbolic tangent interpolation functions) to convert normalized indicator values ​​into HSB or RGB color values, and then writing them into component attribute fields such as "ColorCode" according to component ID, and calling Revit API to update component colors in the 3D view, finally generating a performance heat map with clear layering and structural associations to assist in structural risk positioning and design optimization.

[0118] The S8-2 color mapping function generation process includes the following steps:

[0119] S8-2-1. Establish a normalization function for performance indicators, unifying the range of indicators under different dimensions into the interval [0,1];

[0120] S8-2-2. Construct a five-segment linear interpolation mapping table and divide the normalized value interval into five levels: excellent, good, medium, poor, and over-limit;

[0121] S8-2-3. Map each gear to the HSB color space and set a boundary fuzzy buffer for each gear to reduce critical jumps.

[0122] Specifically, structural performance indicators with different dimensions and scales (such as stress ratio, displacement ratio, stability coefficient, etc.) are normalized separately, and the original data are standardized to values ​​between 0 and 1 by setting maximum values, minimum values ​​or industry standard thresholds; a linear interpolation function is used to map the normalized values ​​into five levels, and an independent color value interval is set for each level to form a linear transition segment; the color values ​​are then mapped to the HSB color space to achieve dynamic adjustment of saturation and brightness with performance level, and a buffer zone is set at the boundaries of adjacent gears. The fuzzy boundary algorithm is used to reduce the interference of color jumps on graphic perception, thereby improving the continuity and interpretability of the heat map.

[0123] The bidirectional control mechanism of the modeling logic in S3 further includes that during the geometric modeling process, parameter nodes and geometric nodes establish a control dependency path through the Python interface, and if the parameter value changes, the geometric node recalculation is triggered;

[0124] At the same time, after the geometric node update is completed, the geometric changes are read in reverse and sent back to the parameter control list, forming a recursive synchronous feedback between parameters and geometry;

[0125] The bidirectional control mechanism of modeling logic completes logic write-back without interrupting the modeling process.

[0126] Specifically, in Dynamo, by customizing Python nodes, using Revit API and Dynamo API interfaces, respectively, the binding relationship between parameters and geometries is established, and the coordinates, direction, size and other attributes of geometric nodes (such as points, lines, and sections) are set as downstream dependencies of parameter nodes.

[0127] When the parameter node input changes, the geometry generation logic is automatically called by triggering the callback function to reconstruct the geometry and reflect the new geometry in real time.

[0128] At the same time, after the geometric node is updated, the secondary interface function is called to analyze its change results, such as node position offset, direction vector change, size difference, etc., and the changed value is updated to the control parameter list through the data rewrite mechanism to realize the return of the parameter value;

[0129] This bidirectional control logic is based on an event-triggered chain and uses a caching mechanism to avoid repeated modeling or endless loops, ensuring that the geometric model is always synchronized with the design parameters, improving the modeling response efficiency and model consistency. It is particularly suitable for the design process of complex bridge components that require frequent adjustments and local control.

[0130] The intelligent modeling and verification system for bridge components based on Dynamo is designed according to the intelligent modeling and verification method for bridge components based on Dynamo.

[0131] The above shows and describes the basic principles, main features and advantages of the present application; those skilled in the art should understand that the present application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only preferred examples of the present application and are not intended to limit the present application; various changes and improvements can be made to the present application without departing from the spirit and scope of the present application, and these changes and improvements all fall within the scope of the claimed present application; the scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. The intelligent modeling and verification method of bridge components based on Dynamo is characterized by: The Dynamo-based intelligent modeling and verification method for bridge components includes the following steps: S1. Preset the geometric logic relationships and parameter control rules of bridge components in the Revit platform, and create family style sketches and geometric prototypes for Dynamo calls; S2. Build a node-driven bridge component generation framework in the Dynamo platform, generating 3D models of structural components by combining geometric lofting paths and control sections. The construction of the geometric lofting path in S2 includes the following steps: S2-1. Sample the main path of the bridge layout line, discretize the nodes and generate an interpolation point set; S2-2. Use the spline interpolation function to smoothly reconstruct the path node set to generate a central lofting curve with a smoothness constraint; The process of generating a smooth curve by spline interpolation in S2-2 further includes the following steps: S2-2-1. Construct the tangent vector field for the initial path nodes and use the rate of change of the tangent angle between nodes as the smoothness evaluation index; The construction of the tangent vector field in S2-2-1 also includes the following steps: S2-2-1-1. Calculate the vector angle between each path node and the two adjacent nodes of each path node to construct a local tangent vector; S2-2-1-2. Perform vector smoothing operation on all the local tangential vectors and use a weighted median filter to remove abnormal fluctuation points; S2-2-1-3. The smoothed vector field guides the normal arrangement direction of the control section in the subsequent geometric lofting, achieving full cross-section orthogonality constraint; S2-2-2. Use Bezier curve segments to transition between nodes and introduce normal control functions to maintain cross-section orthogonality. S2-2-3. Establish a judgment function based on tangent continuity and curvature change, and automatically insert secondary nodes to refine the geometric configuration; S2-3. Arrange control section nodes along the center lofting curve, and set control variables for generating a cross-section of a component having a changing trend at the section node; S2-4. The control variables include section elevation, flange width, bevel angle, and longitudinal slope correction factor, all of which are set as adjustable parameter nodes in Dynamo; S3. Using Python script nodes and built-in function libraries, establish a synchronous execution mechanism for component geometry generation and parameter callbacks, enabling cross-platform bidirectional control of modeling logic. S4. Construct a multi-parameter standard database for bridge components; S5. Based on the family parameter structuring rules, each component parameter is nested and bound to the BIM model, and component physical properties, usage level, construction stage, and material status data are output in an XML structure; S6. Use the model-verification interface program to automatically call the structural analysis platform, import the 3D geometry and attribute model in IFC format, and construct the finite element model; S7. Perform multi-condition verification calculations for bearing capacity, stability, and crack control within the structural analysis platform, and output component response indicators and performance evaluation results; S8. The analysis result data is automatically transferred back to the Dynamo platform environment, and the verification results are displayed in the three-dimensional model of the structural component by component ID mapping, and the structural performance is expressed by a graded thermal map; S9. Implement a visual feedback loop in the Dynamo platform interface.

2. The Dynamo-based intelligent modeling and verification method for bridge components according to claim 1 is characterized in that: The bridge component standard database includes a variety of bridge type construction parameter sets including span level, load standard, prestressing configuration, reinforcement configuration, and flange expansion factor, which are used for configuration comparison and template replacement when the Dynamo platform generates the three-dimensional model of the structural component. The component topology recognition network is constructed through the Dynamo platform and the bridge component parameter set is input to perform multi-factor matching analysis with the standard bridge type database; The matching bridge type is identified through the structural topology priority graph inference algorithm, and the corresponding bridge type logic control network is dynamically loaded to realize the component geometry reconstruction; A structural component reclassification mechanism is introduced during the loading process of the bridge-type logic control network, so that the heterogeneous parameter components are reassigned attribute structures under the new bridge-type template.

3. The intelligent modeling and verification method for bridge components based on Dynamo according to claim 1 is characterized in that: The verification result file exported by the structural analysis platform is read by a middleware script and converted into a parameter mapping dictionary in the Dynamo platform. It is automatically bound to the component family in the three-dimensional model of the structural component according to the component ID and structural parameter type, and graphic coding expression of different performance levels is achieved by setting color segmentation rules. The graphic coding is embedded in the component display properties through the Revit API interface.

4. The Dynamo-based intelligent modeling and verification method for bridge components according to claim 1 is characterized in that: The performance mapping process of the heat map in S8 includes the following steps: S8-1. Set thresholds for the bearing capacity margin and displacement limit ratio performance indicators in the structural verification results; S8-2 uses the color mapping function to map each type of performance indicator value to a color value, and set a five-level classification color segment; S8-3. Write the color field into the Revit component properties according to the component ID and render it in the 3D view to form a visual structural heat map.

5. The Dynamo-based intelligent modeling and verification method for bridge components according to claim 4 is characterized in that: The S8-2 color mapping function generation process includes the following steps: S8-2-1. Establish a normalization function for performance indicators, unifying the range of indicators under different dimensions into the interval [0,1]; S8-2-2. Construct a five-segment linear interpolation mapping table and divide the normalized value interval into five levels: excellent, good, medium, poor, and over-limit; S8-2-3. Map each gear to the HSB color space and set a boundary fuzzy buffer for each gear to reduce critical jumps.

6. The Dynamo-based intelligent modeling and verification method for bridge components according to claim 1 is characterized in that: The bidirectional control mechanism of the modeling logic in S3 further includes establishing a control dependency path between the parameter node and the geometry node through the Python interface during the geometric modeling process, and triggering the recalculation of the geometry node if the parameter value changes; At the same time, after the geometric node update is completed, the geometric changes are read in reverse and sent back to the parameter control list, forming a recursive synchronous feedback between parameters and geometry; The bidirectional control mechanism of the modeling logic completes the logic write-back without interrupting the modeling process.

7. A Dynamo-based intelligent modeling and verification system for bridge components, relating to a Dynamo-based intelligent modeling and verification method for bridge components according to any one of claims 1 to 6, characterized in that: The Dynamo-based intelligent modeling and verification system for bridge components is designed according to the Dynamo-based intelligent modeling and verification method for bridge components.

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