Midas-to-Revit (M2R) Data-driven Structural Modeling System

Abstract

The present invention is a Midas to Revit (M2R) data-driven structural modeling system, comprising: a structural centerline generation module that converts MIDAS GEN data created by a structural designer into Excel and generates structural centerlines in Rhino using the Grasshopper algorithm (a visual programming language and environment used in 3D modeling of Rhino, as well as a graphic algorithm editor); It may include a project setup module that creates Family, Grid, Level, and Material information of structural members in a REVIT project based on MIDAS GEN data created by the structural designer, creates a structural member family corresponding to the member property information of MIDAS GEN using the GRASSHOPPER algorithm, and ensures that the structural member family includes various members used in S-structure, RC-structure, and SRC-structure (structural types of buildings) and is linked to the parameters of the MIDAS GEN data; and a Rhino to REVIT conversion module that models a structural model by linking with a family pre-built in the REVIT project based on the shape and position information of the Rhino structural centerline generated in the structural centerline generation module using the GRASSHOPPER algorithm.

Description

Technology Field

[0001] The present invention relates to a Midas to Revit (M2R) data-driven structural modeling system. Background Technology

[0002] In the domestic construction industry, the roles of structural calculation (structural designer) and structural drawing preparation (architectural designer) are separated.

[0003] Human errors (incorrect, duplicate, or omitted member information) may occur during the process of architectural designers, who have relatively less structural knowledge compared to structural designers, creating drawings based on structural reports.

[0004] Since the collapse of the Geomdan apartment complex in Incheon in April 2023, social interest in the consistency of structural design and construction has been increasing.

[0005] Meanwhile, generally in Korea, structural designers use Midas Zen (structural analysis program) from Midas, while architectural designers use software such as Autodesk's Revit (Architectural Information Modeling software), AutoCAD (drawing software), and McNeil's Rhino Grasshopper (parametric modeling software).

[0006] In other words, when designing a building, the architect prepares architectural drawings and delivers them to the structural designer, who then performs the structural design based on these drawings using structural analysis software such as MIDAS Zen. Subsequently, the structural designer delivers a structural report to the architect, who then creates structural drawings using programs such as Autodesk's Revit (Architectural Information Modeling software) or AutoCAD (drawing software).

[0007] However, since architects and structural designers create deliverables by visually copying drawings and text, various human errors often occur.

[0008] Meanwhile, the aforementioned background technology is technical information that the inventor possessed for the derivation of the present invention or acquired during the process of deriving the present invention, and it cannot necessarily be considered publicly known technology disclosed to the general public prior to the filing of the present invention. Prior art literature

[0009] Korean Registered Patent No. 10-0789121 The problem to be solved

[0010] The objective of the present invention is to provide a Midas to Revit (M2R) data-driven structural modeling system that automatically converts structural design data, for which structural design is completed, into a structural BIM model using an algorithm.

[0011] The technical problems of the present invention are not limited to those mentioned above, and other unmentioned technical problems will be clearly understood by those skilled in the art from the description below. means of solving the problem

[0012] A Midas to Revit (M2R) data-driven structural modeling system according to one embodiment of the present invention converts MIDAS GEN data created by a structural designer into Excel and generates structural centerlines in Rhino by utilizing the Grasshopper algorithm. Structural Centerline Generation Module It may include ;

[0013] A Midas to Revit (M2R) data-driven structural modeling system according to one embodiment of the present invention creates Family, Grid, Level, and Material information of structural members in a Revit project based on MIDAS GEN data created by the structural designer, creates a structural member family corresponding to the member property information of MIDAS GEN using the GRASSHOPPER algorithm, and the structural member family includes various members used in S-structure, RC-structure, and SRC-structure (structural types of buildings) and is linked to the parameters of the MIDAS GEN data. Project Setup Module; and modeling a structural model by linking with pre-built families in the Revit project based on the shape and position information of the Rhino structural centerlines generated by the structural centerline generation module using the above Grasshopper algorithm. Rhino to Revit conversion module; It may include more.

[0014] According to one embodiment, the MIDAS GEN data may include modeling information including NODE, ELEMENT, MATERIAL, PROPERTY, and END CONNECTION.

[0015] According to one embodiment, the structural centerline may include attribute information of the corresponding structural member.

[0016] According to one embodiment, the REVIT mentioned above refers to one of the Building Information Modeling (BIM) software developed by Autodesk, and may refer to software used for architectural, structural, and MEP design, and supporting 3D design and construction information management.

[0017] According to one embodiment, the Rhino (Rhinoceros 3D) may refer to 3D modeling software specialized in NURBS-based surface and curve processing that is widely used in the fields of architecture, industrial design, and engineering.

[0018] According to one embodiment, the Grasshopper refers to a visual algorithm editor for Rhino and is characterized by generating complex shapes and algorithms through a graphic interface without coding, and being used for implementing parametric designs with algorithms suitable for the automation and optimization of modeling tasks.

[0019] According to one embodiment, the Excel is spreadsheet software developed by Microsoft and is a tool for calculating, organizing, and analyzing data, and may be characterized by being capable of processing and visualizing data through formulas.

[0020] According to one embodiment, the Rhino to Revit conversion module may model the structural model at a speed that varies depending on the performance of the CPU, but based on an AMD Ryzen 9 5900X 12-Core Processor (3.70 GHz), it may take about 7 minutes to model 8,000 components.

[0021] According to one embodiment, the Midas to Revit (M2R) data-driven structural modeling system is implemented through Grasshopper, a visual algorithm editor for Rhino, and may be characterized by Rhino.Inside.Revit linking Revit, Rhino, and Grasshopper.

[0022] According to one embodiment, the Rhino.Inside.Revit refers to a plugin that integrates Rhino and Revit to enable the execution of Rhino and Grasshopper within the Revit environment, and is characterized by enabling complex shapes and structures to be easily modeled in Revit through data interaction between the two software programs.

[0023] According to one embodiment, the structural centerline generation module can quantitatively determine performance by utilizing a centerline time reduction judgment algorithm that quantifies the scale and complexity of the structural model and provides a criterion for determining whether the structural centerline generation method has a time reduction effect compared to a conventional method by reflecting the influence of said value on the centerline generation time.

[0024] According to one embodiment, the centerline time reduction determination algorithm can be provided as a criterion for determining whether the centerline generation time is reduced by calculating an estimated time using the number of member elements included in the structural model, the number of connection definitions of structural members, the number of types of structural member attributes, the existing centerline generation time measured and entered by the user, and the time correction coefficient and complexity correction coefficient set by the administrator as input values, and then comparing the value with the existing standard time.

[0025] According to one embodiment, the centerline time reduction determination algorithm comprises a time weight (a M2R It can be composed of ) and the product of the first and second elements.

[0026] According to one embodiment, the first element is the number of structural member elements (E M2R It can be characterized by being composed of a value entered into a logarithmic function after adding 1 to ), designed to reflect the tendency for time to increase as the number of members increases.

[0027] According to one embodiment, the second element is a complexity weight (b) to the result of summing connection complexity and attribute diversity. M2R After multiplying by ), complexity weights (b M2R It can be characterized by being designed to control the effect of complexity on time by adding 1 to the result of multiplying by ).

[0028] According to one embodiment, the centerline time reduction determination algorithm comprises the number of structural member elements (E M2R To reflect that the number of structural member elements (E) increases as ) increases, the increase in operation repetitions is reflected. M2R It is composed including ), and the number of structural member connection definitions (N end ) is the number of structural member elements (E M2R Connection complexity calculated as a ratio value divided by ), and the number of types of structural member attributes (P M2R ) is the number of structural member elements (E M2R Complexity weights (b) that are configured to reflect attribute diversity calculated by the ratio value divided by ) and control the effect of complexity on time. M2R Time weight (a) that matches the time unit M2R It can be characterized by being composed of ) Effects of the invention

[0029] According to one aspect of the present invention described above, the Midas to Revit (M2R) data-driven structural modeling system proposed by the present invention can automatically convert structural design data, for which structural design has been completed, into a structural BIM model.

[0030] In addition, modeling using data-driven parametric modeling can improve speed and accuracy compared to traditional manual methods.

[0031] Accordingly, when a structural designer inputs only structural design data into a provided Excel file and transmits it to an architectural designer, the architectural designer can automatically generate a structural BIM model using the Midas to Revit (M2R) data-driven structural modeling system proposed by the present invention, thereby saving time and enabling efficient work without errors.

[0032] In addition, the Midas to Revit (M2R) data-driven structural modeling system proposed by the present invention can increase productivity per person by reducing manual work in the architectural design field, which is suffering from a labor shortage.

[0033] In addition, a consistent flow of information in BIM and drawing creation can eliminate human error and enhance the consistency of documents.

[0034] In addition, it can enhance public trust in the construction industry by improving the structural safety of buildings.

[0035] The effects of the present invention are not limited to those mentioned above, and various effects may be included within the scope obvious to a person skilled in the art from the contents described below. Brief explanation of the drawing

[0036] FIG. 1 is a diagram illustrating the process of a Midas to Revit (M2R) data-driven structural modeling system according to one embodiment of the present invention. FIG. 2 is a conceptual diagram of a Midas to Revit (M2R) data-driven structural modeling system according to one embodiment of the present invention. FIG. 3 is an example diagram of the generation of a structural centerline and a structural model in a Midas to Revit (M2R) data-driven structural modeling system according to one embodiment of the present invention. FIG. 4 is a diagram illustrating an application example of a Midas to Revit (M2R) data-driven structural modeling system according to one embodiment of the present invention. Specific details for implementing the invention

[0037] The following detailed description of the invention refers to the accompanying drawings, which illustrate specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention. It should be understood that various embodiments of the invention are different but need not be mutually exclusive. For example, specific shapes, structures, and characteristics described herein may be implemented in other embodiments without departing from the spirit and scope of the invention in relation to one embodiment.

[0038] When it is stated that one component is "connected" or "contracted" to another component, it should be understood that while it may be directly connected or contracted to that other component, there may also be other components in between. Conversely, when it is stated that one component is "directly connected" or "directly contracted" to another component, it should be understood that there are no other components in between.

[0039] Furthermore, it should be understood that the location or arrangement of individual components within each disclosed embodiment may be changed without departing from the spirit and scope of the invention. Accordingly, the following detailed description is not intended to be taken in a limiting sense, and the scope of the invention is limited only by the appended claims, including all equivalents thereof, provided appropriately described. Similar reference numerals in the drawings refer to the same or similar functions across various aspects.

[0040] Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the drawings.

[0042] FIG. 1 is a diagram illustrating the process of a Midas to Revit (M2R) data-driven structural modeling system according to one embodiment of the present invention.

[0043] FIG. 2 is a conceptual diagram of a Midas to Revit (M2R) data-driven structural modeling system according to one embodiment of the present invention.

[0044] By utilizing a parametric modeling program (Rhino Grasshopper), data from a structural analysis program (MIDAS GEN) can be automatically converted into a structural BIM model (AUTODESK REVIT).

[0045] Structural models can also be generated using structural design programs other than the MIDAS program (e.g., CSI’s SAP2000).

[0046] The Midas to Rebit (M2R) data-driven structural modeling system (1) may be a self-contained server or a cloud server for providing services according to the present invention, or it may be a peer-to-peer (P2P) set of distributed nodes.

[0047] A Midas to Revit (M2R) data-driven structural modeling system (1) according to one embodiment of the present invention may include a structural centerline generation module (100), a project setup module (500), and a Rhino to Revit conversion module (900).

[0048] The structural centerline generation module (100) converts MIDAS GEN data created by a structural designer into Excel and can generate structural centerlines in Rhino by utilizing the Grasshopper algorithm (a visual programming language and environment used in 3D modeling in Rhino and a graphic algorithm editor).

[0049] The above-described MIDAS GEN data may include modeling information including NODE, ELEMENT, MATERIAL, PROPERTY, and END CONNECTION.

[0050] In addition, the structural centerline may include attribute information of the corresponding structural member.

[0051] Excel is spreadsheet software developed by Microsoft that serves as a tool for calculating, organizing, and analyzing data, and is characterized by its ability to process and visualize data through formulas.

[0052] Meanwhile, the structural centerline generation module (100) can utilize a centerline time reduction judgment algorithm that quantifies the size and complexity of the structural model to quantitatively determine performance and provides a criterion for determining whether the structural centerline generation method has a time reduction effect compared to the conventional method by reflecting the influence of the said value on the centerline generation time.

[0053] The centerline time reduction judgment algorithm may be characterized by being configured to calculate the estimated time required for the structural centerline generation module (100) to generate a centerline based on MIDAS GEN data created by the structural designer, and to determine whether the estimated time is reduced compared to the existing standard time.

[0054] The centerline time reduction judgment algorithm can operate using the number of member elements included in the structural model, the number of connection definitions for structural members, the number of types of structural member attributes, the existing centerline generation time measured and entered by the user, and the time correction coefficient and complexity correction coefficient set by the administrator as input values.

[0055] In other words, the centerline time reduction determination algorithm can determine whether the centerline generation time is reduced by sequentially combining the aforementioned input values ​​to calculate a single estimated time required, and then comparing that value with the existing reference time.

[0056] Centerline generation can usually be processed iteratively at the element (member) level, so the number of structural member elements (E M2RTo reflect the fact that the number of computational iterations increases as ) increases, the centerline time reduction judgment algorithm is based on the number of structural member elements (E M2R It can be characterized by being composed including ).

[0057] However, in large models, time is always the number of structural member elements (E M2R Since it is difficult to conclude that it is directly proportional to ), in order to simplify and smooth this, the number of structural member elements (E M2R It can be characterized by being configured to input a value normalized by adding 1 to ) into a logarithmic function.

[0058] Here, the number of structural member elements (E M2R Adding 1 to ) is the number of structural member elements (E M2R It can mean a preprocessing step to prevent errors by enabling the operation of the logarithmic function even when ) is 0.

[0059] In addition, since it is difficult to explain a complex model based solely on the number of elements, it can be characterized by being configured to additionally reflect connection complexity and attribute diversity.

[0060] The aforementioned connection complexity is the number of structural member connection definitions (N end ) is the number of structural member elements (E M2R It can be calculated as a ratio value divided by ), provided that the number of structural member elements (E) located in the denominator M2R Errors in calculating ratio values ​​can be prevented by adding 1 to ).

[0061] The aforementioned diversity of attributes is the number of types of structural member attributes (P M2R ) is the number of structural member elements (E M2R It can be calculated as a ratio value divided by ), provided that the number of structural member elements (E) located in the denominator M2R Errors in calculating ratio values ​​can be prevented by adding 1 to ).

[0062] In addition, the centerline time reduction judgment algorithm uses complexity weights (b) to adjust the influence of complexity on time all at once. M2R It can be corrected by multiplying by ), and if complexity has almost no effect, the complexity weight (b M2R The effect can be reduced by making ) small.

[0063] Finally, the centerline time reduction judgment algorithm uses time weights (a) to align with actual time units. M2R It can be characterized by being composed of )

[0064] To summarize the above, the centerline time reduction judgment algorithm is the time weight (a M2R It can be characterized as being composed of ) and the product of the first element and the second element.

[0065] The first element is the number of structural member elements (E M2R It can be characterized by being composed of a value entered into a logarithmic function after adding 1 to ), designed to reflect the tendency for time to increase as the number of members increases.

[0066] The second element is the complexity weight (b) on the result of summing connection complexity and attribute diversity. M2R After multiplying by ), complexity weights (b M2R It can be characterized by being designed to control the effect of complexity on time by adding 1 to the result of multiplying by ).

[0067] The centerline time reduction judgment algorithm is based on the number of structural member elements (E M2R It is difficult to conclude that time increases strictly in direct proportion when ) becomes very large, and to simply reflect the phenomenon where the rate of increase becomes gradual in the large value region, the number of structural member elements (E M2R It can be characterized by being configured by inputting ) into a logarithmic function.

[0068] As described in [Table 1] below and the above description, the centerline time reduction judgment algorithm may be characterized by being designed using functions such as logarithmic functions through examples of various tests, and may be characterized by being designed by reflecting actual data distribution and experimental experience.

[0069] [Table 1]

[0070]

[0071] As can be seen from [Table 1] above, based on the first example of 100, even if the input value increases tenfold, the result of the logarithmic function can only increase by about 1.5 times, and as the model becomes larger, the increase can become more gradual, and even if it increases significantly from 8,000 to 20,000, the increase in value is calculated to be relatively small.

[0072] The existing centerline generation time (T), which is each variable used in the centerline time reduction judgment algorithm. base ), number of structural member elements (E M2R ), number of structural member connection definitions (N end ), number of types of structural member attributes (P M2R ), connection complexity ratio (R M2R ), time weight(a M2R ) and complexity weights (b M2R The characteristics of ) are as follows.

[0073] Existing centerline generation time (T base ) may refer to the time taken to generate a structural centerline according to a conventional method that was actually taken to generate a centerline before the application of the present invention or according to a conventional method set as a comparison target.

[0074] In addition, the existing centerline generation time (T base ) may refer to the actual work time measured when a structural designer creates a centerline in the same project using a manual method, existing software tools, or existing work processes.

[0075] Existing centerline generation time (T base ) is the estimated centerline generation time (T) output by the centerline time reduction judgment algorithm. M2R It can be used as the same time unit as ), and the server administrator or operator can select either minutes or seconds to use consistently.

[0076] Existing centerline generation time (T base ) can be derived from values ​​entered after being directly measured by the user through objective measurement means such as a stopwatch, recording of task start and end times, and program execution logs.

[0077] In addition, the existing centerline generation time (T base Since ) may vary from project to project, it is not set as a fixed value and can be derived and utilized from newly measured and entered values ​​for each project unit.

[0078] In the centerline time reduction judgment algorithm, the existing centerline generation time (T base By using ) as a comparison standard, it is possible to determine whether the estimated time according to the method of the present invention is relatively shorter.

[0079] Therefore, the existing centerline generation time (T base ) serves as a baseline for determining whether to shorten, and allows for the comparison of the effects before and after the application of the invention or before and after a process change under the same conditions.

[0080] Number of structural member elements (E M2R ) may mean the total number of structural member element items included in MIDAS GEN data or an Excel file generated by converting said data.

[0081] That is, the number of structural member elements (E M2R ) is the number of basic units constituting the structural model, and one structural member can be defined as one element.

[0082] Number of structural member elements (E M2R) can be derived by directly counting the number of data rows corresponding to the list of structural member elements in the original MIDAS GEN data, or by converting the MIDAS GEN data to Excel and counting the number of rows in the sheet or area where structural member elements are listed in the Excel file.

[0083] The number of structural member elements (E) described above M2R The process of deriving ) may refer to a counting method based on data explicitly present in a file.

[0084] Number of structural member elements (E M2R ) is not a fixed value as it varies depending on the project data, and can be newly calculated for each project.

[0085] Since the centerline generation task is generally performed iteratively at the level of structural member elements, the number of structural member elements (E) in the centerline time reduction judgment algorithm M2R ) can be used as a basic indicator to gauge total throughput.

[0086] The centerline time reduction judgment algorithm is based on the number of structural member elements (E M2R Through ), it is possible to reflect the difference in scale of the structural model so that small-scale and large-scale projects can be compared on the same basis.

[0087] Number of structural member connection definitions (N end ) can mean the total number of end connection items, i.e., connection conditions between structural members included in the MIDAS GEN data, and can represent the number of data defining how structural members are connected to each other.

[0088] Number of structural member connection definitions (N end ) can be derived by directly counting the number of rows corresponding to the end connection item in the MIDAS GEN data or data converted to Excel.

[0089] Since structural member connection definitions are elements that require additional consideration of inter-member relationships during the centerline generation process, a larger number of them may increase the likelihood of condition verification or branching processing. Accordingly, the number of structural member connection definitions (N end ) can be used as an important indicator of the complexity of the structural model.

[0090] The centerline time reduction judgment algorithm is based on the number of structural member connection definitions (N end Through ), the reliability of judgment can be increased by distinguishing between complex models with many connection definitions and relatively simple models.

[0091] Number of types of structural member attributes (P M2R ) may mean the number of different attribute identifiers among the structural member attributes included in the MIDAS GEN data, and may mean not simply the total number of rows of attribute items, but the number of unique attribute types with duplicates removed.

[0092] Number of types of structural member attributes (P M2R ) can be derived by directly calculating the number of remaining items after removing duplicates based on the attribute identifier column in the Excel file, or by using a script to calculate the number of unique values.

[0093] Number of types of structural member attributes (P M2R As the number of ) increases, there is a possibility that the family mapping or attribute branching processing steps will increase during structural centerline generation, which can act as a factor increasing processing time. Therefore, the centerline time reduction judgment algorithm considers the number of types of structural member attributes (P M2R It can be characterized by being designed to reflect the diversity and complexity of the structural model through ).

[0094] Connection complexity ratio (R M2R) is a value representing the proportion of structural member connection definitions relative to the total number of structural member elements, and can be used as an indicator to compare the density of connection definitions on the same standard even if the scale of the structural model changes.

[0095] Connection complexity ratio (R M2R ) is the number of structural member connection definitions (N end ) is the number of structural member elements (E M2R It can be derived by dividing by the value obtained by adding a certain correction value to ).

[0096] As described above, the number of structural member elements (E M2R By reflecting a preprocessing step that adds a constant correction value to ), the centerline time reduction judgment algorithm is based on the number of structural member elements (E M2R It is possible to prevent algorithm implementation errors even when ) is extremely small or non-existent.

[0097] The centerline time reduction judgment algorithm is the connection complexity ratio (R M2R Through this, even if the number of structural member elements differs for each project, it is possible to relatively compare how dense the structure is.

[0098] Time weight (a M2R ) can be characterized as a correction factor for converting a value calculated by combining the number of structural member elements and complexity elements into actual time units, and is used to adjust the result of the centerline time reduction judgment algorithm so that it is expressed at a level similar to actual work time.

[0099] Time weight (a M2R ) can be derived in the following way.

[0100] First, the manager selects one representative project and measures the actual centerline generation time, and then the number of structural member elements (E) of the same project M2R ), number of structural member connection definitions (N end), number of types of structural member attributes (P M2R By applying ) to the centerline time reduction judgment algorithm, time weights (a) are derived from manually set values ​​so that the result becomes similar to actual time. M2R ) can be derived.

[0101] Second, after accumulating actual execution times and structural data information of various projects as records, the time weight (a) is selected from the value that best fits on average and set. M2R ) can be derived.

[0102] Third, in the initial stage, a time weight (a) is also obtained from the set value by setting it to an arbitrary reference value and then adjusting it step by step while checking the difference from the actual result. M2R ) can be derived.

[0103] Time weight (a M2R ) can be used to reduce time deviations caused by differences in PC performance, system environment, and implementation method, and can be compared by applying the same algorithm even in different environments.

[0104] Complexity weight (b M2R ) is a coefficient used to control how much the complexity of structural member connections and property diversity affect the total processing time, and can be utilized to adjust how much the estimated time should increase as complexity increases.

[0105] Complexity weight (b M2R ) can be entered directly by the manager, and can be set based on the actual increase in time after running simple structure models and complex structure models respectively.

[0106] In addition, after accumulating records of actual time and structural complexity information from various projects, a value matching the average trend can be selected, or a reference value can be initially set based on assumed high or low impact, and then gradually adjusted to reflect actual results.

[0107] Complexity weight (b M2R Considering that structural complexity cannot always be assumed to affect time at the same level, it can be set as a flexibly adjustable element.

[0108] Estimated centerline generation time (T) output by the centerline time reduction judgment algorithm M2R ) may refer to the time expected to be required for the structural centerline generation module to generate structural centerlines for a single structural project.

[0109] Estimated centerline generation time (T M2R ) can be managed by unifying it into either minutes or seconds, and consistency in comparison can be maintained by using the same time unit throughout the project.

[0110] Estimated centerline generation time (T M2R ) is not arbitrarily set as a single value, but can be output by sequentially reflecting the number of member elements included in the structural model, the number of connection definitions between structural members, the number of types of structural member attributes, and weight values ​​for time and complexity correction into the centerline time reduction judgment algorithm.

[0111] In other words, multiple input elements representing the scale and complexity of the structural model can be comprehensively considered to output a single estimated time value.

[0112] Estimated centerline generation time (T M2R ) can be used as a key indicator to determine whether the process of generating structural centerlines is shortened in time compared to the actual time required for the existing method being compared.

[0113] Accordingly, in order to objectively explain the effect of the method of the present invention, the corresponding estimated time value is absolutely necessary.

[0114] For example, in the case of a project where there are many structural member elements and the connection definitions and attribute types are also above a certain level, the centerline time reduction judgment algorithm can output one estimated centerline generation time by reflecting these input conditions, and the structural centerline generation module (100) can use this value to subsequently compare and judge with the existing method.

[0115] The centerline time reduction judgment algorithm can sequentially process data values ​​such as the number of structural member elements, the number of connection definitions, and the number of attribute types for a single project, the existing work time measured and entered by the user, and the set weight values.

[0116] First, from the structural data, the number of structural member elements (E M2R ), number of structural member connection definitions (N end ), number of types of structural member attributes (P M2R ) is derived, and based on this, values ​​representing structural complexity are derived, and subsequently, these values ​​are time weights (a M2R ) and complexity weights (b M2R Combined with ), it forms a single estimated centerline generation time (T M2R ) can be output.

[0117] Finally, the estimated centerline generation time (T M2R ) is compared with the actual time of the existing method, and based on the comparison result, the structure centerline generation module (100) can determine whether the structure centerline generation time has been shortened.

[0118] The centerline time reduction judgment algorithm can be implemented in software through the following procedure.

[0119] After converting MIDAS GEN data into an Excel file, the list of structural member elements, the list of connection definitions, and the list of attributes can be analyzed to derive the required quantities. Subsequently, the derived values ​​are sequentially input into internal calculation logic to calculate the estimated time, and a judgment result can be output by comparing it with the standard time entered by the user.

[0120] This implementation method does not require knowledge of complex mathematical operations and can be composed of data counting, simple transformation processing, and comparison operations in that order.

[0121] The judgment criterion of the centerline time reduction judgment algorithm can be set as a single criterion, which may mean whether the estimated centerline generation time is shorter than the existing standard time.

[0122] In addition, the centerline time reduction judgment algorithm can simplify the judgment structure by using a single comparison criterion instead of combining multiple conditions.

[0123] Time weight (a M2R ) and complexity weights (b M2R ) can be used with roles separated into elements for time conversion and elements for controlling complexity effects, and the reference time can be flexibly set for each project.

[0124] The centerline time reduction judgment algorithm can be repeatedly applied by receiving new structural data and reference time for each project when processing multiple projects sequentially.

[0125] The centerline time reduction judgment algorithm, when a version change or implementation improvement is made even for the same project, the time weight (a M2R ) and complexity weights (b M2R By readjusting ), the trend of change over time can be reflected.

[0126] The final judgment result may vary depending on the existing standard time value entered by the user, and the time weight (aM2R ) and complexity weights (b M2R As the user adjusts ), the estimated time itself may change, which allows for judgments tailored to the usage environment and project characteristics.

[0127] The above technical configurations and examples are sufficiently provided so that a person skilled in the art can easily implement the centerline time reduction judgment algorithm and variable definitions of the present invention. Although the present invention is not limited to specific examples and various modifications are possible within its scope, it can be confirmed that a person skilled in the art can obviously understand and realize them.

[0128] The project setup module (500) creates Family, Grid, Level, and Material information of structural members in a REVIT project based on MIDAS GEN data created by the structural designer, creates a structural member family corresponding to the member property information of MIDAS GEN using the GRASSHOPPER algorithm, and the structural member family can be linked with the parameters of the MIDAS GEN data including various members used in S-structure, RC-structure, and SRC-structure (structural types of buildings) (see FIG. 4).

[0129] The aforementioned REVIT refers to one of the BIM (Building Information Modeling) software developed by Autodesk, and may refer to software used in architectural, structural, and MEP design, and supporting 3D design and construction information management.

[0130] The aforementioned Rhino (Rhinoceros 3D) may refer to 3D modeling software specialized in NURBS-based surface and curve processing that is widely used in the fields of architecture, industrial design, and engineering.

[0131] The aforementioned Grasshopper refers to Rhino's visual algorithm editor and is characterized by generating complex shapes and algorithms through a graphical interface without coding, and being used for implementing parametric designs with algorithms suitable for the automation and optimization of modeling tasks.

[0132] Meanwhile, the project setup module (500) may utilize a parameter linkage time reduction judgment algorithm to determine whether to reduce the time for creating structural member information or family information.

[0133] More specifically, the project setup module (500) utilizes a parameter-linked time-saving judgment algorithm to quantify whether the workload is large by considering the project scale and type diversity, and reflects that if a lot of mapping has already been completed, there is less room to further reduce it, and since the perceived speed may differ even for the same project due to differences in the execution environment (CPU, etc.), the judgment criteria can be organized as simple correction factors.

[0134] The project setup module (500) converts MIDAS GEN data (including NODE / ELEMENT / MATERIAL / PROPERTY / END CONNECTION) into Excel and the total number of structural members (N p ), Number of absence attribute types (U p ), completion rate (M p ) derives, and inputs the derived value into the parameter-linked time-saving judgment algorithm to determine the decision score (B p Can output ).

[0135] The project setup module (500) is a decision score (B) output by the parameter linkage time saving judgment algorithm. p ) is the reference value (T pIf it is larger than ), it can be used as a basis for decision to proceed with the procedure of creating structural member information / family information by applying the project setup module.

[0136] As described above, the project setup module (500) can improve work efficiency by utilizing a parameter linkage time reduction judgment algorithm, thereby reducing the risk of performing unnecessary construction (e.g., family mapping work, parameter definition work) first on projects with a low probability of reduction, and applying it first to projects with a high probability of reduction.

[0137] The parameter-linked time-saving judgment algorithm uses the scale of the number of members, diversity of family types, and the degree of incomplete mapping as input values ​​to determine a decision score (B) that can determine whether the project setup module is highly likely to reduce the time for creating structural member information or structural member family information. p Can output ).

[0138] The parameter linkage time-saving judgment algorithm is based on the total number of structural members (N) of the structural model. p ) and number of absence attribute types (U p The first part reflecting ), the completion ratio (M p The second part, which reflects the extent to which parameter mapping has not yet been completed using ), and the relative performance index (C), an execution environment correction factor, considering that the perceived speed may vary depending on the execution environment even for the same calculation. p It may include a third part that reflects ).

[0139] The first part is the total number of structural members (N p After adding 1 to ) and inputting it into the logarithmic function, the absence weight (w) to the result of the logarithmic function p1 The result of multiplying by ) and the number of member attribute types (U p Add 1 to ) and input it into the logarithmic function, and for the result of the logarithmic function, the type weight (w p2It can be characterized as being composed of the sum of the results of multiplying by ).

[0140] The second part is the completion ratio (M) from 1. p It can be characterized by being configured to indicate the extent to which parameter mapping has not been completed to date by subtracting ).

[0141] The third part is the relative performance index (C p It can be characterized by being configured to multiply the reciprocal value of ) divided by 1 by the result of multiplying the first part and the second part.

[0142] As described above, the parameter-linked time-saving judgment algorithm consisting of the first to third parts can increase automation benefits as repetitive tasks increase, so the total number of structural members (N p It can be characterized by being composed by reflecting ).

[0143] In addition, the parameter linkage time-saving judgment algorithm can yield greater automation benefits as the types of family and type definitions and information creation increase, so the number of component attribute types (U p It can be characterized by being composed by reflecting ).

[0144] Here, the total number of structural members (N p ) and number of absence attribute types (U p If used as is, the score becomes too large and the judgment may be skewed to one side, so it is configured to add 1 to each value and input it into a logarithmic function, so it can be characterized by being configured to make the increase gradual while still being large.

[0145] In addition, regarding the parameter linkage time-saving judgment algorithm, if there is already a lot of mapping, the room for further reduction may be small, so the completeness ratio (M) from 1 p It can be characterized by calculating the remaining degree through a normalization process that subtracts ), and multiplying the remaining degree so that the score increases as the amount of incomplete components increases.

[0146] In addition, the parameter-linked time-saving judgment algorithm can be divided to reflect PC performance differences even within the same project, so the relative performance index (C), which is an environment correction factor, p It can be characterized by being composed by reflecting ).

[0149] In other words, the parameter linkage time-saving judgment algorithm is absent (N p There are many ) and family types (U p There are many ) and still mapping(M p It is possible to quantify the intuition that the less ) is done, the higher the likelihood of a greater shortening effect.

[0150] In addition, the parameter-linked time-saving judgment algorithm uses a logarithmic function for the total number of structural members (N p ) or number of absence attribute types (U p It can be characterized by being configured so that the score does not suddenly skyrocket even when ) becomes very large, but increases gradually.

[0151] As described in [Table 2] below and the above description, the parameter-linked time-saving judgment algorithm may be characterized by being designed using functions such as logarithmic functions through examples of various tests, and may be characterized by being designed by reflecting actual data distribution and experimental experience.

[0152] [Table 2]

[0153]

[0154] The total number of structural members (N), each variable used in the parameter-linked time-saving judgment algorithm p ), Number of absence attribute types (U p ), completion rate (M p ), relative performance index (C p ), reference value(T p ), absence weight (w p1 ) and type weights(w p2 The characteristics of ) are as follows.

[0155] Total number of structural members (N p ) can mean the total amount of all structural members included in the MIDAS GEN structural model, and can be derived based on record data, such as structural design data, rather than a value arbitrarily entered by the manager.

[0156] More specifically, after converting the MIDAS GEN model created by the structural designer into Excel format, the sheet containing structural member information within the converted Excel file can be identified, and by counting the number of data rows representing actual structural members one by one in that sheet, the total number of structural members (N p ) can be derived.

[0157] Here, structural members may refer to individual elements that constitute an actual structural model, such as beams, columns, and slabs, defined by the structural designer in MIDAS GEN.

[0158] The parameter-linked time-saving judgment algorithm is based on the total number of structural members (N p The more ) there are, the more repetitive tasks may be performed during the process of creating structural member information, placing structural member families, and creating models.

[0159] Therefore, the total number of structural members (N p ) can be characterized as being utilized in the parameter linkage time reduction judgment algorithm as a factor that must be considered when determining the possibility of a time reduction effect occurring when applying parameter linkage or automation techniques.

[0160] Number of absence attribute types (U p ) can refer to the number of different types among the structural member attributes defined in the MIDAS GEN model, and is not a value arbitrarily entered by the administrator, but can be derived by analyzing the record data called MIDAS GEN data.

[0161] More specifically, after converting MIDAS GEN data into Excel format, a sheet containing PROPERTY information can be identified, and the number of structural member attribute types can be calculated by removing duplicates based on one of the PROPERTY ID, section name, or attribute identifier on that sheet and counting the number of different values.

[0162] Here, the term "structural member attribute type" may refer to the types of attributes that serve as criteria for distinguishing structural members, such as cross-sectional shape, material, and structural form.

[0163] Number of structural member attribute types (U p ) can be directly linked to the number of structural member families or types that need to be created in Revit.

[0164] As the number of attribute types increases, the tasks for family definition, type management, and information entry also increase; therefore, the parameter linkage time reduction judgment algorithm is based on the number of structural member attribute types (U p It can be characterized as being utilized as an important criterion for determining the amount of work required for creating and managing structural member information through ).

[0165] Completion rate ratio (M p ) is an indicator of the extent to which structural member attributes in MIDAS GEN are connected to Revit structural member families or types, and may represent the proportion of attributes that have already been mapped out of the total attributes.

[0166] Completion rate ratio (M p ) can be calculated based on record data, and a mapping table that stores the correspondence relationship between MIDAS GEN's PROPERTY information and Revit families or types can be managed. By comparing the total number of PROPERTYs in this mapping table with the number of PROPERTYs with registered valid correspondence information, the extent to which the current mapping is completed can be derived in the form of a ratio.

[0167] While the mapping task itself is performed by an administrator, the mapping completeness value can be derived by the system based on the data in which the mapping results are recorded.

[0168] If most attributes are already mapped, the amount of work that can be reduced through additional parameter linking may be limited, so the completion rate (M p ) can be utilized in the parameter-linked time-saving judgment algorithm as an element to reflect the degree of unfinished work remaining.

[0169] Relative performance index (C p ) refers to a value representing the relative performance level of the computer execution environment where structural modeling and conversion work is performed, and may refer to a value derived by explicitly receiving input from an administrator or operator rather than a value automatically derived from record data.

[0170] Relative performance index (C p ) can refer to an indicator used to express the relative level of performance compared to a standard execution environment, rather than an absolute performance measurement.

[0171] First, a standard hardware environment can be set, and the administrator can determine how much performance the currently used system has compared to that standard environment, and convert the result of that determination into a numerical value to obtain a relative performance index (C p It can be derived by inputting as ).

[0172] Since perceived processing speed can vary depending on the execution environment even with identical structural data and algorithms, the relative performance index (C p ) can be utilized in the parameter-linked time-saving judgment algorithm as a correction means to adjust the judgment results to the actual work environment.

[0173] Reference value (T p) is the decision score (B) output by the parameter linkage time reduction judgment algorithm. p It is a value that serves as a criterion for determining when time reduction is possible when the level is above a certain threshold, and can be used as a criterion for adjusting the gap in judgment between the output result by the algorithm and whether it is actually applied.

[0174] Reference value (T p ) is entered by the manager and is set by comprehensively considering the organization's operational policies, schedule margins, and risk tolerance range, but since setting the threshold value high leads to a more conservative judgment and setting the threshold value low leads to a more proactive application judgment, it can be derived from a value determined by the manager's intention.

[0175] Absence weight (w p1 ) and type weights(w p2 ) may mean a weight that controls the importance of the number of structural members and a weight that controls the importance of the number of structural member attribute types, respectively.

[0176] Absence weight (w p1 ) and type weights(w p2 ) can be set in one of the following three ways.

[0177] First, the project setup module (500) is formed by a manager directly inputting the component weight (w) considering the project characteristics. p1 ) and type weights(w p2 ) can be derived.

[0178] Second, the project setup module (500) simply compares which of the increase in the number of structural members and the increase in attribute types increased the actual work time more based on past project records, and the member weight (w p1 ) and type weights(w p2 It can be derived by adjusting ).

[0179] Third, the project setup module (500) initially has a component weight (w p1 ) and type weights(w p2 After setting ) to the same value, step by step while checking the actual application results, the absence weight (w p1 ) and type weights(w p2 It can be derived by utilizing a method of adjusting the value of ).

[0180] The parameter-linked time-saving judgment algorithm uses absence weighting (w) because applying the same judgment criteria to all projects can distort judgment results depending on project characteristics. p1 ) and type weights(w p2 You can use ).

[0181] As described above, the parameter linkage time-saving judgment algorithm is an absence weight (w p1 ) and type weights(w p2 By reflecting the nature of the project through ), it becomes possible to make more realistic judgments.

[0182] In the centerline time reduction judgment algorithm, member weights (w) are used to control the influence of the number of structural members and the number of structural member attribute types on the judgment result. p1 ) and type weights(w p2 You can use ).

[0183] The centerline time reduction judgment algorithm is based on the total number of structural members (N p ), Number of absence attribute types (U p ), completion rate (M p ), relative performance index (C p ), reference value(T p ), absence weight (w p1 ) and type weights(w p2 It can receive ) as input and calculate a single judgment score.

[0184] For example, the total number of structural members (N p ) is very large, and the number of absence attribute types (U p) is also diverse, and since parameter mapping is not yet sufficiently completed, the completion rate (M p If ) is low, it can be determined that the structural scale and complexity are large and there is a large amount of remaining work.

[0185] In the case of the example described above, the decision score (B) output by the centerline time reduction judgment algorithm p ) is likely to be larger than the reference value, and accordingly, it can be determined that applying the project setup module (500) is advantageous for reducing time.

[0186] Conversely, the total number of structural members (N) p ) is small, and the number of absence attribute types (U p ) is also simple, and most parameter mappings have already been completed, so the completion rate (M p If ) is high, it can be determined that the amount of work that can be reduced by additional automation is limited.

[0187] In this case, the decision score (B) output by the centerline time reduction judgment algorithm p ) may not meet the standard value, and it may be determined that the time reduction effect is insufficient.

[0188] The centerline time reduction judgment algorithm can be implemented in software through the following step-by-step processing procedure: converting MIDAS GEN data into Excel, aggregating the number of structural members and attribute types, querying a mapping table to calculate the mapping completeness, and comparing it with the standard values ​​entered by the administrator.

[0189] In particular, the completeness of parameter mapping can change over time.

[0190] In the early stages of the project, the judgment score may be high because mapping is nearly incomplete, and as the project progresses and mapping is completed, the judgment score may gradually decrease.

[0191] Through this, the centerline time reduction judgment algorithm can be utilized as a tool to explain the potential for reducing remaining time at each stage of the project.

[0192] The above technical configurations and examples are sufficiently provided so that a person skilled in the art can easily implement the parameter linkage time-saving judgment algorithm and variable definitions of the present invention. Although the present invention is not limited to specific examples and various modifications are possible within its scope, it can be confirmed that a person skilled in the art can obviously understand and realize them.

[0193] The Rhino to Revit conversion module (900) can model a structural model by linking with a family pre-built in the Revit project based on the shape and position information of the Rhino structural centerline generated by the structural centerline generation module using the Grasshopper algorithm.

[0194] The Rhino to Revit conversion module (900) can model the above structural model at a speed that varies depending on the performance of the CPU, but can be based on an AMD Ryzen 9 5900X 12-Core Processor (3.70 GHz) taking about 7 minutes to model 8,000 components.

[0195] Meanwhile, the Midas to Revit (M2R) data-driven structural modeling system (1) according to one embodiment of the present invention is implemented through Grasshopper, a visual algorithm editor for Rhino, and may be characterized by Rhino.Inside.Revit linking Revit, Rhino, and Grasshopper.

[0196] The aforementioned Rhino.Inside.Revit refers to a plugin that integrates Rhino and Revit to enable the execution of Rhino and Grasshopper within the Revit environment, and is characterized by enabling complex shapes and structures to be easily modeled in Revit through data interaction between the two software programs.

[0197] Meanwhile, the Rhino to Revit conversion module (900) converts input values ​​of different units into a single score, which is a monitoring need score (M need You can determine whether to perform monitoring by utilizing a modeling monitoring judgment algorithm that outputs as ).

[0198] In particular, the time item of the modeling monitoring judgment algorithm is configured to be transformed using a logarithmic function, thereby ensuring judgment stability by preventing the score from skyrocketing excessively even when the task is large.

[0199] In addition, the error item of the modeling monitoring judgment algorithm is configured to be transformed using the trigonometric function sin, maintaining a range of 0 to 1; therefore, as the error increases, the output monitoring necessity score (M need ) increases, but prevents excessive reflection during summation, thereby reflecting the risk of reduced accuracy.

[0200] The Rhino to Revit conversion module (900) is a monitoring necessity score (M) output by a modeling monitoring judgment algorithm. need Based on ), you can determine whether to display the monitoring UI, whether to perform intermediate verification, and the stop / retry policy in case of failure.

[0201] Additionally, the Rhino to Revit conversion module (900) can reduce unnecessary overhead or late error detection by utilizing a modeling monitoring judgment algorithm to turn monitoring on only when necessary and turn it off when not necessary.

[0202] The modeling monitoring judgment algorithm is the family linkage time (T fam ), modeling time (T mod ), error rate (E err ), user preference (P user ), CPU performance coefficient (C cpu Using ) as the input value, the monitoring necessity score (M need Can output ).

[0203] More specifically, the modeling monitoring judgment algorithm sums the first item consisting of time factors, the second item consisting of error factors, the third item consisting of user intent factors, and the fourth item consisting of CPU margin factors to obtain a monitoring necessity score (M need It can be characterized by being configured to output ).

[0204] The first item is family linkage time (T fam ) and modeling time (T mod The sum of ) is the reference time (T ref Add 1 to the time ratio divided by ) and input it into the logarithmic function, divide the result of the logarithmic function with the time ratio input by the logarithmic function with 2 input, and the weight (α) to the ratio of the logarithmic function need It can be characterized by being configured to multiply )

[0205] The second item is the error rate (E err After normalizing by multiplying ) by π and dividing by 2, the normalized error rate (E err Input ) into the trigonometric function sin, and at 1, the weight(α need It can be characterized by being configured to multiply the result of the trigonometric function sin by the value obtained by subtracting ).

[0206] The third item is user preference (P), a user intention factor directly entered by the user. user It can be characterized by being composed of adding ) as is.

[0207] The fourth item is the CPU performance coefficient (C) in 1 cpu It can be characterized by being configured such that the monitoring score increases as the CPU slows down, by adding the result of subtracting ).

[0208] The modeling monitoring judgment algorithm determines the monitoring necessity score (M) as time increases. need Although ) needs to increase, if you simply add the absolute time, the monitoring necessity score (M in large projects) need Since ) can become excessively large, it can be characterized by a configuration designed to create a ratio relative to the standard and gradually increase it through log transformation.

[0209] In addition, the modeling monitoring judgment algorithm needs to increase as the error increases, and the error rate (E err Since ) is in the range of 0 to 1, it can be characterized by being designed with a simple configuration as a summation structure that increases within the range of 0 to 1 by utilizing the trigonometric function sin.

[0210] In addition, since the nature of the project differs even under the same time and error conditions, the modeling monitoring judgment algorithm [P] user preference (P user Policies can be applied by reflecting ), and since rework losses can increase as PC performance slows down, the CPU performance coefficient (C cpu It can be characterized by being designed as a configuration that reflects the environment by reflecting ) as a value subtracted from 1.

[0211] In addition, the modeling monitoring judgment algorithm assigns weights (α) to the importance of time and errors. needIt can be characterized by being designed to allow simple adjustment by controlling it as a single unit, enabling adjustment tailored to the operating site.

[0212] As described above, the modeling monitoring judgment algorithm may be characterized by inputting a logarithmic function so that the score increases as time increases but does not explode rapidly.

[0213] Here, the reason for adding 1 before inputting into the logarithmic function is the family linkage time (T) input into the logarithmic function. fam ) and modeling time (T mod The sum of ) is the reference time (T ref This may mean that it is intended to prevent errors that may occur through the logarithmic function when the time ratio divided by ) becomes 0.

[0214] In addition, as described above, the modeling monitoring judgment algorithm has an error rate (E err It can be characterized by being configured to input a normalized value into the trigonometric function sin in order to increase while maintaining the result in the range of 0 to 1.

[0215] As described in [Table 3] below and the above description, the modeling monitoring judgment algorithm may be characterized by being designed using functions such as logarithmic functions through examples of various tests, and may be characterized by being designed to reflect actual data distribution and experimental experience.

[0216] [Table 3]

[0217]

[0218] Family linkage time (T), each variable used in the modeling monitoring judgment algorithm fam ), modeling time (T mod ), error rate (E err ), user preference (P user ), CPU performance coefficient (C cpu ), reference time (T ref ), weight(αneed The characteristics of ) are as follows.

[0219] Family Linkage Time (T fam ) can refer to the time taken to search for and select a structural member family and its type corresponding to the member property of MIDAS GEN in a Revit project, complete type mapping, and prepare it for actual production, and the unit can be used in seconds.

[0220] The Rhino to Revit conversion module (900) has a family linkage time (T fam To derive ), the family linkage start event and the family linkage completion event can be clearly defined within the Rhino to Revit conversion module (900).

[0221] For example, the Family Linkage Start event can be defined as the point at which the iterative process of mapping families and types to a list of properties begins, and the Family Linkage Complete event can be defined as the point at which the mapping results for all properties are finalized and the list of types that can be generated in Revit is ready.

[0222] Then, after recording the time the start event occurred and the time the completion event occurred, respectively, the family linkage time (T) is calculated from the value obtained by subtracting the start event time from the completion event time. fam ) can be derived.

[0223] Here, the recording method may refer to recording using the timer function provided within the Grasshopper component, or recording by saving the Rhino.Inside.Revit execution log in the form of a file log or console log.

[0224] Family Linkage Time (T fam ) can be set to a value that is updated by measuring the actual time for each execution.

[0225] Family Linkage Time (T famIf this is delayed, the start of the entire modeling process may be delayed, and if the modeling proceeds with type mapping errors, it may escalate into a large number of errors later on, so it can be used as a key input to determine the need for monitoring in the modeling monitoring judgment algorithm.

[0226] For example, if the integration start log is 10:00:00 and the integration completion log is 10:01:30, the family integration time can be calculated as 90 seconds.

[0227] In the past, there was a disadvantage in that the monitoring policy was fixed and could not reflect the linkage delay, but the Rhino to Revit conversion module (900) can quickly identify the cause by activating monitoring when the linkage delay is large through a modeling monitoring judgment algorithm that reflects the family linkage time.

[0228] Modeling time (T mod ) may refer to the total time taken to create structural member instances in Revit using the shape and position information of structural centerlines generated in Rhino, and the unit may be seconds.

[0229] The Rhino to Revit conversion module (900) is the modeling time (T mod To derive ), you can define a modeling start event and a modeling completion event, and record the start event time and the completion event time in the execution log.

[0230] Finally, the Rhino to Revit conversion module (900) determines the modeling time (T) from the value obtained by subtracting the start event time from the completion event time. mod ) can be derived.

[0231] Here, the start event can be defined as "the point immediately before the Revit API call to create the first structural member instance," and the completion event can be defined as "the point immediately after the creation of the last structural member instance is terminated, processed as successful or failed."

[0232] Modeling time (T mod ) can be set to a value that is updated by measuring the actual time for each execution.

[0233] Modeling time (T mod The longer the length, the greater the cost of re-execution if an error is missed in the middle, so it can be used as a factor that increases the need for monitoring in modeling monitoring judgment algorithms.

[0234] For example, if the start time is 10:02:00 and the end time is 10:12:00, the modeling time (T mod ) can be derived as 600 seconds.

[0235] Conventionally, applying the same monitoring policy to large-scale models can increase the risk of late error detection; however, the modeling monitoring decision algorithm can contribute to reducing this risk by inducing monitoring to be enabled in large-scale models through the reflection of modeling time.

[0236] Error rate (E err ) can represent a value indicating the ratio of how many events classified as errors occurred compared to the number of modeling attempts, and can be used as a dimensionless ratio between 0 and 1.

[0237] The Rhino to Revit conversion module (900) has an error rate (E err To derive ), it can be implemented to increment the "attempt counter" by 1 for every attempt to generate a component, and at the end of execution, the error rate (E) is derived from the value obtained by dividing the error counter by the attempt counter. err ) can be derived.

[0238] Here, you can define a rule to increase the "error counter" by 1 if any of the following examples occur.

[0239] For example, cases where a mapped family type does not exist in the Revit project can be classified as errors; cases where an exception occurs or creation failure is returned as a result of a Revit API call can be classified as errors; cases where the position or orientation discrepancy between the centerline and the member exceeds a predetermined threshold during the verification phase after creation can be classified as errors; and cases where the number of elements in the creation result does not match the expected number of elements, resulting in omissions, can be classified as errors.

[0240] However, the Rhino to Revit conversion module (900) prevents abnormal situations where the derived result becomes greater than 1, so the final error rate (E err ) can be set to limit not to exceed 1.

[0241] Error rate (E err ) can be derived from a value calculated based on log and counter aggregation for each execution.

[0242] Error rate (E err As ) increases, the risk of the accuracy of the structural model decreasing increases, so it can be used as an input to reflect the situation where there is a greater need to turn on monitoring in the modeling monitoring judgment algorithm to immediately identify the cause of the error and stop or correct it.

[0243] For example, if the number of attempts is 1,000 and the number of errors is 25, the error rate (E err ) can be derived as 0.025.

[0244] While it may be difficult to reflect accuracy issues if judgments are made based solely on time as in the past, the modeling monitoring judgment algorithm uses an error rate (E err By reflecting ) and directly incorporating accuracy issues, monitoring policies can be configured more rationally.

[0245] User preference (P user ) can refer to a value directly input by the manager to indicate how actively the manager wants to perform monitoring, and can be used as a dimensionless value between 0 and 1.

[0246] That is, user preference (P user ) can be derived through UI input that can be manipulated by the user or administrator. For example, a slider with a range from 0 to 1 can be provided in the UI, and the user can be allowed to input a value according to the project purpose.

[0247] At this time, an explanatory phrase indicating that 0 is speed priority and 1 is accuracy priority can be provided so that the administrator can understand the meaning and make a selection.

[0248] User preference (P user The initial value of ) can be set by the administrator determining and distributing the default value, and users can change the value as needed.

[0249] Since monitoring policies may vary depending on delivery deadlines or inspection intensity even in the same environment, user preference (P) is used for the purpose of reflecting user intent as explicit input. user It can be characterized by being configured to use ).

[0250] For example, during the review stage, the manager [considers] user preferences (P user You can enter 0.8 for ), and at the draft stage, the manager can input user preference (P user You can enter 0.2 for ).

[0251] While it is difficult to respond to changes in project objectives when using only conventional fixed logic, the modeling monitoring judgment algorithm [utilizes] user preferences (P user By reflecting ), flexible operation tailored to actual business purposes can be achieved.

[0252] CPU performance coefficient (C cpu) is a performance value ranging from 0 to 1 that indicates how much processing speed the currently running PC has compared to a reference PC, and can be used as a dimensionless value.

[0253] CPU performance coefficient (C cpu ) can be derived by combining a record data-based method and simple measurement, and more specifically, a short performance measurement interval can be performed to actually generate a small amount of sample missing at the start of the conversion module.

[0254] For example, 200 parts can be generated, and the time taken to generate them can be measured to derive the "throughput (parts / second) obtained by dividing the number of completed generations by the time taken." Then, the CPU performance coefficient can be derived by comparing the current throughput with the reference throughput that was previously measured on the reference PC.

[0255] At this time, the CPU performance coefficient (C cpu ) can be calculated as "the value obtained by dividing the current throughput by the reference throughput," and if the value exceeds 1, it can be set to maintain a range of 0 to 1 by limiting it to a maximum of 1.

[0256] Here, the baseline throughput can be set by an administrator through a one-time measurement, and the current throughput can be updated by measuring it in the same way each time it is executed.

[0257] Since a slow PC increases overall task time and late error detection can result in significant losses from re-execution, CPU performance coefficients can be used as an input to induce monitoring to be enabled in slow environments.

[0258] For example, if the baseline throughput is 100 per second and the current throughput is 60 per second, the CPU performance coefficient (C cpu ) can be derived as 0.6.

[0259] Conventional fixed policies that ignore performance can increase rework losses in slow environments, but modeling monitoring decision algorithms utilize CPU performance coefficients (Ccpu By utilizing ), monitoring policies can be reasonably adjusted according to the environment.

[0260] Reference time (T ref ) is a reference value for comparing the total time required, which is the sum of family linkage time and modeling time, and can be used in seconds.

[0261] Reference time (T ref The standard time can be set in one of the following ways: first, by an administrator setting a target time for a representative project and inputting it as the standard time; second, by collecting recent execution logs and calculating the average or median of the total time taken by adding the family integration time and modeling time for each execution, and setting it as the standard time; and third, by starting from a default value and adjusting the standard time by an administrator in increments of a certain percentage, for example, 10 percent, based on feedback during operation such as "monitoring turns on too often" or "turns off too often."

[0262] Reference time (T ref ) can be used to mitigate the problem of absolute time varying depending on the project scale, and can increase judgment stability by enabling relative comparison against a standard rather than judging solely by absolute time.

[0263] weight (α need ) can be used as a dimensionless value ranging from 0 to 1 to determine which of the time-related factors or the error-related factors to prioritize.

[0264] weight (α need ) can be derived in one of the following three ways. First, it can be derived by receiving it as a fixed value input by setting a rule such as "weight 0.7 if time is more important, weight 0.4 if accuracy is more important."

[0265] Second, it can be derived based on historical data by calculating the weight in such a way that if the "ratio of cases where rework occurred due to errors" in past execution logs is high, the weight is lowered, and if the "ratio of cases where errors are few and time is the issue" is high, the weight is increased.

[0266] For example, if 70 percent of the last 20 executions were classified as time issues, the result can be derived by setting the weight to 0.7.

[0267] Third, using a simple numerical adjustment method, starting from a default value of, for example, 0.6, the operator can adjust it in increments of 0.1 based on operational requirements such as "monitoring turns on too often" or "monitoring turns off too often" based on the monitoring results.

[0268] weight (α need Since the priority of time priority and accuracy priority varies from site to site, a single fixed policy may be unsuitable, so it can be used to easily adjust the operation policy with a single value.

[0269] The centerline time reduction judgment algorithm is based on the reference time (T ref ) and weights (α need After setting ) first, in each execution, the family linkage time (T fam ), modeling time (T mod ), error rate (E err ), user preference (P user ), CPU performance coefficient (C cpu Using ) as the input value, the monitoring necessity score (M need It can be implemented in the order of printing ).

[0270] For example, the manager uses the standard time (T ref Set ) to 480 seconds, and weight(α need When ) is set to 0.6, in the case where there are almost no errors and the CPU is good, the family linkage time (T fam Measure ) as 60 seconds and modeling time (Tmod ) is measured at 420 seconds, and the error rate (E err ) is aggregated as 0.02 and user preference (P user ) is input as 0.2, and the CPU performance coefficient (C cpu If ) is derived as 0.9, the monitoring necessity score (M need ) can be output at a low value, in which case the Rhino to Revit switching module (900) can be designed to determine monitoring as OFF.

[0271] Conversely, if the time is long, errors are noticeable, and the CPU is slow, the family linkage time (T fam Measure ) as 200 seconds and modeling time (T mod ) is measured at 1800 seconds, and the error rate (E err ) is aggregated as 0.25 and user preference (P user Enter ) as 0.8 and CPU performance coefficient (C cpu If ) is derived as 0.4, the monitoring necessity score (M need ) can be output at a high level, and in this case, the Rhino to Revit switching module (900) can be designed to determine monitoring as ON.

[0272] The centerline time reduction determination algorithm defines a family linkage start event and a completion event in the Rhino to Revit conversion module (900), records each time as a timer or execution log, and then calculates the value obtained by subtracting the start time from the completion time as the family linkage time (T fam It can be implemented to derive as ).

[0273] In addition, the Rhino to Revit conversion module (900) defines the part creation loop start and completion events, logs each time, and then the value obtained by subtracting the start time from the completion time is the modeling time (T mod It can be implemented to derive as ).

[0274] The Rhino to Revit conversion module (900) increases the number of attempts for each attempt to create a component, increases the number of errors for each predefined error condition that occurs, and at the end of execution, the value obtained by dividing the number of errors by the number of attempts is the error rate (E err It can be implemented to derive as ).

[0275] User preference (P user ) can be implemented to derive by storing the input value received through the administrator interface.

[0276] CPU performance coefficient (C cpu ) can be implemented to generate a small sample of components at the start to measure throughput, compare it with the reference throughput, and derive a ratio.

[0277] Reference time (T ref ) and weights (α need ) can be implemented by setting one of the administrator input method, record data-based method, or simple numerical adjustment method, combining the above input values ​​to calculate a score, and determining whether monitoring is ON or OFF depending on whether the score is above a threshold.

[0278] The centerline time reduction judgment algorithm uses the output monitoring necessity score (M need It can be characterized by being configured to determine whether monitoring is performed by comparing ) and a threshold value.

[0279] The threshold can be set as a value entered by the administrator, and the output monitoring necessity score (M need It can be operated so that monitoring is determined to be ON if it is above the threshold, and monitoring is determined to be OFF if it is below the threshold.

[0280] Monitoring Needs Score (M need The output of ) may be characterized by combining time-related factors, error-related factors, user preference, and CPU performance factors in the form of simple addition, and adjusting the relative importance of time and error among them with weights.

[0281] Reference time (T ref ), weight(α need The threshold value can be set using an administrator input method, a historical data-based method, or a simple numerical adjustment method, allowing it to be adjusted to suit the operating environment.

[0282] The centerline time reduction judgment algorithm can be configured to recalculate the score by updating some input values ​​even during execution.

[0283] For example, whenever a part is created in units of a certain number, the number of attempts and errors are updated, and the error rate up to that point is recalculated to obtain a monitoring necessity score (M need ) can be reflected in the output.

[0284] In addition, the accumulated time during execution is used to update the current modeling time to the median value, and this value is used to determine the monitoring necessity score (M need It can be implemented to reflect this in the output.

[0285] The monitoring necessity score (M) re-output during execution in this manner need You can switch the policy to strengthen monitoring indications or perform intermediate verifications more frequently from the point when ) exceeds the threshold.

[0286] The centerline time reduction judgment algorithm is user preference (P user It can be characterized by being configured to directly reflect ) in the score calculation.

[0287] The administrator [P] user preferences (P user If you enter a high value for ), the monitoring necessity score (M) is the same even under the same time, same error rate, and same CPU performance conditions. need It can be configured to increase ), and as a result, it can be easier to determine whether monitoring is ON.

[0288] The administrator [P] user preferences (P user If you enter a low value, the monitoring necessity score (M) even under the same conditions needIt can be configured to reduce ), and as a result, it can be easier to decide to turn off monitoring.

[0289] Therefore, depending on whether the project phase is a draft or an inspection, or whether the deadline is tight, the manager considers user preference (P user You can easily change the monitoring policy by adjusting ).

[0290] The above technical configurations and examples are sufficiently provided so that a person skilled in the art can easily implement the modeling monitoring judgment algorithm and variable definitions of the present invention. Although the present invention is not limited to specific examples and various modifications are possible within its scope, it can be confirmed that a person skilled in the art can obviously understand and realize them.

[0291] The structure centerline generation module (100), project setup module (500), and Rhino to Revit conversion module (900) can perform one or more of the computation, storage, reference, input / output, and control functions of a general computer, and may include an artificial neural network described later based on input data.

[0292] The structural centerline generation module (100), the project setup module (500), and the Rhino to Revit conversion module (900) may include a processor and memory. The processor may automatically generate a structural model according to the present invention and may include devices capable of performing this. The processor may execute a program or control each module. Program code executed by the processor may be stored in memory. The memory may store relevant information for performing a service according to the present invention or a program for implementing a method. The memory may be volatile memory or non-volatile memory.

[0293] The structure centerline generation module (100), project setup module (500), and Rhino to Revit conversion module (900) can send data to an external device or receive data from an external device using a network.

[0294] The structure centerline generation module (100), project setup module (500), and Rhino to Revit conversion module (900) can train an artificial neural network and can also use an artificial neural network that has been trained. The processor can train or execute an artificial neural network stored in memory, and memory can store an artificial neural network that has been trained. The electronic device that trains the artificial neural network and the electronic device that uses it may be the same, but they may also be separate.

[0295] Artificial intelligence is a computer system that partially implements the functions of the human brain and is capable of learning, speculating, and making judgments on its own. As learning progresses, the probability of extracting the correct answer can increase. Artificial intelligence can be composed of learning and component technologies that utilize it. The learning aspect of AI is an algorithmic technology that classifies and learns features based on input data, while the component technologies may be techniques that utilize these learning algorithms to partially implement the functions of the human brain.

[0296] Artificial intelligence is a technology that facilitates the approach to problems where multiple probabilistic answers are possible, enabling it to logically and probabilistically infer optimal cycles, methods, and plans based on input data. AI inference techniques can include evaluating input data, optimization prediction, knowledge and probability-based reasoning, and preference-based planning.

[0297] Artificial neural networks are learning algorithms in the field of machine learning that programmatically implement the connections between neurons and synapses in the brain. By creating a neural network structure through programming and then training it, artificial neural networks can acquire desired functions. Although errors may exist, they can learn from massive datasets to produce appropriate output data from input data. They have the advantage of being able to obtain output data that has yielded statistically good results and are similar to human reasoning.

[0298] The structural centerline generation module (100), project setup module (500), and Rhino to Revit conversion module (900) can infer individual characteristics and interests by analyzing consumers' online behavior data, social media activities, search history, etc., using an artificial intelligence algorithm built on big data, and may include multiple pre-trained artificial neural networks for this purpose.

[0299] The network is a high-speed backbone network of a large-scale communication network capable of high-capacity, long-distance voice and data services, and may be a next-generation wired and wireless network for providing the Internet or high-speed multimedia services.

[0300] If the network is a mobile communication network, it may be a synchronous mobile communication network or an asynchronous mobile communication network. As an example of an asynchronous mobile communication network, a WCDMA (Wideband Code Division Multiple Access) network may be cited. In this case, although not shown in the drawing, the network may include an RNC (Radio Network Controller). Meanwhile, although a WCDMA network was given as an example, it may be a 3G LTE network, a 4G network, a next-generation communication network such as 5G, or other IP-based IP networks.

[0301] The structure centerline generation module (100), project setup module (500), and Rhino to Revit conversion module (900) and the consumer's terminal can include any terminal capable of exchanging data over a network, such as a desktop computer, laptop, tablet, or smartphone.

[0302] The structural centerline generation module (100), project setup module (500), and Rhino to Revit conversion module (900) may include one or more of the computational function, storage function, reference function, input / output function, and control function that a computer has in order to perform the service according to the present invention.

[0303] The structural centerline generation module (100), the project setup module (500), and the Rhino to Revit conversion module (900) may access a website or install an application to receive the service according to the present invention. The structural centerline generation module (100), the project setup module (500), and the Rhino to Revit conversion module (900) may exchange data through the website or the application.

[0304] The network is a high-speed backbone network of a large-scale communication network capable of high-capacity, long-distance voice and data services, and may be a next-generation wired and wireless network for providing the Internet or high-speed multimedia services.

[0305] If the network is a mobile communication network, it may be a synchronous mobile communication network or an asynchronous mobile communication network. As an example of an asynchronous mobile communication network, a WCDMA (Wideband Code Division Multiple Access) network may be cited. In this case, although not shown in the drawing, the network (300) may include an RNC (Radio Network Controller). Meanwhile, although a WCDMA network was given as an example, it may be a 3G LTE network, a 4G network, a 5G network, or other next-generation communication networks, or other IP-based IP networks.

[0307] Modeling using data-driven parametric modeling can improve speed and accuracy compared to traditional manual methods.

[0308] The main algorithms utilized in the M2R of the present invention are implemented through GRASSHOPPER, a visual algorithm editor for RHINO, and Rhino.Inside.Revit can link REVIT, RHINO, and GRASSHOPPER.

[0309] The Midas to Revit (M2R) data-driven structural modeling system proposed by the present invention was started in 2022 and completed a test application for an in-house project in 2023.

[0310] Referring to Fig. 4, the usability of the S-group, RC-group, and SRC-group was verified by applying it to about 10 projects, including the M2R data-driven structural modeling system project proposed by the present invention.

[0311] During the process of inspecting the structural model generated by the Midas to Revit (M2R) data-driven structural modeling system project proposed by the present invention, structural design errors were discovered, and the structural design could be supplemented by correcting them in consultation with the structural designer.

[0312] In addition, although there are differences depending on the scale of the building, our internal evaluation confirmed that the manpower input for structural modeling could be reduced by 80% compared to the existing manpower input.

[0314] FIG. 3 is an example diagram of the generation of a structural centerline and a structural model in a Midas to Revit (M2R) data-driven structural modeling system according to one embodiment of the present invention.

[0315] Referring to FIG. 3, the operation process of each module of the Midas to Revit (M2R) data-driven structural modeling system according to one embodiment of the present invention can be observed.

[0316] First, the process of converting the structural centerline generation module (100) into Excel and the process of generating the structural centerline ([1] > [2] > [3]) can be observed.

[0317] Secondly, the process of the project setup module (500) interacting with the parameters ([2] > [4]) can be observed.

[0318] Third, we can see the process of modeling the structural model ([3]+[4] > [5]).

[0320] The embodiments described above are for illustrative purposes only, and those skilled in the art will understand that the embodiments described above can be easily modified into other specific forms without altering the technical concept or essential features of the embodiments described above. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. For example, each component described as a single unit may be implemented in a distributed manner, and components described as distributed may likewise be implemented in a combined form.

[0322] The scope of protection sought through this specification is defined by the claims set forth below rather than by the detailed description, and should be interpreted to include all modifications or variations derived from the meaning and scope of the claims and the concept of equivalents. Explanation of the symbols

[0323] 100: Structural Centerline Generation Module

Claims

Claim 1 A structural centerline generation module that converts MIDAS GEN data created by a structural designer into Excel and generates structural centerlines in Rhino using the Grasshopper algorithm (a visual programming language and environment used in 3D modeling in Rhino, as well as a graphic algorithm editor); and a project setup module that creates Family, Grid, Level, and Material information for structural members in a REVIT project based on the MIDAS GEN data created by the structural designer, creates structural member families corresponding to the member property information of MIDAS GEN using the Grasshopper algorithm, and ensures that the structural member families include various members used in S-structure, RC-structure, and SRC-structure (structural types of buildings) and are linked to the parameters of the MIDAS GEN data. and further includes a Rhino to Revit conversion module that models a structural model by linking with a family pre-built in the Revit project based on the shape and position information of the Rhino structural centerline generated by the structural centerline generation module utilizing the Grasshopper algorithm; and the MIDAS GEN data includes modeling information including NODE, ELEMENT, MATERIAL, PROPERTY, and END CONNECTION, and the structural centerline includes attribute information of the corresponding structural member, and Revit refers to one of the BIM (Building Information Modeling) software developed by Autodesk, used in architecture, structure, and MEP design, and refers to software that supports 3D design and construction information management, and Rhino,Rhinoceros 3D refers to 3D modeling software specialized in NURBS-based surface and curve processing widely used in the fields of architecture, industrial design, and engineering; Grasshopper refers to the visual algorithm editor of Rhino, characterized by the creation of complex shapes and algorithms via a graphical interface without coding, and being used for the implementation of parametric design with algorithms suitable for the automation and optimization of modeling tasks; EXCEL is spreadsheet software developed by Microsoft, a tool for data calculation, organization, and analysis, characterized by the ability to process and visualize data through formulas; the Rhino to Revit conversion module is based on the fact that the speed of modeling the structural model varies depending on CPU performance, but it takes approximately 7 minutes to model about 8,000 components using an AMD Ryzen 9 5900X 12-Core Processor (3.70 GHz); and the Midas to Revit (M2R) data-driven structural modeling A Midas to Revit (M2R) data-driven structural modeling system, characterized by being implemented through Grasshopper, a visual algorithm editor for Rhino, and Rhino.Inside.Revit linking Revit, Rhino, and Grasshopper, wherein Rhino.Inside.Revit refers to a plugin that integrates Rhino and Revit to enable the execution of Rhino and Grasshopper within the Revit environment, and enables complex shapes and structures to be easily modeled in Revit through data interaction between the two software programs. Claim 2 delete Claim 3 In claim 1, the structural centerline generation module quantitatively determines performance by utilizing a centerline time reduction judgment algorithm that quantifies the scale and complexity of the structural model and provides a criterion for determining whether the structural centerline generation method has a time reduction effect compared to the conventional method by reflecting the influence of said numerical values ​​on the centerline generation time, thereby forming a Midas to Revit (M2R) data-driven structural modeling system. Claim 4 In claim 2, the centerline time reduction judgment algorithm is a Midas to Revit (M2R) data-driven structural modeling system that calculates an estimated time using the number of member elements included in the structural model, the number of connection definitions of structural members, the number of types of structural member attributes, the existing centerline generation time measured and entered by the user, and the time correction coefficient and complexity correction coefficient set by the administrator as input values, and then provides the value as a criterion for determining whether the centerline generation time is reduced by comparing the value with the existing standard time. Claim 5 In claim 3, the centerline time reduction determination algorithm comprises a time weight (a M2R It consists of ) and the product of the first element and the second element, and the first element is the number of structural member elements (E M2R It is characterized by being designed to reflect the tendency for time to increase as the number of members increases, consisting of a value input into a logarithmic function after adding 1 to ), and the second element is a complexity weight (b) to the result of summing connection complexity and attribute diversity. M2R After multiplying by ), complexity weights (b M2R A Midas to Revit (M2R) data-driven structural modeling system characterized by being designed to control the effect of complexity on time by adding 1 to the result of multiplying by ). Claim 6 In claim 3, the centerline time reduction determination algorithm comprises the number of structural member elements (E M2R To reflect that the number of structural member elements (E) increases as ) increases, the increase in operation repetitions is reflected. M2R Composed including ), and the number of structural member connection definitions (N end ) is the number of structural member elements (E M2R Connection complexity calculated as a ratio value divided by ), and the number of types of structural member attributes (P M2R ) is the number of structural member elements (E M2R It is configured to reflect attribute diversity calculated as a ratio value divided by ), and is a complexity weight (b) that controls the influence of complexity on time. M2R Time weight (a) that matches the time unit M2R A Midas to Revit (M2R) data-driven structural modeling system characterized by being composed of )

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