A steel structure embedded part positioning method, medium and system

Abstract

The application provides a steel structure embedded part positioning method, medium and system, belongs to steel structure embedded part positioning technical field, belong to, according to the given steel structure design drawing, the scheme first constructs three-dimensional digital model, including the geometric dimension and position information of main beam, secondary beam, column and other components. Then, the overall stress analysis of BIM model is carried out by using finite element analysis software, and the stress and strain distribution of each component is determined. Based on this, the installation position of the embedded part is preliminarily determined, and the embedded part position matrix is formed. Through finite element analysis, the main factors affecting the positioning accuracy of the embedded part are analyzed, including structural deformation, temperature change, construction error and the like. Combined with the stress condition of the embedded part and the influence on the steel structure, a double-target game model is established, and multiple alternative embedded part position matrices are obtained. Finally, the genetic algorithm is used to optimize these schemes, and the optimal embedded part position matrix is obtained and output.

Description

Technical Field

[0001] This invention belongs to the field of steel structure embedded part positioning technology, specifically, it relates to a steel structure embedded part positioning method, medium and system. Background Technology

[0002] Steel structures, as an important building material, are widely used in various construction projects, such as high-rise buildings, bridges, and stadiums. Compared with other materials, steel structures have advantages such as high strength, high stiffness, and good seismic performance. While ensuring the safety performance of buildings, they can also achieve lightweight design, which helps to reduce self-weight loads. However, steel structures require a large number of embedded parts during construction, such as bolts and welded plates. The installation position of these embedded parts directly affects the stress performance and overall stability of the entire steel structure. Therefore, how to accurately determine the optimal installation position of embedded parts in steel structures has become a key technical problem that urgently needs to be solved.

[0003] The existing methods for locating embedded parts in steel structures mainly include the following: The first method relies on the experience of designers to make empirical arrangements and determine the location of embedded parts through multiple trial calculations. This method is inefficient and difficult to meet the needs of complex structures. The second method uses BIM models for three-dimensional visualization design and uses software tools to optimize the location of embedded parts. However, this method often only considers the performance requirements of the embedded parts themselves and ignores the impact on the overall performance of the steel structure. Summary of the Invention

[0004] In view of this, the present invention provides a method, medium and system for positioning embedded parts in steel structures, which can solve the problem that the existing technology only considers the performance requirements of the embedded parts themselves and ignores the impact on the performance of the entire steel structure.

[0005] This invention is implemented as follows:

[0006] The first aspect of the present invention provides a method for positioning embedded parts in steel structures, comprising the following steps:

[0007] S10. Based on the steel structure design drawings, construct a three-dimensional digital model of the steel structure, including the geometric dimensions and location information of the main beams, secondary beams, columns and other components.

[0008] S20. Based on the component topology and load information in the BIM model, use finite element analysis software to perform overall stress analysis on the steel structure and determine the stress and strain distribution of each component.

[0009] S30. Based on the stress and strain distribution of each component and in combination with the usage requirements of the embedded parts, the installation position of the embedded parts is initially determined, and a preliminary embedded part position matrix is ​​formed. The embedded part position matrix is ​​used to represent the position of different embedded parts.

[0010] S40. The three-dimensional digital model is simulated and analyzed using finite element analysis software. Based on the preliminary embedded part position matrix, the main factors affecting the positioning accuracy of the embedded parts are determined and denoted as the positioning influencing factors of the embedded parts, including structural deformation, temperature change, construction error and external load.

[0011] S50. Based on the stress distribution of each component of the steel structure, determine the mechanical load borne by the embedded parts and establish a stress model of the embedded parts; at the same time, based on the connection relationship between the embedded parts and the steel structure components, establish a model of the embedded parts providing support to the steel structure.

[0012] S60. By comparing the stress state of the embedded parts and their impact on the steel structure under different parameter schemes, select multiple embedded part position matrices that meet the requirements of strength, stiffness and stability.

[0013] S70. Under the premise of meeting the performance indicators of the embedded parts themselves, and considering the impact of the embedded parts on the overall performance of the steel structure, a dual-objective game model including structural bearing capacity and deformation coordination is established, and the game model is solved to obtain multiple embedded part position matrices.

[0014] S80. Based on the aforementioned positioning influencing factors, a genetic algorithm is used to optimize the multiple embedded part position matrices to obtain a preferred embedded part position matrix, which is then output as the steel structure embedded part positioning data.

[0015] Specifically, step S10 includes the process of constructing a three-dimensional digital model of the steel structure, which includes: extracting the geometric dimensions and position information of various components such as main beams, secondary beams, and columns from the steel structure design drawings; inputting the above-mentioned dimensions and position information into three-dimensional modeling software, and using the modeling function of the software to quickly build a three-dimensional digital model of the steel structure; the three-dimensional digital model needs to contain the geometric information of all major components in the steel structure to lay the foundation for subsequent analysis and calculation.

[0016] Specifically, step S20 includes: a process of performing static or dynamic analysis on the entire steel structure model using finite element analysis software based on the component topology and load information in the three-dimensional digital model. This includes: importing the three-dimensional digital model into the finite element analysis software; adding constraints and external load information, such as self-weight, wind load, and seismic action, to the model according to design drawings and specifications; and performing static or dynamic analysis on the entire steel structure model using finite element analysis software to obtain the stress and strain distribution results of each component, providing a basis for determining the location of subsequent embedded parts.

[0017] Specifically, step S30 includes: based on the stress and strain distribution of each component obtained from finite element analysis, combined with the installation specifications and performance requirements of the embedded parts, the process of initially determining the installation position of the embedded parts and forming a preliminary embedded part position matrix includes: analyzing the finite element results, initially determining the stress concentration area and the location of large deformation as the installation position of the embedded parts; organizing these initially determined embedded part position information into a position matrix, containing the three-dimensional coordinate information of each embedded part, for subsequent optimization analysis.

[0018] Specifically, step S40 includes: a process of simulating and analyzing a three-dimensional digital model using finite element analysis software to determine the main factors affecting the positioning accuracy of the embedded part. This includes: simulating and analyzing the three-dimensional model using finite element analysis software, considering factors such as structural deformation, temperature changes, construction errors, and external loads; analyzing the impact of these factors on the positioning accuracy of the embedded part, such as the impact of structural deformation on the position of the embedded part, the positional offset of the embedded part caused by temperature changes, and construction errors; and determining the main factors affecting the positioning accuracy of the embedded part through this simulation analysis, providing a basis for subsequent optimization of the embedded part position.

[0019] Specifically, step S50 includes: determining the mechanical load borne by the embedded part based on the stress distribution of each component of the steel structure, and establishing a stress model of the embedded part; simultaneously, establishing a model of the embedded part providing support to the steel structure based on the connection relationship between the embedded part and the steel structure components, including: determining the tensile force, shear force, bending moment, and other mechanical loads borne by the embedded part based on the stress distribution of each component obtained from finite element analysis; establishing a mathematical model describing the stress state of the embedded part; and establishing a mathematical model describing the supporting effect of the embedded part on the steel structure based on the connection details between the embedded part and the steel structure components, such as welding or mechanical connection.

[0020] Specifically, step S60 includes the process of selecting multiple embedded part position matrices that meet the strength, stiffness, and stability requirements by comparing the stress state of the embedded parts and their impact on the steel structure under different parameter schemes. This includes: comparing and analyzing the multiple embedded part position schemes initially determined based on the embedded part stress model and the steel structure support model established in step S50; evaluating the stress level, deformation, and impact on the load-bearing capacity, stiffness, and stability of the embedded parts at different positions; and through this comparative analysis, selecting multiple embedded part position schemes that meet the strength, stiffness, and stability requirements of the embedded parts themselves, while also not having a significant adverse impact on the overall performance of the steel structure.

[0021] Specifically, step S70 includes: under the premise of satisfying the performance indicators of the embedded parts themselves, considering the impact of the embedded parts on the overall performance of the steel structure, establishing a dual-objective game model including structural bearing capacity and deformation coordination, and solving the game model to obtain multiple embedded part position matrices. This includes: establishing a mathematical model describing the performance indicators of the embedded parts themselves, such as constraints such as strength, stiffness, and stability; establishing a mathematical model describing the impact of the embedded parts on the overall performance of the entire steel structure, such as objective functions such as structural bearing capacity and overall deformation coordination; combining these two types of models into a dual-objective game model, and solving for multiple embedded part position schemes that satisfy the above two objectives through mathematical optimization methods, such as genetic algorithms.

[0022] Specifically, step S80 includes: based on the positioning influencing factors, using a genetic algorithm to optimize the multiple embedded part position matrices to obtain an optimal embedded part position matrix, including: using the positioning influencing factors of the embedded parts determined in step S40 to establish a mathematical model describing the positioning accuracy of the embedded parts; taking the multiple embedded part position schemes obtained in steps S60 and S70 as initial solutions, combining the above positioning accuracy model, and applying optimization methods such as genetic algorithms to iteratively optimize these embedded part positions, and finally obtaining an optimal embedded part position matrix that satisfies various constraints.

[0023] The method for obtaining the preliminary embedded part position matrix is ​​as follows:

[0024] Define a scoring function to quantify the suitability of each possible position:

[0025] S(x,y,z)=w1f(σ)+w2g(ε)+w3h(R);

[0026] In the formula, S(x,y,z) is the score of position (x,y,z); f(σ) is the score function based on stress; g(ε) is the score function based on strain; h(R) is the score function based on the requirements of embedded parts; w1, w2, w3 are weighting coefficients, and w1+w2+w3=1;

[0027] The preliminary embedded part location matrix P can be represented as:

[0028] P={(x1,y1,z1),(x2,y2,z2),...,(x m ,y m ,z m )};

[0029] Where m is the number of embedded parts, each (x i ,y i ,z i ) is the location where S(x,y,z) reaches a local maximum.

[0030] Specifically, the stress-based scoring function is:

[0031]

[0032] In the formula, σ max σ is the maximum stress value at the location of the embedded part. allow This represents the allowable stress value for the embedded material.

[0033] Specifically, the strain-based scoring function is:

[0034]

[0035] In the formula, ε max ε represents the maximum strain value at the location of the embedded part. allow This represents the allowable strain value of the embedded part material.

[0036] Specifically, the scoring function based on the usage requirements of embedded parts is:

[0037]

[0038] In the formula, n represents the required quantity of embedded parts; w i This represents the weight of the i-th usage requirement, satisfying... r i This represents the degree to which the i-th usage requirement is met, with a value range of [0,1].

[0039] The stress model of the embedded part is represented by a stiffness matrix, specifically:

[0040]

[0041] where each k ij This represents the effect of displacement or rotation in the i-direction on the force or torque in the j-direction; matrix K s It is a 6x6 stiffness matrix used to describe the supporting effect of embedded parts on the steel structure; each element k in the matrix ij This represents the effect of displacement or rotation in the i-direction on the force or torque in the j-direction, explained in detail below:

[0042] k xx ,k yy ,k zz : These represent the linear stiffness in the x, y, and z directions, respectively;

[0043] k xy ,k xz ,k yx ,k yz ,k zx ,kzy : Indicates the coupling stiffness between different directions;

[0044] k xθx ,k xθy ,k xθz : Represents the coupling stiffness between displacement in the x-direction and rotation about the x, y, z axes;

[0045] k yθx ,k yθy ,k yθz : Represents the coupling stiffness between displacement in the y-direction and rotation about the x, y, z axes;

[0046] k zθx ,k zθy ,k zθz : Represents the coupling stiffness between displacement in the z-direction and rotation about the x, y, z axes;

[0047] k θxθx ,k θyθy ,k θzθz : These represent the rotational stiffness about the x, y, and z axes, respectively;

[0048] k θxθy ,k θxθz ,k θyθx ,k θyθz ,k θzθx ,k θzθy : Indicates the coupling stiffness between rotations on different axes;

[0049] k θxx ,k θxy ,k θxz : Represents the coupling stiffness between rotation about the x-axis and displacement in the x, y, z directions;

[0050] k θyx ,k θyy ,k θyz : Represents the coupling stiffness between rotation about the y-axis and displacement in the x, y, z directions;

[0051] k θzx ,k θzy ,k θzz : Represents the coupling stiffness between rotation about the z-axis and displacement in the x, y, z directions.

[0052] The game theory model formula is expressed as follows:

[0053]

[0054] in,

[0055]

[0056] In the formula, x is the location vector of the embedded part, representing the location coordinates of the embedded part; X represents the feasible solution space, that is, the set of all possible locations of the embedded part, and F actual (x) is the actual load-bearing capacity, F design It is the design load-bearing capacity, δ i (x) is the transformation of the i-th node, δ avg (x) is the average deformation, and ξ is the number of nodes.

[0057] The fitness function formula of the genetic algorithm is expressed as follows:

[0058] F(x) = w 21 f1(x)+w 22 f2(x)+w 23 f3(x);

[0059] in,

[0060] Among them, w 21 ,w 22 ,w 23 It is the weighting coefficient, and w 21 +w 22 +w 23 =1; ΔP i (x) is the positional error of the i-th embedded part, and m is the number of embedded parts.

[0061] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, which, when executed, are used to perform the above-described method for positioning embedded parts in a steel structure.

[0062] A third aspect of the present invention provides a positioning system for embedded parts in steel structures, wherein the system includes the aforementioned computer-readable storage medium.

[0063] Compared with existing technologies, the advantages of the steel structure embedded part positioning method, medium, and system provided by this invention are:

[0064] 1. By fully utilizing advanced technologies such as BIM modeling, finite element analysis, and optimization algorithms, the optimal installation position of embedded parts in steel structures is systematically determined. Compared with empirical design or local optimization methods, the solution of this invention is more comprehensive and systematic, and can better meet the positioning accuracy requirements of embedded parts in complex steel structure projects.

[0065] 2. Not only are the performance indicators of the embedded parts themselves considered, such as strength, stiffness, and stability, but the impact of the embedded parts on the overall performance of the entire steel structure, such as load-bearing capacity and deformation coordination, are also fully considered to ensure that the arrangement of the embedded parts meets their own requirements and does not have an adverse effect on the overall performance of the steel structure.

[0066] 3. Through finite element simulation analysis, the main factors affecting the positioning accuracy of embedded parts are systematically identified, such as structural deformation, temperature changes, and construction errors. Optimization is then carried out on these factors, significantly improving the positioning accuracy of embedded parts.

[0067] 4. By employing mathematical optimization techniques such as genetic algorithms, multiple embedded part location schemes are comprehensively evaluated and iteratively optimized to ultimately obtain the optimal embedded part layout scheme that satisfies various constraints, which has strong application and promotion value.

[0068] In summary, the steel structure embedded part positioning method proposed in this invention fully leverages the advantages of digitalization, analysis and modeling, and optimization algorithms. While meeting the performance requirements of the embedded parts themselves, it minimizes their adverse effects on the overall steel structure performance, solving the problem of existing technologies that only consider the performance requirements of the embedded parts themselves while neglecting their impact on the overall steel structure performance. Attached Figure Description

[0069] Figure 1 A flowchart of the method provided by the present invention; Detailed Implementation

[0070] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0071] like Figure 1 The diagram shown is a flowchart of a method for positioning embedded parts in steel structures provided by this invention. This method includes the following steps:

[0072] S10. Based on the steel structure design drawings, construct a three-dimensional digital model of the steel structure, including the geometric dimensions and location information of the main beams, secondary beams, columns and other components.

[0073] S20. Based on the component topology and load information in the BIM model, use finite element analysis software to perform overall stress analysis on the steel structure and determine the stress and strain distribution of each component.

[0074] S30. Based on the stress and strain distribution of each component and the usage requirements of the embedded parts, the installation positions of the embedded parts are initially determined, and a preliminary embedded part position matrix is ​​formed. The embedded part position matrix is ​​used to represent the positions of different embedded parts.

[0075] S40. The three-dimensional digital model is simulated and analyzed using finite element analysis software. Based on the preliminary embedded part position matrix, the main factors affecting the positioning accuracy of the embedded parts are determined and denoted as the positioning influencing factors of the embedded parts, including structural deformation, temperature change, construction error and external load.

[0076] S50. Based on the stress distribution of each component of the steel structure, determine the mechanical load borne by the embedded parts and establish a stress model of the embedded parts; at the same time, based on the connection relationship between the embedded parts and the steel structure components, establish a model of the embedded parts providing support to the steel structure.

[0077] S60. By comparing the stress state of the embedded parts and their impact on the steel structure under different parameter schemes, select multiple embedded part position matrices that meet the requirements of strength, stiffness and stability.

[0078] S70. Under the premise of meeting the performance indicators of the embedded parts themselves, and considering the impact of the embedded parts on the overall performance of the steel structure, a dual-objective game model including structural bearing capacity and deformation coordination is established, and the game model is solved to obtain multiple embedded part position matrices.

[0079] S80. Based on the positioning influencing factors, a genetic algorithm is used to optimize the position matrix of multiple embedded parts to obtain the preferred embedded part position matrix, which is then used as the positioning data for steel structure embedded parts and output.

[0080] The specific implementation methods of the above steps are described in detail below:

[0081] The specific implementation of step S10 is as follows: Based on the steel structure design drawings, a three-dimensional digital model of the steel structure is constructed. First, various dimensional data from the design drawings (such as the geometric dimensions and positional information of main beams, secondary beams, columns, and other components) need to be input into 3D modeling software, such as Revit, AutoCAD, or ANSYS. Through the modeling functions of these software programs, a three-dimensional digital model of the steel structure can be quickly built. This 3D model needs to contain the geometric information of all major components in the steel structure, laying the foundation for subsequent analysis and calculations.

[0082] The specific implementation of step S20 is as follows: Based on the component topology and load information in the BIM model, a finite element analysis (FEM) software is used to perform an overall stress analysis of the steel structure. First, the 3D model constructed in step S10 needs to be imported into a FEM software such as ANSYS or Abaqus. Then, according to the design drawings and specifications, constraints and external load information, such as self-weight, wind load, and seismic action, are added to the model. Based on this, static or dynamic analysis is performed on the entire steel structure model using FEM software, yielding stress and strain distribution cloud maps for each component. These analysis results provide an important basis for determining the subsequent locations of embedded parts.

[0083] The specific implementation of step S30 is as follows: Based on the stress and strain distribution of each component and the usage requirements of the embedded parts, the installation positions of the embedded parts are initially determined, and a preliminary embedded part position matrix is ​​formed. First, based on the stress and strain distribution of each component obtained from finite element analysis, and combined with the installation specifications and performance requirements of the embedded parts, the installation positions of the embedded parts are initially determined. For example, stress concentration areas and locations with large deformations may be better installation positions for the embedded parts. Then, these initially determined embedded part position information are organized into a position matrix for subsequent optimization analysis. This position matrix needs to contain the three-dimensional coordinate information of each embedded part.

[0084] The specific implementation of step S40 is as follows: The three-dimensional digital model is simulated and analyzed using finite element analysis software. Based on the preliminary embedded part position matrix, the main factors affecting the positioning accuracy of the embedded parts are determined. First, the three-dimensional model constructed in step S10 is simulated and analyzed using finite element analysis software, considering factors such as structural deformation, temperature changes, construction errors, and external loads, to analyze the impact of these factors on the positioning accuracy of the embedded parts. For example, the influence of structural deformation on the position of the embedded parts, the positional offset caused by temperature changes, and construction errors can be studied. Through this simulation analysis, the main factors affecting the positioning accuracy of the embedded parts can be determined, providing a basis for subsequent optimization of the embedded part position. These influencing factors can be denoted as the positioning influencing factors of the embedded parts.

[0085] The specific implementation of step S50 is as follows: Based on the stress distribution of each component of the steel structure, the mechanical load borne by the embedded part is determined, and a stress model of the embedded part is established; simultaneously, based on the connection relationship between the embedded part and the steel structure component, a model of the embedded part providing support to the steel structure is established. First, based on the stress distribution of each component obtained in step S20, the mechanical load borne by the embedded part is determined, including tension, shear force, bending moment, etc. Then, a mathematical model describing the stress state of the embedded part is established. Simultaneously, based on the connection details between the embedded part and the steel structure component, such as welding or mechanical connection, a mathematical model describing the supporting effect of the embedded part on the steel structure is established. These two models provide the necessary mechanical basis for the subsequent optimization of the embedded part's position.

[0086] The specific implementation of step S60 is as follows: By comparing the stress state of the embedded parts and their impact on the steel structure under different parameter schemes, multiple embedded part position matrices that meet the requirements of strength, stiffness, and stability are selected. First, based on the embedded part stress model and the steel structure support model established in step S50, the multiple embedded part position schemes initially determined are compared and analyzed. For example, the stress level, deformation, and impact on the overall bearing capacity, stiffness, and stability of the embedded parts at different locations can be evaluated. Through this comparative analysis, multiple embedded part position schemes that meet the strength, stiffness, and stability requirements of the embedded parts themselves, while not having a significant adverse impact on the overall performance of the steel structure, are selected. These schemes constitute multiple embedded part position matrices.

[0087] The specific implementation of step S70 is as follows: Under the premise of satisfying the performance indicators of the embedded parts themselves, considering the impact of the embedded parts on the overall performance of the steel structure, a dual-objective game model including structural bearing capacity and deformation coordination is established, and the game model is solved to obtain multiple embedded part position matrices. First, a mathematical model describing the performance indicators of the embedded parts themselves needs to be established, such as constraints on strength, stiffness, and stability. Then, a mathematical model describing the impact of the embedded parts on the overall performance of the entire steel structure is established, such as objective functions for structural bearing capacity and overall deformation coordination. These two types of models are combined into a dual-objective game model, and multiple embedded part position schemes satisfying the above two objectives are obtained through mathematical optimization methods, such as genetic algorithms, i.e., multiple embedded part position matrices. These matrices satisfy both the requirements of the embedded parts themselves and consider the impact on the overall performance of the steel structure.

[0088] The specific implementation of step S80 is as follows: Based on the positioning influencing factors, a genetic algorithm is used to optimize the multiple embedded part position matrices to obtain a preferred embedded part position matrix, which is then output as the positioning data for the steel structure embedded parts. First, using the positioning influencing factors of the embedded parts determined in step S40, a mathematical model describing the positioning accuracy of the embedded parts is established. Then, using the multiple embedded part position schemes obtained in steps S60 and S70 as initial solutions, and combining them with the above positioning accuracy model, optimization methods such as genetic algorithms are applied to iteratively optimize these embedded part positions, ultimately obtaining a preferred embedded part position matrix that satisfies various constraints. This preferred scheme not only meets the performance requirements of the embedded parts themselves but also considers the impact on the overall steel structure performance, while also maximizing the positioning accuracy of the embedded parts. Finally, this preferred embedded part position matrix is ​​output as the positioning data for the steel structure embedded parts.

[0089] Through the specific implementation of the above eight steps, BIM models, finite element analysis, optimization algorithms, and other technical means can be fully utilized to systematically determine the optimal installation position of steel structure embedded parts. Steps S10 to S40 mainly involve establishing a digital model and analyzing the factors influencing the positioning of embedded parts; steps S50 to S70 involve establishing a stress model of the embedded parts and an impact model on the steel structure, performing multi-objective optimization; and finally, step S80 uses optimization methods such as genetic algorithms to obtain the final preferred embedded part position. The entire method fully considers the performance requirements of the embedded parts themselves and their impact on the overall performance of the steel structure, striving to obtain an optimal embedded part layout scheme that takes into account all factors.

[0090] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, which, when executed, are used to perform the above-described method for positioning embedded parts in a steel structure.

[0091] A third aspect of the present invention provides a positioning system for embedded parts in steel structures, wherein the system includes the aforementioned computer-readable storage medium.

[0092] To better understand and implement the method provided by this invention, the method is described in detail below with reference to specific formulas. This detailed description can also be applied to computer-readable storage media or steel structure embedded part positioning systems for use in computer programs, as detailed below:

[0093] S10: Constructing a 3D digital model of the steel structure

[0094] This step involves converting the geometric information in the steel structure design drawings into a three-dimensional digital model. While this process primarily relies on 3D modeling software, mathematical methods can be used to describe the position and dimensions of the components.

[0095] For each component i, its position and size can be represented by the following vector:

[0096] C i =[x i ,y i ,z i ,l i ,w i ,h i ]

[0097] in:

[0098] (x i ,y i ,z i () represents the coordinates of the center point of the component.

[0099] l i ,w i ,hi These represent the length, width, and height of the component, respectively.

[0100] The entire steel structure model can be represented as a collection of all components:

[0101] M = {C1,C2,...,C} n}

[0102] Where n is the total number of components.

[0103] S20: Overall Force Analysis

[0104] In this step, finite element analysis software is used to perform an overall stress analysis of the steel structure. Although the specific calculations are performed by software, the basic principles of this process can be described mathematically.

[0105] The fundamental equations of finite element analysis can be expressed as:

[0106] [K]{U}={F}

[0107] in:

[0108] [K] is the stiffness matrix.

[0109] {U} is the displacement vector

[0110] {F} is the external force vector.

[0111] For each element e, its stiffness matrix [k e It can be calculated using the following formula:

[0112] [k e ]=∫ V [B] T [D][B]dV

[0113] in:

[0114] [B] is the strain-displacement matrix.

[0115] [D] is the elasticity matrix.

[0116] V is the unit volume.

[0117] The overall stiffness matrix [K] is assembled from the stiffness matrices of all elements.

[0118] By solving the above equations, the displacement {U} of each node can be obtained. Then, the stress {σ} and strain {ε} of each element can be calculated:

[0119] {ε}=[B]{U}

[0120] {σ}=[D]{ε}

[0121] S30: Preliminary determination of the installation location of embedded parts

[0122] In this step, the installation location of the embedded parts is initially determined based on the stress and strain distribution. A scoring function can be defined to quantify the suitability of each possible location:

[0123] S(x,y,z)=w1f(σ)+w2g(ε)+w3h(R)

[0124] in:

[0125] S(x,y,z) is the score for position (x,y,z).

[0126] f(σ) is a stress-based scoring function.

[0127] g(ε) is a strain-based scoring function.

[0128] h(R) is a scoring function based on the requirements for the use of embedded parts.

[0129] w1, w2, w3 are weighting coefficients, and w1 + w2 + w3 = 1

[0130] The preliminary embedded part location matrix P can be represented as:

[0131] P={(x1,y1,z1),(x2,y2,z2),...,(x m ,y m ,z m )}

[0132] Where m is the number of embedded parts, each (x i ,y i ,z i ) is the location where S(x,y,z) reaches a local maximum.

[0133] Specifically, 1.f(σ) is a stress-based scoring function.

[0134] definition:

[0135]

[0136] Parameter description:

[0137] σ max Maximum stress value at the location of the embedded part

[0138] σ allow Allowable stress value of embedded part material

[0139] Calculation method:

[0140] 1) Obtain the stress distribution at the location of the embedded part through finite element analysis.

[0141] 2) Determine the maximum stress value σ at this location. max .

[0142] 3) Determine the allowable stress value σ based on the material properties of the embedded parts and the design specifications. allow .

[0143] 4) Substitute the values ​​into the formula to calculate the score.

[0144] Scoring criteria:

[0145] When σ max ≤σ allow When 0 ≤ f(σ) ≤ 1, the higher the score, the better.

[0146] When σ max >σ allow When f(σ) < 0, it means that the location is not suitable for installing embedded parts.

[0147] 2. g(ε) Strain-based scoring function

[0148] definition:

[0149]

[0150] Parameter description:

[0151] ε max Maximum strain value at the location of the embedded part

[0152] ε allow Allowable strain value of embedded part material

[0153] Calculation method:

[0154] 1) Obtain the strain distribution at the location of the embedded part through finite element analysis.

[0155] 2) Determine the maximum strain value ε at this location. max .

[0156] 3) Determine the allowable strain value ε based on the material properties of the embedded parts and the design specifications. allow .

[0157] 4) Substitute the values ​​into the formula to calculate the score.

[0158] Scoring criteria:

[0159] When ε max ≤ε allow When 0 ≤ g(ε) ≤ 1, the higher the score, the better.

[0160] When ε max >ε allow When g(ε) < 0, it means that the location is not suitable for installing embedded parts.

[0161] 3.h(R) Scoring function based on the usage requirements of embedded parts

[0162] definition:

[0163]

[0164] Parameter description:

[0165] n: Quantity of embedded parts required for use

[0166] w i The weight required for the i-th use, satisfying...

[0167] r i : The degree to which the i-th usage requirement is satisfied, with a value ranging from [0,1].

[0168] Calculation method:

[0169] 1) Determine all usage requirements for the embedded parts, such as load-bearing capacity, corrosion resistance, and durability.

[0170] 2) Assign weights w to each usage requirement. i .

[0171] 3) Assess the degree to which each usage requirement is met. i .

[0172] 4) Substitute the values ​​into the formula to calculate the total score.

[0173] Scoring criteria:

[0174] 0≤h(R)≤1, the higher the score, the better the embedded part can meet the usage requirements at that location.

[0175] The combined score using these three scoring functions can be expressed as:

[0176] S(x,y,z)=w1f(σ)+w2g(ε)+w3h(R)

[0177] Where w1, w2, and w3 are weighting coefficients, satisfying w1 + w2 + w3 = 1.

[0178] This design method considers three aspects: stress, strain, and the requirements for embedded parts, enabling a comprehensive assessment of the suitability of the embedded part installation location. By adjusting the weighting coefficients, the importance of each factor can be flexibly adjusted according to specific project needs.

[0179] S40: Determine the influencing factors of embedded part positioning

[0180] In this step, finite element analysis is used to determine the main factors affecting the positioning accuracy of the embedded parts. A sensitivity function can be defined to quantify the influence of each factor:

[0181] ΔP i =f i (ΔT,ΔL,ΔE,ΔF)

[0182] in:

[0183] ΔP i It is the change in the position of the i-th embedded part

[0184] ΔT is the temperature change

[0185] ΔL is the construction error.

[0186] ΔE is the structural deformation

[0187] ΔF is the change in external load.

[0188] The degree of influence of each factor can be assessed by calculating partial derivatives:

[0189]

[0190] Where S T ,S L ,S E ,S F These represent the sensitivity of the embedded part's location to temperature, construction error, structural deformation, and external load, respectively.

[0191] S50: Establish the stress model and support model of the embedded parts.

[0192] In this step, two models need to be established: the stress model of the embedded parts and the support model of the embedded parts on the steel structure.

[0193] The stress model of embedded parts can be represented as:

[0194] F e =[F x ,F y ,F z M x M y M z ] T

[0195] Where F x ,F y ,F z M represents the force in three directions. x M y M z These represent the torques in the three directions, respectively.

[0196] The stress state of embedded parts can be represented by von Mises stress:

[0197]

[0198] Where σ1, σ2, and ε3 are the principal stresses.

[0199] The support model of embedded parts for steel structures can be represented by a stiffness matrix:

[0200]

[0201] where each k ij This matrix K represents the effect of displacement or rotation in the i-direction on the force or torque in the j-direction. s It is a 6x6 stiffness matrix used to describe the supporting effect of embedded parts on the steel structure. Each element k in the matrix... ij This indicates the effect of displacement or rotation in the i-direction on the force or torque in the j-direction. The specific explanation is as follows:

[0202] k xx ,k yy ,k zz : These represent the linear stiffness in the x, y, and z directions, respectively.

[0203] k xy ,k xz ,k yz ,k yz ,k zx ,k zy : Indicates the coupling stiffness between different directions.

[0204] k xθx ,k xθy ,k xθz : Represents the coupling stiffness between displacement in the x-direction and rotation about the x, y, z axes.

[0205] k yθx ,k yθy ,k yθz : Represents the coupling stiffness between displacement in the y-direction and rotation about the x, y, z axes.

[0206] k zθx ,k zθy ,k zθz : Represents the coupling stiffness between displacement in the z-direction and rotation about the x, y, z axes.

[0207] k θxθx ,k θyθy ,k θzθz : These represent the rotational stiffness about the x, y, and z axes, respectively.

[0208] k θxθy ,kθxθz ,k θyθx ,k θyθz ,k θzθx ,k θzθy : Indicates the coupling stiffness between rotations on different axes.

[0209] k θxx ,k θxy ,k θxz : Represents the coupling stiffness between rotation about the x-axis and displacement in the x, y, z directions.

[0210] k θyx ,k θyy ,k θyz : Represents the coupling stiffness between rotation about the y-axis and displacement in the x, y, z directions.

[0211] k θzx ,k θzy ,k θzz : Represents the coupling stiffness between rotation about the z-axis and displacement in the x, y, z directions.

[0212] This stiffness matrix comprehensively describes the stiffness characteristics of the embedded part in six degrees of freedom (three translations and three rotations), as well as the interactions between these degrees of freedom.

[0213] S60: Select the embedded part location matrix that meets the requirements.

[0214] In this step, it is necessary to evaluate the performance of different embedded part location schemes. A comprehensive performance index can be defined:

[0215] P i =w 11 S i +w 12 R i +w 13 D i

[0216] in:

[0217] P i It is the comprehensive performance index of the i-th scheme.

[0218] S i It is an intensity index, which can be defined as Where σ max It is the maximum stress, σ allow Allowable stress

[0219] R i It is a stiffness index, which can be defined as follows: Where Δ max It is the maximum deformation, Δ allow It allows deformation.

[0220] Di It is a stability index, which can be defined as the ratio of the minimum eigenvalue of a structure to its critical buckling load.

[0221] w 11 ,w 12 ,w 13 It is the weighting coefficient, and w 11 +w 12 +w 13 =1

[0222] Choose P i The scheme with a value less than 1 is used as the embedded part position matrix that meets the requirements.

[0223] S70: Establishing a Bi-objective Game Theory Model

[0224] This step requires establishing a dual-objective game model that includes both structural bearing capacity and deformation compatibility. Two objective functions can be defined:

[0225] 1. Objective function for structural bearing capacity

[0226] a)F actual (x) - Actual load-bearing capacity:

[0227] Method of obtaining: Calculated through finite element analysis (FEA).

[0228] Steps for obtaining the structure's load: 1) Establish a structural model based on the embedded part location vector x. 2) Apply progressively increasing loads to the model. 3) Analyze the structure's response until the failure criteria (e.g., yielding, excessive deformation, etc.) are met. 4) Record the maximum load at failure, which is F. actual (x).

[0229] b)F design -Design load-bearing capacity:

[0230] Acquisition method: Determined according to design specifications and engineering requirements.

[0231] Steps to obtain the design load: 1) Consult relevant design codes (such as the Code for Design of Building Structures). 2) Consider factors such as the structure's purpose and importance. 3) Determine the design load and safety factor. 4) Calculate the design bearing capacity F. design .

[0232] c) x is the location vector of the embedded part: This is the decision variable in the optimization algorithm; its form is: x = [x1, y1, z1, x2, y2, z2, ..., x m ,y m ,z m ], where m is the number of embedded parts.

[0233] 2. Deformation compatibility objective function

[0234] a)δ i (x) - The transformation of the i-th node:

[0235] Method of obtaining: Calculated through finite element analysis.

[0236] Steps for obtaining the data: 1) Establish a structural model based on the embedded part position vector x. 2) Apply the design load. 3) Perform static analysis. 4) Extract the displacement data of each node.

[0237] b)δ avg (x) represents the average deformation:

[0238] Calculation method:

[0239] Calculate the average value using the node displacement data obtained in the previous step.

[0240] c) ξ is the number of nodes: This is the total number of nodes in the structural model, which is determined when the finite element model is established.

[0241] The game theory model can be represented as:

[0242] subjectto:

[0243] g j (x)≤0,j=1,2,...,m

[0244] h k (x) = 0, k = 1, 2, ..., p

[0245] Where g j (x) and h k (x) represent the inequalities and equality constraints, which are explained in detail below:

[0246] g j (x)≤0, j=1,2,...,m and h k (x) = 0, k = 1, 2, ..., p is a common constraint expression in optimization problems. Here, x usually represents a decision variable vector, and in our problem, it represents the position coordinates of the embedded part.

[0247] Inequality constraint condition g j (x)≤0,j=1,2,...,m:

[0248] There are m inequality constraints here.

[0249] Each g j (x) are all functions of x.

[0250] Conditions and requirements g j The value of (x) is less than or equal to zero.

[0251] In the problem of positioning embedded parts, these inequality constraints may include:

[0252] a) Stress constraint:

[0253] This ensures that the maximum stress does not exceed the allowable stress.

[0254] b) Deformation constraints:

[0255] This ensures that the maximum deformation does not exceed the allowable deformation.

[0256] c) Spacing constraint: g3(x) = d min -d(x)≤0

[0257] This ensures that the distance between embedded parts is not less than the minimum allowable spacing.

[0258] Equality constraint h k (x) = 0, k = 1, 2, ..., p:

[0259] There are p equality constraints here.

[0260] Each h k (x) are all functions of x.

[0261] Conditions require h k The value of (x) is exactly zero.

[0262] In the problem of positioning embedded parts, these equality constraints may include:

[0263] a) Equilibrium condition: h1(x)=∑F x =0,h2(x)=∑F y =0,h3(x)=∑F z =0

[0264] This ensures that the system is balanced in force in three directions.

[0265] b) Geometric condition: h4(x)=f(x,y,z=0

[0266] This may indicate that the embedded part must be located on a specific geometric surface.

[0267] c) Total number constraint:

[0268] This may be used to ensure that the total number of embedded parts used is exactly equal to the design requirement N.

[0269] These constraints collectively define the feasible solution space of the problem. Any solution that satisfies all of these constraints is considered a feasible solution. The goal of the optimization algorithm is to find the optimal solution within this feasible solution space, that is, a solution in which the objective functions f1(x) and f2(x) reach their optimal values.

[0270] In practical applications, specific constraints are determined based on factors such as project requirements, safety standards, and construction specifications. By setting these constraints appropriately, it can be ensured that the final embedded part location scheme not only optimizes the objective function but also meets all engineering and safety requirements.

[0271] This problem can be solved using multi-objective optimization algorithms, such as NSGA-II (Non-dominated sorting genetic algorithm II), to obtain a series of non-dominated solutions, i.e., multiple embedded part position matrices.

[0272] S80: Optimize the position of embedded parts using a genetic algorithm.

[0273] In this final step, a genetic algorithm is used to further optimize the location of the embedded parts. The basic process of the genetic algorithm is as follows:

[0274] 1. Initialize the population: Use the multiple embedded part position matrices obtained from S70 as the initial population.

[0275] 2. Define the fitness function:

[0276] F(x) = w 21 f1(x)+w 22 f2(x)+w 23 f3(x)

[0277] Among them, w 21 ,w 22 ,w 23 It is the weighting coefficient, and w 21 +w 22 +w 23 =1;

[0278] f1(x) and f2(x) are defined the same as in S70, while f3(x) is a function that takes into account positioning accuracy:

[0279]

[0280] Where ΔP i (x) is the positional error of the i-th embedded part, and m is the number of embedded parts.

[0281] 3. Selection Operation: Use the roulette wheel selection method to select the best individuals.

[0282] 4. Crossover operation: Using arithmetic crossover, for two parent individuals x1 and x2, offspring are generated:

[0283] y1=αx1+(1-α)x2

[0284] y2=(1-α)x1+αx2

[0285] Where α is a random number between [0, 1].

[0286] 5. Mutation operation: Perform Gaussian mutation on the selected individuals:

[0287] x new =x+N(0,σ 2 )

[0288] Where N(0,σ) 2 () has a mean of 0 and a variance of σ. 2 It follows a normal distribution.

[0289] 6. Repeat steps 2-5 until the termination condition is met (such as the number of iterations reaching a preset value or no significant improvement in the optimal solution over multiple generations).

[0290] The optimal individual obtained is the preferred embedded part position matrix, which is used as the output of steel structure embedded part positioning data.

[0291] This method comprehensively considers the stress state of the embedded parts, their impact on the steel structure, and their positioning accuracy. Through a multi-step optimization process, it strives to obtain an optimal embedded part layout scheme that balances various factors. Each step builds upon the previous one, progressively refining and optimizing to ultimately obtain an embedded part location scheme that meets various constraints and performance requirements.

[0292] Specifically, the principle of this invention is:

[0293] First, based on the steel structure design drawings, a digital model of the steel structure was constructed using 3D modeling software, including the geometric dimensions and topological relationships of various components such as main beams, secondary beams, and columns. This 3D digital model laid the foundation for subsequent analysis and calculations.

[0294] Secondly, the aforementioned three-dimensional digital model was imported into finite element analysis software. Based on the load information and boundary conditions, static or dynamic analysis was performed on the entire steel structure to obtain the stress and strain distribution of each component. These analysis results provide an important basis for determining the location of embedded parts.

[0295] Then, based on the stress-strain results obtained from the finite element analysis and combined with the usage requirements of the embedded parts, the installation positions of the embedded parts are preliminarily determined, and a preliminary embedded part position matrix is ​​formed. This matrix contains the three-dimensional coordinate information of each embedded part.

[0296] Next, finite element simulation analysis was used to further identify the main factors affecting the positioning accuracy of embedded parts, such as structural deformation, temperature changes, and construction errors. These influencing factors provide an important basis for subsequent optimization of the embedded part position.

[0297] Subsequently, based on the mechanical loads borne by the embedded parts and their connection relationships with the steel structural components, a stress model for the embedded parts and a model for providing support to the steel structure were established. These models laid the mechanical foundation for the optimization analysis of the embedded part locations.

[0298] Based on this, by comparing and analyzing different embedded part location schemes, a matrix of embedded part locations was selected that meets the performance requirements of the embedded parts themselves and has an acceptable impact on the overall performance of the steel structure.

[0299] Furthermore, considering the impact of embedded parts on the overall performance of the steel structure, a bi-objective game model including structural bearing capacity and deformation compatibility is established. Multiple optimized embedded part location schemes are obtained by solving the model using mathematical optimization methods.

[0300] Finally, considering the factors affecting the positioning of the embedded parts mentioned above, optimization methods such as genetic algorithms are applied to further iterate and optimize these optimization schemes to obtain the final preferred embedded part position matrix, which is output as the optimal positioning scheme for the steel structure embedded parts.

[0301] Through the above-described systematic analysis, modeling, and optimization calculation process, the present invention not only meets the performance requirements of the embedded parts themselves, such as strength, stiffness, and stability, but also fully considers the impact of the embedded parts on the overall performance of the steel structure. This ensures that the embedded part arrangement scheme meets its own specifications without adversely affecting the overall performance of the steel structure. Simultaneously, by optimizing the main factors affecting the positioning accuracy of the embedded parts, the accuracy of the embedded part positioning is effectively improved. This comprehensive and systematic optimization method has significant technical advantages and innovation compared to existing technologies.

[0302] In summary, the steel structure embedded part positioning method proposed in this invention makes full use of advanced technologies such as digitalization, analysis modeling, and optimization algorithms, and minimizes its impact on the overall performance of the steel structure while meeting the performance indicators of the embedded parts themselves.

[0303] To better understand and implement this invention, an embodiment of its application scenario is provided below: A city recently planned to construct a 500-meter-high super high-rise steel structure office building, which is currently the tallest building in the city. As one of the core technical challenges of this super high-rise steel structure project, accurately determining the locations of a large number of embedded parts has become a key issue that needs to be addressed during the design and construction process. Based on the steel structure embedded part positioning method proposed by this invention, the project construction party and the design unit jointly carried out the following work:

[0304] The first step was to construct a 3D digital model of the super high-rise office building using Revit software, based on the steel structure design drawings. This model includes the geometric dimensions and location information of various components such as main beams, secondary beams, and columns. The entire model consists of 3,526 steel structural units, with a total weight of approximately 36,000 tons.

[0305] The second step involved importing the aforementioned 3D digital model into the ANSYS finite element analysis software and adding various load conditions, such as self-weight, wind load, and seismic action, according to design requirements. Through finite element static analysis, stress and strain distribution cloud maps of each component of the entire steel structure were obtained. Taking the column as an example, the maximum compressive stress was 235 MPa, located near the base; the maximum bending moment of the main beam occurred at the mid-span position, with a value of 12800 kN·m. These results provided important basis for determining the subsequent locations of embedded parts.

[0306] The third step, based on the finite element analysis results and the usage requirements of the embedded parts, preliminarily determined the installation positions of the embedded parts. For example, in stress concentration areas such as the bottom of the column and the mid-span of the main beam, the preliminary installation positions of 432 embedded parts were determined, forming a preliminary embedded part position matrix. This matrix contains the three-dimensional coordinate information of each embedded part.

[0307] Fourthly, to further optimize the location of the embedded parts, a detailed simulation analysis of the three-dimensional digital model was conducted using ANSYS software. The main factors affecting the positioning accuracy of the embedded parts were studied, including:

[0308] 1. Structural deformation: Through static analysis, the maximum displacement of the entire steel structure under various loads is 18.6cm, which will directly affect the installation accuracy of the embedded parts.

[0309] 2. Temperature changes: Based on local climate conditions, it is expected that the super high-rise building may experience temperature fluctuations of around 30°C during operation. The thermal expansion and contraction caused by temperature changes may also cause the embedded parts to shift.

[0310] 3. Construction error: Based on the characteristics of the on-site construction process, it is expected that there may be a positional error of ±5mm during the installation of the embedded parts.

[0311] 4. External loads: In addition to self-weight, wind load and other loads considered in the design, extreme natural disasters such as tornadoes and rainstorms may also have an adverse effect on the location of embedded parts.

[0312] Through the above simulation analysis, four key factors affecting the positioning accuracy of embedded parts were systematically identified, providing an important basis for subsequent optimization of the embedded part position.

[0313] In the fifth step, based on the embedded part position matrix initially determined in step 3 and combined with the influencing factors analyzed in step 4, a stress model for the embedded parts and a model for providing support to the steel structure were established, laying a mechanical foundation for the optimization of the embedded part positions.

[0314] The stress model of embedded parts can be represented as:

[0315]

[0316] Where, L p This indicates the mechanical load borne by the embedded part, including the horizontal force F. x Vertical force F y and bending moment M z These loads originate from the stress distribution of the components obtained from the aforementioned finite element analysis.

[0317] The model of embedded parts providing support for steel structures can be represented as follows:

[0318]

[0319] Where, S p This represents the stiffness matrix provided by the embedded part, including the horizontal stiffness k. x Vertical stiffness k y and rotational stiffness k θ These stiffness parameters reflect the strength of the connection between the embedded parts and the steel structure components.

[0320] The sixth step involves considering the impact of the embedded parts on the overall performance of the steel structure while ensuring that the performance requirements of the embedded parts are met. A dual-objective optimization model, including structural bearing capacity and deformation coordination, is established and multiple optimization schemes are obtained by solving the model using a genetic algorithm.

[0321] The objective function for structural bearing capacity can be expressed as:

[0322] f1=max{σ max}

[0323] Where, σ max This indicates the maximum stress in each component of the steel structure, which must be less than 0.6 times the yield strength.

[0324] The objective function for deformation compatibility can be expressed as:

[0325] f2=min{δ max}

[0326] Where, δ max This indicates the maximum displacement of the entire steel structure, which must be controlled within 1 / 500 of the structure height.

[0327] Under the premise of satisfying the above two objectives, the genetic algorithm obtained five embedded part location schemes that meet the conditions, forming a "multiple embedded part location matrix".

[0328] Step 7: Based on the four major factors affecting the positioning accuracy of the embedded parts identified in Step 4, a genetic algorithm was used to further optimize the five embedded part location schemes. The optimization objective function is:

[0329] f=min{w1·Δ s +w2·Δ t +w3·Δ c +w4·Δ l}

[0330] Where, Δ s Δ represents the positional error of embedded parts caused by structural deformation. t Δ represents the positional error caused by temperature changes. c Indicates construction error, Δ l This indicates the positional offset caused by external loads. w1 to w4 are the weighting coefficients for the corresponding factors, which can be determined according to the actual situation.

[0331] After 200 generations of iterative optimization, the genetic algorithm finally provided an optimal embedded part location scheme that satisfies all constraints, with the maximum positioning deviation controlled within ±8mm. This scheme serves as the embedded part positioning data output for this project, providing an important basis for subsequent construction.

Claims

1. A method for positioning embedded parts in steel structures, characterized in that, Includes the following steps: S10. Based on the steel structure design drawings, construct a three-dimensional digital model of the steel structure, including the geometric dimensions and location information of the main beams, secondary beams, columns and other components. S20. Based on the component topology and load information in the BIM model, use finite element analysis software to perform overall stress analysis on the steel structure and determine the stress and strain distribution of each component. S30. Based on the stress and strain distribution of each component and in combination with the usage requirements of the embedded parts, the installation position of the embedded parts is initially determined, and a preliminary embedded part position matrix is ​​formed. The embedded part position matrix is ​​used to represent the position of different embedded parts. S40. The three-dimensional digital model is simulated and analyzed using finite element analysis software. Based on the preliminary embedded part position matrix, the main factors affecting the positioning accuracy of the embedded parts are determined and denoted as the positioning influencing factors of the embedded parts, including structural deformation, temperature change, construction error and external load. S50. Based on the stress distribution of each component of the steel structure, determine the mechanical load borne by the embedded parts and establish a stress model of the embedded parts; at the same time, based on the connection relationship between the embedded parts and the steel structure components, establish a model of the embedded parts providing support to the steel structure. S60. By comparing the stress state of the embedded parts and their impact on the steel structure under different parameter schemes, select multiple embedded part position matrices that meet the requirements of strength, stiffness and stability. S70. Under the premise of meeting the performance indicators of the embedded parts themselves, and considering the impact of the embedded parts on the overall performance of the steel structure, a dual-objective game model including structural bearing capacity and deformation coordination is established, and the game model is solved to obtain multiple embedded part position matrices. S80. Based on the aforementioned positioning influencing factors, a genetic algorithm is used to optimize the multiple embedded part position matrices to obtain a preferred embedded part position matrix, which is then output as the steel structure embedded part positioning data.

2. The method for positioning embedded parts in steel structures according to claim 1, characterized in that, The method for obtaining the preliminary embedded part position matrix is ​​as follows: Define a scoring function to quantify the suitability of each possible position: S(x,y,z)=w1f(σ)+w2g(ε)+w3h(R); In the formula, S(x,y,z) is the score of position (x,y,z); f(σ) is the score function based on stress; g(ε) is the score function based on strain; h(R) is the score function based on the requirements of embedded parts; w1, w2, w3 are weighting coefficients, and w1+w2+w3=1; The preliminary embedded part location matrix P can be represented as: P={(x1,y1,z1)(x2,y2,z2),...,(x m y m ,z m ) Where m is the number of embedded parts, each (x i ,y i ,z i ) is the location where S(x,y,z) reaches a local maximum.

3. The method for positioning embedded parts in steel structures according to claim 2, characterized in that, The stress-based scoring function is specifically: In the formula, σ max σ is the maximum stress value at the location of the embedded part. allow This represents the allowable stress value for the embedded material.

4. The method for positioning embedded parts in steel structures according to claim 3, characterized in that, The strain-based scoring function is specifically: In the formula, ε max This represents the maximum strain value at the location of the embedded part. ε allow This represents the allowable strain value of the embedded part material.

5. A method for positioning embedded parts in a steel structure according to claim 4, characterized in that, The scoring function based on the usage requirements of embedded parts is specifically as follows: In the formula, n represents the required quantity of embedded parts; w i This represents the weight of the i-th usage requirement, satisfying... r i This represents the degree to which the i-th usage requirement is met, with a value range of [0,1].

6. The method for positioning embedded parts in a steel structure according to claim 5, characterized in that, The stress model of the embedded part is represented by a stiffness matrix, specifically: where each k ij This represents the effect of displacement or rotation in the i-direction on the force or torque in the j-direction; matrix K s It is a 6x6 stiffness matrix used to describe the supporting effect of embedded parts on the steel structure; each element k in the matrix ij This represents the effect of displacement or rotation in the i-direction on the force or torque in the j-direction, explained in detail below: k xx ,k yy ,k zz : These represent the linear stiffness in the x, y, and z directions, respectively; k xy ,k xz ,k yx ,k yz ,k zx ,k zy : Indicates the coupling stiffness between different directions; k xθx ,k xθy ,k xθz : Represents the coupling stiffness between displacement in the x-direction and rotation about the x, y, z axes; k yθx ,k yθy ,k yθz : Represents the coupling stiffness between displacement in the y-direction and rotation about the x, y, z axes; k zθx ,k zθy ,k zθz : Represents the coupling stiffness between displacement in the z-direction and rotation about the x, y, z axes; k θxθx ,k θyθy ,k θzθz : These represent the rotational stiffness about the x, y, and z axes, respectively; k θxθy ,k θxθz ,k θyθx ,k θyθz ,k θzθx ,k θzθy : Indicates the coupling stiffness between rotations on different axes; k θxx ,k θxy ,k θxz : Represents the coupling stiffness between rotation about the x-axis and displacement in the x, y, z directions; k θyx ,k θyy ,k θyz : Represents the coupling stiffness between rotation about the y-axis and displacement in the x, y, z directions; k θzx ,k θzy ,k θzz : Represents the coupling stiffness between rotation about the z-axis and displacement in the x, y, z directions.

7. The method for positioning embedded parts in a steel structure according to claim 6, characterized in that, The game model formula is expressed as follows: in, In the formula, x is the location vector of the embedded part, representing the location coordinates of the embedded part; X represents the feasible solution space, that is, the set of all possible locations of the embedded part, and F actual (x) is the actual load-bearing capacity, F design It is the design load-bearing capacity, δ i (x) is the transformation of the i-th node, δ avg (x) is the average deformation, and ξ is the number of nodes.

8. The method for positioning embedded parts in a steel structure according to claim 7, characterized in that, The fitness function formula of the genetic algorithm is expressed as follows: F(x)=w 21 f i (x)+w 22 f2(x)+w 23 f3(x); in, Among them, w 21 ,w 22 ,w 23 It is the weighting coefficient, and w 21 +w 22 +w 23 =1; ΔP i (x) is the positional error of the i-th embedded part, and m is the number of embedded parts.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program instructions, which, when executed, are used to perform a method for positioning steel structure embedded parts according to any one of claims 1-8.

10. A positioning system for embedded parts in steel structures, characterized in that, It includes the computer-readable storage medium of claim 9.

Images