Optimization design method for reinforcing steel bar through type CFST column-RC beam joint

Abstract

The invention relates to a reinforcing steel bar through type CFST column-RC beam joint optimization design method. The method comprises the steps that all design geometric parameters are determined; establishing a function relationship of the node axial pressure bearing capacity with respect to all the design geometric parameters, and obtaining a node axial pressure bearing capacity response surface equation; and according to the design value of the node axial pressure bearing capacity, solving the node axial pressure bearing capacity response surface equation by adopting a Lagrange multiplier method to obtain the design values of all the design geometric parameters. According to the method, the parameterized design requirement of the steel bar through type CFST column-RC beam joint in a fabricated building can be met, and optimal design of the axial compression bearing capacity of the steel bar through type CFST column-RC beam joint is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of building design, and particularly relates to an optimized design method for a reinforced-through CFST column-RC beam joint. Background Art

[0002] The concrete filled steel tube (CFST) column-reinforced concrete (RC) beam structure has the advantages of high bearing capacity, good ductility, and meeting the construction requirements of prefabricated buildings, etc., and is widely used in high-rise buildings. The reinforced-through CFST column-RC beam joint is a new type of connection joint between CFST columns and RC beams. This joint has a simple structure and clear force transmission. The longitudinal reinforcement of the frame beam completely penetrates through the joint core area. The shear and flexural bearing capacities of the joint are similar to those of ordinary RC beam-column joints. Therefore, the design difficulty is relatively small, which is convenient for engineering promotion and has broad application prospects.

[0003] The purpose of this application is to provide an optimized design method for a reinforced-through CFST column-RC beam joint to meet the parametric design requirements of the axial compressive bearing capacity of the reinforced-through CFST column-RC beam joint in prefabricated buildings and achieve the optimized design of the axial compressive bearing capacity of the reinforced-through CFST column-RC beam joint. Summary of the Invention

[0004] Based on the above description, the present invention provides an optimized design method for a reinforced-through CFST column-RC beam joint to meet the parametric design requirements of the axial compressive bearing capacity of the reinforced-through CFST column-RC beam joint in prefabricated buildings and achieve the optimized design of the axial compressive bearing capacity of the reinforced-through CFST column-RC beam joint.

[0005] The technical solution of the present invention to solve the above technical problems is as follows: This application provides an optimized design method for a reinforced-through CFST column-RC beam joint, and the technical solution adopted is as follows: An optimized design method for a reinforced-through CFST column-RC beam joint includes: Determine all design geometric parameters; Establish a functional relationship between the axial compressive bearing capacity of the joint and all design geometric parameters to obtain the axial compressive bearing capacity response surface equation of the joint; According to the design value of the axial compressive bearing capacity of the joint, use the Lagrange multiplier method to solve the axial compressive bearing capacity response surface equation of the joint to obtain the design values of all design geometric parameters.

[0006] Preferably, when determining all design geometric parameters, use the potential outcome model in the causal inference method to analyze all undetermined geometric parameters to determine the design geometric parameters among all undetermined geometric parameters.

[0007] Preferably, the potential outcome model in the causal inference method is used to analyze all undetermined geometric parameters to determine the design geometric parameters among all undetermined geometric parameters, including: Using the potential outcome model to judge whether each undetermined geometric parameter is a cause geometric parameter and determine all cause geometric parameters; Calculating the causal effect index between each cause geometric parameter and the axial compressive bearing capacity of the node, and determining the design geometric parameters among all cause geometric parameters according to the causal effect index.

[0008] Preferably, using the potential outcome model to judge whether each undetermined geometric parameter is a cause geometric parameter includes: Judging whether each undetermined geometric parameter is a cause geometric parameter respectively. If the undetermined geometric parameter is not the cause geometric parameter of the axial compressive bearing capacity result of the node , then there is: ; In the formula, is the conditional probability of the axial compressive bearing capacity of the node under the intervention , where the axial compressive bearing capacity of the node is the measured value; is the conditional probability of the axial compressive bearing capacity of the node under the intervention , where the axial compressive bearing capacity of the node is the potential outcome; is the specific value of ; Otherwise, the undetermined geometric parameter is the cause geometric parameter of the axial compressive bearing capacity result of the node.

[0009] Preferably, when calculating the causal effect index between each cause geometric parameter and the axial compressive bearing capacity of the node, the causal effect index between each cause geometric parameter and the axial compressive bearing capacity of the node is calculated according to the following formula : ; In the formula, is the cause geometric parameter, = 1 and = 0 respectively represent intervening / non-intervening on the cause geometric parameter; represents the potential outcome of the axial compressive bearing capacity of the node when intervening on the cause geometric parameter; represents the potential outcome of the axial compressive bearing capacity of the node when not intervening on the cause geometric parameter.

[0010] Preferably, when establishing the functional relationship between the axial compression bearing capacity of the joint and all design geometric parameters to obtain the response surface equation of the axial compression bearing capacity of the joint, the forms of the response surface fitting functions include power functions, non-linear functions, BP network models, or quadratic homogeneous polynomials.

[0011] Preferably, a quadratic homogeneous polynomial is used as the response surface fitting function, and the response surface equation of the axial compression bearing capacity of the joint is obtained as follows: ; In the formula, and are different design geometric parameters; is the number of design geometric parameters; , , and are all regression coefficients.

[0012] Preferably, after obtaining the response surface equation of the axial compression bearing capacity of the joint, the coefficient of determination is used to evaluate the accuracy of the response surface equation of the axial compression bearing capacity of the joint. The coefficient of determination is calculated using the following formula: ; In the formula, , , all represent the axial compression bearing capacity of the joint, is the calculated value of the response surface equation of the axial compression bearing capacity of the joint; is the result of the sample test set; is the average value of the results of the sample test set; is the number of test points in the design space.

[0013] Compared with the prior art, the technical solution of the present application has at least the following beneficial technical effects: By establishing the functional relationship between the axial compression bearing capacity of the joint and all design geometric parameters, the present application obtains the response surface equation of the axial compression bearing capacity of the joint, transforms the optimization problem of the joint geometric parameters that have a greater impact on the axial compression bearing capacity, that is, the design set parameters, into a problem of solving the extreme value of a multivariate function with constraints, and according to the design value of the axial compression bearing capacity of the joint, solves the response surface equation of the axial compression bearing capacity of the joint by the Lagrange multiplier method, calculates the optimal solution of the design geometric parameters, so as to obtain the design values of all design geometric parameters. The present application can meet the parametric design requirements of the steel bar through-type CFST column-RC beam joint in prefabricated buildings. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a flowchart of the optimized design method for the steel bar through-type CFST column-RC beam joint provided by the embodiment of the present invention; Figure 2 Schematic diagrams of double-opening joints and single-opening joints in the numerical examples of the embodiments of the present invention; Figure 3 Frequency distribution histograms of the axial compression yield bearing capacity and ultimate bearing capacity of joint samples in the numerical examples of the embodiments of the present invention; Figure 4 Concrete stress nephogram inside the steel pipe in the core area of a typical joint in the numerical example of this embodiment; Figure 5 Schematic diagram of the causal inference result of the geometric parameters of the joint in the numerical example of the embodiments of the present invention; Figure 6 Response surface / curve fitting results of the yield bearing capacity of joints with two structural forms in the numerical examples of the embodiments of the present invention. Detailed implementation manners

[0015] To facilitate the understanding of the present application, the present application will be described more comprehensively below with reference to the relevant drawings. Embodiments of the present application are shown in the drawings. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided to make the disclosure of the present application more thorough and comprehensive.

[0016] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs. The terms used in the specification of this application herein are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0017] It can be understood that spatial relationship terms such as "under", "below", "lower", "beneath", "above", "upper", etc. can be used herein to describe the relationship between one element or feature shown in the figure and other elements or features. It should be understood that, in addition to the orientation shown in the figure, spatial relationship terms also include different orientations of the device during use and operation. For example, if the device in the figure is flipped, the element or feature described as "under other elements" or "beneath it" or "under it" will be oriented "above" other elements or features. Therefore, the exemplary terms "under" and "below" can include both the upper and lower orientations. In addition, the device may also include other orientations (such as rotating 90 degrees or other orientations), and the spatial description terms used herein are accordingly interpreted.

[0018] It should be noted that when a component is considered to be "connected" to another component, it can be directly connected to the other component or connected to the other component through an intermediate component. In the following embodiments, "connection", if there is a transfer of electrical signals or data between the connected circuits, modules, units, etc., should be understood as "electrical connection", "communication connection", etc.

[0019] As used herein, the singular forms "a", "an" and "the" may also include the plural forms unless the context clearly dictates otherwise. It should also be understood that the terms "comprises / include" or "has" etc. specify the presence of the stated features, wholes, steps, operations, components, parts, or combinations thereof, but do not preclude the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof.

[0020] Referring to Figure 1 As shown, the embodiment of the present application provides an optimized design method for a steel bar through - type CFST column - RC beam joint, which includes two steps: joint parameter analysis and joint optimized design.

[0021] Among them, the joint parameter analysis includes: Determine all design geometric parameters.

[0022] The joint optimized design includes: Establish a functional relationship between the axial compression bearing capacity of the joint and all design geometric parameters to obtain the axial compression bearing capacity response surface equation of the joint.

[0023] According to the design value of the axial compression bearing capacity of the joint, use the Lagrange multiplier method to solve the axial compression bearing capacity response surface equation of the joint to obtain the design values of all design geometric parameters.

[0024] When determining all design geometric parameters, use the potential outcome model in the causal inference method to analyze all undetermined geometric parameters to determine the design geometric parameters among all undetermined geometric parameters. Among them, the undetermined geometric parameters of the joint are determined according to experience.

[0025] Causation, confounding, and selection bias can all lead to correlations between variables. Traditional correlation analysis cannot effectively separate the causal relationships between variables, which can easily cause biases in the analysis results. For the optimization design problem of the axial compression bearing capacity of reinforced concrete-filled steel tubular (CFST) column-RC beam joints, using non-causal geometric parameters as independent variables can easily lead to overfitting of the bearing capacity results and reduce the generalizability of the optimization design method. This application uses causal inference to analyze the causal relationship between joint parameters and axial compression bearing capacity. According to existing research, the causal relationship between variables can be separated using the potential outcome model (Rubin Causal Model, RCM). RCM assumes that all variables except for the intervention and potential outcomes are confounding variables, and determines the causal variable by predicting the change in potential outcomes before and after the intervention.

[0026] When using the potential outcome model in the causal inference method to analyze all undetermined geometric parameters to determine the design geometric parameters among all undetermined geometric parameters, it includes: Use the potential outcome model to judge whether each undetermined geometric parameter is a causal geometric parameter and determine all causal geometric parameters; calculate the causal effect index between each causal geometric parameter and the axial compression bearing capacity of the joint, and determine all design geometric parameters according to the causal effect index.

[0027] This embodiment uses RCM to analyze the causal geometric parameters affecting the axial compression bearing capacity of the joint and introduces the do operator to represent the intervention on the joint geometric parameters.

[0028] When using the potential outcome model to judge whether each undetermined geometric parameter is a causal geometric parameter, judge whether each undetermined geometric parameter is a causal geometric parameter respectively. If the undetermined geometric parameter is not a causal geometric parameter for the axial compression bearing capacity result of the joint , then there is: .

[0029] In the formula, is the conditional probability of the axial compression bearing capacity of the joint under the intervention , where the axial compression bearing capacity is the measured value; is the conditional probability of the axial compression bearing capacity of the joint under the intervention , where the axial compression bearing capacity is the potential outcome; is the specific value of.

[0030] Otherwise, if the undetermined geometric parameter is a causal geometric parameter for the axial compression bearing capacity result of the joint , then there is: 。

[0031] According to the above method, it can be judged whether each undetermined geometric parameter is a causal geometric parameter, and then all causal geometric parameters can be determined.

[0032] After determining all the causal geometric parameters, calculate the causal effect index between each causal geometric parameter and the axial compressive bearing capacity of the node according to the following formula : 。

[0033] In the formula, is the causal geometric parameter, = 1 and = 0 respectively represent intervening / not intervening on the causal geometric parameter; represents the potential result of the axial compressive bearing capacity of the node when intervening on the causal geometric parameter; represents the potential result of the axial compressive bearing capacity of the node when not intervening on the causal geometric parameter.

[0034] According to the causal effect index between each causal geometric parameter and the axial compressive bearing capacity of the node , determine the design geometric parameters among all the causal geometric parameters. Specifically, according to the absolute value of the causal effect index to judge whether the corresponding causal geometric parameter is used as a design geometric parameter. For example, taking 0.1 as the reference value, if is less than 0.1, it means that the corresponding causal geometric parameter has a small influence on the axial compressive bearing capacity of the node, and it is considered that this causal geometric parameter is not a design geometric parameter. Otherwise, if is greater than 0.1, it means that the corresponding causal geometric parameter has a large influence on the axial compressive bearing capacity of the node, and this causal geometric parameter is a design geometric parameter. Specifically the value of the judgment reference value is determined according to the specific building.

[0035] The above specific method of using the potential result model in the causal inference method to analyze all undetermined geometric parameters to determine the design geometric parameters among all undetermined geometric parameters is a known technology and will not be elaborated here.

[0036] After determining all the design geometric parameters, establish the functional relationship of the axial compressive bearing capacity of the node with respect to all the design geometric parameters, and obtain the multivariate function of the axial compressive bearing capacity of the node with respect to all the design geometric parameters, that is, the response surface equation of the axial compressive bearing capacity of the node, so as to transform the optimization design problem of all the design geometric parameters into the problem of solving the extreme value of the multivariate function.

[0037] Specifically, the form of the response surface fitting function includes power function, non-linear function, BP network model, quadratic homogeneous polynomial, etc. Considering engineering applications, in this embodiment, the more common quadratic homogeneous polynomial is adopted as the response surface fitting function. Taking the axial compressive bearing capacity of the joint as the dependent variable and the design geometric parameters as the independent variables, the response surface equation of the axial compressive bearing capacity of the joint is as follows: .

[0038] In the formula, and are different design geometric parameters; is the number of design geometric parameters; , , and are all regression coefficients.

[0039] After obtaining the response surface equation of the axial compressive bearing capacity of the joint, the coefficient of determination is used to evaluate the accuracy of the response surface equation. The coefficient of determination is calculated by the following formula: .

[0040] In the formula, , , all represent the axial compressive bearing capacity of the joint, is the calculated value of the response surface equation of the axial compressive bearing capacity of the joint; is the result of the sample test set; is the average value of the results of the sample test set; is the number of test points in the design space, that is, the sample number of the sample test set. The value range of the coefficient of determination is , The larger the value of

[0041] According to the value of to judge the accuracy of the response surface equation of the axial compressive bearing capacity of the joint. For example, when

[0042] is greater than 0.9, it indicates that the fitting effect of the quadratic homogeneous polynomial meets the requirements; otherwise, it indicates that the fitting effect of the quadratic homogeneous polynomial does not meet the requirements. At this time, other forms of response surface fitting functions can be selected.

[0043] Solving the response surface equation of the axial compressive bearing capacity of the joint by the Lagrange multiplier method includes the following steps: (1) Establish the relationship between the axial compression bearing capacity of the joint and the cause geometric parameters according to the response surface equation of the axial compression bearing capacity of the joint, which is expressed by the equation as . .

[0044] (2) Determine the design value of the axial compression bearing capacity of the frame column according to the calculation results of the overall structural model , and establish the constraint condition equation: .

[0045] (3) Establish the Lagrangian function and solve the design value of the cause geometric parameter , and the Lagrangian function is expressed as .

[0046] (4) Check whether the design value of the cause geometric parameter calculated according to the Lagrangian function meets the construction requirements of the joint, and select the final design value of the joint geometric parameter according to the specific design requirements of the joint.

[0047] The following further illustrates the optimized design method of the steel bar through CFST column-RC beam joint provided in this embodiment according to specific calculation examples.

[0048] Take a certain hospital as an example to analyze its steel bar through CFST column-RC beam joint.

[0049] Analysis results of joint parameters The seismic fortification intensity of this hospital is in the 7-degree zone (0.10g). The size of the concrete-filled steel tube column is 600x600x20mm, the height of the thickened area is 900mm, the opening size is 300x250mm, the number of openings is 2, and the size of the frame beam connected to the joint core area is 350x700mm. As Figure 2 shown, according to the engineering application and test results, this embodiment respectively uses the double-opening joint and the single-opening joint for analysis. After the steel tube column in the joint core area is thickened, the failure parts all appear at both ends of the frame column, resulting in the inability to measure the bearing capacity of the joint core area in the test and numerical simulation. To avoid the buckling failure of the non-core area of the frame column joint affecting the calculation of the joint bearing capacity, this embodiment only selects the joint core area for analysis. The cross-sectional dimensions of the concrete-filled steel tube column and the concrete beam are selected as the actual dimensions in this project, and the height h of the thickened part of the joint, the thickness t of the thickened part of the joint, and the height h f of the slab strip between the holes are used as the three independent parameters of the design geometric parameters of the joint, that is, the geometric parameters to be determined. The joint geometric parameters and distribution are shown in Table 1.

[0050] Table 1 Design parameters and calculation methods

[0051] Note: The height hf of the plate strip between holes in a single hole node is taken as 0.

[0052] This embodiment uses the Latin hypercube sampling method to sample node geometry parameter samples. The Latin hypercube sampling method is a stratified sampling method that can make samples evenly distributed within a range of values, ensuring the accuracy of parameter causal inference results. To ensure that the sample results are representative, this embodiment uses a distribution interval that is larger than the commonly used range of values ​​of parameters in engineering for sampling. The Latin hypercube sampling method is used to sample node geometry parameters, and each parameter sample is randomly combined to obtain a node sample. After eliminating node samples that are not representative of the project or unreasonable, finite element analysis is performed to ensure that the calculated node axial compression bearing capacity response surface can cover all possible bearing capacity values ​​that may appear in actual engineering. 300 samples were used for analysis of double-opening and single-opening nodes, and the frequency distribution histograms of the axial compression yield bearing capacity and ultimate bearing capacity of the node samples are shown in Figure 2. Figure 3 , the stress cloud diagram of concrete in steel tube in the core area of ​​typical nodes is shown in Figure 4 .like Figure 3 As shown in the figure, for the two structural forms of nodes, when the nodes enter the elastic-plastic stage, the randomness of the material strength increases, and the volatility of the sample axial compressive bearing capacity increases. The statistical results are consistent with the actual situation. In addition, the statistical results show that the yield bearing capacity level of the double-hole node is about 9% lower than that of the single-hole node, and the ultimate bearing capacity is about 5% lower. At the same time, the yield strength of the double-hole node is about 4.2% lower than that of the single-hole node. Figure 4 As shown in the figure, the concrete in the middle of the core area of ​​the double-hole node is prone to stress concentration, which is more unfavorable for the concrete in the steel pipe column, resulting in a decrease in the bearing capacity of the double-hole node. However, the yield strength ratio of the double-hole node is smaller, so it can be considered that the reliability of the double-hole node is slightly higher than that of the single-hole node. On the other hand, the volatility of the axial compressive bearing capacity of the single-hole node is higher than that of the double-hole node. The main reason is that when the steel pipe wall thickness is thinner and the node height is higher, under the same geometric parameter size, the instability failure of the single-hole node precedes the strength failure. The sample calculation results show that the reliability and stability of the double-hole node are better, but because the stress distribution of the concrete in the core area of ​​the single-hole node is more uniform, the overall axial compressive bearing capacity of the single-hole node is slightly higher than that of the double-hole node.

[0053] For the double-opening and single-opening nodes, causal inference is used to analyze the node geometric parameters in Table 1, and it is determined that the three undetermined geometric parameters are all causal geometric parameters, and the causal inference results are shown in Figure 5 .like Figure 5 As shown in the figure, for the double opening node, the thickness of the thickened part ist plays a decisive and favorable role in the axial compression bearing capacity of the node, and the height of the thickened part h and the height of the slab strip between the holes h f have little influence on the bearing capacity of the node, and show a negative correlation with the node bearing capacity. Combining the results in Figure 3 and Figure 4 it can be determined that although the causal effect index of the height of the slab strip between the holes h f is small and negative, the height of the slab strip between the holes h f can improve the reliability and stability of the double-hole node, while weakening the axial compression bearing capacity of the double-hole node. Its value should be carefully considered in design. For the single-hole node, the thickness of the thickened part t is also the most important geometric parameter affecting the axial compression bearing capacity. Increasing the height of the thickened part h is unfavorable to the axial compression bearing capacity of the node. Since the causal effect index corresponding to the height of the thickened part of the node h is less than 0.1, it indicates that the height of the thickened part of the node has little influence on the axial compression bearing capacity of the node. At the same time, since the height of the thickened part of the node generally depends on the beam height of the adjacent frame beam, the height of the thickened part of the node h is no longer used as an optimization parameter, that is, a design geometric parameter h .

[0054] Node optimization design results In view of the fact that elastic design is generally only carried out for beam-column joints in general engineering, in this embodiment, the CFST column-RC beam joint is optimized according to the yield bearing capacity. Referring to the reference example project, in this embodiment, the height of the thickened part of the node h is taken as 900mm. For the double-hole node, considering the thickness of the thickened part t and the height of the slab strip between the holes h f two geometric parameters are used to fit the response surface of the yield axial compression bearing capacity of the node; for the single-hole node, considering the thickness of the thickened part t one geometric parameter is used to fit the response curve of the yield axial compression bearing capacity of the node. The fitting results of the yield bearing capacity response surface of the nodes with two structural forms are shown in Figure 6 .

[0055] According to Figure 6 the determination coefficient in it can be seen that for the yield bearing capacity of the node, the fitting effect of the quadratic homogeneous polynomial is better. At the same time, due to the height of the slab strip between the holes h fhas little influence on the axial compression bearing capacity. The yield bearing capacity response surface of the double-opening joint is approximately an inclined plane. In addition, the yield bearing capacities of the double-opening and single-opening joints are both positively correlated with the thickness of the thickened part. According to the actual engineering situation, the design value of the axial compression bearing capacity of the joint in this embodiment is 28052 kN. According to the response surface equation of the joint yield bearing capacity, the Lagrange multiplier method is used to calculate the thickness of the thickened part t and the height of the slab strip between the openings h f for the optimal values. The final optimization results after eliminating unreasonable results are shown in Table 2.

[0056] Table 2 Optimized design parameters and corresponding design bearing capacities

[0057] As shown in Table 2, the optimal solutions of the joint geometric parameters are all within a reasonable range. The optimal solution of the thickness t of the thickened part of the double-opening joint is greater than that of the single-opening joint, which is consistent with the above analysis results. At the same time, the difference between the response surface calculation results and the finite element calculation results is within the engineering error, indicating that Figure 6 the response surface (curve) in [reference] does not show overfitting. Thus, it can be seen that the method for solving joint parameters based on causal inference and the response surface method in this application can meet the parametric design requirements of the steel bar penetrating CFST column-RC beam joints in prefabricated buildings.

[0058] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for optimizing the design of a steel bar through-type CFST column-RC beam node, characterized in that: include: Determine all design geometry parameters; Establishing the functional relationship between the node axial compressive bearing capacity and all the design geometric parameters, and obtaining the node axial compressive bearing capacity response surface equation; According to the design value of the node axial compressive bearing capacity, the Lagrange multiplier method is used to solve the node axial compressive bearing capacity response surface equation to obtain the design values ​​of all the design geometric parameters.

2. The method for optimizing the design of the steel bar through-type CFST column-RC beam node according to claim 1 is characterized in that: When determining all the design geometric parameters, the potential result model in the causal inference method is used to analyze all the undetermined geometric parameters to determine the design geometric parameters among all the undetermined geometric parameters.

3. The method for optimizing the design of the steel bar through-type CFST column-RC beam node according to claim 2 is characterized in that: The method of using the potential result model in the causal inference method to analyze all the undetermined geometric parameters to determine the design geometric parameters among all the undetermined geometric parameters includes: Using the potential result model to determine whether each of the undetermined geometric parameters is a cause geometric parameter, and determining all of the cause geometric parameters; The causal effect index between each of the causal geometric parameters and the axial compressive bearing capacity of the node is calculated, and the design geometric parameters among all the causal geometric parameters are determined according to the causal effect index.

4. The method for optimizing the design of the steel bar through-type CFST column-RC beam node according to claim 3 is characterized in that: The adopting of the potential result model to judge whether each of the undetermined geometric parameters is a cause geometric parameter includes: It is judged whether each of the undetermined geometric parameters is the cause geometric parameter. Non-nodal axial compression bearing capacity results The reason for the geometric parameters is: ； In the formula, For intervention The conditional probability of the axial compressive bearing capacity of the lower node, where the axial compressive bearing capacity of the node is the measured value; For intervention The conditional probability of the lower node axial compressive capacity, where the node axial compressive capacity is the potential result; for The specific value of Otherwise, the undetermined geometric parameters is the result of the axial compressive bearing capacity of the node The reason is the geometric parameters.

5. The method for optimizing the design of the steel bar through-type CFST column-RC beam node according to claim 3 is characterized in that: When calculating the causal effect index between each of the causal geometric parameters and the node axial compressive bearing capacity, the causal effect index between each of the causal geometric parameters and the node axial compressive bearing capacity is calculated according to the following formula: : ； In the formula, is the geometric parameter of the cause, =1 and =0 means intervention / no intervention on the cause geometric parameters respectively; represents the potential results of the axial compressive bearing capacity of the node when intervening on the causal geometric parameters; Represents the potential results of the nodal axial compressive capacity without intervention in the causal geometric parameters.

6. The method for optimizing the design of the steel bar through-type CFST column-RC beam node according to claim 1 is characterized in that: When the functional relationship of the node axial compressive bearing capacity with respect to all the design geometric parameters is established to obtain the response surface equation of the node axial compressive bearing capacity, the response surface fitting function includes a power function, a nonlinear function, a BP network model or a quadratic homogeneous polynomial.

7. The method for optimizing the design of the steel bar through-type CFST column-RC beam node according to claim 6 is characterized in that: Using a quadratic homogeneous polynomial as the response surface fitting function, the response surface equation for the axial compressive bearing capacity of the node is obtained as follows: ； In the formula, and For different design geometric parameters; is the number of design geometric parameters; , , and All are regression coefficients.

8. The method for optimizing the design of the steel bar through-type CFST column-RC beam node according to claim 1, characterized in that: After obtaining the node axial compression bearing capacity response surface equation, the determination coefficient Evaluate the accuracy of the response surface equation for the axial compressive bearing capacity of the node and determine the coefficients The calculation is done using the following formula: ； In the formula, , , Both represent the axial compressive bearing capacity of the node. is the calculated value of the response surface equation for the axial compressive bearing capacity of the node; is the sample test set result; is the average of the sample test set results.

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