Intelligent structural design method and system for reinforcing constraint steel pipe concrete

Abstract

The invention discloses a structural design method and system of intelligent reinforced constraint steel pipe concrete, and relates to the technical field of data processing, and the method comprises the steps: carrying out structural design parameter analysis according to a target design task, and obtaining a steel pipe concrete structural design scheme; modeling according to the concrete-filled steel tube structure design scheme to obtain a structure design model; performing multi-dimensional performance simulation according to the structural design model to obtain a structural performance simulation result; judging whether the structural performance simulation result meets a preset performance constraint or not; and if the structural performance simulation result does not meet the predetermined performance constraint, enhancing the concrete filled steel tube structural design scheme according to the predetermined performance constraint. The technical problem that design enhancement adjustment cannot be automatically carried out when the structural performance does not meet the design requirement in the prior art is solved, and the technical effect of improving the meeting capacity of the structural design scheme for the preset performance constraint is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a structure design method and system for intelligent enhancement of constraint steel pipe concrete. BACKGROUND

[0002] In the process of steel pipe concrete structure design, the structure is usually parameterized and performance evaluated by relying on artificial experience or fixed model. When the simulation result does not meet the design requirement, the engineering personnel often need to manually modify the design scheme and re-verify. The process is tedious and inefficient. Especially in the face of complex application scenarios or multiple performance constraint conditions, there is a lack of effective automatic mechanism to dynamically adjust and enhance the design scheme, and it is difficult to achieve efficient matching between structure performance and design requirements. SUMMARY

[0003] The present application provides a structure design method and system for intelligent enhancement of constraint steel pipe concrete, which is used to solve the technical problem that the prior art cannot automatically adjust and enhance the design when the structure performance does not meet the design requirement.

[0004] In view of the above problems, the present application provides a structure design method and system for intelligent enhancement of constraint steel pipe concrete.

[0005] In a first aspect of the present application, a structure design method for intelligent enhancement of constraint steel pipe concrete is provided, which comprises:

[0006] According to the target design task, the structure design parameters are analyzed to obtain a steel pipe concrete structure design scheme. A structure design model is obtained according to the steel pipe concrete structure design scheme. A structure performance simulation result is obtained according to the structure design model. It is judged whether the structure performance simulation result meets the predetermined performance constraint. If the structure performance simulation result does not meet the predetermined performance constraint, the steel pipe concrete structure design scheme is enhanced according to the predetermined performance constraint.

[0007] In a second aspect of the present application, a structure design system for intelligent enhancement of constraint steel pipe concrete is provided, which comprises:

[0008] A parameter analysis module is configured to perform structural design parameter analysis according to a target design task, and obtain a concrete-filled steel tube structure design scheme; a modeling module is configured to perform modeling according to the concrete-filled steel tube structure design scheme, and obtain a structural design model; a simulation module is configured to perform multi-dimensional performance simulation according to the structural design model, and obtain a structural performance simulation result; a judgment module is configured to judge whether the structural performance simulation result meets a predetermined performance constraint; and an enhancement processing module is configured to perform enhancement processing on the concrete-filled steel tube structure design scheme according to the predetermined performance constraint, if the structural performance simulation result does not meet the predetermined performance constraint.

[0009] The one or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0010] The present application performs structural design parameter analysis according to a target design task, and obtains a concrete-filled steel tube structure design scheme; performs modeling according to the concrete-filled steel tube structure design scheme, and obtains a structural design model; performs multi-dimensional performance simulation according to the structural design model, and obtains a structural performance simulation result; judges whether the structural performance simulation result meets a predetermined performance constraint; and performs enhancement processing on the concrete-filled steel tube structure design scheme according to the predetermined performance constraint, if the structural performance simulation result does not meet the predetermined performance constraint. The present application solves the technical problem that the prior art cannot automatically perform design enhancement adjustment when the structural performance does not meet the design requirements, and achieves the technical effect of improving the ability of the structural design scheme to meet the predetermined performance constraint, by introducing an automatic enhancement processing mechanism based on the performance simulation result. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort based on these drawings.

[0012] Figure 1 The structural design method for the intelligent enhancement constraint concrete-filled steel tube provided in the embodiments of the present application is shown in the flowchart.

[0013] Figure 2 The structural design system structure for the intelligent enhancement constraint concrete-filled steel tube provided in the embodiments of the present application is shown in the structural diagram.

[0014] The reference signs are explained as follows: parameter analysis module 11, modeling module 12, simulation module 13, judgment module 14, and enhancement processing module 15. DETAILED DESCRIPTION

[0015] The application provides a structural design method and system for intelligently enhanced constraint steel pipe concrete, aiming to solve the technical problem that the prior art cannot automatically perform design enhancement adjustment when the structural performance does not meet the design requirements, and introduces an automatic enhancement processing mechanism based on performance simulation results, so as to improve the technical effect of the satisfaction of the predetermined performance constraint of the structural design scheme.

[0016] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the application.

[0017] It should be noted that any variants of the terms "comprise" and "have" are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server comprising a series of steps or units does not have to be limited to the clearly listed steps or units, but can include other steps or modules that are not clearly listed or inherent to the process, method, product or device.

[0018] Embodiment one, as shown in the application provides a structural design method for intelligently enhanced constraint steel pipe concrete, which comprises: Figure 1

[0019] Step S100: performing structural design parameter analysis according to a target design task to obtain a steel pipe concrete structure design scheme.

[0020] In the embodiments of the application, when performing structural design parameter analysis according to a target design task, first, the target design task is subjected to semantic analysis, and key design requirement features for structural design are extracted. Then, a steel pipe concrete structure design model is constructed based on a machine learning method. Finally, the extracted design requirement features are input into the model, and a steel pipe concrete structure design scheme meeting the requirements of the target task is output.

[0021] Further, the method provided in the embodiments of the application, wherein the steel pipe concrete structure design scheme is obtained by performing structural design parameter analysis according to a target design task, further comprises:

[0022] performing semantic analysis according to the target design task to determine design requirement features; constructing a steel pipe concrete structure design model based on machine learning; inputting the design requirement features into the steel pipe concrete structure design model to obtain the steel pipe concrete structure design scheme.

[0023] ​In the embodiments of the present application, first, for the input target design task text, a semantic parsing algorithm in natural language processing (NLP) is used, such as a text vector encoding method based on a BERT model, to perform semantic understanding and structured processing on the target design task, and to extract design requirement features therefrom. These features include design load level, structural purpose, environmental category, construction limitation condition and the like.

[0024] Subsequently, when constructing a steel pipe concrete structure design model, a supervised deep neural network (DNN) training method is used, with various input features (such as use function, specification level, load parameter) and corresponding structure design parameters (such as steel pipe outer diameter, wall thickness, concrete grade, constraint member configuration) in historical structure design cases as training samples, to constantly optimize network weights through a back propagation algorithm, so that the model has mapping capability between input requirement features and output design parameters.

[0025] After the model is constructed, the design scheme generation link is executed, the extracted design requirement features are used as input vectors, which are input into the trained steel pipe concrete structure design model, a set of structure design parameter combinations is output through layer-by-layer activation calculation, to constitute a steel pipe concrete structure design scheme.

[0026] Step S200: modeling according to the steel pipe concrete structure design scheme to obtain a structure design model.

[0027] In the embodiments of the present application, when modeling according to the steel pipe concrete structure design scheme, first, an initial structure model is constructed according to the steel pipe concrete structure design scheme, and deviation detection is performed on the model to identify modeling errors, then the model is optimized and corrected according to the detection results, to generate a structure design model meeting the design requirements.

[0028] Further, in the method provided by the embodiments of the present application, modeling according to the steel pipe concrete structure design scheme to obtain a structure design model further includes:

[0029] According to the steel pipe concrete structure design scheme, an initial structure model is constructed, deviation detection is performed on the initial structure model according to the steel pipe concrete structure design scheme to obtain a modeling deviation detection result, and the initial structure model is optimized according to the modeling deviation detection result to generate the structure design model.

[0030] In the embodiments of the present application, first, according to the key parameter information such as the component geometric size (such as the outer diameter of the steel pipe, the wall thickness), the concrete grade, the component length, the joint connection form and the like in the concrete-filled steel tube structure design scheme, a parameterized modeling method is used to build an initial structure model in a modeling platform (such as Autodesk Revit). Specifically, by defining family parameters (including cross-sectional size, material type, construction boundary condition and the like) and calling a component template library, the initial structure model is built by automatically laying out key components such as steel pipe columns, concrete core materials and steel hoops.

[0031] Next, a geometric topology consistency checking method is used to detect modeling deviations of the initial structure model. The method extracts the topology elements (such as edges, faces and bodies) and their connection relationships in the model, and compares them with the corresponding parameter set in the concrete-filled steel tube structure design scheme, to identify whether there are problems such as offset, size error, connection node error or missing components in the geometric components in the model. For example, if the outer diameter of a component does not match the design value, or a connection node is not correctly arranged, the corresponding item will be marked as a deviation item and output as the modeling deviation detection result.

[0032] Subsequently, the initial structure model is corrected according to the modeling deviation detection result, and a rule-driven parameter correction method is used. Specifically, the built-in design rule library (such as component size tolerance range and node configuration logic) is called to automatically adjust the abnormal components detected. For example, if it is detected that the wall thickness of a component is less than the lower limit of the design tolerance, the original design parameter is replaced by correction according to the rule; if the node type configuration is incorrect, the correct node component is automatically matched and replaced or reconnected. After this process, a structure design model is generated which is completely consistent with the structure design scheme.

[0033] Step S300: performing multi-dimensional performance simulation according to the structure design model to obtain a structure performance simulation result.

[0034] In the embodiments of the present application, when performing multi-dimensional performance simulation according to the structure design model, the structure bearing coefficient, the seismic coefficient and the safety coefficient are obtained by respectively simulating and analyzing the bearing performance, the seismic performance and the safety performance of the structure design model, and the performance indexes are integrated to form the structure performance simulation result.

[0035] Further, in the method provided by the embodiments of the present application, the multi-dimensional performance simulation according to the structure design model to obtain the structure performance simulation result further includes:

[0036] The structure bearing coefficient, the structure seismic coefficient, and the structure safety coefficient are added to the structure performance simulation result.

[0037] In the embodiment of the present application, when the structure design model is subjected to bearing performance simulation analysis, according to the target design task, a design application scenario is constructed, and the action forms and boundary conditions such as static load, live load, and wind load are determined. Then, the structure design model is subjected to simulation bearing test under the design application scenario, and the stress response data of the key components are obtained, including stress distribution, displacement curve, and ultimate bearing point, etc., to form simulation bearing test data. In the performance evaluation stage, a bearing performance evaluation model trained based on big data is used. The model is obtained by training historical engineering data and typical working condition samples. The test data are input into the model, and the corresponding structure bearing coefficient is automatically output.

[0038] Similarly, in the seismic performance simulation analysis process, a seismic action scenario is also constructed according to the target design task, including seismic intensity, response spectrum parameters or time history record, etc., and the seismic input is applied to the structure design model, and the dynamic response of the model under the action of the earthquake is obtained, such as acceleration response, inter-story drift, residual deformation, etc., to form simulation seismic test data. On this basis, the data are input into the seismic performance evaluation model. The model is also trained based on a large number of seismic simulation samples, and the structure seismic coefficient is output by pattern recognition and parameter matching, which represents the ability of the structure to resist earthquake impact.

[0039] In the safety performance simulation analysis, a safety evaluation scenario containing uncertain factors (such as material fluctuation, construction error, load disturbance, etc.) is constructed according to the target task, and multiple rounds of Monte Carlo simulation or random variable test are performed to generate simulation safety test data of the structure under different disturbance conditions. Then, the data are input into the trained safety performance evaluation model. The model integrates a large number of structure failure cases and reliability analysis samples, and outputs the structure safety coefficient, which is used to evaluate the overall operation reliability of the structure in an uncertain environment.

[0040] Finally, the structure bearing coefficient, the structure seismic coefficient, and the structure safety coefficient are added to the structure performance simulation result.

[0041] Further, the method provided by the embodiment of the present application, wherein the structure design model is subjected to bearing performance simulation analysis to obtain the structure bearing coefficient, further comprises:

[0042] According to the target design task, a design application scenario is constructed; a simulation bearing test is performed on the structural design model according to the design application scenario, and simulation bearing test data is obtained; a bearing performance evaluation is performed according to the simulation bearing test data, and the structural bearing coefficient is generated.

[0043] In the embodiments of the present application, first, according to the parameter information such as the structural purpose, load level, support condition and construction environment contained in the target design task, a structural working condition construction method is used to construct a design application scenario.

[0044] Then, based on the constructed design application scenario, a finite element analysis is performed on the structural design model to simulate the stress process of the structural design model under different load working conditions. The process includes meshing the structural model, setting the stress-strain relationship of the material, and gradually applying the load, and calculating the stress, strain, displacement and other response parameters of each part of the structure during the loading process. Through simulation, the complete response process of the structure under static load or combined load is obtained, and simulation bearing test data is formed.

[0045] Finally, a bearing performance evaluation is performed according to the simulation bearing test data. In this process, a bearing performance evaluation model is trained based on big data, and the simulation bearing test data is input into the model for analysis, and the structural bearing coefficient is output.

[0046] Further, in the method provided by the embodiments of the present application, the bearing performance evaluation is performed according to the simulation bearing test data, and the structural bearing coefficient is generated, which further comprises:

[0047] Based on big data, a bearing performance evaluation model is trained; the simulation bearing test data is input into the bearing performance evaluation model, and the structural bearing coefficient is obtained.

[0048] In the embodiments of the present application, first, a large number of representative historical structure simulation samples are collected, the sample data includes the finite element simulation results of the structure under different loads and boundary conditions, the key response features such as maximum stress, maximum displacement, yield load, load-displacement curve slope are extracted, and the real structural bearing coefficient is labeled by technical experts for each sample as a supervised learning target label. Then, a regression type supervised learning method (such as random forest regression) is used to train the model on the above samples. During the training process, the feature vector of each sample is taken as the input, and the bearing coefficient labeled by the expert is taken as the output, the model parameters are continuously updated by minimizing the error (such as mean square error) between the predicted value and the real label until the fitting of the model on the training set reaches a stable accuracy. Through the process, the bearing performance evaluation model is obtained.

[0049] After the training is completed, the simulation bearing test data obtained by the actual simulation is input into the bearing performance evaluation model, and the corresponding structural bearing coefficient is output after the model analysis.

[0050] Step S400: judging whether the structural performance simulation result meets the predetermined performance constraint.

[0051] Further, the method provided by the application further comprises:

[0052] The predetermined performance constraint comprises a bearing predetermined constraint, a seismic predetermined constraint and a safety predetermined constraint.

[0053] In the embodiments of the application, the predetermined performance constraint comprises a bearing predetermined constraint, a seismic predetermined constraint and a safety predetermined constraint, which are all performance coefficients pre-set according to design requirements. The corresponding structural bearing coefficient, structural seismic coefficient and structural safety coefficient in the structural performance simulation result are compared with the above predetermined performance constraints one by one to determine whether the design performance requirement is met.

[0054] Step S500: if the structural performance simulation result does not meet the predetermined performance constraint, the steel pipe concrete structure design scheme is enhanced according to the predetermined performance constraint.

[0055] In the embodiments of the application, when any performance coefficient (i.e. the structural bearing coefficient, the structural seismic coefficient or the structural safety coefficient) in the structural performance simulation result is lower than the corresponding predetermined performance constraint, it is considered that the structural performance simulation result does not meet the predetermined performance constraint, and the steel pipe concrete structure design scheme is enhanced at this time.

[0056] In the enhancement process, a parameter adjustment method is adopted to optimize the key design parameters affecting the structural performance, such as increasing the steel pipe wall thickness, improving the concrete strength grade, modifying the component cross-section size or optimizing the constraint component arrangement method. After the design parameter adjustment is completed, the system re-executes the whole process of structural modeling, multi-dimensional performance simulation and performance coefficient calculation to obtain the updated structural bearing coefficient, seismic coefficient and safety coefficient, and the comparison with the predetermined performance constraint is performed again. The enhancement process can be repeated until all performance coefficients meet the corresponding predetermined constraint, and the enhancement of the structural design scheme is completed.

[0057] In the embodiments of the application, as described above, the embodiments of the application have at least the following technical effects:

[0058] This application analyzes structural design parameters based on the target design task to obtain a steel-concrete composite structure design scheme; models the steel-concrete composite structure based on the design scheme to obtain a structural design model; performs multi-dimensional performance simulation based on the structural design model to obtain structural performance simulation results; determines whether the structural performance simulation results meet predetermined performance constraints; if the structural performance simulation results do not meet the predetermined performance constraints, enhances the steel-concrete composite structure design scheme according to the predetermined performance constraints. This invention solves the technical problem of existing technologies that cannot automatically perform design enhancement adjustments when structural performance does not meet design requirements. By introducing an automatic enhancement processing mechanism based on performance simulation results, it achieves the technical effect of improving the ability of the structural design scheme to meet predetermined performance constraints.

[0059] Example 2, based on the same inventive concept as the intelligent reinforced confined steel tube concrete structural design method in the foregoing examples, such as... Figure 2 As shown, this application provides an intelligent reinforced confined steel-tube concrete structural design system. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0060] The parameter parsing module 11 is used to parse structural design parameters according to the target design task to obtain a steel-concrete composite structure design scheme; the modeling module 12 is used to model the steel-concrete composite structure design scheme to obtain a structural design model; the simulation module 13 is used to perform multi-dimensional performance simulation based on the structural design model to obtain structural performance simulation results; the judgment module 14 is used to judge whether the structural performance simulation results meet the predetermined performance constraints; and the enhancement processing module 15 is used to enhance the steel-concrete composite structure design scheme according to the predetermined performance constraints if the structural performance simulation results do not meet the predetermined performance constraints.

[0061] Furthermore, the system is also used to implement the following functions:

[0062] Semantic parsing is performed based on the target design task to determine the design requirement features; a steel-concrete composite structure design model is constructed based on machine learning; the design requirement features are input into the steel-concrete composite structure design model to obtain the steel-concrete composite structure design scheme.

[0063] Furthermore, the system is also used to implement the following functions:

[0064] Based on the steel-concrete composite structure design scheme, an initial structural model is constructed; deviation detection is performed on the initial structural model according to the steel-concrete composite structure design scheme to obtain the modeling deviation detection results; the initial structural model is optimized based on the modeling deviation detection results to generate the structural design model.

[0065] Further, the system is further configured to implement the following functions:

[0066] Performing bearing performance simulation analysis on the structure design model to obtain a structure bearing coefficient; performing seismic performance simulation analysis on the structure design model to obtain a structure seismic coefficient; performing safety performance simulation analysis on the structure design model to obtain a structure safety coefficient; and adding the structure bearing coefficient, the structure seismic coefficient and the structure safety coefficient to the structure performance simulation result.

[0067] Further, the system is further configured to implement the following functions:

[0068] According to the target design task, a design application scenario is constructed; according to the design application scenario, a simulation bearing test is performed on the structure design model to obtain simulation bearing test data; and according to the simulation bearing test data, a bearing performance evaluation is performed to generate the structure bearing coefficient.

[0069] Further, the system is further configured to implement the following functions:

[0070] Based on big data, a bearing performance evaluation model is trained; and the simulation bearing test data is input into the bearing performance evaluation model to obtain the structure bearing coefficient.

[0071] Further, the system is further configured to implement the following functions:

[0072] The predetermined performance constraints include bearing predetermined constraints, seismic predetermined constraints and safety predetermined constraints.

[0073] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The above describes a specific embodiment of the present application. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0074] The above only describes the preferred embodiments of the present application and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0075] The present specification and drawings are only exemplary descriptions of the present application, and are considered to cover any and all modifications, changes, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art can make various modifications and changes to the present application without departing from the scope of the present application. Thus, if these modifications and changes of the present application belong to the scope of the present application and its equivalents, the present application intends to include these modifications and changes.

Claims

1. A structural design method of a smart reinforced confined steel tube concrete, characterized by, The method comprises: performing structural design parameter analysis according to a target design task to obtain a concrete-filled steel tube structure design scheme; modeling according to the concrete-filled steel tube structure design scheme to obtain a structural design model; performing multi-dimensional performance simulation according to the structural design model to obtain a structural performance simulation result; judging whether the structural performance simulation result meets predetermined performance constraints; if the structural performance simulation result does not meet the predetermined performance constraints, performing enhancement processing on the concrete-filled steel tube structure design scheme according to the predetermined performance constraints.

2. The method of designing a structure of the intelligent reinforced confined steel tube concrete as claimed in claim 1, wherein, Performing structural design parameter analysis according to a target design task to obtain a concrete-filled steel tube structure design scheme, comprising: performing semantic analysis according to the target design task to determine design requirement characteristics; constructing a concrete-filled steel tube structure design model based on machine learning; inputting the design requirement characteristics into the concrete-filled steel tube structure design model to obtain the concrete-filled steel tube structure design scheme.

3. The method of designing a structure of the intelligent reinforced confined steel tube concrete as claimed in claim 1, wherein Modeling according to the concrete-filled steel tube structure design scheme to obtain a structural design model, comprising: constructing an initial structural model according to the concrete-filled steel tube structure design scheme; performing deviation detection on the initial structural model according to the concrete-filled steel tube structure design scheme to obtain a modeling deviation detection result; optimizing the initial structural model according to the modeling deviation detection result to generate the structural design model.

4. The method of designing a structure of the intelligent reinforced concrete with steel tube according to claim 1, wherein, Performing multi-dimensional performance simulation according to the structural design model to obtain a structural performance simulation result, comprising: performing bearing performance simulation analysis on the structural design model to obtain a structural bearing coefficient; performing seismic performance simulation analysis on the structural design model to obtain a structural seismic coefficient; performing safety performance simulation analysis on the structural design model to obtain a structural safety coefficient; adding the structural bearing coefficient, the structural seismic coefficient, and the structural safety coefficient to the structural performance simulation result.

5. The method of designing a structure of the intelligent reinforced concrete with steel tube according to claim 4, wherein, Performing bearing performance simulation analysis on the structural design model to obtain a structural bearing coefficient, comprising: constructing a design application scenario according to the target design task; performing simulated bearing test on the structural design model according to the design application scenario to obtain simulated bearing test data; performing bearing performance evaluation according to the simulated bearing test data to generate the structural bearing coefficient.

6. The method of designing a structure of the intelligent reinforced concrete with steel tube according to claim 5, wherein, Performing bearing performance evaluation according to the simulated bearing test data to generate the structural bearing coefficient, comprising: training a bearing performance evaluation model based on big data; inputting the simulated bearing test data into the bearing performance evaluation model to obtain the structural bearing coefficient.

7. The method of designing a structure of the intelligent reinforced concrete with steel tube according to claim 1, wherein The predetermined performance constraints include bearing predetermined constraints, seismic predetermined constraints, and safety predetermined constraints.

8. A structural design system of a smart reinforced confined steel tube concrete, characterized by, The system is used to perform the intelligent enhancement constraint concrete-filled steel tube structure design method according to any one of claims 1-7, and the system comprises: a parameter analysis module configured to perform structural design parameter analysis according to a target design task to obtain a concrete-filled steel tube structure design scheme; a modeling module configured to model according to the concrete-filled steel tube structure design scheme to obtain a structural design model; a simulation module configured to perform multi-dimensional performance simulation according to the structural design model to obtain a structural performance simulation result; A judging module is configured to judge whether the structural performance simulation result meets a predetermined performance constraint. An enhancement processing module is configured to perform enhancement processing on the concrete-filled steel tube structure design scheme according to the predetermined performance constraint if the structural performance simulation result does not meet the predetermined performance constraint.

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