A building design method and system based on multi-objective optimization

Abstract

The application provides a building design method and system based on multi-objective optimization. The method improves the seismic performance and material stress dissipation efficiency of the building through multi-objective optimization. First, the building structure parameters such as the period ratio, the inter-story drift angle, the axial compression ratio, the dynamic load simulation data and the material stress wave propagation data are collected. Based on these data, the dynamic coupling factor between the structure parameters and the dynamic load is generated, and the matching relationship between the axial compression ratio and the stress wave peak propagation rate is adjusted. The non-dominated sorting strategy is used to optimize the above parameters collaboratively to obtain the initial structure parameter combination. Finally, the dynamic load simulation data is used as a constraint condition for iterative correction to generate a structure parameter combination that meets the requirements of seismic redundancy and stress dissipation balance. The technical scheme provided by the application can improve the safety and stability of building design.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of architectural design, and particularly relates to a building design method and system based on multi-objective optimization. BACKGROUND

[0002] In the process of modern urbanization, high-rise buildings as an important way of urban space utilization are increasing. In the face of natural disasters such as earthquakes, how to ensure that high-rise buildings meet structural safety while optimizing material stress dissipation becomes a key technical requirement. Especially under dynamic loads such as seismic waves, buildings need to have sufficient seismic redundancy to protect the safety of residents' lives and property. In addition, with the development of building material science, how to efficiently use the characteristics of new materials to improve the overall performance of building structures is also an important part of architectural design that cannot be ignored.

[0003] At present, one of the existing solutions for the above technical requirements is to use a simulation method based on finite element analysis for building design and optimization. This method establishes a detailed digital model of a high-rise building, combines specific geological conditions and expected dynamic loads (such as seismic waves), and accurately predicts the response of the building structure under different loads. This method can effectively evaluate the stability and safety of the building structure under specific conditions and provide data support for designers to optimize building design parameters.

[0004] However, although the simulation method based on finite element analysis performs well in structural analysis, it has certain limitations. First, because it needs to handle a large number of complex calculation tasks, it has high requirements for computer hardware resources, increasing costs and time consumption. Second, finite element analysis mainly relies on pre-set assumptions and boundary values, making it difficult to fully consider all variable changes in actual engineering, especially in multi-objective optimization problems. This may result in a design scheme that theoretically meets the requirements but may not achieve the best results in actual application. SUMMARY

[0005] The present application provides a building design method and system based on multi-objective optimization to solve the problem of low safety and poor stability in building design in the prior art.

[0006] In a first aspect, the present application provides a building design method based on multi-objective optimization, comprising:

[0007] Obtaining a structure parameter set, dynamic load simulation data and material stress wave propagation data of a target high-rise building, wherein the structure parameter set includes a period ratio, a story drift angle and an axial compression ratio, and the dynamic load simulation data is generated based on the spectral characteristics of seismic waves;

[0008] According to the dynamic change trend of the period ratio and the interlayer displacement angle, and in combination with the time distribution characteristics of the dynamic load simulation data, a dynamic coupling factor of the structure parameters and the dynamic load is generated;

[0009] Based on the spatial attenuation gradient of the material stress wave propagation data, the matching relationship between the axial compression ratio and the stress wave peak propagation rate is adjusted;

[0010] According to the dynamic coupling factor and the matching relationship, the period ratio, the interlayer displacement angle, and the axial compression ratio are optimized by a non-dominated sorting strategy to generate an initial structure parameter combination;

[0011] The dynamic load simulation data is fed back as a constraint boundary condition of the initial structure parameter combination, and after iterative correction, a high-rise building structure parameter combination that satisfies the seismic redundancy and material stress dissipation balance is generated.

[0012] Optionally, the initial structure parameter combination is generated by optimizing the period ratio, the interlayer displacement angle, and the axial compression ratio by a non-dominated sorting strategy, comprising:

[0013] Based on the dynamic coupling coefficient and the stress wave attenuation relationship, a multi-objective solution space with the period ratio, the interlayer displacement angle, and the axial compression ratio as variables is constructed;

[0014] The amplitude mutation interval of the dynamic load data is extracted, and the corresponding time-varying correlation value is set as the dynamic constraint boundary of the multi-objective solution space to generate a lateral displacement resistance interval and a stress dissipation interval;

[0015] In the overlapping domain of the lateral displacement resistance interval and the stress dissipation interval, a candidate solution set that satisfies the seismic redundancy and material dissipation synchronous convergence is screened, the attenuation coefficient of the period ratio, the interlayer displacement angle, and the axial compression ratio is iteratively corrected according to the real-time feedback of the dynamic constraint boundary, and an initial structure parameter combination is generated.

[0016] Optionally, the candidate solution set that satisfies the seismic redundancy and material dissipation synchronous convergence is screened in the overlapping domain of the lateral displacement resistance interval and the stress dissipation interval, comprising:

[0017] Based on the multi-dimensional space intersection of the lateral displacement resistance interval and the stress dissipation interval, a seismic redundancy quantification index and a material dissipation quantification index of each solution vector in the overlapping domain are established;

[0018] The maximum interlayer displacement angle offset under the amplitude mutation of the dynamic load is calculated by the seismic redundancy quantification index, and the attenuation gradient on the stress wave propagation path is calculated by the material dissipation quantification index;

[0019] establish a synchronous convergence condition, and require that the maximum inter-story drift angle offset is not more than the upper limit of the lateral displacement resistance interval, and the ratio of the attenuation gradient and the lower limit of the stress dissipation interval is greater than the real-time feedback coefficient of the dynamic constraint boundary;

[0020] Within the overlapping domain, the non-dominated solutions in the space of the solution vector that satisfy the seismic redundancy quantification index and the material dissipation quantification index are traversed with the synchronous convergence condition as a constraint, and the corresponding parameter combinations of the period ratio, inter-story drift angle and axial compression ratio are extracted;

[0021] According to the distribution density of the parameter combinations, the segmented weight of the period ratio, the iteration step of the inter-story drift angle and the correction amplitude of the axial compression ratio are dynamically adjusted to generate a candidate solution set.

[0022] Optionally, the maximum inter-story drift angle offset under the dynamic load amplitude mutation is calculated through the seismic redundancy quantification index, comprising:

[0023] The extreme points and duration of the dynamic load amplitude mutation interval are extracted, an association matrix of the amplitude gradient and the duration is constructed, a displacement transfer relationship between the dynamic load amplitude mutation interval and the inter-story drift angle is established, and a floor displacement increment is output;

[0024] The time history response of the floor displacement increment is calculated by traversing the dynamic load amplitude mutation interval, the displacement cumulative influence factor is obtained by time domain integration, and the corresponding inter-story drift angle instantaneous offset is extracted based on the displacement cumulative influence factor;

[0025] The inter-story drift angle instantaneous offset is dynamically matched with the upper limit of the lateral displacement resistance interval, and the maximum value is selected as the maximum inter-story drift angle offset.

[0026] Optionally, after the dynamic load simulation data is fed back as the constraint boundary condition of the initial structure parameter combination, and iteratively corrected, a high-rise building structure parameter combination that satisfies the balance of seismic redundancy and material stress dissipation is generated, comprising:

[0027] Based on the time distribution characteristics of the dynamic load simulation data, the time segments are divided and the load amplitude extreme points and duration of the time segments are extracted to generate a dynamic load constraint sequence;

[0028] According to the load amplitude extreme value in the dynamic load constraint sequence, time-varying constraint boundaries of the period ratio, inter-story drift angle and axial compression ratio are established;

[0029] Based on the material stress wave propagation data, the stress wave propagation path length and attenuation gradient of each floor are calculated to generate a dynamic matching coefficient of the axial compression ratio and the attenuation gradient;

[0030] The period ratio fluctuation range, inter-story drift angle offset and axial compression ratio decay rate of the high-rise building structure parameters are dynamically coupled with the time-varying constraint boundary, and when the high-rise building structure parameters meet the time-varying constraint boundary and are within the dynamic matching coefficient range, a high-rise building structure parameter combination is generated.

[0031] Optionally, the calculation of the stress wave propagation path length and the attenuation gradient of each floor, and the generation of the dynamic matching coefficient of the axial compression ratio and the attenuation gradient, comprises:

[0032] The three-dimensional coordinate node sequence of the stress wave propagation path of each floor is extracted, the Euclidean distance of adjacent nodes is calculated according to the geometric topological relationship between the nodes, and the stress wave propagation path length of each floor is accumulated;

[0033] Based on the stress wave propagation path length of each floor, stress wave peak value data is extracted, a peak value attenuation sequence is constructed, and a weighted average of the peak value attenuation sequence is performed to generate a stress wave attenuation gradient;

[0034] The dynamic correlation between the axial compression ratio and the attenuation gradient is established, the attenuation gradient is taken as an input variable, and the stress wave propagation path length of each floor is taken as a weight coefficient, so as to analyze the initial dynamic matching coefficient of the axial compression ratio of each floor;

[0035] Based on the floor position, the initial dynamic matching coefficient is spatially interpolated to eliminate the coefficient mutation between adjacent floors, and the dynamic matching coefficient of the axial compression ratio and the attenuation gradient is generated.

[0036] Optionally, the generation of the dynamic coupling factor of the structure parameter and the dynamic load in combination with the time distribution characteristics of the dynamic load simulation data comprises:

[0037] The load amplitude mean, load amplitude variance and load main frequency component of the dynamic load simulation data are extracted to generate a load statistical feature vector;

[0038] Based on the time sequence curve of the period ratio, the period ratio fluctuation slope of the time segment of the dynamic load simulation data and the fluctuation difference between adjacent time segments are analyzed to generate a period ratio trend coefficient;

[0039] Based on the time sequence curve of the inter-story drift angle, the displacement angle extreme value difference and displacement increment accumulation rate of the time segment are extracted to generate an inter-story drift angle trend coefficient;

[0040] An association matrix of the load statistical feature vector, the period ratio trend coefficient and the inter-story drift angle trend coefficient is established, an initial coupling component is obtained by associating the load amplitude mean with the period ratio trend coefficient, a displacement response component is obtained by associating the load main frequency component with the inter-story drift angle trend coefficient, and an initial dynamic coupling factor is generated by superimposing the initial coupling component and the displacement response component;

[0041] According to the time-varying relationship of the initial dynamic coupling factor, a time-varying weight matrix is constructed, time delay is eliminated, and a dynamic coupling factor of the structural parameters and the dynamic load is generated.

[0042] In a second aspect, the application provides a building design system based on multi-objective optimization, comprising:

[0043] An acquisition module acquires a structural parameter set of a target high-rise building, dynamic load simulation data, and material stress wave propagation data, wherein the structural parameter set includes a period ratio, a story drift angle, and an axial compression ratio, and the dynamic load simulation data is generated based on seismic wave spectrum characteristics;

[0044] A generation module generates a dynamic coupling factor of the structural parameters and the dynamic load according to the dynamic change trend of the period ratio and the story drift angle, and in combination with the time distribution characteristics of the dynamic load simulation data;

[0045] An adjustment module adjusts a matching relationship between the axial compression ratio and the stress wave peak propagation rate based on a spatial attenuation gradient of the material stress wave propagation data;

[0046] An optimization module performs multi-objective collaborative optimization on the period ratio, the story drift angle, and the axial compression ratio through a non-dominated sorting strategy according to the dynamic coupling factor and the matching relationship, and generates an initial structural parameter combination;

[0047] A correction module feeds back the dynamic load simulation data as a constraint boundary condition of the initial structural parameter combination, and after iterative correction, generates a high-rise building structural parameter combination that satisfies the balance of seismic redundancy and material stress dissipation.

[0048] In a third aspect, the application provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a building design method based on multi-objective optimization as described in the first aspect above.

[0049] In a fourth aspect, the application provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, a building design method based on multi-objective optimization as described in the first aspect is implemented.

[0050] In the embodiments of the present application, the structural parameter set of the target high-rise building, the dynamic load simulation data and the material stress wave propagation data are obtained, wherein the structural parameter set includes the period ratio, the inter-story drift angle and the axial compression ratio, and the dynamic load simulation data is generated based on the spectral characteristics of seismic waves; a dynamic coupling factor of the structural parameters and the dynamic load is generated according to the dynamic change trend of the period ratio and the inter-story drift angle, combined with the time distribution characteristics of the dynamic load simulation data; the matching relationship between the axial compression ratio and the peak stress wave propagation rate is adjusted based on the spatial attenuation gradient of the material stress wave propagation data; the period ratio, the inter-story drift angle and the axial compression ratio are multi-objective cooperatively optimized through a non-dominated sorting strategy according to the dynamic coupling factor and the matching relationship, to generate an initial structural parameter combination; and the dynamic load simulation data is fed back as a constraint boundary condition of the initial structural parameter combination, and after iterative correction, a high-rise building structural parameter combination satisfying the balance of seismic redundancy and material stress dissipation is generated.

[0051] The technical scheme of the present application has the following beneficial effects:

[0052] The present application ensures that the building design is based on a detailed data set, including key structural parameters such as period ratio, inter-story drift angle and axial compression ratio, as well as dynamic load simulation data generated based on the spectral characteristics of seismic waves and material stress wave propagation data. This provides a solid data foundation for subsequent analysis. By analyzing the dynamic change trend of the period ratio and the inter-story drift angle, a dynamic coupling factor reflecting their interaction is formed. This factor helps to more accurately evaluate the response behavior of the building under dynamic loads such as earthquakes. The relationship between the axial compression ratio and the peak stress wave propagation rate is optimized based on the spatial attenuation gradient of the material stress wave propagation, to improve the efficiency of building materials and enhance the safety of the structure. The period ratio, inter-story drift angle and axial compression ratio are multi-objective cooperatively optimized using a non-dominated sorting strategy, to find the best design parameter combination that satisfies the balance of seismic performance and material stress dissipation. The dynamic load simulation data is used as a feedback mechanism to iteratively correct the initial structural parameter combination until the final design scheme that meets both the seismic redundancy requirements and effectively dissipates material stress is obtained.

[0053] Further, by constructing a multi-objective solution space containing the period ratio, inter-story drift angle and axial compression ratio, and setting dynamic constraint boundaries in the amplitude mutation interval of the dynamic load data, the lateral displacement resistance interval and stress dissipation interval are identified. On this basis, a candidate solution set that satisfies the simultaneous convergence of seismic redundancy and material dissipation is selected, and the attenuation coefficients of each parameter are iteratively adjusted according to real-time feedback, to finally form the optimized initial structural parameter combination. This method greatly enhances the comprehensive consideration of seismic performance and material stress dissipation characteristics in the building design process, and by introducing dynamic constraint boundaries and real-time feedback mechanisms, the optimization process is more closely related to the actual situation, significantly improving the safety, reliability and economy of the building structure.

[0054] These aspects or other aspects of the present application will be made clearer in the following description of embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the description of embodiments or prior art. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0056] Figure 1 A flow chart of a building design method based on multi-objective optimization provided by the present application is shown;

[0057] Figure 2 A structural schematic diagram of a building design system based on multi-objective optimization provided by the present application is shown;

[0058] Figure 3 A structural schematic diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0059] In order to enable those skilled in the art to better understand the technical solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.

[0060] In some of the descriptions in the specification and claims of the present application and the above-mentioned drawings, a plurality of operations appearing in a specific order are included, but it should be clearly understood that these operations can be executed in the order appearing in the text or in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes can include more or fewer operations, and the operations can be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in the text are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence. Also, "first" and "second" are not of different types.

[0061] The project aims to improve the seismic performance of high-rise buildings through multi-objective optimization. First, obtain the structure parameter set, dynamic load simulation data, and material stress wave propagation data of the target high-rise building. Then, by analyzing the dynamic change trend of the period ratio and the inter-story drift angle, combined with the time distribution characteristics of the dynamic load simulation data, generate the dynamic coupling factor of the structure parameters and the dynamic load. Feedback the dynamic load data as a constraint boundary condition to ensure that the final structure parameter combination not only meets the seismic redundancy requirement, but also realizes the balance of material stress dissipation, thereby optimizing the overall design of high-rise buildings.

[0062] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0063] Figure 1 A flowchart of a building design method based on multi-objective optimization is provided for the embodiments of the present application, as shown in Figure 1 The method comprises:

[0064] 101, obtaining the structure parameter set, dynamic load simulation data, and material stress wave propagation data of the target high-rise building, wherein the structure parameter set includes period ratio, inter-story drift angle, and axial compression ratio, and the dynamic load simulation data is generated based on the seismic wave spectrum characteristics;

[0065] In this step, the structure parameter set includes period ratio (reflecting the vibration frequency characteristics of the building under the action of earthquake), inter-story drift angle (measuring the relative horizontal displacement between adjacent floors and the ratio of layer height, evaluating the lateral stiffness of the building), and axial compression ratio (the ratio of the axial pressure borne by the column to its carrying capacity, used to ensure the safety of the column).

[0066] The dynamic load simulation data is generated based on the seismic wave spectrum characteristics, including the vibration conditions that the building may encounter under different earthquake magnitudes and source distances, providing a reference for the seismic response of building design.

[0067] The material stress wave propagation data describes the variation of stress wave in material with time and space, which helps to understand the response mechanism of building materials under stress.

[0068] In the embodiments of the present application, first, the basic structural parameters of the target high-rise building are obtained from design drawings and specifications. Next, professional software is used to simulate seismic activity in a specific area to generate dynamic load simulation data. Then, material stress wave propagation data is collected through experimental or numerical simulation methods to analyze the behavior of materials under different loading conditions. Finally, these data are integrated to form a comprehensive dataset for subsequent steps.

[0069] In a new office building project, the design team extracts key parameters such as period ratio, inter-story drift angle, and axial compression ratio from the design scheme, and uses seismic engineering software to generate dynamic load simulation data for typical seismic characteristics in the region. At the same time, stress wave propagation data is obtained through testing of concrete samples, providing a basis for optimization design.

[0070] 102、According to the dynamic change trend of the period ratio and the inter-story drift angle, combined with the time distribution characteristics of the dynamic load simulation data, a dynamic coupling factor of structure parameters and dynamic load is generated;

[0071] In this step, the dynamic coupling factor is a quantitative index that reflects the degree of mutual influence between structure parameters and dynamic load. This factor is calculated based on the time variation trend of the period ratio and the inter-story drift angle, as well as the time distribution characteristics of the dynamic load, and is used to guide the multi-objective optimization process and enhance the seismic performance of buildings.

[0072] Dynamic load simulation data is generated based on seismic wave spectrum characteristics, including possible seismic conditions that buildings may encounter under different magnitudes and source distances, providing seismic response references for building design.

[0073] In the embodiments of the present application, first, the variation trend of the period ratio and the inter-story drift angle with time is analyzed. Next, the key time periods are identified in combination with the time distribution characteristics of the dynamic load. Then, the dynamic coupling factor in these time periods is calculated using statistical methods. Finally, the dynamic coupling factor is applied to the optimization process to ensure that the structure design can effectively cope with the challenges brought by earthquakes.

[0074] In the office building project, the design team analyzed the variation pattern of the period ratio and the inter-story drift angle with time, and found that these two parameters showed significant correlation in certain time periods. Combined with the previously generated dynamic load simulation data, the team calculated the dynamic coupling factor in specific time periods, providing a basis for further optimization.

[0075] 103、Based on the spatial attenuation gradient of the material stress wave propagation data, adjust the matching relationship between the axial compression ratio and the peak stress wave propagation rate;

[0076] In this step, the matching relationship between the axial compression ratio and the peak propagation rate of the stress wave refers to adjusting the axial compression ratio according to the spatial attenuation gradient of the material's stress wave to achieve optimal material properties and structural stability. This relationship ensures that building materials can effectively disperse stress during an earthquake, improving the overall safety of the building.

[0077] Spatial attenuation gradient refers to the law governing the phenomenon that the intensity of stress waves gradually weakens as the propagation distance increases during the propagation of stress waves within a material.

[0078] In this embodiment, firstly, the stress wave propagation path length for each floor is extracted from the material stress wave propagation data. Next, a stress wave peak attenuation sequence is constructed, and the attenuation gradient is calculated. Then, the axial compression ratio is adjusted according to the attenuation gradient to match the stress wave peak propagation rate. Finally, the adjusted axial compression ratio is incorporated into the overall structural parameter system in preparation for further optimization.

[0079] In a new office building project, the design team first calculated the stress wave propagation path length and its spatial attenuation gradient for each floor. The team constructed a stress wave peak attenuation sequence and determined the attenuation rate and pattern of stress waves in different floor materials. Next, based on these analyses, they adjusted the axial compression ratio to ensure it matched the stress wave peak propagation rate, optimizing the performance of building materials under seismic loading. This enhanced the building's safety. Ultimately, effective stress management was achieved throughout the entire building structure, providing a solid foundation for subsequent steps.

[0080] 104. Based on the dynamic coupling factor and matching relationship, the period ratio, inter-story drift angle and axial compression ratio are optimized in a multi-objective manner using a non-dominated sorting strategy to generate an initial combination of structural parameters.

[0081] In this step, the non-dominated sorting strategy is a multi-objective optimization algorithm that aims to find a set of solutions that are not dominated by any other solution on any objective.

[0082] The period ratio refers to the ratio between the natural vibration period of a building when it is subjected to seismic excitation and the standard reference period.

[0083] Inter-story drift angle is an important parameter that measures the ratio of the relative horizontal displacement between adjacent floors to the story height. It is used to assess the lateral stiffness and overall stability of a building.

[0084] The axial compression ratio refers to the ratio of the axial pressure a column bears to its bearing capacity. It is a key indicator used to assess the safety of a column under stress.

[0085] In this embodiment, firstly, an optimization objective is set based on the dynamic coupling factor and matching relationship. Next, a non-dominated sorting algorithm is applied to optimize the period ratio, inter-story drift angle, and axial compression ratio. Then, a set of non-dominated solutions is selected as a candidate solution set. Finally, considering all indicators, an optimal combination of initial structural parameters is determined.

[0086] During the design process of the office building project, the design team first set optimization objectives based on the dynamic coupling factor and the matching relationship between the axial compression ratio and the peak stress wave propagation rate obtained in previous steps. Next, a non-dominated sorting algorithm was applied to optimize the period ratio, inter-story drift angle, and axial compression ratio. Subsequently, a set of non-dominated solutions was selected as a candidate solution set. After multiple iterations and verifications, the team finally determined an optimal combination of initial structural parameters. This set of parameters not only improved the building's seismic performance but also ensured the effective dispersion of material stress, laying the foundation for the project's success.

[0087] 105. Feed back the dynamic load simulation data as the constraint boundary conditions of the initial structural parameter combination, and after iterative correction, generate a high-rise building structural parameter combination that satisfies seismic redundancy and material stress dissipation equilibrium.

[0088] In this step, the combination of structural parameters for high-rise buildings refers to a set of key structural parameter values ​​determined for a specific high-rise building design after a series of analysis and optimization processes have been completed.

[0089] Boundary constraints refer to the transformation of various external factors affecting architectural design into specific limitations, which are used to guide and constrain parameter selection and optimization during the design process.

[0090] The initial structural parameter set is a set of basic structural parameter values ​​generated in the early stages of architectural design based on a multi-objective optimization strategy. These parameters mainly include the period ratio, inter-story drift angle, and axial compression ratio.

[0091] In this embodiment, firstly, the dynamic load simulation data is converted into constraint boundary conditions. Next, these conditions are applied to an initial combination of structural parameters. Then, through multiple iterative corrections, the system is gradually optimized until all constraints are met. Finally, the final combination of structural parameters is confirmed to ensure good performance under actual seismic conditions.

[0092] In the final stage of the office building project, the design team converted the dynamic load simulation data into constraint boundary conditions for the structural parameter combination. First, based on the seismic activity characteristics of the specific region, the team set detailed constraints. Then, these conditions were applied to the preliminary structural parameter combination, and through multiple rounds of iterative corrections, the combination was continuously optimized until all constraints were met. Every detail was ensured to meet safety standards. After fine-tuning, a structural parameter combination was finally confirmed that not only met the seismic requirements but also balanced material stress dissipation. This achievement significantly improved the safety and reliability of the building, ensuring its stability under actual seismic conditions.

[0093] In summary, steps 101 to 105, through systematic analysis of high-rise building structural parameters, dynamic load simulation data, and material stress wave propagation data, and the use of advanced computing techniques and optimization algorithms, not only improve the safety and seismic performance of building design, but also ensure effective dispersion of material stress, greatly enhancing the overall stability and reliability of the building. Each step is closely connected, from data collection to the final determination of the structural parameter combination, forming a complete solution.

[0094] To further improve the seismic performance and material utilization efficiency of high-rise building structural design, the scheme is based on the relationship between dynamic coupling coefficients and stress wave attenuation, and constructs a multi-objective solution space with period ratio, inter-story drift angle, and axial compression ratio as variables. According to the amplitude mutation interval of dynamic load data, dynamic constraint boundaries are set to generate lateral displacement resistance intervals and stress dissipation intervals, so as to select candidate solutions that meet the synchronization convergence of seismic redundancy and material dissipation in the overlapping domain of these intervals. In some embodiments, the multi-objective collaborative optimization of the period ratio, inter-story drift angle, and axial compression ratio in step 104 generates an initial structural parameter combination, including:

[0095] 201. Based on the relationship between dynamic coupling coefficients and stress wave attenuation, a multi-objective solution space is constructed with the period ratio, inter-story drift angle, and axial compression ratio as variables;

[0096] In step 201, dynamic coupling coefficients refer to coefficients established based on the relationship between structural parameters and dynamic loads, used to measure the interaction between the two. Stress wave attenuation relationship involves the phenomenon that stress waves within materials weaken as they propagate over distance, which is a key indicator for evaluating the performance of building materials under dynamic loads such as earthquakes. The period ratio refers to the relative lateral displacement of a building structure within one period compared to its height, the inter-story drift angle represents the relative displacement angle between floors of a building under horizontal forces, and the axial compression ratio reflects the proportion of axial pressure on a column or wall to the compressive capacity of the cross-section of the member. The multi-objective solution space is a mathematical model that includes the above variables, aiming to find the optimal design parameters.

[0097] In the embodiments of the present application, first, a multi-objective solution space is constructed with period ratio, inter-story drift angle and axial compression ratio as variables based on the relationship between dynamic coupling coefficient and stress wave attenuation. Then, the response characteristics of these variables under different conditions are analyzed using computer simulation technology and incorporated into the solution space. Next, an appropriate algorithm (such as genetic algorithm) is used to explore the solution space and identify potential optimization paths. Finally, a multi-dimensional solution space is formed by integrating all the information as the basis for subsequent optimization.

[0098] 202. Extract the amplitude mutation interval of dynamic load data, set the corresponding time-varying correlation value as the dynamic constraint boundary of the multi-objective solution space, and generate the lateral displacement interval and stress dissipation interval;

[0099] In step 202, the amplitude mutation interval refers to the part of the dynamic load data that has a significant change, which is used to identify the time period that may have a significant impact on the building structure. The time-varying correlation value is a data set that describes the characteristics of dynamic load changes over time, and has a direct impact on the lateral displacement interval and stress dissipation interval. The lateral displacement interval refers to the range that ensures the stability of the building under lateral force, and the stress dissipation interval refers to the range of the material's ability to absorb and dissipate energy.

[0100] In the embodiments of the present application, first, the amplitude mutation interval in the dynamic load data is extracted and its characteristics are analyzed. Then, the corresponding time-varying correlation value is set as the dynamic constraint boundary of the multi-objective solution space to generate the lateral displacement interval and stress dissipation interval. Then, numerical simulation method is used to verify the rationality and effectiveness of these intervals. Finally, these intervals are adjusted according to the actual engineering requirements to ensure both the safety of the building and the economic requirements.

[0101] 203. In the overlapping domain of the lateral displacement interval and the stress dissipation interval, filter the candidate solution set that meets the seismic redundancy and material dissipation synchronous convergence, and iteratively correct the attenuation coefficients of the period ratio, inter-story drift angle and axial compression ratio according to the real-time feedback of the dynamic constraint boundary to generate the initial structure parameter combination.

[0102] In step 203, the candidate solution set is composed of a group of solutions that meet certain conditions, which refers to solutions that meet both seismic redundancy and material dissipation synchronous convergence requirements. The real-time feedback mechanism of the dynamic constraint boundary can adjust the limiting conditions in the optimization process according to the changes in the external environment to improve the quality of the solution. The attenuation coefficient is a parameter that describes the speed of reduction of a physical quantity over time or space, which is mainly used to adjust the period ratio, inter-story drift angle and axial compression ratio in this context.

[0103] In the application examples, first, the overlapping region of the lateral displacement resistance interval and the stress dissipation interval is identified. Then, the candidate solution set that meets the synchronous convergence condition is screened out in this region. Then, the attenuation coefficients of the period ratio, inter-story drift angle and axial compression ratio are iteratively adjusted using real-time feedback information of the dynamic constraint boundary. Finally, after multiple iterations of optimization, a set of optimal initial structure parameter combinations is obtained, ensuring that the building has excellent seismic performance and material utilization efficiency.

[0104] The following is a specific example:

[0105] In a new high-rise office building project, the design team first establishes a multi-objective solution space based on the dynamic coupling coefficient and stress wave attenuation relationship, covering key parameters such as period ratio, inter-story drift angle and axial compression ratio. Then, the team extracts the amplitude mutation interval of the dynamic load data and sets the corresponding dynamic constraint boundary conditions to generate the lateral displacement resistance interval and the stress dissipation interval. On this basis, the team screens out the candidate solution set that meets the synchronous convergence condition and iteratively corrects the attenuation coefficients of the period ratio, inter-story drift angle and axial compression ratio, finally obtaining a set of initial structure parameter combinations that not only have excellent seismic performance but also effectively disperse stress, laying a solid foundation for the success of the project.

[0106] In summary, steps 201 to 203 not only improve the scientificity and rationality of building design, but also greatly enhance the safety and stability of buildings in the face of natural disasters such as earthquakes through fine parameter optimization, achieving efficient use of building materials while ensuring the overall performance of the structure.

[0107] To solve the problem of seismic redundancy and material dissipation synchronous convergence in high-rise building design, the scheme establishes the seismic redundancy quantitative index and material dissipation quantitative index of each solution vector in the overlapping domain of the lateral displacement resistance interval and the stress dissipation interval. By calculating the maximum inter-story drift angle offset and the attenuation gradient on the stress wave propagation path, it ensures that these indicators meet the synchronous convergence condition, and traverses the solution vector space with this constraint to extract the parameter combination of period ratio, inter-story drift angle and axial compression ratio that meets the condition. In some embodiments, step 203 of screening the candidate solution set that meets the seismic redundancy and material dissipation synchronous convergence in the overlapping domain of the lateral displacement resistance interval and the stress dissipation interval includes:

[0108] 301、Based on the multi-dimensional space intersection of the lateral displacement resistance interval and the stress dissipation interval, establish the seismic redundancy quantitative index and material dissipation quantitative index of each solution vector in the overlapping domain;

[0109] In step 301, the multidimensional spatial intersection refers to determining the spatial range in which two or more variables act together using mathematical methods. The seismic redundancy quantification index is a measure of a structure's ability to maintain stability under extreme conditions, including parameters such as the maximum inter-story drift angle, used to assess a building's performance during an earthquake. The material dissipation quantification index assesses a material's ability to absorb and dissipate energy by calculating the attenuation gradient along the stress wave propagation path, involving material properties and stress wave characteristics.

[0110] In this embodiment, the overlapping portion of the lateral displacement resistance interval and the stress dissipation interval is first identified as the intersection of the multidimensional space. Next, based on this intersection, quantitative indices for seismic redundancy and material dissipation are constructed for each solution vector. Then, numerical simulation technology is used to analyze each solution to ensure it meets preset safety standards. Finally, all information is integrated to form a comprehensive evaluation system to guide subsequent design optimization.

[0111] 302. Calculate the maximum inter-story drift angle offset under sudden change in dynamic load amplitude using the seismic redundancy quantification index, and calculate the attenuation gradient on the stress wave propagation path using the material dissipation quantification index;

[0112] In step 302, abrupt changes in dynamic load amplitude refer to significant changes in load intensity over a specific time period, which are crucial to the impact on building structures. The maximum inter-story drift angle offset is an important indicator for assessing a building's resistance to lateral displacement. The attenuation gradient describes the rate at which the intensity of a stress wave decreases along its propagation path, reflecting the material's ability to dissipate energy.

[0113] In this embodiment, the maximum inter-story drift angle offset is first calculated based on dynamic load data, particularly abrupt amplitude changes. Next, the attenuation gradient is calculated according to the stress wave propagation characteristics of the material. Then, these indicators are compared under different design schemes to find the optimal solution. Finally, the design scheme is adjusted based on the calculation results to ensure that the building structure can both resist lateral displacement and effectively dissipate stress.

[0114] 303. Establish synchronous convergence conditions, and require that the maximum inter-story drift angle offset does not exceed the upper limit of the anti-lateral displacement interval, and that the ratio of the attenuation gradient to the lower limit of the stress dissipation interval is greater than the real-time feedback coefficient of the dynamic constraint boundary.

[0115] In step 303, the synchronous convergence condition is a set of predefined criteria that require the design parameters to simultaneously meet the requirements of seismic redundancy and material dissipation. The real-time feedback coefficients of the dynamic constraint boundary adjust the constraints during the optimization process in real time according to changes in the external environment, thereby improving the quality of the solution. This step ensures that the final design scheme is both safe and efficient.

[0116] In the embodiments of the present application, first, the synchronous convergence condition is set, it is clear that the maximum inter-story drift angle offset does not exceed the upper limit of the lateral displacement resistance interval, and the ratio of the attenuation gradient and the lower limit of the stress dissipation interval is greater than the real-time feedback coefficient of the dynamic constraint boundary. Then, the iterative algorithm is used to continuously adjust the parameters until the best combination is found. Then, the actual effect of the design scheme is verified by using the simulation tool. Finally, the most suitable scheme is selected by considering the economy and safety.

[0117] 304. In the overlapping domain, the non-dominated solutions that satisfy the seismic redundancy quantification index and the material dissipation quantification index in the solution vector space are traversed with the synchronous convergence condition as the constraint, and the parameter combinations of the corresponding period ratio, inter-story drift angle and axial compression ratio are extracted;

[0118] In step 304, the non-dominated solution is all solutions that cannot be simultaneously superior to the rest of the solutions in the multi-objective optimization process. The parameter combinations of the period ratio, inter-story drift angle and axial compression ratio represent the specific numerical values of different design schemes. The distribution density refers to the distribution of these combinations in the parameter space, which is used to guide the further optimization direction.

[0119] In the embodiments of the present application, first, all solution vectors in the overlapping domain are traversed to find non-dominated solutions that satisfy the synchronous convergence condition. Then, the parameter combinations of the period ratio, inter-story drift angle and axial compression ratio corresponding to the best performance are extracted. Then, the distribution density of these combinations is analyzed to identify potential optimization areas. Finally, based on the analysis results, the optimization strategy is adjusted to generate a set of high-quality candidate solution sets.

[0120] 305. According to the distribution density of the parameter combinations, the segmented weight of the period ratio, the iteration step of the inter-story drift angle and the correction amplitude of the axial compression ratio are dynamically adjusted to generate a candidate solution set.

[0121] In step 305, the segmented weight, iteration step and correction amplitude respectively refer to different adjustment methods of the period ratio, inter-story drift angle and axial compression ratio in the optimization process. The segmented weight determines how to balance the importance of each parameter at different stages; the iteration step affects the speed and accuracy of searching the solution space; and the correction amplitude controls the degree of parameter adjustment at each iteration.

[0122] In the embodiments of the present application, first, according to the distribution density of the parameter combinations, the segmented weight of the period ratio, the iteration step of the inter-story drift angle and the correction amplitude of the axial compression ratio are dynamically adjusted. Then, the optimization algorithm is re-run using the adjusted parameter settings to find a better solution set. Then, through multiple iterations, the optimal solution is gradually approached. Finally, based on the optimization results, the final candidate solution set is generated to ensure that the building design meets the safety and has good economy.

[0123] The following is a specific example:

[0124] In a new office building project, the design team first established a multi-dimensional space intersection and constructed a seismic redundancy and material dissipation quantification index. Then, through the calculation model, the maximum inter-story drift angle offset and attenuation gradient were found, and the synchronous convergence condition was set. Then, in the overlapping domain, the non-dominated solution set that meets the conditions was found by traversing the solution vector. After that, based on the distribution density, the optimization parameters were adjusted, and a set of efficient candidate solutions were successfully generated. This not only improves the safety of the building, but also maximizes the economic benefits.

[0125] In summary, through steps 301 to 305, the fine optimization of high-rise building structure parameters is achieved, improving the safety performance of the building when facing natural disasters, while also optimizing material utilization and reducing construction costs. This method provides a new approach and technical means for complex structure design, making the design more scientific and reasonable.

[0126] To solve the problem of calculating the maximum inter-story drift angle offset under dynamic load, the scheme first constructs the correlation matrix of amplitude gradient and duration, then obtains the displacement cumulative influence factor through time domain integration, and finally extracts the corresponding inter-story drift angle instantaneous offset based on this factor, ensuring that it does not exceed the upper limit of the lateral displacement interval. In some embodiments, the maximum inter-story drift angle offset under dynamic load amplitude mutation is calculated by the seismic redundancy quantification index in step 302, including:

[0127] 401、Extract the extreme points and duration of the dynamic load amplitude mutation interval, construct the correlation matrix of amplitude gradient and duration, establish the displacement transfer relationship between the dynamic load amplitude mutation interval and the inter-story drift angle, and output the floor displacement increment;

[0128] In step 401, the extreme points and duration of the dynamic load amplitude mutation interval refer to the maximum or minimum value of the load intensity and its corresponding time length within a certain time period. The correlation matrix of amplitude gradient and duration is used to describe the relationship between these extreme points and their corresponding duration. The displacement transfer relationship is established based on the above data to evaluate the displacement change of the floor under dynamic load. The floor displacement increment represents the additional displacement of the floor within a certain time.

[0129] In the embodiments of the present application, first, the extreme points and duration of the dynamic load amplitude mutation interval are extracted, and the correlation matrix of amplitude gradient and duration is constructed. Then, according to this matrix, the displacement transfer relationship between the dynamic load amplitude mutation interval and the inter-story drift angle is established. Then, the floor displacement increment in each time interval is calculated using numerical simulation technology. Finally, all the calculated floor displacement increments are output as the basis for subsequent analysis.

[0130] 402. traversing the dynamic load amplitude mutation interval, calculating the time history response of the floor displacement increment, obtaining the displacement cumulative influence factor by time domain integration, and extracting the corresponding inter-story drift angle instantaneous offset based on the displacement cumulative influence factor;

[0131] In step 402, the time history response refers to the reaction of the structure to the change of dynamic load over time. The displacement cumulative influence factor is an index to measure the cumulative displacement influence of the floor during the entire loading process. The inter-story drift angle instantaneous offset is the change amount of the relative displacement angle of the floor at a certain time.

[0132] In the embodiments of the present application, first, the dynamic load amplitude mutation interval is traversed, and the time history response of the floor displacement increment in each time interval is calculated. Then, the displacement cumulative influence factor is obtained by time domain integration method. Then, based on this factor, the inter-story drift angle instantaneous offset corresponding to each time point is extracted. Finally, all the instantaneous offset data are integrated to form a database that comprehensively reflects the behavior of the building structure under the action of dynamic load.

[0133] 403. dynamically matching the inter-story drift angle instantaneous offset with the upper limit of the lateral displacement resistance interval, and screening the maximum value as the maximum inter-story drift angle offset.

[0134] In step 403, the upper limit of the lateral displacement resistance interval is the maximum inter-story drift angle offset allowed in the design, which is used to ensure the safety of the building. The dynamic matching process refers to the process of comparing the calculated inter-story drift angle instantaneous offset with the upper limit of the lateral displacement resistance interval. The maximum inter-story drift angle offset is the maximum value screened out, which represents the maximum displacement that may occur in all considered time periods.

[0135] In the embodiments of the present application, first, the calculated inter-story drift angle instantaneous offset is dynamically matched with the upper limit of the lateral displacement resistance interval. Then, the maximum value among all instantaneous offsets is screened out. Then, it is verified whether the maximum value meets the safety standard. Finally, the maximum inter-story drift angle offset is confirmed and recorded as a key parameter for evaluating the safety of building design.

[0136] The following is a specific example:

[0137] In a new office building project, the design team first extracts the extreme points and duration of the dynamic load amplitude mutation interval, establishes the correlation matrix of amplitude gradient and duration, determines the displacement transfer relationship between dynamic load amplitude mutation interval and inter-story drift angle, and calculates the floor displacement increment. Then, by traversing the entire dynamic load amplitude mutation interval, the time history response of the floor displacement increment is calculated, the displacement cumulative influence factor is obtained, and the instantaneous offset of the inter-story drift angle is extracted accordingly. Then, the instantaneous offset is dynamically matched with the upper limit of the lateral displacement interval, and the maximum inter-story drift angle offset is finally selected, ensuring that the design scheme meets the safety requirements and has good economy.

[0138] In summary, through steps 401 to 403, the maximum inter-story drift angle offset of high-rise buildings under dynamic load is accurately calculated, improving the safety and reliability of building design. This method not only helps to identify potential risk points, but also provides a scientific basis for optimizing the selection of building materials and structural design, thereby improving the overall quality and durability of the building.

[0139] To solve the problem of balancing seismic redundancy and material stress dissipation in high-rise building design, the scheme uses the time distribution characteristics of dynamic load simulation data to divide time segments and extract load amplitude extreme points and duration, establish time-varying constraint boundaries of period ratio, inter-story drift angle and axial compression ratio, and calculate stress wave propagation path length and attenuation gradient of each floor based on material stress wave propagation data, dynamically adjust the axial compression ratio, and generate high-rise building structure parameter combinations that meet the requirements. In some embodiments, the dynamic load simulation data is fed back as a constraint boundary condition for the initial structure parameter combination in step 105, and after iterative correction, a high-rise building structure parameter combination that balances seismic redundancy and material stress dissipation is generated, including:

[0140] 501、Based on the time distribution characteristics of the dynamic load simulation data, divide the time segments and extract the load amplitude extreme points and duration of the time segments to generate a dynamic load constraint sequence;

[0141] In step 501, the time distribution characteristics refer to the pattern of dynamic load change over time, which includes the frequency, amplitude and its variation over time of the load. The load amplitude extreme point is the maximum or minimum value of the load in a certain time period, which is used to identify the key point of load intensity. The duration refers to the length of time when these extreme points appear, which is crucial for understanding the impact of load on the structure. The dynamic load constraint sequence is a sequence composed of these extreme points and their corresponding duration, which serves as the basis for subsequent design optimization.

[0142] In the embodiments of the present application, first, the time distribution characteristics of dynamic load simulation data are analyzed to identify the load amplitude extreme points and duration in different time segments. Then, based on this information, a dynamic load constraint sequence is generated. Next, numerical analysis techniques are used to evaluate the characteristics of each time segment to ensure that it accurately reflects the actual load conditions. Finally, all the calculation results are integrated into a comprehensive constraint sequence as the basis for the optimization process.

[0143] 502. Based on the load amplitude extreme values in the dynamic load constraint sequence, time-varying constraint boundaries for the period ratio, inter-story drift angle, and axial compression ratio are established;

[0144] In step 502, the time-varying constraint boundaries are a series of limiting conditions established based on the load amplitude extreme values in the dynamic load constraint sequence, aiming to guide the design adjustment of the period ratio, inter-story drift angle, and axial compression ratio. These boundary conditions will dynamically adjust with time and load changes to ensure that the design scheme meets the latest safety standards. The period ratio, inter-story drift angle, and axial compression ratio are key parameters for measuring the seismic resistance and structural stability of buildings.

[0145] In the embodiments of the present application, first, the time-varying constraint boundaries for the period ratio, inter-story drift angle, and axial compression ratio are determined based on the load amplitude extreme values in the dynamic load constraint sequence. Next, algorithmic models are used to simulate the performance of building structures under different constraint conditions. Then, the constraint boundaries are continuously adjusted based on the simulation results until the optimal solution is found. Finally, the effectiveness of these boundary conditions is verified, and they are applied to the final design scheme to ensure the safety and stability of the building.

[0146] 503. Based on the material stress wave propagation data, the stress wave propagation path lengths and attenuation gradients of each floor are calculated, and a dynamic matching coefficient of the axial compression ratio and the attenuation gradient is generated;

[0147] In step 503, the stress wave propagation path length is the distance from the stress wave source to each floor, reflecting how stress waves propagate within the building. The attenuation gradient describes the speed at which stress waves decrease in strength along the propagation path, and is an important indicator of the material's ability to absorb and dissipate energy. The dynamic matching coefficient is established based on the relationship between the axial compression ratio and the attenuation gradient, and is used to optimize the selection of the axial compression ratio to ensure that the material can effectively dissipate stress.

[0148] In the embodiments of the present application, first, the stress wave propagation path lengths and attenuation gradients of each floor are calculated, and the dynamic matching coefficient of the axial compression ratio and the attenuation gradient is generated using these data. Next, numerical simulation methods are used to evaluate the stress wave propagation effect under different axial compression ratios. Then, the matching coefficient is adjusted based on the evaluation results to ensure that it meets both safety requirements and improves material utilization. Finally, the optimized matching coefficient is applied in the design to form a more reasonable axial compression ratio configuration, improving the overall performance of the building.

[0149] 504. Coupling the period ratio fluctuation range, the inter-story drift angle offset, and the axial compression ratio decay rate of the high-rise building structure parameters with the time-varying constraint boundary, generating a high-rise building structure parameter combination when the high-rise building structure parameters all meet the time-varying constraint boundary and are within the dynamic matching coefficient range.

[0150] In step 504, the period ratio fluctuation range refers to the variation interval of the period ratio under different conditions, the inter-story drift angle offset is the variation amount of the relative displacement angle between floors, and the axial compression ratio decay rate represents the speed at which the axial compression ratio decreases over time or load increase. These parameters, combined with the dynamic matching coefficient and the time-varying constraint boundary, can generate a high-rise building structure parameter combination that balances seismic redundancy and material stress dissipation.

[0151] In the embodiments of the present application, the period ratio fluctuation range, the inter-story drift angle offset, and the axial compression ratio decay rate are first coupled with the dynamic matching coefficient and the time-varying constraint boundary. Then, these parameters are iteratively corrected until all parameters meet the constraint conditions. Then, the actual effect of the final design scheme is verified using a simulation tool. Finally, the best high-rise building structure parameter combination is confirmed and recorded based on the verification results, ensuring that the design scheme is both safe and efficient.

[0152] The following is a specific example:

[0153] In a new office building project, the design team first analyzed the time distribution characteristics of the dynamic load simulation data, divided the time segments, extracted the load amplitude extreme points and duration, and generated a dynamic load constraint sequence. Then, the time-varying constraint boundaries of the period ratio, inter-story drift angle, and axial compression ratio were established based on the sequence. Then, the stress wave propagation path length and attenuation gradient of each floor were calculated, and the dynamic matching coefficient of the axial compression ratio and the attenuation gradient was generated. Finally, the period ratio fluctuation range, the inter-story drift angle offset, and the axial compression ratio decay rate were combined with these constraint conditions, and after multiple iterations, a high-rise building structure parameter combination that balances seismic redundancy and material stress dissipation was obtained.

[0154] In summary, the fine optimization of high-rise building structure parameters is achieved through steps 501 to 504, not only improving the safety performance of the building when facing natural disasters, but also optimizing material utilization and reducing construction costs. This method provides a new way of thinking and technical means for complex structure design, making the design more scientific and reasonable, and enhancing the overall quality and durability of the building.

[0155] To solve the problem of accurately calculating the stress wave propagation path length and attenuation gradient of each floor in high-rise building design, in the calculation of the stress wave propagation path length and attenuation gradient of each floor, the three-dimensional coordinate node sequence of the stress wave propagation path of each floor is extracted to calculate the Euclidean distance of adjacent nodes, and then a peak attenuation sequence is generated and weighted average is performed on the sequence, the dynamic correlation between the axial compression ratio and the attenuation gradient is analyzed, and finally the dynamic matching coefficient of the axial compression ratio and the attenuation gradient is generated. In some embodiments, the calculation of the stress wave propagation path length and the attenuation gradient of each floor in step 503 generates the dynamic matching coefficient of the axial compression ratio and the attenuation gradient, including:

[0156] 601、extract the three-dimensional coordinate node sequence of the stress wave propagation path of each floor, calculate the Euclidean distance of adjacent nodes according to the geometric topological relationship between nodes, and accumulate to obtain the stress wave propagation path length of each floor;

[0157] In step 601, the three-dimensional coordinate node sequence refers to a set of coordinates describing the positions of points within the building structure, and each node represents a specific location. The Euclidean distance is the straight-line distance between two points, which is used to quantify the actual distance between adjacent nodes. By calculating these distances and accumulating them, the stress wave propagation path length of each floor can be obtained, which is the basis for evaluating how stress waves propagate inside the building.

[0158] In the embodiments of the present application, first, the three-dimensional coordinate node sequence of the stress wave propagation path of each floor is extracted. Then, based on the geometric topological relationship between nodes, the Euclidean distance between adjacent nodes is calculated using mathematical methods. Then, the distances of all adjacent nodes are added up to obtain the stress wave propagation path length of each floor. Finally, integrate all floor data to form a comprehensive stress wave propagation path length database as the basis for subsequent analysis.

[0159] 602、based on the stress wave propagation path length of each floor, extract the peak value data of the stress wave, construct a peak attenuation sequence, and perform weighted average on the peak attenuation sequence to generate the attenuation gradient of the stress wave;

[0160] In step 602, the peak attenuation sequence is a sequence composed of peak values of stress waves along their propagation paths, reflecting the trend of stress wave intensity with distance. Weighted average is a process of averaging values in the sequence after assigning different weights, used to calculate the attenuation gradient of the stress wave, i.e. the speed of stress wave intensity decreasing with distance. The attenuation gradient is crucial for understanding the energy absorption and dissipation capacity of materials.

[0161] In the embodiments of the present application, first, the stress wave peak value data is extracted according to the stress wave propagation path length of each floor to construct a peak value attenuation sequence. Then, the numerical analysis technique is used to perform weighted average processing on the peak value attenuation sequence to calculate the attenuation gradient of the stress wave. Then, the effectiveness of the calculation result is verified to ensure that it can accurately reflect the propagation characteristics of the stress wave in the building material. Finally, the calculated attenuation gradient is used as an important parameter in the subsequent optimization process to guide the design decision.

[0162] 603, establish the dynamic correlation between the axial compression ratio and the attenuation gradient, take the attenuation gradient as the input variable, and take the stress wave propagation path length of each floor as the weight coefficient, analyze the initial dynamic matching coefficient of the axial compression ratio of each floor;

[0163] In step 603, the dynamic correlation between the axial compression ratio and the attenuation gradient refers to the process of establishing the relationship between the two to guide the selection of the axial compression ratio. The initial dynamic matching coefficient is a coefficient calculated based on the attenuation gradient and the stress wave propagation path length, which aims to optimize the selection of the axial compression ratio to enable the material to effectively dissipate stress. This coefficient takes into account the influence of the floor position to adapt to different building requirements. The axial compression ratio is a measure of the ratio of the axial pressure borne by a column or wall to its cross-sectional compressive capacity.

[0164] In the embodiments of the present application, first, the dynamic correlation model between the axial compression ratio and the attenuation gradient is established, taking the attenuation gradient as the input variable and the stress wave propagation path length of each floor as the weight coefficient. Then, the initial dynamic matching coefficient of the axial compression ratio of each floor is analyzed using an algorithm. Then, the matching coefficient is adjusted through multiple iterations until the optimal solution is found. Finally, the final initial dynamic matching coefficient is confirmed and recorded to provide a basis for subsequent design, ensuring the safety and effectiveness of the design scheme.

[0165] 604, based on the floor position, spatially interpolate the initial dynamic matching coefficient to eliminate the sudden change of the coefficient between adjacent floors, and generate the dynamic matching coefficient of the axial compression ratio and the attenuation gradient.

[0166] In step 604, spatial interpolation is a method of estimating unknown point data based on known point data, which is used to eliminate the problem of sudden change of the coefficient between adjacent floors. By spatially interpolating the initial dynamic matching coefficient, the dynamic matching coefficient of the axial compression ratio and the attenuation gradient with smooth transition can be generated, ensuring the consistency and rationality of the design scheme. Spatial interpolation helps to improve the continuity and prediction accuracy of the model.

[0167] In the embodiments of the present application, first, the initial dynamic matching coefficients are spatially interpolated based on the floor positions to eliminate the sudden changes of the coefficients between adjacent floors. Then, the dynamic matching coefficients of the axial compression ratio and the attenuation gradient are generated using interpolation techniques. Then, the actual effect of the new coefficients is verified by simulation to ensure that they can achieve smooth transition between different floors. Finally, all data are integrated to generate the final dynamic matching coefficients of the axial compression ratio and the attenuation gradient, which are applied to building design to improve the overall design quality.

[0168] The following is a specific example:

[0169] In a new office building project, the design team first extracts the three-dimensional coordinate node sequence of the stress wave propagation path of each floor, calculates the Euclidean distance of adjacent nodes, and obtains the length of the stress wave propagation path of each floor. Then, based on these lengths, the stress wave peak value data is extracted, the peak value attenuation sequence is constructed, and the weighted average processing is performed to obtain the attenuation gradient of the stress wave. Then, the dynamic correlation between the axial compression ratio and the attenuation gradient is established, and the initial dynamic matching coefficients of the axial compression ratio of each floor are analyzed. Finally, the sudden changes of the coefficients between adjacent floors are eliminated by spatial interpolation, and the dynamic matching coefficients of the axial compression ratio and the attenuation gradient with smooth transition are generated, ensuring that the design scheme is both safe and efficient.

[0170] In summary, through steps 601 to 604, the fine optimization of high-rise building structure parameters is achieved, which not only improves the safety performance of the building when facing natural disasters, but also optimizes the material utilization rate and reduces the construction cost. This method provides a new idea and technical means for complex structure design, making the design more scientific and reasonable, enhancing the overall quality and durability of the building, and improving the design efficiency and accuracy.

[0171] In order to solve the problem of generating dynamic coupling factors in high-rise building design, the present scheme combines the time distribution characteristics of dynamic load simulation data to generate the dynamic coupling factors of structure parameters and dynamic load. First, the load statistical feature vector is extracted, then the trend coefficients of the period ratio and the inter-story drift angle are analyzed, and then these coefficients are associated with the load feature vector to generate the initial dynamic coupling factor. Finally, by constructing a time-varying weight matrix, the time delay is eliminated, and the generation of the dynamic coupling factor is completed. In some embodiments, the step 102 of generating the dynamic coupling factors of structure parameters and dynamic load by combining the time distribution characteristics of the dynamic load simulation data includes:

[0172] 701、extracting the load amplitude mean, load amplitude variance and load main frequency component of the dynamic load simulation data to generate a load statistical feature vector;

[0173] In step 701, the load amplitude mean value is the average amplitude in the dynamic load data, representing the average level of load intensity. The load amplitude variance describes the dispersion of load amplitude, reflecting the stability of load fluctuation. The load main frequency component refers to the most important frequency component in the load signal, which is crucial for understanding the load characteristics. These parameters together constitute the load statistical feature vector, which is used to quantify and analyze the characteristics of dynamic load.

[0174] In the embodiments of the present application, first, the load amplitude mean value, load amplitude variance and load main frequency component in the dynamic load simulation data are extracted. Then, based on these parameters, the load statistical feature vector is generated. Next, the load characteristics of each time slice are evaluated through data analysis techniques to ensure that they accurately reflect the actual load conditions. Finally, all the calculation results are integrated to form a comprehensive load statistical feature vector, which serves as the basis for subsequent design optimization.

[0175] 702, based on the period ratio time series curve, analyze the period ratio fluctuation slope and adjacent time slice fluctuation difference of the time slice of the dynamic load simulation data, and generate a period ratio trend coefficient;

[0176] In step 702, the period ratio time series curve shows the trend of the period ratio changing over time. The period ratio fluctuation slope is the speed of the period ratio changing over time, which is used to measure the rate of change of the period ratio. The adjacent time slice fluctuation difference compares the change amplitude of the period ratio in different time periods, which helps to identify the key points of trend change. The period ratio trend coefficient integrates the above information and is used to guide the adjustment of structural parameters.

[0177] In the embodiments of the present application, first, based on the period ratio time series curve, the period ratio fluctuation slope and adjacent time slice fluctuation difference of the time slice of the dynamic load simulation data are analyzed. Then, the period ratio trend coefficient is calculated using an algorithm model. Then, the optimization strategy is continuously adjusted according to the calculation results until the optimal solution is found. Finally, the effectiveness of these coefficients is verified and they are applied to the final design scheme to ensure the safety and stability of the building.

[0178] 703, based on the inter-story drift angle time series curve, extract the drift angle extreme value difference and drift increment cumulative rate of the time slice, and generate an inter-story drift angle trend coefficient;

[0179] In step 703, the inter-story drift angle time series curve shows the trend of the inter-story drift angle changing over time. The drift angle extreme value difference is the maximum difference of the inter-story drift angle in each time slice, which is used to measure the maximum displacement change. The drift increment cumulative rate is the speed of the inter-story drift angle accumulating over time, which reflects the response speed of the building under dynamic load. The inter-story drift angle trend coefficient integrates these information and is used to guide the adjustment of structural parameters.

[0180] In the embodiments of the present application, first, the displacement angle extreme value difference and displacement increment accumulation rate of the time slice are extracted based on the time sequence curve of the interlayer displacement angle. Then, the interlayer displacement angle trend coefficient is generated using numerical analysis method. Then, the design scheme is continuously optimized according to the calculation results until the best performance is achieved. Finally, the actual effect of these coefficients is verified to ensure that they can accurately reflect the behavior of the building structure under dynamic load.

[0181] 704、establishing a correlation matrix of the load statistical feature vector, the period ratio trend coefficient and the interlayer displacement angle trend coefficient, obtaining an initial coupling component by correlating the load amplitude mean value with the period ratio trend coefficient, obtaining a displacement response component by correlating the load main frequency component with the interlayer displacement angle trend coefficient, and superimposing the initial coupling component and the displacement response component to generate an initial dynamic coupling factor;

[0182] In step 704, the correlation matrix is a method for quantifying the relationship between multiple variables. The initial coupling component is a parameter obtained by correlating the load amplitude mean value with the period ratio trend coefficient, which is used to describe the interaction between the load and the structure parameters. The displacement response component is a parameter obtained by correlating the load main frequency component with the interlayer displacement angle trend coefficient, which is used to evaluate the response of the structure to dynamic load. Superimposing these two components can generate an initial dynamic coupling factor.

[0183] In the embodiments of the present application, first, the correlation matrix of the load statistical feature vector, the period ratio trend coefficient and the interlayer displacement angle trend coefficient is established. Then, the initial coupling component is obtained by correlating the load amplitude mean value with the period ratio trend coefficient, and the displacement response component is obtained by correlating the load main frequency component with the interlayer displacement angle trend coefficient. Then, the two components are superimposed to generate an initial dynamic coupling factor. Finally, the effectiveness of the coupling factor is verified, and it is applied to the design to optimize the selection of structure parameters.

[0184] 705、according to the time-varying relationship of the initial dynamic coupling factor, constructing a time-varying weight matrix to eliminate time delay and generating a dynamic coupling factor of the structure parameters and the dynamic load.

[0185] In step 705, the time-varying weight matrix is a method for dynamically adjusting the weight, which is used to eliminate time delay and improve the accuracy of the model. By constructing the time-varying weight matrix, the dynamic coupling relationship between the structure parameters and the dynamic load can be more accurately described, so as to generate the dynamic coupling factor of the structure parameters and the dynamic load, which is used to guide the final design decision.

[0186] In the embodiments of the present application, first, a time-varying weight matrix is constructed according to the time-varying relationship of the initial dynamic coupling factor. Then, the weight of each parameter is adjusted using the matrix to eliminate time delay. Then, through multiple iterations of correction, it is ensured that all parameters meet the latest constraint conditions. Finally, the best dynamic coupling factor is confirmed and recorded based on the verification result, ensuring that the design scheme is both safe and efficient.

[0187] The following is a specific example:

[0188] In a new office building project, the design team first extracted the load amplitude mean, load amplitude variance, and load main frequency component of the dynamic load simulation data. Then, the period ratio fluctuation slope and the fluctuation difference of adjacent time segments were analyzed to generate the period ratio trend coefficient. Then, the displacement angle extreme difference and displacement increment cumulative rate were extracted to generate the inter-story drift angle trend coefficient. Then, the correlation matrix was established to generate the initial dynamic coupling factor, and the time delay was eliminated by constructing the time-varying weight matrix, and finally the dynamic coupling factor between the structure parameters and the dynamic load was generated, ensuring the safety and effectiveness of the design scheme.

[0189] In summary, steps 701 to 705 achieve fine modeling of the complex relationship between high-rise building structure parameters and dynamic loads, not only improving the safety performance of buildings when facing natural disasters, but also optimizing material utilization and reducing construction costs. This method provides a new way of thinking and technical means for complex structure design, making the design more scientific and reasonable, enhancing the overall quality and durability of the building, and improving design efficiency and accuracy.

[0190] Figure 2 A structural diagram of a building design system based on multi-objective optimization is provided for the embodiments of the present application, as shown in Figure 2 The system includes:

[0191] An acquisition module 21 acquires a set of structure parameters of a target high-rise building, dynamic load simulation data, and material stress wave propagation data, wherein the set of structure parameters includes a period ratio, an inter-story drift angle, and an axial compression ratio, and the dynamic load simulation data is generated based on the spectral characteristics of seismic waves.

[0192] A generation module 22 generates a dynamic coupling factor between structure parameters and dynamic loads according to the dynamic change trend of the period ratio and the inter-story drift angle, and in combination with the time distribution characteristics of the dynamic load simulation data.

[0193] An adjustment module 23 adjusts the matching relationship between the axial compression ratio and the stress wave peak propagation rate based on the spatial attenuation gradient of the material stress wave propagation data.

[0194] The optimization module 24 performs multi-objective collaborative optimization on the period ratio, interlayer displacement angle and axial pressure ratio through a non-dominated sorting strategy according to the dynamic coupling factor and matching relationship, and generates an initial structure parameter combination;

[0195] The correction module 25 feeds back the dynamic load simulation data as a constraint boundary condition of the initial structure parameter combination, and generates a high-rise building structure parameter combination satisfying the balance between seismic redundancy and material stress dissipation after iterative correction.

[0196] Figure 2 The building design system based on multi-objective optimization can perform Figure 1 The building design method based on multi-objective optimization has the implementation principle and technical effects which will not be repeated. The specific operation manner of each module and unit of the building design system based on multi-objective optimization in the above embodiment has been described in detail in the embodiment related to the method, and will not be described in detail here.

[0197] In one possible design, Figure 2 The building design system based on multi-objective optimization can be implemented as a computing device, such as a computer. Figure 3 As shown in the figure, the computing device can include a storage component 31 and a processing component 32.

[0198] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32.

[0199] The processing component 32 is configured to perform the above Figure 1 The building design method based on multi-objective optimization.

[0200] The processing component 32 can include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component can also be one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic elements, for executing the above method.

[0201] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component can be implemented by any type of volatile or nonvolatile storage devices, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic or optical disk.

[0202] Of course, the computing device can also necessarily include other components, such as an input / output interface, a display component, a communication component, etc.

[0203] The input / output interface provides an interface between the processing component and peripheral interface modules, which can be output devices, input devices, etc.

[0204] The communication component is configured to facilitate wired or wireless communication between the computing device and other devices, etc.

[0205] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform, and the computing device can be a cloud server, and the processing component, the storage component, etc. can be a basic server resource rented or purchased from the cloud computing platform.

[0206] The embodiment of the application further provides a computer storage medium, which stores a computer program, and the computer program can implement the above-mentioned Figure 1 The embodiment shown in the figure is a building design method based on multi-objective optimization.

[0207] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-mentioned system, device and unit can refer to the corresponding process in the foregoing method embodiment, which will not be described here.

[0208] The device embodiment described above is only schematic, wherein the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, that is, they can be located in one place, or distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment scheme. Those skilled in the art can understand and implement it without creative labor.

[0209] Those skilled in the art can clearly understand the implementation of the various embodiments by means of software and the necessary general hardware platform from the above description of the embodiments, and of course, the embodiments can also be implemented by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part of the prior art that contributes to the technical solutions can be embodied in the form of a software product. The computer software product can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, and the like, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0210] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A building design method based on multi-objective optimization, characterized in that, include: The structural parameter set, dynamic load simulation data, and material stress wave propagation data of the target high-rise building are obtained. The structural parameter set includes period ratio, inter-story drift angle, and axial compression ratio. The dynamic load simulation data is generated based on the seismic wave spectral characteristics. Based on the dynamic variation trend of the period ratio and inter-story drift angle, and combined with the time distribution characteristics of the dynamic load simulation data, a dynamic coupling factor between structural parameters and dynamic load is generated. Based on the spatial attenuation gradient of the material stress wave propagation data, the matching relationship between the axial compression ratio and the peak propagation rate of the stress wave is adjusted. Based on the dynamic coupling factor and matching relationship, the period ratio, inter-story drift angle and axial compression ratio are optimized in a multi-objective collaborative manner using a non-dominated sorting strategy to generate an initial combination of structural parameters. The dynamic load simulation data is fed back as the constraint boundary conditions of the initial structural parameter combination. After iterative correction, a high-rise building structural parameter combination that satisfies seismic redundancy and material stress dissipation equilibrium is generated.

2. The method according to claim 1, characterized in that, The process of performing multi-objective collaborative optimization of the period ratio, inter-story drift angle, and axial compression ratio using a non-dominated sorting strategy to generate an initial combination of structural parameters includes: Based on the relationship between dynamic coupling coefficient and stress wave attenuation, a multi-objective solution space is constructed with the period ratio, inter-layer displacement angle and axial compression ratio as variables. Extract the amplitude abrupt change interval of dynamic load data, set the corresponding time-varying correlation value as the dynamic constraint boundary of the multi-objective solution space, and generate the anti-lateral displacement interval and stress dissipation interval; Within the overlapping region of the anti-lateral displacement interval and the stress dissipation interval, candidate solution sets that satisfy the synchronous convergence of seismic redundancy and material dissipation are selected. Based on the real-time feedback of the dynamic constraint boundary, the attenuation coefficients of the period ratio, inter-story drift angle and axial compression ratio are iteratively corrected to generate the initial structural parameter combination.

3. The method according to claim 2, characterized in that, Within the overlapping region of the anti-lateral displacement interval and the stress dissipation interval, a candidate solution set that satisfies the synchronous convergence of seismic redundancy and material dissipation is selected, including: Based on the multidimensional spatial intersection of the anti-lateral displacement interval and the stress dissipation interval, a seismic redundancy quantification index and a material dissipation quantification index are established for each solution vector in the overlapping domain. The maximum inter-story drift angle offset under sudden changes in dynamic load amplitude is calculated using the seismic redundancy quantification index, and the attenuation gradient on the stress wave propagation path is calculated using the material dissipation quantification index. Establish synchronous convergence conditions, and require that the maximum inter-story drift angle offset does not exceed the upper limit of the anti-lateral displacement interval, and that the ratio of the attenuation gradient to the lower limit of the stress dissipation interval is greater than the real-time feedback coefficient of the dynamic constraint boundary. Within the overlapping domain, constrained by the synchronous convergence condition, traverse the non-dominated solutions in the solution vector space that satisfy the seismic redundancy quantification index and the material dissipation quantification index, and extract the corresponding parameter combinations of period ratio, inter-story drift angle and axial compression ratio. Based on the distribution density of the parameter combination, the segment weight of the period ratio, the iteration step size of the interlayer displacement angle, and the correction magnitude of the axial compression ratio are dynamically adjusted to generate a candidate solution set.

4. The method according to claim 3, characterized in that, The calculation of the maximum inter-story drift angle offset under abrupt changes in dynamic load amplitude using the seismic redundancy quantification index includes: Extract the extreme points and durations of the dynamic load amplitude mutation interval, construct the correlation matrix between the amplitude gradient and the duration, establish the displacement transmission relationship between the dynamic load amplitude mutation interval and the inter-story drift angle, and output the floor displacement increment. Traverse the range of abrupt changes in the dynamic load amplitude, calculate the time history response of the floor displacement increment, obtain the displacement cumulative influence factor through time domain integration, and extract the corresponding instantaneous offset of the inter-story drift angle based on the displacement cumulative influence factor. The instantaneous offset of the inter-story drift angle is dynamically matched with the upper limit of the anti-lateral displacement interval, and the maximum value is selected as the maximum inter-story drift angle offset.

5. The method according to claim 1, characterized in that, The process of feeding back the dynamic load simulation data as constraint boundary conditions for the initial structural parameter combination, and iteratively correcting them to generate a high-rise building structural parameter combination that satisfies seismic redundancy and material stress dissipation equilibrium, includes: Based on the time distribution characteristics of the dynamic load simulation data, time segments are divided and the extreme points of load amplitude and duration of the time segments are extracted to generate a dynamic load constraint sequence. Based on the extreme values ​​of the load amplitude in the dynamic load constraint sequence, time-varying constraint boundaries for period ratio, inter-story drift angle, and axial compression ratio are established. Based on material stress wave propagation data, the propagation path length and attenuation gradient of stress waves on each floor are calculated, and a dynamic matching coefficient between the axial compression ratio and the attenuation gradient is generated. The period ratio fluctuation range, inter-story drift angle offset, and axial compression ratio attenuation rate of the high-rise building structural parameters are dynamically coupled with the time-varying constraint boundary. When all the high-rise building structural parameters satisfy the time-varying constraint boundary and are within the range of the dynamic matching coefficient, a combination of high-rise building structural parameters is generated.

6. The method according to claim 5, characterized in that, The calculation of the stress wave propagation path length and attenuation gradient for each floor, and the generation of a dynamic matching coefficient between the axial compression ratio and the attenuation gradient, includes: Extract the three-dimensional coordinate node sequence of the stress wave propagation path of each floor, calculate the Euclidean distance between adjacent nodes based on the geometric topological relationship between nodes, and accumulate the stress wave propagation path length of each floor. Based on the stress wave propagation path length of each floor, peak stress wave data is extracted, a peak attenuation sequence is constructed, and a weighted average of the peak attenuation sequence is performed to generate the attenuation gradient of the stress wave. A dynamic correlation between the axial compression ratio and the attenuation gradient is established. The attenuation gradient is used as an input variable, and the stress wave propagation path length of each floor is used as a weighting coefficient. The initial dynamic matching coefficient of the axial compression ratio of each floor is analyzed. Spatial interpolation is performed on the initial dynamic matching coefficients based on the floor location to eliminate abrupt changes in coefficients between adjacent floors, thereby generating dynamic matching coefficients between the axial compression ratio and the attenuation gradient.

7. The method according to claim 1, characterized in that, The generation of a dynamic coupling factor between structural parameters and dynamic loads, based on the time distribution characteristics of the dynamic load simulation data, includes: Extract the mean load amplitude, variance of load amplitude, and dominant frequency component of the dynamic load simulation data to generate a load statistical feature vector; Based on the time series curve of the period ratio, the period ratio fluctuation slope of the time segment of the dynamic load simulation data and the fluctuation difference between adjacent time segments are analyzed to generate the period ratio trend coefficient. Based on the time-series curve of inter-story drift angle, the extreme difference of drift angle and the cumulative rate of drift increment of the time segment are extracted to generate the inter-story drift angle trend coefficient. Establish the correlation matrix of the load statistical feature vector, the period ratio trend coefficient and the inter-story drift angle trend coefficient, correlate the load amplitude mean with the period ratio trend coefficient to obtain the initial coupling component, correlate the load dominant frequency component with the inter-story drift angle trend coefficient to obtain the displacement response component, and superimpose the initial coupling component and the displacement response component to generate the initial dynamic coupling factor. Based on the time-varying relationship of the initial dynamic coupling factor, a time-varying weight matrix is ​​constructed to eliminate time delay and generate the dynamic coupling factor between structural parameters and dynamic loads.

8. A building design system based on multi-objective optimization, characterized in that, include: The acquisition module acquires the set of structural parameters, dynamic load simulation data, and material stress wave propagation data of the target high-rise building. The set of structural parameters includes the period ratio, inter-story drift angle, and axial compression ratio. The dynamic load simulation data is generated based on the seismic wave spectral characteristics. The generation module generates a dynamic coupling factor between structural parameters and dynamic loads based on the dynamic variation trends of the period ratio and inter-story drift angle, combined with the time distribution characteristics of the dynamic load simulation data. The adjustment module adjusts the matching relationship between the axial compression ratio and the peak propagation rate of the stress wave based on the spatial attenuation gradient of the material stress wave propagation data. The optimization module performs multi-objective collaborative optimization of the period ratio, inter-story drift angle and axial compression ratio based on the dynamic coupling factor and matching relationship through a non-dominated sorting strategy to generate an initial combination of structural parameters. The correction module feeds back the dynamic load simulation data as the constraint boundary conditions of the initial structural parameter combination. After iterative correction, it generates a high-rise building structural parameter combination that satisfies seismic redundancy and material stress dissipation equilibrium.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the multi-objective optimization-based architectural design method as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The system contains a computer program that, when executed by a computer, implements a multi-objective optimization-based architectural design method as described in any one of claims 1 to 7.

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