A simplified elastic analysis and design method for a partition structure integrated model

Abstract

The present application relates to the technical field of building structure engineering, in particular to a simplified elastic analysis and design method for integrated model of partition wall structure, which proposes a set of simplified elastic analysis and design method based on physical mechanism and data driving. The method is not a simple application of traditional empirical coefficient, but an intelligent calibration based on a large number of refined nonlinear analysis results, forming a practical design tool which can be directly used for engineering design, seamlessly connected with the existing specification and has clear physical meaning. The quantitative correlation between elastic and nonlinear performance is established, which not only guarantees the theoretical rigor, but also has strong engineering practicability. As a preposition and supplement of refined nonlinear analysis, the method forms a complete technical system from rapid scheme design to accurate performance verification.

Description

Technical Field

[0001] This invention relates to the field of building structure engineering technology, specifically a simplified elastic analysis and design method for an integrated model of partition wall structures, which is particularly applicable to high-rise buildings, public buildings with strict seismic fortification requirements, and reinforcement projects of existing buildings in urban renewal. Background Technology

[0002] In the seismic analysis and design of building structures, especially frame and frame-shear wall structures, the influence of non-load-bearing partition walls, such as masonry infill walls and ALC wall panels, has always been a critical and complex issue. Traditional design methods typically use a "period reduction factor" to empirically consider the contribution of partition walls to the overall structural stiffness. For example, the current "Technical Specification for Concrete Structures of High-Rise Buildings" stipulates that the period reduction factor is 0.6 to 0.7 for frame structures, 0.7 to 0.8 for frame-shear wall structures, 0.8 to 0.9 for frame-core tube structures, and 0.8 to 1.0 for shear wall structures. While this method is simple, it has fundamental flaws: First, it is a macroscopic and general empirical coefficient that cannot reflect the actual spatial distribution of the partition wall in the structure, its own nonlinear mechanical properties, and the complex interaction mechanism between it and the main structure. This leads to a significant deviation between the calculation model and the actual dynamic response, especially in the elastoplastic stage where the error is even greater. Second, with the advancement of building industrialization, various lightweight and high-performance prefabricated partition walls are widely used. Their material properties and connection methods with the main structure are very different from traditional masonry, making the applicability of empirical coefficients based on traditional masonry statistics questionable. Designs often tend to be conservative or unsafe.

[0003] In terms of structural measures, the industry has tried many improvements to mitigate the damage to partition walls during earthquakes, such as installing flexible connections at the top of the wall, using lightweight materials, or setting control joints. These methods have alleviated the damage to the partition walls themselves to some extent, but they have not fundamentally solved the problems of the difficulty in quantifying their stiffness contribution and the unclear mechanism of their collaborative work with the main structure during earthquakes. In recent years, although some studies have attempted to embed or connect energy dissipation devices in partition walls, their designs are mostly based on simplified calculations or experiments on isolated components, failing to place them in the overall structure for integrated collaborative analysis and optimization. This makes it impossible to accurately assess their energy dissipation efficiency and even more difficult to achieve performance-based refined design.

[0004] Meanwhile, some existing solutions use nonlinear simulation for design. Although it is highly accurate, it is computationally complex and time-consuming, resulting in efficiency bottlenecks in its widespread application in preliminary engineering design or routine projects. Furthermore, current mainstream design processes and standards are still largely based on elasticity analysis theory. Therefore, how to quickly and accurately assess the performance of partition wall systems in the early stages of design and seamlessly integrate them with existing standards presents a new challenge. Summary of the Invention

[0005] The purpose of this invention is to provide a simplified elastic analysis and design method for an integrated model of partition wall structures, addressing the aforementioned problems.

[0006] The technical solution adopted in this invention is as follows: a simplified elastic analysis and design method for an integrated model of partition wall structures, applicable to the simplified elastic analysis and design of prefabricated vibration-damping partition wall structural systems. The prefabricated vibration-damping partition wall structural system includes partition walls, frame beams, frame columns, and dampers, and includes the following steps:

[0007] S1. The "intelligent calibration-layered weighting" model of the equivalent elastic stiffness of the partition wall is established. The stiffness contribution of the partition wall is decomposed into three key sub-item coefficients: material, location, and connection structure. Parametric analysis is performed, and these coefficients are intelligently calibrated based on machine learning algorithms to form a coefficient table or simplified formula that can be directly used.

[0008] S2. The "equivalent linear-equivalent damping" dual-mode transformation of the damper connection converts the complex mechanical behavior of the nonlinear damper into the "equivalent linear stiffness" and "equivalent additional damping ratio" that can be identified and calculated by elastic analysis software.

[0009] S3. Construction of the "elastic-nonlinear" correlation prediction model for the overall structural performance, establishing a quantitative correlation between elastic analysis results and nonlinear performance indicators;

[0010] S4. Design process integration and toolbox integration: This integrates components including a quick calculator for the equivalent stiffness of partition walls, a damper parameter selection chart, a displacement amplification factor lookup table, and a damage level prediction module. Based on the input structural information, it determines the performance of the partition wall structure and outputs a design scheme.

[0011] Optionally, in S1, the key component coefficients are as follows: ;

[0012] in, It contributes to the equivalent elastic stiffness of the partition wall; , These are the elastic modulus and moment of inertia of the partition wall material, respectively.

[0013] This is a material correction factor;

[0014] This is the location influence coefficient;

[0015] These are the connection construction coefficients.

[0016] Optionally, the machine learning algorithm includes training a prediction model using random forest and support vector regression to analyze the coefficients of the prefabricated vibration-damping partition wall structure system. Intelligent prediction:

[0017] ;

[0018] in, It is a prediction function trained based on machine learning algorithms (such as random forest, support vector regression, etc.);

[0019] It also generates coefficient lookup tables and simplified formulas applicable to different engineering conditions for querying purposes.

[0020] Optionally, in S2, regarding the equivalent linear stiffness, it is based on the initial stiffness of the nonlinear damper. and expected displacement amplitude Define the equivalent stiffness in elasticity analysis :

[0021] ;

[0022] in, This is the damper's initial slip displacement;

[0023] This represents the stiffness of the damper after it slides.

[0024] Optionally, in S2, regarding the equivalent additional damping ratio, based on the hysteresis curve of the nonlinear damper, the equivalent viscous damping ratio corresponding to its energy dissipation capacity is derived through the energy analysis method of the equivalent additional damping ratio. :

[0025] ;

[0026] in, The coefficient of friction of the damper's friction interface;

[0027] This refers to the normal preload applied to the friction surface of the damper;

[0028] The energy consumed by the damper in one hysteresis cycle;

[0029] This represents the maximum elastic strain energy of the structure at the maximum displacement Δ.

[0030] Optionally, in step S3, a quantitative correlation is established between the elasticity analysis results and the nonlinear performance index, and the nonlinear performance is predicted through elasticity analysis, including displacement prediction and damage prediction.

[0031] Optionally, in step S3, regarding displacement prediction, based on a large number of nonlinear time history analysis results, the displacement amplification coefficient spectrum is obtained through statistical regression. :

[0032] ;

[0033] Where T is the structural period;

[0034] a, b, and c are coefficients determined through statistical regression analysis;

[0035] For elastic analysis of displacement;

[0036] It is a nonlinear displacement.

[0037] Furthermore, in S3, regarding damage prediction, a correlation model between elastic response parameters and the damage level of the partition wall is established using a machine learning algorithm. :

[0038] ;

[0039] in, ( ) is a prediction function trained based on a machine learning algorithm for predicting damage levels;

[0040] The predicted damage level;

[0041] This represents the maximum inter-story drift angle.

[0042] For base shear force;

[0043] Structural form is one of the input parameters, referring to specific structural system classifications such as frame structure and frame-shear wall structure.

[0044] Furthermore, in S4, based on the input structural information, the coefficients of the prefabricated vibration-damping partition wall structural system are first determined. Then calculate the equivalent stiffness of the partition wall. An elastic analysis model is established, and then the equivalent stiffness of the damper is added. and equivalent viscous damping ratio Modal analysis and response spectrum analysis are performed to obtain elastic response results. Then, a management model is applied to predict nonlinear performance, and finally, it is determined whether the performance meets the standards.

[0045] Furthermore, if the performance is deemed satisfactory, a design scheme is output; if the performance is deemed unsatisfactory, parameters are adjusted and the system is returned to re-determine the coefficients of the prefabricated vibration-damping partition wall structure system. .

[0046] The beneficial effects of this invention are:

[0047] 1. A simplified elasticity analysis and design method based on physical mechanisms and data-driven approaches is proposed. This method is not a simple application of traditional empirical coefficients, but rather an intelligent calibration based on a large number of refined nonlinear analysis results. It forms a practical design tool that can be directly used in engineering design, is seamlessly integrated with current standards, and has clear physical meaning.

[0048] 2. Establish a quantitative correlation between elasticity and nonlinear performance, ensuring both theoretical rigor and strong engineering applicability. This serves as a prerequisite and supplement to refined nonlinear analysis, forming a complete technical system from rapid scheme design to accurate performance verification. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of a prefabricated vibration-damping partition wall structure system;

[0050] Figure 2 A flowchart illustrating a simplified elastic analysis and design method for an integrated model of a partition wall structure;

[0051] Figure 3 Flowchart for design process integration and toolkit integration;

[0052] Figure 4 This is a schematic diagram of the structure of an electronic device.

[0053] The attached figures are labeled as follows:

[0054] 1 is a prefabricated partition wall panel, 2 is the first frame beam, 3 is the second frame beam, 4 is the first frame column, 5 is the second frame column, 6 is the energy-dissipating flexible filling layer between the wall side and the main structure, 7 is the bottom connection of the wall panel, 8 is the energy-dissipating flexible filling layer between the top of the wall and the main structure, 9 is the steel plate capping, and 10 is the shock absorber. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described in the accompanying drawings can generally be arranged and designed in various different configurations.

[0056] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0057] It should be noted that, unless otherwise specified, the embodiments and features described in this invention can be combined with each other.

[0058] like Figure 2 As shown, a simplified elastic analysis and design method for an integrated partition wall structure model is applicable to the simplified elastic analysis and design of prefabricated vibration-damping partition wall structural systems. The prefabricated vibration-damping partition wall structural system includes partition walls, frame beams, frame columns, and dampers, and includes the following steps:

[0059] S1. The "intelligent calibration-layered weighting" model of the equivalent elastic stiffness of the partition wall is established. The stiffness contribution of the partition wall is decomposed into three key sub-item coefficients: material, location, and connection structure. Parametric analysis is performed, and these coefficients are intelligently calibrated based on machine learning algorithms to form a coefficient table or simplified formula that can be directly used.

[0060] S2. The "equivalent linear-equivalent damping" dual-mode transformation of the damper connection converts the complex mechanical behavior of the nonlinear damper into the "equivalent linear stiffness" and "equivalent additional damping ratio" that can be identified and calculated by elastic analysis software.

[0061] S3. Construction of the "elastic-nonlinear" correlation prediction model for the overall structural performance, establishing a quantitative correlation between elastic analysis results and nonlinear performance indicators;

[0062] S4. Design process integration and toolbox integration: This integrates components including a quick calculator for the equivalent stiffness of partition walls, a damper parameter selection chart, a displacement amplification factor lookup table, and a damage level prediction module. Based on the input structural information, it determines the performance of the partition wall structure and outputs a design scheme.

[0063] The purpose of this design is to propose a simplified elasticity analysis and design method based on physical mechanisms and data-driven approaches. This method is not a simple application of traditional empirical coefficients, but rather an intelligent calibration based on a large amount of refined nonlinear analysis results. It forms a practical design tool that can be directly used in engineering design, seamlessly integrates with current standards, and has clear physical meaning. Establishing a quantitative correlation between elasticity and nonlinear performance ensures both theoretical rigor and strong engineering applicability. As a prerequisite and supplement to refined nonlinear analysis, it forms a complete technical system from rapid scheme design to accurate performance verification.

[0064] It should be noted that in S1, the machine learning algorithm adopts random forest or support vector regression, with a training data sample size of ≥1000 groups and a prediction error of ≤10%. In S2, the calculation error of "equivalent linear stiffness" is ≤5%, and the calculation error of "equivalent additional damping ratio" is ≤8%. In S3, the correlation model adopts multiple linear regression or neural network algorithm, with a prediction accuracy of ≥85%. In S4, the toolbox interfaces with PKPM or YJK design software through API, shortening the design cycle by 40%.

[0065] At the same time, it should be noted that in this embodiment, as Figure 1 As shown, it is a prefabricated vibration-damping partition wall structure system. In this system, the main structure is composed of the first frame beam 2, the second frame beam 3, the first frame column 4, and the second frame column 5. Prefabricated partition wall panels 1 are installed in this main structure. The top of the prefabricated partition wall panel 1 abuts against the first frame beam 2, the bottom abuts against the second frame beam 3, and the two sides abut against the first frame column 4 and the second frame column 5, respectively. In order to reduce energy loss, flexible energy-dissipating filling layers 6 and 8 are filled at the contact points between the wall side and the main structure and between the wall top and the main structure, respectively. In order to improve the connection with the first frame beam 2, i.e. the top frame beam, and to reduce vibration, a steel plate capping 9 and a vibration damper 10 are also installed on it.

[0066] Meanwhile, S1 aims to overcome the drawbacks of the traditional "one-size-fits-all" approach to period reduction coefficients and construct a variable-weight equivalent stiffness model that considers multiple influencing factors. The key component coefficients are as follows:

[0067] ;

[0068] in, It contributes to the equivalent elastic stiffness of the partition wall;

[0069] , These are the elastic modulus and moment of inertia of the partition wall material, respectively.

[0070] This is a material correction factor;

[0071] This is the location influence coefficient;

[0072] These are the connection construction coefficients.

[0073] Simultaneously, through the design of orthogonal numerical experiments and the implementation of extensive parametric analyses using refined nonlinear models, a database of "design parameters - equivalent stiffness" was established. Machine learning algorithms, including random forest and support vector regression, were used to train prediction models for the coefficients of the prefabricated vibration-damping partition wall structure system. Intelligent prediction:

[0074] ;

[0075] It also generates coefficient lookup tables and simplified formulas applicable to different engineering conditions for querying. It should be noted that the material type in the input parameters includes ALC, masonry, etc., the aspect ratio ranges from 1 to 4, the damper friction coefficient μ is 0.2 to 0.5, the position coordinates include X / Y / Z three-dimensional coordinates, and the coefficient lookup table contains at least 20 typical working conditions, and the simplified formula error is ≤5%.

[0076] In S2, to address the complex behavior of the damper in the elastic phase, the nonlinear damper is represented by a simplified model of "linear stiffness element + additional damping ratio". Regarding the equivalent linear stiffness, it is based on the initial stiffness of the nonlinear damper. and expected displacement amplitude Define the equivalent stiffness in elasticity analysis :

[0077] ;

[0078] in, This is the damper's initial slip displacement;

[0079] This represents the stiffness of the damper after it slides.

[0080] Regarding the equivalent additional damping ratio, based on the hysteresis curve of the nonlinear damper, the equivalent viscous damping ratio corresponding to its energy dissipation capacity is derived through the energy analysis method of the equivalent additional damping ratio. :

[0081] ;

[0082] in, The coefficient of friction of the damper's friction interface;

[0083] This refers to the normal preload applied to the friction surface of the damper;

[0084] The energy consumed by the damper in one hysteresis cycle;

[0085] This represents the maximum elastic strain energy of the structure at the maximum displacement Δ.

[0086] In S3, a quantitative correlation is established between elasticity analysis results and nonlinear performance indices, enabling the prediction of nonlinear performance through elasticity analysis. This includes displacement prediction and damage prediction. For displacement prediction, based on extensive nonlinear time history analysis results, the displacement amplification factor spectrum is obtained through statistical regression. :

[0087] ;

[0088] Where T is the structural period;

[0089] a, b, and c are coefficients determined through statistical regression analysis;

[0090] For elastic analysis of displacement;

[0091] It is a nonlinear displacement.

[0092] Regarding damage prediction, machine learning algorithms are used to establish a correlation model between elastic response parameters and the damage level of the partition wall. :

[0093] ;

[0094] in, ( ) is a prediction function trained based on a machine learning algorithm for predicting damage levels;

[0095] The predicted damage level;

[0096] This represents the maximum inter-story drift angle.

[0097] This is the base shear force.

[0098] like Figure 3 As shown, in S4, based on the input of basic structural information, the coefficients of the prefabricated vibration-damping partition wall structural system are first determined. Then calculate the equivalent stiffness of the partition wall. An elastic analysis model is established, and then the equivalent stiffness of the damper is added. and equivalent viscous damping ratio Modal analysis and response spectrum analysis are performed to obtain elastic response results. Then, a management model is applied to predict nonlinear performance. Finally, it is determined whether the performance meets the standards. If the performance meets the standards, a design scheme is output. If the performance does not meet the standards, the parameters are adjusted and the system is returned to re-determine the coefficients of the prefabricated vibration-damping partition wall structure system. .

[0099] It should be noted that the development and supporting design toolkit in this embodiment includes a rapid calculator for the equivalent stiffness of partition walls, a damper parameter selection chart, a displacement amplification factor lookup table, a damage level prediction module, and interface plugins for mainstream design software such as PKPM and YJK, achieving seamless integration with the current design process. This simplified elastic analysis and design method, through the above four steps, constructs a complete technical chain from parameter calibration, component simplification, performance correlation, process integration to adaptive optimization. It combines physical mechanisms with big data analysis, comprehensively considers multiple influencing factors, and establishes a quantitative correlation between elastic and nonlinear performance for the first time. This ensures both theoretical rigor and strong engineering practicality, serving as a prerequisite and supplement to refined nonlinear analysis, forming a complete technical system from rapid scheme design to accurate performance verification.

[0100] It should also be noted that, in order to further improve the feasibility of the entire simplified elastic analysis and design method, this embodiment also provides a preliminary integrated refined modeling and simulation method for partition wall structures based on physical reality. Based on this, in order to further bridge the gap between high-precision simulation and engineering practice efficiency, this embodiment develops a practical simplified design tool based on physical mechanisms and data-driven approaches, capable of effectively predicting nonlinear performance through elastic analysis results. This allows the advanced concept of integrated partition wall design to be efficiently integrated into daily engineering design processes. The integrated refined modeling and simulation method for partition wall structures based on physical reality is applicable to the calculation, simulation, and design of prefabricated vibration-damping partition wall structures. The prefabricated vibration-damping partition wall structure system includes prefabricated partition wall panels 1, frame beams, frame columns, and vibration dampers 10, and includes the following steps:

[0101] A1. Establishment of a parametric geometry, material and failure criterion model for failure simulation: A physically realistic refined numerical model of the prefabricated partition wall panel 1 is established in the prefabricated vibration damping partition wall structure system and failure criteria are integrated.

[0102] A2. Define the mechanical model and linkage mechanism of the connection interface, simulate the connection behavior between the prefabricated partition wall panel 1 and the main structure composed of frame beams and frame columns in the prefabricated vibration damping partition wall structure system, and establish the linkage rules between the main structure and the failure state of the prefabricated partition wall panel 1.

[0103] A3. Full-process nonlinear co-simulation and failure simulation: Perform full-process nonlinear simulation including the "failure-exit-internal force redistribution" mechanism in the prefabricated vibration-damping partition wall structure system;

[0104] A4. Multi-objective performance-driven parameter automated optimization design transforms the design problem of prefabricated vibration-damping partition wall structure system into a mathematical optimization problem. It finds the optimal design parameters through automated iteration and finally outputs scientifically optimized connection construction parameters.

[0105] The purpose of this design is to construct a complete and closed technical chain, from refined physical modeling to the definition of connection and failure linkage algorithms, to full-process nonlinear simulation, and finally to automated multi-objective parameter optimization. This integrates the entire lifecycle behavior of the partition wall subsystem—from "operation-damage-failure-impact"—into the overall structural performance design using rigorous mathematical models and numerical algorithms. This forms a highly innovative, computable, and verifiable dedicated design system, thereby achieving quantitative and performance-based design of the vibration damping connection structure. By introducing a series of mathematical models, criteria, and linkage algorithms with clear physical meaning, it improves upon the traditional black-box experience of the "period reduction factor."

[0106] In this embodiment, under the prefabricated vibration damping partition wall structure system above, in A1, the prefabricated partition wall panel 1 in the prefabricated vibration damping partition wall structure system is treated as an independent unit for refined geometric modeling. The prefabricated partition wall panel 1 is given a nonlinear material constitutive model, and a comprehensive failure criterion function based on strain, damage, and energy dissipation is integrated for the prefabricated partition wall panel 1 as the numerical basis for simulating the entire process behavior of "working-damage-failure".

[0107] Meanwhile, the nonlinear material constitutive model, taking widely used concrete as an example, is expressed using the concrete damage plasticity model as follows:

[0108] ;

[0109] in Let Cauchy stress tensor describe the stress state at any point inside the partition wall material;

[0110] The damage variable (0≤d≤1) characterizes the degree of material stiffness degradation, where 0 represents intact and 1 represents complete failure. It is derived from the tensile damage evolution variable. and variables of compressive damage evolution Definition of a rule;

[0111] For tensile damage evolution variables;

[0112] For the evolution of pressure damage;

[0113] is the initial elastic stiffness tensor, representing the stiffness properties of a material when it is undamaged;

[0114] The total strain tensor is the total deformation of the material under load.

[0115] The plastic strain tensor represents the portion of the material that undergoes irreversible plastic deformation.

[0116] Furthermore, the comprehensive destruction criterion function is expressed as follows:

[0117] ;

[0118] in,

[0119] To comprehensively assess damage indicators, a comprehensive set of parameters is used to determine whether a partition wall has reached a state of damage. When the value is ≥1.0, the component is deemed damaged;

[0120] This represents the actual strain value of the partition wall under the current load;

[0121] This represents the ultimate strain value that the partition wall material can withstand.

[0122] This is the ratio of the critical point strain to the ultimate strain;

[0123] It is a comprehensive damage index that combines tensile and compressive damage, and has ;

[0124] This refers to the cumulative dissipated energy consumed by the partition wall during the loading process;

[0125] The maximum energy dissipation capacity that the partition wall material can dissipate;

[0126] This represents the cumulative energy consumption ratio.

[0127] The purpose of this design is to model each partition wall as an independent unit (solid / shell element) with realistic geometric dimensions within the overall structural finite element model. The key is to assign a physically realistic nonlinear material constitutive model to these components. Furthermore, the creative extension lies in integrating a clear and quantifiable failure criterion for each partition wall component while defining the material constitutive model. This criterion is based on a multi-parameter fusion synthesis function, used to intelligently trigger the subsequent "exit" mechanism.

[0128] Meanwhile, in this embodiment A2, a nonlinear force-displacement model of the wall-top damper 10 is defined, and a linkage algorithm is set: when the partition wall is detected to have reached a failure state, an instruction is automatically triggered to reduce the connection efficiency, thereby physically simulating the dynamic transformation of the connection system from "cooperative work" to "failure isolation". The force-displacement model, taking a friction damper as an example, is expressed as follows:

[0129] ;

[0130] in, The output force of the shock absorber (10);

[0131] The initial locking stiffness of the damper 10 when it is not sliding;

[0132] / This is the initial sliding displacement;

[0133] The friction coefficient of the friction interface;

[0134] This refers to the normal preload applied to the friction surface;

[0135] The stiffness of the damper after sliding;

[0136] The relative displacement between the two ends of the damper;

[0137] The relative velocity between the two ends of the damper;

[0138] During simulation, when a certain partition unit in prefabricated partition panel 1 is detected to meet the following conditions... When the value is ≥1.0, the following instructions are executed:

[0139] If ≥1.0, then set: μ→μr(eg,0.1μ) and / or →0;

[0140] The study physically simulated the decline in top connection performance after severe wall damage.

[0141] The purpose of this design is to accurately reproduce the mechanical behavior of the physical connection structure in digital space and establish linkage rules with the failure of the partition wall. Parametric nonlinear connection units are established between the top of the wall and the main structural beams to simulate a damping device. The innovative integration lies in defining a linkage algorithm between this connection unit and the failure of the partition wall itself, physically simulating the state of the top connection performance degradation after severe wall damage, achieving a dynamic and realistic transformation from "cooperative work" to "failure isolation." Simultaneously, "contact pairs" are used between the bottom of the wall and the floor slab to simulate possible sliding and separation, while "compression-only" gap units or nonlinear springs are set between the wall sides and vertical components to simulate the behavior of flexible sealant materials. This set of refined boundary condition simulations together constitutes the true mechanical boundary of the partition wall in the structure.

[0142] Meanwhile, in embodiment A3, nonlinear static / dynamic analysis is performed on the model constructed in A1. When a partition unit in the prefabricated partition wall panel 1 is judged to be damaged, its stiffness matrix is ​​modified and the unloading force is calculated to simulate its "retirement from work" state, as shown in the following formula:

[0143] , ;

[0144] ;

[0145] in, The element stiffness matrix describes the stiffness characteristics of a single partition wall element;

[0146] This is the stiffness reduction factor after the partition wall unit fails.

[0147] This represents the unloading force vector in case of failure of the partition wall unit;

[0148] Let be the displacement vector at the moment the partition wall unit fails.

[0149] Furthermore, in A3, the unloading force will be... As an equivalent load applied in reverse, it triggers a dynamic redistribution of internal forces in the global system solution. The nonlinear dynamic equations of the overall system are solved based on the updated system tangent stiffness matrix. The following formula exists:

[0150] ;

[0151] in, The internal force vector includes the nonlinear contributions of all partition wall units, all wall top dampers 10, and the main structure in the prefabricated partition wall panel 1 in real time.

[0152] The mass matrix of the structural system;

[0153] Let be the damping matrix representing the inherent damping characteristics of the structure;

[0154] Let be the acceleration vector of each node in the system;

[0155] The velocity vectors of each node in the system;

[0156] This is the vector of external forces that varies with time.

[0157] The purpose of this design is to perform nonlinear static / dynamic analysis on the integrated model and implement a failure exit mechanism through a customized numerical strategy. When a partition wall element is determined to be failed, it "exits" the failure process by modifying its element stiffness matrix and calculating the unloading force. As an equivalent load applied in reverse, it automatically triggers dynamic redistribution of internal forces in the global system solution, while the internal force vector The simulation incorporates the nonlinear contributions of all partitions, dampers, and the main structure in real time. This process enables the simulation to accurately track the entire system response from the initial elastic state, through partial component failure, to the final state.

[0158] In this embodiment, in A4, the parameters of the damper 10 are used as design variables, and the comprehensive objective function is to control structural deformation and improve energy dissipation efficiency. Performance constraints are set, and the simulation process of A1-A3 is called as a performance evaluator. The optimization algorithm is used to automatically iterate and finally output the scientifically optimized connection construction parameters.

[0159] The parameters of the damper 10 are used as design variables and are expressed as follows:

[0160] ;

[0161] in, These represent the mechanical parameters of different dampers;

[0162] The objective function is:

[0163] ;

[0164] in, The overall objective function;

[0165] The maximum inter-story drift angle response of the structure across all floors is a measure of the structure's deformation performance.

[0166] Total energy dissipation of the damper system Accounting for a portion of total earthquake input energy The ratio is a measure of energy efficiency;

[0167] and The weighting coefficients are used to adjust the relative importance of the two sub-objectives of controlling deformation and improving energy consumption in the optimization, and satisfy the following conditions: + =1;

[0168] The performance constraints are as follows:

[0169] ;

[0170] ;

[0171] ;

[0172] ;

[0173] in, The deformation performance constraint function is given, and the maximum inter-story drift angle of the structure is given. Not exceeding the allowable value ;

[0174] Due to energy consumption performance constraints, and the energy consumption ratio of the damper 10... Not lower than the preset minimum requirement value ;

[0175] As a damage control constraint, and the proportion of damage to the partition wall unit. Not exceeding the permissible proportion of damage .

[0176] The purpose of this design is to transform the design problem into a constrained mathematical optimization problem in order to find the optimal damper design parameter vector. This optimization algorithm based on a surrogate model automatically calls the simulation process of A1-A3 as a performance evaluator, iterates until the optimal solution that satisfies all performance constraints (such as displacement limit under a major earthquake, lower limit of energy consumption ratio, and upper limit of damage ratio) is found, and outputs the scientifically optimized connection construction parameters.

[0177] Based on refined nonlinear analysis, this innovative approach simplifies elasticity analysis, forming a complete technical system of "rapid solution design + accurate performance verification." Using extensive parametric analysis results, machine learning algorithms are employed to establish intelligent correlations between design parameters and performance indicators. A practical toolkit seamlessly integrates with mainstream design software, meeting the precise analysis needs of high-standard projects while providing efficient design tools for routine engineering projects. This significantly lowers the technical application threshold and shortens the design cycle.

[0178] In one embodiment of this application, an electronic device for implementing a simplified flexible analysis and design method for an integrated partition structure model is also provided. The device includes a bus and a computer program stored in the memory and executable on the processor, such as an integrated fine modeling and simulation program for physically real partition structures.

[0179] Figure 4Only an electronic device with memory and processor is shown. Those skilled in the art will understand that the structure shown does not constitute a limitation on the electronic device and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0180] Combination Figure 4 The memory in the electronic device stores multiple computer-readable instructions to implement a simplified elastic analysis and design method for an integrated model of a partition structure, and the processor can execute the multiple instructions to achieve this.

[0181] Specifically, the processor's implementation method for the above instructions can be found in the description of the relevant steps in the corresponding embodiment of the figure, and will not be repeated here.

[0182] Those skilled in the art will understand that the schematic diagram is merely an example of an electronic device and does not constitute a limitation on the electronic device. The electronic device may be a bus-type structure or a star-type structure. The electronic device may also include more or fewer other hardware or software than shown in the diagram, or different component arrangements. For example, the electronic device may also include input / output devices, network access devices, etc.

[0183] It should be noted that electronic devices are merely examples. Other existing or future electronic products that are suitable for this application should also be included within the scope of protection of this application and are incorporated herein by reference.

[0184] The memory includes at least one type of readable storage medium, which can be non-volatile or volatile. The readable storage medium includes flash memory, portable hard drives, multimedia cards, card-type memory (e.g., SD or DX memory), magnetic storage, magnetic disks, optical disks, etc. In some embodiments, the memory can be an internal storage unit of an electronic device, such as a portable hard drive. In other embodiments, the memory can be an external storage device of the electronic device, such as a plug-in portable hard drive, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. The memory can be used not only to store application software and various types of data installed in the electronic device, such as code for integrated and refined modeling and simulation of physically real partition structures, but also to temporarily store data that has been output or will be output.

[0185] In some embodiments, a processor can be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits packaged with the same or different functions. This includes combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor is the control unit of an electronic device, connecting various components of the device through various interfaces and circuits. It executes programs or modules stored in the memory (e.g., executing a program for integrated, detailed modeling and simulation of a physically real partition structure) and calls data stored in the memory to perform various functions and process data within the electronic device.

[0186] The processor executes the operating system of the electronic device and various installed applications. The processor executes the applications to implement the steps in the above-described embodiments of the simplified elastic analysis and design method for an integrated partition structure model, such as the steps shown in the figure.

[0187] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete this application. The one or more modules / units may be a series of computer-readable instruction segments capable of performing a specific function, which describe the execution process of the computer program in an electronic device. For example, the computer program may be divided into a receiving module, a preprocessing module, a projection module, and a determining module.

[0188] The integrated unit implemented as a software functional module described above can be stored in a computer-readable storage medium. This software functional module, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, computer equipment, or network device, etc.) or processor to execute portions of the simplified elastic analysis and design method for an integrated partition structure model as described in the various embodiments of this application.

[0189] When modules / units integrated into an electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware devices. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above.

[0190] This application provides a simplified flexible analysis and design method for an integrated model of partition wall structures, which can be applied to one or more electronic devices. An electronic device is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0191] The computer program includes computer program code, which may be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory, and other memory.

[0192] Furthermore, the computer-readable storage medium may primarily include a stored program area and a stored data area, wherein the stored program area may store the operating system, an application program required for at least one function, etc.; and the stored data area may store data created based on the use of blockchain nodes, etc.

[0193] The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus. The bus is configured to implement the connection and communication between the memory and at least one processor, etc.

[0194] This application also provides a computer-readable storage medium (not shown), which stores computer-readable instructions. These computer-readable instructions are executed by a processor in an electronic device to implement the simplified elastic analysis and design method for an integrated partition structure model as described in any of the above embodiments.

[0195] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0196] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0197] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0198] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices described in the specification may also be implemented by a single unit or device through software or hardware. Terms such as "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0199] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A simplified elastic analysis and design method for an integrated partition wall structure model, applicable to the simplified elastic analysis and design of prefabricated vibration-damping partition wall structural systems, wherein the prefabricated vibration-damping partition wall structural system includes partition walls, frame beams, frame columns, and dampers, characterized in that, Includes the following steps: S1. The "intelligent calibration-layered weighting" model of the equivalent elastic stiffness of the partition wall is established. The stiffness contribution of the partition wall is decomposed into three key sub-item coefficients: material, location, and connection structure. Parametric analysis is performed, and these coefficients are intelligently calibrated based on machine learning algorithms to form a coefficient table or simplified formula that can be directly used. S2. The "equivalent linear-equivalent damping" dual-mode of the damper connection transforms the complex mechanical behavior of the nonlinear damper into the "equivalent linear stiffness" and "equivalent additional damping ratio" that can be identified and calculated by elastic analysis software. S3. Construction of the "elastic-nonlinear" correlation prediction model for the overall structural performance, establishing a quantitative correlation between elastic analysis results and nonlinear performance indicators; S4. Design process integration and toolbox integration: This integrates components including a quick calculator for the equivalent stiffness of partition walls, a damper parameter selection chart, a displacement amplification factor lookup table, and a damage level prediction module. Based on the input structural information, it determines the performance of the partition wall structure and outputs a design scheme.

2. The simplified elastic analysis and design method for an integrated model of partition wall structures according to claim 1, characterized in that, In S1, the key component coefficients are as follows: ； in, It contributes to the equivalent elastic stiffness of the partition wall; , These are the elastic modulus and moment of inertia of the partition wall material, respectively. This is a material correction factor; This is the location influence coefficient; These are the connection construction coefficients.

3. The simplified elastic analysis and design method for an integrated model of partition wall structures according to claim 2, characterized in that, The machine learning algorithm includes training a prediction model using random forest and support vector regression to analyze the coefficients of the prefabricated vibration-damping partition wall structure system. Intelligent prediction: ； in, It is a prediction function trained based on a machine learning algorithm; It also generates coefficient lookup tables and simplified formulas applicable to different engineering conditions for querying purposes.

4. The simplified elastic analysis and design method for an integrated model of partition wall structures according to claim 1, characterized in that, In S2, regarding the equivalent linear stiffness, it is based on the initial stiffness of the nonlinear damper. and expected displacement amplitude Define the equivalent stiffness in elasticity analysis : ； in, This is the damper's initial slip displacement; This represents the stiffness of the damper after it slides.

5. The simplified elastic analysis and design method for an integrated model of partition wall structures according to claim 4, characterized in that, In S2, regarding the equivalent additional damping ratio, based on the hysteresis curve of the nonlinear damper, the equivalent viscous damping ratio corresponding to its energy dissipation capacity is derived through the energy analysis method of the equivalent additional damping ratio. : ； in, The coefficient of friction of the damper's friction interface; This refers to the normal preload applied to the friction surface of the damper; The energy consumed by the damper in one hysteresis cycle; This represents the maximum elastic strain energy of the structure at the maximum displacement Δ.

6. The simplified elastic analysis and design method for an integrated model of partition wall structures according to claim 5, characterized in that, In step S3, a quantitative correlation is established between the elasticity analysis results and the nonlinear performance index. Nonlinear performance is predicted through elasticity analysis, including displacement prediction and damage prediction.

7. The simplified elastic analysis and design method for an integrated model of partition wall structures according to claim 6, characterized in that, In S3, regarding displacement prediction, based on a large number of nonlinear time history analysis results, the displacement amplification coefficient spectrum is obtained through statistical regression. : ； Where T is the structural period; a, b, and c are coefficients determined through statistical regression analysis; For elastic analysis of displacement; It is a nonlinear displacement.

8. The simplified elastic analysis and design method for an integrated model of partition wall structures according to claim 6, characterized in that, In section S3, regarding damage prediction, a correlation model between elastic response parameters and the damage level of the partition wall is established using machine learning algorithms. : ； in, ( ) is a prediction function trained based on a machine learning algorithm for predicting damage levels; The predicted damage level; This represents the maximum inter-story drift angle. This is the base shear force.

9. A simplified elastic analysis and design method for an integrated model of a partition wall structure according to claim 7 or 8, characterized in that, In step S4, based on the input structural information, the coefficients of the prefabricated vibration-damping partition wall structure system are first determined. Then calculate the equivalent stiffness of the partition wall. An elastic analysis model is established, and then the equivalent stiffness of the damper is added. and equivalent viscous damping ratio Modal analysis and response spectrum analysis are performed to obtain elastic response results. Then, a management model is applied to predict nonlinear performance, and finally, it is determined whether the performance meets the standards.

10. A simplified elastic analysis and design method for an integrated model of a partition wall structure according to claim 9, characterized in that, If the performance meets the standards, the design scheme is output; if the performance fails to meet the standards, the parameters are adjusted and the system is returned to re-determine the coefficients of the prefabricated vibration-damping partition wall structure system. .

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