High-rise building simplified model construction method based on particle swarm optimization algorithm

Abstract

The invention relates to the technical field of building model construction, and discloses a high-rise building simplified model construction method based on a particle swarm optimization algorithm, and the method comprises the steps: obtaining design parameters of a high-rise building, constructing a refined finite element model, simplifying the refined finite element model, and obtaining a high-rise building initial simplified model; the method comprises the following steps: acquiring actual monitoring data, determining modal frequency and modal shape of a high-rise building based on the actual monitoring data, and optimizing simplified model parameters of the high-rise building by using a particle swarm optimization algorithm by taking the minimum comprehensive error of a modal analysis result based on actual measurement data and a model calculation modal result as an optimization target. Obtaining an optimized high-rise building simplified model; and verifying and adjusting the optimized high-rise building simplified model. According to the method, iterative search is carried out through the particle swarm optimization algorithm, the model parameters are optimized, the model parameters are made to better conform to actual measurement data, and the precision and reliability of the simplified model are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building model construction, and particularly to a method for constructing a simplified model of a high-rise building based on a particle swarm optimization algorithm. Background Art

[0002] Constructing a finite element model of a high-rise building plays an important role in fields such as building design, structural analysis, and seismic performance evaluation. The finite element model can effectively simulate the stress and deformation of the building structure under various loads (such as wind load, seismic load), providing a scientific basis for building design optimization and safety assessment.

[0003] However, the traditional method for constructing a refined finite element model has the following problems: 1. The model is too complex: To ensure calculation accuracy, it is usually necessary to finely model each component of the building (such as beams, columns, walls), resulting in a large number of model nodes and elements and a complex structural system. 2. It requires excessive computing resources: The high-precision model will significantly increase the amount of calculation, resulting in a long calculation time and large storage requirements for simulation analysis, and may even exceed the processing capacity of general engineering calculation platforms. 3. The modeling efficiency is low: The process of manually constructing a complex model is rather cumbersome, and it takes a long time to adjust parameters and optimize the model, affecting the progress of engineering projects. 4. It is difficult to meet the rapid analysis requirements: In the early stage of building design or emergency analysis, it is often necessary to quickly obtain analysis results, while the traditional refined model cannot meet this real-time requirement. Especially in the application of structural health monitoring of high-rise buildings, system identification and response reconstruction usually rely on the fusion of monitoring data and calculation models. However, due to limited monitoring points, the degrees of freedom of the refined finite element model are much larger than the number of sensors, which leads to low calculation efficiency and easily divergent results.

[0004] Therefore, there is an urgent need for a method for constructing a simplified model of a high-rise building based on a particle swarm optimization algorithm to construct a simplified and reasonable finite element model of a high-rise building on the premise of ensuring analysis accuracy, thereby reducing the calculation complexity and improving the calculation efficiency. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for constructing a simplified model of a high-rise building based on a particle swarm optimization algorithm, aiming to solve the above problems.

[0006] The present invention provides a method for constructing a simplified model of a high-rise building based on a particle swarm optimization algorithm, including:

[0007] Obtain the design parameters of the high-rise building, construct a refined finite element model based on the design parameters, and simplify the refined finite element model by using a layer model simplification method to obtain a simplified model of the high-rise building;

[0008] Obtain actual monitoring data, determine the modal frequencies and modal shapes of high-rise buildings based on the actual monitoring data, take the minimum comprehensive error between the modal analysis results based on the actual monitoring data and the modal analysis results calculated by the model as the optimization goal, and use the particle swarm optimization algorithm to optimize the parameters of the simplified model of high-rise buildings to obtain an optimized simplified model of high-rise buildings;

[0009] Verify the optimized simplified model of high-rise buildings. If the verification result shows that the error value exceeds the preset threshold, adjust the parameters of the particle swarm optimization algorithm and re-optimize the parameters of the simplified model of high-rise buildings.

[0010] Preferably, the design parameters include geometric features, material properties, and structural layouts;

[0011] The geometric features include building height, number of floors, building shape, building size, and site category;

[0012] The material properties include the elastic modulus, density, Poisson's ratio, and damping ratio of the material;

[0013] The structural layout includes the building structure plane layout, structural safety level, structural system, seismic fortification intensity, and design earthquake grouping.

[0014] Preferably, the layer model simplification method includes stiffness equivalent simplification and mass concentration simplification.

[0015] Preferably, during the simplification process, extract and retain the key parameters affecting the dynamic characteristics. The key parameters include the stiffness matrix and the mass matrix.

[0016] Preferably, after obtaining the actual monitoring data, preprocess the actual monitoring data. The preprocessing includes zero drift correction processing and filtering processing.

[0017] Preferably, using the particle swarm optimization algorithm to optimize the parameters of the simplified model of high-rise buildings includes: [[ID=z8]]

[0018] Initialize the number of individuals, velocity, position, learning factor, and inertia weight of the particle swarm;

[0019] Take the stiffness matrix of the simplified model of high-rise buildings as the optimization variable. The optimization variable is the equivalent shear coefficient of the stiffness matrix, and its value range is ±20% of the initial value. Use the particle swarm optimization algorithm to search for the optimal solution in the multi-dimensional space;

[0020] By iteratively updating the velocity and position of the particles, dynamically adjust the parameters of the simplified model of high-rise buildings to minimize the error between the modal frequencies and modal shapes calculated by the simplified model of high-rise buildings and the modal frequencies and modal shapes obtained based on the actual monitoring data.

[0021] Preferably, when optimizing the parameters of the simplified model of a high-rise building, a constraint error condition is set. The constraint error condition is that the comprehensive error between the modal analysis results obtained from the simplified model of the high-rise building and the modal analysis results based on actual monitoring data is within an acceptable range, where the acceptable range is [0%, 5%].

[0022] Preferably, verifying the optimized simplified model of the high-rise building includes: performing modal analysis on the optimized simplified model, calculating the modal frequencies and modal vibration modes of the optimized simplified model, comparing the calculation results with the modal analysis based on actual monitoring data, and determining the error value.

[0023] Preferably, the parameters of the particle swarm optimization algorithm include the number of particles, learning factors, and inertia weight.

[0024] Preferably, the method further includes: when the operating or environmental conditions of the high-rise building change, obtaining real-time monitoring data and updating the simplified model of the high-rise building.

[0025] Compared with the prior art, the beneficial effects of the present invention are as follows. Through dynamic adjustment in the iterative search process, the model parameters of the present invention are more in line with actual measurement data, improving the accuracy of the simplified model. By adopting the layer model simplification method, the refined finite element model of the high-rise building is divided into multiple simplified layer units, and the stiffness, mass, and damping characteristics of different floors are equivalently simplified. While reducing the degrees of freedom and complexity of the model, the key parameters that have an important impact on the overall dynamic characteristics of the building are retained. Taking the stiffness matrix parameters of the simplified model as the optimization variables, an optimization model is constructed with the goal of minimizing the comprehensive error between the modal analysis results based on actual monitoring data and the modal results calculated by the model. By iteratively optimizing and adjusting the parameters of the simplified model, the errors between the calculated modal frequencies and modal vibration modes and the measured modal frequencies and modal vibration modes are controlled within an acceptable range, improving the accuracy and reliability of the simplified model. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings according to the provided drawings without creative efforts.

[0027] Figure 1 It is a schematic flowchart of a method for constructing a simplified model of a high-rise building based on a particle swarm optimization algorithm according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0029] As Figure 1 shown, the present invention provides a method for constructing a simplified model of a high-rise building based on a particle swarm optimization algorithm, including:

[0030] Obtain the design parameters of the high-rise building, construct a refined finite element model based on the design parameters, and simplify the refined finite element model by using a layer model simplification method to obtain a simplified model of the high-rise building.

[0031] Obtain the actual monitoring data, determine the modal frequencies and modal shapes of the high-rise building based on the actual monitoring data, and use the particle swarm optimization algorithm to optimize the parameters of the simplified model of the high-rise building with the goal of minimizing the comprehensive error between the modal analysis results based on the actual monitoring data and the model calculation modal analysis results, so as to obtain an optimized simplified model of the high-rise building.

[0032] Verify the optimized simplified model of the high-rise building. If the verification result shows that the error value exceeds the preset threshold, adjust the parameters of the particle swarm optimization algorithm and re-optimize the parameters of the simplified model of the high-rise building.

[0033] The particle swarm optimization algorithm has strong global search ability and optimization efficiency, and performs excellently in the field of multi-objective optimization. Introducing the particle swarm optimization algorithm into the construction process of the simplified model of the high-rise building can significantly improve the modeling efficiency and computational performance while ensuring the model accuracy.

[0034] The present invention can significantly improve the construction efficiency and accuracy of the simplified model of the high-rise building. By constructing a refined finite element model and applying a layer model simplification method, the basic structural characteristics of the high-rise building can be quickly obtained, providing a reliable basis for subsequent optimization work. At the same time, using the actual monitoring data to determine the optimization goal makes the optimization process closer to the actual situation and improves the practicality of the optimization results. In addition, by continuously verifying and adjusting the parameters of the particle swarm optimization algorithm, the optimal solution can be gradually approached, further improving the accuracy of the simplified model of the high-rise building. This method for constructing a simplified model of a high-rise building based on a particle swarm optimization algorithm not only has the characteristics of high efficiency and accuracy, but also can provide strong support for the structural design and safety assessment of high-rise buildings.

[0035] The specific beneficial effects are as follows: Refined finite element model simplification: By obtaining the design parameters of high-rise buildings, a refined finite element model is constructed and simplified using the layer model simplification method. On the premise of ensuring a certain accuracy, the complexity and computational volume of the model are greatly reduced. This simplified model can quickly conduct a preliminary assessment of the dynamic characteristics of high-rise buildings, providing a basis for subsequent optimization.

[0036] Goal-oriented optimization: Taking the actual monitoring data, the optimization goal is determined to minimize the comprehensive error between the modal analysis results based on the measured data and the modal results calculated by the model, making the optimization process more targeted. The particle swarm optimization algorithm can efficiently search the optimal solution space to optimize the parameters of the simplified model, further improving the accuracy of the model and making it closer to the actual dynamic characteristics of high-rise buildings.

[0037] Multi-parameter collaborative optimization: The particle swarm optimization algorithm can simultaneously optimize multiple parameters of the simplified model, fully considering the mutual influence between parameters, avoiding the problem of local optimal solutions caused by independent parameter adjustment in traditional optimization methods, and thus obtaining a global optimal or near-optimal combination of simplified model parameters, improving the optimization effect.

[0038] Intelligent search and convergence: This algorithm has the ability of intelligent search, can quickly locate to a better area in the complex solution space, and continuously iteratively updates through the group collaboration and individual learning mechanisms, gradually approaching the optimal solution, with good convergence, ensuring the stability and reliability of the optimization process.

[0039] Error control and model adjustment: By verifying the optimized simplified model of high-rise buildings and making adjustments according to the verification results, the accuracy and reliability of the model are effectively guaranteed. When the error value exceeds the preset threshold, the parameters of the particle swarm optimization algorithm are adjusted and re-optimized, enabling the model to adaptively correct the deviation, further improving the fit between the model and the actual building, and enhancing the adaptability of the model under different working conditions.

[0040] Actual data-driven optimization and verification: The entire method is based on actual monitoring data, not only making the optimization goal more in line with the actual situation, but also being able to promptly discover problems existing in the model and make improvements through comparison and verification with actual data, realizing a closed-loop feedback from model construction, optimization to verification, and improving the application value of the model in actual engineering.

[0041] In some embodiments of the present application, the design parameters include geometric features, material properties, and structural layouts; the geometric features include building height, number of building floors, building shape, building size, and site category; the material properties include the elastic modulus, density, Poisson's ratio, and damping ratio of the material; the structural layouts include the building structure plane layout, structural safety grade, structural system, seismic fortification intensity, and design earthquake group.

[0042] It can be understood that by comprehensively considering the geometric characteristics, material properties, and structural layout of high-rise buildings, the actual situation of high-rise buildings can be comprehensively and accurately reflected, providing a reliable basis for subsequent model simplification and optimization. The refined finite element model also includes key structural components such as beams, columns, and shear walls, fully reflecting the mechanical properties of the building.

[0043] In some embodiments of the present application, the layer model simplification method includes stiffness equivalent simplification and mass concentration simplification.

[0044] It can be understood that the stiffness equivalent simplification simplifies the complex floor structure into a single layer model with equivalent stiffness by calculating the equivalent stiffness of each floor, thus greatly reducing the complexity of the model. The mass concentration simplification is to concentrate the mass of the floor on one or several nodes to simplify the mass distribution and improve the calculation efficiency. The combination of these two simplification methods not only reduces the complexity of the model but also retains the main dynamic characteristics of high-rise buildings.

[0045] In some embodiments of the present application, during the simplification process, key parameters affecting dynamic characteristics are extracted and retained, and the key parameters include the stiffness matrix and the mass matrix.

[0046] It can be understood that by extracting and retaining key parameters such as the stiffness matrix and the mass matrix, the accuracy of the simplified model in dynamic analysis can be ensured. The stiffness matrix reflects the stiffness characteristics of the structure and is an important parameter in structural dynamic analysis; the mass matrix describes the mass distribution of the structure and is crucial for the calculation of dynamic responses.

[0047] In some embodiments of the present application, after obtaining the actual monitoring data, the actual monitoring data is preprocessed, and the preprocessing includes zero drift correction processing and filtering processing.

[0048] It can be understood that by performing zero drift correction processing and filtering processing on the actual monitoring data, the noise and errors in the data can be effectively removed, improving the accuracy and reliability of the data. The zero drift correction processing can eliminate the zero drift caused by factors such as temperature changes after the sensor has worked for a long time, ensuring the stability of the data; the filtering processing can remove high-frequency noise, making the data smoother and facilitating subsequent analysis and processing. The data after preprocessing can more truly reflect the dynamic characteristics of high-rise buildings, providing strong support for subsequent model construction and dynamic analysis.

[0049] In some embodiments of the present application, the particle swarm optimization algorithm is used to optimize the parameters of the simplified high-rise building model, including: initializing the number of individuals, velocity, position, learning factor, and inertia weight of the particle swarm; using the stiffness matrix of the simplified high-rise building model as the optimization variable, where the optimization variable is the equivalent shear coefficient of the stiffness matrix, and the value range is ±20% of the initial value, and using the particle swarm optimization algorithm to search for the optimal solution in the multi-dimensional space; by iteratively updating the velocity, position, and inertia weight of the particles, dynamically adjusting the parameters of the simplified high-rise building model to minimize the error between the modal frequencies and modal shapes calculated by the simplified high-rise building model and the modal frequencies and modal shapes obtained based on the actual monitoring data.

[0050] It can be understood that optimizing the parameters of the simplified high-rise building model by the particle swarm optimization algorithm can significantly improve the accuracy and applicability of the model. This method avoids the disadvantages of the traditional trial-and-error method, which is time-consuming, laborious, and has limited effects. By using an intelligent search algorithm to quickly find the optimal solution in the multi-dimensional space, it greatly improves the efficiency and accuracy of model parameter optimization. For the optimized simplified high-rise building model, the comprehensive error between the calculated modal analysis results and the modal analysis results obtained based on the measured data is minimized, and it can better simulate the actual dynamic response of high-rise buildings, providing a more reliable basis for fields such as structural health monitoring, seismic design, and disaster warning.

[0051] In some embodiments of the present application, when optimizing the parameters of the simplified high-rise building model, a constraint error condition is set, and the constraint error condition is that the comprehensive error between the modal analysis results calculated by the simplified high-rise building model and the modal analysis results based on the actual monitoring data is within an acceptable range, where the acceptable range is [0%, 5%].

[0052] It can be understood that by setting the constraint error condition, it can be ensured that the optimized simplified high-rise building model is within the controllable error range, further improving the reliability and practicality of the model. This method not only ensures the optimization effect of the model parameters but also avoids the problem of model distortion caused by over-optimization or unreasonable optimization.

[0053] In some embodiments of the present application, the optimized simplified high-rise building model is verified, including: performing modal analysis on the optimized simplified model, calculating the modal frequencies and modal shapes of the optimized simplified model, comparing the calculation results with the modal analysis based on the actual monitoring data, and determining the error value.

[0054] It can be understood that by verifying the optimized simplified high-rise building model, the accuracy and reliability of the model can be ensured. This verification step not only improves the practical application value of the model but also provides strong support for subsequent high-rise building design and analysis.

[0055] In some embodiments of the present application, the parameters of the particle swarm optimization algorithm include the number of particles, learning factors, and inertia weight.

[0056] It can be understood that by reasonably setting the number of particles, learning factors, and inertia weight, the exploration ability and exploitation ability of the algorithm can be balanced, thereby improving the convergence speed and accuracy of the algorithm. This setting not only enhances the applicability of the particle swarm optimization algorithm in the construction of simplified models of high-rise buildings, but also provides a basis for the further optimization and improvement of the algorithm. In addition, reasonable parameter configuration can also reduce the running time of the algorithm and improve the overall work efficiency, making this method more efficient and practical in practical applications.

[0057] In some embodiments of the present application, the parameters of the particle swarm optimization algorithm further include particle dimension, acceleration constant, maximum velocity, maximum number of iterations, and termination condition.

[0058] In some embodiments of the present application, the method further includes: when the operation or environmental conditions of the high-rise building change, acquiring real-time monitoring data and updating the simplified model of the high-rise building.

[0059] It can be understood that by dynamically updating the simplified model of the high-rise building through real-time monitoring data, it can ensure that the model always reflects the actual situation of the building, improving the accuracy and reliability of the model. This step not only enhances the adaptability of the model to changes in the operation or environmental conditions of the high-rise building, but also provides a more accurate basis for subsequent model application and analysis. In addition, dynamically updating the model can also timely detect and correct possible errors or deviations, thereby further improving the reliability and stability of this method in practical applications.

[0060] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

[0061] For the system provided by the above embodiments, only the above division of each functional module is used for illustration. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiments can be combined into one module, or further split into multiple sub-modules to complete all or part of the functions described above. For the names of the modules and steps involved in the embodiments of the present invention, they are only used to distinguish each module or step and are not regarded as an improper limitation of the present invention.

[0062] Those skilled in the art should be able to realize that the modules and method steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. The programs corresponding to the software modules and method steps can be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the technical field. To clearly illustrate the interchangeability of electronic hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in the form of electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

Claims

1. A method for constructing a simplified model of a high-rise building based on the particle swarm optimization algorithm, characterized in that, Including: Obtain the design parameters of the high-rise building, construct a refined finite element model based on the design parameters, and simplify the refined finite element model using the layer model simplification method to obtain a simplified high-rise building model; Obtain the actual monitoring data, determine the modal frequencies and modal vibration modes of the high-rise building based on the actual monitoring data, and use the particle swarm optimization algorithm to optimize the parameters of the simplified high-rise building model with the goal of minimizing the comprehensive error between the modal analysis results based on the actual monitoring data and the modal analysis results calculated by the model, so as to obtain an optimized simplified high-rise building model; Verify the optimized simplified high-rise building model. If the verification result shows that the error value exceeds the preset threshold, adjust the parameters of the particle swarm optimization algorithm and re-optimize the parameters of the simplified high-rise building model.

2. The method for constructing a simplified model of a high-rise building based on the particle swarm optimization algorithm according to claim 1, wherein, The design parameters include geometric features, material properties, and structural layout; The geometric features include building height, number of floors, building shape, building size, and site category; The material properties include elastic modulus, density, Poisson's ratio, and damping ratio of the material; The structural layout includes building structural plane layout, structural safety grade, structural system, seismic fortification intensity, and design earthquake group.

3. The method for constructing a simplified model of a high-rise building based on the particle swarm optimization algorithm according to claim 1, characterized in that, The layer model simplification method includes stiffness equivalent simplification and mass concentration simplification.

4. The method for constructing a simplified model of a high-rise building based on the particle swarm optimization algorithm according to claim 1, characterized in that, During the simplification process, extract and retain the key parameters affecting the dynamic characteristics. The key parameters include the stiffness matrix and the mass matrix.

5. The method for constructing a simplified model of a high-rise building based on the particle swarm optimization algorithm according to claim 1, wherein After obtaining the actual monitoring data, preprocess the actual monitoring data. The preprocessing includes zero drift correction processing and filtering processing.

6. The method for constructing a simplified model of a high-rise building based on the particle swarm optimization algorithm according to claim 1, characterized in that, Using the particle swarm optimization algorithm to optimize the parameters of the simplified high-rise building model includes: Initialize the number of individuals, velocity, position, learning factor, and inertia weight of the particle swarm; Take the stiffness matrix of the simplified high-rise building model as the optimization variable. The optimization variable is the equivalent shear coefficient of the stiffness matrix, and its value range is ±20% of the initial value. Use the particle swarm optimization algorithm to search for the optimal solution in the multi-dimensional space; Dynamically adjust the parameters of the simplified high-rise building model by iteratively updating the velocity and position of the particles, so that the error between the modal frequencies and modal vibration modes calculated by the simplified high-rise building model and the modal frequencies and modal vibration modes obtained based on the actual monitoring data is minimized.

7. The method for constructing a simplified model of a high-rise building based on the particle swarm optimization algorithm according to claim 1, wherein When optimizing the parameters of the simplified high-rise building model, a constraint error condition is set. The constraint error condition is that the comprehensive error between the modal analysis results calculated by the simplified high-rise building model and the modal analysis results based on the actual monitoring data is within an acceptable range, where the acceptable range is [0%, 5%].

8. The method for constructing a simplified model of a high-rise building based on the particle swarm optimization algorithm according to claim 1, wherein Verify the optimized simplified high-rise building model, including: perform modal analysis on the optimized simplified model, calculate the modal frequencies and modal vibration modes of the optimized simplified model, compare the calculation results with the modal analysis based on the actual monitoring data, and determine the error value.

9. The method for constructing a simplified model of a high-rise building based on the particle swarm optimization algorithm according to claim 1, characterized in that, The parameters of the particle swarm optimization algorithm include the number of particles, learning factor, and inertia weight.

10. The method for constructing a simplified model of a high-rise building based on the particle swarm optimization algorithm according to claim 1, wherein, The method further includes: when the operation or environmental conditions of the high-rise building change, obtain real-time monitoring data and update the simplified high-rise building model.

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