Temperature driving structure parameter identification method based on uncertainty sampling

Abstract

The invention discloses a temperature-driven structure parameter identification method based on uncertainty sampling, relates to the technical field of structure health monitoring, and aims to overcome the defects of sensitive environmental interference, low calculation efficiency and insufficient uncertainty quantization in an existing structure parameter identification method. By establishing an initial finite element model, structure surface temperature change and temperature-induced static response data are synchronously collected; after data preprocessing and steady-state uniform temperature field screening, high-sensitivity structural parameters are screened based on parameter perturbation analysis; adopting a transition expansion Markov chain Monte Carlo algorithm to sample posterior distribution, and reducing the calculation complexity of a high-dimensional parameter space through an intermediate distribution transition mechanism; and finally outputting a parameter posteriori expectation and a covariance matrix. The method does not need manual excitation, remarkably improves long-term monitoring efficiency and damage identification reliability, and is suitable for state evaluation and safety diagnosis of civil engineering structures such as bridges and space steel structures.

Description

Technical Field

[0001] The present invention belongs to the technical field of structural health monitoring, and particularly relates to a temperature-driven structural parameter identification method based on uncertainty sampling. Background Technique

[0002] Structural damage identification methods are mainly divided into two categories: data-driven and model-based. The data-driven method discriminates the damage state by comparing the changes in monitoring data before and after damage, but it cannot quantitatively evaluate physical parameters (such as stiffness, boundary constraints), resulting in insufficient damage mechanism analysis ability; the model-based structural parameter identification method makes the calculated response of the finite element model approximate the actual monitoring data by modifying the finite element model parameters. Although it can achieve quantitative evaluation, traditional technologies have significant limitations: methods based on dynamic modal parameters (such as frequency, mode shape) are easily interfered by environmental factors such as wind loads and traffic vibrations, with a low signal-to-noise ratio, making it difficult to support long-term health monitoring; while methods based on static responses (stress, deflection) have the advantages of small data volume and strong anti-noise ability, but still rely on artificial excitation loading, and their engineering applicability is limited.

[0003] The temperature-driven structural parameter identification technology directly establishes a complete input-output transfer function by synchronously measuring the environmental temperature change (input excitation) and the temperature-induced response (output) of the structure, avoiding the need for external excitation. However, this method has two major bottlenecks: firstly, conventional deterministic optimization methods ignore multi-source uncertainties such as monitoring data noise, finite element model simplification errors, and environmental variations, resulting in low reliability of the identification results; secondly, although Bayesian inference can quantify uncertainties, the solution of its posterior probability density depends on Markov chain Monte Carlo (MCMC) sampling, with low computational efficiency, and the improved transitional Markov chain Monte Carlo (TMCMC) algorithm has a sharp increase in computational cost due to the need to repeatedly calculate auxiliary parameters during iteration, making it difficult to be applied to large-scale civil engineering structures. Summary of the Invention

[0004] To solve the above technical problems, the present invention proposes a temperature-driven structural parameter identification method based on uncertainty sampling to solve the problems existing in the above prior art.

[0005] In a first aspect, to achieve the above object, the present invention provides a temperature-driven structural parameter identification method based on uncertainty sampling, including the following steps: Establish an initial finite element model of the structure; Based on the initial finite element model, conduct a temperature sensitivity analysis, select the structural components sensitive to temperature changes to deploy strain sensors, and synchronously collect the static response of the structure under the temperature environment change and the structural surface temperature change data; Preprocess the static response and temperature data, and separate the temperature-induced static response component from the overall static response monitoring data; Screening of strain response data under steady-state and uniform temperature fields; Perform perturbation analysis on the structural parameters to be identified, and select the structural parameters that are sensitive to temperature-induced strain response as the parameters to be identified; The posterior distribution is defined by the prior distribution and likelihood function of the parameter to be identified, and the transition extended Markov chain Monte Carlo algorithm is used to sample the posterior distribution, in which a series of intermediate distributions are introduced to gradually transition the prior distribution to the posterior distribution to obtain the posterior sample set; The posterior expectation and posterior covariance matrix of the parameters to be identified are calculated based on the posterior sample set.

[0006] Optionally, the process of establishing an initial finite element model of the structure includes: establishing the initial finite element model according to structural geometric dimensions, component cross-sectional information, material parameters and constraints.

[0007] Optionally, the process of synchronously collecting data of static response of the structure under temperature environment changes and temperature change of the structure surface includes: Apply unit temperature change to the structure for temperature sensitivity analysis; Select temperature-sensitive components to arrange vibrating-wire or fiber Bragg grating strain sensors along the axial direction; set strain and temperature data to synchronous low-frequency sampling.

[0008] Optionally, the process of preprocessing the static response and temperature data includes: Discrete outliers exceeding three times the standard deviation of the monitoring data were removed and replaced with adjacent values, and continuous outliers were replaced with cubic spline interpolation; Based on the correlation between environmental data and monitoring data, a neural network model was established to fill in the missing values; Low-pass filtering is used to reduce data noise; The temperature-induced static response components are separated by principal component analysis or empirical mode decomposition.

[0009] Optionally, the process of screening the strain response data under steady-state and uniform temperature fields includes: avoiding the period of non-uniform temperature fields caused by sunlight factors, and selecting monitoring data when the structure is in thermal equilibrium.

[0010] Optionally, the process of performing perturbation analysis on the structural parameters to be identified includes: The support constraint stiffness and node connection stiffness are used as pre-selected parameters; Analyze the sensitivity of temperature-induced strain response by parameter perturbation; Parameters with screening sensitivity reaching the threshold are used as parameters to be identified.

[0011] Second aspect, the present invention also provides a temperature-driven structural parameter identification system based on uncertainty sampling for implementing a temperature-driven structural parameter identification method based on uncertainty sampling. The system includes: A model construction module for establishing an initial finite element model of the structure; A data acquisition module for performing temperature sensitivity analysis based on the initial finite element model, selecting temperature-sensitive components to deploy strain sensors, and synchronously collecting static response and surface temperature change data; A preprocessing module for removing outliers, filling missing values, denoising, and separating the temperature-induced static response component from the static response and temperature data; A data screening module for screening strain response data under steady state and uniform temperature fields; A parameter screening module for performing perturbation analysis on the structural parameters to be identified and screening the parameters sensitive to the temperature-induced strain response; An inversion calculation module for defining the posterior distribution through the prior distribution and the likelihood function, sampling the posterior distribution using the transitional extended Markov chain Monte Carlo algorithm, and obtaining the posterior sample set; A parameter output module for calculating the posterior expectation and posterior covariance matrix of the parameters to be identified based on the posterior sample set.

[0012] Optionally, the model construction module includes: A geometry processing unit for inputting the structural geometry size and component cross-section information; A material loading unit for inputting material parameters; A constraint configuration unit for configuring boundary constraint conditions and generating an initial finite element model.

[0013] Third aspect, the present invention also provides a computer terminal device, including: One or more processors; A memory coupled to the processor for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a temperature-driven structural parameter identification method based on uncertainty sampling.

[0014] Fourth aspect, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements a temperature-driven structural parameter identification method based on uncertainty sampling.

[0015] Compared with the prior art, the present invention has the following advantages and technical effects: A temperature-driven structural parameter identification method and system based on uncertainty sampling provided by the present invention significantly improve the efficiency and accuracy of structural parameter identification. By driving static responses with temperature, the need for external artificial excitation is avoided, enabling long-term automated monitoring. Combining the sampling mechanism of the transitional extended Markov chain Monte Carlo algorithm effectively overcomes the computational bottleneck in high-dimensional parameter spaces and reduces the inversion time. Using steady-state temperature field data screening and temperature-induced response separation techniques enhances robustness under environmental disturbances. At the same time, parameter uncertainty is quantified based on Bayesian posterior distributions, providing a reliable basis for structural damage diagnosis. The overall solution addresses the deficiencies of traditional methods in terms of engineering applicability, computational efficiency, and uncertainty quantification. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which form a part of this invention, are used to provide a further understanding of the invention. The schematic embodiments and descriptions thereof of the invention are used to explain the invention and shall not constitute an improper limitation to the invention. In the drawings: Figure 1 is a flowchart of the implementation of the temperature-driven structural parameter identification method based on uncertainty sampling according to an embodiment of the present invention; Figure 2 is a schematic diagram of the structure to be identified according to an embodiment of the present invention; Figure 3 is a schematic diagram of sensors for synchronous monitoring of static responses and temperatures arranged along the axial direction of temperature-sensitive members according to an embodiment of the present invention; Figure 4 is a schematic diagram of the sensor layout positions according to an embodiment of the present invention; Figure 5 is a schematic diagram of the process of screening steady-state and uniform temperature field strain response data according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments may be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0018] It should be noted that the steps shown in the flowchart of the accompanying drawings may be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than here.

[0019] Description of the related technologies of the present invention: Structural damage identification methods are mainly divided into two categories: data-driven and model-based (i.e., structural parameter identification). Data-driven methods identify damage by analyzing the data changes before and after damage, but lack the ability to quantitatively evaluate physical parameters; while structural parameter identification methods achieve quantitative evaluation of the structural state by modifying the finite element model to make its response approximate the actual monitoring data. Among the existing structural parameter identification techniques, the method based on dynamic modal parameters is vulnerable to environmental factors and is difficult to support long-term health monitoring; the method based on static responses (stress, deflection) has advantages such as small data volume and strong anti-noise ability. Especially for temperature-driven structural parameter identification, by analyzing the correlation between temperature-induced responses and structural parameters, its main advantage is that it can measure both external excitation (i.e., temperature change) and structural response (i.e., temperature-induced response) simultaneously, so as to obtain a complete transfer function (i.e., input-output relationship). However, when using temperature-driven structural parameter identification, conventional deterministic methods tend to ignore the influence of multi-source uncertainties such as monitoring data noise, model simplification error, and environmental variation on the identification results. Bayesian inference provides a theoretical framework for dealing with uncertain parameter identification, but the solution of its posterior probability density depends on Markov chain Monte Carlo sampling. Transitional Markov chain Monte Carlo improves the sampling efficiency by introducing a transitional distribution, but it needs to repeatedly calculate auxiliary parameters during iteration, resulting in too high computational cost and restricting its application in large-scale projects.

[0020] To address the above bottlenecks, the present invention proposes a temperature-driven structural parameter identification method based on uncertainty sampling. This method takes the temperature effect as the input of the structural system, utilizes the correlation between the temperature-induced response of the structure and structural parameters such as the stiffness and boundary constraints of the structure, constructs an objective function by minimizing the difference between the measured and numerically calculated temperature-induced responses, and uses the transitional extended Markov chain Monte Carlo algorithm (TEMCMC) to sample the posterior distribution. This method reduces the model calculation cost through a surrogate model, and combines the adaptive transitional distribution mechanism of the transitional extended Markov chain Monte Carlo algorithm to significantly improve the Bayesian inversion efficiency while ensuring the search accuracy in the high-dimensional parameter space, providing a new path for temperature-driven uncertain structural parameter identification. Through theoretical innovation and algorithm optimization, the patent aims to break through the limitations of traditional methods in engineering applicability, computational efficiency, and uncertainty quantification, and provide a more reliable quantitative evaluation tool for structural health monitoring.

[0021] Embodiment 1 As Figure 1 shown, this embodiment provides a temperature-driven structural parameter identification method based on uncertainty sampling, including: Establish an initial finite element model of the structure; Based on the initial finite element model, conduct temperature sensitivity analysis, select the structural components sensitive to temperature changes to deploy strain sensors, and synchronously collect the static response of the structure under temperature environment changes and the data of the surface temperature change of the structure; Preprocess the static response and temperature data, and separate the temperature-induced static response component from the overall static response monitoring data; Screen the strain response data under steady-state and uniform temperature fields; Perform perturbation analysis on the structural parameters to be identified, and screen the structural parameters sensitive to the temperature-induced strain response as the parameters to be identified; Define the posterior distribution through the prior distribution and likelihood function of the parameters to be identified, and sample the posterior distribution using the transitional extended Markov chain Monte Carlo algorithm, where the prior distribution is gradually transitioned to the posterior distribution by introducing a series of intermediate distributions to obtain a posterior sample set; Calculate the posterior expectation and posterior covariance matrix of the parameters to be identified based on the posterior sample set.

[0022] As an implementation manner in this embodiment, the process of establishing the initial finite element model of the structure includes: establishing the initial finite element model according to the structural geometric dimensions, member section information, material parameters, and constraint conditions.

[0023] Specifically, step A: Taking Figure 2 the space grid steel structure as an example, first establish the initial finite element model of the structure according to the original design information such as the space grid steel structure dimensions, member section information, material parameters, and constraint conditions.

[0024] As an implementation manner in this embodiment, the process of synchronously collecting the static response of the structure under temperature environment changes and the data of the structural surface temperature changes includes: Apply a unit temperature change to the structure for temperature sensitivity analysis; Select temperature-sensitive members and arrange vibrating wire type or fiber Bragg grating type strain sensors along the axial direction; set the strain and temperature data for synchronous low-frequency sampling.

[0025] Specifically, based on the established initial finite element model, apply a unit temperature change to the structure and perform temperature sensitivity analysis on the structure. Select the members among the numerous members of the structure that are more sensitive to temperature changes and arrange strain sensors. Set the strain and temperature data for synchronous low-frequency sampling, and synchronously collect the static response of the structure under temperature environment changes and the data of the structural surface temperature changes.

[0026] More specifically, step B: Based on the initial finite element model established in step A, apply a unit temperature change to the structure and perform temperature sensitivity analysis on the structure. Select the members (such as the members shown in the attachment Figure 3 ) among the numerous members of the structure that are more sensitive to temperature changes, arrange strain sensors along the axial direction of the members. It is advisable to use vibrating wire type or fiber Bragg grating type sensors for the arranged sensors. The positions of all the arranged sensors are as shown in the attachment Figure 4As shown, the strain and temperature data are set for synchronous low-frequency sampling, and the static response of the structure under changing temperature environments and the change in the surface temperature of the structure data are synchronously collected.

[0027] As an implementation manner in this embodiment, the process of preprocessing the static response and temperature data includes: Eliminating discrete outliers exceeding 3 times the standard deviation of the monitoring data and replacing them with adjacent values, and using cubic spline interpolation to replace continuous outliers; Establishing a neural network model based on the correlation between environmental data and monitoring data to fill in missing values; Performing data noise reduction using low-pass filtering; Separating the temperature-induced static response component through principal component analysis or empirical mode decomposition.

[0028] Specifically, preprocessing the monitored static response and temperature data, such as eliminating outliers, filling in missing values, and reducing data noise. For relatively complex environmental changes, the structural static response monitoring data obtained by the monitoring system under normal service conditions is the result of the combined action of multiple factors. Among these influencing factors, temperature is one of the main factors affecting the change in the structural monitoring response. By preprocessing the data through methods such as principal component analysis and empirical mode decomposition, the temperature-induced static response component is separated from the overall static response monitoring data for subsequent structural parameter identification.

[0029] More specifically, step C: Preprocessing the measured temperature and strain monitoring data. Due to signal transmission failures caused by system noise, power outages, etc., and the influence of other irresistible factors, abnormal values, noise, and data loss will occur in the data during long-term monitoring. These data anomalies directly lead to the failure of the structural parameter identification process. Therefore, first preprocess the original temperature and strain response data, and the preprocessing process includes eliminating outliers, filling in missing values, and reducing data noise. Specifically, for discrete and sporadic outlier data, data exceeding 3 times the standard deviation of the data during the monitoring period is replaced with adjacent values, while for a large number of continuous outlier data within a period of time, cubic spline interpolation is used for data replacement. For missing values, a neural network prediction model is established using the correlation between environmental data and temperature and strain data to fill in the missing data. In addition, there may be high-frequency noise in the original strain data, and low-pass filtering is used for noise reduction processing.

[0030] As an implementation manner in this embodiment, the process of screening the strain response data under steady-state and uniform temperature fields includes: Avoiding the non-uniform temperature field period caused by sunlight exposure factors and selecting the monitoring data when the structure is in a thermal equilibrium state.

[0031] Specifically, data screening: The thermodynamic behavior of the structure under non-uniform and non-steady-state temperature fields is relatively complex, and there is an obvious nonlinear relationship between the structural response and the temperature change. It is difficult to identify the structural parameters using the monitoring data under non-uniform and non-steady-state temperature fields. Therefore, it is necessary to screen the static response and temperature data collected synchronously to select the strain response of the structure under steady-state and uniform temperature fields.

[0032] Further, step D: screen the data used for structural parameter identification. Theoretically, under the influence of uniform temperature, each rod is mainly deformed in the axial direction, and the temperature-induced strain measured by the strain gauge shows an obvious linear relationship with the surface temperature change of the structure. However, during the day, due to the influence of factors such as sunlight, there is a large temperature difference between different components, and there is also an obvious temperature gradient in the cross-sectional direction of the same component. Due to the spatial distribution and time-varying nature of the non-uniform temperature field, it is difficult to reflect the overall temperature field distribution of the structure through a limited number of sensors. In addition, the non-uniform temperature field will cause a certain degree of non-uniform deformation of the components. Therefore, the daytime temperature and strain data show a large nonlinear correlation, and it is difficult to directly use the daytime data for parameter identification. As shown in the attached Figure 5 As shown, the temperature from 18:00 to 6:00 the next day and only the temperature-induced response data are selected for further structural parameter identification.

[0033] As an implementation manner in this embodiment, the process of performing perturbation analysis on the structural parameters to be identified includes: The support constraint stiffness and node connection stiffness are used as pre-selected parameters; Analyze the sensitivity of temperature-induced strain response by parameter perturbation; Parameters with screening sensitivity reaching the threshold are used as parameters to be identified.

[0034] Specifically, the determination of the structural parameters to be identified: It takes a long time to identify the structural parameters of all units as the parameters to be identified, and when the noise is large during the monitoring process or the stiffness damage caused by structural damage is too large, the uncertainty of parameter identification increases. The identification process may have problems such as the structural response is not sensitive to parameter changes and the solution process does not converge. In order to improve the calculation efficiency and parameter identification accuracy, the pre-selected structural parameters are perturbed, the sensitivity of the static response is analyzed, and the structural parameters that are more sensitive to the temperature-induced strain response are selected as the parameters to be identified. θ ={ θ 1 , θ 2 , … θ n}, where each parameter can represent the elastic modulus of the member, constraint stiffness, etc.

[0035] More specifically, step E: For grid shell steel structures, boundary conditions such as support constraints and the stiffness of connecting components such as joints are the main parameters affecting the accuracy of the finite element model. Here, these two types of parameters are taken as the main parameters to be identified. Perturb the preselected structural parameters, analyze the sensitivity of the static response, and screen out the structural parameters that are more sensitive to the temperature-induced strain response as the parameters to be identified. θ ={ θ 1 , θ 2 , … θ n}, where each parameter can represent the elastic modulus of the member, the constraint stiffness, etc.

[0036] Specifically, posterior distribution inference based on uncertainty sampling: Determine the overall prior distribution through the prior information of the parameters to be identified, calculate its likelihood function at the same time, and finally define the posterior distribution of the parameters to be identified through its prior distribution and likelihood function. To achieve effective sampling of the posterior distribution, the transitional extended Markov chain Monte Carlo (TEMCMC) method is adopted. Its core idea is to gradually transition the initial prior distribution to the target posterior distribution by introducing a series of intermediate distributions, thereby improving the sampling efficiency and the ability to explore the distribution.

[0037] (1) Set the sequence of transitional parameters: Set a group of transitional factors , satisfying: , and construct a series of intermediate distributions accordingly: , where is the intermediate distribution function, is β k step likelihood function; (2) Generate initial samples: Generate an initial sample set from the prior distribution . (3) Iteratively advance in stages: For each stage k , perform the following operations in sequence: Calculate the weighting factor of the current sample; Resample according to the weights to obtain equally weighted samples; Use Markov chain Monte Carlo sampling (such as Metropolis-Hastings) to perturb the samples to obtain the samples of the next stage; Adaptively adjust to keep the sampling efficiency stable. When , the iteration terminates, and the current sample is the approximate posterior sample.

[0038] More specifically, step F: First, assume that n the prior information of the parameters to be identified all conforms to U ( a , b ) uniform distribution, and its upper and lower bounds a andb Determined through trial calculations and assuming that n the distributions of the parameters are independent of each other, the overall prior distribution is: Let the thermally induced static response of be subject to a Gaussian distribution of observation errors, and the likelihood function is defined as: where is the monitored value of the thermally induced response, is the calculated value of the thermally induced response model obtained by inputting the temperature change data synchronously monitored with into the finite element model; m is the error variance of the monitored value, and are respectively n and m operation steps. Finally, the Bayesian process of updating the posterior distribution of the structural parameter and θ using the prior information of the monitoring data θ can be expressed as: .

[0039] Furthermore, TEMCMC sampling is further used to identify the structural parameters. The specific steps are as follows: Step F.1: Define the sequence of transition distributions: Define a series of transition parameters that satisfy: , and accordingly define the transition distribution at the k th stage as: , when , , that is, the prior distribution; when , that is, the posterior distribution.

[0040] Step F.2: Generate initial samples: Generate a sample set from the prior distribution , N is the number of samplings of the sample set.

[0041] Step F.3: Iteratively advance the intermediate distribution: For each stage k= 0, 1,..., K −1, first calculate the sample weights: For the sample k at the th stage, calculate its importance weight: ; then normalize the weights and calculate the effective sample number; then systematically resample the sample according to the normalized weights to obtain equally weighted samples ; finally, with As the initial point, parallel Markov chain Monte Carlo sampling (such as Metropolis-Hastings) is performed to generate , making it follow the distribution of the next stage . Continuously execute the above process to determine whether the posterior distribution stage ( ) is reached. Otherwise, continue the iteration of the next stage. The finally obtained is the sample set approximating the posterior distribution.

[0042] Step G: Parameter estimation based on samples: After obtaining the posterior sample set , further analysis can obtain the posterior expectation (parameter estimation value) of the parameter to be identified: . The posterior covariance matrix is: .

[0043] Based on this, a temperature-driven structural parameter identification method based on uncertainty sampling provided by an embodiment of the present invention is used to solve the problems of inaccurate parameter estimation, low calculation efficiency, and difficulty in quantifying uncertainty in current structural parameter identification. Different from the structural parameter identification method based on dynamic response, the present invention directly uses the temperature-induced static response for structural parameter identification, and the advantages are that no external excitation loading is required, the test operation is simple, and the health monitoring of the structure under long-term service conditions can be realized; compared with the structural parameter identification method based on traditional static response, the present invention combines Bayesian inference with the transitional extended Markov chain Monte Carlo algorithm, and significantly improves the posterior sampling efficiency through the intermediate distribution and parallel sampling mechanism, avoiding problems such as low sampling efficiency and poor result convergence in the high-dimensional parameter space; at the same time, the present invention introduces the temperature-driven response as the identification information source, which can effectively reflect damage information such as structural stiffness degradation or boundary condition changes on the premise of ensuring the identification accuracy. Generally speaking, the present invention provides a theoretical method and technical support with high precision, high efficiency, and strong robustness for steel structure parameter identification, safety performance evaluation, and maintenance during long-term service, and has wide engineering promotion and application value.

[0044] Embodiment 2 In this embodiment, a computer terminal device is provided, including: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the methods in the above embodiments.

[0045] In this embodiment, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the methods in the above embodiments are implemented.

[0046] In this embodiment, an electronic device is further provided, which includes a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the method in the above embodiment.

[0047] The above program can run in the processor, or can also be stored in the memory (or referred to as a computer-readable medium). The computer-readable medium includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0048] These computer programs can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate computer-implemented processing. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 The steps corresponding to different steps can be implemented by different modules.

[0049] In this embodiment, such a device or system is provided. The system is called a temperature-driven structural parameter identification system based on uncertainty sampling, and includes: A model construction module, which is used to establish an initial finite element model of the structure; A data acquisition module, which is used to perform temperature sensitivity analysis based on the initial finite element model, select temperature-sensitive components to deploy strain sensors, and synchronously collect static response and surface temperature change data; A preprocessing module, which is used to remove outliers, fill in missing values, denoise, and separate the temperature-induced static response components from the static response and temperature data; A data screening module, which is used to screen the strain response data under steady state and uniform temperature fields; A parameter screening module, which is used to perform perturbation analysis on the structural parameters to be identified and screen the parameters sensitive to the temperature-induced strain response; An inversion calculation module is used to define the posterior distribution through the prior distribution and the likelihood function, and to sample the posterior distribution and obtain a posterior sample set using the transition extended Markov chain Monte Carlo algorithm; The parameter output module is used to calculate the posterior expectation and posterior covariance matrix of the parameters to be identified based on the posterior sample set.

[0050] As an implementation in this embodiment, the model building module includes: A geometry processing unit, used to input structural geometry and component cross-section information; Material loading unit, used to input material parameters; The constraint configuration unit is used to configure boundary constraints and generate an initial finite element model.

[0051] As an implementation method in this embodiment, the data acquisition module includes: Sensitivity analysis unit, used to apply unit temperature change to the structure and analyze component sensitivity; A sensor deployment unit, used for arranging vibrating-wire or fiber Bragg grating strain sensors along the axial direction of the temperature-sensitive component; The synchronous acquisition unit is used to synchronously acquire strain and temperature data in a low-frequency sampling mode.

[0052] As an implementation method in this embodiment, the preprocessing module includes: Outlier processing unit, used to remove discrete outliers exceeding 3 times the standard deviation and replace them with adjacent values, and replace continuous outliers with cubic spline interpolation; A missing value compensation unit is used to establish a neural network model based on the correlation of environmental data to fill in the missing values; A noise reduction unit for eliminating high frequency noise using a low pass filter; Thermotropic separation unit is used to separate the thermotropic static response components by principal component analysis or empirical mode decomposition.

[0053] As an implementation method in this embodiment, the data screening module includes: Non-uniform field recognition unit, used to detect non-uniform temperature field periods caused by sunshine factors; The thermal balance screening unit is used to select monitoring data when the structure is in a thermal equilibrium state.

[0054] As an implementation method in this embodiment, the parameter screening module includes: Boundary parameter loading unit, used to load support constraint stiffness and node connection stiffness as pre-selected parameters; A sensitivity analysis unit, used to calculate the sensitivity of temperature-induced strain response by parameter perturbation; A threshold screening unit for screening parameters with sensitivity reaching the threshold as parameters to be recognized.

[0055] This system or device is used to implement the functions of the methods in the above embodiments. Each module in this system or device corresponds to each step in the method, and those that have been described in the method will not be elaborated here.

[0056] Through the above implementation manner, the problem of temperature-driven structural parameter identification based on uncertainty sampling in the related art is solved, so as to ensure that the problems existing in the prior art can be solved.

[0057] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A temperature-driven structural parameter identification method based on uncertainty sampling, characterized in that The following steps are involved: Create an initial finite element model of the structure; Based on the initial finite element model, temperature sensitivity analysis is performed, and strain sensors are arranged on structural components that are sensitive to temperature changes, so as to simultaneously collect data on the static response of the structure under temperature changes and the temperature change of the structure surface; Preprocess the static response and temperature data, and separate the temperature-induced static response component from the overall static response monitoring data; Screening of strain response data under steady-state and uniform temperature fields; Perform perturbation analysis on the structural parameters to be identified, and select the structural parameters that are sensitive to temperature-induced strain response as the parameters to be identified; The posterior distribution is defined by the prior distribution and likelihood function of the parameter to be identified, and the transition extended Markov chain Monte Carlo algorithm is used to sample the posterior distribution, in which a series of intermediate distributions are introduced to gradually transition the prior distribution to the posterior distribution to obtain the posterior sample set; The posterior expectation and posterior covariance matrix of the parameters to be identified are calculated based on the posterior sample set.

2. The method according to claim 1, wherein The process of establishing the initial finite element model of the structure includes: establishing the initial finite element model according to the structural geometric dimensions, component cross-section information, material parameters and constraint conditions.

3. The method according to claim 1, characterized in that, The process of synchronously collecting the static response of the structure under the temperature environment change and the temperature change data of the structure surface includes: Apply unit temperature change to the structure for temperature sensitivity analysis; Select temperature-sensitive components to arrange vibrating-wire or fiber Bragg grating strain sensors along the axial direction; set strain and temperature data to synchronous low-frequency sampling.

4. The method according to claim 1, characterized in that, The process of preprocessing the static response and temperature data includes: Discrete outliers exceeding three times the standard deviation of the monitoring data were removed and replaced with adjacent values, and continuous outliers were replaced with cubic spline interpolation; Based on the correlation between environmental data and monitoring data, a neural network model was established to fill in the missing values; Low-pass filtering is used to reduce data noise; The temperature-induced static response components are separated by principal component analysis or empirical mode decomposition.

5. The method according to claim 1, wherein The process of screening the strain response data under the steady state and uniform temperature field includes: avoiding the non-uniform temperature field period caused by the sunshine factor, and selecting the monitoring data when the structure is in a thermal equilibrium state.

6. The method according to claim 1, characterized in that The process of performing perturbation analysis on the structural parameters to be identified comprises: The support constraint stiffness and node connection stiffness are used as pre-selected parameters; Analyze the sensitivity of temperature-induced strain response by parameter perturbation; Parameters with screening sensitivity reaching the threshold are used as parameters to be identified.

7. A temperature-driven structural parameter identification system based on uncertainty sampling, characterized in that, The system comprises: A model building module, used to build an initial finite element model of the structure; The data acquisition module is used to perform temperature sensitivity analysis based on the initial finite element model, select temperature sensitive components to arrange strain sensors, and simultaneously collect static response and surface temperature change data; The preprocessing module is used to remove outliers, fill missing values, reduce noise and separate temperature-induced static response components from static response and temperature data; Data screening module, used to screen strain response data under steady state and uniform temperature field; Parameter screening module, used to perform perturbation analysis on the structural parameters to be identified and screen the parameters that are sensitive to temperature-induced strain response; An inversion calculation module, configured to define a posterior distribution through a prior distribution and a likelihood function, sample the posterior distribution by using a transitional extended Markov chain Monte Carlo algorithm, and obtain a posterior sample set; A parameter output module, configured to calculate a posterior expectation and a posterior covariance matrix of a parameter to be identified based on the posterior sample set.

8. The system according to claim 7, characterized in that The model construction module includes: A geometry processing unit, configured to input structural geometry dimensions and component cross-section information; A material loading unit, configured to input material parameters; A constraint configuration unit, configured to configure boundary constraint conditions and generate an initial finite element model.

9. A computer terminal device, characterized in that, Including: One or more processors; A memory, coupled to the processor, configured to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the temperature-driven structural parameter identification method based on uncertainty sampling according to any one of claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the temperature-driven structural parameter identification method based on uncertainty sampling according to any one of claims 1-6 is implemented.

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