A method for identifying temperature-driven structural parameters based on uncertainty sampling

Abstract

The present invention discloses a temperature-driven structural parameter identification method based on uncertainty sampling, which relates to the field of structural health monitoring technology. To address the shortcomings of existing structural parameter identification methods, such as sensitivity to environmental interference, low computational efficiency, and insufficient uncertainty quantification, the invention uses temperature as a natural excitation source. By establishing an initial finite element model, the temperature change of the structural surface and the temperature-induced static response data are simultaneously collected. After data preprocessing and steady-state uniform temperature field screening, highly sensitive structural parameters are screened based on parameter perturbation analysis. The transition extended Markov chain Monte Carlo algorithm is used to sample the posterior distribution, and the computational complexity of the high-dimensional parameter space is reduced through the intermediate distribution transition mechanism. The final output is the parameter posterior expectation and covariance matrix. This method does not require artificial excitation, significantly improves long-term monitoring efficiency and damage identification reliability, and is suitable for the condition assessment and safety diagnosis of civil engineering structures such as bridges and spatial steel structures.

Description

Technical Field

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

[0002] Structural damage identification methods are mainly divided into two categories: data-driven and model-based. Data-driven methods identify damage states by comparing changes in monitoring data before and after damage, but they cannot quantitatively assess physical parameters (such as stiffness and boundary constraints), resulting in insufficient damage mechanism analysis capabilities. Model-based structural parameter identification methods modify finite element model parameters to make their calculated responses closer to actual monitoring data. Although they can achieve quantitative assessment, traditional technologies have significant limitations. Methods based on dynamic modal parameters (such as frequency and mode shape) are easily affected by environmental factors such as wind loads and traffic vibrations, have low signal-to-noise ratios, and are difficult to support long-term health monitoring. While methods based on static responses (stress and deflection) have the advantages of small data volumes and strong noise resistance, they still rely on artificial excitation loading, limiting their engineering applicability.

[0003] Temperature-driven structural parameter identification technology directly establishes a complete input-output transfer function by simultaneously measuring ambient temperature changes (input excitation) and the structural temperature-induced response (output), eliminating the need for external excitation. However, this method suffers from two major bottlenecks: First, conventional deterministic optimization methods ignore multiple sources of uncertainty, such as monitoring data noise, finite element model simplification errors, and environmental variability, resulting in low reliability of identification results. Second, while Bayesian inference can quantify uncertainty, its posterior probability density solution relies on Markov Chain Monte Carlo (MCMC) sampling, which is computationally inefficient. Furthermore, the improved Transition Markov Chain Monte Carlo (TMCMC) algorithm requires repeated calculation of auxiliary parameters during iteration, which significantly increases computational cost and makes it difficult to apply to large civil engineering structures. Summary of the Invention

[0004] In order 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-mentioned prior art.

[0005] In a first aspect, to achieve the above-mentioned object, the present invention provides a method for identifying temperature-driven structural parameters based on uncertainty sampling, comprising the following steps:

[0006] Create an initial finite element model of the structure;

[0007] Conduct temperature sensitivity analysis based on the initial finite element model, select structural components that are sensitive to temperature changes and deploy strain sensors to simultaneously collect data on the static response of the structure under temperature changes and the temperature change of the structure surface;

[0008] Preprocess the static response and temperature data, and separate the temperature-induced static response component from the overall static response monitoring data;

[0009] Screening of strain response data under steady-state and uniform temperature fields;

[0010] 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;

[0011] 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. The prior distribution is gradually transitioned to the posterior distribution by introducing a series of intermediate distributions to obtain the posterior sample set.

[0012] The posterior expectation and posterior covariance matrix of the parameters to be identified are calculated based on the posterior sample set.

[0013] Optionally, the process of establishing the 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 constraint conditions.

[0014] Optionally, the process of synchronously collecting data on the static response of the structure under temperature environment changes and the temperature change of the structure surface includes:

[0015] Apply unit temperature change to the structure to perform temperature sensitivity analysis;

[0016] Select temperature-sensitive components and arrange vibrating wire or fiber Bragg grating strain sensors along the axial direction; set the strain and temperature data to synchronous low-frequency sampling.

[0017] Optionally, the process of preprocessing the static response and temperature data includes:

[0018] 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;

[0019] Based on the correlation between environmental data and monitoring data, a neural network model was established to fill in missing values;

[0020] Low-pass filtering is used to reduce data noise;

[0021] The temperature-induced static response components are separated by principal component analysis or empirical mode decomposition.

[0022] 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.

[0023] Optionally, the process of performing perturbation analysis on the structural parameters to be identified includes:

[0024] The support constraint stiffness and node connection stiffness are used as pre-selected parameters;

[0025] Analyze the sensitivity of temperature-induced strain response through parameter perturbation;

[0026] Parameters with screening sensitivity reaching the threshold are used as parameters to be identified.

[0027] In a second aspect, the present invention further provides a temperature-driven structural parameter identification system based on uncertainty sampling, which is used to implement a temperature-driven structural parameter identification method based on uncertainty sampling, and the system comprises:

[0028] Model building module, used to build the initial finite element model of the structure;

[0029] The data acquisition module is used to perform temperature sensitivity analysis based on the initial finite element model, select temperature-sensitive components to deploy strain sensors, and simultaneously collect static response and surface temperature change data;

[0030] 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;

[0031] Data screening module, used to screen strain response data under steady-state and uniform temperature fields;

[0032] Parameter screening module, used to perform perturbation analysis on the structural parameters to be identified and screen parameters that are sensitive to temperature-induced strain response;

[0033] 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 using the transition extended Markov chain Monte Carlo algorithm to obtain a posterior sample set;

[0034] 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.

[0035] Optionally, the model building module includes:

[0036] Geometry processing unit, used to input structural geometry and component cross-section information;

[0037] Material loading unit, used to input material parameters;

[0038] The constraint configuration unit is used to configure boundary constraints and generate an initial finite element model.

[0039] In a third aspect, the present invention further provides a computer terminal device, comprising:

[0040] one or more processors;

[0041] a memory, coupled to the processor, for storing one or more programs;

[0042] 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.

[0043] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a temperature-driven structural parameter identification method based on uncertainty sampling.

[0044] Compared with the prior art, the present invention has the following advantages and technical effects:

[0045] The present invention provides a temperature-driven structural parameter identification method and system based on uncertainty sampling. This method significantly improves the efficiency and accuracy of structural parameter identification, avoids the need for external artificial excitation through temperature-driven static response, and achieves long-term automated monitoring. Combining the sampling mechanism of the transition-extended Markov chain Monte Carlo algorithm effectively overcomes the computational bottleneck of high-dimensional parameter space and reduces inversion time. Steady-state temperature field data screening and temperature-induced response separation techniques are utilized to enhance robustness under environmental interference. Furthermore, parameter uncertainty is quantified based on the Bayesian posterior distribution, providing a reliable basis for structural damage diagnosis. This overall solution addresses the shortcomings of traditional methods in terms of engineering applicability, computational efficiency, and uncertainty quantification. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings, which constitute part of the present invention, are provided to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are provided to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0047] Figure 1 This is a flow chart of an implementation method of a temperature-driven structural parameter identification method based on uncertainty sampling according to an embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram of a structure to be identified according to an embodiment of the present invention;

[0049] Figure 3 Schematic diagram of a static response and temperature synchronization monitoring sensor arranged along the axial direction of a temperature sensitive rod according to an embodiment of the present invention;

[0050] Figure 4 A schematic diagram of the sensor layout according to an embodiment of the present invention;

[0051] Figure 5Schematic diagram of the steady-state and uniform temperature field strain response data screening process according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can 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.

[0053] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0054] Description of the related technologies of the present invention:

[0055] Structural damage identification methods are primarily categorized as data-driven and model-based (i.e., structural parameter identification). Data-driven methods identify damage based on pre- and post-damage data changes but lack the ability to quantitatively evaluate physical parameters. Structural parameter identification methods, on the other hand, modify finite element models to make their responses approximate actual monitoring data, enabling quantitative assessment of the structural condition. Among existing structural parameter identification techniques, methods based on dynamic modal parameters are susceptible to environmental interference and lack support for long-term health monitoring. Methods based on static responses (stress and deflection) offer advantages such as reduced data size and strong noise immunity. Temperature-driven structural parameter identification, in particular, analyzes the correlation between temperature-induced responses and structural parameters. Its primary advantage lies in simultaneously measuring both the external stimulus (i.e., temperature change) and the structural response (i.e., temperature-induced response), thereby obtaining a complete transfer function (i.e., input-output relationship). However, when using temperature-driven structural parameter identification, conventional deterministic methods tend to overlook the impact of multiple sources of uncertainty on the identification results, such as monitoring data noise, model simplification errors, and environmental variability. Bayesian inference provides a theoretical framework for addressing uncertainty in parameter identification, but its posterior probability density solution relies on Markov chain Monte Carlo sampling. Transition Markov Chain Monte Carlo improves sampling efficiency by introducing transition distribution, but it requires repeated calculation of auxiliary parameters during iteration, resulting in high computational cost, which restricts its application in large-scale projects.

[0056] To address the aforementioned bottlenecks, the present invention proposes a temperature-driven structural parameter identification method based on uncertainty sampling. This method uses temperature effects as the input of the structural system, exploits 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 Transition Extended Markov Chain Monte Carlo algorithm (TEMCMC) to sample the posterior distribution. This method reduces the model's computational cost through a surrogate model and, combined with the adaptive transition distribution mechanism of the Transition Extended Markov Chain Monte Carlo algorithm, significantly improves the efficiency of Bayesian inversion while ensuring the accuracy of high-dimensional parameter space searches, providing a new path for temperature-driven uncertainty structural parameter identification. Through theoretical innovation and algorithm optimization, the patent aims to overcome the limitations of traditional methods in engineering applicability, computational efficiency, and uncertainty quantification, providing a more reliable quantitative assessment tool for structural health monitoring.

[0057] Example 1

[0058] like Figure 1 As shown, this embodiment provides a temperature-driven structural parameter identification method based on uncertainty sampling, including:

[0059] Create an initial finite element model of the structure;

[0060] Conduct temperature sensitivity analysis based on the initial finite element model, select structural components that are sensitive to temperature changes and deploy strain sensors to simultaneously collect data on the static response of the structure under temperature changes and the temperature change of the structure surface;

[0061] Preprocess the static response and temperature data, and separate the temperature-induced static response component from the overall static response monitoring data;

[0062] Screening of strain response data under steady-state and uniform temperature fields;

[0063] 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;

[0064] 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. The prior distribution is gradually transitioned to the posterior distribution by introducing a series of intermediate distributions to obtain the posterior sample set.

[0065] The posterior expectation and posterior covariance matrix of the parameters to be identified are calculated based on the posterior sample set.

[0066] As an implementation method 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, component cross-sectional information, material parameters and constraint conditions.

[0067] Specifically, step A: Figure 2 Taking the grid steel structure as an example, the initial finite element model of the structure is first established based on the original design information such as the grid steel structure size, component cross-section information, material parameters and constraints.

[0068] As an implementation method of this embodiment, the process of synchronously collecting the static response of the structure under temperature environment changes and the structure surface temperature change data includes:

[0069] Apply unit temperature change to the structure to perform temperature sensitivity analysis;

[0070] Select temperature-sensitive components and arrange vibrating wire or fiber Bragg grating strain sensors along the axial direction; set the strain and temperature data to synchronous low-frequency sampling.

[0071] Specifically, based on the establishment of the initial finite element model, a unit temperature change is applied to the structure to perform a temperature sensitivity analysis of the structure. Strain sensors are placed on components that are more sensitive to temperature changes among the many components of the structure. Strain and temperature data are set to synchronous low-frequency sampling to analyze the static response of the structure under temperature changes. Changes in surface temperature of the structure Data is collected synchronously.

[0072] More specifically, step B: Based on step A, establish an initial finite element model, apply unit temperature change to the structure, and perform temperature sensitivity analysis on the structure. Select the rods that are more sensitive to temperature change among the many components of the structure, such as the attached Figure 3 As shown, strain sensors are arranged in the axial direction of the rod. Vibrating wire or fiber Bragg grating sensors are preferred. The positions of all sensors are shown in the attached figure. Figure 4 As shown. Set the strain and temperature data to synchronous low-frequency sampling, and the static response of the structure under temperature environment changes Changes in surface temperature of the structure Data is collected synchronously.

[0073] As an implementation method of this embodiment, the process of preprocessing the static response and temperature data includes:

[0074] 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;

[0075] Based on the correlation between environmental data and monitoring data, a neural network model was established to fill in missing values;

[0076] Low-pass filtering is used to reduce data noise;

[0077] The temperature-induced static response components are separated by principal component analysis or empirical mode decomposition.

[0078] Specifically, the monitored static response and temperature data are preprocessed by removing outliers, filling missing values, and reducing data noise. For more complex environmental changes, the structural static response monitoring data obtained by the monitoring system under normal service conditions is the result of the interaction of multiple factors. Among these influencing factors, temperature is one of the main factors affecting the changes in the structural monitoring response. Data preprocessing methods such as principal component analysis and empirical mode decomposition are used to separate the temperature-induced static response components from the overall static response monitoring data for subsequent structural parameter identification.

[0079] More specifically, step C: preprocessing the measured temperature and strain monitoring data. Due to signal transmission failures caused by system noise, power outages, and other irresistible factors, outliers, noise, and data loss will appear in the data during the long-term monitoring process. These data anomalies directly lead to the failure of the structural parameter identification process. Therefore, the original temperature and strain response data are first preprocessed. The preprocessing process includes the removal of outliers, filling in missing values, and data noise reduction. Specifically, for discrete and sporadic outlier data, data that exceeds 3 times the standard deviation of the data during the monitoring period will be replaced with neighboring values, while for more continuous outlier data within a period of time, cubic spline interpolation is used for data replacement. For missing values, the correlation between environmental data and temperature and strain data is used to establish a neural network prediction model 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.

[0080] As an implementation method in this embodiment, 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.

[0081] Specifically, data screening: The thermodynamic behavior of the structure under non-uniform and non-steady temperature fields is relatively complex, and there is an obvious nonlinear relationship between the structural response and temperature changes. It is difficult to identify structural parameters using monitoring data under non-uniform and non-steady 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.

[0082] Furthermore, step D: screening the data used for structural parameter identification. Theoretically, under the influence of uniform temperature, each rod mainly deforms 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 factors such as sunlight, there are large temperature differences 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, so the daytime temperature and strain data show a large nonlinear correlation, and it is difficult to directly use daytime data for parameter identification. As shown in the attached Figure 5 As shown in the figure, the temperature and temperature-induced response data from 18:00 to 6:00 the next day are selected for further structural parameter identification.

[0083] As an implementation method in this embodiment, the process of performing perturbation analysis on the structural parameters to be identified includes:

[0084] The support constraint stiffness and node connection stiffness are used as pre-selected parameters;

[0085] Analyze the sensitivity of temperature-induced strain response through parameter perturbation;

[0086] Parameters with screening sensitivity reaching the threshold are used as parameters to be identified.

[0087] 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. In addition, when there is a lot of noise 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 cause problems such as the structural response being insensitive to parameter changes and the solution process not converging. 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.

[0088] More specifically, step E: For grid steel structures, boundary conditions such as support constraints and the stiffness of connectors such as nodes are the main parameters that affect the accuracy of the finite element model. Here, these two parameters are considered as the main parameters to be identified. Perturb the pre-selected structural parameters, analyze the sensitivity of the static response, and select 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, constraint stiffness, etc.

[0089] Specifically, the posterior distribution inference based on uncertainty sampling involves determining the overall prior distribution of the parameter to be identified using prior information about it, simultaneously calculating its likelihood function, and finally defining the posterior distribution of the parameter to be identified using the prior distribution and likelihood function. To effectively sample the posterior distribution, the Transitional Extended Markov Chain Monte Carlo (TEMCMC) method, with its core concept of gradually transitioning the initial prior distribution to the target posterior distribution by introducing a series of intermediate distributions, thereby improving sampling efficiency and distribution exploration capabilities.

[0090] (1) Set the transition parameter sequence: set a set of transition factors ,satisfy: , and construct a series of intermediate distributions based on this: ,in is the intermediate distribution function, for β k The likelihood function of the step;

[0091] (2) Initial sample generation: from prior distribution Generate an initial sample set;

[0092] (3) Iterative advancement in stages: For each stage k , the following operations are performed in sequence: calculate the weighting factor of the current sample; resample according to the weight to obtain equal-weighted samples; use Markov chain Monte Carlo sampling (such as Metropolis-Hastings) to perturb the sample to obtain the sample of the next stage; adaptively adjust , so that the sampling efficiency remains stable. When , the iteration terminates, and the current sample is the approximate posterior sample.

[0093] More specifically, step F: First, assume n The prior information of the parameters to be identified all meet the U ( a , b ) is uniformly distributed, with upper and lower bounds a and b Determined by trial calculation, assuming n The distributions of the parameters are independent of each other, and the overall prior distribution is: ;

[0094] Assume that the structure temperature-induced static response The observation error obeys the Gaussian distribution, and the likelihood function is defined as: ,in is the monitoring value of the temperature-induced response, It will be with Synchronously monitored temperature change data The calculated value of the temperature-induced response model obtained by inputting the finite element model; is the error variance of the monitoring value, m is the number of structural response test samples, and They are n and m Finally, by monitoring the data and θ The prior information on the structural parameters θ The Bayesian process of updating the posterior distribution of can be expressed as: .

[0095] TEMCMC sampling is further used to identify the structural parameters. The specific steps are as follows:

[0096] Step F.1: Define a sequence of transition distributions: Define a series of transition parameters ,satisfy: , based on which the first k The transition distribution of the stages is: ,when hour, , that is, the prior distribution; when hour, That is the posterior distribution.

[0097] Step F.2: Initial sample generation: from prior distribution Generate sample set , N is the number of sampling times of the sample set.

[0098] Step F.3: Iteratively advance the intermediate distribution: For each stage k= 0, 1, ..., K −1First calculate the sample weight: k Stage samples , calculate its importance weight: ; Then normalize the weights and calculate the effective number of samples; then, according to the normalized weights, Perform systematic resampling to obtain equally weighted samples ; Finally As the initial point, perform parallel Markov chain Monte Carlo sampling (such as Metropolis-Hastings) to generate , so that it obeys the distribution of the next stage . Continue to execute the above process to determine whether the posterior distribution stage has been reached ( ), otherwise continue to the next stage iteration. The final This is the sample set that approximates the posterior distribution.

[0099] Step G: Sample-based parameter estimation: After obtaining the posterior sample set After that, further analysis can obtain the posterior expectation (parameter estimate) of the parameter to be identified: The posterior covariance matrix is: .

[0100] Based on this, an embodiment of the present invention provides a temperature-driven structural parameter identification method based on uncertainty sampling, which is used to address the current problems of inaccurate parameter estimation, low computational efficiency, and difficulty in quantifying uncertainty in structural parameter identification. Unlike structural parameter identification methods based on dynamic response, the present invention directly identifies structural parameters using temperature-induced static response. This method has the advantages of not requiring external excitation loading, simplifying testing, and enabling health monitoring of structures under long-term service conditions. Compared with traditional structural parameter identification methods based on static response, the present invention combines Bayesian inference with the transition-extended Markov chain Monte Carlo algorithm. Through an intermediate distribution and parallel sampling mechanism, it significantly improves the efficiency of posterior sampling, avoiding problems such as low sampling efficiency and poor convergence in high-dimensional parameter spaces. Furthermore, the present invention introduces temperature-driven response as an identification information source, effectively reflecting damage information such as structural stiffness degradation or boundary condition changes while ensuring identification accuracy. Overall, the present invention provides a high-precision, high-efficiency, and highly robust theoretical method and technical support for parameter identification, safety performance assessment, and maintenance of steel structures during long-term service, with broad engineering application value.

[0101] Example 2

[0102] In this embodiment, a computer terminal device is provided, including:

[0103] one or more processors;

[0104] a memory, coupled to the processor, for storing one or more programs;

[0105] 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.

[0106] 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 method in the above embodiment is implemented.

[0107] In this embodiment, an electronic device is further provided, including a memory and a processor. The memory stores a computer program, and the processor is configured to run the computer program to execute the method in the above embodiment.

[0108] The above program can be executed in a processor or stored in a memory (or computer-readable medium). Computer-readable media includes both permanent and non-permanent, removable and non-removable media, and can be implemented using any method or technology to store information. 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 cassettes, magnetic tape, 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.

[0109] These computer programs can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps of the functions specified in one or more blocks can be implemented by different modules corresponding to different steps.

[0110] This embodiment provides such a device or system. The system is called a temperature-driven structural parameter identification system based on uncertainty sampling, and includes:

[0111] Model building module, used to build the initial finite element model of the structure;

[0112] The data acquisition module is used to perform temperature sensitivity analysis based on the initial finite element model, select temperature-sensitive components to deploy strain sensors, and simultaneously collect static response and surface temperature change data;

[0113] 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;

[0114] Data screening module, used to screen strain response data under steady-state and uniform temperature fields;

[0115] Parameter screening module, used to perform perturbation analysis on the structural parameters to be identified and screen parameters that are sensitive to temperature-induced strain response;

[0116] 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 using the transition extended Markov chain Monte Carlo algorithm to obtain a posterior sample set;

[0117] 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.

[0118] As an implementation in this embodiment, the model building module includes:

[0119] Geometry processing unit, used to input structural geometry and component cross-section information;

[0120] Material loading unit, used to input material parameters;

[0121] The constraint configuration unit is used to configure boundary constraints and generate an initial finite element model.

[0122] As an implementation method of this embodiment, the data acquisition module includes:

[0123] Sensitivity analysis unit, used to apply unit temperature changes to the structure and analyze component sensitivity;

[0124] A sensor deployment unit, used for arranging vibrating wire or fiber Bragg grating strain sensors along the axial direction of the temperature sensitive component;

[0125] The synchronous acquisition unit is used to synchronously acquire strain and temperature data in a low-frequency sampling mode.

[0126] As an implementation method in this embodiment, the preprocessing module includes:

[0127] Outlier processing unit, used to remove discrete outliers exceeding three times the standard deviation and replace them with adjacent values, and to replace continuous outliers with cubic spline interpolation;

[0128] Missing value compensation unit, used to establish a neural network model based on the correlation of environmental data to fill in missing values;

[0129] A noise reduction unit for eliminating high-frequency noise using a low-pass filter;

[0130] The temperature-induced separation unit is used to separate the temperature-induced static response components through principal component analysis or empirical mode decomposition.

[0131] As an implementation in this embodiment, the data screening module includes:

[0132] Non-uniform field recognition unit, used to detect periods of non-uniform temperature fields caused by sunlight factors;

[0133] The thermal balance screening unit is used to select monitoring data when the structure is in thermal equilibrium.

[0134] As an implementation method of this embodiment, the parameter screening module includes:

[0135] Boundary parameter loading unit, used to load support constraint stiffness and node connection stiffness as pre-selected parameters;

[0136] Sensitivity analysis unit, used to calculate the sensitivity of temperature-induced strain response through parameter perturbation;

[0137] The threshold screening unit is used to screen parameters whose sensitivity reaches a threshold as parameters to be identified.

[0138] The system or device is used to implement the functions of the method in the above-mentioned embodiment. Each module in the system or device corresponds to each step in the method, which has been explained in the method and will not be repeated here.

[0139] Through the above implementation, the problem of temperature-driven structural parameter identification based on uncertainty sampling in the related art is solved, thereby ensuring that the problems existing in the prior art are solved.

[0140] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection 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; Conduct temperature sensitivity analysis based on the initial finite element model, select structural components that are sensitive to temperature changes and deploy strain sensors 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. The prior distribution is gradually transitioned to the posterior distribution by introducing a series of intermediate distributions 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, characterized in that 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 temperature environment changes and the temperature change data of the structure surface includes: Apply unit temperature change to the structure to perform temperature sensitivity analysis; Select temperature-sensitive components and arrange vibrating wire or fiber Bragg grating strain sensors along the axial direction; set the strain and temperature data to synchronous low-frequency sampling.

4. The method according to claim 1, wherein 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 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 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.

6. The method according to claim 1, characterized in that 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 through 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: Model building module, used to build the 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 deploy 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 fields; Parameter screening module, used to perform perturbation analysis on the structural parameters to be identified and screen parameters that are sensitive to 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 using the transition extended Markov chain Monte Carlo algorithm to obtain a posterior sample set; 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.

8. The system according to claim 7, characterized in that The model building module includes: 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.

9. A computer terminal device, characterized in that: include: 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 temperature-driven structural parameter identification method based on uncertainty sampling according to any one of claims 1 to 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 to 6 is implemented.

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