CN110717287B - Spatial steel structure support rigidity identification method based on temperature strain - Google Patents
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Abstract
The invention discloses a spatial steel structure support rigidity identification method based on temperature strain, which comprises the following steps: (1) A sensor capable of synchronously collecting strain and temperature data is arranged at a member connected with a support to be identified in the structure; collecting strain and temperature data of the component; (2) Performing outlier replacement, noise reduction and downsampling on the strain and temperature data, increasing the reliability of the data and reducing the number of analysis samples; (3) Carrying out principal component analysis on the strain data processed in the step (2), and extracting a temperature strain component in the strain data; (4) Establishing an initial finite element model of the space steel structure according to design data, wherein the initial value of the rigidity of the support is the design value of the support; (5) And constructing a rigidity identification index by using the difference between the calculated temperature strain and the actually measured temperature strain of the structure, and continuously iterating the calculation to minimize the rigidity identification index value by changing the rigidity value of the support of the finite element model, so as to finally obtain the actual rigidity value of the support of the structure.
Description
Technical Field
The invention relates to the field of support rigidity identification and safety diagnosis of space steel structures in civil engineering, in particular to a support rigidity identification method based on temperature strain.
Background
The support is an important component for connecting space steel structures such as a net rack and a net shell with a lower support, necessary constraint is provided for the space steel structures, and the working state of the support is closely related to the operation safety of the structures. In the long-term service process of the space steel structure, the space steel structure is subjected to the comprehensive effects of environmental factors and human factors, so that the diseases of the support of the structure are frequently generated. The most important manifestations of structural support damage are changes in stiffness, for example, poor construction or overrun of load, resulting in support fracture and void, reduced stiffness, etc. The rigidity change of the support directly affects the whole stress state of the structure, and hidden danger is brought to the safe operation of the structure. Therefore, early damage of the structural support is timely and accurately identified, and the method has important engineering application value.
The damage identification method based on the reference finite element model is widely studied as a common health monitoring method, and the method is used for identifying structural damage by comparing the difference between actual monitoring data and reference finite element calculation data when the structure is damaged, and the establishment of an accurate finite element model is the basis of the method. However, the finite element model constrained by the structural member and the support cannot fully reflect the actual behavior of the structure, and the parameters of the finite element model such as structural rigidity, constraint rigidity and the like are not fully consistent with the actual parameters, so that the parameters of the finite element model are necessarily identified and corrected through monitoring data.
At present, two common structural rigidity identification methods and numerical model correction methods exist, one is an identification method based on vibration modal parameters, and the other is an identification method based on structural static response.
The vibration-based identification method is characterized in that the acceleration data of the structure vibration is monitored, the acceleration data is utilized to carry out modal analysis to obtain the modal parameters of the structure, and the structural rigidity identification and model correction are realized by comparing the structural modal parameters monitored by the structure with the calculated modal parameters by utilizing the relation between the structural modal parameters and the structural physical parameters as the structural characteristic parameters (such as structural rigidity, quality and the like) are considered to be changed to directly influence the modal parameters (such as frequency, vibration mode and the like) of the structure. But the modal parameters of the structure are not only related to the properties of the structure itself, but also are very susceptible to environmental factors; in addition, for the large-span space steel structure, the mode parameters are mutually overlapped and difficult to accurately obtain, the input excitation of the structure vibration is not clear, the dynamic monitoring data storage capacity is large, and the like, so that the application of the method in the space steel structure rigidity identification is greatly limited.
The structure static force based identification method monitors static force response information of the structure by applying static force load to the structure, and realizes structure rigidity identification and model correction by comparing the static force response monitored by the structure with the calculated static force response. Compared with the recognition method based on the modal parameters, the recognition method based on the static response has higher precision, is less influenced by environmental factors and noise, but has higher difficulty in applying the static load to the spatial steel structure and is not easy to realize.
The temperature effect of the space steel structure is obvious under the influence of factors such as four seasons replacement, solar radiation and the like, and the temperature load is one of main loads born by the space steel structure in a normal service state. The support form and the support rigidity of the space steel structure have obvious correlation with the temperature effect of the structure, and the change of the support rigidity can lead to the change of the temperature response of the rod piece near the support, so that the rigidity of the structure support can be identified by monitoring the temperature response of the structure.
Disclosure of Invention
The invention aims to overcome the defects of the existing structure support rigidity identification method based on vibration modal parameters and static response, and provides a space steel structure support rigidity identification method based on temperature strain.
The invention aims at realizing the following technical scheme:
the spatial steel structure support rigidity identification method based on temperature strain is characterized by comprising the following steps of:
(1) Arranging a sensor and collecting data: a sensor capable of synchronously collecting strain and temperature data is arranged at a member connected with a support to be identified in the structure; synchronously collecting strain and temperature data of the component;
(2) Data preprocessing: performing outlier replacement, noise reduction and downsampling on the strain and temperature data, increasing the reliability of the data and reducing the number of analysis samples;
(3) Separating and extracting temperature strain: carrying out principal component analysis on the strain data processed in the step (2), and extracting a temperature strain component in the data, namely, actually measured temperature strain;
(4) Establishing an initial finite element model: establishing an initial finite element model of the space steel structure according to design data, wherein the initial value of the rigidity of the support is the design value of the support;
(5) Stiffness identification: taking the temperature data pretreated in the step (2) as load, inputting the initial finite element model established in the step (4), and calculating the temperature strain at the same position as the actual structure monitoring position in the finite element model, namely calculating the temperature strain; . Constructing a judging index of the support rigidity identification by utilizing the difference between the calculated temperature strain and the actually measured temperature strain extracted in the step (3), and gradually reducing the judging index value of the support rigidity identification by changing the support rigidity value of the finite element model through continuous iterative calculation, so as to finally achieve convergence, wherein the obtained support rigidity value of the finite element model is the actual rigidity value of the structural support.
Furthermore, in the step (1), the acquisition frequency of the strain and the temperature data is ensured to be consistent, the measurement precision is ensured, and the influence of external interference on the measurement data is reduced; the sensor is a temperature self-compensation fiber grating strain sensor or a vibrating wire strain sensor with temperature compensation.
Further, in the step (2), the specific method of outlier replacement and noise reduction processing is as follows:
(201) Replacing the outlier: taking data with window length of L from the data acquired initially, and averaging and standard deviation of the data, wherein the data points meet the requirement ofI.e., if the difference between a certain measured value and the mean value is greater than 3 times the standard deviation, then the data adjacent to the data point is replaced; the window is then moved sequentially along the data until all the data is covered, where x i For the ith data point, +.>The sigma is the standard deviation of the data in the L length;
(202) Noise is removed: denoising by adopting a moving average method, taking data of a window from the data acquired initially, averaging the data, replacing all the data in the window with the average value, and then sequentially moving the window until all the data are covered by the data.
Further, in step (2), in order to reduce the calculation amount and time of the subsequent analysis, the data is downsampled, that is, the original monitoring data is sampled at intervals of several data, so that a new sample with a smaller data amount is obtained for the subsequent analysis calculation.
Further, the temperature strain separated and extracted in the step (3) is a stress strain under a temperature load, and is not a thermal strain of free thermal expansion of the rod.
In the step (3), a principal component analysis method is adopted to perform separation and extraction, principal component analysis is performed on the strain data processed in the step (2), a first-order principal component is reserved as a conversion matrix of the principal component analysis, and the temperature strain component in the strain can be obtained by performing back projection after the data is projected.
Further, in step (4), before the support stiffness is identified, the structure is initially modeled by finite elements according to design data, parameters of the model such as coordinates of each node, dimensions of each rod, and material characteristics are input according to the design data, initial values of constraint stiffness are obtained from the parameter values given by the design data, and are recorded as p (0) 。
Further, in the step (5), the environmental temperature change is regarded as a static load, so that the finite element analysis process of the structure under the action of the temperature load is the same as the analysis process under the general static load. Inputting the temperature data processed in the step (2) into a finite element model, and calculating the strain of the structure under the action of temperature load, namely calculating the temperature strain: { ε } = [ B ]][K(p)]- 1 { F (DeltaT) } where [ B ]]For the strain change matrix, it is determined only by the cell node coordinates, [ K (p)]The structural rigidity matrix after introducing boundary conditions is a function of structural parameters p including the rigidity of the support; { F (ΔT) } is an excitation load, which is a function of the temperature change ΔT; calculating the temperature strain { ε } and the measured temperature strain { ε }, and M the residual { r } between is denoted as { r (p) } = { ε } M Residual { r } is a function of the structural related parameters and is a discrimination index for identifying the rigidity of the support; the process of identifying the rigidity of the structural support is to optimize the rigidity parameter p of the structural support through continuous iteration, so that the discrimination index { r } norm of the rigidity identification of the support is minimum: e (p) = { r } T {r}＝||{r}|| 2 →min；
Searching a minimum solution meeting E (p) as a minimum value optimizing process about p, adopting a genetic algorithm to perform optimizing calculation, and selecting a residual norm as a proper value of the genetic algorithmThe value of the fitness function is continuously calculated in an iterative mode through selection, crossing and mutation operation until the final iterative rigidity value meets the termination condition p (t+1) -p (t) ||/p (t) And (3) outputting a result which is the final result of the rigidity identification of the support.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
the invention discloses a spatial steel structure support rigidity identification method based on temperature strain, which is used for solving the problems that the support rigidity is inconsistent with the actually measured support rigidity in the current spatial steel structure finite element modeling, and the spatial steel structure support damage is difficult to effectively judge. Different from a rigidity identification method based on vibration, the rigidity identification method directly carries out rigidity identification by utilizing temperature strain without complex modal analysis, thereby improving identification precision; in addition, the temperature load can be regarded as a static load, so that compared with vibration-based high-frequency acceleration data monitoring, the sampling frequency of temperature strain monitoring is smaller, the required monitoring data amount is less, and the data storage cost is reduced. Compared with a static force-based rigidity identification method, the external load input is temperature, and because the temperature is an environmental factor, the static force load is not required to be applied manually, so that the problem of difficult application of the static force load in the space steel structure support rigidity identification process is avoided, and theoretical methods and technical supports are provided for the identification of the space steel structure support rigidity, the safety performance evaluation and the maintenance in the long-term service process.
Drawings
FIG. 1 is a flow chart for identifying the stiffness of a support based on the temperature strain of the structure according to the present invention.
FIG. 2 is a schematic view of a support to be identified for stiffness and a corresponding strain monitoring rod.
Fig. 3 is a schematic diagram of a temperature data preprocessing process.
Fig. 4 is a schematic diagram of a strain data preprocessing process.
Fig. 5 is a schematic diagram before and after separation and extraction of temperature strain components in strain.
FIG. 6 is a schematic diagram of an iteration of the seat stiffness identification process.
Detailed Description
The optimal implementation mode of the invention provides a spatial steel structure support rigidity identification method based on temperature strain. In order to make the objects, features and advantages of the present invention more visual and clear, the best mode of the invention will be clearly and completely described in connection with the description of the drawings of the present invention, so that the skilled person can quickly understand the method of the present invention.
Fig. 1 is a schematic diagram of a specific implementation flow of a spatial steel structure support rigidity identification method based on temperature strain according to an embodiment of the present invention. The method comprises the following specific steps:
step A: taking a space reticulated shell of the attached figure 2 as an example, black points are marked as the positions of supports with rigidity to be identified, thick lines are marked as components connected with the supports with rigidity to be identified, and sensors capable of synchronously acquiring strain and temperature data are arranged at thick line marked rod pieces; and collecting stress strain and temperature data of the component.
In the step, the acquisition frequency of strain and temperature data is ensured to be consistent, meanwhile, the measurement precision is ensured, the influence of external interference such as noise on the measurement data is reduced as much as possible, and a temperature self-compensating fiber bragg grating strain sensor or a vibrating wire strain sensor is preferably selected as the sensor;
and (B) step (B): in the obtained actual monitoring data, due to the influence of internal factors and external environmental factors of the sensor, partial abnormal values and noise interference exist in the monitoring data, and compared with the original values, the abnormal values and the noise interference are too large or too small, so that the normal analysis of the data is directly influenced, such as the figure 3 (a) and the figure 4 (a); before damage analysis, the abnormal value in the data is replaced, noise reduction treatment is carried out on the data, the reliability of the data is improved, and the number of analysis samples is reduced; the method comprises the following steps:
1) Replacing the outlier: taking data with window length of L from the data acquired initially, and averaging and standard deviation of the data, wherein the data points meet the requirement ofI.e. the difference between a measured value and the mean value is greater than 3 times the standard deviationReplacing with data adjacent to the data point; the window is then moved sequentially along the data until all the data is covered, where x i For the ith data point, +.>The sigma is the standard deviation of the data in the L length; the effects after the temperature and strain data outliers are replaced are shown in fig. 3 (b) and fig. 4 (b).
2) Noise is removed: denoising by adopting a moving average method, taking data of a smaller window from the data acquired initially, averaging the data, replacing all the data in the window with the average value, and then sequentially moving the window along the data until all the data are covered; the effect of the temperature and strain data after noise reduction is shown in fig. 3 (c) and fig. 4 (c).
3) Downsampling: in the monitoring process, in order to meet different analysis requirements, the sampling frequency set by the collector is generally higher, and in order to reduce the calculation amount and time of the subsequent analysis, the data are downsampled, that is, the original monitoring data are sampled at intervals of a few data, so that new samples with smaller data amount are obtained for the subsequent analysis, and the effect of noise reduction of the temperature and the stress data is shown in fig. 3 (d) and fig. 4 (d).
Step C: the research on signals such as actual measurement strain of a space steel structure in a normal service state shows that the environment temperature is the most important factor influencing signal change, but the strain response also contains components under other load effects inevitably, and in order to ensure the effectiveness of the subsequent support rigidity identification based on the temperature response, the temperature strain is separated and extracted, and the extracted temperature strain is the stress strain under the temperature load, but not the thermal strain of free thermal expansion of a rod piece. And C, performing separation and extraction by adopting a principal component analysis method, performing principal component analysis on the strain data processed in the step B, retaining a first-order principal component as a conversion matrix of the principal component analysis, projecting the data, and performing back projection to obtain a temperature strain component in the strain, wherein the effects before and after the separation of the temperature strain component in the strain are shown in figure 5.
Step D: before the support rigidity is identified, the structure is initially finite element modeled according to design data, parameters of the model, such as coordinates of each node, node size, rod size, material characteristics and the like, are input according to the design data, and the initial value of constraint rigidity can be obtained as a parameter value given by the design data and recorded as p (0) 。
Step E: the environmental temperature change has extremely small amplitude in a period of time and can be regarded as a static load, so that the finite element analysis process of the structure under the action of the temperature load is the same as the analysis process under the action of the general static load, the temperature data processed in the step B are input into a finite element model, and the strain of the structure under the action of the temperature load is calculated: { ε } = [ B ]][K(p)] -1 { F (ΔT) }. In the formula [ B ]]For the strain change matrix, it is determined only by the cell node coordinates, [ K (p)]The structural rigidity matrix after introducing boundary conditions is a function of structural parameters p including the rigidity of the support; { F (ΔT) } is an excitation load and is a function of the temperature change ΔT. Calculating the temperature strain { ε } and the measured temperature strain { ε }, and M the residual { r } between can be expressed as { r (p) } = { ε } M ＝[B][K(p)]- 1 {F(ΔT)}-{ε} M 。
The residual { r } is a function of the structural related parameters and is a discrimination index for the stiffness identification of the support. The process of identifying the rigidity of the structural support is to optimize the rigidity parameter p of the structural support through continuous iteration, so that the discrimination index { r } norm of the rigidity identification of the support is minimum: e (p) = { r } T {r}＝||{r}|| 2 →min。
Searching for the minimum solution meeting E (p) as the minimum value optimizing process about p, carrying out optimizing calculation by adopting a genetic algorithm, selecting a residual norm as an fitness function value of the genetic algorithm, carrying out iterative calculation continuously through operations such as selection, crossing, variation and the like, and continuously reducing the iterative process E (p), wherein the iterative process E (p) is shown in the figure 6 until the final iterative rigidity value meets the termination condition I P (t+1) -p (t) ||/p (t) And (3) outputting a result which is the final result of the rigidity identification of the support.
The invention is not limited to the embodiments described above. The above description of specific embodiments is intended to describe and illustrate the technical aspects of the present invention, and is intended to be illustrative only and not limiting. Numerous specific modifications can be made by those skilled in the art without departing from the spirit of the invention and scope of the claims, which are within the scope of the invention.
Claims (1)
1. The spatial steel structure support rigidity identification method based on temperature strain is characterized by comprising the following steps of:
(1) Arranging a sensor and collecting data: a sensor capable of synchronously collecting strain and temperature data is arranged at a member connected with a support to be identified in the structure; synchronously collecting strain and temperature data of the component; the method has the advantages that the acquisition frequency of strain and temperature data is ensured to be consistent, meanwhile, the measurement precision is ensured, and the influence of external interference on the measurement data is reduced; the sensor is a temperature self-compensation fiber grating strain sensor or a vibrating wire strain sensor with temperature compensation;
(2) Data preprocessing: performing outlier replacement, noise reduction and downsampling on the strain and temperature data, increasing the reliability of the data and reducing the number of analysis samples; the specific method for outlier replacement and noise reduction processing is as follows:
(201) Replacing the outlier: taking data with window length of L from the data acquired initially, and averaging and standard deviation of the data, wherein the data points meet the requirement ofI.e., if the difference between a certain measured value and the mean value is greater than 3 times the standard deviation, then the data adjacent to the data point is replaced; the window is then moved sequentially along the data until all the data is covered, where x i For the ith data point, +.>The sigma is the standard deviation of the data in the L length;
(202) Noise is removed: denoising by adopting a moving average method, starting from data acquired initially, taking data of a window, averaging the data, replacing all the data in the window with the average value, and then sequentially moving the window along the data until all the data are covered;
in order to reduce the calculation amount and time of the subsequent analysis, the data are downsampled, namely, the original monitoring data are sampled once at intervals of a plurality of data, so that a new sample with smaller data amount is obtained for the subsequent analysis calculation;
(3) Separating and extracting temperature strain: carrying out principal component analysis on the strain data processed in the step (2), and extracting a temperature strain component in the data, namely, actually measured temperature strain; the separated and extracted temperature strain is stress strain under temperature load, but not thermal strain of free thermal expansion of the rod piece; separating and extracting by adopting a principal component analysis method, carrying out principal component analysis on the strain data processed in the step (2), reserving a first-order principal component as a conversion matrix of the principal component analysis, projecting the data, and then carrying out back projection to obtain a temperature strain component in the strain;
(4) Establishing an initial finite element model: establishing an initial finite element model of the space steel structure according to design data, wherein the initial value of the rigidity of the support is the design value of the support; before the support rigidity is identified, the structure is initially finite element modeled according to design data, the parameter values of the model comprise the coordinates of each node, the size of each rod and the material characteristics, the parameter values given by the design data are obtained according to the initial values of the constraint rigidity input according to the design data, and are recorded as p (0) ；
(5) Stiffness identification: taking the temperature data pretreated in the step (2) as load, inputting the initial finite element model established in the step (4), and calculating the temperature strain at the same position as the actual structure monitoring position in the finite element model, namely calculating the temperature strain; constructing a judging index of the support rigidity identification by utilizing the difference between the calculated temperature strain and the actually measured temperature strain extracted in the step (3), and gradually reducing the judging index value of the support rigidity identification by changing the support rigidity value of the finite element model and continuously carrying out iterative calculation to finally achieve convergence, wherein the obtained support rigidity value of the finite element model is the actual support of the structureA stiffness value; the environmental temperature change is regarded as a static load, so that the finite element analysis process of the structure under the action of the temperature load is the same as the analysis process under the general static load; inputting the temperature data processed in the step (2) into a finite element model, and calculating the strain of the structure under the action of temperature load, namely calculating the temperature strain: { ε } = [ B ]][K(p)] -1 { F (DeltaT) } where [ B ]]For the strain change matrix, it is determined only by the cell node coordinates, [ K (p)]The structural rigidity matrix after introducing boundary conditions is a function of structural parameters p including the rigidity of the support; { F (ΔT) } is an excitation load, which is a function of the temperature change ΔT; calculating the temperature strain { ε } and the measured temperature strain { ε }, and M the residual { r } between is denoted as { r (p) } = { ε } M Residual { r } is a function of the structural related parameters and is a discrimination index for identifying the rigidity of the support; the process of identifying the rigidity of the structural support is to optimize the rigidity parameter p of the structural support through continuous iteration, so that the discrimination index { r } norm of the rigidity identification of the support is minimum: e (p) = { r } T {r}＝||{r}|| 2 →min；
Searching a minimum solution meeting E (p) as a minimum value optimizing process about p, adopting a genetic algorithm to perform optimizing calculation, selecting a residual norm as an fitness function value of the genetic algorithm, and continuously performing iterative calculation through selection, crossing and mutation operations until the final iterative rigidity value meets a termination condition p (t+1) -p (t) ||/p (t) And (3) outputting a result which is the final result of the rigidity identification of the support.
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