CN110717287A - Temperature strain-based rigidity identification method for space steel structure support - Google Patents
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Abstract
The invention discloses a method for identifying the rigidity of a space steel structure support based on temperature strain, which comprises the following steps: (1) arranging a sensor capable of synchronously acquiring strain and temperature data at a component connected with a support to be identified in the structure; collecting strain and temperature data of the component; (2) carrying out abnormal value replacement, noise reduction and down-sampling treatment on the strain and temperature data, increasing the reliability of the data and reducing the number of analysis samples; (3) performing 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 the design data, wherein the initial value of the rigidity of the support is the design value of the initial finite element model; (5) and constructing a rigidity identification index by utilizing the difference between the calculated temperature strain and the actually measured temperature strain of the structure, and continuously performing iterative calculation to minimize the rigidity identification index value by changing the support rigidity value of the finite element model so as to finally obtain the actual rigidity value of the structure support.
Description
Technical Field
The invention relates to the field of support rigidity identification and safety diagnosis of a space steel structure in civil engineering, in particular to a support rigidity identification method based on temperature strain.
Background
The support is an important component for connecting the space steel structure such as the net rack, the reticulated shell and the like with the lower support, provides necessary constraint for the space steel structure, and is closely related to the operation safety of the structure in the working state. In the long-term service process of the space steel structure, the support of the structure is frequently damaged due to the comprehensive action of environmental factors and human factors. The most important performance of the damage of the structural support is the change of rigidity, for example, poor construction or over-load, the fracture and the void of the support, the reduction of the rigidity and the like. The change of the rigidity of the support directly affects the stress state of the whole structure, and hidden danger is brought to the safe operation of the structure. Therefore, early damage of the structural support can be identified timely and accurately, and the method has important engineering application value.
The damage identification method based on the reference finite element model is widely researched as a common health monitoring method, the method identifies the structural damage by comparing the difference between actual monitoring data and reference finite element calculation data when the structure is damaged, and the method is based on establishing an accurate finite element model. However, since the finite element model constrained by the structural member and the support cannot completely reflect the real behavior of the structure, and the finite element model parameters such as the structural rigidity and the constraint rigidity are not completely consistent with the actual parameters, it is necessary to identify and correct the finite element model parameters through monitoring data.
At present, two common structural rigidity identification methods and numerical model correction methods are provided, wherein one method is an identification method based on vibration mode parameters, and the other method is an identification method based on structural static response.
The vibration-based identification method comprises the steps of monitoring acceleration data of structural vibration, carrying out modal analysis by using the acceleration data to obtain modal parameters of a structure, and considering that the change of structural characteristic parameters (structural rigidity, quality and the like) can directly influence the modal parameters (frequency, vibration mode and the like) of the structure, so that the structural rigidity identification and model correction are realized by using the relation between the structural modal parameters and structural physical parameters and comparing the structural modal parameters to ensure that the structural monitored modal parameters are consistent with the calculated modal parameters. However, the modal parameters of the structure are not only related to the properties of the structure itself, but also are easily influenced by environmental factors; in addition, for a large-span space steel structure, modal parameters of the large-span space steel structure are mutually superposed and are difficult to accurately obtain, the input excitation of structural vibration is not clear, the storage capacity of dynamic monitoring data is large, and the like, so that the application of the method in the identification of the rigidity of the space steel structure is greatly limited.
The identification method based on the structural static force monitors the static response information of the structure by applying the static load to the structure, and realizes structural rigidity identification and model correction by comparing the static response monitored by the structure with the calculated static response. Compared with the identification method based on modal parameters, the identification method based on static response has higher precision and is less influenced by environmental factors and noise, but the static load application difficulty is higher for the space 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-season replacement, solar radiation and the like, and the temperature load is one of main loads borne 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 cause the temperature response of a rod piece near the support to change, so that the structural support rigidity can be identified by monitoring the structural temperature response.
Disclosure of Invention
The invention aims to overcome the defects of the existing structural support rigidity identification method based on vibration modal parameters and static response, and provides a spatial steel structure support rigidity identification method based on temperature strain.
The purpose of the invention is realized by the following technical scheme:
a space steel structure support rigidity identification method based on temperature strain is characterized by comprising the following steps:
(1) arranging sensors, and acquiring data: arranging a sensor capable of synchronously acquiring strain and temperature data at a component connected with a support to be identified in the structure; synchronously acquiring strain and temperature data of the component;
(2) data preprocessing: carrying out abnormal value replacement, noise reduction and down-sampling treatment on the strain and temperature data, increasing the reliability of the data and reducing the number of analysis samples;
(3) separation and extraction of temperature strain: performing principal component analysis on the strain data processed in the step (2), and extracting a temperature strain component in the data, namely the measured temperature strain;
(4) establishing an initial finite element model: establishing an initial finite element model of the space steel structure according to the design data, wherein the initial value of the rigidity of the support is the design value of the initial finite element model;
(5) rigidity identification: inputting the temperature data preprocessed in the step (2) as a load into the initial finite element model established in the step (4), and calculating the temperature strain at the same position in the finite element model as the monitoring position of the actual structure, namely calculating the temperature strain; . And (4) constructing a judgment index for identifying the rigidity of the support by using the difference between the calculated temperature strain and the actually measured temperature strain extracted in the step (3), and changing the rigidity value of the support of the finite element model to continuously perform iterative calculation to gradually reduce the judgment index value for identifying the rigidity of the support and finally achieve convergence, wherein the support rigidity value of the finite element model obtained at the moment is the actual rigidity value of the structural support.
Furthermore, in the step (1), the strain and temperature data acquisition frequency should be 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 step (2), the specific method of outlier replacement and noise reduction processing is as follows:
(201) replacement of outliers: starting from data collected at the beginning, taking data with the window length of L, calculating the mean value and standard deviation of the data, and when the data points meet the requirements
When the difference between a certain measured value and the mean value is more than 3 times of the standard deviation, replacing the measured value with the data adjacent to the data point; then sequentially moving the window by delaying the data until all the data are coveredWherein x isiThe ith data point of (i) is,
the mean value of the data in the length L is shown, and the sigma is the standard deviation of the data in the length L;
(202) removing noise: denoising by adopting a moving average method, taking data of a window from the initially collected data, 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.
Further, in step (2), in order to reduce the amount and time of subsequent analysis calculation, the data is down-sampled, that is, the original monitoring data is sampled once every several data intervals, so as to obtain a new sample with a smaller data amount for subsequent analysis calculation.
Further, the temperature strain extracted separately in step (3) is a stress strain under a temperature load, not a thermal strain of the free thermal expansion of the rod member.
Further, in the step (3), a principal component analysis method is adopted for separation and extraction, principal component analysis is carried out 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 after the data is projected, back projection is carried out, so that a temperature strain component in strain can be obtained.
Further, in the step (4), before the support stiffness identification is carried out, initial finite element modeling is carried out on the structure according to design data, the parameter values of the model such as the coordinates of each node, the size of the node, the size of a rod piece and the material characteristics are input according to the design data, the initial value of the constraint stiffness is the parameter value given by the design data and is recorded as p(0)。
Further, in the step (5), the environmental temperature change is regarded as 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. 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: { epsilon } ═ B][K(p)]-1{ F (. DELTA.T) }, in which, [ B ] is]Is strain ofThe change matrix is determined only by the coordinates of the unit nodes, [ K (p) ]]The structural rigidity matrix after the boundary condition is introduced is a function of a structural parameter p including the support rigidity; { F (Δ T) } is the excitation load, which is a function of the temperature change Δ T; calculating temperature strain { epsilon } and actually measured temperature strain { epsilon }MThe residual { r } between them is denoted by { r (p) } ═ epsilon } - { epsilon }MThe residual error { r } is a function of the structure-related parameters and is a judgment index for identifying the rigidity of the support; the process of identifying the rigidity of the structural support is that the rigidity parameter p of the structural support is continuously iterated and optimized, so that the norm of a judgment index { r } norm of the rigidity identification of the support is minimum: e (p) { r }T{r}＝||{r}||2→min；
Searching the minimum solution meeting the requirement of E (p) as the minimum value optimizing process about p, performing optimizing calculation by adopting a genetic algorithm, selecting a residual norm as a fitness function value of the genetic algorithm, and continuously performing iterative calculation through selection, intersection and variation until the final iterative stiffness value meets the termination condition | | | p(t+1)-p(t)||/p(t)If the value is less than Tol, the output result is the final result of the support rigidity identification.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
the invention discloses a temperature strain-based method for identifying the rigidity of a spatial steel structure support, which is used for solving the problems that the support rigidity is inconsistent with the actually measured support rigidity in the existing spatial steel structure finite element modeling and the damage of the spatial steel structure support is difficult to effectively judge. Different from a rigidity identification method based on vibration, the rigidity identification method directly identifies the rigidity by using temperature strain without complex modal analysis, thereby improving the identification precision; in addition, because the temperature load can be regarded as a static load, compared with the high-frequency acceleration data monitoring based on vibration, the sampling frequency of temperature strain monitoring is small, the required monitoring data amount is small, and the data storage cost is reduced. Compared with a rigidity identification method based on static force, the external load input is temperature, and the temperature is an environmental factor, so that the problem of difficulty in static load application in the rigidity identification process of the space steel structure support is solved without artificial application, and a theoretical method and a technical support are provided for the rigidity identification, safety performance evaluation and maintenance of the space steel structure support in the long-term service process.
Drawings
FIG. 1 is a flow chart of the present invention for identifying stiffness of a mount based on structural temperature strain.
Fig. 2 is a schematic view of a support to be identified in stiffness and a corresponding strain monitoring bar.
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 a temperature strain component in strain.
FIG. 6 is a schematic diagram of an iteration of a mount stiffness identification process.
Detailed Description
The best embodiment of the invention provides a space steel structure support rigidity identification method based on temperature strain. In order to make the objects, features and advantages of the present invention more intuitive and clear, preferred embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings, which will facilitate the skilled person to understand the method of the present invention quickly.
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 the spatial reticulated shell shown in fig. 2 as an example, black dots are marked as the positions of the supports with rigidity to be identified, thick lines are marked as members connected with the supports with rigidity to be identified, and sensors capable of synchronously acquiring strain and temperature data are arranged at the thick line marking rod pieces; and collecting stress strain and temperature data of the component.
In the step, the strain is ensured to be consistent with the acquisition frequency of temperature data, 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-compensation fiber grating strain sensor or a vibrating wire type strain sensor is preferably selected as the sensor;
and B: in the obtained actual monitoring data, due to the influence of factors inside the sensor and factors outside the environment, there exist some abnormal values and noise interferences in the monitoring data, and these abnormal values and noise interferences are too large or too small compared with the original values, which directly affect the normal analysis of the data, as shown in fig. 3(a) and fig. 4 (a); therefore, before damage analysis, abnormal values in the data are replaced, noise reduction processing 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 specific steps:
1) replacement of outliers: starting from data collected at the beginning, taking data with the window length of L, calculating the mean value and standard deviation of the data, and when the data points meet the requirements
When the difference between a certain measured value and the mean value is more than 3 times of the standard deviation, replacing the measured value with the data adjacent to the data point; then sequentially moving the window along the data until all data are covered, wherein xiThe ith data point of (i) is,the mean value of the data in the length L is shown, and the sigma is the standard deviation of the data in the length L; the effect of the temperature and strain data after replacement with outliers is shown in fig. 3(b) and fig. 4 (b).
2) Removing noise: denoising by adopting a moving average method, taking data of a smaller window from initially acquired data, averaging the data, replacing all data in the window with the average value, and then moving the window in sequence along the data until all data are covered; the effect of noise reduction on the temperature and strain data is shown in fig. 3(c) and fig. 4 (c).
3) Down-sampling: in the monitoring process, in order to meet different analysis requirements, the sampling frequency set by the acquisition instrument is generally higher, in order to reduce the subsequent analysis calculation amount and time, the data is downsampled, that is, the original monitoring data is sampled once every several data, so that a new sample with a small data amount is obtained for subsequent analysis, and the effect of denoising the temperature and stress data is shown in fig. 3(d) and fig. 4 (d).
And C: the research on signals such as the measured strain of the space steel structure in a normal service state shows that the environmental temperature is the most main factor influencing the signal change, but the strain response inevitably contains other components under the action of load, and the extracted temperature strain refers to the stress strain under the temperature load and is not the thermal strain of the free thermal expansion of the rod piece in order to ensure the effectiveness of subsequent support stiffness identification based on the temperature response. And (3) separating and extracting by adopting a principal component analysis method, performing principal component analysis on the strain data processed in the step (B), reserving a first-order principal component as a conversion matrix of the principal component analysis, projecting the data, and performing back projection on the data to obtain a temperature strain component in the strain, wherein the effects of the temperature strain component in the strain before and after separation are shown in the attached figure 5.
Step D: before the support stiffness identification is carried out, initial finite element modeling should be carried out on the structure according to design data, the parameter values of the model, such as the coordinates of each node, the size of the node, the size of a rod piece, the material characteristics and the like, should be input according to the design data, the initial value of the constraint stiffness can be the parameter values given by the design data and is recorded as p(0)。
Step E: the range of the change of the environmental temperature is extremely small in a period of time, and the change can be regarded as 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 is input into a finite element model, and the strain of the structure under the action of the temperature load is calculated: { epsilon } ═ B][K(p)]-1{ F (Δ T) }. Wherein [ B ]]The strain change matrix is determined only by the coordinates of the unit nodes, [ K (p) ]]The structural rigidity matrix after the boundary condition is introduced is a function of a structural parameter p including the support rigidity; { F (Δ T) } is the excitation load, and is a function of the temperature change Δ T. Calculating temperature strain { epsilon } and actually measured temperature strain { epsilon }MThe residual { r } between can be expressed as { r (p) } ═ epsilon } - { epsilon }M＝[B][K(p)]-1{F(ΔT)}-{ε}M。
And residual errors { r } are functions of structure related parameters and are judgment indexes of support rigidity identification. Structural support stiffness discriminationThe process is that the structural support rigidity parameter p is optimized through continuous iteration, so that the judgment index { r } norm of the support rigidity identification is minimum: e (p) { r }T{r}＝||{r}||2→min。
Searching the minimum solution meeting the requirement of E (p) as the minimum value optimizing process about p, performing optimizing calculation by adopting a genetic algorithm, selecting a residual norm as a fitness function value of the genetic algorithm, continuously performing iterative calculation through operations such as selection, intersection, variation and the like, and continuously reducing the iterative process E (p) until the final iterative stiffness value meets the termination condition | | p as shown in figure 6(t+1)-p(t)||/p(t)If the value is less than Tol, the output result is the final result of the support rigidity identification.
The present invention is not limited to the above-described embodiments. The foregoing description of the specific embodiments is intended to describe and illustrate the technical solutions of the present invention, and the above specific embodiments are merely illustrative and not restrictive. Those skilled in the art can make many changes and modifications to the invention without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (8)
1. A space steel structure support rigidity identification method based on temperature strain is characterized by comprising the following steps:
(1) arranging sensors, and acquiring data: arranging a sensor capable of synchronously acquiring strain and temperature data at a component connected with a support to be identified in the structure; synchronously acquiring strain and temperature data of the component;
(2) data preprocessing: carrying out abnormal value replacement, noise reduction and down-sampling treatment on the strain and temperature data, increasing the reliability of the data and reducing the number of analysis samples;
(3) separation and extraction of temperature strain: performing principal component analysis on the strain data processed in the step (2), and extracting a temperature strain component in the data, namely the measured temperature strain;
(4) establishing an initial finite element model: establishing an initial finite element model of the space steel structure according to the design data, wherein the initial value of the rigidity of the support is the design value of the initial finite element model;
(5) rigidity identification: inputting the temperature data preprocessed in the step (2) as a load into the initial finite element model established in the step (4), and calculating the temperature strain at the same position in the finite element model as the monitoring position of the actual structure, namely calculating the temperature strain; . And (4) constructing a judgment index for identifying the rigidity of the support by using the difference between the calculated temperature strain and the actually measured temperature strain extracted in the step (3), and changing the rigidity value of the support of the finite element model to continuously perform iterative calculation to gradually reduce the judgment index value for identifying the rigidity of the support and finally achieve convergence, wherein the support rigidity value of the finite element model obtained at the moment is the actual rigidity value of the structural support.
2. The method for identifying the rigidity of the spatial steel structure support based on the temperature strain is characterized in that in the step (1), the strain is ensured to be consistent with the acquisition frequency of the temperature data, 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.
3. The method for identifying the rigidity of the spatial steel structure support based on the temperature strain is characterized in that in the step (2), the specific methods of abnormal value replacement and noise reduction processing are as follows:
(201) replacement of outliers: starting from data collected at the beginning, taking data with the window length of L, calculating the mean value and standard deviation of the data, and when the data points meet the requirements
When the difference between a certain measured value and the mean value is more than 3 times of the standard deviation, replacing the measured value with the data adjacent to the data point; then sequentially moving the window to cover all the data by delaying the data, wherein xiThe ith data point of (i) is,
the mean value of the data in the length L is shown, and the sigma is the standard deviation of the data in the length L;
(202) removing noise: denoising by adopting a moving average method, taking data of a window from the initially collected data, 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.
4. The method for identifying the rigidity of the spatial steel structure support based on the temperature strain is characterized in that in the step (2), in order to reduce the subsequent analysis calculation amount and time, the data is subjected to down-sampling, namely, the original monitoring data is sampled once every several data, so that a new sample with a small data amount is obtained for the subsequent analysis calculation.
5. The method for identifying the space steel structure support rigidity based on the temperature strain is characterized in that the temperature strain extracted in the step (3) is the stress strain under the temperature load instead of the thermal strain of the free thermal expansion of the rod.
6. The method for identifying the rigidity of the spatial steel structure support based on the temperature strain is characterized in that in the step (3), a principal component analysis method is adopted for separation and extraction, principal component analysis is carried out 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 data is projected and then subjected to inverse projection, so that a temperature strain component in the strain can be obtained.
7. The method for identifying the rigidity of the spatial steel structure support based on the temperature strain as claimed in claim 1, wherein in the step (4), before the identification of the rigidity of the support, the structure is subjected to initial finite element modeling according to design data, the values of parameters of the model such as coordinates of each node, the size of the node, the size of a rod and the material characteristics are input according to the design data, the initial value of the constraint rigidity takes the parameter values given by the design data,is denoted by p(0)。
8. The method for identifying the rigidity of the spatial steel structure support based on the temperature strain is characterized in that in the step (5), the environmental temperature change is regarded as the static load, so that the finite element analysis process of the structure under 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: { epsilon } ═ B][K(p)]-1{ F (. DELTA.T) }, in which, [ B ] is]The strain change matrix is determined only by the coordinates of the unit nodes, [ K (p) ]]The structural rigidity matrix after the boundary condition is introduced is a function of a structural parameter p including the support rigidity; { F (Δ T) } is the excitation load, which is a function of the temperature change Δ T; calculating temperature strain { epsilon } and actually measured temperature strain { epsilon }MThe residual { r } between them is denoted by { r (p) } ═ epsilon } - { epsilon }MThe residual error { r } is a function of the structure-related parameters and is a judgment index for identifying the rigidity of the support; the process of identifying the rigidity of the structural support is that the rigidity parameter p of the structural support is continuously iterated and optimized, so that the norm of a judgment index { r } norm of the rigidity identification of the support is minimum: e (p) { r }T{r}＝||{r}||2→min；
Searching the minimum solution meeting the requirement of E (p) as the minimum value optimizing process about p, performing optimizing calculation by adopting a genetic algorithm, selecting a residual norm as a fitness function value of the genetic algorithm, and continuously performing iterative calculation through selection, intersection and variation until the final iterative stiffness value meets the termination condition | | | p(t+1)-p(t)||/p(t)If the value is less than Tol, the output result is the final result of the support rigidity identification.
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| CN116738554B (en) * | 2023-08-14 | 2023-11-14 | 中铁二局集团有限公司 | Arc crescent member optimization method and system with support |
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