A comprehensive analysis method for isothermal field of structural static load response under fragmented time

Abstract

The present invention relates to a comprehensive analysis method of the isothermal field of static load response of a structure under fragmented time, which belongs to the field of civil structure monitoring. For the massive monitoring data obtained from civil facility safety monitoring systems, such as bridge monitoring systems, tunnel monitoring systems, road monitoring systems, dam monitoring systems, building monitoring systems, etc., signal processing, pattern recognition and other technologies are used to systematically cut off the influence of temperature factors on the original monitoring signals, weaken or eliminate the combined effects of various other factors such as live loads, random errors, and test errors, so that technicians can extract the static load response of the structure submerged in the original signal, and judge whether there is any abnormality in the operation status of the civil structure based on this. The present invention provides a scientific reference basis for technicians to manage and maintain in-service civil structures and ensure operational safety.

Description

Technical Field

[0001] The present invention belongs to the field of civil structure monitoring and relates to a comprehensive analysis method of isothermal field of static load response of structures under fragmented time, including but not limited to bridge monitoring, tunnel monitoring, road monitoring, dam monitoring, building monitoring, etc. Background Art

[0002] Currently, many civil engineering facilities are equipped with safety monitoring systems, such as those for bridges, tunnels, roads, dams, and buildings. However, because civil structures are sensitive to temperature, the raw signals captured by sensors are influenced by not only static loads but also live loads, random errors, testing errors, and other factors. This makes it difficult for technicians to understand the dynamics of structures under static loads from the massive amount of monitoring data. Directly determining the normality of raw monitoring signals based on thresholds can easily lead to misdiagnosis. Summary of the Invention

[0003] In view of this, the object of the present invention is to provide a comprehensive analysis method for isothermal field of static load response of structures under fragmented time.

[0004] In order to achieve the above object, the present invention provides the following technical solutions:

[0005] A method for isothermal field comprehensive analysis of static load response of a structure under fragmented time, the method comprising the following steps:

[0006] S1: Select civil structure monitoring data as the object to be analyzed, and evaluate the changes in the static load of the structure during the monitoring period to be analyzed;

[0007] S2: Determine the monitoring period to be analyzed and clarify the start and end time of monitoring data collection;

[0008] S3: Classify the acquired monitoring periods according to different temperature fields to obtain n sample sets {X1, X2, ...X i …,X n}, exclude the influence of temperature factors;

[0009] S4: Calculate each sample set to obtain the feature set {Y1, Y2, ...Y i …,Y n}, eliminate the influence of live load, random error and test error;

[0010] S5: Use principal component analysis to analyze the feature set {Y1, Y2, ...Y i …,Y n} to reduce the dimension and obtain the training sample set {Z1, Z2, …Z j …,Z m}, where m≤n;

[0011] S6: The time series of monitoring data has the characteristics of temporal attribute fragmentation under different temperature fields; the convolutional neural network is used to reduce the dimension of the feature set {Z1, Z2, ... Z j …,Z m} to train, and finally obtain the variation q of the static load of the civil structure during the monitoring period; the dimension is the same as the original monitoring dimension;

[0012] S7: Set the allowable threshold value k of the structural static load response. The dimension of the threshold value k is the same as the dimension of the original monitoring value.

[0013] S8: When q ≥ k, it is considered that the static load of the civil structure has undergone a large change within the specified monitoring period.

[0014] Optionally, the S1 is specifically:

[0015] The civil engineering structure monitoring data includes: crack data, strain data, inclination data, deflection data and displacement data, which are civil engineering structure monitoring data affected by temperature factors.

[0016] Optionally, in S2, the monitoring period to be analyzed is greater than 6 months.

[0017] Optionally, in S4, for a sample set X under a certain temperature field i have:

[0018] (1) Use median filtering to eliminate the combined effects of live load, random error, and test error, and improve the signal-to-noise ratio;

[0019] (2) The wavelet method is used to perform multi-level decomposition and reconstruction on the filtered sample data to obtain the low-frequency information of the signal; the Daubechies wavelet is selected as the wavelet method, and the number of decomposition layers is 5 to 10 layers.

[0020] Optionally, in S5, the sample set {Z1, Z2, ... Z j …,Z m} Feature information ≥ samples before dimensionality reduction {Y1,Y2,…Y i …,Y n}Collection feature information *90%.

[0021] A computer device includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the method described is implemented when the processor executes the computer program.

[0022] A computer-readable storage medium stores a computer program, which implements the method when executed by a processor.

[0023] The beneficial effects of the present invention are as follows: For civil engineering facility safety monitoring systems, such as bridge monitoring systems, tunnel monitoring systems, road monitoring systems, dam monitoring systems, and building monitoring systems, the massive amount of monitoring data obtained is systematically removed by using signal processing, pattern recognition, and other technologies to reduce or eliminate the combined effects of various other factors, such as live loads, random errors, and test errors. This allows technicians to extract the static load response of the structure submerged in the original signal and, based on this, determine whether the civil structure's operational status is abnormal. The present invention provides a scientific reference for technicians to manage and maintain in-service civil structures and ensure operational safety.

[0024] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0026] Figure 1 Schematic diagram of median filtering;

[0027] Figure 2 Schematic diagram of wavelet decomposition;

[0028] Figure 3 It is a technical flow chart;

[0029] Figure 4 is the static load response change of the strain measuring point A of bridge;

[0030] Figure 5 It is the change in static load response of the crack measuring point B in the building. DETAILED DESCRIPTION

[0031] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0032] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0033] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "back", etc. indicating directions or positional relationships, they are based on the directions or positional relationships shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0034] See also Figures 1 to 3 , is a comprehensive analysis method for the static load response of a structure under fragmented time at isothermal field, the method comprising the following steps:

[0035] S1: Select civil structure monitoring data as the object to be analyzed, and evaluate the changes in the static load of the structure during the monitoring period to be analyzed;

[0036] S2: Determine the monitoring period to be analyzed and clarify the start and end time of monitoring data collection;

[0037] S3: Classify the acquired monitoring periods according to different temperature fields to obtain n sample sets {X1, X2, ...X i …,X n}, exclude the influence of temperature factors;

[0038] S4: Calculate each sample set to obtain the feature set {Y1, Y2, ...Y i …,Y n}, eliminate the influence of live load, random error and test error;

[0039] S5: Use principal component analysis to analyze the feature set {Y1, Y2, ...Y i …,Y n} to reduce the dimension and obtain the training sample set {Z1, Z2, …Z j …,Z m}, where m≤n;

[0040] S6: The time series of monitoring data has the characteristics of temporal attribute fragmentation under different temperature fields; the convolutional neural network is used to reduce the dimension of the feature set {Z1, Z2, ... Z j …,Z m} to train, and finally obtain the variation q of the static load of the civil structure during the monitoring period; the dimension is the same as the original monitoring dimension;

[0041] S7: Set the allowable threshold value k of the structural static load response. The dimension of the threshold value k is the same as the dimension of the original monitoring value.

[0042] S8: When q ≥ k, it is considered that the static load of the civil structure has undergone a large change within the specified monitoring period.

[0043] Optionally, the S1 is specifically:

[0044] The civil engineering structure monitoring data includes: crack data, strain data, inclination data, deflection data and displacement data, which are civil engineering structure monitoring data affected by temperature factors.

[0045] In S2, the monitoring period to be analyzed is greater than 6 months.

[0046] In S4, for a sample set X under a certain temperature field, i have:

[0047] (1) Use median filtering to eliminate the combined effects of live load, random error, and test error, and improve the signal-to-noise ratio;

[0048] (2) The wavelet method is used to perform multi-level decomposition and reconstruction on the filtered sample data to obtain the low-frequency information of the signal; the Daubechies wavelet is selected as the wavelet method, and the number of decomposition layers is 5 to 10 layers.

[0049] Optionally, in S5, the sample set {Z1, Z2, ... Z j …,Z m} Feature information ≥ samples before dimensionality reduction {Y1,Y2,…Y i …,Y n}Collection feature information *90%.

[0050] Case 1:

[0051] A domestic bridge A is equipped with strain measuring points S1-3. From August 14, 2015 to September 20, 2017, the strain value of this measuring point fluctuated with temperature fluctuations, with the peak-to-peak value reaching 170με. If the value is simply judged from the monitoring value, the state of this measuring point is abnormal. However, the change in the static load response of the structure of the bridge strain measuring point calculated by the patented technology of this invention during the 769-day monitoring period is less than 20με (e.g. Figure 4(as shown), i.e., q < k (k = 100 με). Taking into account the effects of residual noise and calculation errors, it is assumed that the structure of this measuring point has not changed. Field verification shows that the actual operating status of Bridge A is consistent with the calculation and analysis results of this invention.

[0052] Case 2:

[0053] A crack measuring point C1-1 is set up in a domestic building B. From January 1, 2021 to August 31, 2021, the crack size at this measuring point fluctuated with temperature fluctuations, with the peak-to-peak value reaching 0.10mm. If we judge it simply by the size of the monitored value, the state of this measuring point is abnormal. However, the change in the static load response of the structure of the crack measuring point of this building during the 243-day monitoring period is calculated by the patented technology of this invention to be 0.01mm (as shown in Figure 2). Figure 5 (as shown), i.e., q < k (k = 0.08 mm). Considering the effects of residual noise and calculation errors, it is assumed that the structure of this measuring point has not changed. Field verification shows that the actual operating status of Building B is consistent with the calculation and analysis results of this invention.

[0054] It should be appreciated that embodiments of the present invention can be implemented or practiced by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable memory. The methods can be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with a computer program, wherein the storage medium so configured causes the computer to operate in a specific and predefined manner, according to the methods and figures described in the specific embodiments. Each program can be implemented in a high-level procedural or object-oriented programming language to communicate with the computer system. However, if desired, the program can be implemented in assembly or machine language. In any case, the language can be a compiled or interpreted language. In addition, the program can be run on a programmed application-specific integrated circuit for this purpose.

[0055] Furthermore, the operations of the processes described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by the context. The processes described herein (or variations and / or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) that is executed collectively on one or more processors, by hardware, or a combination thereof. The computer program includes a plurality of instructions that can be executed by one or more processors.

[0056] Furthermore, the method can be implemented in any type of computing platform that is operably connected to a suitable computer, including but not limited to a personal computer, a minicomputer, a mainframe, a workstation, a network or distributed computing environment, a separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, etc. Various aspects of the present invention can be implemented as machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, an optical read and / or write storage medium, RAM, ROM, etc., so that it can be read by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the process described herein. In addition, the machine-readable code, or portions thereof, can be transmitted via a wired or wireless network. When such media includes instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor, the invention described herein includes these and other different types of non-transitory computer-readable storage media. When programmed according to the method and technique for isothermal field comprehensive analysis of static load response of structures under fragmented time, the present invention also includes the computer itself.

[0057] The computer program can be applied to input data to perform the functions described herein, thereby converting the input data to generate output data that is stored in a non-volatile memory. The output information can also be applied to one or more output devices such as a display. In a preferred embodiment of the present invention, the converted data represents a physical and tangible object, including a specific visual depiction of the physical and tangible object produced on the display.

[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for isothermal field comprehensive analysis of structural static load response under fragmented time, characterized by: The method comprises the following steps: S1: Selecting civil structure monitoring data as an object to be analyzed, and evaluating changes in the static load of the object to be analyzed during the monitoring period to be analyzed; the civil structure monitoring data includes: crack data, strain data, inclination data, deflection data, and displacement data of the civil structure monitoring data affected by temperature factors; S2: Determine the monitoring period to be analyzed and clarify the start and end time of monitoring data collection; in S2, the monitoring period to be analyzed is greater than 6 months; S3: Classify the acquired monitoring periods according to different temperature fields to obtain n sample sets {X1, X2, ...X i …,X n }, exclude the influence of temperature factors; S4: Calculate each sample set to obtain the feature set {Y1, Y2, ...Y i …,Y n }, eliminating the influence of live load, random error and test error; in S4, for a sample set X under a certain temperature field i have: (1) Use median filtering to eliminate the combined effects of live load, random error, and test error, and improve the signal-to-noise ratio; (2) Using the wavelet method to perform multi-level decomposition and reconstruction on the filtered sample data to obtain the low-frequency information of the signal; the wavelet method uses Daubechies wavelet, and the number of decomposition layers is 5 to 10; S5: Use principal component analysis to analyze the feature set {Y1, Y2, ...Y i …,Y n } to reduce the dimension and obtain the training sample set {Z1, Z2, …Z j …,Z m }, where m≤n; in S5, the sample set {Z1, Z2, ... Z j …,Z m } Feature information ≥ samples before dimensionality reduction {Y1,Y2,…Y i …,Y n }Collection feature information * 90%; S6: The time series of monitoring data has the characteristics of temporal attribute fragmentation under different temperature fields; the convolutional neural network is used to reduce the dimension of the feature set {Z1, Z2, ... Z j …,Z m } to train, and finally obtain the variation q of the static load of the civil structure during the monitoring period; the dimension is the same as the original monitoring dimension; S7: Set the allowable threshold value k of the structural static load response. The dimension of the threshold value k is the same as the dimension of the original monitoring value. S8: When q ≥ k, it is considered that the static load of the civil structure has undergone a large change within the specified monitoring period.

2. A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the computer program, the method according to claim 1 is implemented.

3. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to claim 1 is implemented.

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