A Chinese-style beam pier intelligent detection method and detection system

Abstract

The present invention relates to the technical field of inspection of middle cross beams and piers, and discloses an intelligent inspection method and inspection system for middle cross beams and piers. The method includes: fitting and generating a crack damage test model of the middle cross beam and pier according to the geological attribute information and bearing attribute information of the middle cross beam and pier. Collecting the crack position, crack geometric image data and deformation data of the crack area before damage of the middle cross beam and pier according to the crack damage test model, and determining them as preset crack data information. Setting an image climbing acquisition module along the setting direction of the middle cross beam and pier, and collecting the surface image information of the middle cross beam and pier based on the image climbing acquisition module. Extracting the crack data information of the middle cross beam and pier based on the surface image information, and determining the repair score to be carried out for the middle cross beam and pier according to the coincidence degree between the crack data information and the preset crack data information, and outputting it. Through the crack damage test model and combined with the crack data information, the present invention can accurately evaluate the damage condition of the middle cross beam and pier.

Description

Technical Field

[0001] The present invention relates to the technical field of inspection of middle cross beams and piers, and more particularly, to an intelligent inspection method and inspection system for middle cross beams and piers. Background Art

[0002] With the continuous development and aging of urban infrastructure, the health monitoring of structures such as bridges and piers has become an important task to ensure their safety and service life. As a key supporting component in the bridge structure, the load-bearing capacity and stability of the beam pier directly affect the safety of the entire bridge. Therefore, accurately monitoring the crack development and damage degree of the beam pier, and timely detecting potential risks are of crucial significance for preventing accidents and formulating scientific maintenance strategies.

[0003] Traditional inspection methods for beam piers usually rely on manual inspections and visual inspections, which have problems such as high labor intensity, low inspection accuracy, and large subjective errors. Although non-destructive inspection technologies such as ultrasonic, infrared imaging, and X-ray can effectively improve inspection accuracy, these methods are still restricted by factors such as inspection environment, equipment complexity, and cost, and it is difficult to achieve real-time, comprehensive, and accurate monitoring.

[0004] Therefore, there is an urgent need to invent an inspection technology for middle cross beams and piers to solve the problems of low inspection accuracy, large subjective errors, and inability to achieve real-time, comprehensive, and accurate monitoring in traditional inspection methods for beam piers. Especially when facing complex environments and high-cost non-destructive inspection technologies, it is difficult to be effectively applied to the intelligent monitoring of middle cross beams and piers. Summary of the Invention

[0005] In view of this, the present invention proposes an intelligent inspection method and inspection system for middle cross beams and piers, aiming to solve the problems of low inspection accuracy, large subjective errors, and inability to achieve real-time, comprehensive, and accurate monitoring in traditional inspection methods for beam piers. Especially when facing complex environments and high-cost non-destructive inspection technologies, it is difficult to be effectively applied to the intelligent monitoring of middle cross beams and piers.

[0006] The present invention proposes an intelligent inspection method for middle cross beams and piers, including:

[0007] Fitting and generating a crack damage test model of the middle cross beam pier according to the geological attribute information and load-bearing attribute information of the middle cross beam pier, where the geological attribute information includes geological soil density, geological soil friction coefficient, and geological soil compressibility, and the load-bearing attribute information includes the size of the middle cross beam pier, the concrete strength of the middle cross beam pier, the prestress of the middle cross beam pier, the load-bearing deformation characteristics of the middle cross beam pier, and the loading method of the middle cross beam pier;

[0008] Collect the crack position, crack geometric image data before damage, and deformation data of the crack area of the middle beam pier column according to the crack damage test model, and determine them as preset crack data information;

[0009] Set up an image climbing acquisition module along the setting direction of the middle beam pier column, and collect the surface image information of the middle beam pier column based on the image climbing acquisition module;

[0010] Extract the crack data information of the middle beam pier column based on the surface image information, and determine the repair score to be repaired of the middle beam pier column according to the coincidence degree between the crack data information and the preset crack data information, and output it.

[0011] Further, when fitting and generating the crack damage test model of the middle beam pier column according to the geological attribute information and bearing attribute information of the middle beam pier column, it includes:

[0012] Generate a geological-structure interaction model according to the geological soil density, geological soil friction coefficient, geological soil compressibility, middle beam pier column size, middle beam pier column concrete strength, middle beam pier column prestress, middle beam pier column bearing deformation characteristics, and loading method of the middle beam pier column;

[0013] Simulate the geological-structure interaction model based on finite element analysis, and obtain the crack development path and crack propagation data of the middle beam pier column under each load after simulation;

[0014] Generate load correlation formulas according to the crack development path and crack propagation data of the middle beam pier column under each load, cluster according to the load relationships in each load correlation formula, and establish the crack damage test model according to the clustering results.

[0015] Further, when collecting the crack position, crack geometric image data before damage, and deformation data of the crack area of the middle beam pier column according to the crack damage test model, it includes:

[0016] Based on the stress intensity factor analysis of fracture mechanics, obtain the deformation data before the occurrence of each crack in the crack damage test model and the occurrence position of each crack;

[0017] Conduct fracture simulation on the occurrence position of each crack based on numerical simulation, and obtain the propagation speed and propagation direction of each crack at each preset time period and each preset load;

[0018] Obtain the distance metric between the propagation speeds of each crack and the propagation directions of each crack, and establish a distance matrix according to the distance metric;

[0019] Iteratively cluster the expansion speeds and expansion directions of each of the cracks according to the distance matrix respectively;

[0020] Based on the expansion speed and expansion direction of the cracks after clustering, determine the characteristic vector of the crack types for the cracks, and based on the characteristic vectors of the crack types of each of the cracks, determine the positions of each of the cracks before the damage of the middle crossbeam pier column. Based on the characteristic vectors of the crack types, determine the geometric image data of the cracks. Based on the deformation data before the occurrence of the cracks corresponding to the characteristic vectors of the crack types, determine the deformation data of the crack region.

[0021] Further, when determining the repair score to be determined for the middle crossbeam pier column according to the coincidence degree between the crack data information and the preset crack data information, it includes:

[0022] Obtain the positions of each of the cracks, and based on the positions of each of the cracks, determine the crack position coincidence degree between the positions of each of the cracks in the middle crossbeam and the positions of the preset crack regions:

[0023] ;

[0024] where η w is the crack position coincidence degree, w i is the weight coefficient of the i-th preset crack region, M is the total number of positions of the preset crack regions, χR i (P j ) is the indicator function indicating whether the crack position P j falls within the i-th preset region Ri, and N is the total number of cracks;

[0025] Determine the initial repair score to be determined for the middle crossbeam pier column according to the crack position coincidence degree.

[0026] Further, when determining the initial repair score to be determined for the middle crossbeam pier column according to the crack position coincidence degree, it includes:

[0027] Determine the initial repair score according to the relationship between the crack position coincidence degree and the first preset crack position coincidence degree and the second preset crack position coincidence degree configured in advance;

[0028] When the crack position coincidence degree is lower than the first preset crack position coincidence degree, then determine that the initial repair score is L1;

[0029] When the crack position coincidence degree is higher than or equal to the first preset crack position coincidence degree and the crack position coincidence degree is lower than the second preset crack position coincidence degree, then determine that the initial repair score to be determined is L2;

[0030] When the coincidence degree of the crack positions is higher than or equal to the second preset crack position coincidence degree, the initial score to be repaired is determined as L3;

[0031] Among them, the first preset crack position coincidence degree is less than the second preset crack position coincidence degree, and L1 < L2 < L3.

[0032] Further, when determining the initial score to be repaired of the middle crossbeam pier column as Li, i = 1, 2, 3, it includes:

[0033] Image information of each preset crack area will be obtained, and deformation data of the middle crossbeam pier column located in the preset crack area will be obtained according to the image information;

[0034] According to the relationship between the deformation data and the preset deformation data, effective evaluation data is determined. According to the relationship between the effective evaluation data and the corresponding preset deformation data, an adjustment coefficient is determined, and the initial score to be repaired Li is adjusted according to the adjustment coefficient;

[0035] Among them, when determining the effective evaluation data according to the relationship between the deformation data and the preset deformation data, it includes:

[0036] When the deformation data is consistent with the preset deformation data, it is determined that the deformation data is not the effective evaluation data;

[0037] When the deformation data is greater than the preset deformation data, it is determined that the deformation data is the effective evaluation data.

[0038] Further, when determining the adjustment coefficient according to the relationship between the effective evaluation data and the corresponding preset deformation data, it includes:

[0039] Obtain the deformation difference between each effective evaluation data and the corresponding preset deformation data:

[0040] ;

[0041] Among them, F is the average value of the absolute values of the deformation differences, K is the total number of effective evaluation data, S e K is the effective evaluation data, S p K is the corresponding preset deformation data;

[0042] According to the relationship between the average value of the absolute values and the pre-configured first preset deformation difference and second preset deformation difference, the adjustment coefficient is determined:

[0043] When the average value of the absolute values is less than the first preset deformation difference, the adjustment coefficient is determined to be B1;

[0044] When the average value of the absolute values is greater than or equal to the first preset deformation difference and less than the second preset deformation difference, the adjustment coefficient is determined to be B2;

[0045] When the average value of the absolute values is greater than or equal to the second preset deformation difference, the adjustment coefficient is determined to be B3;

[0046] Wherein, the first preset deformation difference is less than the second preset deformation difference, and B1 < B2 < B3 < 1.5.

[0047] Further, when the adjustment coefficient is determined to be Bi, i = 1, 2, 3, it includes:

[0048] Obtain the crack images of each of the preset crack regions in the surface image, perform gray value processing according to the crack images, and determine the size and depth of each crack;

[0049] Extract the average crack size and crack average depth in the crack geometric image data of each of the preset crack regions, and determine them as the preset crack size and preset crack depth;

[0050] According to the size of the crack, the preset crack size, the depth of the crack, and the preset crack depth, determine a correction coefficient, and correct the adjustment coefficient Bi according to the correction coefficient:

[0051] ;

[0052] Wherein, Q is the correction coefficient, V is the total number of preset crack regions, We is the size of the crack, WE is the preset crack size, Re is the depth of the crack, RE is the preset crack depth, zv and Pv are weight coefficients, and both zv and Pv are not zero.

[0053] Compared with the prior art, the beneficial effects of the present invention are as follows: By comprehensively considering the geological attribute information and bearing attribute information of the beam pier column, an accurate crack damage test model is established. This model can not only simulate the crack development of the beam pier column under different geological conditions and load effects, but also provide early warnings for the occurrence and expansion of cracks. This model based on the interaction between geology and structure overcomes the problem in traditional methods that the influence of geological factors on the beam pier column cannot be fully considered, making the detection results more comprehensive and accurate. Secondly, through the image climbing acquisition module, the method can obtain high-quality image data on the surface of the beam pier column in real time. The introduction of this technology makes the detection process more efficient and intelligent. Compared with traditional manual inspections, the image acquisition module can continuously monitor the crack changes of the beam pier column, avoiding the situation where crack damage cannot be detected in time due to insufficient coverage or too long inspection cycle of manual inspections. After the image data is processed, by extracting the crack data information, accurate geometric data and deformation data can be generated for each crack, providing a basis for subsequent analysis and evaluation. In addition, by comparing the coincidence degree between the actually collected crack data and the preset crack data, the damage degree of the beam pier column can be intelligently evaluated and a basis for maintenance decisions can be provided. Through this accurate evaluation mechanism, potential crack areas can be detected in time, and corresponding maintenance plans can be formulated according to the severity of the cracks, avoiding the problems of over-maintenance or omission of serious cracks. The advantages of this intelligent detection method lie in its high efficiency, accuracy and automation degree, which can significantly improve the health monitoring quality of the beam pier column and the accuracy of maintenance decisions. Finally, by combining big data analysis and machine learning technologies, it can continuously optimize itself according to the real-time collected data, improving the prediction performance and detection accuracy. With the accumulation of monitoring data, it can gradually adapt to the crack characteristics of different types of bridges, providing more accurate technical support for the safety management of various bridges. Through this intelligent detection and evaluation mechanism, more accurate and timely maintenance can be achieved, reducing the bridge management cost and increasing the service life of the bridge, thus providing strong technical support for ensuring public traffic safety.

[0054] On the other hand, the present application also provides a Chinese beam pier column intelligent detection system, including:

[0055] A ring guide frame sleeved on the outside of the Chinese beam pier column, and a moving frame is further arranged between the ring guide frame and the outer wall of the Chinese beam pier column;

[0056] A plurality of image acquisition modules are provided, and the plurality of image acquisition modules are arranged side by side along the circumferential direction of the ring guide frame. The image acquisition modules are fixedly connected to the ring guide frame. Among them, the acquisition end of the image acquisition module is arranged opposite to the outer wall of the Chinese beam pier column, and the image acquisition module is used to acquire the surface image of the Chinese beam pier column;

[0057] The central control module is configured to fit and generate a crack damage test model of the middle beam pier column according to the geological attribute information and bearing attribute information of the middle beam pier column. The central control module is further configured to collect the crack position, crack geometric image data before damage of the middle beam pier column and the deformation data of the crack area according to the crack damage test model, and determine them as preset crack data information;

[0058] The output module is electrically connected to the image acquisition module and the central control module respectively. The output module is configured to acquire the surface image information of the middle beam pier column acquired by the image acquisition module; the output module is further configured to extract the crack data information of the middle beam pier column based on the surface image information, and determine the maintenance score to be repaired of the middle beam pier column according to the coincidence degree between the crack data information and the preset crack data information, and output it.

[0059] Furthermore, it further includes:

[0060] The fluid propulsion module is arranged between two adjacent image acquisition modules. One end of the fluid propulsion module is fixedly connected to the ring guide frame. The fluid propulsion module is used to drive the ring guide frame to move along the vertical direction of the pier column.

[0061] It can be understood that the intelligent detection method and detection system of a middle beam pier column in the above embodiments of the present invention have the same beneficial effects, which will not be elaborated here. Description of the Drawings

[0062] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. And throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0063] Figure 1 It is a flow block diagram of an intelligent detection method for a middle beam pier column provided by an embodiment of the present invention;

[0064] Figure 2 It is a structural schematic diagram of an intelligent detection system for a middle beam pier column provided by an embodiment of the present invention;

[0065] Figure 3 It is a top view of an intelligent detection system for a middle beam pier column provided by an embodiment of the present invention;

[0066] Figure 4 It is a working schematic diagram of an intelligent detection system for a middle beam pier column provided by an embodiment of the present invention;

[0067] Among them, 111 is a fixed frame; 112 is a driving wheel; 113 is a telescopic connecting arm; 120 is an annular guide frame; 121 is a semi-rail; 122 is a first connecting rail; 123 is a second connecting rail; 130 is a fluid propulsion module; 140 is an image acquisition module; 210 is a moving base; 310 is a pier column. Detailed implementation manners

[0068] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0069] As Figure 1 shown, in some embodiments of the present application, the present embodiment provides a method for intelligent detection of pier columns of intermediate cross beams, including:

[0070] Step S100: Generate a crack damage test model of the pier column of the intermediate cross beam according to the geological attribute information and bearing attribute information of the pier column of the intermediate cross beam.

[0071] Specifically, the geological attribute information includes geological soil density, geological soil friction coefficient, and geological soil compressibility, and the bearing attribute information includes the size of the pier column of the intermediate cross beam, the concrete strength of the pier column of the intermediate cross beam, the prestress of the pier column of the intermediate cross beam, the bearing deformation characteristics of the pier column of the intermediate cross beam, and the loading method of the pier column of the intermediate cross beam.

[0072] Specifically, when generating a crack damage test model of the pier column of the intermediate cross beam according to the geological attribute information and bearing attribute information of the pier column of the intermediate cross beam, it includes: generating a geological-structure interaction model according to the geological soil density, geological soil friction coefficient, geological soil compressibility, the size of the pier column of the intermediate cross beam, the concrete strength of the pier column of the intermediate cross beam, the prestress of the pier column of the intermediate cross beam, the bearing deformation characteristics of the pier column of the intermediate cross beam, and the loading method of the pier column of the intermediate cross beam. Simulate the geological-structure interaction model based on finite element analysis, and obtain the crack development path and crack propagation data of the pier column of the intermediate cross beam under each load. Generate load correlation formulas according to the crack development path and crack propagation data of the pier column of the intermediate cross beam under each load, cluster according to the load relationships in the load correlation formulas, and establish a crack damage test model according to the clustering results.

[0073] It is understandable that the structural performance of beam piers is jointly affected by geological property information (such as soil density, friction coefficient, and compressibility) and bearing property information (such as beam pier column dimensions, concrete strength, prestress, bearing deformation characteristics, and loading methods). These properties reflect the actual behavior of beam piers under different geological environments and load actions, providing key parameters for the construction of the crack damage model. Next, a geological-structure interaction model is generated based on geological properties and bearing properties. By comprehensively considering the interaction between soil and beam piers, this model can accurately reflect the influence of geological conditions on the formation of cracks in beam piers and lay the foundation for the simulation of crack damage. The geological-structure interaction model can fully consider factors such as the dimensions of beam piers, concrete strength, prestress, etc., as well as the compressibility and friction characteristics of soil, ensuring the comprehensiveness and accuracy of the simulation results. Then, finite element analysis technology is used to simulate the geological-structure interaction model. Finite element analysis can accurately calculate the stress distribution, deformation conditions, and initial conditions of crack formation in beam piers under different load conditions. The simulation data generated in this step provides detailed basic data for the subsequent analysis of crack development paths and crack propagation laws. According to the simulated crack development paths and crack propagation data, the next step is to generate various load correlation equations. The load correlation equations describe the relationship between the occurrence and propagation of cracks in beam piers under different load conditions. By analyzing these load correlation equations, the characteristics and laws of crack development under different load conditions can be identified, providing necessary reference data for the construction of the crack damage test model. Finally, based on the load relationships in the load correlation equations, further cluster analysis is carried out. Cluster analysis can identify similar patterns of crack propagation under different loads, and by classifying and summarizing the development characteristics of cracks under different working conditions, the crack damage test model can be optimized and established. This model can accurately predict the critical points of crack occurrence and propagation paths of beam piers in actual use, providing a scientific basis for the health monitoring and maintenance of beam piers and ensuring the long-term stability and safety of the structure.

[0074] It can be seen that by combining the geological attribute information and bearing attribute information of the middle beam pier columns to construct a crack damage test model, it is possible to provide a more accurate and scientific basis for the crack development and damage prediction of the beam pier columns. By considering the interaction between the geological environment and the structural characteristics, it helps to comprehensively understand the influence of different geological conditions on the generation of cracks in the beam pier columns, thereby improving the accuracy of crack detection and repair prediction. Secondly, by simulating the geological-structure interaction model based on finite element analysis, the crack propagation path and damage data of the middle beam pier columns under different load conditions can be accurately obtained. This simulation analysis provides comprehensive numerical data support for the crack development process, helping engineers identify potential crack risk points and optimize the structural design and maintenance strategies. In addition, when analyzing the crack development characteristics under different load conditions through the generated load correlation formula, it has high scientificity and operability. By deeply analyzing the relationship between the load and the crack propagation, it can provide a quantitative basis for the safety assessment and subsequent maintenance of the beam pier columns. Especially under complex working conditions, this correlation formula can help predict the change of the structural bearing capacity and avoid potential threats to the structural safety caused by cracks. Finally, by performing cluster analysis on the load relationships in the load correlation formula, the crack propagation patterns under different working conditions can be effectively extracted, and then a crack damage test model can be established. This cluster analysis provides strong data support for the prediction and repair of cracks under different load conditions, and provides a quantitative basis for structural maintenance, making the priority and resource allocation of the maintenance work more scientific and reasonable.

[0075] Step S200: According to the crack damage test model, collect the crack position, crack geometric image data, and deformation data of the crack area before the damage of the middle beam pier column, and determine them as the preset crack data information.

[0076] Specifically, when collecting the crack position, crack geometric image data, and deformation data of the crack area before the damage of the middle beam pier column according to the crack damage test model, it includes: based on the stress intensity factor analysis of fracture mechanics, obtain the deformation data before the occurrence of each crack in the crack damage test model and the occurrence position of each crack. Conduct fracture simulation on the occurrence position of each crack based on numerical simulation, and obtain the propagation speed and propagation direction of each crack at each preset time period and each preset load. Obtain the distance metric between the propagation speeds of each crack and the propagation directions of each crack, and establish a distance matrix according to the distance metric. Conduct iterative clustering on the propagation speeds of each crack and the propagation directions of each crack respectively according to the distance matrix. Determine the type characteristic vector of the crack according to the propagation speed and propagation direction of the clustered crack, and according to the type characteristic vector of each crack, determine the position of each crack before the damage of the middle beam pier column, determine the crack geometric image data according to the type characteristic vector of the crack, and determine the deformation data of the crack area according to the deformation data before the occurrence of the crack corresponding to the type characteristic vector of the crack.

[0077] It is understandable that based on the stress intensity factor analysis of fracture mechanics, the deformation data before the occurrence of cracks and the occurrence positions of the cracks in the crack damage test model are obtained. This analysis method fully considers the relationship between crack propagation and structural stress. By accurately obtaining the deformation information of each crack, it provides a scientific basis for subsequent crack prediction and evaluation. In addition, through numerical simulation of fracture simulation for the occurrence positions of the cracks, the propagation behavior of the cracks under different loads and time periods can be simulated, and then the propagation speed and direction of the cracks can be analyzed. Secondly, by performing distance measurement on the propagation speed and direction of the cracks, a distance matrix is constructed, and this matrix provides a quantitative description of the relationship and mutual influence between the cracks. Based on this distance matrix, iterative clustering analysis is carried out to determine the similarity and interaction between the cracks. The propagation speed and direction of the cracks after clustering will be used to further determine the characteristic vectors of the crack types. Through this process, the typical propagation patterns of the cracks can be extracted, and the specific type characteristics of the cracks can be defined according to these patterns, so as to more accurately identify and classify the cracks in subsequent analysis. Finally, based on the characteristic vectors of the crack types, the positions of each crack, the geometric image data of the cracks, and the deformation data of the crack regions before the damage of the middle beam pier columns can be accurately determined. Furthermore, the comprehensive monitoring and accurate prediction of the crack positions, shapes, and propagation behaviors are effectively realized.

[0078] It can be seen that through the stress intensity factor analysis based on fracture mechanics, the deformation data and the positions of the cracks before the occurrence of the cracks can be accurately obtained, thus providing a scientific and quantitative basis for the prediction and monitoring of the cracks. This method can comprehensively evaluate the stress state of the structure under different loads, provide basic data for subsequent crack propagation path analysis and structural health monitoring, and improve the accuracy of crack identification and damage assessment. Secondly, through numerical simulation and crack propagation behavior analysis, combined with distance measurement and clustering analysis of the crack propagation speed and direction, the mutual relationship and propagation trend between the cracks can be effectively analyzed. This process systematizes the crack propagation patterns and can identify the potential influence between different cracks, providing guidance for subsequent repair and strengthening measures of the structure, thus improving the comprehensiveness and accuracy of crack detection. Finally, according to the characteristic vectors of the crack types, the geometric image data and deformation data of the cracks can be accurately extracted. This provides detailed and specific information for the health assessment of the structure, helping to determine the specific positions, shapes, and deformation conditions of the cracks before the damage of the middle beam pier columns. This method can not only enhance the accuracy and real-time performance of crack detection, but also give early warnings of potential structural problems at an early stage, thus providing a strong basis for maintenance decision-making and repair, helping to extend the service life of the structure and improve safety.

[0079] Step S300: Set up an image climbing acquisition module along the installation direction of the middle crossbeam pier column, and acquire the surface image information of the middle crossbeam pier column based on the image climbing acquisition module.

[0080] Step S400: Extract the crack data information of the middle crossbeam pier column based on the surface image information, and determine the maintenance score to be repaired for the middle crossbeam pier column according to the coincidence degree between the crack data information and the preset crack data information, and output it.

[0081] Specifically, when determining the maintenance score to be repaired for the middle crossbeam pier column according to the coincidence degree between the crack data information and the preset crack data information, it includes: obtaining the positions of each crack, and determining the crack position coincidence degree between each crack position in the middle crossbeam and the preset crack area position according to the positions of each crack:

[0082] .

[0083] Among them, η w is the crack position coincidence degree, w i is the weight coefficient of the i-th preset crack area, M is the total number of preset crack area positions, χR i (P j ) is the indicator function indicating whether the crack position P j falls within the i-th preset area Ri, and N is the total number of cracks. Determine the initial maintenance score to be repaired for the middle crossbeam pier column according to the crack position coincidence degree.

[0084] Specifically, when determining the initial maintenance score to be repaired for the middle crossbeam pier column according to the crack position coincidence degree, it includes: determining the initial maintenance score according to the relationship between the crack position coincidence degree and the pre-configured first preset crack position coincidence degree and the second preset crack position coincidence degree. When the crack position coincidence degree is lower than the first preset crack position coincidence degree, the initial maintenance score is determined to be L1. When the crack position coincidence degree is higher than or equal to the first preset crack position coincidence degree and lower than the second preset crack position coincidence degree, the initial maintenance score to be repaired is determined to be L2. When the crack position coincidence degree is higher than or equal to the second preset crack position coincidence degree, the initial maintenance score is determined to be L3. Among them, the first preset crack position coincidence degree is less than the second preset crack position coincidence degree, and L1 < L2 < L3.

[0085] Specifically, when determining the initial maintenance scores Li of the middle beam piers, where i = 1, 2, 3, it includes: obtaining the image information of each preset crack area, and obtaining the deformation data of the middle beam piers located in the preset crack area based on the image information. According to the relationship between the deformation data and the preset deformation data, determining the effective evaluation data, according to the relationship between the effective evaluation data and the corresponding preset deformation data, determining the adjustment coefficient, and adjusting the initial maintenance score Li according to the adjustment coefficient. Among them, when determining the effective evaluation data according to the relationship between the deformation data and the preset deformation data, it includes: when the deformation data is consistent with the preset deformation data, it is determined that the deformation data is not the effective evaluation data. When the deformation data is greater than the preset deformation data, it is determined that the deformation data is the effective evaluation data.

[0086] Specifically, when determining the adjustment coefficient according to the relationship between the effective evaluation data and the corresponding preset deformation data, it includes: obtaining the deformation difference between each effective evaluation data and the corresponding preset deformation data:

[0087] 。

[0088] Among them, F is the average value of the absolute values of the deformation differences, K is the total number of effective evaluation data, S e K is the effective evaluation data, S p K is the corresponding preset deformation data. According to the relationship between the average value of the absolute values and the pre-configured first preset deformation difference and the second preset deformation difference, determining the adjustment coefficient: when the average value of the absolute values is less than the first preset deformation difference, it is determined that the adjustment coefficient is B1. When the average value of the absolute values is greater than or equal to the first preset deformation difference and less than the second preset deformation difference, it is determined that the adjustment coefficient is B2. When the average value of the absolute values is greater than or equal to the second preset deformation difference, it is determined that the adjustment coefficient is B3. Among them, the first preset deformation difference is less than the second preset deformation difference, and B1 < B2 < B3 < 1.5.

[0089] Specifically, when determining that the adjustment coefficient is Bi, where i = 1, 2, 3, it includes: obtaining the crack images of each preset crack area in the surface image, and performing gray value processing on the crack images to determine the size and depth of each crack. Extracting the average crack size and crack average depth in the crack geometric image data of each preset crack area, and determining them as the preset crack size and preset crack depth. According to the size of the crack and the preset crack size and the depth of the crack and the preset crack depth, determining the correction coefficient, and correcting the adjustment coefficient Bi according to the correction coefficient:

[0090] 。

[0091] Among them, Q is the correction coefficient, V is the total number of preset crack regions, We is the size of the crack, WE is the preset crack size, Re is the depth of the crack, RE is the preset crack depth, zv and Pv are weight coefficients, and neither zv nor Pv is zero.

[0092] It can be understood that based on the calculation of the crack position coincidence degree, by analyzing the coincidence degree between the crack position and the preset crack region position, the distribution of cracks on the middle beam pier can be accurately evaluated. The calculation formula of the crack position coincidence degree combines the weight coefficient of the preset crack region and the indicator function of whether the crack is located in this region, providing a quantitative basis for the subsequent repair score to be determined. Through this method, the influence degree of different cracks can be scientifically determined, and then it can be evaluated whether the structure needs maintenance. Then, by setting multiple preset crack position coincidence degree thresholds, the crack position coincidence degree is compared with the preset standard to determine the preliminary repair score to be determined. Through this classification method, the crack problems of the middle beam pier can be effectively graded and the corresponding repair priorities can be provided. This provides a clear repair guide for decision-makers and optimizes the allocation of repair resources. After the preliminary repair score to be determined is obtained, the score is further adjusted in combination with the relationship between the deformation data and the preset deformation data. If the deformation data is greater than the preset deformation data, this data is regarded as valid evaluation data and can correct the score. This method effectively takes into account the structural deformation caused by cracks and enhances the accuracy of the score through the deformation data, ensuring that the repair evaluation is more comprehensive and accurate. In addition, the average value of the deformation data difference is used to calculate the adjustment coefficient. By setting the preset deformation difference interval, the repair score to be determined is corrected according to different degrees of deformation differences. This mechanism can dynamically adjust the repair priority to ensure timely repair in the case of large deformation and avoid potential structural risks. Finally, in combination with the geometric size and depth information of the crack, as well as the application of the correction coefficient, the score is further optimized. The changes in the size and depth of the crack directly affect its repair requirements. Therefore, by performing gray-scale processing on the crack image, extracting the geometric data of the crack, and comparing it with the preset size and depth, the correction coefficient can be accurately calculated, and finally the repair score to be determined is finally corrected.

[0093] It can be seen that by calculating the coincidence degree of crack positions, the matching degree between the actual distribution of cracks in the middle beam piers and the preset crack areas can be efficiently evaluated. This method ensures very precise crack positioning, avoids the errors of manual inspection, and improves the accuracy and reliability of crack evaluation. The preliminary maintenance score obtained based on the crack position coincidence degree provides a scientific basis for subsequent repair decisions and reduces the risk of potential structural problems being overlooked. Secondly, by dividing the crack position coincidence degree into different thresholds, the maintenance tasks can be effectively graded, thereby determining the maintenance priorities. For example, when the crack coincidence degree is low, the maintenance score is L1, indicating a low urgency for maintenance; when the crack coincidence degree is high, the score is L3, suggesting that emergency maintenance is required. This hierarchical management method not only ensures the timely restoration of structural health but also optimizes the use of maintenance resources, enabling the most serious problems to be addressed first. In addition, by introducing deformation data to adjust the initial maintenance score, the impact of cracks on the structure can be more accurately reflected. The deformation data, as supplementary evaluation information, helps to capture potential structural deformation problems that may occur during the crack development process, further improving the accuracy of maintenance evaluation. This method combines the crack problem with the deformation problem, comprehensively considers the structural health status, and avoids the evaluation deviation caused by a single indicator. At the same time, by setting the interval of deformation difference, the system can dynamically adjust the initial score according to the actually measured deformation difference. According to different deformation differences, the introduction of adjustment coefficients can refine the maintenance score, ensuring the timeliness and accuracy of structural maintenance. This flexible adjustment mechanism makes the score more in line with the actual situation, effectively avoids the errors caused by deformation differences, and makes the repair decision more reasonable. Finally, by extracting the size and depth information from the crack images and correcting the adjustment coefficients with correction factors, the impact degree of cracks can be further accurately evaluated. The geometric characteristics of cracks, especially the size and depth, are crucial for repair decisions. Through this method, the maintenance score not only depends on the crack position but also considers the severity of the cracks, ensuring the efficiency and pertinence of the structural repair plan and reducing unnecessary maintenance costs.

[0094] In the above embodiments, by comprehensively considering the geological attribute information and bearing attribute information of beam piers, an accurate crack damage test model was established. This model can not only simulate the crack development of beam piers under different geological conditions and load effects, but also provide early warnings for the occurrence and expansion of cracks. This model based on the interaction between geology and structure overcomes the problem in traditional methods that the influence of geological factors on beam piers cannot be fully considered, making the detection results more comprehensive and accurate. Secondly, through the image climbing acquisition module, the method can obtain high-quality image data on the surface of beam piers in real time. The introduction of this technology makes the detection process more efficient and intelligent. Compared with traditional manual inspections, the image acquisition module can continuously monitor the crack changes of beam piers, avoiding the situation where crack damage cannot be detected in time due to insufficient coverage or too long inspection cycles of manual inspections. After the image data is processed, by extracting crack data information, accurate geometric data and deformation data can be generated for each crack, providing a basis for subsequent analysis and evaluation. In addition, by comparing the coincidence degree between the actually collected crack data and the preset crack data, the damage degree of beam piers can be intelligently evaluated and a basis for maintenance decisions can be provided. Through this precise evaluation mechanism, potential crack areas can be detected in time, and corresponding maintenance plans can be formulated according to the severity of the cracks, avoiding the problems of over-maintenance or omission of serious cracks. The advantages of this intelligent detection method lie in its high efficiency, accuracy and automation level, which can significantly improve the quality of health monitoring of beam piers and the accuracy of maintenance decisions. Finally, by combining big data analysis and machine learning technologies, it can continuously self-optimize according to the real-time collected data, improving the prediction performance and detection accuracy. With the accumulation of monitoring data, it can gradually adapt to the crack characteristics of different types of bridges, providing more accurate technical support for the safety management of various bridges. Through this intelligent detection and evaluation mechanism, more precise and timely maintenance can be achieved, reducing the bridge management cost and increasing the service life of the bridge, thus providing strong technical support for ensuring public traffic safety.

[0095] In another preferred manner based on the above embodiments, as Figures 2 - 4 shown, this embodiment provides a Chinese-style beam pier intelligent detection system, including: a ring guide frame, an image acquisition module 140, a central control module, an output module, and a fluid propulsion module 130.

[0096] Specifically, the annular guide frame is sleeved outside the middle crossbeam pier column, and a moving frame is further arranged between the annular guide frame and the outer wall of the middle crossbeam pier column. A number of image acquisition modules 140 are provided, and the number of image acquisition modules 140 are arranged side by side along the circumferential direction of the annular guide frame. The image acquisition module 140 is fixedly connected to the annular guide frame. Among them, the acquisition end of the image acquisition module 140 is arranged opposite to the outer wall of the middle crossbeam pier column, and the image acquisition module 140 is used to acquire the surface image of the middle crossbeam pier column. The central control module is configured to fit and generate a crack damage test model of the middle crossbeam pier column according to the geological attribute information and bearing attribute information of the middle crossbeam pier column. The central control module is further configured to collect the crack position, crack geometric image data and deformation data of the crack area before damage of the middle crossbeam pier column according to the crack damage test model, and determine it as the preset crack data information. The output module is electrically connected to the image acquisition module 140 and the central control module respectively. The output module is configured to acquire the surface image information of the middle crossbeam pier column acquired by the image acquisition module 140. The output module is further configured to extract the crack data information of the middle crossbeam pier column based on the surface image information, and determine the maintenance score to be repaired of the middle crossbeam pier column according to the coincidence degree between the crack data information and the preset crack data information, and output it. The fluid propulsion module 130 is arranged between two adjacent image acquisition modules 140. One end of the fluid propulsion module 130 is fixedly connected to the annular guide frame, and the fluid propulsion module 130 is used to drive the annular guide frame to move along the vertical direction of the pier column.

[0097] Preferably, the annular guide frame includes: a fixed frame 111, a driving wheel 112 and a telescopic connecting arm 113. Two groups of fixed frames 111 are provided, and the two groups of fixed frames 111 are respectively arranged outside the two pier columns 310. The driving wheel 112 is arranged between the fixed frame 111 and the outer wall of the pier column 310. One end of the driving wheel 112 is rotatably connected to the fixed frame 111. Among them, the driving wheel 112 is provided with a braking unit. Two groups of telescopic connecting arms 113 are provided, and the two groups of telescopic connecting arms 113 are arranged opposite to each other between the two groups of fixed frames 111. Both ends of the telescopic connecting arm 113 are fixedly connected to the outer walls of the two groups of fixed frames 111. Among them, the material of the driving wheel 112 is a non-slip material such as rubber.

[0098] Specifically, the fixed frame 111, as the basic structure of the mobile frame, is arranged on the outer sides of the two pier columns 310, providing a stable support foundation for the entire device. The position of the fixed frame 111 ensures that the mobile frame can be firmly fixed on the pier columns 310 and can withstand various forces and pressures during the movement and operation processes. Secondly, the driving wheels 112 are located between the fixed frame 111 and the outer side walls of the pier columns 310. The driving wheels 112 are rotationally connected to provide power transmission and control for the mobile frame. The driving wheels 112 can not only make the mobile frame move smoothly along the surface of the pier columns 310, but also, through the provided braking unit, can timely stop or fix the position of the mobile frame when needed, ensuring the safety and accuracy of the detection process. Finally, the telescopic connecting arms 113, as the adjustable part of the mobile frame, are arranged between the two groups of fixed frames 111. The design of these telescopic connecting arms 113 enables the mobile frame to adapt to pier columns 310 with different diameters and heights. The length adjustment ability of the telescopic connecting arms 113 enables them to closely connect the two groups of fixed frames 111 and maintain the stability and balance of the overall structure. This design not only enhances the applicability and flexibility of the mobile frame, but also enables it to operate efficiently and complete the detection tasks in complex bridge structures.

[0099] Preferably, the ring guide frame 120 includes: a half-rail 121, a first connecting rail 122, a second connecting rail 123, a distance measuring unit, and a control unit. The half-rail 121 is arranged above the fixed frame 111 and is fixedly connected to the fixed frame 111. The first connecting rail 122 and the second connecting rail 123 are respectively arranged on the side of the pier column 310 away from the half-rail 121. One end of the first connecting rail 122 is rotationally connected to one end of the half-rail 121, one end of the second connecting rail 123 is rotationally connected to the other end of the half-rail 121, and the other end of the second connecting rail 123 is rotationally connected to the other end of the first connecting rail 122, so as to form the ring guide frame 120 between the half-rail 121, the first connecting rail 122, and the second connecting rail 123. The distance measuring unit is arranged at one end of the first connecting rail 122 adjacent to the second connecting rail 123, and the distance measuring unit is used to obtain the real-time distance between the first connecting rail 122 and the middle beam of the middle beam pier column. The control unit is electrically connected to the distance measuring unit, the first connecting rail 122, and the second connecting rail 123 respectively. Among them, the control unit is configured with a preset distance range, and the control unit is also used to respectively control the rotation angles between the first connecting rail 122 and the second connecting rail 123 and the half-rail 121 according to the relationship between the real-time distance and the preset distance range.

[0100] Specifically, by setting the semi-rail 121 above the fixed frame 111 and firmly connecting it to the fixed frame 111, a stable support foundation is provided for the entire ring guide frame 120. The position of the semi-rail 121 ensures that the ring guide frame 120 can move and be guided smoothly around the top of the pier column 310. Secondly, the first connecting rail 122 and the second connecting rail 123 are respectively arranged on one side of the pier column 310, at a position far from the semi-rail 121. One end of the first connecting rail 122 is rotatably connected to one end of the semi-rail 121, one end of the second connecting rail 123 is rotatably connected to the other end of the semi-rail 121, and the other end of the second connecting rail 123 is also rotatably connected to the other end of the first connecting rail 122. This setting enables a closed-loop ring guide frame 120 structure to be formed among the semi-rail 121, the first connecting rail 122, and the second connecting rail 123, ensuring smooth movement of the device in the vertical direction along the pier column 310. In addition, the ranging unit is located at one end between the first connecting rail 122 and the second connecting rail 123, and its main function is to obtain the distance between the first connecting rail 122 and the pier column of the middle cross beam in real time. The ranging unit precisely measures the distance from the surface of the pier column 310 to the ring guide frame 120. Finally, the control unit is electrically connected to the ranging unit, the first connecting rail 122, and the second connecting rail 123 respectively. By configuring a preset distance range, it monitors and analyzes the actual distance data obtained by the ranging unit in real time. According to the relationship between the real-time distance and the preset distance range, the control unit can intelligently adjust the rotation angles between the first connecting rail 122 and the second connecting rail 123 and the semi-rail 121. This intelligent control mechanism ensures that the ring guide frame 120 can flexibly respond to the presence of the middle cross beam during the climbing process. When the ring guide frame 120 encounters the middle cross beam, the control unit immediately adjusts the rotation angles of the first connecting rail 122 and the second connecting rail 123, causing the ring guide frame 120 to change from the closed state to the open-and-close state. This open-and-close state allows the ring guide frame 120 to smoothly bypass the middle cross beam, ensuring that the device can pass through smoothly and continue to move along the surface of the pier column 310 (see Figure 4 as shown). After the ring guide frame 120 passes the middle cross beam, the control unit adjusts the rotation angles of the first connecting rail 122 and the second connecting rail 123 again, causing the ring guide frame 120 to return from the open-and-close state to the closed state. This design ensures that the acoustic detection module can accurately detect the pier column 310 above the middle cross beam when the ring guide frame 120 is closed, effectively supporting the safety assessment and maintenance work of the bridge structure.

[0101] It can be understood that through the control of the ranging unit and the control unit, once the ring guide frame 120 encounters the middle cross beam, the control unit will quickly and accurately adjust the rotation angles of the first connecting rail 122 and the second connecting rail 123, prompting the ring guide frame 120 to change from the closed state to the open-and-close state (see Figure 4As shown in the figure). The design of this open - and - close state enables the ring guide frame 120 to smoothly avoid the middle cross - beam, thus ensuring that the entire device can continue to move smoothly and unobstructed along the surface of the pier column 310. Once the ring guide frame 120 successfully bypasses the middle cross - beam, the control unit will execute precise adjustment operations again to restore the rotation angles of the first connecting rail 122 and the second connecting rail 123, making the ring guide frame 120 return from the open - and - close state to the closed state. The design of this mechanism ensures that the acoustic wave detection module can accurately and comprehensively detect the pier column 310 above the middle cross - beam in the closed state of the ring guide frame 120, providing strong support for the safety assessment and maintenance of the bridge structure.

[0102] Preferably, the acoustic wave detection module includes: a moving base 210 and an ultrasonic detection unit 220. There are two sets of moving bases 210, and the two sets of moving bases 210 are respectively arranged at one end of the two sets of ring guide frames 120. Among them, a driving sheave group is arranged at the bottom of the moving base 210, and the moving base 210 is slidably connected to the ring guide frame 120 through the driving sheave group. The ultrasonic detection unit 220 is arranged above the moving base 210 and is fixedly connected to the moving base 210. Among them, the detection end of the ultrasonic detection unit 220 is arranged corresponding to the outer side wall of the pier column 310, so that when the moving base 210 drives the ultrasonic detection unit 220 to move along the direction of the ring guide frame 120, the ultrasonic detection unit 220 is used to detect the concrete quality of the pier column 310.

[0103] Specifically, the moving base 210 is designed in two sets and is respectively installed at one end of the two sets of ring guide frames 120. This setting ensures that the acoustic wave detection module can cover the entire circumference of the pier column 310, thus comprehensively evaluating the concrete quality of the two pier columns 310 of the middle cross - beam. Each set of moving bases 210 is equipped with a driving sheave group at the bottom. These sheaves are closely connected to the ring guide frame 120, enabling the moving base 210 to slide stably along the track direction, ensuring the accuracy and continuity of the detection. Secondly, the ultrasonic detection unit 220, as a component fixed above the moving base 210, is the key equipment for realizing non - destructive detection of concrete quality. The detection end of the ultrasonic detection unit 220 is arranged corresponding to the outer side wall of the pier column 310. Such a design can ensure that ultrasonic waves can effectively penetrate the concrete structure to reach the predetermined detection depth and accuracy. When the moving base 210 moves along the direction of the ring guide frame 120, the ultrasonic detection unit 220 can accurately obtain the reflection signals from the surface to the interior of the pier column 310, thereby analyzing the acoustic wave propagation characteristics of the concrete and detecting whether there are cracks, cavities or other structural defects in the pier column 310.

[0104] It can be understood that in an inspection device for the surface of a middle crossbeam pier column in this application, through the fixed setting of the ring guide frame and the image acquisition module 140, the device can perform all-round and multi-angle inspections on the surface of the pier column, thereby improving the comprehensiveness and accuracy of the inspection. Secondly, by combining the surface image and acoustic wave information, the early warning module can establish a comprehensive score in real time and automatically judge whether the quality of the pier column is qualified. This automated evaluation and early warning function significantly reduces manual intervention and improves the inspection efficiency. Finally, the setting of the fluid propulsion module 130 enables the ring guide frame to move along the vertical direction of the pier column, achieving high-altitude coverage of the inspection and further ensuring the inspection consistency and reliability at different heights.

[0105] Those skilled in the art should understand that the embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can adopt the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0106] This application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of this application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one or more flows Figure 1 or multiple flows and / or blocks

[0107] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one or more flows Figure 1 or multiple flows and / or blocks

[0108] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide for implementing the functions in the processFigure 1 one process or multiple processes and / or boxes Figure 1 steps of the functions specified in one box or multiple boxes.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific implementation manners of the present invention, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. An intelligent detection method for center beam piers, characterized in that: include: A crack damage test model of the center beam pier column is generated by fitting according to the geological attribute information and bearing attribute information of the center beam pier column, wherein the geological attribute information includes geological soil density, geological soil friction coefficient and geological soil compressibility, and the bearing attribute information includes the size of the center beam pier column, the concrete strength of the center beam pier column, the prestress of the center beam pier column, the bearing deformation characteristics of the center beam pier column and the loading mode of the center beam pier column; Collecting preset crack data information according to the crack damage test model, wherein the preset crack data information includes a preset crack position of the center tie beam pier before damage, crack geometric image data, and preset deformation data of a preset crack area; An image climbing acquisition module is arranged along the arrangement direction of the middle tie beam pier, and surface image information of the middle tie beam pier is collected based on the image climbing acquisition module; Extracting crack data information of the center tie beam pier column based on the surface image information, and determining a repair score of the center tie beam pier column according to the overlap between the crack data information and the preset crack data information, and outputting the score; When the crack damage test model of the middle beam pier column is generated by fitting according to the geological attribute information and the bearing attribute information of the middle beam pier column, it includes: Generate a geological-structural interaction model according to the geological soil density, geological soil friction coefficient, geological soil compressibility, center tie beam pier column size, center tie beam pier column concrete strength, center tie beam pier column prestressing, center tie beam pier column bearing deformation characteristics and center tie beam pier column loading mode; The geological-structural interaction model is simulated based on finite element analysis, and crack development paths and crack extension data of the center tie beam pier under various loads after simulation are obtained; generating load correlation equations according to the crack development paths and crack extension data of the center beam pier under various loads, clustering according to the load relationships in the load correlation equations, and establishing the crack damage test model according to the clustering results; When the preset crack data information is collected according to the crack damage test model, and the preset crack data information includes the preset crack position of the center tie beam pier before damage, the crack geometric image data and the preset deformation data of the preset crack area, it includes: Based on the stress intensity factor analysis of fracture mechanics, the preset deformation data before each crack occurs in the crack damage test model and the occurrence position of each crack are obtained; Based on numerical simulation, fracture simulation is performed on the occurrence position of each of the cracks, and the expansion speed and expansion direction of each of the cracks at each preset time period and each preset load are obtained; Obtaining distance metrics between each expansion speed of each of the cracks and each expansion direction of the cracks, and establishing a distance matrix according to the distance metrics; Iteratively clustering the expansion speeds of the cracks and the expansion directions of the cracks according to the distance matrix; According to the expansion speed and expansion direction of the cracks after clustering, the type characteristic vector of the crack is determined, and according to the type characteristic vector of each crack, the preset crack positions before the damage of the center tie beam pier are determined, according to the type characteristic vector of the crack, the crack geometric image data is determined, and according to the preset deformation data before the crack occurs corresponding to the type characteristic vector of the crack, the preset deformation data of the preset crack area is determined.

2. The intelligent detection method for center beam piers according to claim 1, characterized in that: Determining the score of the center beam pier to be repaired according to the overlap between the crack data information and the preset crack data information includes: The positions of the cracks are obtained, and according to the positions of the cracks, the overlap between the positions of the cracks in the center tie beam and the positions of the preset crack regions is determined: ; Among them, η w is the overlap of crack positions, w i is the weight coefficient of the i-th preset crack area, M is the total number of preset crack area positions, χR i (P j ) is the crack position P j An indicator function of whether it falls within the i-th preset crack region Ri, where N is the total number of cracks; The initial score for maintenance of the center tie beam pier is determined according to the overlap of the crack positions.

3. The intelligent detection method for center beam piers as claimed in claim 2, characterized in that: When determining the initial score of the center tie beam pier column to be repaired according to the overlap of the crack positions, it includes: Determine the initial score to be repaired according to the relationship between the crack position coincidence degree and the pre-configured first preset crack position coincidence degree and the second preset crack position coincidence degree; When the crack position coincidence is lower than the first preset crack position coincidence, the initial score to be repaired is determined to be L1; When the crack position coincidence is higher than or equal to the first preset crack position coincidence, and the crack position coincidence is lower than the second preset crack position coincidence, the initial score to be repaired is determined to be L2; When the crack position coincidence degree is higher than or equal to the second preset crack position coincidence degree, the initial score to be repaired is determined to be L3; Wherein, the first preset crack position overlap is smaller than the second preset crack position overlap, and L1<L2<L3.

4. The intelligent detection method for center beam piers as claimed in claim 3 is characterized in that: When the initial maintenance score of the middle tie beam pier is determined to be Li, i=1,2,3, it includes: The image information of each preset crack area is obtained, and the preset deformation data of the center tie beam pier located in the preset crack area is obtained according to the image information; Determine effective evaluation data according to the relationship between the deformation data and the preset deformation data, determine an adjustment coefficient according to the relationship between the effective evaluation data and the corresponding preset deformation data, and adjust the initial score Li for repair according to the adjustment coefficient; Wherein, determining the effective evaluation data according to the relationship between the deformation data and the preset deformation data includes: When the deformation data is consistent with the preset deformation data, it is determined that the deformation data is not the valid evaluation data; When the deformation data is greater than the preset deformation data, the deformation data is determined to be valid evaluation data.

5. The intelligent detection method for center beam piers as claimed in claim 4, characterized in that: Determining the adjustment coefficient according to the relationship between the effective evaluation data and the corresponding preset deformation data includes: Obtaining the deformation difference between each of the effective evaluation data and the corresponding preset deformation data: ; Among them, F is the average value of the absolute value between each deformation difference, K is the total number of valid evaluation data, S e K To effectively evaluate the data, S p K is the corresponding preset deformation data; The adjustment coefficient is determined according to the relationship between the average value of the absolute value and the pre-configured first preset deformation difference value and the second preset deformation difference value: When the average value of the absolute values ​​is less than the first preset deformation difference value, the adjustment coefficient is determined to be B1; When the average value of the absolute values ​​is greater than or equal to the first preset deformation difference value, and the average value of the absolute values ​​is less than the second preset deformation difference value, the adjustment coefficient is determined to be B2; When the average value of the absolute values ​​is greater than or equal to the second preset deformation difference value, the adjustment coefficient is determined to be B3; The first preset deformation difference value is smaller than the second preset deformation difference value, and B1<B2<B3<1.

5.

6. The intelligent detection method for center beam piers as claimed in claim 5, characterized in that: When the adjustment coefficient is determined to be Bi, i=1, 2, 3, it includes: Acquire a crack image of each of the preset crack areas in the surface image, and perform grayscale value processing according to the crack image to determine the size and depth of each of the cracks; Extracting the average crack size and the average crack depth from the crack geometric image data of each of the preset crack areas, and determining them as the preset crack size and the preset crack depth; According to the size of the crack and the preset crack size and the depth of the crack and the preset crack depth, a correction coefficient is determined, and the adjustment coefficient Bi is corrected according to the correction coefficient: ； Among them, Q is the correction coefficient, V is the total number of preset crack areas, We is the size of the crack, WE is the preset crack size, Re is the depth of the crack, RE is the preset crack depth, zv and Pv are weight coefficients, and both zv and Pv are not zero.

7. An intelligent detection system for a central beam pier column, applicable to an intelligent detection method for a central beam pier column as claimed in any one of claims 1 to 6, characterized in that: include: A ring guide frame is sleeved on the outer side of the middle beam pier, wherein a movable frame is further arranged between the ring guide frame and the outer side wall of the middle beam pier; A plurality of image acquisition modules are provided, and the plurality of image acquisition modules are arranged side by side along the circumference of the ring guide frame, and the image acquisition module is fixedly connected to the ring guide frame, wherein the acquisition end of the image acquisition module is arranged opposite to the outer side wall of the middle tie beam pier, and the image acquisition module is used to acquire the surface image of the middle tie beam pier; A central control module is configured to fit and generate a crack damage test model of the central tie beam pier column according to geological attribute information and bearing attribute information of the central tie beam pier column, and the central control module is further configured to collect preset crack data information according to the crack damage test model, wherein the preset crack data information includes a preset crack position of the central tie beam pier column before damage, crack geometric image data, and preset deformation data of a preset crack area; An output module is electrically connected to the image acquisition module and the central control module, respectively, and the output module is configured to acquire surface image information of the center tie beam pier column acquired by the image acquisition module; the output module is also configured to extract crack data information of the center tie beam pier column based on the surface image information, and determine the maintenance score of the center tie beam pier column according to the overlap between the crack data information and the preset crack data information, and output it.

8. The intelligent detection system for center beam piers as claimed in claim 7, characterized in that it also includes: A fluid propulsion module is arranged between two adjacent image acquisition modules. One end of the fluid propulsion module is fixedly connected to the ring guide frame. The fluid propulsion module is used to drive the ring guide frame to move along the vertical direction of the pier.

Images