Testing method for reduction coefficient of circular ground-connected wall's annular stiffness based on multi-source images

Abstract

The present invention discloses a method for testing the annular stiffness reduction coefficient of a circular ground-connected wall based on multi-source images, which relates to the technical field of building safety assessment. The deformation degree is obtained according to the change analysis of the damage characteristics. If the deformation degree exceeds the expectation, the corresponding annular stiffness reduction coefficient is constructed through image analysis and actual observation respectively, and the difference degree is calculated. If the difference degree exceeds the difference degree threshold, the reliability of the annular stiffness calculation model is calculated by the annular stiffness reduction coefficient obtained by the test. If the reliability is lower than the reliability threshold, the annular stiffness calculation model is optimized; the service life of the annular ground-connected wall is predicted using a wall load-bearing digital twin model. If the predicted service life is lower than the expectation, the corresponding sub-area is maintained after the corresponding wall maintenance strategy is given by the wall maintenance knowledge map. The walls of each sub-area are maintained in a targeted manner, thereby extending the service life of the wall under various conditions and improving the efficiency of subsequent maintenance.

Description

Technical Field

[0001] The present invention relates to the technical field of building safety assessment, and in particular to a method for testing the annular stiffness reduction coefficient of a circular ground-connected wall based on multi-source images. Background Art

[0002] The annular ground wall, also known as the underground continuous wall, is an annular structure that is widely used in various foundation projects. Its main function is to provide stability and support for the underground structure, prevent soil and water infiltration, and withstand various loads from the surrounding environment. The construction process of the annular ground wall usually includes multiple steps such as guide wall design, trenching construction, steel cage production and installation, and concrete pouring. The design and construction of the guide wall is the basis of the underground continuous wall construction, and its internal clearance size needs to be accurately calculated according to the actual project requirements. Trenching construction uses equipment such as trenching machines, rotary drilling drills, and slot milling machines to ensure that the shape and size of the trench meet the design requirements. The production and installation of the steel cage requires strict control of the size and processing accuracy of the steel bars to ensure that it fits tightly with the trench. Finally, the underground continuous wall is formed into a whole through concrete pouring to provide a stable support structure.

[0003] The annular stiffness reduction factor is a crucial parameter in underground continuous wall engineering, which is used to describe the degree of reduction in the annular stiffness of the wall due to various factors. These influencing factors may include the reduction of the effective thickness of the wall caused by the polygon replacing the "true circle", the reduction of the effective thickness of the wall caused by the verticality error of the ground-connected wall, the weakening of the annular stiffness of the wall caused by the thickness of the mud skin between adjacent slots, the influence of the type of slot joints (rigid, hinged and flexible), and the quantitative influence of uneven ground overload on the annular stiffness.

[0004] In the Chinese invention patent with application publication number CN116305465A, a method for determining the bearing capacity of the frame columns of the combined support system of the outer steel frame and the inner straight tube of the water tower is involved. The frame columns in the combined support system of the outer steel frame and the inner straight tube of the water tower are connected to the inner straight tube of the water tower through radial beams, and horizontal supports are arranged between the beams. The frame columns are connected to the circumferential beams along the tangent direction of the straight tube through vertical supports. The beam-column connection working conditions and beam-column parameters are used as variables, and the calculated length coefficient of the frame column is calculated by the beam-column line stiffness ratio and the beam line stiffness combination reduction coefficient. The bearing capacity of the frame column is obtained according to the bearing capacity calculation formula, and a method for determining the bearing capacity of the frame columns of the combined support system of the outer steel frame and the inner straight tube of the water tower is introduced. The combined support system of the outer steel frame and the inner straight tube of the water tower has a simple structure, clear force transmission, convenient construction, and is economical and safe.

[0005] Combined with the above application and the contents of the prior art:

[0006] After the construction of the annular ground wall is completed, in order to ensure the availability of the wall, ensure that its service life can reach the expected level, and that its anti-seismic ability and other mechanical properties can meet expectations, it is necessary to test the annular stiffness reduction coefficient of the wall. The annular stiffness reduction coefficient can be used to determine whether the wall is qualified and available. However, due to environmental conditions, if the wall is in abnormal conditions for a long time, for example, the rainfall in the area where the wall is located is in a long-term state, the groundwater level is high, and at the same time, if the daily temperature difference is also large, the aging of the wall may be accelerated, for example, cracks may appear quickly on the wall surface, thereby shortening the service life of the wall.

[0007] Although the annular stiffness reduction coefficient is generally a fixed value, it will also change under the above abnormal environmental conditions. If the annular stiffness reduction coefficient is still estimated based on the inherent annular stiffness calculation model, there may be a certain difference between the estimated value and the actual value. If this difference is ignored, the safety hazards caused by the wall may be ignored. At the same time, considering that the annular stiffness reduction coefficient may change to a certain extent, the existing annular stiffness reduction coefficient test method is usually a one-time test, and it is difficult to output results continuously, so it is difficult to provide guidance for the safety management of the wall.

[0008] To this end, the present invention provides a method for testing the annular stiffness reduction coefficient of a circular ground-connected wall based on multi-source images. Summary of the invention

[0009] 1. Technical issues to be solved

[0010] In view of the deficiencies of the prior art, the present invention provides a method for testing the annular stiffness reduction coefficient of a circular ground-connected wall based on multi-source images. The deformation degree is obtained by analyzing the changes in damage characteristics. If the deformation degree exceeds expectations, the corresponding annular stiffness reduction coefficient is constructed through image analysis and actual observation respectively, and the difference degree is calculated. If the difference degree exceeds the difference degree threshold, the reliability of the annular stiffness calculation model is calculated by the annular stiffness reduction coefficient obtained by the test. If the reliability is lower than the reliability threshold, the annular stiffness calculation model is optimized. The service life of the annular ground-connected wall is predicted using a wall bearing digital twin model. If the predicted service life is lower than expected, the corresponding sub-area is maintained after the corresponding wall maintenance strategy is given by the wall maintenance knowledge map. The walls of each sub-area are maintained in a targeted manner to extend the service life of the wall under various conditions, thereby solving the problems raised in the background technology.

[0011] (II) Technical solution

[0012] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for testing the annular stiffness reduction coefficient of a circular ground-connected wall based on multi-source images, comprising: constructing an environmental condition coefficient To from environmental condition data in an area adjacent to the wall; if the obtained environmental condition coefficient To exceeds a condition threshold, collecting the size specifications and material data of the annular ground-connected wall to construct a multi-source data set;

[0013] Preprocess the image data in the multi-source data set and perform feature recognition to extract the damage features of the annular ground connection wall. Summarize the extracted damage features and construct a damage feature set. Analyze the changes in the damage features to obtain the deformation degree Bo. If the deformation degree Bo exceeds expectations, issue a model building instruction to the outside.

[0014] Combined with the environment in which the wall is located, the annular stiffness calculation model of the annular ground-connected wall is established. Combined with the annular stiffness calculation model, the corresponding annular stiffness reduction coefficient is constructed through image analysis and actual observation respectively. The difference Co is obtained by calculating the difference between the two. If the difference Co exceeds the difference threshold, a test instruction is issued to the outside. Among them, the difference Co is obtained by calculating the image coefficient Ts and the observation coefficient Gs in the following way:

[0015]

[0016] Among them, Ts i is the image coefficient in the i-th sub-region, Gs i is the observation coefficient in the i-th sub-region, F2 and F1 are weight coefficients, 0≤F1≤1, 0≤F2≤1, i=2,…3, p, p is the number of the sub-region;

[0017] The wall is tested under different conditions, and the reliability Ko of the annular stiffness calculation model is calculated by the annular stiffness reduction coefficient obtained from the test. If the reliability Ko is lower than the reliability threshold, the annular stiffness calculation model is optimized, and the annular stiffness reduction coefficient is periodically constructed by the optimized annular stiffness calculation model.

[0018] The service life of the annular ground-connected wall is predicted using the wall-bearing digital twin model. If the predicted service life is lower than expected, the corresponding maintenance priority Yo is obtained for each sub-area of ​​the wall. After the corresponding wall maintenance strategy is given by the wall maintenance knowledge graph, the wall maintenance strategy is executed in sequence according to the maintenance priority Yo to maintain the corresponding sub-areas.

[0019] Furthermore, the geographical location, surrounding environment, soil conditions and rainfall conditions of the annular ground-connected wall are collected to construct an environmental condition set of the annular ground-connected wall; an environmental condition coefficient To is constructed from the data in the environmental condition set, and if the obtained environmental condition coefficient To exceeds the condition threshold, an image acquisition instruction is issued to the outside;

[0020] After receiving the image acquisition instruction, the size specifications and material data of the annular ground-connected wall are collected to build a three-dimensional model of the wall; multi-source image data during the design, construction and operation of the relevant annular ground-connected wall are collected, including but not limited to satellite images, drone images, and ground scanning images.

[0021] Furthermore, the environmental condition coefficient To is obtained by linearly normalizing the rainfall Rp and temperature difference Wp in each detection period, mapping the corresponding data values ​​to the interval [0, 1], and then according to the following method:

[0022]

[0023] Where i = 1, 2, ..., m, m is the number of detection cycles, Rp i is the rainfall in the i-th detection period, Rp avg is the mean rainfall amount, is its qualified standard value; Wp i is the temperature difference within the i-th detection cycle, Wp avg is the mean temperature difference, is its qualified standard value; weight coefficient: 0≤F1≤1, 0≤F2≤1, and F1+F2=1.

[0024] Furthermore, the image data in the multi-source data set are preprocessed, and the preprocessed image data are summarized to construct a preprocessed wall image set; the preprocessed image data are subjected to image recognition detection using an edge detection algorithm to extract the damaged features of the annular ground-connected wall, and the position information of each damaged feature on the wall is determined, and the extracted damaged features are summarized to construct a damaged feature set.

[0025] Furthermore, according to the position of the damage feature on the three-dimensional model of the wall, the proximity center Cu of each damage feature is obtained, and according to the influence range of each damage feature, the spreading area Mu of each damage feature is determined;

[0026] The spread area Mu and the proximity center Cu of the damage feature are linearly normalized, and the corresponding data values ​​are mapped to the interval [0, 1], and then the deformation degree Bo is constructed according to the following method:

[0027]

[0028] Among them, k2 and k1 are weight coefficients, 0≤k1≤1, 0≤k2≤1, and k1 2 +k2 2 =1,Mu i is the spreading area of ​​the i-th damage feature, is the acceptable value of the spreading area, Cu iis the closeness center of the i-th damaged feature, is the acceptable value of the proximity center, n is the number of damaged features, i = 2, ... 3, n.

[0029] Furthermore, after receiving the model building instruction, a mechanical model is established according to the material, structural characteristics and soil-structure interaction of the annular ground-connected wall in combination with mechanical theory, and a hoop stiffness calculation model of the annular ground-connected wall is established by deducing the mechanical model; the wall is divided into several equivalent sub-areas, and in each sub-area, the deformation data extracted by image analysis is used as input, and the hoop stiffness reduction coefficient is output by the hoop stiffness calculation model, which is used as the image coefficient Ts; the actual observation data of the wall is used as input, and the hoop stiffness reduction coefficient is output by the hoop stiffness calculation model, which is used as the observation coefficient Gs.

[0030] Further, after receiving the test instruction, different test conditions are set, the wall is tested on site under different conditions, and the annular stiffness reduction coefficient is calculated from the test data, and the average value is used as the test coefficient Cs;

[0031] The reliability Ko of the circumferential stiffness calculation model is constructed by the image coefficient Ts and the test coefficient Cs, wherein the image coefficient Ts and the test coefficient Cs in each sub-area correspond one to one, according to the following method:

[0032]

[0033] Among them, Ts i and Cs i are the image coefficient and test coefficient of the ith sub-region, Ts avg With Cs avg is the corresponding mean, i = 2, ... 3, p, p is the number of the sub-area, Zo i is the reliability interval value of the ith sub-region, Zo avg is the corresponding mean.

[0034] Furthermore, if the reliability Ko is lower than the reliability threshold, the annular stiffness calculation model is optimized, and the annular stiffness reduction coefficient is output by the optimized annular stiffness calculation model. If the obtained annular stiffness reduction coefficient is lower than expected, an alarm command is issued to the outside; if it is not lower than expected, a monitoring cycle is set, and images of the wall are collected regularly. After the image data is processed and selected as input, the annular stiffness reduction coefficient is constructed again by the optimized annular stiffness calculation model.

[0035] Furthermore, a wall data set is constructed, and a digital twin model of wall load is obtained through training. The extreme conditions of wall use are used as preset test conditions. Under the preset test conditions, the digital twin model of wall load is used to predict the service life of the annular ground-connected wall. If the predicted service life is lower than expected, a wall maintenance instruction is issued.

[0036] Taking the maintenance of the ring-shaped ground wall as the target word, after deep search and building entity relationships, a wall maintenance knowledge graph is built; according to the correspondence between the wall maintenance strategy and the damage characteristics in each sub-area, the wall maintenance knowledge graph gives the corresponding wall maintenance strategy; among them, the wall maintenance strategy includes the selection of reinforcement timing, methods and materials; according to the maintenance priority, the wall maintenance strategy is executed, and the corresponding sub-areas are maintained in turn.

[0037] Furthermore, after receiving the maintenance instruction, the annular ground connection wall is divided into several sub-areas, and the damage characteristics of each sub-area are obtained from the prediction data; according to the distribution state of the damage characteristics in each sub-area, the maintenance priority Yo in each sub-area is calculated and obtained as follows:

[0038] The spread area Mu and the proximity center Cu of the damage features in each sub-area are linearly normalized, and the corresponding data values ​​are mapped to the interval [0, 1], and then according to the following method:

[0039]

[0040] Among them, the weight coefficient is 0≤ρ≤1, 0≤ζ≤1, and 0.6≤ρ+ζ≤1.2, Po is the middle priority value; among them, i=2,…3, p, p is the number of the sub-area, Po avg is the mean of the median priorities.

[0041] (III) Beneficial effects

[0042] The present invention provides a method for testing the annular stiffness reduction coefficient of a circular ground-connected wall based on multi-source images, which has the following beneficial effects:

[0043] 1. By constructing the environmental condition coefficient To, the environmental conditions in the area where the wall is located are judged according to the value of the environmental condition coefficient To, and then the state and life of the wall are predicted, so that the wall can be maintained in time.

[0044] 2. Use the trained image recognition model to identify the preprocessed wall image and obtain the corresponding recognition results. By judging the damage features obtained through recognition, it is possible to make a judgment on the current wall safety and determine whether the wall needs maintenance based on this.

[0045] 3. The deformation degree Bo is pre-constructed according to the degree of damage of the wall, and several damage characteristics are comprehensively considered. The current deformation of the wall is evaluated according to the deformation degree, so that when judging the damage and deformation of the wall, the judgment result given is more accurate and covers a wider range.

[0046] 4. Perform a preliminary calculation of the circumferential stiffness reduction coefficient of the current wall based on the circumferential stiffness calculation model; after determining the various condition parameters in the area where the wall is located, re-derive the circumferential stiffness calculation model. When analyzing and calculating the current wall, the results given are more reliable.

[0047] 5. The image recognition results and actual observation and measurement data are used as input respectively, and the circumferential stiffness calculation model outputs the corresponding circumferential stiffness reduction coefficient. On the basis of wall segmentation, the difference Co between the two is constructed to judge the gap between the image recognition data and the actual observation, and the constructed difference Co is used to describe the current gap.

[0048] 6. Obtain the corresponding annular stiffness reduction coefficient under different conditions, and then construct the reliability Ko. Judge the reliability of the image coefficient obtained based on image recognition, that is, the corresponding annular stiffness reduction coefficient, based on the reliability Ko. Set a monitoring cycle to periodically collect image data, output the corresponding annular stiffness reduction coefficient based on the optimized model, and send an alarm to the outside based on whether the annular stiffness reduction coefficient is abnormal, so as to implement a periodic alarm strategy. When there are changes in the wall, timely processing and response can be carried out. If there are hidden dangers, avoid further expansion of the hidden dangers.

[0049] 7. The trained wall-bearing digital twin model predicts the wall life in the current state, and further judges the wall status based on the obtained prediction data, thereby achieving secondary verification, ensuring the reliability of the alarm, and reducing the risk of invalid maintenance.

[0050] 8. Based on image recognition, damage features and pre-built wall maintenance knowledge graphs are obtained. The wall maintenance knowledge graph provides corresponding wall maintenance strategies as a reference maintenance plan, saving time in formulating maintenance strategies and performing targeted maintenance on the walls of each sub-area, thereby extending the service life of the wall under various conditions and improving subsequent maintenance efficiency.

[0051] 9. By calculating the corresponding maintenance priority Yo according to the degree of damage in each sub-area, the sub-areas of the wall can be maintained in an orderly manner when necessary, with priority given to the locations with the greatest impact, gradually reducing existing safety hazards and improving current maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1It is a structural schematic diagram of a method for testing the annular stiffness reduction coefficient of a circular ground-connected wall according to the present invention;

[0053] Figure 2 This is a schematic diagram of the test process based on drones of the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] See also Figure 1 and Figure 2 The present invention provides a method for testing the annular stiffness reduction coefficient of a circular ground-connected wall based on multi-source images, comprising: step 1, constructing an environmental condition coefficient To from environmental condition data in an area adjacent to the wall, if the obtained environmental condition coefficient To exceeds a condition threshold, collecting the size specifications and material data of the annular ground-connected wall, and constructing a multi-source data set;

[0056] The step 1 includes the following contents:

[0057] Step 101: collect the geographical location, surrounding environment, soil conditions, rainfall conditions, etc. of the annular ground-connected wall, construct an environmental condition set for the annular ground-connected wall, and set a detection period, for example, 1 day;

[0058] The environmental condition coefficient To is constructed from the data in the environmental condition set, wherein the rainfall Rp and temperature difference Wp in each detection period are linearly normalized, and the corresponding data values ​​are mapped to the interval [0, 1], and then according to the following method:

[0059]

[0060] Where i = 1, 2, ..., m, m is the number of detection cycles, Rp i is the rainfall in the i-th detection period, Rp avg is the mean rainfall amount, is its qualified standard value; Wp i is the temperature difference within the i-th detection cycle, Wp avg is the mean temperature difference, is its qualified standard value;

[0061] Weight coefficient: 0≤F1≤1, 0≤F2≤1, and F1+F2=1; the weight coefficient is obtained by referring to the hierarchical analysis method;

[0062] Considering that environmental conditions will have a certain impact on the wall, and thus affect the value of its hoop stiffness reduction factor, for example:

[0063] Soil conditions: Soil type, density, moisture content, compressibility and thickness of soil layers will directly affect the stability of the ground wall. Different soil conditions will affect the lateral earth pressure of the ground wall, the lateral deformation of the soil and the bearing capacity of the foundation.

[0064] Groundwater level: Changes in groundwater level are critical to the stability of the ground wall. High groundwater levels may cause soil liquefaction, increase lateral earth pressure, reduce the bearing capacity of the soil, and thus affect the stability of the ground wall.

[0065] Earthquake action: Earthquake force will produce lateral thrust on the ground wall, increase the lateral deformation of the soil, and thus affect the stability of the ground wall. Therefore, earthquake action is one of the important factors affecting the stability of the ground wall.

[0066] Rainfall conditions: Rainfall can cause changes in soil moisture content, increase the lateral pressure of the soil, and may cause soil liquefaction or slope stability problems, thus affecting the stability of the ground wall.

[0067] Temperature changes: Temperature changes can affect the mechanical properties of soil and ground wall materials, such as causing expansion or contraction of the soil and materials, thus affecting the stability of the ground wall.

[0068] Soil layer changes: The properties of soil layers at different depths may be different, such as clay, sand, rock, etc. These differences will affect the stability of the ground wall.

[0069] Combined with historical data and the safety management expectations of the wall, the condition threshold is set in advance; if the environmental condition coefficient To obtained exceeds the condition threshold, it means that if the wall continues to be under the current conditions, the ring stiffness reduction coefficient of the wall may change to a certain extent; in order to continue to maintain the safety of the wall, it is necessary to continuously monitor and identify the ring stiffness reduction coefficient for timely processing. At this time, an image acquisition instruction is issued to the outside;

[0070] Step 102: after receiving the image acquisition instruction, collect the size specifications and material data of the annular ground-connected wall and construct a three-dimensional model of the wall; collect multi-source image data during the design, construction and operation of the relevant annular ground-connected wall, including but not limited to satellite images, drone images, and ground scanning images; the image acquisition device includes a drone and a ground scanning device, etc., and the collected image data are aggregated to construct a multi-source data set;

[0071] When using, combine the contents in steps 101 and 102:

[0072] After the construction of the annular ground wall is completed, the environmental condition coefficient To is constructed, and the environmental conditions in the area where the wall is located are judged according to the value of the environmental condition coefficient To, and then the state and life of the wall are predicted, so that the wall can be maintained in time.

[0073] Combined with the above application and the contents of the prior art:

[0074] After the construction of the annular ground wall is completed, in order to ensure the availability of the wall, ensure that its service life can reach the expected level, and that its anti-seismic ability and other mechanical properties can meet expectations, it is necessary to test the annular stiffness reduction coefficient of the wall. The annular stiffness reduction coefficient can be used to determine whether the wall is qualified and available. However, due to environmental conditions, if the wall is in abnormal conditions for a long time, for example, the rainfall in the area where the wall is located is in a long-term state, the groundwater level is high, and at the same time, if the daily temperature difference is also large, the aging of the wall may be accelerated, for example, cracks may appear quickly on the wall surface, thereby shortening the service life of the wall.

[0075] Although the annular stiffness reduction coefficient is usually a fixed value, it will also change under the above abnormal environmental conditions. If the annular stiffness reduction coefficient is still estimated based on the inherent annular stiffness calculation model, there may be a certain difference between the estimated value and the actual value. If this difference is ignored, the safety hazards caused by the wall may be ignored. At the same time, considering that the annular stiffness reduction coefficient may change to a certain extent, the existing annular stiffness reduction coefficient test method is usually a one-time test, and it is difficult to output results continuously, so it is difficult to provide guidance for the safety management of the wall.

[0076] Step 2: Preprocess the image data in the multi-source data set and perform feature recognition to extract the damage features of the annular ground connection wall, summarize the extracted damage features and construct a damage feature set, and obtain the deformation degree Bo based on the change analysis of the damage features. If the deformation degree Bo exceeds expectations, issue a model construction instruction to the outside;

[0077] The step 2 includes the following contents:

[0078] Step 201, preprocessing the image data in the multi-source data set, including: data cleaning, screening out images with qualified quality, and excluding images whose quality is affected by factors such as weather and light; image correction and image enhancement, performing geometric correction and radiation correction on the selected images to ensure the accuracy and comparability of the images; aggregating the preprocessed image data to construct a preprocessed wall image set;

[0079] Step 202: pre-set identification standards based on the quality management of the wall; use the edge detection algorithm to perform image recognition detection on the pre-processed image data, extract the damage features of the annular ground-connected wall, including cracks, deformation, etc., and determine the position information of each damage feature on the wall, and summarize the extracted damage features to construct a damage feature set;

[0080] When in use, after preprocessing the collected images, the trained image recognition model is used to identify the preprocessed wall images after the construction is completed, and the corresponding recognition results are obtained. By judging the current wall safety based on the damaged features obtained by recognition, it is possible to determine whether the wall needs maintenance based on this starting point.

[0081] Step 203: According to the position of the damage feature on the three-dimensional wall model, the proximity center Cu of each damage feature is obtained through proximity center analysis. At the same time, according to the influence range of each damage feature, the spread area Mu of each damage feature is determined;

[0082] The spread area Mu and the proximity center Cu of the damage feature are linearly normalized, and the corresponding data values ​​are mapped to the interval [0, 1], and then the deformation degree Bo is constructed according to the following method:

[0083]

[0084] Among them, k2 and k1 are weight coefficients, 0≤k1≤1, 0≤k2≤1, and k1 2 +k2 2 =1,Mu i is the spreading area of ​​the i-th damage feature, is the acceptable value of the spreading area, Cu i is the closeness center of the i-th damaged feature, is the acceptable value of the proximity center, n is the number of damaged features, i = 2, ... 3, n;

[0085] Based on historical data and management expectations for wall quality, a deformation threshold is pre-set; if the deformation Bo exceeds expectations, that is, the deformation exceeds the deformation threshold, it means that the deformation of the wall is serious and may not meet current usage expectations. There may be safety hazards in subsequent use. At this time, in order to ensure the reliability of the wall, a model building instruction is issued to the outside;

[0086] When using, combine the contents in steps 201 to 203:

[0087] On the basis of the identified damage features, the deformation degree Bo is pre-constructed according to the damage degree of the wall. Several damage features are comprehensively considered, and the current deformation of the wall is evaluated according to the deformation degree. Therefore, when judging the damage and deformation of the wall, the judgment result given is more accurate and covers a wider range.

[0088] Step 3: Based on the environment in which the wall is located, a hoop stiffness calculation model of the annular ground-connected wall is established. In combination with the hoop stiffness calculation model, the corresponding hoop stiffness reduction coefficient is constructed through image analysis and actual observation respectively. The difference Co is obtained based on the difference between the two. If the difference Co exceeds the difference threshold, a test instruction is issued to the outside.

[0089] The step three includes the following contents:

[0090] Step 301: after receiving the model building instruction, a mechanical model is established according to the material and structural characteristics of the annular ground-connected wall and the soil-structure interaction in combination with mechanical theory, and a hoop stiffness calculation model of the annular ground-connected wall is established by deducing the mechanical model;

[0091] The derivation method is common knowledge in this field, and the following contents may be referred to;

[0092] The hoop stiffness calculation model is usually used to evaluate the resistance of pipes, rings or other annular structures to external or internal pressure. The calculation is mainly based on the mechanical properties of the material and the geometric dimensions of the structure. The following are the general steps and considerations of the hoop stiffness calculation model:

[0093] Determine material properties: First, you need to know the elastic modulus (E) of the material used, which reflects the material's ability to resist elastic deformation when subjected to force. In addition, you also need to know the material's Poisson's ratio (v), which describes the degree to which the material expands perpendicular to the direction of pressure when subjected to unidirectional pressure.

[0094] Measuring structure dimensions: You need to know the inner diameter (d) and outer diameter (D) of the ring structure, as well as the cross-sectional thickness (t) of the ring. These dimensions will be used to calculate geometric parameters such as the cross-sectional area and moment of inertia of the ring.

[0095] Establishing a mechanical model: Based on the theory of elastic mechanics, a mechanical model of the ring structure can be established. This model usually involves equilibrium equations, geometric equations, and physical equations. The equilibrium equations describe the stress state of the structure, the geometric equations describe the deformation of the structure, and the physical equations establish the relationship between stress and strain.

[0096] Derive the stiffness formula: Based on the mechanical model, the calculation formula for the hoop stiffness can be derived. This formula is usually a function of material properties (E, v) and structural dimensions (d, D, t). For example, a common hoop stiffness formula may be K = E × t / (Dd) or K = E × t^3 / (12 × (1-v^2) × R), where K represents the hoop stiffness.

[0097] Perform the calculation: Substituting the known material properties and structure dimensions into the stiffness formula, the hoop stiffness can be calculated.

[0098] It should be noted that the calculation model of hoop stiffness may vary depending on the specific application scenario and structural form. Therefore, in practical applications, it is necessary to select the appropriate model and formula according to the specific situation and consider various possible boundary conditions and influencing factors. In addition, for complex structures or situations where more factors need to be considered, more advanced methods such as finite element analysis may be required to calculate and analyze stiffness.

[0099] According to the existing mechanical model and various condition parameters in the area where the wall is located, a hoop stiffness calculation model is given, so that the hoop stiffness reduction coefficient of the current wall can be preliminarily calculated based on the hoop stiffness calculation model; and after determining the various condition parameters in the area where the wall is located, the hoop stiffness calculation model is re-derived, and when the current wall is analyzed and calculated, the results given are more reliable.

[0100] It should be noted that the method of obtaining the annular stiffness reduction coefficient of the annular joint strength through image analysis is as follows:

[0101] Image preprocessing: preprocessing the image to remove noise, enhance contrast, or perform other necessary operations;

[0102] Feature extraction: Use image processing technology to extract features that are strongly related to the ring connection;

[0103] Ring structure recognition: After extracting features, image analysis algorithms are used to identify and locate ring structures in the image.

[0104] Parameter measurement: measure key parameters such as diameter, thickness, shape, etc. Used to calculate the annular stiffness reduction factor.

[0105] Calculation of stiffness reduction coefficient: Based on the measured annular structure parameters and combined with the principles of material mechanics and structural mechanics, the calculation formula for the annular stiffness reduction coefficient can be derived, and these parameters can be substituted into the formula for calculation.

[0106] Step 302: the wall is divided into a number of equal sub-regions. In each sub-region, the deformation data extracted by image analysis is used as input, and the annular stiffness calculation model outputs the annular stiffness reduction coefficient, which is used as the image coefficient Ts; the actual observation data of the wall is used as input, and the annular stiffness calculation model outputs the annular stiffness reduction coefficient, which is used as the observation coefficient Gs;

[0107] Step 303: The difference Co is calculated by combining the image coefficient Ts with the observation coefficient Gs in the following manner:

[0108]

[0109] Among them, Ts i is the image coefficient in the i-th sub-region, Gs i is the observation coefficient in the i-th sub-region, F2 and F1 are weight coefficients, 0≤F1≤1, 0≤F2≤1, i=2,…3, p, p is the number of the sub-region, and its value is consistent with the previous value, or obtained by referring to the hierarchical analysis method;

[0110] A difference threshold is pre-set based on historical data and the degree of tolerance for deviations between different calculation methods. If the difference Co exceeds the difference threshold, it means that there is a large difference between the two current calculation methods. In this case, a test instruction is issued to the outside.

[0111] When using, combine the contents in steps 301 to 303:

[0112] The image recognition results and actual observation and measurement data are used as input respectively, and the circumferential stiffness calculation model outputs the corresponding circumferential stiffness reduction coefficient. On the basis of wall segmentation, the difference Co between the two is constructed to judge the gap between the image recognition data and the actual observation. The constructed difference Co is used to describe the current gap. If the current difference Co is large, it means that the current estimation result or the recognition result is not reliable.

[0113] Step 4: Test the wall under different conditions, and calculate the reliability Ko of the annular stiffness calculation model based on the annular stiffness reduction coefficient obtained from the test. If the reliability Ko is lower than the reliability threshold, optimize the annular stiffness calculation model, and periodically construct the annular stiffness reduction coefficient based on the optimized annular stiffness calculation model.

[0114] The step 4 includes the following contents:

[0115] Step 401: after receiving the test instruction, different test conditions are set, the wall is tested on site under different conditions, and the hoop stiffness reduction coefficient is calculated from the test data, which is used as the test coefficient Cs; considering that the test coefficient can be obtained under different conditions, there are multiple test coefficients, and the average of the test coefficients is used as the final test coefficient Cs;

[0116] The test conditions are:

[0117] Soil conditions: The hoop stiffness of the ground-connected wall is affected by the soil. Different types of soil have different strength and deformation characteristics, which will affect the hoop stiffness of the ground-connected wall structure. Therefore, the characteristics of the soil are an important factor affecting the hoop stiffness.

[0118] Wall material and shape: The hoop stiffness of the ground-anchored wall also depends on the material and shape of the wall. For example, the hoop stiffness of a ground-anchored wall made of reinforced concrete and steel sheet piles will be different from that of a ground-anchored wall made of wood or other materials. In addition, the geometric shape of the wall (such as wall thickness, wall height, geometric configuration of the wall, etc.) will also have an impact on the hoop stiffness.

[0119] Wall support and earth pressure distribution: The hoop stiffness of the ground-connected wall is also affected by the support method of the wall and the distribution of earth pressure. For example, if there is an effective support system behind the ground-connected wall, such as anchors or support piles, then its hoop stiffness may be enhanced. In addition, the distribution of earth pressure will also have an impact on the stiffness of the ground-connected wall.

[0120] Loading conditions: The hoop stiffness of the ground-connected wall will also be affected by the loading conditions. For example, if the ground-connected wall needs to withstand large horizontal loads, such as earthquakes or lateral earth pressure, then its hoop stiffness needs to be increased accordingly.

[0121] The reliability Ko of the circumferential stiffness calculation model is constructed by the image coefficient Ts and the test coefficient Cs, wherein the image coefficient Ts and the test coefficient Cs in each sub-area correspond one to one, according to the following method:

[0122]

[0123] Among them, Ts i and Cs i are the image coefficient and test coefficient of the ith sub-region, Ts avg With Cs avg is the corresponding mean, i = 2, ... 3, p, p is the number of the sub-region, Zo i is the reliability interval value of the ith sub-region, Zo avg is the corresponding mean;

[0124] When in use, through actual testing and verification, the corresponding annular stiffness reduction coefficients are obtained under different conditions, and then the reliability Ko is constructed. The reliability of the image coefficient obtained based on image recognition, that is, the reliability of the corresponding annular stiffness reduction coefficient, is judged according to the reliability Ko. If its reliability is insufficient, the corresponding annular stiffness calculation model needs to be optimized;

[0125] Step 402: pre-set a reliability threshold based on historical data and management expectations for wall safety; if the reliability Ko is not lower than the reliability threshold, it means that the derived annular stiffness calculation model can meet the current use needs; if it is lower than the reliability threshold, the annular stiffness reduction factor output by the annular stiffness calculation model is not reliable enough, and the annular stiffness calculation model is optimized to obtain the optimized annular stiffness calculation model;

[0126] The optimization of the hoop stiffness calculation model can be referred to as follows:

[0127] Model refinement: refine the hoop stiffness calculation model, taking into account more physical factors and boundary conditions, such as material nonlinearity, temperature effects, dynamic loads, etc.

[0128] Parameter optimization: Optimize the parameters in the model to find the best combination of parameters. This can be achieved through experimental data, simulation analysis or optimization algorithms. The optimization parameters can include the elastic modulus, Poisson's ratio, cross-sectional dimensions, etc. of the material.

[0129] Algorithm improvement: Use more advanced algorithms for solving problems, use higher-order finite element methods, boundary element methods or spectral methods, etc. to improve calculation accuracy and efficiency. You can also consider using parallel computing technology to speed up the solution of large-scale problems.

[0130] Experimental verification: Verify the accuracy of the model through experiments, obtain experimental data, and compare and analyze the model prediction results. Continue to modify and optimize the model based on the experimental results to improve the reliability of the model.

[0131] Among them, the optimization of the hoop stiffness calculation model is a multifaceted process that requires comprehensive consideration of model refinement, parameter optimization, algorithm improvement, experimental verification, model integration and expansion, as well as code optimization and tool development. By continuously optimizing and improving the model, its accuracy and practicality in engineering design and analysis can be improved.

[0132] Step 403: Generally speaking, the annular stiffness reduction coefficient is a fixed value. However, in fact, under certain conditions, the annular stiffness reduction coefficient may also vary to a certain extent. The following are some factors that may cause the annular stiffness reduction coefficient to change:

[0133] Soil changes: If soil conditions change, such as soil inhomogeneity, moisture changes or seasonal changes, this may lead to changes in the hoop stiffness reduction factor. This is because the mechanical properties of the soil are related to the hoop stiffness.

[0134] Construction quality: The construction quality of the ground-connected wall may affect its hoop stiffness. If the construction quality is poor, such as the eccentricity of the wall, the verticality of the wall, the horizontality of the wall, etc., it may lead to a change in the hoop stiffness reduction factor.

[0135] Load changes: If the load conditions of the surrounding environment change, such as an increase or decrease in lateral earth pressure, seismic load, etc., it may affect the annular stiffness of the ground-connected wall, resulting in a change in the annular stiffness reduction factor.

[0136] Structural aging: Over time, the material and structure of the ground-anchored wall may age, such as the age of the concrete, cracks in the wall, etc. These aging phenomena may lead to changes in the hoop stiffness reduction factor.

[0137] It should be noted that the change in the hoop stiffness reduction factor may be gradual or abrupt, depending on the specific changing factors and time scale.

[0138] The ring stiffness reduction coefficient is output by the optimized ring stiffness calculation model. If the obtained ring stiffness reduction coefficient is lower than expected, it means that there are certain safety hazards in the current wall, and an alarm instruction is issued to the outside. If it is not lower than expected, a monitoring cycle is set to regularly collect images of the wall. After the image data is processed and selected for input, the ring stiffness reduction coefficient is constructed again by the optimized ring stiffness calculation model.

[0139] When used, combine the contents in steps 401 to 403:

[0140] By optimizing the current hoop stiffness calculation model, setting a monitoring cycle, periodically collecting image data, and outputting the corresponding hoop stiffness reduction coefficient based on the optimized model, an alarm is issued to the outside based on whether the hoop stiffness reduction coefficient is abnormal, thereby implementing a periodic alarm strategy. When there are changes in the wall, timely processing and response can be carried out, and if there are hidden dangers, further expansion of the hidden dangers can be avoided.

[0141] Step 5: Use the wall load-bearing digital twin model to predict the service life of the annular ground-connected wall. If the predicted service life is lower than expected, analyze and obtain the corresponding maintenance priority Yo for each sub-area of ​​the wall. After the corresponding wall maintenance strategy is given by the wall maintenance knowledge graph, the wall maintenance strategy is executed in sequence according to the maintenance priority Yo to maintain the corresponding sub-areas;

[0142] The step five includes the following contents:

[0143] Step 501: after receiving the alarm instruction, the wall structure data, material data, force data and environmental data are collected, and a wall data set is constructed after being summarized. Part of the data in the wall data set is extracted as sample data, and an initial model is constructed using a neural convolutional network. The initial model is trained with the sample data to obtain a trained wall load-bearing digital twin model; the extreme conditions of the wall use are used as preset test conditions, and under the preset test conditions, the wall load-bearing digital twin model is used to predict the service life of the annular ground-connected wall. If the obtained predicted service life is lower than the expected service life, a wall maintenance instruction is issued;

[0144] During use, after receiving the alarm command, the trained wall-bearing digital twin model predicts the wall life in the current state, and makes further judgment on the wall status based on the obtained prediction data, thereby achieving secondary verification, ensuring the reliability of the alarm, and reducing the risk of invalid maintenance.

[0145] Step 502: After receiving the maintenance instruction, the annular ground connection wall is divided into several sub-areas, and the damage characteristics of each sub-area are obtained from the prediction data; according to the distribution state of the damage characteristics in each sub-area, the maintenance priority Yo in each sub-area is calculated and obtained as follows:

[0146] The spread area Mu and the proximity center Cu of the damage features in each sub-area are linearly normalized, and the corresponding data values ​​are mapped to the interval [0, 1], and then according to the following method:

[0147]

[0148] Among them, the weight coefficient is 0≤ρ≤1, 0≤ζ≤1, and 0.6≤ρ+ζ≤1.2, and Po is the middle value of the priority;

[0149]

[0150] Where i = 2, ... 3, p, p is the number of the sub-area, Po avg is the mean of the median priority values;

[0151] Step 503: Taking the maintenance of the ring-shaped ground wall as the target word, after deep search and after building entity relationships, a wall maintenance knowledge graph is built; using the trained matching model, according to the correspondence between the wall maintenance strategy and the damage characteristics in each sub-area, the wall maintenance knowledge graph gives the corresponding wall maintenance strategy;

[0152] Among them, the wall maintenance strategy includes the selection of reinforcement timing, methods and materials; according to the maintenance priority, the wall maintenance strategy is implemented and the corresponding sub-areas are maintained in turn;

[0153] When using, combine the contents in steps 501 to 503:

[0154] After confirming that the wall needs maintenance, the damage features and the pre-built wall maintenance knowledge graph are obtained based on image recognition. The wall maintenance knowledge graph provides the corresponding wall maintenance strategy as a reference maintenance plan, saving time in formulating maintenance strategies and performing targeted maintenance on the walls of each sub-area, thereby extending the service life of the wall under various conditions and improving the efficiency of subsequent maintenance.

[0155] Furthermore, by calculating the corresponding maintenance priority Yo according to the degree of damage in each sub-area, the sub-areas of the wall can be maintained in an orderly manner when necessary, with priority given to the locations with the greatest impact, gradually reducing existing safety hazards and improving current maintenance efficiency.

[0156] It should be noted that the construction method of the circular ground wall maintenance knowledge graph is as follows:

[0157] Determine the goal and scope of the knowledge graph: First, it is necessary to clarify the goal of building the knowledge graph for the ring-connected wall maintenance, such as to provide decision support, optimize the maintenance process, or improve maintenance efficiency. At the same time, determine the coverage of the knowledge graph, including the fields, concepts, entities involved, and the relationships between them.

[0158] Entity recognition and relationship extraction: Identify entities related to the maintenance of the ring-type ground wall from relevant text, data or expert knowledge, such as equipment, materials, processes, fault types, etc. Then, extract the relationships between these entities, such as the connection relationship between equipment, the correspondence between materials and processes, and the causal relationship between fault types and causes.

[0159] Ontology construction: Ontology is the core of the knowledge graph, which describes the concepts, attributes and relationships in the field. In the field of ring-type ground wall maintenance, it is necessary to build a corresponding ontology, including defining concepts, attributes and relationships, as well as the hierarchical structure and logical relationship between them.

[0160] Knowledge fusion and reasoning: Integrate and unify knowledge from different sources, formats or structures to eliminate redundant and contradictory information. At the same time, use reasoning technology to derive new knowledge or conclusions from existing knowledge to enrich the content of the knowledge graph.

[0161] Knowledge graph storage and query: Select appropriate knowledge graph storage technology, such as graph database, relational database, etc., to store the constructed knowledge graph. At the same time, provide a query interface to facilitate users to retrieve and query knowledge.

[0162] In the construction process, a variety of methods and technologies can be used, such as rule-based methods, statistical methods, deep learning-based methods, etc. These methods have their own advantages and disadvantages, and appropriate methods can be selected and used in combination according to actual conditions.

[0163] The analytic hierarchy process, or AHP for short, is a decision-making method that decomposes elements related to decision-making into levels such as goals, criteria, and plans, and then conducts qualitative and quantitative analysis. This method is particularly suitable for decision-making problems with target systems that have hierarchical and staggered evaluation indicators and target values ​​that are difficult to describe quantitatively.

[0164] The core of the AHP is to decompose complex decision-making problems into different hierarchical structures, including the target layer, criterion layer, sub-criterion layer and solution layer. By solving the eigenvector of the judgment matrix, the priority weight of each element of each layer to an element of the previous layer can be determined, and then the final weight of each alternative solution to the total goal can be recursively merged by the weighted sum method. The one with the largest final weight is the optimal solution.

[0165] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0166] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0167] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0168] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only some logical function divisions. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0169] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0170] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0171] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0172] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0173] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for testing the annular stiffness reduction coefficient of a circular ground-connected wall based on multi-source images, characterized in that: include, The environmental condition coefficient is constructed from the environmental condition data in the area adjacent to the wall. , if the environmental condition coefficient is obtained When the condition threshold is exceeded, the dimension specifications and material data of the annular ground-connected wall are collected to construct a multi-source data set; among them, the environmental condition coefficient The method of obtaining is to calculate the rainfall in each detection cycle. and temperature difference Perform linear normalization and map the corresponding data values ​​to the interval Then according to the following method: ； in, , is the number of detection cycles, For the The rainfall in the detection period is is the mean rainfall amount, is its qualified standard value; For the The temperature difference within a detection cycle, is the mean temperature difference, is its qualified standard value; weight coefficient: , ,and ; Preprocess the image data in the multi-source data set and perform feature recognition to extract the damage features of the annular ground connection wall. Summarize the extracted damage features and construct a damage feature set. Then, analyze the changes in the damage features to obtain the deformation degree. , if the deformation Exceeding expectations, issuing model building instructions to the outside; among them, obtaining the proximity center of each damaged feature based on the position of the damaged feature on the 3D wall model , according to the influence range of each damage feature, determine the spread area of ​​each damage feature ; Preprocess the image data in the multi-source data set, summarize the preprocessed image data, and construct a preprocessed wall image set; use the edge detection algorithm to perform image recognition detection on the preprocessed image data, extract the damage features of the annular ground-connected wall, and determine the position information of each damage feature on the wall, and summarize the extracted damage features to construct a damage feature set; The spread area of ​​the damaged feature and closeness centrality Perform linear normalization and map the corresponding data values ​​to the interval Then the deformation degree is constructed as follows : ； in, is the weight coefficient, , ,and , For the The spread area of ​​the damaged feature, is the acceptable value of the spread area, For the The closeness center of the damaged features, is the acceptable value of the proximity centrality, is the number of damaged features, ; After receiving the model building instruction, a mechanical model is established based on the material, structural characteristics and soil-structure interaction of the annular ground-connected wall in combination with mechanical theory, and the annular stiffness calculation model of the annular ground-connected wall is derived from the mechanical model; the wall is divided into several equivalent sub-areas, and in each sub-area, the deformation data extracted by image analysis is used as input, and the annular stiffness reduction coefficient is output by the annular stiffness calculation model, which is used as the image coefficient ; The actual observation data of the wall is used as input, and the annular stiffness calculation model outputs the annular stiffness reduction factor, which is used as the observation coefficient ; Combined with the environment in which the wall is located, the annular stiffness calculation model of the annular ground-connected wall is established. Combined with the annular stiffness calculation model, the corresponding annular stiffness reduction coefficient is constructed through image analysis and actual observation, and the difference degree is obtained based on the difference between the two. , if the difference If the difference threshold is exceeded, a test instruction is issued to the outside; Combined observation coefficient Calculate the difference , as follows: ； in, For the The image coefficients in the sub-regions are For the The observed coefficients in the sub-regions are is the weight coefficient, , , , is the number of the sub-region; The wall is tested under different conditions, and the reliability of the hoop stiffness calculation model is calculated based on the hoop stiffness reduction factor obtained from the test. , if the reliability Below the reliability threshold, the hoop stiffness calculation model is optimized, and the hoop stiffness reduction coefficient is periodically constructed by the optimized hoop stiffness calculation model; after receiving the test instruction, different test conditions are set, the wall is tested on site under different conditions, and the hoop stiffness reduction coefficient is calculated from the test data, and its average is used as the test coefficient ; By image coefficient And test coefficient Reliability of building hoop stiffness calculation model , where the image coefficients in each sub-region are And test coefficient One-to-one correspondence, in the following manner: ； in, and Respectively The image coefficients and test coefficients of the sub-regions, and is the corresponding mean, , is the number of the sub-region, For the The reliability interval value of the sub-region, is the corresponding mean; Use the wall load digital twin model to predict the service life of the annular ground-connected wall. If the predicted service life is lower than expected, analyze each sub-area of ​​the wall to obtain the corresponding maintenance priority After the corresponding wall maintenance strategy is given by the wall maintenance knowledge graph, according to the maintenance priority , execute the wall maintenance strategy in sequence, and maintain the corresponding sub-areas. After receiving the maintenance instruction, the annular ground-connected wall is divided into several sub-areas, and the damage characteristics of each sub-area are obtained from the prediction data; according to the distribution state of the damage characteristics in each sub-area, the maintenance priority in each sub-area is calculated. , as follows: the spread area of ​​the damage features in each sub-region and closeness centrality Perform linear normalization and map the corresponding data values ​​to the interval Then according to the following method: ； Among them, the weight coefficient is , ,and , is the middle priority value; , is the number of the sub-region, is the mean of the median priorities.

2. The method for testing the circular ground-connected wall annular stiffness reduction coefficient based on multi-source images according to claim 1 is characterized in that: Collect the geographical location, surrounding environment, soil conditions and rainfall conditions of the annular ground-connected wall to construct the environmental condition set of the annular ground-connected wall; construct the environmental condition coefficient from the data in the environmental condition set , if the environmental condition coefficient is obtained When the condition threshold is exceeded, an image acquisition instruction is issued to the outside; After receiving the image acquisition instruction, the size specifications and material data of the annular ground-connected wall are collected to build a three-dimensional model of the wall; multi-source image data during the design, construction and operation of the relevant annular ground-connected wall are collected, including satellite images, drone images, and ground scanning images.

3. The method for testing the circular ground-connected wall annular stiffness reduction coefficient based on multi-source images according to claim 2 is characterized in that: If reliability If the value is lower than the reliability threshold, the annular stiffness calculation model is optimized, and the annular stiffness reduction coefficient is output by the optimized annular stiffness calculation model. If the obtained annular stiffness reduction coefficient is lower than expected, an alarm command is issued to the outside. If it is not lower than expected, a monitoring cycle is set, and images of the wall are collected regularly. After the image data is processed and input is selected, the annular stiffness reduction coefficient is constructed again by the optimized annular stiffness calculation model.

4. The method for testing the circular ground-connected wall annular stiffness reduction coefficient based on multi-source images according to claim 3 is characterized in that: Build a wall data set, train and obtain a digital twin model of wall load, use the extreme conditions of wall use as preset test conditions, and use the digital twin model of wall load to predict the service life of the annular ground-connected wall under the preset test conditions. If the predicted service life is lower than expected, issue a wall maintenance instruction. Taking the maintenance of the ring-shaped ground wall as the target word, after deep search and building entity relationships, a wall maintenance knowledge graph is built; according to the correspondence between the wall maintenance strategy and the damage characteristics in each sub-area, the wall maintenance knowledge graph gives the corresponding wall maintenance strategy; among them, the wall maintenance strategy includes the selection of reinforcement timing, methods and materials; according to the maintenance priority, the wall maintenance strategy is executed, and the corresponding sub-areas are maintained in turn.

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