Analysis Method, Device and Storage Medium for Dead Load Stress State of Existing Bridges

Abstract

The present invention provides a method, device and storage medium for analyzing the dead load stress state of an existing bridge. The analysis method includes: obtaining a first series of data on the current status of the bridge structure; obtaining distribution characteristic data of structural diseases at key parts of the bridge, performing parameter sensitivity analysis on the distribution characteristic data of structural diseases, and determining a corresponding second series of data that may cause structural diseases; performing bridge disease inversion identification, including: comparing the simulation data output based on the first simulation model with the distribution characteristic data to determine the second series of data that meet the conditions; calling a second simulation model pre-generated based on the first series of data and the second series of data that meet the conditions to obtain a third series of data for characterizing the internal force state, stress state and geometric state of each structural part of the bridge under its own weight, and determining the dead load stress state before the bridge is demolished. By using the analysis method of the present invention, the stress state of the bridge can be analyzed more accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge construction inspection and evaluation, and particularly relates to a method, device and storage medium for analyzing the dead load stress state of existing bridges. Background Art

[0002] Since the initial construction of bridges in our country has been decades until now, during which due to the sharp increase in traffic volume, continuous changes in external environmental conditions, and factors such as concrete shrinkage creep and prestress loss, a large number of existing bridge structures are severely damaged. Limited by the existing detection means, the data obtained is incomplete, and it is impossible to accurately obtain the theoretical calculation parameters such as material properties, loads, and mechanical indexes. It is difficult to accurately master its true stress state, resulting in the inability to effectively analyze the bridge stress state.

[0003] Therefore, a method, device and storage medium for analyzing the dead load stress state of existing bridges are needed to at least partially solve the above technical problems. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method, device and storage medium for analyzing the dead load stress state of existing bridges to at least solve one of the problems in the prior art.

[0005] One aspect of the present invention provides a method for analyzing the dead load stress state of existing bridges, and the analysis method includes the following steps:

[0006] Obtain a first series of data on the current status of the bridge structure;

[0007] Obtain the distribution characteristic data of structural diseases in key parts of the bridge, conduct a parameter sensitivity analysis on the distribution characteristic data of structural diseases, and determine a corresponding second series of data that may cause structural diseases;

[0008] Conduct bridge disease inversion identification, including: comparing the simulation data output based on the first simulation model with the distribution characteristic data to determine the second series of data that meet the conditions;

[0009] Call a second simulation model pre-generated based on the first series of data and the second series of data that meet the conditions, obtain a third series of data for characterizing the internal force state, stress state and geometric state of each structural part of the bridge under its own weight, and determine the dead load stress state before the bridge is demolished.

[0010] In some embodiments of the present invention, the analysis method further includes establishing a second simulation model, including:

[0011] Adjust the parameters of the first simulation model based on the first series of data and the second series of data that meet the conditions to determine the second simulation model.

[0012] In some embodiments of the present invention, the analysis method further includes establishing a first simulation model, including:

[0013] Establishing an initial finite element model based on the original bridge design drawing data;

[0014] Adjusting the parameters of the initial finite element model based on the first series of data to obtain the first simulation model.

[0015] In some embodiments of the present invention, the obtaining of the first series of data on the current situation of the bridge structure includes:

[0016] Conducting a current situation test on the bridge structure and obtaining the first series of data based on the test results;

[0017] Wherein, the first series of data includes structural action parameters, structural material parameters, and structural damage parameters. And / or

[0018] The obtaining of the distribution characteristic data of the structural diseases at the key parts of the bridge includes:

[0019] Detecting the bridge diseases and obtaining the distribution characteristic data of the structural diseases at the key parts with prominent structural forces;

[0020] Wherein, the key parts include the mid-span, 1 / 4 span, and support positions of the main girder, as well as the top and bottom of the bridge pier.

[0021] In some embodiments of the present invention, the performing of parameter sensitivity analysis on the distribution characteristic data to determine the corresponding second series of data that may cause structural diseases includes:

[0022] Performing parameter sensitivity analysis on the distribution characteristic data to obtain multiple key sensitive parameters, and the sequence of the multiple key sensitive parameters sorted from high to low according to the sensitivity degree constitutes the second series of data.

[0023] In some embodiments of the present invention, the determining of the second series of data that meets the conditions by comparing the simulation data output based on the first simulation model with the distribution characteristic data includes:

[0024] Adjusting the values of the second series of data to change the simulation data output by the first simulation model;

[0025] Comparing the simulation data with the distribution characteristic data. If the two match, the second series of data at this time is the second series of data that meets the conditions; otherwise, continue to adjust the values of the second series of data until the output simulation data matches the distribution characteristic data.

[0026] In some embodiments of the present invention, when adjusting the values of the second series of data, the data with a higher sensitivity degree in the second series of data is preferentially adjusted.

[0027] In some embodiments of the present invention, the third series of data includes stress, internal force, and displacement data of bridge structural parts.

[0028] The second aspect of the present invention further provides an electronic device, which includes:

[0029] A first data acquisition module, configured to acquire a first series of data on the current status of the bridge structure;

[0030] A second data acquisition module, configured to acquire distribution characteristic data of structural diseases of key parts of the bridge, perform parameter sensitivity analysis on the distribution characteristic data of structural diseases, and determine a corresponding second series of data that may cause structural diseases;

[0031] An inversion identification module, configured to perform bridge disease inversion identification, including: comparing the simulation data output based on the first simulation model with the distribution characteristic data to determine the second series of data that meets the conditions;

[0032] A determination module, configured to call a second simulation model pre-generated based on the first series of data and the second series of data that meets the conditions, obtain a third series of data for characterizing the internal force state, stress state, and geometric state of each structural part of the bridge under its own weight, and determine the dead load stress state before the bridge is demolished.

[0033] The third aspect of the present invention further provides an analysis device for the dead load stress state of an existing bridge, and the analysis device includes:

[0034] A memory, configured to store computer-executable instructions;

[0035] A processor, configured to implement the analysis method described in the above embodiments when executing the computer-executable instructions stored in the memory.

[0036] The fourth aspect of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the analysis method described in the above embodiments is implemented.

[0037] According to the analysis method of the embodiments of the present invention, in order to obtain relevant data required for analyzing the stress state of a bridge, on the one hand, a first series of data regarding the current situation of the bridge structure is obtained through the current situation test and detection of the bridge structure. On the other hand, for the data that cannot be obtained through the current situation test and detection of the bridge structure and the data with relatively large errors obtained through the current situation test and detection of the bridge structure (i.e., the second series of data that meets the conditions), they are obtained through the macroscopic disease inversion and identification method of the key parts of the bridge. The data acquisition is more efficient and accurate. Then, based on the first series of data and the second series of data that meets the conditions, a second simulation model can output a more accurate third series of data for analyzing the stress state of the bridge, thereby obtaining the dead load stress state of the existing bridge that is more in line with the actual situation. Through this analysis method, the true state of the existing bridge can be evaluated more accurately, effectively ensuring construction safety.

[0038] The additional advantages, objects, and features of the present invention will be partially described below and will become partially apparent to those of ordinary skill in the art after studying the following text, or can be learned from the practice of the present invention. The objects and other advantages of the present invention can be achieved and obtained by the structure specifically pointed out in the specification and the drawings.

[0039] Those skilled in the art will understand that the objects and advantages that can be achieved by the present invention are not limited to the above specifically described, and the above and other objects that the present invention can achieve will be more clearly understood according to the following detailed description. Brief Description of the Drawings

[0040] The drawings described herein are used to provide a further understanding of the present invention, form a part of this application, and do not limit the present invention. The components in the drawings are not drawn to scale, but only to illustrate the principles of the present invention. In order to facilitate the illustration and description of some parts of the present invention, the corresponding parts in the drawings may be enlarged, that is, they may become larger relative to other components in the exemplary device actually manufactured according to the present invention. In the drawings:

[0041] Figure 1 is a flowchart of the analysis method according to an embodiment of the present invention;

[0042] Figure 2 is a flowchart of the analysis method according to another embodiment of the present invention;

[0043] Figure 3 is a partial method flowchart of the analysis method according to an embodiment of the present invention;

[0044] Figure 4 is the detection list of the first series of data in the analysis method according to an embodiment of the present invention;

[0045] Figure 5Identification diagram for stiffness inversion based on load test in the analysis method according to an embodiment of the present invention;

[0046] Figure 6 Identification diagram for prestress loss inversion based on the deflection result of the bridge main girder in the analysis method according to an embodiment of the present invention;

[0047] Figure 7 Schematic block diagram of an electronic device according to an embodiment of the present invention;

[0048] Figure 8 Schematic block diagram of an analysis device according to an embodiment of the present invention. Detailed implementation manners

[0049] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with the implementation manners and the accompanying drawings. Herein, the schematic implementation manners of the present invention and their descriptions are used to explain the present invention, but do not limit the present invention.

[0050] Herein, it should also be noted that in order to avoid obscuring the present invention due to unnecessary details, only the structures and / or processing steps closely related to the solution according to the present invention are shown in the drawings, while other details less related to the present invention are omitted.

[0051] It should be emphasized that the term "including / containing" when used herein refers to the presence of features, elements, steps or components, but does not exclude the presence or addition of one or more other features, elements, steps or components.

[0052] Herein, it should also be noted that if not specifically stated, the term "connection" in this article can not only refer to direct connection, but also represent indirect connection with an intermediate.

[0053] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar components, or the same or similar steps.

[0054] First, reference will be made to Figure 1 Describe the existing bridge dead load stress state analysis method 100 according to an embodiment of the present application.

[0055] As Figure 1 shown, the analysis method 100 may include the following steps:

[0056] In step S110, obtain a first series of data on the current status of the bridge structure.

[0057] In step S120, distribution characteristic data of structural diseases in key parts of the bridge are obtained, and parameter sensitivity analysis is performed on the distribution characteristic data of the structural diseases to determine a corresponding second series of data that may cause structural diseases.

[0058] In step S130, bridge disease inversion identification is performed, including: determining a second series of data that meet the conditions by comparing the simulation data output based on the first simulation model with the distribution characteristic data.

[0059] In step S140, a second simulation model pre-generated based on the first series of data and the second series of data that meet the conditions is called to obtain a third series of data for characterizing the internal force state, stress state, and geometric state of each structural part of the bridge under its own weight, and the dead load stress state before the bridge demolition is determined.

[0060] In the embodiment of the present application, in order to obtain relevant theoretical calculation data required for bridge stress state analysis, on the one hand, the first series of data on the current situation of the bridge structure can be obtained by carrying out on-site tests on the bridge structure. On the other hand, for the data that cannot be obtained through on-site tests on the bridge structure and the data with large errors obtained through on-site tests on the bridge structure, that is, the second series of data that meet the conditions, they can be obtained through the macroscopic disease inversion identification method for key parts of the bridge. Then, based on the first series of data and the second series of data that meet the conditions, a second simulation model is generated, and finally, a more accurate third series of data for analyzing the bridge stress state is output, so as to determine the dead load stress state before the bridge demolition.

[0061] Through the description of the above process, it can be seen that according to the analysis method 100 of the embodiment of the present application, compared with the traditional method, this analysis method obtains relevant data required for bridge stress state analysis from two aspects, adopts the combination of on-site detection of the bridge structure and macroscopic disease inversion identification, makes the obtained data more comprehensive and accurate, obtains the dead load stress state of the existing bridge that is more consistent with the actual situation, can more accurately evaluate the true state of the existing bridge, and effectively guarantees the construction safety. Moreover, when obtaining the second series of data that meet the conditions, only the macroscopic disease inversion identification of the key parts of the bridge is carried out. Since the stress characteristics of the key parts of the bridge are clear, the diseases are prominent and regular, the obtained data is more accurate. At the same time, the macroscopic disease inversion identification object is limited to the key parts of the bridge, greatly reducing the amount of inversion identification data and obtaining data more efficiently.

[0062] The content of each of the above steps of the analysis method 100 according to the embodiment of the present application will be specifically described below.

[0063] In the embodiment of the present application, the first series of data on the current situation of the bridge structure are obtained in step S110. Specifically, refer to Figure 2 and Figure 4, in order to accurately and quickly analyze the true stress state of the dead load of existing bridges, the first series of data selects several parameters with the largest influence weights that can be determined through parameter sensitivity analysis. For example, key consideration is given to structural action parameters (structural self-weight, prestress, cable force), structural material parameters (elastic modulus, stiffness, strength), and structural damage parameters (such as the effects of cracking, shrinkage creep, settlement, prestress loss, etc.). Among them, structural action parameters include direct action parameters (loads) and indirect action parameters (prestress, cable force). To obtain the first series of data, on-site tests can be carried out on the bridge structure, and the first series of data can be obtained based on the test results. Specifically, corresponding structural inspection items can be inspected by using a variety of inspection methods, and the first series of data can be obtained based on the inspection results. The first series of data can be data directly obtained from the inspection results or data indirectly reflected by the inspection results. For example, structural dimension parameters and weight parameters can be obtained through the inspection results of component volume, and structural stiffness parameters can be obtained through load tests. Figure 4 Lists the main bridge calculation parameters and common inspection methods.

[0064] The test inspection method should select a method with high reliability, high precision, and less manual intervention to minimize test errors. The on-site test inspection of the bridge structure should comply with relevant specifications and instrument operation requirements, be carried out strictly in accordance with the regulations, and convert the inspection results into quantifiable parameter values.

[0065] In the embodiment of the present application, in step S120, distribution characteristic data of structural diseases at key parts of the bridge are obtained, and parameter sensitivity analysis is performed on the distribution characteristic data of structural diseases to determine the corresponding second series of data that may cause structural diseases.

[0066] For existing bridges, their structural diseases refer to diseases such as cracking and deformation caused by structural stress, and the corresponding distribution characteristic data mainly refer to specific numerical values, distribution areas, etc. of diseases such as cracks and deformations. To improve the efficiency of data analysis and ensure accuracy, due to the clear stress characteristics, prominent and regular diseases at the key parts of the bridge, compared with the existing random sampling or full sampling methods, the present application obtains the distribution characteristic data of structural diseases at the key parts of the bridge. Among them, the key parts of the bridge include the mid-span, 1 / 4 span, and support positions of the main girder, as well as the top and bottom of the bridge pier. To obtain the distribution characteristic data of structural diseases at the key parts of the bridge, it can be obtained by detecting the bridge diseases. Among them, the means of detecting bridge diseases adopt existing technologies and will not be described here.

[0067] After obtaining the distribution characteristic data of the structural diseases in the key parts of the bridge, in order to find out the generation mechanism of the structural diseases in the key parts of the bridge, parametric sensitivity analysis can be performed on the distribution characteristic data of the structural diseases to obtain the corresponding second series of data that may trigger the structural diseases. Among them, the second series of data generally includes multiple data, but it does not exclude that the second series of data corresponding to some distribution characteristic data is only a single data. The second series of data corresponding to the distribution characteristic data of different structural diseases is not the same or not completely the same.

[0068] The parametric sensitivity analysis method adopted in this application is a prior art and will not be elaborated here. Generally speaking, parametric sensitivity analysis is to study how the uncertainty of the output of a mathematical model or system (numerical or others) is allocated to different sources of the uncertainty of the input. The sensitivity of each input is usually represented by a numerical value, called the sensitivity index. There are several forms of the sensitivity index: 1. First-order index: Measure the contribution to the output variance only through a single input; 2. Second-order index: Measure the contribution of the interaction of two inputs to the output variance; 3. Total-order index: Measure the contribution of the model input to the output variance, including its first-order effect (the input changes alone) and all higher-order interactions.

[0069] For example, parametric sensitivity analysis can calculate the change of the corresponding distribution characteristic data by adjusting the percentage parameters in the bridge finite element model. By analyzing different parameters, the sensitivity degree of each parameter to the distribution characteristic data can be determined, and the key sensitive parameters that are more sensitive to the structural diseases can be determined. For example, the mid-span deflection may be caused by prestress loss, stiffness, and self-weight, etc.

[0070] Furthermore, in order to perform bridge disease inversion identification in the subsequent step S130 and more quickly determine the second series of data that meet the conditions, the multiple key sensitive parameters obtained after parametric sensitivity analysis can be sorted in descending order of sensitivity degree. The sequence formed after such sorting constitutes the second series of data. Continuing with the example of the mid-span deflection mentioned above, the key sensitive parameters of the mid-span deflection include prestress loss, stiffness, and self-weight, among which the sensitivity degree of prestress loss is the highest. Then, when performing inversion identification of the mid-span deflection disease in the subsequent step S130, the parameter of prestress loss is targeted first.

[0071] In the embodiment of this application, the bridge disease inversion identification in step S130 includes: determining the second series of data that meet the conditions by comparing the simulation data output based on the first simulation model with the distribution characteristic data.

[0072] Through bridge disease inversion identification, some data types and their specific values that cannot be obtained through structural condition detection and have large errors in the current condition detection results can be obtained, that is, the second series of data that meet the conditions. The second series of data that meet the conditions, or the objects that need to be inversely identified, include structural stiffness, prestress, and the cable forces of stay cables in cable-stayed bridges. Among them, structural stiffness and prestress generally refer to the stiffness and prestress of the main girder of the bridge.

[0073] Reference Figure 3 , in order to obtain the second series of data that meet the conditions, step S130 may include the following steps:

[0074] In step S131, the values of the second series of data are adjusted to change the simulation data output by the first simulation model.

[0075] In step S132, the simulation data is compared with the distribution characteristic data. If the two match, the second series of data at this time is the second series of data that meet the conditions; otherwise, the values of the second series of data are continuously adjusted until the output simulation data matches the distribution characteristic data.

[0076] Moreover, when adjusting the values of the second series of data, the data with higher sensitivity in the second series of data is preferentially adjusted, that is, starting from the data with the highest sensitivity in the second series of data, and then adjusting the data therein in the order of sensitivity from high to low, so as to more quickly determine the second series of data that meet the conditions.

[0077] Still taking the mid-span deflection deformation mentioned above as an example, the key sensitive parameters of the mid-span deflection deformation include prestress loss, stiffness, and self-weight. Among them, the error of the prestress loss data in the current condition detection results is relatively large. Therefore, more accurate data can be obtained through disease inversion identification. Based on the first simulation model, the prestress loss data is input and continuously adjusted. The first simulation model will correspondingly output the mid-span deflection deformation data. The mid-span deflection deformation data output by the model is numerically compared with the mid-span deflection deformation results obtained in the structural condition test in step S110. If the two match, the prestress loss data at this time is the prestress loss data that meet the conditions; otherwise, the prestress loss data is continuously adjusted until the mid-span deflection deformation data output by the model matches the mid-span deflection deformation results obtained in the structural condition test in step S110.

[0078] For example, reference Figure 5 , the structural stiffness is inversely identified through the test results of the load test.

[0079] Specifically, the structural response data of two stages are obtained through the load test at the completion stage and the current load test respectively. The structural response data mainly includes the deflection response data of the static load test and the frequency response data of the dynamic load test. Then, the structural verification coefficient at the completion stage and the current stage structural equivalent coefficient corrected by the stiffness damage coefficient are calculated respectively. Adjust the value of the stiffness damage coefficient until the equivalent coefficient is consistent with the verification coefficient result to obtain the stiffness identification result, that is, the actual stiffness of the structure = the design stiffness × the stiffness damage coefficient.

[0080] Among them, the stiffness damage coefficient is the stiffness reduction ratio due to structural damage factors, that is, the stiffness reduction coefficient. The design stiffness is the stiffness of the structure under the design parameter values.

[0081] Furthermore, according to the relevant regulations of the "Code for Inspection and Evaluation of Bearing Capacity of Highway Bridges", the deflection response data is obtained based on the static load test data to invert the structural stiffness parameters, and the deflection verification coefficient is defined as follows:

[0082]

[0083] In the formula: S1 is the measured deflection value of the measuring point under the test load at the completion stage; S d1 is the theoretically calculated deflection value of the measuring point under the test load at the completion stage (the model established by using the design parameters, that is, the design model).

[0084] To determine the structural stiffness damage condition, the static load test conditions are loaded using the first simulation model, and the deflection equivalent coefficient ξ2 is defined as:

[0085]

[0086] In the formula: S2 is the measured deflection value of the measuring point under the current stage test load; S d2 is the theoretically calculated deflection value of the measuring point under the current stage test load (the first calculation is carried out using the first simulation model, and subsequent new calculation results are continuously obtained by adjusting the stiffness damage coefficient).

[0087] Adjust the value of the stiffness damage coefficient in the first simulation model to obtain the corresponding theoretically calculated deflection value S d2 , by calculating the equivalent coefficient ξ2, until the equivalent coefficient ξ2 is consistent with the verification coefficient ξ1, determine that the actual stiffness of the structure = the design stiffness × the stiffness damage coefficient.

[0088] Furthermore, through the frequency response results of the dynamic load test, the structural stiffness is inversely identified, and the frequency verification coefficient β1 and the equivalent coefficient β2 are defined respectively as:

[0089]

[0090] Where: ω1 and ω2 are respectively the calculated values of the structural frequencies under the test loads at the completion stage and the current stage (the design model is used at the completion stage, and the first simulation model is used at the current stage); ω d1 and ω d2 are respectively the measured frequencies of the structure under the test loads at the completion stage and the current stage.

[0091] Similar to the above calculation process, in the first simulation model, adjust the value of the stiffness damage coefficient to obtain the corresponding theoretically calculated frequency value ω d2 . By calculating the equivalent coefficient β2 until the result of the equivalent coefficient β2 is consistent with the verification coefficient β1, determine that the actual stiffness of the structure = design stiffness × stiffness damage coefficient.

[0092] In addition, in order to obtain some data types and their specific values that cannot be detected through the current structural condition and have large errors in the current structural condition detection results, the following methods can also be used.

[0093] Refer to Figure 6 , and inversely identify the deflection-sensitive parameters through the monitoring results of the long-term deflection deformation diseases of the structure.

[0094] Specifically, conduct a parameter sensitivity analysis to determine the key sensitive parameters that are more sensitive to the deflection deformation. The key sensitive parameters of the deflection deformation generally include prestress, stiffness, and self-weight. According to the results of the parameter sensitivity analysis, sort multiple key sensitive parameters in descending order of sensitivity, take the key sensitive parameters as the identification targets, and establish a multi-parameter deformation influence matrix. Use the influence matrix to solve the values of each parameter. The specific formula is as follows:

[0095] [K]{δ}={L}

[0096] Where: {δ} is the sensitive parameter vector; [K] is the deflection influence matrix, representing the deflection change of the measuring points caused by the change of the sensitive parameters; {L} is the structural measuring point deflection vector, which is the column vector of the deflections of each measuring point of the structure, and its form is related to the number of measuring points. Taking Figure 6 as an example, {L}={L1, L2, L3, L4, L5} T .

[0097] Among them, in the embodiment of the present application, before performing step S131, a first simulation model is also established. The steps of establishing the first simulation model include: establishing an initial finite element model (i.e., the design model) based on the original bridge design drawing data. Adjusting the parameters of the initial finite element model based on the first series of data to obtain the first simulation model.

[0098] In an embodiment of the present application, in step S140, a second simulation model pre-generated based on the first series of data and the second series of data that meet the conditions is called to obtain a third series of data for characterizing the internal force state, stress state, and geometric state of each structural part of the bridge under its own weight, and the dead load stress state of the bridge before demolition is determined.

[0099] Based on the first series of data and the second series of data that meet the conditions obtained in the foregoing steps, the values of relevant model calculation parameters in the final model (i.e., the second simulation model) can be determined. Therefore, in an embodiment of the present application, before performing step S140, a second simulation model is further established. The specific process of establishing the second simulation model is as follows: The parameters of the first simulation model are adjusted based on the first series of data and the second series of data that meet the conditions to determine the second simulation model.

[0100] Then, the internal force state, stress state, and geometric state of each structural part of the bridge under its own weight are obtained by using the built-in function of the second simulation model, that is, the dead load stress state of the bridge before demolition is determined. Among them, the third series of data includes stress, internal force, and displacement data of the bridge structural parts.

[0101] In an embodiment of the present application, various models mentioned, such as the initial finite element model and the second simulation model, etc., can be existing models or models obtained by adjusting the parameters of existing models. For example, finite element simulation analysis can be performed using MIDAS software or ABAQUS software.

[0102] Based on the above description, according to the analysis method 100 of the embodiment of the present application, in order to obtain relevant data required for analyzing the stress state of the bridge, on the one hand, the first series of data about the current situation of the bridge structure is obtained through the current situation test and detection of the bridge structure, and on the other hand, for the data that cannot be obtained through the current situation test and detection of the bridge structure, and the data with large errors obtained through the current situation test and detection of the bridge structure, they are obtained through the macroscopic disease inversion and identification method of the key parts of the bridge. The data is obtained more efficiently and accurately. Then, the second simulation model generated based on the first series of data and the second series of data that meet the conditions can output more accurate third series of data for analyzing the stress state of the bridge, so as to obtain the dead load stress state of the existing bridge that is more consistent with the actual situation. Through this analysis method, the true state of the existing bridge can be more accurately evaluated, and the construction safety can be effectively guaranteed.

[0103] Reference Figure 7 Next, an electronic device 200 provided on the other hand of the present application is described. The electronic device 200 is used to implement the analysis method of the embodiment of the present invention.

[0104] The electronic device 200 may include a first data acquisition module 210, a second data acquisition module 220, an inversion recognition module 230, and a determination module 240.

[0105] Specifically, the first data acquisition module 210 is configured to acquire a first series of data on the current status of the bridge structure.

[0106] The second data acquisition module 220 is configured to acquire distribution characteristic data of structural diseases at key parts of the bridge, perform parameter sensitivity analysis on the distribution characteristic data of structural diseases, and determine a corresponding second series of data that may cause structural diseases.

[0107] The inversion recognition module 230 is configured to perform bridge disease inversion recognition, including: comparing the simulation data output based on the first simulation model with the distribution characteristic data to determine the second series of data that meet the conditions.

[0108] The determination module 240 is configured to call a second simulation model pre-generated based on the first series of data and the second series of data that meet the conditions, obtain a third series of data for characterizing the internal force state, stress state, and geometric state of each structural part of the bridge under its own weight, and determine the dead load stress state before the bridge is demolished.

[0109] The above exemplarily shows the analysis method 100 according to the embodiments of the present application. Next, in combination with Figure 8 Describe the existing bridge dead load stress state analysis device 300 provided by another aspect of the present application.

[0110] Refer to Figure 8 To describe an example analysis device 300 for implementing the analysis method of the embodiments of the present invention.

[0111] The analysis device 300 may include one or more processors 321, one or more memories 322, and may further include an input device 323 and an output device 324. These components are interconnected through a bus system 325 and / or other forms of connection mechanisms (not shown). It should be noted that Figure 8 The components and structures of the shown analysis device 300 are only exemplary and not restrictive. According to needs, the device may also have other components and structures.

[0112] The processor 321 may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the analysis device 300 to perform desired functions.

[0113] The memory 322 may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage media, and the processor 321 may run the program instructions to implement the client functions (implemented by the processor) in the embodiments of the present invention described herein and / or other desired functions. Various application programs and various data may also be stored in the computer-readable storage media, such as various data used and / or generated by the application programs, etc.

[0114] The input device 323 may be a device used by a user to input instructions, and may include one or more of a keyboard, a mouse, a microphone, a touch screen, etc. In addition, the input device 323 may also be any interface for receiving information.

[0115] The output device 324 may output various information (such as images or sounds) to the outside (such as a user), and may include one or more of a display, a speaker, etc. In addition, the output device 324 may also be any other device with an output function.

[0116] Exemplarily, the exemplary analysis device 300 for implementing the analysis method 100 according to the embodiments of the present invention may be applied to electronic devices such as terminal devices (such as mobile phones), tablet computers, laptop computers, ultra-mobile personal computers (UMPCs), handheld computers, netbooks, personal digital assistants (PDAs), wearable devices (such as smart watches, smart glasses or smart helmets, etc.), augmented reality (AR) / virtual reality (VR) devices, smart home devices, in-vehicle computers, etc., and the embodiments of the present application do not make any restrictions on this.

[0117] Reference Figure 8, the analysis device 300 according to an embodiment of the present application includes a processor 321 and a memory 322. The memory 322 stores an executable program run by the processor 321. When the executable program is run by the processor 321, the processor 321 is caused to execute the analysis method 100 according to an embodiment of the present application described above. Those skilled in the art can understand the specific operations of the analysis device according to an embodiment of the present application in combination with the content described above. For the sake of brevity, the specific details are not described here, and only some main operations of the processor 321 are described.

[0118] In an embodiment of the present application, when the executable program is run by the processor 321, the processor 321 is caused to execute the following steps: obtaining a first series of data on the current status of the bridge structure. Obtaining distribution characteristic data of structural diseases in key parts of the bridge, performing parameter sensitivity analysis on the distribution characteristic data of structural diseases, and determining a corresponding second series of data that may cause structural diseases. Performing bridge disease inversion identification, including: comparing the simulation data output based on the first simulation model with the distribution characteristic data to determine the second series of data that meet the conditions. Invoking a second simulation model pre-generated based on the first series of data and the second series of data that meet the conditions to obtain a third series of data for characterizing the internal force state, stress state, and geometric state of each structural part of the bridge under its own weight, and determining the dead load stress state before the bridge is demolished.

[0119] In an embodiment of the present application, when the executable program is run by the processor 321, the processor 321 is further caused to execute the following steps: adjusting the parameters of the first simulation model based on the first series of data and the second series of data that meet the conditions to determine the second simulation model.

[0120] In an embodiment of the present application, when the executable program is run by the processor 321, the processor 321 is further caused to execute the following steps: establishing an initial finite element model based on the original bridge design drawing materials. Adjusting the parameters of the initial finite element model based on the first series of data to obtain the first simulation model.

[0121] In an embodiment of the present application, when the executable program is run by the processor 321, the processor 321 is further caused to execute the following steps: performing parameter sensitivity analysis on the distribution characteristic data to obtain a plurality of key sensitive parameters, and a sequence formed by sorting the plurality of key sensitive parameters in descending order of sensitivity constitutes the second series of data.

[0122] In one embodiment of the present application, when the executable program is run by the processor 321, the processor 321 is further caused to perform the following steps: perform value adjustment on the second series of data to change the simulation data output by the first simulation model. Compare the simulation data with the distribution characteristic data. If the two match, the second series of data at this time is the second series of data that meets the conditions; otherwise, continue to perform value adjustment on the second series of data until the output simulation data matches the distribution characteristic data.

[0123] In addition, according to an embodiment of the present application, the present invention further provides a storage medium on which a computer program is stored, and when the computer program is run by a processor, it is used to execute the corresponding steps of the analysis method 100 of the embodiment of the present application. The storage medium may include, for example, a memory card of a smart phone, a storage component of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.

[0124] Based on the above description, for the analysis method 100 according to an embodiment of the present application, the relevant data required for the analysis of the bridge stress state is obtained from two aspects, and the combination of the current situation detection of the bridge structure and the inversion recognition of macroscopic diseases is adopted, so that the obtained data is more comprehensive and accurate, and the dead load stress state of the existing bridge that is more consistent with the actual situation is obtained, and the true state of the existing bridge can be evaluated more accurately, effectively ensuring the construction safety. And when obtaining the second series of data that meets the conditions, only the inversion recognition of macroscopic diseases of the key parts of the bridge is carried out. Since the stress characteristics of the key parts of the bridge are clear, the diseases are prominent and regular, the obtained data is more accurate. At the same time, the object of the inversion recognition of macroscopic diseases is limited to the key parts of the bridge, which greatly reduces the amount of inversion recognition data and makes the obtained data more efficient.

[0125] Although example embodiments have been described herein with reference to the drawings, it should be understood that the above example embodiments are merely exemplary and are not intended to limit the scope of the present application. Those of ordinary skill in the art can make various changes and modifications therein without departing from the scope and spirit of the present application. All such changes and modifications are intended to be included within the scope of the present application as claimed in the appended claims.

[0126] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in connection with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0127] In several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed.

[0128] In the specification provided herein, a large number of specific details are set forth. However, it can be understood that the embodiments of this application can be practiced without these specific details. In some instances, well-known methods, structures, and technologies have not been shown in detail so as not to obscure the understanding of this specification.

[0129] Similarly, it should be understood that, in order to streamline this application and assist in understanding one or more of the various inventive aspects, in the description of the exemplary embodiments of this application, the various features of this application are sometimes grouped together into a single embodiment, figure, or description thereof. However, the methods of this application should not be construed as reflecting the intention that the claimed application requires more features than are expressly recited in each claim. Rather, as reflected in the corresponding claims, the inventive point lies in being able to solve the corresponding technical problems with fewer features than all the features of a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into the detailed description, where each claim itself serves as a separate embodiment of this application.

[0130] Those skilled in the art can understand that, except for features that are mutually exclusive, any combination can be used to combine all the features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all the processes or units of any method or device so disclosed. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) can be replaced by an alternative feature that provides the same, equivalent, or similar purpose.

[0131] In addition, those skilled in the art can understand that although some of the embodiments described herein include certain features included in other embodiments rather than other features, the combination of features of different embodiments is meant to be within the scope of this application and forms different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0132] Each component embodiment of this application can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art should understand that a microprocessor or a digital signal processor (DSP) can be used in practice to implement some or all of the functions of some of the modules according to the embodiments of this application. This application can also be implemented as a device program (such as a computer program and a computer program product) for executing part or all of the methods described herein. Such a program for implementing this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0133] It should be noted that the above embodiments illustrate rather than limit this application, and those skilled in the art can design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in the claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim listing several devices, several of these devices can be embodied by the same item of hardware. The use of the words first, second, and third, etc. does not denote any order. These words can be interpreted as names.

[0134] As described above, this is only the specific implementation manner of this application or the description of the specific implementation manner, and the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by this application, and all of them should be covered by the protection scope of this application. The protection scope of this application shall be subject to the protection scope of the claims.

Claims

1. A method for analyzing the dead load stress state of an existing bridge, characterized in that, The analysis method includes: Obtaining a first series of data on the current status of the bridge structure; wherein, the first series of data includes structural action parameters, structural material parameters, and structural damage parameters; Obtaining distribution characteristic data of structural diseases at key parts of the bridge, performing parameter sensitivity analysis on the distribution characteristic data of structural diseases to obtain multiple key sensitive parameters, and a sequence formed by sorting the multiple key sensitive parameters in descending order of sensitivity constitutes a corresponding second series of data that may cause structural diseases; wherein, the second series of data includes prestress loss, structural stiffness, and structural self-weight; Performing bridge disease inversion identification, including: comparing the simulation data output based on a pre-established first simulation model with the distribution characteristic data to determine the second series of data that meets the conditions, specifically: adjusting the values of the second series of data, changing the simulation data output by the first simulation model, comparing the simulation data with the distribution characteristic data, and if the two match, the second series of data at this time is the second series of data that meets the conditions; Invoking a second simulation model established by adjusting the parameters of the first simulation model based on the first series of data and the second series of data that meets the conditions, obtaining a third series of data for characterizing the internal force state, stress state, and geometric state of each structural part of the bridge under self-weight, and determining the dead load stress state before the bridge is demolished.

2. The analysis method according to claim 1, characterized in that, It also includes establishing a second simulation model, including: Adjusting the parameters of the first simulation model based on the first series of data and the second series of data that meets the conditions to determine the second simulation model.

3. The analysis method according to claim 1 or 2, characterized in that, It also includes establishing a first simulation model, including: Establishing an initial finite element model based on the original bridge design drawing materials; Adjusting the parameters of the initial finite element model based on the first series of data to obtain the first simulation model.

4. The analysis method according to claim 1, characterized in that, The obtaining of the first series of data on the current status of the bridge structure includes: Conducting current status test and detection on the bridge structure, and obtaining the first series of data based on the test results; and / or The obtaining of the distribution characteristic data of structural diseases at key parts of the bridge includes: Detecting bridge diseases and obtaining the distribution characteristic data of structural diseases at key parts where the structure is stressed prominently; Among them, the key parts include the mid-span, 1 / 4 span, and support positions of the main girder, as well as the top and bottom of the bridge pier.

5. The analysis method according to claim 1, characterized in that, It also includes inversely identifying the deflection sensitive parameters through the monitoring results of the long-term downward deflection deformation diseases of the structure, specifically including: Performing parameter sensitivity analysis on the downward deflection deformation diseases to determine the key sensitive parameters that are more sensitive to the downward deflection deformation. The key sensitive parameters of the downward deflection deformation include prestress, stiffness, and self-weight; According to the results of the parameter sensitivity analysis, sorting the multiple key sensitive parameters in descending order of sensitivity, taking the key sensitive parameters as the identification targets, and establishing a multi-parameter deformation influence matrix; Using the influence matrix to solve the values of each parameter, and the specific formula is as follows: In the formula: is the sensitive parameter vector; is the deflection influence matrix, representing the deflection change of the measuring points caused by the change of sensitive parameters; is the structural measuring point deflection vector, which is the column vector of the deflections of each measuring point of the structure, and its form is related to the number of measuring points.

6. The analysis method according to claim 1, characterized in that, The determining of the second series of data that meets the conditions by comparing the simulation data output based on the first simulation model with the distribution characteristic data also includes: Adjusting the values of the second series of data to change the simulation data output by the first simulation model; Compare the simulation data with the distribution characteristic data. If the two do not match, continue to adjust the values of the second series of data until the output simulation data matches the distribution characteristic data.

7. The analysis method according to claim 6, characterized in that, When adjusting the values of the second series of data, preferentially adjust the data with higher sensitivity in the second series of data.

8. The analysis method according to claim 1, characterized in that, The third series of data includes stress, internal force and displacement data of the bridge structural parts.

9. An analysis device for the dead load stress state of an existing bridge, characterized in that, The analysis device includes: A memory for storing computer-executable instructions; A processor for implementing the analysis method according to any one of claims 1 to 8 when executing the computer-executable instructions stored in the memory.

10. A computer-readable storage medium storing computer-executable instructions, characterized in that, When the computer-executable instructions are executed by the processor, the analysis method according to any one of claims 1 to 8 is implemented.

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