A method, terminal and medium for predicting fatigue damage degree of suspension bridge cables

Abstract

The present invention provides a method, terminal and medium for predicting fatigue damage of suspension bridge cables, and belongs to the technical field of fatigue of suspension bridge cables in bridge engineering. The prediction method comprises: setting vehicle data information on the suspension bridge, and calculating vehicle loads under normal traffic flow and congested traffic flow respectively; setting a predetermined time span period, and calculating fatigue stress amplitude data of suspension bridge cables in each month in the time span period by finite element analysis software according to vehicle loads under normal traffic flow and congested traffic flow; using hash statistics to count fatigue stress amplitude data of each month, and drawing cable stress amplitude diagram according to counting results. The present invention can realize the prediction and evaluation of fatigue state of suspension bridge cables under vehicle load, make up for the problem of untimely prediction of fatigue damage of cables, greatly improve the damage prediction efficiency of suspension bridge cables, and the damage prediction system is of great significance to the maintenance and replacement of cables.

Description

Technical Field

[0001] The invention belongs to the technical field of fatigue of suspension bridge cables of bridge engineering, and in particular relates to a method, a terminal and a medium for predicting fatigue damage degree of suspension bridge cables. Background Art

[0002] In recent years, my country has made remarkable achievements in the construction of transportation infrastructure, especially in the field of long-span bridges, where the number of constructions has far surpassed the world. As a symbolic structural form of long-span bridges, suspension bridges occupy an important position in bridge construction with their beautiful shape and excellent spanning capacity. Suspension bridges are mainly composed of key components such as main cables 1, suspenders 2, stiffening beams 4 and main towers 3. Figure 1 As shown, the main cable 1 is the core load-bearing component of the upper structure, which is closely connected with the stiffening beam through the sling 2 to form a stable and reliable force transmission system.

[0003] As the key link between the main cable and the stiffening beam, the stress state of cable 2 is directly related to the overall safety performance and normal use function of the suspension bridge. During the operation of the suspension bridge, the cable is subjected to complex and variable loads, especially the frequent impact of vehicle loads, which makes the fatigue damage problem of cable 1 increasingly prominent. Once cable 1 suffers fatigue fracture, it will not only cause a significant change in the stress state of the entire bridge, but may also cause partial or entire bridge instability, posing a serious threat to the safety and durability of the bridge structure.

[0004] With the acceleration of urbanization, urban traffic volume has increased dramatically, especially during peak hours in the morning and evening. The surge in traffic volume has further aggravated the fatigue damage risk of suspension bridge cables. Cables 1 are very prone to fatigue fracture under the continuous action of vehicle loads, which will not only cause huge economic losses, but also may cause serious casualties. Therefore, how to accurately predict and evaluate the damage degree of suspension bridge cables under vehicle fatigue loads has become a key issue that needs to be solved in the field of bridge engineering.

[0005] However, there are still many deficiencies in the prediction of fatigue damage of suspension bridge cables in China, especially the lack of efficient and rapid prediction methods and systems. As a result, in actual projects, the monitoring and early warning of fatigue damage of suspension cables are often not timely enough, making it difficult to effectively ensure the safety and durability of bridge structures.

[0006] Therefore, developing a construction method, prediction method, system, terminal and medium for predicting fatigue damage degree of suspension bridge cables is of great significance for improving the safety performance of bridge structures and extending their service life. Summary of the invention

[0007] In view of the deficiencies in the prior art, the present invention proposes a method, terminal and medium for predicting fatigue damage of suspension bridge cables, so as to solve the technical problem in the prior art that damage prediction of suspension bridge cables under vehicle fatigue load is not timely.

[0008] The technical solution adopted by the present invention is:

[0009] In a first aspect of the embodiment, a method for predicting fatigue damage of suspension bridge cables is provided, comprising:

[0010] Set the vehicle data information on the suspension bridge and calculate the vehicle load under normal traffic flow and congested traffic flow respectively;

[0011] A predetermined time span period is set, and according to the vehicle loads under normal traffic flow and congested traffic flow, the fatigue stress amplitude data of the suspension bridge cables in each month in the time span period is calculated by finite element analysis software;

[0012] The fatigue stress amplitude data of each month are counted using the hash statistical method, and the sling stress amplitude diagram is drawn based on the counting results;

[0013] The damage prediction results based on fatigue damage degree are obtained according to the stress amplitude diagram of the sling.

[0014] Furthermore, the vehicle data information on the suspension bridge is set to calculate the vehicle load under normal traffic flow and congested traffic flow respectively, including:

[0015] Modify the mass and speed data information of vehicles with different axle weights;

[0016] Load calculations are performed according to vehicle load information under normal traffic flow and congested traffic flow.

[0017] Furthermore, the fatigue stress amplitude data of the suspension bridge cables in each month in the time span period are calculated by finite element analysis software according to the vehicle loads under normal traffic flow and congested traffic flow, including:

[0018] Obtain cable force data of suspension bridge based on vehicle loads under normal and congested traffic flows;

[0019] The cable force data and cable cross-sectional area of ​​the suspension bridge are input into the finite element analysis software to calculate and obtain the fatigue stress amplitude data of the suspension bridge cable.

[0020] Furthermore, the cable force data of the suspension bridge cables are obtained by a prediction system of the suspension bridge cables under vehicle load, and the prediction system includes:

[0021] Cable force dynamic measuring instrument and cable cross-sectional area acquisition device;

[0022] Sling stress data acquisition device;

[0023] Sling stress data transmission device;

[0024] Sling stress data counting medium;

[0025] Cable stress data terminal prediction system.

[0026] Furthermore, the prediction system also includes a refined analysis model of the suspension bridge cables under vehicle loads.

[0027] Further, the construction method of the refined analysis model includes:

[0028] Obtaining the initial suspension bridge-vehicle load driving model and the initial suspension bridge cable damage degree model analysis model;

[0029] The speed and mass of virtual vehicles with different axle loads under different working conditions are set in the initial suspension bridge-vehicle load driving model to simulate normal traffic flow and congested traffic flow, and the cable stress amplitude data corresponding to the initial suspension bridge-vehicle load driving model are obtained;

[0030] The cable stress amplitude data and the corresponding vehicle driving information are used as sample data to adjust the initial suspension bridge cable damage degree analysis model to obtain a first suspension bridge cable damage degree model analysis model;

[0031] Adjusting the bridge span in the initial suspension bridge-vehicle load driving model to obtain a first-span suspension bridge-vehicle load driving model, a second-span suspension bridge-vehicle load driving model, and a third suspension bridge-vehicle load driving model;

[0032] The speeds and masses of virtual vehicles with different axle loads under different working conditions are set in the first-span suspension bridge-vehicle load driving model, the second-span suspension bridge-vehicle load driving model, and the third-span suspension bridge-vehicle load driving model to simulate normal traffic flow and congested traffic flow, respectively, to obtain the first cable stress amplitude data corresponding to the first-span suspension bridge-vehicle load driving model, the second cable stress amplitude data corresponding to the second-span suspension bridge-vehicle load driving model, and the third cable stress amplitude data corresponding to the third-span suspension bridge-vehicle load driving model;

[0033] The first cable stress amplitude data and the corresponding vehicle driving information, the second cable stress amplitude data and the corresponding vehicle driving information, and the third cable stress amplitude data and the corresponding vehicle driving information are used as sample data to adjust the first suspension bridge cable damage degree analysis model to obtain the second suspension bridge cable damage degree analysis model, which is a refined analysis model.

[0034] Furthermore, the cable stress amplitude data includes the cable stress amplitude, and the speed and mass of virtual vehicles with different axle loads under different working conditions are set in the initial suspension bridge-vehicle load driving model to simulate normal traffic flow and congested traffic flow, and the cable stress amplitude data corresponding to the initial suspension bridge-vehicle load driving model are obtained, including:

[0035] In the initial suspension bridge-vehicle load driving model, the speed and mass of virtual vehicles with different axle loads under different working conditions are set to simulate normal traffic flow and congested traffic flow, and the cable force data of the cables in the initial suspension bridge-vehicle load driving model are collected by a cable stress data acquisition device to obtain a cable force data set;

[0036] The cable force data set and the cable cross section in the initial suspension bridge-vehicle load driving model are used to calculate the cable stress amplitude to obtain the cable stress amplitude data.

[0037] Furthermore, the cable stress amplitude calculation is performed using the cable force data set and the cable cross section in the initial suspension bridge-vehicle load driving model to obtain the cable stress amplitude data, including:

[0038] The sling stress amplitude is calculated by the method shown in the following formula to obtain the sling stress amplitude data:

[0039]

[0040] Among them, F is the cable force data of the sling; A is the cross-sectional area of ​​the sling, and σ is the stress amplitude of the sling.

[0041] In a second aspect of the embodiment, a terminal is provided, comprising a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute any method described in the first aspect of the embodiment.

[0042] In a third aspect of the embodiment, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the method as described in any one of the first aspect of the embodiment.

[0043] It can be seen from the above technical solution that the beneficial technical effects of the present invention are as follows:

[0044] 1. The present invention can realize the prediction and evaluation of the fatigue state of the suspension bridge cable under vehicle load, which makes up for the problem of untimely prediction of fatigue damage of the cable, greatly improves the damage prediction efficiency of the suspension bridge cable, and the damage prediction system is of great significance to the maintenance and replacement of the cable. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the prior art description. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0046] Figure 1 It is a schematic diagram of a suspension bridge in the background technology of the present invention;

[0047] Figure 2 This is a flow chart of a method according to an embodiment of the present invention;

[0048] Figure 3 This is a schematic cross-sectional view of a suspension cable of a suspension bridge according to an embodiment of the present invention;

[0049] Figure 4 Schematic diagram of a terminal according to an embodiment of the present invention.

[0050] Reference numerals:

[0051] 1-main cable; 2-sling; 3-main tower; 4-stiffening beam, 31-main cable outer protective agent, 32-middle anti-corrosion belt; 33-bottom layer anti-corrosion paste, 34-main cable strand steel wire, 35-outer strand. DETAILED DESCRIPTION

[0052] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0053] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should have the common meanings understood by those skilled in the art to which the present invention belongs.

[0054] Example

[0055] This embodiment provides a method for predicting fatigue damage of suspension bridge cables. Figure 2 As shown, including:

[0056] S1: Establish a refined analysis model of suspension bridge cables under vehicle loads;

[0057] S2: Create a prediction system for suspension bridge cables under vehicle loads;

[0058] S3: Input the cable force data and cable cross-sectional area of ​​the suspension bridge in the prediction system into the integrated trained ANSYS program, output the fatigue stress amplitude data of the suspension bridge cable according to the ANSYS program, perform the counting by hash statistics method and give the cable stress amplitude diagram;

[0059] S4: The vehicle load calculation under normal traffic flow and congested traffic flow is adopted, and the vehicle data information on the bridge is modified. Vehicles under different traffic flows are allowed to travel on the bridge, and a time span of 10 years is given. The stress amplitude data of the cable is collected every month, and the output value of the stress amplitude of the cable is used as the training sample 1 through the counting of the hash statistical method. The training sample 1 is input into the ANSYS program again to correct and improve the ANSYS program model, and the prediction results of the fatigue damage degree of the cable under different traffic flows are obtained.

[0060] In step S1, the specific steps of establishing a refined analysis model of the suspension bridge cables under vehicle loads include:

[0061] S1.1: Taking three suspension bridges with different spans in the coastal area as examples, the superstructure model of the suspension bridge is established using the ANSYS program, and different speeds and masses are assigned to vehicles with different axle weights.

[0062] The specific implementation method is as follows: Based on the bridge design drawing information, an initial suspension bridge-vehicle load driving model and an initial suspension bridge cable damage degree model analysis model are obtained by modeling in three-dimensional software.

[0063] Then, the speed and mass of virtual vehicles with different axle loads under different working conditions are set in the initial suspension bridge-vehicle load driving model.

[0064] S1.2: Allow vehicles with different axle weights to travel on the bridge and establish a suspension bridge-vehicle load driving model under normal traffic flow and congested traffic flow.

[0065] The initial suspension bridge-vehicle load driving model obtained by three-dimensional modeling is imported into the finite element analysis software, and the speed and mass of virtual vehicles with different axle loads under different working conditions are set in the initial suspension bridge-vehicle load driving model to simulate normal traffic flow and congested traffic flow, and the cable stress amplitude data corresponding to the initial suspension bridge-vehicle load driving model is obtained;

[0066] Specifically, the speed and mass of virtual vehicles with different axle loads under different working conditions are set in the initial suspension bridge-vehicle load driving model to simulate normal traffic flow and congested traffic flow, and the cable force data of the cables in the initial suspension bridge-vehicle load driving model are collected by a cable stress data acquisition device to obtain a cable force data set;

[0067] The cable force data set and the cable cross section in the initial suspension bridge-vehicle load driving model are used to calculate the cable stress amplitude to obtain the cable stress amplitude data.

[0068] Specifically, the cable stress amplitude calculation is performed using the cable force data set and the cable cross section in the initial suspension bridge-vehicle load driving model to obtain the cable stress amplitude data, including:

[0069] The sling stress amplitude is calculated by the method shown in the following formula to obtain the sling stress amplitude data:

[0070]

[0071] Among them, F is the cable force data of the sling; A is the cross-sectional area of ​​the sling, and σ is the stress amplitude of the sling.

[0072] S1.3: Assign the speed and mass of the vehicle under different working conditions. When the vehicle is driving under normal traffic flow and congested traffic flow, the stress amplitude data of the sling is packaged and named as training sample 1. The training sample 1 is input into the ANSYS program, and the sling stress amplitude diagram is output through counting by hash statistics method.

[0073] In a specific implementation, since step S1.1 takes three suspension bridges with different spans as an example, it is necessary to adjust the bridge span in the initial suspension bridge-vehicle load driving model to obtain the first span suspension bridge-vehicle load driving model, the second span suspension bridge-vehicle load driving model and the third suspension bridge-vehicle load driving model;

[0074] Then, when assigning the speed and mass of the vehicle under different working conditions, the speed and mass of virtual vehicles with different axle weights under different working conditions can be set in the first-span suspension bridge-vehicle load driving model, the second-span suspension bridge-vehicle load driving model and the third-span suspension bridge-vehicle load driving model to simulate normal traffic flow and congested traffic flow, and obtain the first cable stress amplitude data corresponding to the first-span suspension bridge-vehicle load driving model, the second cable stress amplitude data corresponding to the second-span suspension bridge-vehicle load driving model, and the third cable stress amplitude data corresponding to the third-span suspension bridge-vehicle load driving model;

[0075] Specifically, the initial suspension bridge cable damage model analysis model obtained in the three-dimensional modeling is imported into the finite element analysis software, and the cable stress amplitude data and the corresponding vehicle driving information are used as sample data to adjust the initial suspension bridge cable damage model analysis model in the finite element software to obtain the first suspension bridge cable damage model analysis model.

[0076] Specifically, the cable cross-sectional area information can be obtained from the design drawings of the suspension bridge cables.

[0077] Specifically, the bridge span in the initial suspension bridge-vehicle load driving model in the finite element software is adjusted to obtain the first span suspension bridge-vehicle load driving model, the second span suspension bridge-vehicle load driving model and the third suspension bridge-vehicle load driving model;

[0078] Specifically, the speeds and masses of virtual vehicles with different axle weights under different working conditions are set in the finite element software to simulate normal traffic flow and congested traffic flow in the first span suspension bridge-vehicle load driving model, the second span suspension bridge-vehicle load driving model and the third span suspension bridge-vehicle load driving model, respectively, to obtain the first cable stress amplitude data corresponding to the first span suspension bridge-vehicle load driving model, the second cable stress amplitude data corresponding to the second span suspension bridge-vehicle load driving model, and the third cable stress amplitude data corresponding to the third span suspension bridge-vehicle load driving model.

[0079] Specifically, the first cable stress amplitude data and the corresponding vehicle driving information, the second cable stress amplitude data and the corresponding vehicle driving information, and the third cable stress amplitude data and the corresponding vehicle driving information are used as sample data to adjust the first suspension bridge cable damage analysis model to obtain the second suspension bridge cable damage analysis model.

[0080] S1.4: Continue to assign different speeds and masses to different types of vehicles, and continue to let the vehicles travel on bridges of different spans under normal traffic flow and congested traffic flow, and finally obtain the stress amplitude characteristics of the suspension bridge cables. Input these bridge and vehicle information into the ANSYS program in turn. The program continues to output the stress amplitude of the cables and gives damage prediction results based on damage fatigue.

[0081] After assigning different types of vehicles different speeds and masses, the bridge and vehicle information is input into the ANSYS program again, and the stress amplitude output values ​​of different suspension bridge cables are used as training samples A, B, and C respectively. The training samples A, B, and C are input into the ANSYS program again to correct and improve the ANSYS program model, and finally the stress amplitude data under different traffic flows are obtained. Fatigue damage correction is performed in real time according to the growth of traffic volume in congested traffic flow, and the prediction results of cable damage degree are given.

[0082] The first cable stress amplitude data and the corresponding vehicle driving information, the second cable stress amplitude data and the corresponding vehicle driving information, and the third cable stress amplitude data and the corresponding vehicle driving information are used as sample data to adjust the first suspension bridge cable damage analysis model to obtain the second suspension bridge cable damage analysis model, which is a suspension bridge cable fatigue damage prediction model.

[0083] In step S2, the specific steps of creating a prediction system for suspension bridge cables under vehicle loads include:

[0084] S2.1 Information on cable forces and cross-sectional areas of suspension bridge cables;

[0085] S2.2 Cable stress data acquisition device;

[0086] S2.3 Sling stress data transmission device;

[0087] S2.4 Sling stress data counting medium;

[0088] S2.5 sling stress data terminal prediction system.

[0089] The cable force information of the suspension bridge S2.1 can be measured by a cable force dynamic measuring instrument installed on the upper surface of the suspension bridge cable. When a vehicle passes through the bridge, the cable force dynamic measuring instrument can collect the cable force data of the cable; the cable cross-sectional area information can be obtained from the design drawings of the suspension bridge cable, such as Figure 3 shown.

[0090] The S2.2 sling stress data acquisition device calculates the ratio of the cable force data measured by the cable force dynamic measuring instrument to the cross-sectional area of ​​the sling. After the stress data is obtained, the acquisition device can collect and store it in real time. The stress calculation formula is as follows:

[0091]

[0092] Where: F is the measured sling force data; A is the cross-sectional area of ​​the sling.

[0093] The S2.3 sling stress data transmission device transmits the stress data collected by the stress data collection device to the data counting medium for counting, playing the role of transmitting data.

[0094] The S2.4 sling stress data counting medium mainly adopts the counting method of hash statistics, which can classify and integrate the large amount of acquired data. The counting algorithm has the characteristics of one-way encryption and high sensitivity, that is, after statistics, it can only follow the set route to the next stage path to ensure that data will not be leaked or missing.

[0095] The S2.5 cable stress data terminal prediction system draws and calculates the cable stress amplitude diagram based on the data statistically classified by the hash statistical method, and gives the prediction results under the fatigue damage degree of the suspension bridge cable.

[0096] In step S3, the cable force data and cable cross-sectional area of ​​the suspension bridge in the prediction system are input into the integrated edited and trained ANSYS program, and the fatigue stress amplitude data of the suspension bridge cables are output according to the ANSYS program. The hash statistical method is used for counting and the cable stress amplitude diagram is given. Finally, the cable damage prediction result is given.

[0097] In step S4, the vehicle load calculation under normal traffic flow and congested traffic flow is adopted to modify the vehicle data information on the bridge, so that vehicles under different traffic flows are allowed to travel on the bridge, and a time span of 10 years is given. The cable stress amplitude data of each month is collected, and the output value of the cable stress amplitude is used as training sample 1 through the counting of the hash statistical method. The training sample 1 is input into the ANSYS program again, so as to correct and improve the ANSYS program model, and the prediction results of the cable fatigue damage degree under different traffic flows are obtained, which specifically include:

[0098] S4.1: Modify the mass and speed data information of vehicles with different axle loads;

[0099] S4.2: Carry out load calculations according to vehicle load information under normal traffic flow and congested traffic flow respectively;

[0100] S4.3: Let vehicles with different traffic flows travel on suspension bridges with different spans, and give the span information of vehicles with different axle loads traveling on the bridge for 20 years. Collect the cable stress amplitude data every month within 10 years, and count and aggregate them in turn through the hash statistical method;

[0101] S4.4: The stress amplitude output values ​​of different suspension bridge cables are used as training samples A, B, and C respectively. The training samples A, B, and C are input into the ANSYS program again to correct and improve the ANSYS program model, and finally the stress amplitude data under different traffic flows are obtained. Fatigue damage correction is performed in real time according to the growth of traffic volume in congested traffic flow, and the prediction result of cable damage degree is given.

[0102] The prediction results of fatigue damage degree of the suspension bridge cables include:

[0103] Slight damage: It indicates that the steel wire inside the sling is more likely to break as the fatigue load time increases under the cyclic action of fatigue load. Traffic control can be strengthened to reduce the number of times the sling is subjected to fatigue load.

[0104] Moderate damage: indicates that the stress of the steel wire inside the cable has reached the yield point under the cyclic action of fatigue load, but it has not broken or a few steel wires have broken inside. At this time, according to the traffic conditions, the traffic on one side is closed and the vehicle flow is controlled or even the traffic on the whole bridge is closed, and the corresponding cable is replaced.

[0105] Severe damage: This means that under the cyclic effect of fatigue load, most of the steel wires in a single cable are broken or multiple cables are broken as a whole. At this time, traffic should be closed to prevent vehicles from going on the bridge, and the safety status of the bridge should be evaluated. Based on the evaluation results, the cables should be partially or completely replaced.

[0106] In one embodiment, the prediction system of the suspension bridge cable under vehicle load includes: suspension bridge cable force, cross-sectional area information, cable stress data acquisition device, cable stress data transmission device, cable stress data counting medium, and cable stress data terminal prediction system.

[0107] The cable force information of the suspension bridge cable can be measured by a cable force dynamic measuring instrument installed on the upper surface of the suspension bridge cable. When a vehicle passes through the bridge, the cable force dynamic measuring instrument can collect cable force data information of the cable; the cable cross-sectional area information can be obtained through the design drawings of the suspension bridge cable.

[0108] The cable stress data acquisition device compares the cable force data measured by the cable force dynamic measuring instrument with the cable cross-sectional area. After the stress data is obtained, the acquisition device can collect and store it in real time. The stress calculation formula is as follows:

[0109]

[0110] Where: F is the measured sling force data; A is the cross-sectional area of ​​the sling.

[0111] The sling stress data transmission device transmits the stress data collected by the stress data collection device to the data counting medium for counting, and plays the role of an intermediate medium for transmission.

[0112] The sling stress data counting medium mainly adopts the counting method of hash statistics, which can classify and integrate the large amount of acquired data. The counting algorithm has the characteristics of one-way encryption and high sensitivity, that is, after statistics, it can only follow the set route to the next stage path to ensure that data will not be leaked or missing.

[0113] The cable stress data terminal prediction system draws and calculates the cable stress amplitude diagram based on the data statistically classified by the hash statistical method, and gives the prediction result under the fatigue damage degree of the suspension bridge cable.

[0114] The system in this embodiment uses the created suspension bridge cable under vehicle load based on the vehicle driving conditions of different speeds and masses under normal traffic flow and congested traffic flow. The prediction system can realize the collection and transmission of cable stress information, and count the cable stress amplitude information through hash statistics, and finally input it into the integrated edited and trained ANSYS program. After repeated iterations, the cable stress amplitude data under different working conditions of vehicles with different axle loads are output, and fatigue damage correction is performed in real time according to the growth of traffic volume in congested traffic flow, and the damage degree is divided into grades, and the fatigue damage prediction results of the cable are given. The research and development of this damage assessment system will greatly fill the gap in the field of damage prediction of suspension bridge cables under vehicle fatigue load, effectively predict the damage degree of the cable, and provide a basis for the replacement of the cable in the later stage. The advantages of this damage prediction system are reliable data, high sensitivity, and good stability. It can realize the prediction and evaluation of the fatigue state of the suspension bridge cable under vehicle load. Therefore, the establishment of this damage prediction system makes up for the problem of untimely prediction of cable fatigue damage, greatly improves the damage prediction efficiency of the suspension bridge cable, and the damage prediction system is of great significance to the maintenance and replacement of the cable.

[0115] This embodiment provides a terminal, such as Figure 4 As shown, it includes a processor, an input device, an output device and a memory, and the processor, input device, output device and memory are interconnected, wherein the memory is used to store a computer program, and the computer program includes program instructions, and the processor is configured to call the program instructions to execute the steps described in any one of the methods for predicting fatigue damage of a suspension bridge cable, for example.

[0116] This embodiment also provides a computer-readable storage medium, which stores a computer program. The computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the steps described in any one of the methods for predicting fatigue damage of a suspension bridge cable.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for predicting fatigue damage of suspension bridge cables, characterized in that: include: Set the vehicle data information on the suspension bridge and calculate the vehicle load under normal traffic flow and congested traffic flow respectively; A predetermined time span period is set, and according to the vehicle loads under normal traffic flow and congested traffic flow, the fatigue stress amplitude data of the suspension bridge cables in each month in the time span period is calculated by finite element analysis software; The fatigue stress amplitude data of each month are counted using the hash statistical method, and the sling stress amplitude diagram is drawn based on the counting results; The damage prediction results based on fatigue damage degree are obtained according to the stress amplitude diagram of the sling.

2. A method for predicting fatigue damage of suspension bridge cables according to claim 1, characterized in that: The vehicle data information on the suspension bridge is set to calculate the vehicle load under normal traffic flow and congested traffic flow respectively, including: Modify the mass and speed data information of vehicles with different axle weights; Load calculations are performed according to vehicle load information under normal traffic flow and congested traffic flow.

3. A method for predicting fatigue damage of suspension bridge cables according to any one of claims 1 to 2, characterized in that: The fatigue stress amplitude data of the suspension bridge cables in each month in the time span period are calculated by finite element analysis software according to the vehicle loads under normal traffic flow and congested traffic flow, including: Obtain cable force data of suspension bridge based on vehicle loads under normal and congested traffic flows; The cable force data and cable cross-sectional area of ​​the suspension bridge are input into the finite element analysis software to calculate and obtain the fatigue stress amplitude data of the suspension bridge cable.

4. A method for predicting fatigue damage of suspension bridge cables according to claim 3, characterized in that: The cable force data of the suspension bridge cables are obtained by a prediction system of the suspension bridge cables under vehicle load, wherein the prediction system comprises: Cable force dynamic measuring instrument and cable cross-sectional area acquisition device; Sling stress data acquisition device; Sling stress data transmission device; Sling stress data counting medium; Cable stress data terminal prediction system.

5. A method for predicting fatigue damage of suspension bridge cables according to claim 4, characterized in that: The prediction system also includes a refined analysis model of suspension bridge cables under vehicle loads.

6. A method for predicting fatigue damage of suspension bridge cables according to claim 5, characterized in that: The method for constructing a refined analysis model includes: Obtaining the initial suspension bridge-vehicle load driving model and the initial suspension bridge cable damage degree model analysis model; The speed and mass of virtual vehicles with different axle loads under different working conditions are set in the initial suspension bridge-vehicle load driving model to simulate normal traffic flow and congested traffic flow, and the cable stress amplitude data corresponding to the initial suspension bridge-vehicle load driving model are obtained; The cable stress amplitude data and the corresponding vehicle driving information are used as sample data to adjust the initial suspension bridge cable damage degree analysis model to obtain a first suspension bridge cable damage degree analysis model; Adjusting the bridge span in the initial suspension bridge-vehicle load driving model to obtain a first-span suspension bridge-vehicle load driving model, a second-span suspension bridge-vehicle load driving model, and a third suspension bridge-vehicle load driving model; The speeds and masses of virtual vehicles with different axle loads under different working conditions are set in the first-span suspension bridge-vehicle load driving model, the second-span suspension bridge-vehicle load driving model, and the third-span suspension bridge-vehicle load driving model to simulate normal traffic flow and congested traffic flow, respectively, to obtain the first cable stress amplitude data corresponding to the first-span suspension bridge-vehicle load driving model, the second cable stress amplitude data corresponding to the second-span suspension bridge-vehicle load driving model, and the third cable stress amplitude data corresponding to the third-span suspension bridge-vehicle load driving model; The first cable stress amplitude data and the corresponding vehicle driving information, the second cable stress amplitude data and the corresponding vehicle driving information, and the third cable stress amplitude data and the corresponding vehicle driving information are used as sample data to adjust the first suspension bridge cable damage degree analysis model to obtain the second suspension bridge cable damage degree analysis model, which is a refined analysis model.

7. A method for predicting fatigue damage of suspension bridge cables according to claim 6, characterized in that: The cable stress amplitude data includes the cable stress amplitude, and the speed and mass of virtual vehicles with different axle loads under different working conditions are set in the initial suspension bridge-vehicle load driving model to simulate normal traffic flow and congested traffic flow, and the cable stress amplitude data corresponding to the initial suspension bridge-vehicle load driving model is obtained, including: In the initial suspension bridge-vehicle load driving model, the speed and mass of virtual vehicles with different axle loads under different working conditions are set to simulate normal traffic flow and congested traffic flow, and the cable force data of the cables in the initial suspension bridge-vehicle load driving model are collected by a cable stress data acquisition device to obtain a cable force data set; The cable force data set and the cable cross section in the initial suspension bridge-vehicle load driving model are used to calculate the cable stress amplitude to obtain the cable stress amplitude data.

8. A method for predicting fatigue damage of suspension bridge cables according to claim 7, characterized in that: The cable stress amplitude calculation is performed using the cable force data set and the cable cross section in the initial suspension bridge-vehicle load driving model to obtain the cable stress amplitude data, including: The sling stress amplitude is calculated by the method shown in the following formula to obtain the sling stress amplitude data: Among them, F is the cable force data of the sling; A is the cross-sectional area of ​​the sling, and σ is the stress amplitude of the sling.

9. A terminal, characterized in that: The method comprises a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 8.

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