Life estimation method of cable of cable-stayed bridge based on big data

Abstract

The present invention discloses a method for estimating the life of a bridge cable of a cable-stayed bridge based on big data, comprising: acquiring historical environmental data based on big data to establish an environmental cloud database; calling target historical environmental data corresponding to the area where a target cable-stayed bridge is located in the environmental cloud database; real-time monitoring of damage parameters of the bridge cable of the target cable-stayed bridge; and predicting the remaining life of subsequent bridge cables according to the target historical environmental data based on the influence coefficient of environmental data on the damage parameters. The present invention predicts the environmental data of the area where the target cable-stayed bridge is located by adopting big data technology, and predicts the life of the cable-stayed cable according to the influence amplitude of different environmental data on the damage parameters of the cable-stayed cable, which is beneficial to improving the estimation and prediction accuracy of the life of the cable-stayed cable.

Description

Technical Field

[0001] The present invention belongs to the technical field of computer big data, and in particular relates to a method for estimating the life of a bridge steel cable of a cable-stayed bridge based on big data. Background Art

[0002] With the rapid development of the domestic transportation industry, cable-stayed bridges are widely used in infrastructure construction due to their strong spanning capacity, simple structure, and convenient construction. They have become an important hub across rivers, canyons, and bays. A cable-stayed bridge is a combined force system bridge. The cable-stayed bridge transmits the dead load and live load borne by the main beam to the cable tower through the cable-stayed cables. As the main load-bearing component in the cable-stayed bridge system, the cable-stayed cables must have high strength, fatigue resistance, durability, and good corrosion resistance. The main body of the currently commonly used cable-stayed bridge adopts steel plating, filling with anti-rust grease, and extrusion polyethylene sheathing to prevent steel wire corrosion. Most of its cable-stayed cables use PE sheaths, which are easily worn and damaged due to long-term exposure to the natural environment. This situation can easily cause corrosive media to enter the interior of the cable-stayed cables through the gaps. Cable wires often rust due to erosion by corrosive media, among which pitting is the most common and most dangerous local corrosion form. As the degree of corrosion continues to increase, the effective cross-sectional area of ​​the cable wire also decreases, which will seriously affect the working safety of the inclined cable and even cause major economic losses and safety accidents.

[0003] In order to solve the above problems, Chinese patent CN113591355A discloses a method for monitoring the corrosion degree of bridge cable steel wires based on big data, which includes establishing a digital cloud database of cable bridges; establishing a digital cloud database of corrosion pits of bridge cable steel wires; using measuring instruments to monitor the damage parameters of bridge cable steel wire corrosion pits in real time; establishing a local database to classify, store and analyze the damage parameters of the monitored bridge cable steel wire corrosion pits; matching the data analysis results obtained after analyzing the local database on the monitored corrosion pit damage parameter data with the corrosion pits; After the cloud database is obtained, the surface crack type equivalent to the corrosion pit is determined; after matching the surface crack type equivalent to the corrosion pit with the cloud database of the cable-stayed bridge, a finite element model of the bridge cable steel wire structure with equivalent surface cracks is established according to the equivalent surface crack type, the size data of the bridge cable steel wire and the material data of the bridge cable steel wire, and the stress intensity factor is calculated through the finite element model; the remaining fatigue life of the monitored bridge cable steel wire structure is calculated according to the stress intensity factor; a bridge maintenance plan is formulated or a risk warning is issued according to the remaining fatigue life. Through this method, the problem of corrosion pits extending to cracks in common bridge cable structures can be simplified, and the accuracy of the remaining life assessment of cable-stayed bridges can be improved. In addition, the present invention also combines cloud-based big data with real-time monitoring of cable-stayed bridges, so that the bridge cable structure can be regionalized and intelligent, which is conducive to improving the efficiency of bridge cable structure monitoring.

[0004] From the above, it can be seen that the existing monitoring method only combines the damage parameters of the cable-stayed steel cable at the current moment to monitor the life of the cable and formulate a maintenance plan. In actual situations, due to the influence of environmental factors corresponding to different regions and different seasons, the variation range of the damage parameters of the cable-stayed steel cable is different, which in turn causes certain errors in the life monitoring of the cable-stayed steel cable using the damage parameters. Summary of the invention

[0005] In view of this, an object of the present invention is to provide a method for estimating the life of bridge cables of a cable-stayed bridge based on big data, so as to solve the above-mentioned problems.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A method for estimating the life of a cable of a cable-stayed bridge based on big data, comprising:

[0008] Based on big data, historical environmental data is obtained to establish an environmental cloud database;

[0009] Call the target historical environmental data corresponding to the area where the target cable-stayed bridge is located in the environmental cloud database;

[0010] Real-time monitoring of damage parameters of the bridge cables of the target cable-stayed bridge;

[0011] Based on the influence coefficient of environmental data on damage parameters, the remaining life of subsequent bridge cables is predicted according to the target historical environmental data.

[0012] Furthermore, based on big data, historical environmental data is obtained to establish an environmental cloud database, including:

[0013] Based on big data technology, historical environmental data of several cable-stayed bridges in each preset time period in the area where they are located are obtained;

[0014] According to the preset data classification method, a number of historical environmental data are classified and stored to generate an environmental cloud database.

[0015] Furthermore, the target historical environmental data corresponding to the area where the target cable-stayed bridge is located in the environmental cloud database is called, including:

[0016] Obtain the information of the cable-stayed bridge to be tested uploaded by the user, and combine it with the bridge database to obtain the information of the target cable-stayed bridge;

[0017] The target cable-stayed bridge area information carried in the target cable-stayed bridge information is obtained, and the corresponding target historical environment data in the environment cloud database is called according to the target cable-stayed bridge area information.

[0018] Furthermore, the damage parameters of the bridge cables of the target cable-stayed bridge are monitored in real time, including:

[0019] Obtain the network address of the data transfer device uploaded by the user, and establish a data transmission channel according to the network address; wherein the receiving end of the data transfer device is connected to the output ends of several Internet of Things measuring instruments, and the measurement data of the several Internet of Things measuring instruments include multiple measurement types;

[0020] According to the data transmission channel, a number of measurement data of the bridge cables of the target cable-stayed bridge are obtained, and the number of measurement data are judged according to the preset cable measurement standard, and the measurement data that do not meet the preset cable measurement standard are screened out as damage data;

[0021] According to the preset damage association relationship, the damage data is classified and counted to form a plurality of damage parameters; wherein each damage parameter includes at least two damage data.

[0022] Furthermore, based on the influence coefficient of environmental data on damage parameters, the remaining life of subsequent bridge cables is predicted according to the target historical environmental data, including:

[0023] Acquire target cable-stayed bridge information and determine the type of steel cables of the target cable-stayed bridge;

[0024] Based on big data technology, the measured life curve of the same type of cables as the target cable-stayed bridge is obtained under different environmental data and preset time periods;

[0025] Calling the standard predicted life change curve of the same type of steel cables as the target cable-stayed bridge at the factory stage pre-stored in the bridge database;

[0026] Determine the influence coefficient of different environmental data on the damage parameters of the current type of steel cable within a preset time period based on the standard predicted life change curve and the measured life change curve;

[0027] Get current time data;

[0028] According to the current time data, based on the target historical environmental data of the target cable-stayed bridge, determine the subsequent predicted environmental data corresponding to the current time data of the target cable-stayed bridge;

[0029] Determine the corresponding influence coefficient according to the predicted environmental data, predict the damage degree of the damage parameters of the bridge cables of the target cable-stayed bridge, and obtain the damage degree prediction curve that changes with time;

[0030] The subsequent life changes of bridge cables are predicted according to the damage degree prediction curve, and the remaining life of subsequent bridge cables is determined.

[0031] Furthermore, based on big data technology, the measured life curve of the same type of cables as the target cable-stayed bridge under different environmental data and preset time periods is obtained, including:

[0032] Based on big data technology, a set of damage parameters of several cables of the same type as the target cable-stayed bridge at each time node under different environmental data within a preset time period is obtained; wherein the set of damage parameters includes multiple damage parameters;

[0033] According to the correlation between the damage parameters in the damage parameter set and the life of the corresponding type of steel cable, the remaining life of the steel cable corresponding to the damage parameter set at each time node under different environmental data is determined;

[0034] According to the corresponding remaining life of the steel cable at each time node, a curve chart of the remaining life of the steel cable changing with time is constructed;

[0035] Obtaining a number of remaining life curves of steel cables under the same environmental data, and performing curve fitting on the remaining life curves of the steel cables based on a curve fitting method to obtain a life variation curve under the current environmental data;

[0036] The life variation curves under all environmental data are integrated to obtain the measured life variation curves of the same type of cables as the target cable-stayed bridge under different environmental data and preset time periods.

[0037] Furthermore, according to the standard predicted life change curve and the measured life change curve, the influence coefficient of different environmental data on the damage parameters of the current type of steel cable within a preset time period is determined, including:

[0038] Obtaining the first slope of the data point corresponding to each time node of the standard predicted life change curve to obtain a first slope set;

[0039] Obtain the second slope of the data point corresponding to each time node of the measured life change curve under any environmental data to obtain a second slope set;

[0040] Calculate the slope difference between the first slope in the first slope set and the corresponding second slope in the second slope set;

[0041] The average value of all slope differences under the same environmental data is calculated as the undetermined value;

[0042] The unknown values ​​under all environmental data are integrated to obtain the influence coefficient of different environmental data on the damage parameters of the current type of steel cable within the preset time period.

[0043] Furthermore, the corresponding influence coefficient is determined according to the predicted environmental data, and the damage degree of the damage parameters of the bridge cables of the target cable-stayed bridge is predicted to obtain a damage degree prediction curve that changes with time, including:

[0044] Determine a first influence coefficient corresponding to the damage parameter of the current type of steel cable according to the predicted environmental data;

[0045] Obtain the time variation curve of the standard damage degree of the current type of steel cable under the premise of no maintenance event;

[0046] Based on the judgment method of the damage degree in the standard damage degree versus time curve, according to the damage parameters of the bridge cables of the target cable-stayed bridge, the current first damage degree of the bridge cables of the target cable-stayed bridge is determined;

[0047] According to the data point corresponding to the first damage degree on the standard damage degree versus time curve as the cutoff point, the cutoff point is used as the new origin, all the original points to the left of the cutoff point are abandoned, and the initial damage degree prediction curve of the bridge cable of the current target cable-stayed bridge is obtained;

[0048] The first influence coefficient is obtained as a constraint condition, and the preset nonlinear regression model is used to refit the initial damage degree prediction curve to obtain the damage degree prediction curve that changes with time.

[0049] The beneficial effects of the present invention are:

[0050] The present invention provides a method for estimating the life of a bridge steel cable of a cable-stayed bridge based on big data. The method predicts the environmental data of the area where the target cable-stayed bridge is located by adopting big data technology, and predicts the life of the cable-stayed steel cable according to the influence of different environmental data on the damage parameters of the cable-stayed steel cable, which is beneficial to improving the estimation and prediction accuracy of the life of the cable-stayed steel cable.

[0051] Other advantages, objectives and features of the present invention will be described in the following description and will be apparent to those skilled in the art to some extent, or they may be taught from the practice of the present invention. The purposes and other advantages of the present invention may be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0052] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0054] Figure 1 A method flow chart of a method for estimating the life of a bridge cable of a cable-stayed bridge based on big data in an embodiment of the present invention;

[0055] Figure 2 It is a flow chart of the remaining life prediction process of a bridge steel cable in a bridge steel cable life estimation method of a cable-stayed bridge based on big data in an embodiment of the present invention;

[0056] Figure 3 The present invention is a flowchart of a prediction process of a damage degree prediction curve that varies with time in a method for estimating the life of a cable of a cable-stayed bridge based on big data in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0058] See also Figure 1 The present invention proposes a method for estimating the life of a cable of a cable-stayed bridge based on big data, comprising:

[0059] S101. Based on big data, obtain historical environmental data and establish an environmental cloud database;

[0060] S102, calling the target historical environmental data corresponding to the area where the target cable-stayed bridge is located in the environmental cloud database;

[0061] S103, monitoring damage parameters of bridge cables of the target cable-stayed bridge in real time;

[0062] S104, based on the influence coefficient of environmental data on damage parameters, predict the remaining life of subsequent bridge cables according to target historical environmental data;

[0063] The working principle of the above technical scheme is as follows: This scheme provides a method for estimating the life of bridge cables of cable-stayed bridges based on big data, which aims to predict the environmental data of the area where the target cable-stayed bridge is located by adopting big data technology, and at the same time predict the life of the cable-stayed cable according to the influence of different environmental data on the damage parameters of the cable-stayed cable, so as to improve the accuracy of life estimation. Specifically, firstly, based on big data technology, historical environmental data of the areas where several cable-stayed bridges are located are obtained to establish an environmental cloud database. It is worth mentioning that after the first establishment is completed, unless there is a large change in the environmental data of a certain area, the content in the cloud database will no longer be updated, in order to ensure the consistency of the life estimation of all cables. When the cloud database is built, the target historical environmental data corresponding to the area where the target cable-stayed bridge to be detected is located in the environmental cloud database, and the monitoring damage parameters of the bridge cables of the target cable-stayed bridge are obtained in real time, and based on the influence coefficient of the environmental data on the damage parameters, the remaining life of the subsequent bridge cables is predicted according to the target historical environmental data;

[0064] The beneficial effect of the above technical scheme is: through the above technical scheme, combined with the influence of environmental parameters, the life of the cable-stayed cable is further estimated. Compared with subsequent life monitoring based only on the damage parameters at the current moment, this scheme can further improve the accuracy of life monitoring.

[0065] In one embodiment, based on big data, historical environmental data is acquired to establish an environmental cloud database, including:

[0066] Based on big data technology, historical environmental data of several cable-stayed bridges in each preset time period in the area where they are located are obtained;

[0067] According to the preset data classification method, a number of historical environmental data are classified and stored to generate an environmental cloud database;

[0068] The working principle of the above technical solution is: based on big data technology, historical environmental data of several cable-stayed bridges in each preset time period in the area where they are located are obtained. The setting of the preset time period can be divided according to the quarter. In this solution, it is preferably divided according to the seasonal climate, because different seasonal climates have different corrosion degrees on the PE sleeve, such as the rainy season, etc. At the same time, in addition to conventional meteorological data, historical environmental data can also include earthquake data. For example, a basin area in China with constant small earthquakes can more accurately predict the subsequent cable life. It also includes regular extreme weather events. Then, according to the preset data classification method, several historical environmental data are classified and stored to generate an environmental cloud database. The preset data classification method is preferably classified according to the different regions where the cable-stayed bridges are located. The division of regions is preferably town-level division, which is beneficial to improve the accuracy of monitoring;

[0069] The beneficial effect of the above technical solution is: through the above technical solution, the construction of the environmental cloud database is completed, which is beneficial to improving the estimation accuracy in the subsequent cable life estimation.

[0070] In one embodiment, calling the target historical environmental data corresponding to the area where the target cable-stayed bridge is located in the environmental cloud database includes:

[0071] Obtain the information of the cable-stayed bridge to be tested uploaded by the user, and combine it with the bridge database to obtain the information of the target cable-stayed bridge;

[0072] Obtain the target cable-stayed bridge area information carried in the target cable-stayed bridge information, and call the corresponding target historical environment data in the environment cloud database according to the target cable-stayed bridge area information;

[0073] The working principle of the above technical solution is as follows: in order to reduce the use of human resources, this solution preferably adopts the method of autonomously calling corresponding data to predict the subsequent life estimation. First, the information of the cable-stayed bridge to be tested uploaded by the user is obtained. The information of the cable-stayed bridge to be tested can also be autonomously determined according to the preset cable-stayed bridge detection cycle. Then, the target cable-stayed bridge information is screened and obtained in combination with the bridge database. The bridge database is a comprehensive database of all bridge data. The target cable-stayed bridge information preferably includes the maintenance information of the target cable-stayed bridge, the area where it is located, the main person in charge, etc.; if the information of the cable-stayed bridge to be tested is determined autonomously, it is necessary to check the maintenance information of the target cable-stayed bridge and confirm it with the main person in charge. If the information of the cable-stayed bridge to be tested is uploaded by the user, the area where the target cable-stayed bridge is located carried in the target cable-stayed bridge information is directly obtained, and according to the area where the target cable-stayed bridge is located, the corresponding target historical environmental data in the environmental cloud database is called;

[0074] The beneficial effect of the above technical solution is: through the above technical solution, the call of the target historical environmental data is completed, providing reliable data support for subsequent life estimation.

[0075] In one embodiment, real-time monitoring of damage parameters of bridge cables of a target cable-stayed bridge includes:

[0076] Obtain the network address of the data transfer device uploaded by the user, and establish a data transmission channel according to the network address; wherein the receiving end of the data transfer device is connected to the output ends of several Internet of Things measuring instruments, and the measurement data of the several Internet of Things measuring instruments include multiple measurement types;

[0077] According to the data transmission channel, a number of measurement data of the bridge cables of the target cable-stayed bridge are obtained, and the number of measurement data are judged according to the preset cable measurement standard, and the measurement data that do not meet the preset cable measurement standard are screened out as damage data;

[0078] According to the preset damage association relationship, the damage data is classified and counted to form multiple damage parameters; wherein each damage parameter includes at least two damage data;

[0079] The working principle of the above technical scheme is as follows: in order to reduce the use of human resources and reduce the error of manual data transmission, this scheme preferably adopts autonomous monitoring to obtain the damage parameters of bridge cables. Specifically, the corresponding measuring devices and data transfer equipment are arranged on the cable-stayed bridge to be tested in advance. The method of personnel coordination and the method of pre-setting the cable-stayed bridge detection cycle can be adopted. If the method of pre-setting the cable-stayed bridge detection cycle is adopted, the corresponding measuring device needs to be arranged on the cable-stayed bridge in advance, or a measuring device with good weather resistance is used to be fixed at the corresponding position for a long time waiting for the background terminal to start. It is worth mentioning that if the long-term fixation requires additional consideration of the accuracy of the measuring device, the error detection algorithm should be configured accordingly; during specific monitoring, the network address of the data transfer device uploaded by the user is obtained, and a data transmission channel is established according to the network address; wherein, the receiving end of the data transfer device is connected to the output end of several Internet of Things measuring instruments, and the measurement data of several Internet of Things measuring instruments include multiple measurement types. It is preferred to determine the type of Internet of Things measuring instruments according to the specific measurement data type; then, according to the data transmission channel, several bridge cables of the target cable-stayed bridge are obtained. Measurement data, judging a number of measurement data according to a preset steel cable measurement standard, screening out measurement data that do not meet the preset steel cable measurement standard as damage data, the setting of the preset steel cable measurement standard is preferably determined according to the national standard file of the corresponding steel cable material, and finally, according to the preset damage association relationship, classifying and counting the damage data to form a plurality of damage parameters; wherein, each damage parameter includes at least two damage data, and the preset damage association relationship is used to characterize the influence relationship between any two or more damage data, and according to the distinction of the influence degree, the types of all damage data that have been previously counted are subjected to damage association judgment, and the damage data with the highest influence degree between the damage data are converted into a class of damage parameters, for example, the influence degree between A data and B data is α, and the influence degree between B data and C data is β, at this time α is greater than β, then A data and B data are divided into a class of damage parameters, and C data and B data are not converted into a class of damage parameters, and the specific association division is preferably selected according to the random forest method, and finally, the independence of each subtree is improved by pruning operation to ensure that there is no duplication of elements in all subtrees;

[0080] The beneficial effect of the above technical solution is: through the above technical solution, the selection of damage parameters is completed, and the damage data is integrated into damage parameters according to the damage correlation. There is no need to redesign the algorithm for the impact of each damage data on the life of the wire rope separately. It is only necessary to design the algorithm for each damage parameter in advance and retain the weight of each damage data in the damage parameter. When any damage data in the damage parameter does not exist, only the corresponding weight needs to be adjusted. There is no need to redesign the overall algorithm framework of the damage parameter, which is beneficial to reduce the calculation time of subsequent life estimation, improve monitoring efficiency, and provide reliable data support for subsequent life estimation.

[0081] See also Figure 2 In one embodiment, based on the influence coefficient of environmental data on damage parameters, the remaining life of subsequent bridge cables is predicted according to target historical environmental data, including:

[0082] S201, obtaining target cable-stayed bridge information, and determining the type of steel cables of the bridge cables of the target cable-stayed bridge;

[0083] S202. Based on big data technology, obtain the measured life variation curve of the same type of steel cables as the target cable-stayed bridge under different environmental data and preset time periods;

[0084] S203, calling a standard predicted life change curve of the same type of steel cables as the target cable-stayed bridge at the factory stage, which is pre-stored in the bridge database;

[0085] S204, determining the influence coefficient of different environmental data within a preset time period on the damage parameters of the current type of steel cable according to the standard predicted life change curve and the measured life change curve;

[0086] S205, obtaining current time data;

[0087] S206, determining subsequent predicted environmental data corresponding to the current time data of the target cable-stayed bridge based on the target historical environmental data of the target cable-stayed bridge according to the current time data;

[0088] S207, determining a corresponding influence coefficient according to the predicted environmental data, predicting the damage degree of the damage parameters of the bridge cables of the target cable-stayed bridge, and obtaining a damage degree prediction curve that changes with time;

[0089] S208, predicting the subsequent life span change of the bridge steel cables according to the damage degree prediction curve, and determining the remaining life span of the subsequent bridge steel cables;

[0090] The working principle of the above technical solution is: obtain the information of the target cable-stayed bridge, determine the cable type of the bridge cables of the target cable-stayed bridge, due to the difference in the anti-corrosion performance of different materials, the cable type here preferably refers to the cable material type and the protective cover material type; then based on big data technology, obtain the measured life change curve of the same type of steel cables as the target cable-stayed bridge under different environmental data and preset time periods, and the preset time period is preferably determined according to the corresponding environmental data. For example, if the environmental data includes seasonal climate, the preset time period is the time range corresponding to the corresponding seasonal climate; if the environmental data includes regular severe climate, the preset time period is the historical regular time range corresponding to the severe climate; then call the standard predicted life change curve of the same type of steel cables as the target cable-stayed bridge at the factory stage pre-stored in the bridge database, and the standard predicted life change curve is the life under an ideal normal environment. life change curve, the ideal normal environment includes but is not limited to suitable humidity, temperature, ideal environment without wind and frost invasion, etc.; according to the standard predicted life change curve and the measured life change curve, determine the influence coefficient of different environmental data in the preset time period on the damage parameters of the current type of steel cable, and obtain the current time data, according to the current time data, based on the target historical environmental data of the target cable-stayed bridge, determine the subsequent predicted environmental data corresponding to the current time data of the target cable-stayed bridge. Under normal circumstances, only predicting the environmental data within the same seasonal climate time period is beneficial to reducing the prediction error; determine the corresponding influence coefficient according to the predicted environmental data, predict the damage degree of the damage parameters of the bridge steel cable of the target cable-stayed bridge, and obtain the damage degree prediction curve that changes with time; finally, predict the life change of the subsequent bridge steel cables according to the damage degree prediction curve, and determine the remaining life of the subsequent bridge steel cables;

[0091] The beneficial effect of the above technical solution is: through the above technical solution, the life of the cable-stayed steel cable is estimated in combination with the prediction of environmental data, which is beneficial to improve the accuracy of the estimation compared with the existing technology.

[0092] In one embodiment, based on big data technology, the measured life curve of the same type of steel cables as the target cable-stayed bridge under different environmental data and preset time periods is obtained, including:

[0093] Based on big data technology, a set of damage parameters of several cables of the same type as the target cable-stayed bridge at each time node under different environmental data within a preset time period is obtained; wherein the set of damage parameters includes multiple damage parameters;

[0094] According to the correlation between the damage parameters in the damage parameter set and the life of the corresponding type of steel cable, the remaining life of the steel cable corresponding to the damage parameter set at each time node under different environmental data is determined;

[0095] According to the corresponding remaining life of the steel cable at each time node, a curve chart of the remaining life of the steel cable changing with time is constructed;

[0096] Obtaining a number of remaining life curves of steel cables under the same environmental data, and performing curve fitting on the remaining life curves of the steel cables based on a curve fitting method to obtain a life variation curve under the current environmental data;

[0097] Integrate the life variation curves under all environmental data to obtain the measured life variation curves of the same type of cables as the target cable-stayed bridge under different environmental data and preset time periods;

[0098] The working principle of the above technical solution is: based on big data technology, a set of damage parameters of several steel cables of the same type as the target cable-stayed bridge at each time node under different environmental data within a preset time period is obtained; wherein, the preset time period is divided into multiple time nodes according to a predetermined time division rule, the changes of the damage parameter sets corresponding to adjacent time nodes are in line with expectations, and the damage parameter set corresponding to each time node includes all damage parameters of the same time node under the same environmental data; according to the correlation between the damage parameters in the damage parameter set and the life of the corresponding type of steel cable, the remaining life of the steel cable corresponding to the damage parameter set at each time node under different environmental data is determined, and preferably the effect of each damage parameter on the life of the steel cable designed in advance is calculated. The affected life estimation algorithm is jointly calculated; according to the corresponding remaining life of the steel cable at each time node, a curve diagram of the remaining life of the steel cable that changes with time is constructed, wherein the horizontal axis is time and the vertical axis is the remaining life of the steel cable; several remaining life curve diagrams of the steel cables under the same environmental data are obtained, and based on the curve fitting method, curve fitting is performed on several remaining life curve diagrams of the steel cables to obtain the life change curve under the current environmental data, and the error of a single historical data is reduced by fitting all the corresponding curves. The curve fitting method is relatively mature and will not be described in detail here; the life change curves under all environmental data are integrated to obtain the measured life change curves of the same type of steel cables as the target cable-stayed bridge under different environmental data and preset time periods;

[0099] The beneficial effect of the above technical solution is: through the above technical solution, the determination of the measured life change curve is completed, and the error of a single historical data is reduced by fitting all corresponding curves, which is beneficial to improving the reliability of the measured life change curve.

[0100] In one embodiment, according to the standard predicted life change curve and the measured life change curve, the influence coefficient of different environmental data within a preset time period on the damage parameter of the current type of steel cable is determined, including:

[0101] Obtaining the first slope of the data point corresponding to each time node of the standard predicted life change curve to obtain a first slope set;

[0102] Obtain the second slope of the data point corresponding to each time node of the measured life change curve under any environmental data to obtain a second slope set;

[0103] Calculate the slope difference between the first slope in the first slope set and the corresponding second slope in the second slope set;

[0104] The average value of all slope differences under the same environmental data is calculated as the undetermined value;

[0105] Integrate the undetermined values ​​under all environmental data to obtain the influence coefficient of different environmental data on the damage parameters of the current type of steel cable within a preset time period;

[0106] The working principle of the above technical solution is: obtain the first slope of the data point corresponding to each time node of the standard predicted life change curve to obtain the first slope set, and at the same time obtain the second slope of the data point corresponding to each time node of the measured life change curve under any environmental data to obtain the second slope set, that is, the tangent slope of the corresponding data point, which is used to characterize the rate of change of the data point; calculate the slope difference between the first slope in the first slope set and the corresponding second slope in the second slope set, for example, the point corresponding to the Y-axis where the origin is located is taken as the starting point, recorded as 1, and each subsequent point is marked with a serial number addition, then each first slope in the first slope set has a sequence mark, and similarly, each second slope in the second slope set has the same sequence mark, when subtracting the first slope from the second slope, it is necessary to ensure that the sequence marks of the two slopes to be subtracted are the same, so as to ensure the consistency of the data participating in the subtraction operation; then calculate the average value of all slope differences under the same environmental data as the pending value; integrate the pending values ​​under all environmental data to obtain the influence coefficient of different environmental data on the damage parameters of the current type of steel cable within a preset time period;

[0107] The beneficial effect of the above technical solution is: through the above technical solution, the local change rate of each data point is used to determine the actual error of the two curves, and then the influence coefficient of different environmental data on the damage parameters of the current type of steel cable is calculated, which is beneficial to improve the reliability of the influence coefficient.

[0108] See also Figure 3 In one embodiment, the corresponding influence coefficient is determined according to the predicted environmental data, and the damage degree of the damage parameter of the bridge cable of the target cable-stayed bridge is predicted to obtain a damage degree prediction curve that changes with time, including:

[0109] S301, determining a first influence coefficient corresponding to a damage parameter of a current type of steel cable according to predicted environmental data;

[0110] S302, obtaining a curve of standard damage degree over time of the current type of steel cable under the premise that no maintenance event occurs;

[0111] S303, based on the damage degree judgment method in the standard damage degree versus time curve, and according to the damage parameters of the bridge cables of the target cable-stayed bridge, determining the current first damage degree of the bridge cables of the target cable-stayed bridge;

[0112] S304, taking the data point corresponding to the first damage degree on the standard damage degree versus time curve as the truncation point, taking the truncation point as the new origin, abandoning all original points to the left of the truncation point, and obtaining an initial damage degree prediction curve of the bridge cables of the current target cable-stayed bridge;

[0113] S305, obtaining a first influence coefficient as a constraint condition, using a preset nonlinear regression model, refitting the initial damage degree prediction curve graph, and obtaining a damage degree prediction curve that changes with time;

[0114] The working principle of the above technical solution is: determine the first influence coefficient corresponding to the damage parameter of the current type of steel cable according to the predicted environmental data, and then obtain the standard damage degree change curve of the current type of steel cable under the premise of no maintenance event, that is, under the premise of no personnel maintenance, the standard damage degree change curve of the current type of steel cable with the passage of time, which can be calculated and collected by cutting off a part of the steel cable material in a laboratory simulation environment; based on the judgment method of the damage degree in the standard damage degree change curve over time, according to the damage parameters of the bridge steel cable of the target cable-stayed bridge, determine the current first damage degree of the bridge steel cable of the target cable-stayed bridge; and use the data point corresponding to the first damage degree on the standard damage degree change curve over time as the truncation point, use the truncation point as the new origin, abandon all original points to the left of the truncation point, and obtain the initial damage degree prediction curve of the bridge steel cable of the current target cable-stayed bridge, that is, abandon the part of the curve on the side close to the origin of the truncation point. line, retain the remaining curve, and construct an initial damage degree prediction curve with the truncation point as the new origin; finally, obtain the first influence coefficient as a constraint condition, use the preset nonlinear regression model to refit the initial damage degree prediction curve, and obtain the damage degree prediction curve that changes with time, wherein the selection of the preset nonlinear regression model is determined by the judgment mechanism of the damage degree, and is used to select a suitable nonlinear regression model. The selected model needs to be able to reasonably describe the change of damage over time and contain parameters that can represent the first influence coefficient. The specific parameters of the model can be estimated by nonlinear least squares method or other nonlinear optimization methods. In this process, the first influence coefficient is used as a constraint condition to ensure that the results of the model will not deviate from expectations. Finally, the estimated parameters are substituted into the nonlinear regression model to determine the fitting curve. It is worth noting that the subsequent diagnosis and verification methods of the fitting curve are relatively mature in the prior art, so they will not be described one by one here.

[0115] The beneficial effect of the above technical solution is: through the above technical solution, the original estimated curve is refitted with the first influence coefficient generated by the environmental difference, combined with the comprehensive life evaluation of environmental data, which is beneficial to improve the accuracy of the cable life estimation.

[0116] Finally, it should be noted that the above preferred 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 through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A method for estimating the life of steel cables of a cable-stayed bridge based on big data, characterized in that: include: Based on big data, historical environmental data is obtained to establish an environmental cloud database; Call the target historical environmental data corresponding to the area where the target cable-stayed bridge is located in the environmental cloud database; Real-time monitoring of damage parameters of the bridge cables of the target cable-stayed bridge; Based on the influence coefficient of environmental data on damage parameters, the remaining life of subsequent bridge cables is predicted according to the target historical environmental data; Among them, based on the influence coefficient of environmental data on damage parameters, the remaining life of subsequent bridge cables is predicted according to the target historical environmental data, including: Acquire target cable-stayed bridge information and determine the type of steel cables of the target cable-stayed bridge; Based on big data technology, the measured life curve of the same type of cables as the target cable-stayed bridge is obtained under different environmental data and preset time periods; Calling the standard predicted life change curve of the same type of steel cables as the target cable-stayed bridge at the factory stage pre-stored in the bridge database; Determine the influence coefficient of different environmental data on the damage parameters of the current type of steel cable within a preset time period based on the standard predicted life change curve and the measured life change curve; Get current time data; According to the current time data, based on the target historical environmental data of the target cable-stayed bridge, determine the subsequent predicted environmental data corresponding to the current time data of the target cable-stayed bridge; Determine the corresponding influence coefficient according to the predicted environmental data, predict the damage degree of the damage parameters of the bridge cables of the target cable-stayed bridge, and obtain the damage degree prediction curve that changes with time; The subsequent life changes of bridge cables are predicted according to the damage degree prediction curve, and the remaining life of subsequent bridge cables is determined.

2. The method for estimating the life of a cable of a cable-stayed bridge based on big data according to claim 1 is characterized in that: Based on big data, historical environmental data is obtained to establish an environmental cloud database, including: Based on big data technology, historical environmental data of several cable-stayed bridges in each preset time period in the area where they are located are obtained; According to the preset data classification method, a number of historical environmental data are classified and stored to generate an environmental cloud database.

3. The method for estimating the life of a cable of a cable-stayed bridge based on big data according to claim 1 is characterized in that: Call the target historical environmental data corresponding to the area where the target cable-stayed bridge is located in the environmental cloud database, including: Obtain the information of the cable-stayed bridge to be tested uploaded by the user, and combine it with the bridge database to obtain the information of the target cable-stayed bridge; The target cable-stayed bridge area information carried in the target cable-stayed bridge information is obtained, and the corresponding target historical environment data in the environment cloud database is called according to the target cable-stayed bridge area information.

4. The method for estimating the life of a cable of a cable-stayed bridge based on big data according to claim 1 is characterized in that: Real-time monitoring of damage parameters of the target cable-stayed bridge’s bridge cables, including: Obtain the network address of the data transfer device uploaded by the user, and establish a data transmission channel according to the network address; wherein the receiving end of the data transfer device is connected to the output ends of several Internet of Things measuring instruments, and the measurement data of the several Internet of Things measuring instruments include multiple measurement types; According to the data transmission channel, a number of measurement data of the bridge cables of the target cable-stayed bridge are obtained, and the number of measurement data are judged according to the preset cable measurement standard, and the measurement data that do not meet the preset cable measurement standard are screened out as damage data; According to the preset damage association relationship, the damage data is classified and counted to form a plurality of damage parameters; wherein each damage parameter includes at least two damage data.

5. The method for estimating the life of a cable of a cable-stayed bridge based on big data according to claim 1 is characterized in that: Based on big data technology, the measured life curve of the same type of cables as the target cable-stayed bridge under different environmental data and preset time periods is obtained, including: Based on big data technology, a set of damage parameters of several cables of the same type as the target cable-stayed bridge at each time node under different environmental data within a preset time period is obtained; wherein the set of damage parameters includes multiple damage parameters; According to the correlation between the damage parameters in the damage parameter set and the life of the corresponding type of steel cable, the remaining life of the steel cable corresponding to the damage parameter set at each time node under different environmental data is determined; According to the corresponding remaining life of the steel cable at each time node, a curve chart of the remaining life of the steel cable changing with time is constructed; Obtaining a number of remaining life curves of steel cables under the same environmental data, and performing curve fitting on the remaining life curves of the steel cables based on a curve fitting method to obtain a life variation curve under the current environmental data; The life variation curves under all environmental data are integrated to obtain the measured life variation curves of the same type of cables as the target cable-stayed bridge under different environmental data and preset time periods.

6. The method for estimating the life of steel cables of a cable-stayed bridge based on big data according to claim 1, characterized in that: According to the standard predicted life change curve and the measured life change curve, determine the influence coefficient of different environmental data on the damage parameters of the current type of steel cable within the preset time period, including: Obtaining the first slope of the data point corresponding to each time node of the standard predicted life change curve to obtain a first slope set; Obtain the second slope of the data point corresponding to each time node of the measured life change curve under any environmental data to obtain a second slope set; Calculate the slope difference between the first slope in the first slope set and the corresponding second slope in the second slope set; The average value of all slope differences under the same environmental data is calculated as the undetermined value; The unknown values ​​under all environmental data are integrated to obtain the influence coefficient of different environmental data on the damage parameters of the current type of steel cable within the preset time period.

7. The method for estimating the life of steel cables of a cable-stayed bridge based on big data according to claim 1, characterized in that: The corresponding influence coefficient is determined according to the predicted environmental data, and the damage degree of the damage parameters of the bridge cables of the target cable-stayed bridge is predicted to obtain the damage degree prediction curve that changes with time, including: Determine a first influence coefficient corresponding to the damage parameter of the current type of steel cable according to the predicted environmental data; Obtain the time variation curve of the standard damage degree of the current type of steel cable under the premise of no maintenance event; Based on the judgment method of the damage degree in the standard damage degree versus time curve, according to the damage parameters of the bridge cables of the target cable-stayed bridge, the current first damage degree of the bridge cables of the target cable-stayed bridge is determined; According to the data point corresponding to the first damage degree on the standard damage degree versus time curve as the cutoff point, the cutoff point is used as the new origin, all the original points to the left of the cutoff point are abandoned, and the initial damage degree prediction curve of the bridge cable of the current target cable-stayed bridge is obtained; The first influence coefficient is obtained as a constraint condition, and the preset nonlinear regression model is used to refit the initial damage degree prediction curve to obtain the damage degree prediction curve that changes with time.

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