Bridge life prediction method, device, equipment, medium and product

Abstract

The application provides a bridge life prediction method, device, equipment, medium and product, and relates to the technical field of data processing. The method comprises the following steps: constructing a time-varying structural resistance model according to time-varying rules corresponding to a plurality of bridge life factors; the bridge life factors comprise at least two of a synergistic working coefficient between steel bars and concrete, concrete strength, steel bar area, and steel bar strength; constructing a load effect model according to load data of a vehicle live load and crowd load data; constructing a structure function function according to the time-varying structural resistance model and the load effect model; and predicting the remaining service life of a target bridge according to structure reliability parameters of the target bridge and the structure function function. The application can improve the prediction accuracy of the bridge life.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a bridge life prediction method, device, equipment, medium and product. BACKGROUND

[0002] With the increase of bridge service life, a large number of bridges enter the performance decline period and face the severe challenges of structural aging and material performance degradation. In current bridge management practice, there are generally problems such as durability loss exceeding expectations and maintenance decision lag, which not only causes a large amount of maintenance fund waste, but also may cause major safety accidents.

[0003] The existing bridge life evaluation method mainly has the following technical defects: the traditional resistance model only uses static deterioration coefficients and cannot accurately reflect the time-varying characteristics of concrete strength, steel performance and their synergistic working relationship. Deterministic design values are generally used in load effect analysis, ignoring the random process characteristics of vehicle live load and crowd load. In addition, the existing evaluation method is mostly based on deterministic models and cannot effectively handle the randomness of material parameter dispersion and environmental action. In summary, the existing technology is not conducive to improving the bridge life prediction accuracy. SUMMARY

[0004] The present application provides a bridge life prediction method, device, equipment, medium and product to solve the defect that the existing technology is not conducive to improving the bridge life prediction accuracy, and to realize improving the bridge life prediction accuracy.

[0005] The present application provides a bridge life prediction method, which comprises:

[0006] According to the time-varying law corresponding to a plurality of bridge life factors, a time-varying structural resistance model is constructed; the bridge life factors include at least two of the synergistic working coefficient between steel and concrete, concrete strength, steel area, and steel strength;

[0007] According to the load data of vehicle live load and the crowd load data, a load effect model is constructed;

[0008] According to the time-varying structural resistance model and the load effect model, a structural function function is constructed;

[0009] According to the structural reliability parameters of the target bridge and the structural function function, the remaining service life of the target bridge is predicted.

[0010] According to the bridge life prediction method provided by the present application, before the time-varying structural resistance model is constructed according to the time-varying law corresponding to a plurality of bridge life factors, it further comprises:

[0011] The concrete strength is simulated by using a non-stationary random process to obtain the time-varying law of the concrete strength; and / or,

[0012] establishing a time-varying rule of the steel bar area by using the annual average corrosion speed; and / or,

[0013] establishing a time-varying rule of the steel bar strength by using a non-stationary random process to simulate the steel bar strength; and / or,

[0014] establishing a time-varying rule of the coefficient of joint work by using the coefficient of joint work to simulate the degradation relationship of the bonding performance between the steel bar and the concrete.

[0015] According to the bridge life prediction method provided by the application, the load effect model is constructed, which comprises:

[0016] simulating the crowd load data by using a stationary binomial random process to obtain a crowd load probability model;

[0017] obtaining a vehicle load probability model according to the load data of the vehicle live load;

[0018] constructing the load effect model according to the crowd load probability model and the vehicle load probability model.

[0019] According to the bridge life prediction method provided by the application, the bridge life prediction method further comprises:

[0020] obtaining a mapping relationship between a structure safety level and a reliability parameter;

[0021] determining a reference reliability parameter of the target bridge according to the structure safety level of the target bridge;

[0022] determining a structure reliability parameter of the target bridge according to the bridge structure data of the target bridge and the reference reliability parameter.

[0023] According to the bridge life prediction method provided by the application, the structure reliability parameter of the target bridge is determined according to the bridge structure data of the target bridge and the reference reliability parameter.

[0024] drawing a reliability parameter change curve according to the structure function function;

[0025] obtaining the structure reliability parameter of the target bridge and a matching result of the structure reliability parameter and the reliability parameter change curve;

[0026] predicting the remaining service life of the target bridge according to the matching result.

[0027] According to the bridge life prediction method provided by the application, the remaining service life of the target bridge is predicted, which comprises:

[0028] According to the bridge structure of the target bridge, a plurality of sub-regions of the target bridge are obtained;

[0029] Local environment parameters of each sub-region are obtained;

[0030] According to the structural reliability parameters of the target bridge and the structure function function, a life prediction model of each sub-region is determined;

[0031] According to the life prediction results of each sub-region, a life prediction result of the target bridge is determined.

[0032] The application also provides a bridge life prediction device, comprising:

[0033] A time-varying structure resistance model construction module is configured to construct a time-varying structure resistance model according to time-varying rules corresponding to a plurality of bridge life factors, wherein the bridge life factors include at least two of a synergistic working coefficient between steel bars and concrete, concrete strength, steel bar area, and steel bar strength;

[0034] A load effect model construction module is configured to construct a load effect model according to load data of vehicle live load and crowd load data;

[0035] A structure function function construction module is configured to construct a structure function function according to the time-varying structure resistance model and the load effect model;

[0036] A prediction module is configured to predict the remaining service life of the target bridge according to structural reliability parameters of the target bridge and the structure function function.

[0037] The application also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the bridge life prediction method of any of the above when executing the computer program.

[0038] The application also provides a non-transitory computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to implement the bridge life prediction method of any of the above.

[0039] The application also provides a computer program product, comprising a computer program, wherein the computer program is executed by a processor to implement the bridge life prediction method of any of the above.

[0040] The bridge life prediction method, device, equipment, medium and product provided by the application, according to the time-varying law corresponding to a plurality of bridge life factors, a time-varying structure resistance model is constructed; the bridge life factors include at least two of the synergistic working coefficient between steel and concrete, concrete strength, steel area, and steel strength; according to the load data of the vehicle live load and the crowd load data, a load effect model is constructed; according to the time-varying structure resistance model and the load effect model, a structure function function is constructed; according to the structure reliability parameters of the target bridge and the structure function function, the remaining service life of the target bridge is predicted. In this way, the structure function function is established by constructing the time-varying structure resistance model and the load effect model, and the remaining life is predicted based on the structure reliability parameters, which solves the defects of the static deterioration coefficient and the deterministic load analysis in the traditional method, and is beneficial to improving the bridge life prediction accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0042] Figure 1 It is a flowchart of the bridge life prediction method provided by the application.

[0043] Figure 2 It is a data diagram corresponding to the bridge life prediction method provided by the application.

[0044] Figure 3 It is a schematic diagram of the function function probability distribution density curve provided by the application.

[0045] Figure 4 It is a structural diagram of the bridge life prediction device provided by the application.

[0046] Figure 5 It is a structural diagram of the electronic device provided by the application. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical scheme and advantages of the application more clear, the technical scheme in the application will be described clearly and completely in combination with the drawings in the application. Obviously, the described embodiments are some embodiments of the application, not all embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor belong to the protection scope of the application.

[0048] The following will be combined with Figures 1-3The bridge life prediction method provided by the application is described as follows Figure 1 The bridge life prediction method provided by the application is described as follows

[0049] S101, a time-varying structure resistance model is constructed according to time-varying rules corresponding to multiple bridge life factors, wherein the bridge life factors include at least two of a synergistic working coefficient between steel bars and concrete, concrete strength, steel bar area, and steel bar strength.

[0050] The synergistic working coefficient includes a bonding performance.

[0051] S102, a load effect model is constructed according to load data of a vehicle live load and crowd load data.

[0052] S103, a structure function function is constructed according to the time-varying structure resistance model and the load effect model.

[0053] S104, a remaining service life of a target bridge is predicted according to structure reliability parameters of the target bridge and the structure function function.

[0054] The time-varying structure resistance model refers to a mathematical model capable of reflecting changes of bridge structure resistance with time, and can be constructed by superimposing time-varying rules of multiple bridge life factors such as concrete strength, steel bar area, steel bar strength, and a synergistic working coefficient of steel bars and concrete. The model solves the deficiency of traditional methods that only use a product of degradation coefficients to describe structure resistance by introducing a multi-factor synergistic time-varying rule.

[0055] The bridge life factor refers to a key parameter affecting degradation of bridge structure performance, and specifically includes at least two parameters of concrete strength, steel bar area, steel bar strength, and a synergistic working coefficient of steel bars and concrete. Selection of a multi-factor combination can more comprehensively represent coupling of material performance degradation and interface bonding failure, and avoid limitations of single-factor analysis.

[0056] The load effect model refers to a statistical model describing random distribution rules of a vehicle live load and crowd load, and specifically can simulate crowd load by using a stationary binomial random process and establish a probability model in combination with measured vehicle load data. The model solves the problem of ignoring actual load fluctuations in traditional design value methods by quantifying load randomness.

[0057] The structure function function refers to a limit state equation for evaluating reliability of a bridge structure, and is specifically constructed by dynamically coupling the time-varying structure resistance model and the load effect model. The function can reflect a probability relationship between resistance attenuation and load effect in real time, and provides a mathematical basis for reliability calculation.

[0058] The remaining service life prediction refers to a dynamic matching process based on the specific reliability parameters of the target bridge and the structural function function. Specifically, it can be realized by comparing and analyzing the reliability parameter change curve and the real-time monitoring data. This method replaces deterministic prediction with probability evaluation, solving the defect of ignoring the distribution characteristics of random variables in the empirical method.

[0059] In the present application, a dynamic reliability evaluation system is established, which includes multi-factor time-varying rules and random load effects. By probabilistically coupling time-varying processes such as concrete strength degradation, steel corrosion, and interface bonding degradation with random loads, a structural function function is constructed to quantify the bridge life attenuation law, and precise prediction of the remaining life based on the probability method is realized.

[0060] Specifically, first, a time-varying structural resistance model is constructed according to the time-varying rules of multiple bridge life factors. These life factors include at least two of concrete strength, steel area, steel strength, and steel and concrete synergistic work coefficient, including bonding performance. By establishing the time-varying rules of these key parameters, the variation of bridge structural resistance with time can be comprehensively reflected.

[0061] Next, a load effect model is constructed according to the load data of vehicle live load and crowd load data. This step takes into account the randomness and time-varying characteristics of actual loads, avoiding the errors caused by using deterministic design values in traditional methods.

[0062] Then, based on the constructed time-varying structural resistance model and load effect model, a structural function function is constructed. This function comprehensively considers the dynamic changes of structural resistance and load effect, and can more accurately describe the performance state of the bridge structure.

[0063] Finally, according to the structural reliability parameters of the target bridge and the constructed structural function function, the remaining service life of the target bridge is predicted. This step combines the actual condition of the bridge with the theoretical model, realizing the scientific prediction of the remaining life of the bridge.

[0064] Throughout the process, each step is closely connected, forming a complete bridge life prediction system. By considering the time-varying characteristics and randomness of multiple key factors, this method can more accurately reflect the actual state and performance evolution process of the bridge structure, thereby improving the accuracy and reliability of life prediction.

[0065] In one possible implementation, the scheme of the present application is implemented as follows: first, for a certain bridge, historical monitoring data of concrete strength, reinforcement area, reinforcement strength, and reinforcement and concrete cooperation coefficient are collected. These data are analyzed using a non-stationary random process model to establish the time-varying law of each parameter. For example, the concrete strength can adopt a lognormal distribution model, and the reinforcement area can establish a linear decay model based on the annual average corrosion rate.

[0066] Secondly, vehicle load data of the bridge are collected, including vehicle type, weight, and passing frequency, and the like. At the same time, crowd load data, such as pedestrian flow and distribution, are obtained. Using extreme value distribution theory and a random process model, a probability model of vehicle live load and crowd load is constructed, and then a comprehensive load effect model is established.

[0067] Then, the time-varying structure resistance model and the load effect model are substituted into the limit state equation in the structure reliability theory to construct a structure function. The function reflects the reliability level of the bridge structure at different time points.

[0068] Finally, according to the design service life and safety level of the bridge, a target reliability index is determined. The index is compared and analyzed with the structure function, and the time when the bridge reaches the target reliability index is solved by a numerical calculation method, that is, the predicted remaining service life.

[0069] In one application scenario, the monitoring data can be updated regularly and the model parameters can be adjusted to improve the accuracy and adaptability of the prediction.

[0070] Through the above scheme, the present application can effectively solve the problems of oversimplified resistance model and insufficient consideration of load effect in the traditional bridge life prediction method. By establishing a time-varying resistance model of multi-factor synergistic action and combining a dynamic load effect model, the performance degradation process of the bridge structure is accurately described. This method considers the randomness and time-varying nature of material performance, environmental factors, and load characteristics, significantly improving the accuracy of life prediction. At the same time, since a probability and statistics method is used, the scheme can better evaluate the reliability level of the bridge, providing a more reliable scientific basis for bridge maintenance and management decision-making. In addition, the flexibility of the method makes it applicable to bridges of different types and environmental conditions, and has a wide application prospect.

[0071] Specifically, before constructing the time-varying structure resistance model according to the time-varying law of the plurality of bridge life factors, the method further includes:

[0072] simulating the concrete strength using a non-stationary random process to obtain the time-varying law of the concrete strength; and / or,

[0073] establishing the time-varying law of the reinforcement area using the annual average corrosion rate; and / or,

[0074] a non-stationary random process is used to simulate the reinforcement strength to obtain a time-varying law of the reinforcement strength; and / or,

[0075] a coefficient of synergistic work is used to simulate the degradation relationship of the bonding performance between the reinforcement and the concrete to obtain a time-varying law of the coefficient of synergistic work.

[0076] Specifically, the time-varying law of the concrete strength is obtained by superimposing an initial strength statistical parameter and a time-dependent function to extend the 28-day strength data to a probability distribution in a continuous time domain, for example, an exponential function is used to describe the mean value attenuation law of the strength, and the standard deviation linearly increases with time; the model of the reinforcement area is based on the annual average corrosion rate to establish a deterministic degradation trend, while the initial area statistical characteristics are retained, for example, the corrosion rate is converted into an area reduction rate function, and the time-varying mean value is formed by multiplying the initial area mean value; the model of the reinforcement strength is combined with the initial strength parameter and the time variable through a non-stationary random process, for example, a power function is used to describe the strength degradation path, and a random disturbance term is introduced to represent the influence of environmental factors; the model of the coefficient of synergistic work is used to convert the physical damage process of the bonding performance into a quantifiable mathematical relationship by establishing a function of the ratio of the corrosion crack width to the reinforcement diameter. In the construction process, the deterministic degradation trend and the random fluctuation component are separated, the physical mechanism of the material performance degradation is retained, and the mathematical expression of the probability characteristics is realized, so that the prediction accuracy of the time-varying structural resistance model is improved.

[0077] In a possible implementation manner, the scheme of the present application is implemented as follows: before the time-varying structural resistance model is constructed, the time-varying laws of a plurality of bridge life factors are first established. Specifically, a non-stationary random process is used to simulate the concrete strength to obtain a time-varying law of the concrete strength. Further, the annual average corrosion rate is used to establish a time-varying law of the reinforcement area. Thus, a non-stationary random process is used to simulate the reinforcement strength to obtain a time-varying law of the reinforcement strength. Finally, a coefficient of synergistic work is used to simulate the degradation relationship of the bonding performance between the reinforcement and the concrete to obtain a time-varying law of the coefficient of synergistic work.

[0078] It should be noted that the determination manner of the time-varying law is only used as an example and is not specifically limited.

[0079] Through the above technical scheme, the present application realizes the accurate modeling of the key factors of the bridge life. Since the non-stationary random process and the time-varying model are used, the change laws of the factors with time can be more accurately reflected.

[0080] The application further provides a method for constructing the load effect model, which comprises the following steps: simulating crowd load data by using a stationary binomial random process to obtain a crowd load probability model; obtaining a vehicle load probability model according to load data of a vehicle live load; and constructing the load effect model according to the crowd load probability model and the vehicle load probability model.

[0081] In a possible implementation manner, the scheme of the application is implemented as follows.

[0082] In the construction of the load effect model, the crowd load probability model is obtained by simulating crowd load data by using a stationary binomial random process.

[0083] Secondly, the vehicle load probability model is obtained according to load data of a vehicle live load.

[0084] Finally, the crowd load probability model and the vehicle load probability model are superimposed to construct a complete load effect model.

[0085] Through the above technical scheme, the load effect actually borne by the bridge can be simulated more accurately.

[0086] The application further provides a mapping relationship between a structure safety level and reliability parameters, determines a reference reliability parameter of a target bridge according to a structure safety level of the target bridge, and determines a structure reliability parameter of the target bridge according to bridge structure data of the target bridge and the reference reliability parameter.

[0087] The mapping relationship between the structure safety level and the reliability parameter is established by querying industry specifications or based on historical detection data, for example, the bridge is divided into first, second and third safety levels, which correspond to different reliability indexes or failure probability thresholds. The determination of the benchmark reliability parameter depends on the safety level division in the bridge design file, for example, the first safety level corresponds to a reliability index β≥4.2, and the second safety level corresponds to 3.7≤β<4.2. The bridge structure data includes material performance degradation detection results, load history records or component geometric size measured values, which are combined with the benchmark parameter to adjust the parameter value through weighted calculation or probability correction model.

[0088] Specifically, first, the design safety level of the target bridge is called from the bridge management system, the corresponding benchmark reliability index is extracted by matching the pre-stored mapping relationship table. Then, the current concrete carbonation depth and steel corrosion rate data of the bridge are obtained through non-destructive detection, and the resistance attenuation coefficient is calculated; the live load statistical characteristics are corrected combined with the vehicle load dynamic monitoring data. The resistance attenuation coefficient and the live load correction result are input into the parameter updating model, the benchmark reliability index is dynamically adjusted based on the Bayesian probability method, and the reliability parameter reflecting the current state is obtained.

[0089] Through the above technical solution, the structural reliability parameter of the target bridge can be accurately determined. Therefore, reliable input data is provided for subsequent bridge life prediction, and the accuracy of life prediction is improved.

[0090] The application further proposes to draw a reliability parameter change curve according to a structure function, obtain the structural reliability parameter of the target bridge and the matching result of the structural reliability parameter and the reliability parameter change curve, and predict the remaining service life of the target bridge according to the matching result.

[0091] The reliability parameter change curve has a time variable as the horizontal axis and a reliability index as the vertical axis. The structural reliability parameter of the target bridge is obtained through field detection or monitoring data acquisition, including concrete carbonation depth, steel corrosion rate, crack width and other parameters. The matching result can determine the fitting degree of the actual parameter and the theoretical curve by using the least square method or correlation analysis method. The remaining life prediction is calculated by setting a reliability threshold, and the time point at which the actual parameter curve intersects with the threshold.

[0092] Specifically, the reliability parameter change curve reflects the probabilistic distribution relationship between structural resistance and load effect over time, and the theoretical decay trend is generated through numerical simulation. The structural reliability parameters of the target bridge are collected and time-aligned with the theoretical curve for deviation analysis, such as using dynamic time warping algorithm to eliminate the differences in time series. The matching results are evaluated by calculating the residual sum of squares or correlation coefficient to assess the degree of agreement between the theoretical model and the actual state. If the residual exceeds the preset threshold, the model parameter correction mechanism is triggered. The remaining service life prediction is extrapolated to the critical time point when the reliability index falls below the target value, such as when the reliability index drops to the preset minimum safety factor, the remaining life is the time difference from the current time to the critical point.

[0093] Further, the method further comprises: obtaining a plurality of sub-regions of the target bridge according to the bridge structure of the target bridge; acquiring local environmental parameters of each sub-region; determining a life prediction model of each sub-region according to the structural reliability parameters of the target bridge and the structural performance function; and determining a life prediction result of the target bridge according to the life prediction results of each sub-region.

[0094] Specifically, a plurality of sub-regions of the target bridge are obtained according to the bridge structure of the target bridge; the entire bridge structure is divided into a plurality of sub-regions, and the size of each sub-region is determined according to the variation of structural characteristics and environmental conditions; for example, the bridge deck, main girder, pier and other main components are divided into a plurality of sub-regions; local environmental parameters of each sub-region are acquired; environmental monitoring sensors are installed in each sub-region to collect local temperature, humidity, salt fog concentration and other environmental parameter data; at the same time, non-destructive testing techniques are used to regularly collect structural parameters such as concrete strength and steel corrosion degree of each sub-region; a life prediction model of each sub-region is determined according to the structural reliability parameters of the target bridge and the structural performance function; and a multi-scale spatial analysis method is used to determine a life prediction result of the target bridge according to the life prediction results of each sub-region.

[0095] Therefore, determining the life prediction result of the target bridge according to the life prediction results of each sub-region can further improve the accuracy of bridge life prediction. By dividing the bridge into a plurality of sub-regions and predicting the life of each sub-region, the structural characteristics and environmental condition differences of different parts of the bridge can be fully considered, and errors caused by overall prediction can be avoided. At the same time, using a multi-scale spatial analysis method can comprehensively consider the mutual influence between each sub-region, so as to obtain a more comprehensive and accurate overall life prediction result of the bridge. This method not only improves the prediction accuracy, but also identifies the key areas that deteriorate faster, providing more targeted guidance for bridge maintenance and management.

[0096] The application further provides a method for predicting the service life of a target bridge based on the service life prediction results of the sub-regions, comprising: identifying a key sub-region with a relatively fast deterioration rate; obtaining a correlation between deterioration processes of adjacent sub-regions; and determining the service life prediction result of the target bridge based on the service life prediction results of the sub-regions, the key sub-region, and the correlation between the deterioration processes.

[0097] The key sub-region with the relatively fast deterioration rate can be identified by collecting real-time data of concrete strength, steel corrosion degree, and rust expansion crack width of each sub-region, calculating the deterioration rate of each sub-region, and screening the sub-regions exceeding a preset threshold as the key region. The correlation between the deterioration processes of adjacent sub-regions can be obtained by analyzing the load transmission path and environmental parameter similarity of adjacent sub-regions, establishing a correlation coefficient matrix, for example, the load correlation coefficient of the main beam and the pier can be calculated based on a finite element model. The service life prediction result can be determined based on the service life prediction results of the sub-regions, the key sub-region, and the correlation between the deterioration processes by using a weighted fusion algorithm, assigning a higher weight to the service life prediction value of the key sub-region, and correcting the prediction value of adjacent regions by the correlation coefficient.

[0098] In the application, the method is also specifically described based on a specific application scenario. Figure 2 is a data schematic diagram corresponding to the bridge service life prediction method provided by the application, which will be specifically described below in combination with Figure 2 .

[0099] The reliability of a structure is the ability to complete a predetermined function within a specified time and under specified conditions, and the reliability of a structure is a quantitative index of the reliability of the structure.

[0100] The reliability of a structure can be simply expressed as the logical relationship between the action effect S and the structural resistance effect R of the structure, so that the function function of completing the predetermined function within a certain time domain under the set conditions can be expressed as:

[0101] ;

[0102] Wherein, the structural resistance R represents the ability of the bridge structure to resist structural damage and deformation, such as the ultimate bending moment of the bearing structure, the maximum stress limit, the maximum deflection, and the fatigue limit; the various action effects S of the bridge are mainly the dead load and live load effects. When the action effect S exceeds the resistance, the predetermined structure function cannot be completed, i.e. Z<0, and the probability at this time is the structural function failure , otherwise it can be called the reliability probability, or the reliability ; Z represents the structure function function.

[0103] As shown in Figure 3 , the distance from the origin to the average value is The data can be expressed directly corresponding to Avoid the cumbersome calculation workload brought by multiple integrals. The smaller, The greater, and vice versa The smaller. Thus can be used as an important indicator of structural reliability, The calculation method of As shown in the following formula:

[0104] ;

[0105] Wherein, Indicates the mean value of structural resistance, Indicates the mean value of load effect, Indicates the variance of structural resistance, Indicates the variance of load effect.

[0106] The natural life of the bridge is also called the service life or the durability of the structure, which refers to the time that the bridge still has its predetermined use function under normal use and normal maintenance conditions. The remaining service life of the bridge refers to the difference between the service life of the bridge and the used life.

[0107] The present application considers the influence of concrete strength, steel strength, steel corrosion degree and steel and concrete synergistic work coefficient on the bridge structure resistance attenuation, establishes a resistance attenuation model of the bridge structure, and the load of the highway bridge is mainly composed of dead load and live load. Due to the changes of the structural resistance attenuation and the maximum distribution of the load, the reliability of the bridge structure changes with time, the reliability is introduced into the bridge life prediction, and the reliability limit equation of a certain limit state is derived. The reliability index in the specification is introduced into the remaining life, and the remaining service life of the bridge structure under the specified index is predicted.

[0108] Specifically, the present application adopts the bearing capacity life criterion as the termination standard of the service life of the reinforced concrete or prestressed concrete bridge.

[0109] As Figure 2 Indicated, the data involved in the present application include:

[0110] 1. Time-varying rule of concrete strength: the concrete strength is simulated as the product of strength and a certain function, and the present application simulates the concrete strength by using a non-stationary random process, which is more reasonable. The concrete strength obeys normal distribution, and the mean value and standard deviation change with the service time of the structure.

[0111] ;

[0112] ;

[0113] ;

[0114] ;

[0115] with the average and standard deviation of the change of concrete strength with time , with the average and standard deviation of the change of concrete strength with time , the average and standard deviation of the change of concrete strength with time represent the natural logarithm.

[0116] 2, the time-varying law of the area of the steel bar: the corrosion of the steel bar is divided into overall corrosion and local corrosion, and the superposition effect of overall corrosion and local corrosion is difficult to simulate, the present application adopts the annual average corrosion speed to establish a corrosion model of the steel bar as the time-varying law of the area of the steel bar.

[0117] ;

[0118] ;

[0119] with the average and standard deviation of the change of the area of the steel bar with time , with the average and standard deviation of the change of the area of the steel bar with time , the average and standard deviation of the change of the area of the steel bar with time

[0120] ;

[0121] ;

[0122] wherein, ; ; ; ; ; ; ; .

[0123] 3, the time-varying law of the strength of the steel bar: due to the unevenness of the material, the uncertainty of the environmental variable and the different forces of each part of the steel bar, the strength of the steel bar is similar to the strength of the concrete, the present application fits the strength of the steel bar and obtains the following change law:

[0124] ;

[0125] ;

[0126] With the average and standard deviation of the steel strength over time the average and standard deviation of the steel initial strength, respectively. With the average and standard deviation of the steel strength over time, respectively. With the average and standard deviation of the steel strength over time, respectively.

[0127] ;

[0128] ;

[0129] Wherein, ; ; ; ; ; ; ; .

[0130] 4. Time-varying law of the bonding performance between the steel and the concrete: the bonding performance between the steel and the concrete is the basis for the work of the concrete bridge, and the corrosion of the steel can cause the decline of the bonding performance between the steel and the concrete, and the present application adopts the synergistic working coefficient to simulate the change of the decline of the bonding performance.

[0131] Synergistic working coefficient is taken as follows:

[0132] ;

[0133] In the formula, is the width of the corrosion and expansion crack, and d is the maximum diameter of the steel.

[0134] 5. Time-varying structural resistance: the statistical parameters of the structural resistance of the basic component are shown in Table 1, and R k in Table 1 represents the standard value of the structural resistance effect, and the structural resistance is calculated according to the following formula:

[0135] .

[0136] Wherein is the importance coefficient of the bridge and culvert structure; is the design value of the bending moment; fcu is the design value of concrete axial compressive strength; b is the width of rectangular section or the width of web of T-shaped section; x is the height of concrete compression zone; h is the effective height of section; fcu is the design value of concrete axial compressive strength; b is the width of rectangular section or the width of web of T-shaped section; x is the height of concrete compression zone; As is the sectional area of longitudinal ordinary steel in compression zone; As is the distance from the resultant point of ordinary steel in compression zone to the edge of tension zone; fcu is the design value of concrete axial compressive strength; b is the width of rectangular section or the width of web of T-shaped section; x is the height of concrete compression zone; fcu is the design value of concrete axial compressive strength; b is the width of rectangular section or the width of web of T-shaped section; x is the height of concrete compression zone; As is the sectional area of longitudinal ordinary steel in compression zone; As is the distance from the resultant point of ordinary steel in compression zone to the edge of tension zone;

[0137] Table 1

[0138]

[0139] 6、Further consider the effects of vehicle live load and crowd load effect: First, construct a vehicle load probability model, vehicle weight or axle weight, vehicle spacing, axle spacing affect the effects generated in the bridge structure, it is difficult to directly introduce vehicle load into bridge reliability analysis, through a large number of calculations of different bridge types and various spans, obtain the control effect of various load effects. The calculation is divided into two cases of general running state and dense running state. The statistical results are applicable to various bridge types and various spans, and the ratio of the standard load effect value specified in the linear specification is taken as the statistical analysis of the effect. The statistical parameters of vehicle load effect are shown in Table 2.

[0140] Table 2

[0141]

[0142] A crowd load probability model is constructed, and the bridge crowd load is a variable action on the structure that changes with time. A random process probability model is generally used to describe it, and it is a stationary binomial random process:

[0143]

[0144] Among them, represent the maximum value distribution; represent time; represent the standard value of crowd load; mean ; standard deviation . ​

[0145] 7. Reliability index: when calculating the bearing capacity, different reliability indexes are taken for different structural safety levels, and the safety levels refer to the General Specification for Design of Highway Bridges and Culverts (JTGD60-2015), as shown in Table 3.

[0146] Table 3

[0147]

[0148] The present application mainly considers that the in-service bridge has different degrees of attenuation changes after a certain service life, and various influencing factors such as concrete strength, steel strength and synergistic working performance are functions of time change, and are also normal distribution random processes; the dead load and live load of the structure can also be described as a stationary random process. Therefore, the technical life of the structure is affected by multiple random variables, the present application introduces the reliability of the structure based on the reliability and random variables to predict the life of the in-service concrete bridge, and for different structural safety levels, corresponding reliability indexes are adopted, thereby providing technical support for bridge dynamic maintenance decision-making.

[0149] The bridge life prediction device provided by the present application is described below, and the bridge life prediction device described below can be correspondingly referred to the bridge life prediction method described above. As shown in Figure 4 The bridge life prediction device provided by the present application includes the following modules:

[0150] The time-varying structure resistance model construction module 410 is configured to construct a time-varying structure resistance model according to time-varying laws corresponding to a plurality of bridge life factors; the bridge life factors include at least two of a synergistic working coefficient between steel and concrete, concrete strength, steel area, and steel strength;

[0151] The load effect model construction module 420 is configured to construct a load effect model according to load data of vehicle live load and crowd load data;

[0152] The structure function function construction module 430 is configured to construct a structure function function according to the time-varying structure resistance model and the load effect model;

[0153] The prediction module 440 is configured to predict the remaining service life of the target bridge according to the structural reliability parameters of the target bridge and the structure function function.

[0154] Figure 5 An example of an entity structure schematic diagram of an electronic device is shown in Figure 5As shown, the electronic device can include a processor 510, a communications interface 520, a memory 530, and a communications bus 540, wherein the processor 510, the communications interface 520, and the memory 530 complete mutual communication through the communications bus 540. The processor 510 can invoke a logic instruction in the memory 530 to execute a bridge life prediction method, which includes: constructing a time-varying structural resistance model according to time-varying laws corresponding to a plurality of bridge life factors; the bridge life factors include at least two of a synergistic work coefficient between steel bars and concrete, concrete strength, steel bar area, and steel bar strength; constructing a load effect model according to load data of a vehicle live load and crowd load data; constructing a structure function function according to the time-varying structural resistance model and the load effect model; and predicting a remaining service life of a target bridge according to a structure reliability parameter of the target bridge and the structure function function.

[0155] In addition, the logic instruction in the memory 530 described above can be implemented in the form of a software function unit and sold or used as an independent product when used, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

[0156] In another aspect, the present application also provides a computer program product comprising a computer program, which can be stored on a non-transitory computer readable storage medium, and the computer program, when executed by a processor, causes a computer to perform the bridge life prediction method provided by any of the above methods, which comprises: constructing a time-varying structural resistance model according to time-varying rules corresponding to a plurality of bridge life factors; the bridge life factors include at least two of a synergistic working coefficient between steel bars and concrete, concrete strength, steel bar area, and steel bar strength; constructing a load effect model according to load data of vehicle live load and crowd load data; constructing a structure function function according to the time-varying structural resistance model and the load effect model; and predicting the remaining service life of the target bridge according to the structure reliability parameters of the target bridge and the structure function function.

[0157] In yet another aspect, the present application also provides a non-transitory computer readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the bridge life prediction method provided by any of the above methods, which comprises: constructing a time-varying structural resistance model according to time-varying rules corresponding to a plurality of bridge life factors; the bridge life factors include at least two of a synergistic working coefficient between steel bars and concrete, concrete strength, steel bar area, and steel bar strength; constructing a load effect model according to load data of vehicle live load and crowd load data; constructing a structure function function according to the time-varying structural resistance model and the load effect model; and predicting the remaining service life of the target bridge according to the structure reliability parameters of the target bridge and the structure function function.

[0158] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the present embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0159] Those skilled in the art can clearly understand the technical solutions of the various embodiments from the above description of the embodiments, and the various embodiments can be implemented by means of software with the necessary general hardware platforms, and of course, can also be implemented by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part of the prior art that makes a contribution, can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, and the like, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0160] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for some technical features therein; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method of predicting the life of a bridge, characterized by, The method comprises the following steps: constructing a time-varying structural resistance model according to time-varying rules corresponding to a plurality of bridge life factors, wherein the bridge life factors include at least two of a synergistic working coefficient between steel bars and concrete, concrete strength, steel bar area, and steel bar strength; constructing a load effect model according to load data of vehicle live load and crowd load data; constructing a structure function according to the time-varying structural resistance model and the load effect model; predicting a remaining service life of the target bridge according to a structure reliability parameter of the target bridge and the structure function; the step of predicting the remaining service life of the target bridge according to the structure reliability parameter of the target bridge and the structure function comprises: drawing a reliability parameter change curve according to the structure function; obtaining the structure reliability parameter of the target bridge and a matching result of the structure reliability parameter and the reliability parameter change curve; predicting the remaining service life of the target bridge according to the matching result; dividing the bridge structure of the target bridge into a plurality of sub-regions according to the bridge structure of the target bridge, wherein the size of each sub-region is determined according to the variation degree of structure characteristics and environmental conditions; determining a life prediction result of the target bridge according to life prediction results of the sub-regions; the step of determining the life prediction result of the target bridge according to the life prediction results of the sub-regions comprises: real-time collecting concrete strength, steel bar corrosion degree, and rust expansion crack width data of each sub-region, calculating a deterioration speed of each sub-region, and screening the sub-regions exceeding a preset threshold as key sub-regions; analyzing load transmission paths and environmental parameter similarity of adjacent sub-regions, and establishing a correlation coefficient matrix; weighting and fusing life prediction results corresponding to each sub-region, wherein a higher weight is given to the life prediction value of the key sub-region in the weighting and fusing, and the prediction value of adjacent sub-regions is corrected through the correlation coefficient matrix.

2. The bridge life prediction method according to claim 1, characterized by, Before the step of constructing a time-varying structural resistance model according to time-varying rules corresponding to a plurality of bridge life factors, the method further comprises: simulating concrete strength by using a non-stationary random process to obtain a time-varying rule of the concrete strength; and / or, establishing a time-varying rule of the steel bar area by using an annual average corrosion speed; and / or, simulating steel bar strength by using a non-stationary random process to obtain a time-varying rule of the steel bar strength; and / or, simulating a decay relationship of bonding performance between steel bars and concrete by using a synergistic working coefficient to obtain a time-varying rule of the synergistic working coefficient.

3. The bridge life prediction method according to claim 1, characterized by, The step of constructing a load effect model comprises: simulating the crowd load data by using a stationary binomial random process to obtain a crowd load probability model; obtaining a vehicle load probability model according to the load data of vehicle live load; constructing the load effect model according to the crowd load probability model and the vehicle load probability model.

4. The bridge life prediction method according to claim 1, characterized by, The method further comprises: obtaining a mapping relationship between a structure safety level and a reliability parameter; determining a reference reliability parameter of the target bridge according to a structure safety level of the target bridge; According to the bridge structure data of the target bridge and the reference reliability parameter, a structure reliability parameter of the target bridge is determined.

5. A bridge life prediction device characterized by comprising: Comprise: A time-varying structural resistance model construction module is configured to construct a time-varying structural resistance model according to time-varying rules corresponding to a plurality of bridge life factors, wherein the bridge life factors include at least two of a synergistic working coefficient between steel bars and concrete, concrete strength, steel bar area, and steel bar strength; A load effect model construction module is configured to construct a load effect model according to load data of vehicle live loads and crowd load data; A structure function function construction module is configured to construct a structure function function according to the time-varying structural resistance model and the load effect model; A prediction module is configured to predict a remaining service life of the target bridge according to a structure reliability parameter of the target bridge and the structure function function; The prediction of the remaining service life of the target bridge according to the structure reliability parameter of the target bridge and the structure function function comprises: Drawing a reliability parameter change curve according to the structure function function; Obtaining a structure reliability parameter of the target bridge and a matching result of the structure reliability parameter and the reliability parameter change curve; Predicting the remaining service life of the target bridge according to the matching result; According to the bridge structure of the target bridge, the entire bridge structure is divided into a plurality of sub-regions, and the size of each sub-region is determined according to the variation degree of structure characteristics and environmental conditions; The life prediction result of the target bridge is determined according to the life prediction results of the sub-regions; The determination of the life prediction result of the target bridge according to the life prediction results of the sub-regions comprises: Real-time collection of concrete strength, steel bar corrosion degree, and rust expansion crack width data of each sub-region, calculation of the deterioration speed of each sub-region, and screening of the sub-regions exceeding the preset threshold as key sub-regions; Analysis of the load transfer path and environmental parameter similarity of adjacent sub-regions, establishment of a correlation coefficient matrix; Weighted fusion of the life prediction results corresponding to each sub-region, wherein a higher weight is given to the life prediction value of the key sub-region in the weighted fusion, and the prediction value of adjacent sub-regions is corrected through the correlation coefficient matrix.

6. An electronic device comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized in that, The processor executes the computer program to realize the bridge life prediction method according to any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the bridge life prediction method according to any one of claims 1 to 4.

8. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to realize the bridge life prediction method according to any one of claims 1 to 4.

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