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

Abstract

The invention provides a bridge life prediction method and device, equipment, a medium and a product, and relates to the technical field of data processing, and the method comprises the steps: constructing a time-varying structure resistance model according to a time-varying rule corresponding to a plurality of bridge life factors; the bridge life factor comprises at least two of a cooperative work coefficient between a steel bar and concrete, concrete strength, a steel bar area and steel bar strength; constructing a load effect model according to the load data of the vehicle live load and the crowd load data; constructing a structure performance function according to the time-varying structure resistance model and the load effect model; and predicting the remaining service life of the target bridge according to the structural reliability parameter of the target bridge and the structural performance function. The bridge life prediction precision can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular, to a method, device, equipment, medium and product for predicting the life of a bridge. Background Art

[0002] As the service life of bridges increases, a large number of bridges enter the performance decline period and face severe challenges such as structural aging and material property degradation. In current bridge management practices, there are generally problems such as durability loss exceeding expectations and maintenance decision-making lag, which not only cause huge waste of maintenance funds, but may also lead to major safety accidents.

[0003] The existing bridge life assessment methods mainly have the following technical defects: The traditional resistance model only uses static deterioration coefficients and fails to accurately reflect the time-varying characteristics of concrete strength, steel bar performance and their collaborative working relationship. In the analysis of load effects, deterministic design values are generally used, ignoring the stochastic process characteristics of vehicle live loads and pedestrian loads. In addition, the current assessment methods are mostly based on deterministic models and cannot effectively handle the discreteness of material parameters and the randomness of environmental actions. In summary, the existing technologies are not conducive to improving the accuracy of bridge life prediction. Summary of the Invention

[0004] The present invention provides a method, device, equipment, medium and product for predicting the life of a bridge, aiming to solve the defect that the existing technologies are not conducive to improving the accuracy of bridge life prediction and to achieve the improvement of the accuracy of bridge life prediction.

[0005] The present invention provides a method for predicting the life of a bridge, which includes: Constructing a time-varying structural resistance model according to the time-varying laws corresponding to multiple bridge life factors; the bridge life factors include at least two of the collaborative working coefficient between steel bars and concrete, concrete strength, steel bar area, and steel bar strength; Constructing a load effect model according to the load data of vehicle live loads and pedestrian load data; Constructing a structural performance function according to the time-varying structural resistance model and the load effect model; Predicting the remaining service life of the target bridge according to the structural reliability parameters of the target bridge and the structural performance function.

[0006] According to the method for predicting the life of a bridge provided by the present invention, before constructing a time-varying structural resistance model according to the time-varying laws corresponding to multiple bridge life factors, it further includes: Simulating the concrete strength by using a non-stationary stochastic process to obtain the time-varying law of the concrete strength; and / or, Establishing the time-varying law of the steel bar area by using the annual average corrosion rate; and / or, Simulate the steel bar strength using a non - stationary random process to obtain the time - varying law of the steel bar strength; and / or, Simulate the decay relationship of the bond performance between the steel bar and the concrete using a coefficient of composite action to obtain the time - varying law of the coefficient of composite action.

[0007] According to a bridge life prediction method provided by the present invention, the construction of the load effect model includes: Simulate the crowd load data using a stationary binomial random process to obtain a crowd load probability model; Obtain a vehicle load probability model according to the load data of the vehicle live load; Construct the load effect model according to the crowd load probability model and the vehicle load probability model.

[0008] According to a bridge life prediction method provided by the present invention, it further includes: Obtain the mapping relationship between the structural safety level and the reliability parameters; Determine the reference reliability parameters of the target bridge according to the structural safety level of the target bridge; Determine the structural reliability parameters of the target bridge according to the bridge structure data of the target bridge and the reference reliability parameters.

[0009] According to a bridge life prediction method provided by the present invention, predicting the remaining service life of the target bridge according to the structural reliability parameters of the target bridge and the structural function function includes: Draw a reliability parameter change curve according to the structural function function; Obtain the structural reliability parameters of the target bridge and the matching result between the structural reliability parameters and the reliability parameter change curve; Predict the remaining service life of the target bridge according to the matching result.

[0010] According to a bridge life prediction method provided by the present invention, predicting the remaining service life of the target bridge includes: Obtain multiple sub - regions of the target bridge according to the bridge structure of the target bridge; Obtain the local environmental parameters of each sub - region; Determine the life prediction model of each sub - region according to the structural reliability parameters of the target bridge and the structural function function; Determine the life prediction result of the target bridge according to the life prediction results of each sub - region.

[0011] The present invention also provides a bridge life prediction device, including: A time-varying structural resistance model construction module, configured to construct a time-varying structural resistance model according to the time-varying laws corresponding to multiple bridge life factors; the bridge life factors include at least two of: the cooperative working coefficient between steel bars and concrete, the concrete strength, the steel bar area, and the steel bar strength; A load effect model construction module, configured to construct a load effect model according to the load data of vehicle live load and the load data of pedestrian load; A structural performance function construction module, configured to construct a structural performance function according to the time-varying structural resistance model and the load effect model; A prediction module, configured to predict the remaining service life of the target bridge according to the structural reliability parameters of the target bridge and the structural performance function.

[0012] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where when the processor executes the computer program, the bridge life prediction method as described in any one of the above is implemented.

[0013] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the bridge life prediction method as described in any one of the above is implemented.

[0014] The present invention also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the bridge life prediction method as described in any one of the above is implemented.

[0015] The bridge life prediction method, device, equipment, medium, and product provided by the present invention construct a time-varying structural resistance model according to the time-varying laws corresponding to multiple bridge life factors; the bridge life factors include at least two of: the cooperative working coefficient between steel bars and concrete, the concrete strength, the steel bar area, and the steel bar strength; construct a load effect model according to the load data of vehicle live load and the load data of pedestrian load; construct a structural performance function according to the time-varying structural resistance model and the load effect model; predict the remaining service life of the target bridge according to the structural reliability parameters of the target bridge and the structural performance function. In this way, by constructing a time-varying structural resistance model and a load effect model to establish a structural performance function, and predicting the remaining life based on the structural reliability parameters, the defects of static deterioration coefficients and deterministic load analysis in traditional methods are solved, which is beneficial to improving the accuracy of bridge life prediction. Description of the Drawings

[0016] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the accompanying drawings required in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0017] Figure 1 It is a schematic flowchart of a bridge life prediction method provided by the present invention.

[0018] Figure 2 It is a schematic data diagram corresponding to the bridge life prediction method provided by the present invention.

[0019] Figure 3 It is a schematic diagram of the probability distribution density curve of the function function provided by the present invention.

[0020] Figure 4 It is a schematic structural diagram of a bridge life prediction device provided by the present invention.

[0021] Figure 5 It is a schematic structural diagram of an electronic device provided by the present invention. Detailed implementation manners

[0022] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0023] The following will describe Figures 1-3 the bridge life prediction method provided by the present invention. As Figure 1 shown, the present invention proposes a bridge life prediction method, including the following steps: S101. Construct a time-varying structural resistance model according to the time-varying laws corresponding to multiple bridge life factors; the bridge life factors include at least two of the collaborative working coefficient between steel bars and concrete, concrete strength, steel bar area, and steel bar strength.

[0024] Among them, the collaborative working coefficient includes bond performance.

[0025] S102. Construct a load effect model according to the load data of vehicle live load and crowd load data.

[0026] S103. Construct a structural function function according to the time-varying structural resistance model and the load effect model.

[0027] S104. Predict the remaining service life of the target bridge according to the structural reliability parameters of the target bridge and the structural performance function.

[0028] Among them, the time-varying structural resistance model refers to a mathematical model that can reflect the change of the bridge structural resistance over time. Specifically, it can be constructed by superimposing the time-varying laws of multiple bridge life factors such as concrete strength, steel bar area, steel bar strength, and the coefficient of collaborative work between steel bars and concrete. By introducing the time-varying law of multi-factor coordination, this model solves the deficiency of the traditional method that only describes the structural resistance by the product of deterioration coefficients.

[0029] Among them, the bridge life factor refers to the key parameter that affects the degradation of the bridge structural performance, specifically including at least two parameters among concrete strength, steel bar area, steel bar strength, and the coefficient of collaborative work between steel bars and concrete. Selecting a multi-factor combination can more comprehensively characterize the coupling effect of material property degradation and interface bond failure, and avoid the limitation of single-factor analysis.

[0030] The load effect model refers to a statistical model that describes the random distribution law of vehicle live load and pedestrian load. Specifically, the pedestrian load can be simulated by a stationary binomial random process, and a probability model can be established by combining the measured vehicle load data. By quantifying the characteristics of load randomness, this model solves the problem that the traditional design value method ignores the actual load fluctuation.

[0031] The structural performance function refers to the limit state equation used to evaluate the structural reliability of the bridge. Specifically, it is constructed by dynamically coupling the time-varying structural resistance model and the load effect model. This function can reflect the probability relationship between resistance decay and load effect in real time, providing a mathematical basis for reliability calculation.

[0032] The prediction of the remaining service life refers to a dynamic matching process based on the specific reliability parameters and structural performance function of the target bridge. Specifically, it can be realized by the comparative analysis of the reliability parameter change curve and the real-time monitoring data. By replacing the deterministic prediction with probability evaluation, this method solves the defect that the empirical method ignores the distribution characteristics of random variables.

[0033] In the present invention, a dynamic reliability evaluation system including the time-varying law of multi-factors and random load effects is established. By probabilistically coupling the time-varying processes such as concrete strength degradation, steel bar corrosion, and interface bond recession with random loads, a structural performance function that can quantify the bridge life attenuation law is constructed, realizing accurate prediction of the remaining life based on the probability method.

[0034] Specifically, first, a time-varying structural resistance model is constructed based on the time-varying laws corresponding to multiple bridge life factors. These life factors include at least two of concrete strength, steel bar area, steel bar strength, and the coefficient of collaborative work between steel bars and concrete. Among them, the coefficient of collaborative work includes bond performance. By establishing the time-varying laws of these key parameters, the variation law of the bridge structural resistance over time can be comprehensively reflected.

[0035] Next, a load effect model is constructed according to the load data of vehicle live load and pedestrian load data. This step takes into account the randomness and time-varying characteristics of the actual load, avoiding the errors caused by using deterministic design values in traditional methods.

[0036] Then, based on the constructed time-varying structural resistance model and load effect model, a structural performance 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.

[0037] Finally, according to the structural reliability parameters of the target bridge and the constructed structural performance 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.

[0038] Throughout the process, each step is closely linked, 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, thus improving the accuracy and reliability of life prediction.

[0039] In a possible implementation manner, the solution of the present invention is specifically implemented as follows: First, for a certain large bridge, historical monitoring data of concrete strength, steel bar area, steel bar strength, and the coefficient of collaborative work between steel bars and concrete are collected. These data are analyzed using a non-stationary random process model to establish the time-varying laws of each parameter. For example, the concrete strength can adopt a lognormal distribution model, and the steel bar area can establish a linear attenuation model based on the annual average corrosion rate.

[0040] Secondly, the vehicle load data of the bridge are collected, including information such as vehicle type, weight, and passing frequency. At the same time, pedestrian load data, such as pedestrian flow and distribution, are obtained. Using extreme value distribution theory and random process models, probability models of vehicle live load and pedestrian load are constructed, and then a comprehensive load effect model is established.

[0041] Then, the time-varying structural resistance model and load effect model are substituted into the limit state equation in the structural reliability theory to construct a structural performance function. This function reflects the reliability level of the bridge structure at different time points.

[0042] Finally, according to the designed service life and safety level of the bridge, the target reliability index is determined. By comparing and analyzing this index with the structural performance function, the time when the bridge reaches the target reliability index is solved through numerical calculation methods, which is the predicted remaining service life.

[0043] In an 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.

[0044] Through the above solution, the present invention can effectively solve the problems of over-simplification of the resistance model and insufficient consideration of load effects in traditional bridge life prediction methods. By establishing a time-varying resistance model considering the synergistic effect of multiple factors and combining with a dynamic load effect model, an accurate description of the degradation process of the bridge structural performance is achieved. This method takes into account the randomness and time-variation of material properties, environmental factors and load characteristics, significantly improving the accuracy of life prediction. At the same time, due to the use of probability and statistics methods, this solution can better evaluate the reliability level of the bridge, providing a more reliable scientific basis for bridge maintenance and management decisions. In addition, the flexibility of this method enables it to adapt to bridges of different types and environmental conditions, having a wide application prospect.

[0045] Specifically, before constructing the time-varying structural resistance model according to the time-varying laws corresponding to multiple bridge life factors, the method further includes: Simulating the concrete strength using a non-stationary random process to obtain the time-varying law of the concrete strength; and / or, Establishing the time-varying law of the steel bar area using the annual average corrosion rate; and / or, Simulating the steel bar strength using a non-stationary random process to obtain the time-varying law of the steel bar strength; and / or, Simulating the degradation relationship of the bond performance between the steel bar and the concrete using a cooperative working coefficient to obtain the time-varying law of the cooperative working coefficient.

[0046] Specifically, the time-varying law of concrete strength is obtained by superimposing the statistical parameters of the initial strength and the time-related function, and the 28-day strength data is extended to the probability distribution in the continuous time domain. For example, an exponential function is used to describe the attenuation law of the strength mean value, and the standard deviation increases linearly with time; the model of the steel bar area is based on the annual average corrosion rate to establish a deterministic degradation trend, while retaining the statistical characteristics of the initial area. For example, the corrosion rate is converted into an area reduction rate function, which is multiplied by the average value of the initial area to form a time-varying mean value; the model of the steel bar strength combines the initial strength parameters 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 perturbation term is introduced to characterize the influence of environmental factors; the model of the coefficient of cooperative work converts the physical damage process of the bond performance into a quantifiable mathematical relationship by establishing a ratio function of the width of the rust expansion crack to the diameter of the steel bar. In the process of constructing these models, by separating the deterministic degradation trend and the random fluctuation component, not only the physical mechanism of the degradation of material properties is retained, but also the mathematical expression of the probability characteristics is realized, which improves the prediction accuracy of the time-varying structural resistance model.

[0047] In a possible implementation manner, the solution of the present invention is specifically implemented as follows: Before constructing the time-varying structural resistance model, first establish the time-varying laws of multiple bridge life factors. Specifically, a non-stationary random process is used to simulate the concrete strength to obtain the time-varying law of the concrete strength. Further, the annual average corrosion rate is used to establish the time-varying law of the steel bar area. Thus, a non-stationary random process is used to simulate the steel bar strength to obtain the time-varying law of the steel bar strength. Finally, the coefficient of cooperative work is used to simulate the degradation relationship between the bond performance of the steel bar and the concrete to obtain the time-varying law of the coefficient of cooperative work.

[0048] It should be noted that the above determination method of the time-varying law is only an example and is not specifically limited.

[0049] Through the above technical solution, the present invention realizes the accurate modeling of the key factors of the bridge life. Due to the adoption of the non-stationary random process and the time-varying model, it can more accurately reflect the variation law of each factor with time.

[0050] The present invention further proposes a method for constructing a load effect model, including: using a stationary binomial random process to simulate the crowd load data to obtain a crowd load probability model; obtaining a vehicle load probability model according to the load data of the vehicle live load; and constructing a load effect model according to the crowd load probability model and the vehicle load probability model.

[0051] In a possible implementation manner, the solution of the present invention is specifically implemented as follows: When constructing the load effect model, first, the crowd load data is simulated using a stationary binomial random process to obtain the crowd load probability model. Specifically, by collecting the pedestrian flow data on the bridge, a relationship function between the crowd density and time is established. Then, the binomial distribution is used to describe the probability of people arriving on the bridge deck per unit time, and it is extended to a continuous-time random process.

[0052] Secondly, based on the load data of the vehicle live load, the vehicle load probability model is obtained. The specific steps include: collecting traffic flow data and analyzing the vehicle type distribution; establishing the probability density function of the vehicle weight; considering the vehicle dynamic effect and introducing the impact factor.

[0053] Finally, the crowd load probability model and the vehicle load probability model are superimposed to construct a complete load effect model. During the superposition process, the correlation between the two loads is considered, and the combined effect is described using the joint distribution function.

[0054] Through the above technical solutions, the present invention can more accurately simulate the load effect actually borne by the bridge. Due to considering the randomness and correlation of the crowd load and the vehicle load, the limitations of the traditional deterministic method are avoided. This load effect analysis method based on the probability model can better reflect the actual load conditions borne by the bridge during its service life, providing more reliable input data for the subsequent construction of the structural function function and life prediction.

[0055] The present invention further proposes to obtain the mapping relationship between the structural safety level and the reliability parameters; determine the reference reliability parameters of the target bridge according to the structural safety level of the target bridge; and determine the structural reliability parameters of the target bridge according to the bridge structure data of the target bridge and the reference reliability parameters.

[0056] Among them, the mapping relationship between the structural safety level and the reliability parameters is established by querying industry specifications or based on historical detection data. For example, the bridge is divided into first-level, second-level, and third-level safety levels, corresponding to different reliability indices or failure probability thresholds respectively. The determination of the reference reliability parameters depends on the safety level division in the bridge design document. For example, the first-level safety level corresponds to a reliability index β≥4.2, and the second level corresponds to 3.7≤β<4.2. The bridge structure data includes the detection results of material property degradation, the load history record, or the measured values of the component geometric dimensions. When combined with the reference parameters, the parameter values are adjusted through weighted calculation or probability correction models.

[0057] Specifically, first, retrieve the design safety level of the target bridge from the bridge management system, match the pre-stored mapping relation table, and extract the corresponding benchmark reliability index. Subsequently, obtain the current concrete carbonation depth and steel corrosion rate data of the bridge through non-destructive testing, and calculate the resistance attenuation coefficient; combine the vehicle load dynamic monitoring data to correct the statistical characteristics of the live load. Input the resistance attenuation coefficient and the live load correction result into the parameter update model, and dynamically adjust the benchmark reliability index based on the Bayesian probability method to obtain the reliability parameters reflecting the current state.

[0058] Through the above technical solutions, the present invention can accurately determine the structural reliability parameters of the target bridge. Thereby, it provides reliable input data for subsequent bridge life prediction and improves the accuracy of life prediction.

[0059] The present invention further proposes to draw a reliability parameter change curve according to the structural function function; obtain the structural reliability parameters of the target bridge and the matching result between the structural reliability parameters and the reliability parameter change curve; predict the remaining service life of the target bridge according to the matching result.

[0060] Among them, the horizontal axis of the reliability parameter change curve is the time variable, and the vertical axis is the reliability index; the structural reliability parameters of the target bridge are collected through on-site inspection or monitoring data, including parameters such as concrete carbonation depth, steel corrosion rate, and crack width; the matching result can use the least squares method or the correlation analysis method to determine the fitting degree of the actual parameters and the theoretical curve; the remaining life prediction is to calculate the time point when the actual parameter curve intersects the threshold by setting a reliability threshold.

[0061] Specifically, the reliability parameter change curve reflects the probability distribution relationship between the structural resistance and the load effect over time, and generates a theoretical attenuation trend through numerical simulation. After the structural reliability parameters of the target bridge are collected, time series alignment and deviation analysis are performed with the theoretical curve, such as using the dynamic time warping algorithm to eliminate the time series difference. The matching result evaluates the coincidence degree between the theoretical model and the actual state by calculating the sum of squared residuals or the correlation coefficient. If the residual exceeds the preset threshold, the model parameter correction mechanism is triggered. The remaining service life prediction extrapolates the matched curve to the critical time point where the reliability index is lower than the target value. For example, 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.

[0062] Further, predicting the remaining service life of the target bridge includes: obtaining multiple sub-regions of the target bridge according to the bridge structure of the target bridge; acquiring the local environmental parameters of each sub-region; determining the life prediction model of each sub-region according to the structural reliability parameters and the structural function function of the target bridge; and determining the life prediction result of the target bridge according to the life prediction results of each sub-region.

[0063] Specifically, obtaining multiple sub-regions of the target bridge according to the bridge structure of the target bridge; dividing the entire bridge structure into multiple sub-regions, and determining the size of each sub-region according to the change degree of structural characteristics and environmental conditions; for example, dividing the main components such as bridge decks, main girders, and bridge piers into several sub-regions respectively; acquiring the local environmental parameters of each sub-region; installing environmental monitoring sensors in each sub-region to collect local environmental parameter data such as temperature, humidity, and salt fog concentration; at the same time, regularly collecting the structural parameters such as concrete strength and steel bar corrosion degree of each sub-region by using non-destructive testing techniques; determining the life prediction model of each sub-region according to the structural reliability parameters and the structural function function of the target bridge; and determining the life prediction result of the target bridge by using a multi-scale spatial analysis method according to the life prediction results of each sub-region.

[0064] Thus, determining the life prediction result of the target bridge according to the life prediction results of each sub-region is beneficial to further improving the accuracy of bridge life prediction. By dividing the bridge into multiple sub-regions and predicting the life respectively, the structural characteristics and environmental condition differences of different parts of the bridge can be fully considered, avoiding the errors that may be brought by overall prediction. At the same time, by using the multi-scale spatial analysis method, the mutual influence between each sub-region can be comprehensively considered, so as to obtain a more comprehensive and accurate overall bridge life prediction result. This method not only improves the prediction accuracy, but also can identify the key regions with faster deterioration in the bridge, providing more targeted guidance for the maintenance and management of the bridge.

[0065] The present invention further proposes determining the life prediction result of the target bridge according to the life prediction results of each sub-region, including: identifying the key sub-regions with faster deterioration rate; obtaining the correlation of the deterioration process between adjacent sub-regions; and determining the life prediction result of the target bridge based on the life prediction results of each sub-region, the key sub-regions, and the correlation of the deterioration process.

[0066] Among them, the key sub-regions with a faster deterioration rate can be identified by collecting the concrete strength, steel corrosion degree, and rust expansion crack width data of each sub-region in real time, calculating the deterioration rate of each sub-region, and screening the sub-regions that exceed the preset threshold as the key regions. Obtaining the correlation of the deterioration process between adjacent sub-regions can be achieved by analyzing the load transfer path and environmental parameter similarity of adjacent sub-regions, and establishing a correlation coefficient matrix. For example, the load correlation coefficient between the main girder and the pier can be calculated based on a finite element model. Determining the life prediction result based on the life prediction results, key sub-regions, and deterioration process correlation of each sub-region can adopt a weighted fusion algorithm, assigning a higher weight to the life prediction value of the key sub-region, and correcting the prediction values of adjacent regions through the correlation coefficient.

[0067] In the present invention, the method is also specifically described based on a specific application scenario. Figure 2 It is a data schematic diagram corresponding to the bridge life prediction method provided by the present invention. Now, it will be specifically described in conjunction with Figure 2 for specific illustration.

[0068] The reliability of a structure is the ability to complete the predetermined function within a specified time and under specified conditions. The reliability of a structure is a quantitative index of the structure's reliability.

[0069] The structural reliability can mainly be simplified as the logical relationship between two random variables, the action effect S of the structure and the resistance effect R of the structure. Thus, within a certain time domain and under set conditions, the performance function for completing the predetermined function can be expressed as: ; Among them: The structural resistance R represents the ability of the bridge structure to resist structural damage and deformation, such as the ultimate moment of the load-bearing structure, the maximum stress limit, the maximum deflection, and the fatigue limit, etc.; 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 structural function cannot be completed, that is, Z < 0. At this time, the probability is the structural function failure , and vice versa, it can be called the reliability probability, or reliability ; Z represents the structural performance function.

[0070] As Figure 3 shown, the distance from the origin to the average value is . It can be expressed by the directly corresponding , avoiding the cumbersome calculation workload brought by multiple integrals. The smaller , the larger , and vice versa is smaller. Therefore, can be used as an important index to measure the structural reliability. Among them, the calculation method of ; Among them, represents the mean value of structural resistance, represents the mean value of load effect, represents the variance of structural resistance, represents the variance of load effect.

[0071] The natural life of a bridge is also called the service life or durability life of the structure, which refers to the time when the bridge still has its intended use function under normal use and normal maintenance conditions. The remaining service life of a bridge is the difference between the service life of the bridge and the years of use.

[0072] The present invention considers the influence of concrete strength, steel bar strength, degree of steel bar corrosion, and the coefficient of collaborative work between steel bars and concrete on the attenuation of the resistance of bridge structures, establishes a resistance attenuation model of bridge structures, and the loads of highway bridges mainly consist of dead loads and live loads, and establishes a load model of in-service bridges. Due to the attenuation of the resistance of the structure and the change of the maximum load distribution, the reliability of the bridge structure changes with time. The reliability is introduced into the prediction of the bridge life, and the reliability limit equation of a certain limit state is derived. The reliability index in the code is introduced into the remaining life to predict the remaining service life of the bridge structure under the specified index.

[0073] Specifically, for reinforced concrete or prestressed concrete bridges, the present invention adopts the bearing capacity life criterion as the end standard of the service life of the structure.

[0074] As Figure 2 shown, the data involved in the present invention include: 1. The time-varying law of concrete strength: The concrete strength is simulated as the product of strength and a certain function. It is more reasonable to simulate the concrete strength with a non-stationary random process in the present invention. The concrete strength follows a normal distribution, and the mean value and standard deviation change with the service time of the structure.

[0075] ; ; ; ; and are the mean value and standard deviation of the change of concrete strength with time , and are respectively the mean value and standard deviation of the 28-day strength of concrete. and are respectively the change laws of the mean value and standard deviation of strength with time. represents The natural logarithm of

[0076] 2. Time-varying law of steel bar area: The corrosion of steel bars is divided into overall corrosion and local corrosion. It is difficult to simulate the superposition effect of overall corrosion and local corrosion. In the present invention, the annual average corrosion rate is adopted to establish a corrosion model of steel bars as the time-varying law of the steel bar area.

[0077] ; ; and are the average value and standard deviation of the strength of the steel bar area varying with time per year, and are the average value and standard deviation of the initial area of the steel bar, respectively. and are the variation laws of the average value and standard deviation of the steel bar area with time, respectively.

[0078] ; ; Among them, ; ; ; ; ; ; ; .

[0079] 3. Time-varying law of steel bar strength: Due to the inhomogeneity of materials, the uncertainty of environmental variables, and the different forces on each part of the steel bar, the steel bar strength is similar to the concrete strength. In the present invention, the steel bar strength is fitted to obtain the following variation law: ; ; and are the average value and standard deviation of the steel bar strength varying with time per year, and are the average value and standard deviation of the initial strength of the steel bar, respectively. and are the variation laws of the average value and standard deviation of the steel bar strength with time, respectively.

[0080] ; ; Among them, ; ; ; ; ; ; ; 。

[0081] 4. Time-varying law of the bond performance between steel bars and concrete: The bond performance between steel bars and concrete is the basis for the operation of concrete bridges. Steel bar corrosion can lead to a decline in the bond performance between steel bars and concrete. In this invention, the coefficient of cooperative work is used to simulate the change of bond performance degradation.

[0082] Coefficient of cooperative work adopts the following formula: ; In the formula: is the width of the rust expansion crack, and d is the maximum diameter of the steel bar.

[0083] 5. Time-varying structural resistance: The statistical parameters of the structural resistance of basic components are shown in Table 1. R in Table 1 k represents the standard value of the structural resistance effect. The structural resistance is calculated with reference to the following formula: <( 。

[0084] Among them is the importance coefficient of the bridge and culvert structure; is the design value of the bending moment; is the design value of the axial compressive strength of concrete; b is the width of the rectangular section or the web width of the T-shaped section; x is the height of the concrete compression zone; is the effective height of the section; is the design value of the compressive strength of longitudinal ordinary steel bars; is the cross-sectional area of longitudinal ordinary steel bars in the compression zone; is the distance from the resultant force point of the ordinary steel bars in the compression zone to the edge of the tension zone; is the design value of the compressive strength of longitudinal prestressed steel bars; is the stress of the prestressed steel bars when the normal stress of the concrete at the resultant force point of the longitudinal prestressed steel bars in the compression zone is zero; is the cross-sectional area of longitudinal prestressed steel bars in the compression zone; is the distance from the resultant force point of the prestressed steel bars to the edge of the compression zone. For flexural members, when the structure is designed for the ultimate limit state of bearing capacity, the prestressing force should not be regarded as an action, and the prestressed steel bars should be regarded as part of the structural resistance. However, in indeterminate structures such as continuous beams, the secondary effect caused by the prestressing force should be considered.

[0085] Table 1

[0086] 6. Further consider the effects of vehicle live loads and pedestrian loads: First, construct a vehicle load probability model. The vehicle weight or axle weight, vehicle spacing, and axle spacing affect the effects generated in the bridge structure. It is difficult to directly introduce vehicle loads into the bridge reliability analysis. Through a large number of calculations for various spans of different bridge types, various load effects with a controlling role are obtained. The calculations are carried out in two cases: normal operating state and intensive operating state. The statistical results are applicable to various bridge types and various spans, and the ratio to the standard load effect value specified in the linear code is used for the statistical analysis of the effects. The statistical parameters of the vehicle load effects are shown in Table 2.

[0087] Table 2

[0088] Construct a pedestrian load probability model. The pedestrian load on the bridge is a variable action that changes with time acting on the structure. Generally, a stochastic process probability model is used to describe it, and it is a stationary binomial stochastic process: ; where, represents the maximum value distribution; represents time; represents the standard value of the pedestrian load; the mean ; the standard deviation .

[0089] 7. Reliability index: When performing bearing capacity calculations, different reliability indices are taken for different structural safety levels. The safety levels refer to the General Code for Design of Highway Bridges and Culverts (JTGD60 - 2015), as shown in Table 3.

[0090] Table 3

[0091] In this invention, for in - service bridges after a certain service life, there are varying degrees of attenuation changes. Each influencing factor such as concrete strength, steel bar strength, and collaborative working performance is a function that changes with time and is also a stochastic process subject to a normal distribution; the dead loads and live loads of the structure can also be described as stationary stochastic processes. Therefore, the technical life of the structure is affected by multi - factor random variables. This invention introduces the reliability of the structure and, based on reliability and random variables, predicts the life of in - service concrete bridges. For different structural safety levels, corresponding reliability indices are adopted to provide technical support for bridge dynamic maintenance decisions.

[0092] Next, the bridge life prediction device provided by this invention will be described. The bridge life prediction device described below can be mutually corresponding and referred to with the bridge life prediction method described above. As Figure 4 shown, the bridge life prediction device provided by this invention includes the following modules: The time-varying structural resistance model construction module 410 is used to construct a time-varying structural resistance model according to the time-varying laws corresponding to multiple bridge life factors; the bridge life factors include at least two of the coefficient of cooperative work between steel bars and concrete, concrete strength, steel bar area, and steel bar strength; The load effect model construction module 420 is used to construct a load effect model according to the load data of vehicle live load and the load data of pedestrian load; The structural performance function construction module 430 is used to construct a structural performance function according to the time-varying structural resistance model and the load effect model; The prediction module 440 is used to predict the remaining service life of the target bridge according to the structural reliability parameters of the target bridge and the structural performance function.

[0093] Figure 5 An example of the physical structure diagram of an electronic device is shown as Figure 5 shown. The electronic device may include: a processor 510, a communication interface 520, a memory 530, and a communication bus 540. Among them, the processor 510, the communication interface 520, and the memory 530 complete mutual communication through the communication bus 540. The processor 510 can call the logical instructions in the memory 530 to execute the bridge life prediction method, which includes: constructing a time-varying structural resistance model according to the time-varying laws corresponding to multiple bridge life factors; the bridge life factors include at least two of the coefficient of cooperative work between steel bars and concrete, concrete strength, steel bar area, and steel bar strength; constructing a load effect model according to the load data of vehicle live load and the load data of pedestrian load; constructing a structural performance function according to the time-varying structural resistance model and the load effect model; predicting the remaining service life of the target bridge according to the structural reliability parameters of the target bridge and the structural performance function.

[0094] In addition, when the logical instructions in the above-mentioned memory 530 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, external hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0095] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the bridge life prediction method provided by the above-mentioned various methods. The method includes: constructing a time-varying structural resistance model according to the time-varying laws corresponding to multiple bridge life factors; the bridge life factors include at least two of the cooperation coefficient between steel bars and concrete, concrete strength, steel bar area, and steel bar strength; constructing a load effect model according to the load data of vehicle live load and the load data of pedestrian load; constructing a structural performance function according to the time-varying structural resistance model and the load effect model; predicting the remaining service life of the target bridge according to the structural reliability parameters of the target bridge and the structural performance function.

[0096] In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is implemented to execute the bridge life prediction method provided by the above-mentioned various methods. The method includes: constructing a time-varying structural resistance model according to the time-varying laws corresponding to multiple bridge life factors; the bridge life factors include at least two of the cooperation coefficient between steel bars and concrete, concrete strength, steel bar area, and steel bar strength; constructing a load effect model according to the load data of vehicle live load and the load data of pedestrian load; constructing a structural performance function according to the time-varying structural resistance model and the load effect model; predicting the remaining service life of the target bridge according to the structural reliability parameters of the target bridge and the structural performance function.

[0097] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative work.

[0098] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several 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 each embodiment or some parts of the embodiments.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the lifespan of a bridge, characterized in that, Including: Construct a time-varying structural resistance model according to the time-varying laws corresponding to multiple bridge life factors; the bridge life factors include at least two of the cooperation coefficient between steel bars and concrete, concrete strength, steel bar area, and steel bar strength; Construct a load effect model according to the load data of vehicle live load and crowd load data; Construct a structural performance function according to the time-varying structural resistance model and the load effect model; Predict the remaining service life of the target bridge according to the structural reliability parameters of the target bridge and the structural performance function.

2. The bridge life prediction method according to claim 1, wherein Before constructing a time-varying structural resistance model according to the time-varying laws corresponding to multiple bridge life factors, it further includes: Simulate the concrete strength using a non-stationary random process to obtain the time-varying law of the concrete strength; and / or, Establish the time-varying law of the steel bar area using the annual average corrosion rate; and / or, Simulate the steel bar strength using a non-stationary random process to obtain the time-varying law of the steel bar strength; and / or, Simulate the decay relationship of the bond performance between steel bars and concrete using the cooperation coefficient to obtain the time-varying law of the cooperation coefficient.

3. The bridge life prediction method according to claim 1, characterized in that, The constructing of the load effect model includes: Simulate the crowd load data using a stationary binomial random process to obtain a crowd load probability model; Obtain a vehicle load probability model according to the load data of the vehicle live load; Construct 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 in that It also includes: Obtain the mapping relationship between the structural safety level and the reliability parameters; Determine the reference reliability parameters of the target bridge according to the structural safety level of the target bridge; Determine the structural reliability parameters of the target bridge according to the bridge structure data of the target bridge and the reference reliability parameters.

5. The bridge life prediction method according to claim 1, wherein The predicting the remaining service life of the target bridge according to the structural reliability parameters of the target bridge and the structural performance function includes: Draw a reliability parameter change curve according to the structural performance function; Obtain the structural reliability parameters of the target bridge and the matching result between the structural reliability parameters and the reliability parameter change curve; Predict the remaining service life of the target bridge according to the matching result.

6. The bridge life prediction method according to any one of claims 1 to 5, characterized in that, The predicting the remaining service life of the target bridge includes: Obtain multiple sub-regions of the target bridge according to the bridge structure of the target bridge; Obtain the local environmental parameters of each sub-region; Determine the life prediction model of each sub-region according to the structural reliability parameters of the target bridge and the structural performance function; Determine the life prediction result of the target bridge according to the life prediction results of each sub-region.

7. A bridge life prediction device, characterized in that, Including: A time-varying structural resistance model construction module for constructing a time-varying structural resistance model according to the time-varying laws corresponding to multiple bridge life factors; the bridge life factors include at least two of the cooperation coefficient between steel bars and concrete, concrete strength, steel bar area, and steel bar strength; A load effect model construction module for constructing a load effect model according to the load data of vehicle live load and crowd load data; A structural function construction module, configured to construct a structural function according to the time-varying structural resistance model and the load effect model; A prediction module, configured to predict the remaining service life of the target bridge according to the structural reliability parameters of the target bridge and the structural function; 8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized in that, When the processor executes the computer program, the bridge life prediction method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is executed by a processor, the bridge life prediction method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, the bridge life prediction method according to any one of claims 1 to 6 is implemented.

Images