A long-term stiffness prediction method for the negative moment region of a steel-concrete composite beam bridge

Abstract

The present invention relates to the technical field of negative moment zone calculation, and specifically relates to a method for predicting the long-term stiffness of the negative moment zone of a steel-concrete composite beam bridge. A method for predicting the long-term stiffness of the negative moment zone of a steel-concrete composite beam bridge includes: constructing a time-varying degradation model of stud connectors under the coupling action of corrosion and fatigue and a full-time domain degradation model of the concrete bridge deck according to test data, constructing a stiffness degradation model of the negative moment zone of the steel-concrete composite beam bridge, and predicting the long-term stiffness of the negative moment zone of the steel-concrete composite beam bridge, etc. In the process of predicting the stiffness of the negative moment zone of the steel-concrete composite bridge, the present invention takes into account the degradation mechanism of stud connectors under the coupling action of corrosion and fatigue, considers the pitting corrosion situation under environmental influence and the stud crack situation under vehicle load influence, and considers the influence of bridge deck cracks in the pitting corrosion situation, and considers the mutual relationship between the influence of stud pitting corrosion, the influence of stud cracks and the influence of bridge deck cracks, and accurately analyzes the stiffness of the negative moment zone of the steel-concrete composite bridge.

Description

Technical Field

[0001] The present invention relates to the technical field of negative bending moment zone calculation, and particularly relates to a method for predicting the long-term stiffness of the negative bending moment zone of a steel-concrete composite beam bridge. Background Art

[0002] Steel-concrete composite beam bridges have been widely used in bridge engineering because they combine the respective advantages of steel and concrete. However, during service, the problem of long-term stiffness degradation in the negative bending moment zone has become increasingly prominent, affecting the durability and safety of the bridge. Existing literature shows that for traditional RC beams under the combined action of corrosion and fatigue, only diseases such as steel bar corrosion, fatigue fracture, and the decline of the steel-concrete bond force are considered, and relevant diseases can be better simulated by reducing relevant parameters in the beam model. However, in steel-concrete composite beams, the stiffness of the negative bending moment zone is affected by the deterioration responses of multiple components such as the concrete bridge deck and steel-concrete connectors. Therefore, it is urgent to explore the relationship between the deterioration degree of each component and the stiffness degradation of the composite beam to better predict the stiffness of the negative bending moment zone of the steel-concrete composite beam bridge. Summary of the Invention

[0003] In the process of predicting the stiffness of the negative bending moment zone of a steel-concrete composite bridge, the present invention considers the deterioration mechanism of stud connectors under the combined action of corrosion and fatigue, takes into account the pitting corrosion situation under environmental influence and the stud crack situation under vehicle load influence, and considers the influence of bridge deck cracks in the pitting corrosion situation, and also considers the mutual relationship among the influence of stud pitting corrosion, the influence of stud cracks, and the influence of bridge deck cracks, so as to accurately analyze the stiffness of the negative bending moment zone of the steel-concrete composite bridge; it also takes into account the uncertainty of the key parameters of the steel-concrete composite bridge during the prediction process, is data-driven through actual monitoring data, and generates the range of future stiffness change trends through Monte Carlo simulation, providing a more robust and accurate prediction result.

[0004] The present invention provides a method for predicting the long-term stiffness of the negative bending moment zone of a steel-concrete composite beam bridge, including:

[0005] Constructing a time-varying model for the deterioration of stud connectors under the combined action of corrosion and fatigue and a full-time domain model for the deterioration of the concrete bridge deck according to test data;

[0006] Coupling and calculating the time-varying model for the deterioration of stud connectors and the full-time domain model for the deterioration of the concrete bridge deck to obtain a stiffness degradation model for the negative bending moment zone of the steel-concrete composite beam bridge;

[0007] Based on the actual bridge monitoring data and the simulated bridge monitoring data extended by the Bayesian method, determine the upper and lower bounds of the key parameters of the steel-concrete composite bridge through the maximum density interval, and perform Monte Carlo simulation according to the upper and lower bounds of the key parameters of the steel-concrete composite bridge. Sample a number of analysis groups of the key parameters of the steel-concrete composite bridge, send all the analysis groups of the key parameters of the steel-concrete composite bridge into the stiffness degradation model of the negative moment area of the steel-concrete composite beam bridge for calculation, and count the stiffness change trend of the negative moment area of the steel-concrete composite beam bridge to achieve the long-term stiffness prediction of the negative moment area of the steel-concrete composite beam bridge.

[0008] Preferably, it includes: constructing a time-varying deterioration model of stud connectors under the coupling action of corrosion and fatigue according to test data, specifically including the following steps:

[0009] Adopt the electrochemical corrosion test method and fatigue loading test to simulate the coupling action of corrosion and fatigue, obtain test data, and extract the stud size, stiffness of stud connectors, pitting corrosion rate, stud crack propagation rate, concrete cover thickness, chloride ion diffusion rate, concrete fatigue crack width, depth of concrete fatigue crack width, load force and number of load cycles from the test data;

[0010] Numerically simulate the mapping relationship between the pitting corrosion rate and the stud size, concrete cover thickness, chloride ion diffusion rate, concrete fatigue crack width and depth of concrete fatigue crack width, and construct the first time-varying deterioration model of stud connectors;

[0011] Numerically simulate the mapping relationship between the stud crack propagation rate and the stud size, concrete cover thickness, load force and number of load cycles, and construct the second time-varying deterioration model of stud connectors;

[0012] Couple and calculate the first time-varying deterioration model of stud connectors and the second time-varying deterioration model of stud connectors to construct a time-varying deterioration model of stud connectors, and adjust the variable parameters of the time-varying deterioration model of stud connectors according to the mapping relationship between the stiffness of stud connectors and the pitting corrosion rate and stud crack propagation rate.

[0013] Preferably, construct a full-time domain deterioration model of the concrete bridge deck under the coupling action of corrosion and fatigue according to test data, specifically including the following steps:

[0014] Extract the stiffness of the concrete bridge deck, degree of steel bar corrosion, internal cavity distribution, bridge deck crack length and bridge deck crack width from the test data;

[0015] Before the stud does not break, numerically simulate the mapping relationship between the stiffness of the concrete bridge deck and the degree of steel bar corrosion, internal cavity distribution, bridge deck crack length and bridge deck crack width, and construct the first full-time domain deterioration model of the concrete bridge deck;

[0016] After the stud breaks, an interface slip reduction coefficient is introduced to correct the full-time domain model of the deterioration of the first concrete bridge deck, and a second full-time domain model of the deterioration of the concrete bridge deck is constructed;

[0017] The full-time domain model of the deterioration of the first concrete bridge deck and the full-time domain model of the deterioration of the second concrete bridge deck are coupled and calculated to construct a full-time domain model of the deterioration of the concrete bridge deck.

[0018] Preferably, based on the actual bridge monitoring data and the simulated bridge monitoring data extended by the Bayesian method, the upper and lower bounds of the key parameters of the steel-concrete composite bridge are determined through the maximum density interval, and the specific steps are as follows:

[0019] For each key parameter of the steel-concrete composite bridge, collect the time series of the corresponding key parameter of the steel-concrete composite bridge, perform Bayesian inference on the time series of the key parameter of the steel-concrete composite bridge through the Bayesian method, construct the probability distribution corresponding to the key parameter of the steel-concrete composite bridge, and then calculate the maximum density interval with a confidence level of μ of the key parameter of the steel-concrete composite bridge based on the probability distribution corresponding to the key parameter of the steel-concrete composite bridge, as the upper and lower bounds corresponding to the key parameter of the steel-concrete composite bridge.

[0020] Preferably, and perform Monte Carlo simulation according to the upper and lower bounds of the key parameters of the steel-concrete composite bridge, and sample a number of analysis groups of the key parameters of the steel-concrete composite bridge, and the specific steps are as follows:

[0021] Divide the key parameters of the steel-concrete composite bridge into epistemic uncertain variables and random uncertain variables. For each epistemic uncertain variable, construct a bounded cumulative distribution function according to the upper and lower bounds corresponding to the epistemic uncertain variable and the probability distribution corresponding to the epistemic uncertain variable;

[0022] Traverse the key parameters of the steel-concrete composite bridge. For each key parameter of the steel-concrete composite bridge, if the key parameter of the steel-concrete composite bridge is an epistemic uncertain variable, extract the value of the key parameter of the steel-concrete composite bridge from the bounded cumulative distribution function. If the key parameter of the steel-concrete composite bridge is a random uncertain variable, extract the value of the key parameter of the steel-concrete composite bridge from the probability distribution function; until all the key parameters of the steel-concrete composite bridge are traversed, form the values of all the key parameters of the steel-concrete composite bridge into an analysis group of the key parameters of the steel-concrete composite bridge; repeat the operation of traversing the key parameters of the steel-concrete composite bridge several times to construct several analysis groups of the key parameters of the steel-concrete composite bridge.

[0023] Preferably, the coupled calculation refers to a model that performs coupled calculation through loading in a finite element analysis model and is solved by the finite element method.

[0024] Preferably, for the missing data in the time series of the key parameters of the steel-concrete composite bridge, a deep learning network is designed through the AGT platform to predict and supplement the missing time series detection data

[0025] The present invention has the following advantages:

[0026] In the process of predicting the stiffness of the negative moment area of the steel-concrete composite bridge, the present invention takes into account the deterioration mechanism of the stud connectors under the coupling action of corrosion and fatigue, considers the pitting corrosion situation under environmental influence and the stud crack situation under vehicle load influence, and takes into account the influence of the bridge deck crack in the pitting corrosion situation, and considers the mutual relationship among the influence of stud pitting corrosion, the influence of stud crack and the influence of bridge deck crack, so as to accurately analyze the stiffness of the negative moment area of the steel-concrete composite bridge; it also takes into account the uncertainty of the key parameters of the steel-concrete composite bridge during the prediction process, is data-driven by actual monitoring data, and generates the future stiffness change trend range through Monte Carlo simulation, providing a more robust and accurate prediction result. Description of the Drawings

[0027] Figure 1 It is a schematic flow chart of the long-term stiffness prediction method for the negative moment area of the steel-concrete composite beam bridge adopted in the embodiment of the present invention.

[0028] Figure 2 It is a schematic flow chart of the deterioration and failure process of the stud connector under the coupling action of corrosion and fatigue in the embodiment of the present invention. Detailed Embodiments

[0029] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention.

[0030] As Figure 1 shown, a long-term stiffness prediction method for the negative moment area of a steel-concrete composite beam bridge includes:

[0031] Construct a time-varying model for the deterioration of stud connectors under the coupling action of corrosion and fatigue and a full-time domain model for the deterioration of the concrete bridge deck according to the test data, specifically including the following steps:

[0032] Couple the time-varying model for the deterioration of stud connectors and the full-time domain model for the deterioration of the concrete bridge deck to obtain a stiffness degradation model for the negative moment area of the steel-concrete composite beam bridge; the coupling calculation refers to a model that performs coupling calculation by loading through a finite element analysis model and is solved by the finite element method;

[0033] Based on the actual bridge monitoring data and the simulated bridge monitoring data extended by the Bayesian method, determine the upper and lower bounds of the key parameters of the steel-concrete composite bridge through the maximum density interval, and perform Monte Carlo simulation according to the upper and lower bounds of the key parameters of the steel-concrete composite bridge. Sample a number of analysis groups of the key parameters of the steel-concrete composite bridge, send all the analysis groups of the key parameters of the steel-concrete composite bridge into the stiffness degradation model of the negative moment area of the steel-concrete composite beam bridge for calculation, and count the stiffness change trend of the negative moment area of the steel-concrete composite beam bridge to achieve the long-term stiffness prediction of the negative moment area of the steel-concrete composite beam bridge;

[0034] Construct a time-varying deterioration model of stud connectors under the coupling action of corrosion and fatigue according to the test data, which specifically includes the following steps:

[0035] Adopt the electrochemistry corrosion test method and fatigue loading test to simulate the coupling action of corrosion and fatigue, obtain the test data, and extract the stud size, stud connector stiffness, pitting corrosion rate, stud crack propagation rate, concrete cover thickness, chloride ion diffusion rate, concrete fatigue crack width, concrete fatigue crack width depth, load force and load cycle number from the test data; among them, the concrete fatigue crack width and the concrete fatigue crack width depth environment affect the degree of environmental pitting of the stud connector;

[0036] Numerically simulate the mapping relationship between the pitting corrosion rate and the stud size, concrete cover thickness, chloride ion diffusion rate, concrete fatigue crack width and concrete fatigue crack width depth, and construct the first time-varying deterioration model of the stud connector;

[0037] Numerically simulate the mapping relationship between the stud crack propagation rate and the stud size, concrete cover thickness, load force and load cycle number, and construct the second time-varying deterioration model of the stud connector;

[0038] Couple and calculate the first time-varying deterioration model of the stud connector and the second time-varying deterioration model of the stud connector to construct the time-varying deterioration model of the stud connector, and adjust the variable parameters of the time-varying deterioration model of the stud connector according to the mapping relationship between the stud connector stiffness and the pitting corrosion rate and the stud crack propagation rate. One form of the time-varying deterioration model of the stud connector is , where K total (t) is the stiffness of the stud connector corresponding to time t, K total (0) is the initial stiffness of the stud connector, α is the influence of the pitting corrosion rate on the stiffness of the stud connector, and β is the influence of the stud crack propagation rate on the stiffness of the stud connector;

[0039] This application uses the electrochemically accelerated corrosion test method to explore the entire process of corrosion deterioration of stud connectors under service conditions. And through the X-ray tomography test method, the corrosion state of the studs, the crack propagation pattern, and the steel-concrete bond condition during the test process are quantitatively observed to obtain test data. Considering that the stiffness of the negative moment area of the steel-concrete composite beam bridge is determined by the stiffness of the stud connectors and the stiffness of the bridge deck, therefore, the deterioration process of the stud connectors under the coupling action of corrosion and fatigue is first explored. And further considering that pitting corrosion caused by environmental effects and crack conditions under vehicle cyclic loads will occur during the use of stud connectors, both of which will affect the stiffness of the stud connectors, and the effects of the two are mutual. Refer to Figure 2 For deterioration stage ①, the action of fatigue load is manifested as cracks appearing on the bridge deck. At this time, the studs will be affected by the outside world and pitting corrosion will occur. The pitting corrosion situation can be analyzed based on the first time-varying deterioration model of stud connectors. For deterioration stage ②, considering the stress concentration effect after the formation of pitting pits leads to the generation and propagation of fatigue cracks in the studs, the growth rate of pitting pits is calculated based on the first time-varying deterioration model of stud connectors. At the same time, the propagation rate of fatigue cracks in the studs is calculated based on the second time-varying deterioration model of stud connectors. Taking the depth of the pitting pit reaching the critical value of crack nucleation as the threshold for the growth of pitting pits to transition to the propagation of fatigue cracks in the studs, and as the time lower boundary of this deterioration stage in the failure model. For deterioration stage ③, the failure time boundary of the steel-concrete connection is determined based on the time-varying deterioration model of stud connectors, that is, the studs break. There are two criteria for judging the failure of stud connectors. One is that the depth of the pitting pit reaches the failure boundary, and the other is that the crack of the stud reaches the failure boundary.

[0040] According to the test data, a full-time domain model for the deterioration of the concrete bridge deck under the coupling action of corrosion and fatigue is constructed, which specifically includes the following steps:

[0041] Extract the stiffness of the concrete bridge deck, the degree of steel bar corrosion, the internal cavity distribution, the crack length of the bridge deck, and the crack width of the bridge deck from the test data;

[0042] Before the studs break, the mapping relationship between the stiffness of the concrete bridge deck and the degree of steel bar corrosion, the internal cavity distribution, the crack length of the bridge deck, and the crack width of the bridge deck is numerically simulated, and the first full-time domain model for the deterioration of the concrete bridge deck is constructed;

[0043] After the studs break, an interface slip reduction coefficient is introduced to correct the first full-time domain model for the deterioration of the concrete bridge deck, and the second full-time domain model for the deterioration of the concrete bridge deck is constructed;

[0044] The first full-time domain model for the deterioration of the concrete bridge deck and the second full-time domain model for the deterioration of the concrete bridge deck are coupled and calculated to construct a full-time domain model for the deterioration of the concrete bridge deck.

[0045] When analyzing the deterioration of the concrete bridge deck, considering that after the stud connector cracks, the stiffness of the stud connector drops to the critical value, which will lead to a sharp increase in the slip at the stud-concrete interface. On this basis, the correlation coefficient between the stiffness reduction amplitude and the interface slip amount in the negative moment area of the steel-concrete composite beam bridge is calculated by the finite element method, and is used as the interface slip reduction coefficient to correct the full-time domain model of the first concrete bridge deck deterioration.

[0046] Based on the actual bridge monitoring data and the simulated bridge monitoring data extended by the Bayesian method, the upper and lower bounds of the key parameters of the steel-concrete composite bridge are determined through the maximum density interval, and the specific steps are as follows:

[0047] For each key parameter of the steel-concrete composite bridge, where the key parameters of the steel-concrete composite bridge include the initial stiffness of the bridge deck, the initial stiffness of the stud connector, the vehicle load, the chloride ion diffusion rate, etc., collect the corresponding time series of the key parameters of the steel-concrete composite bridge, perform Bayesian inference on the time series of the key parameters of the steel-concrete composite bridge by the Bayesian method, construct the probability distribution corresponding to the key parameters of the steel-concrete composite bridge, and then calculate the maximum density interval with a confidence level of μ for the key parameters of the steel-concrete composite bridge based on the probability distribution corresponding to the key parameters of the steel-concrete composite bridge. Here, μ is generally 95%, which is used as the upper and lower bounds corresponding to the key parameters of the steel-concrete composite bridge; it should be noted that the time series of the key parameters of the steel-concrete composite bridge here are combined with the actual design requirements of the bridge project and obtained on-site of the bridge. For the missing data in the time series of the key parameters of the steel-concrete composite bridge, the AGT platform can be used to design a deep learning network to predict and supplement the missing time series detection data;

[0048] Execute Monte Carlo simulation according to the upper and lower bounds of the key parameters of the steel-concrete composite bridge, and sample to obtain several analysis groups of the key parameters of the steel-concrete composite bridge. The specific steps are as follows:

[0049] Divide the key parameters of the steel-concrete composite bridge into epistemic uncertain variables and random uncertain variables. For each epistemic uncertain variable, construct a bounded cumulative distribution function according to the upper and lower bounds corresponding to the epistemic uncertain variable and the probability distribution corresponding to the epistemic uncertain variable. The bounded cumulative distribution function is generally F(x)=P(X≤x), which represents the probability that X appears before a certain value x; it should be noted that the epistemic uncertain variables here are due to limited understanding of the system, resulting in uncertain value ranges for some parameters. For example, the initial stiffness of the bridge deck and the initial stiffness of the stud connector have an error of ±5% under different construction qualities; the random uncertain variables are the random fluctuations existing in nature or during use. For example, the vehicle load is random and different every day;

[0050] Traverse the key parameters of the steel-concrete composite bridge. For each key parameter of the steel-concrete composite bridge, if the key parameter of the steel-concrete composite bridge is an epistemic uncertain variable, extract the value of the key parameter of the steel-concrete composite bridge from the bounded cumulative distribution function. If the key parameter of the steel-concrete composite bridge is a random uncertain variable, extract the value of the key parameter of the steel-concrete composite bridge from the probability distribution function. Here, the probability distribution function can be a normal distribution or a lognormal distribution. The vehicle load generally adopts a normal distribution. Until the traversal of the key parameters of the steel-concrete composite bridge is completed, form the key parameter analysis group of the steel-concrete composite bridge with all the values of the key parameters of the steel-concrete composite bridge. Repeat the operation of traversing the key parameters of the steel-concrete composite bridge several times to construct several key parameter analysis groups of the steel-concrete composite bridge. Send the key parameter analysis groups generated here into the stiffness degradation model of the negative moment area of the steel-concrete composite beam bridge for calculation one by one. For example, calculate the stiffness change in the negative moment area after 20 years, and several groups of stiffness changes in the negative moment area of the steel-concrete composite beam bridge will be obtained. For example, the stiffness drops by 30% and the stiffness drops by 25%. Conduct a probability distribution statistics on all the stiffness changes in the negative moment area of the steel-concrete composite beam bridge to obtain a 95% confidence range: the stiffness drops by 30% - 40%, which can provide a more robust and accurate prediction result.

[0051] In the process of predicting the stiffness of the negative moment area of the steel-concrete composite bridge in this application, the degradation mechanism of the stud connector under the coupling action of corrosion and fatigue is considered. The pitting situation under the influence of the environment and the stud crack situation under the influence of the vehicle load are considered. And the influence of the bridge deck crack is considered in the pitting situation. And the mutual relationship among the influence of stud pitting, the influence of stud crack and the influence of bridge deck crack is considered to accurately analyze the stiffness of the negative moment area of the steel-concrete composite bridge. The uncertainty of the key parameters of the steel-concrete composite bridge in the prediction process is also considered. Driven by the actual monitoring data, the Monte Carlo simulation is used to generate the future range of stiffness change trends, providing a more robust and accurate prediction result.

[0052] It should be understood that for those of ordinary skill in the art, improvements or transformations can be made according to the above description, and all such improvements and transformations should fall within the protection scope of the appended claims of the present invention. The parts not described in detail in this specification belong to the well-known prior art of those skilled in the art.

Claims

1. A method for predicting the long-term stiffness of a steel-concrete composite beam bridge in the negative bending moment zone, characterized in that: include: Based on the test data, a time-varying model of welded stud connector degradation under the action of corrosion-fatigue coupling and a full-time domain model of concrete bridge deck degradation were constructed. The time-varying degradation model of welded nail connectors and the full-time domain degradation model of concrete bridge deck are coupled to obtain the stiffness degradation model of the negative moment zone of steel-concrete composite beam bridge. Based on the actual bridge monitoring data and the simulated bridge monitoring data extended by the Bayesian method, the upper and lower bounds of the key parameters of the steel-concrete composite bridge are determined through the maximum density interval, and Monte Carlo simulation is performed according to the upper and lower bounds of the key parameters of the steel-concrete composite bridge. Several key parameter analysis groups of the steel-concrete composite bridge are sampled and all the key parameter analysis groups of the steel-concrete composite bridge are sent to the stiffness degradation model of the negative moment zone of the steel-concrete composite beam bridge for calculation, and the stiffness change trend of the negative moment zone of the steel-concrete composite beam bridge is statistically analyzed to realize the long-term stiffness prediction of the negative moment zone of the steel-concrete composite beam bridge.

2. The method for predicting long-term stiffness in negative moment zone of a steel-concrete composite beam bridge according to claim 1 is characterized in that: include: Based on the test data, a time-varying model of the degradation of welded stud connectors under the action of corrosion-fatigue coupling is constructed, which specifically includes the following steps: Electrochemical corrosion test method and fatigue loading test are used to simulate the corrosion-fatigue coupling effect, obtain test data, and extract welding nail size, welding nail connection stiffness, pitting corrosion rate, welding nail crack growth rate, concrete cover thickness, chloride ion diffusion rate, concrete fatigue crack width, concrete fatigue crack width depth, load force and load cycle number from the test data; The mapping relationship between pitting corrosion rate and welding nail size, concrete cover thickness, chloride ion diffusion rate, concrete fatigue crack width, and concrete fatigue crack width depth is numerically simulated to construct a time-varying degradation model of the first welding nail connection. The mapping relationship between the crack growth rate of the weld stud and the size of the weld stud, the thickness of the concrete cover, the load force and the number of load cycles is numerically simulated to construct a time-varying degradation model of the second weld stud connection. The first weld rivet connector degradation time-varying model and the second weld rivet connector degradation time-varying model are coupled and calculated to construct the weld rivet connector degradation time-varying model, and the variable parameters of the weld rivet connector degradation time-varying model are adjusted according to the mapping relationship between the weld rivet connector stiffness and the pitting corrosion rate and the weld rivet crack growth rate.

3. The method for predicting long-term stiffness in negative moment zone of a steel-concrete composite beam bridge according to claim 2 is characterized in that: Based on the test data, a full time domain model of concrete bridge deck degradation under the action of corrosion-fatigue coupling is constructed, which includes the following steps: Extract concrete bridge deck stiffness, steel bar corrosion degree, internal void distribution, bridge deck crack length and bridge deck crack width from test data; Before the welding nails break, the mapping relationship between the concrete bridge deck stiffness and the degree of steel corrosion, the internal void distribution, the bridge deck crack length and the bridge deck crack width is digitized to construct the first concrete bridge deck degradation full time domain model; After the welding nail breaks, the interface slip reduction coefficient is introduced to correct the first concrete bridge deck degradation full time domain model, and the second concrete bridge deck degradation full time domain model is constructed; The first concrete bridge deck degradation full time domain model and the second concrete bridge deck degradation full time domain model are coupled and calculated to construct a concrete bridge deck degradation full time domain model.

4. The method for predicting long-term stiffness in negative moment zone of a steel-concrete composite beam bridge according to claim 3 is characterized in that: Based on the actual bridge monitoring data and the simulated bridge monitoring data extended by the Bayesian method, the upper and lower bounds of the key parameters of the steel-concrete composite bridge are determined through the maximum density interval, which specifically includes the following steps: For each key parameter of the steel-concrete composite bridge, the corresponding time series of the key parameters of the steel-concrete composite bridge is collected, and the Bayesian inference of the key parameters of the steel-concrete composite bridge is performed through the Bayesian method to construct the probability distribution corresponding to the key parameters of the steel-concrete composite bridge. Then, based on the probability distribution corresponding to the key parameters of the steel-concrete composite bridge, the maximum density interval with the confidence level μ of the key parameters of the steel-concrete composite bridge is calculated as the upper and lower bounds of the key parameters of the steel-concrete composite bridge.

5. The method for predicting long-term stiffness in negative moment zone of steel-concrete composite beam bridge according to claim 4 is characterized in that: Monte Carlo simulation is performed according to the upper and lower bounds of the key parameters of the steel-concrete composite bridge, and several key parameter analysis groups of the steel-concrete composite bridge are sampled, which specifically includes the following steps: The key parameters of steel-concrete composite bridges are divided into epistemic uncertain variables and random uncertain variables. For each epistemic uncertain variable, a bounded cumulative distribution function is constructed according to the upper and lower bounds corresponding to the epistemic uncertain variable and the probability distribution corresponding to the epistemic uncertain variable. Traverse the key parameters of the steel-concrete composite bridge. For each key parameter of the steel-concrete composite bridge, if the key parameter of the steel-concrete composite bridge is a cognitive uncertain variable, extract the key parameter value of the steel-concrete composite bridge from the bounded cumulative distribution function; if the key parameter of the steel-concrete composite bridge is a random uncertain variable, extract the key parameter value of the steel-concrete composite bridge from the probability distribution function; until the traversal of the key parameters of the steel-concrete composite bridge is completed, all the key parameter values ​​of the steel-concrete composite bridge are combined into a steel-concrete composite bridge key parameter analysis group; repeat the operation of traversing the key parameters of the steel-concrete composite bridge several times to construct several steel-concrete composite bridge key parameter analysis groups.

6. The method for predicting long-term stiffness in negative moment zone of steel-concrete composite beam bridge according to claim 5, characterized in that: Coupling calculation refers to the model that is loaded through the finite element analysis model for coupling calculation and solved by the finite element method.

7. The method for predicting long-term stiffness in negative moment zone of steel-concrete composite beam bridge according to claim 6 is characterized in that: Aiming at the missing data in the time series of key parameters of steel-concrete composite bridges, a deep learning network was designed through the AGT platform to predict and supplement the missing time series detection data.

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