Prestressed concrete continuous box girder bridge time-varying reliability assessment method based on actually measured traffic flow

Abstract

The invention discloses a method for evaluating the time-varying reliability of a prestressed concrete continuous box girder bridge based on actually measured traffic flow, which comprises the following steps of: establishing a finite element model of the prestressed concrete continuous box girder bridge according to a bridge structure design drawing, and calculating and defining the section attribute of each section in the longitudinal bridge direction; determining the most unfavorable section position of the bridge structure; the time-varying bearing capacity of the most unfavorable section is calculated through the constructed degradation model; preprocessing the data acquired by the dynamic weighing system; establishing the preprocessed dynamic weighing data into a numerical model to realize the construction of a traffic flow model; performing bridge dynamic analysis to obtain a load effect; calculating a time-varying reliability index in the service period of the bridge; and comparing the calculated time-varying reliable index with a target reliable index in the specification. According to the method, the time-varying reliability of the in-service prestressed concrete continuous box girder bridge in the service period can be accurately evaluated, and subsequent management and maintenance work is guided.

Description

Technical Field

[0001] The present invention belongs to the technical field of bridge engineering health monitoring and reliability assessment, and specifically relates to a time-varying reliability assessment method for a prestressed concrete continuous box girder bridge based on measured traffic flow. Background Art

[0002] Prestressed concrete continuous box girder bridges are widely used in critical transportation networks such as highways and urban expressways due to their excellent load-bearing performance. However, as they age, bridges face challenges such as concrete degradation, steel corrosion, and decreased bond strength, leading to a continuous decline in their load-bearing capacity. Furthermore, increasing traffic volume further exacerbates the live load effects on bridges. Therefore, a sound assessment of bridge reliability over its service life is crucial for ensuring its long-term safe operation.

[0003] At present, there have been many research advances in the reliability assessment of in-service bridges. However, the current bridge reliability assessment mainly has the following problems:

[0004] Static assessment methods: They cannot reflect time-varying degradation effects, leading to delayed maintenance decisions;

[0005] Simplified degradation model: It focuses on the impact of a single factor on bearing capacity without comprehensively considering various factors affecting bearing capacity;

[0006] Single load model: It relies on standard lane loads and ignores the randomness and dynamic growth characteristics of actual traffic flow.

[0007] Existing research has mostly focused on conventional reinforced concrete beam bridges or static load scenarios, lacking analysis methods for coupling random traffic flow with time-varying resistance for continuous box girder bridges. Therefore, a new technical solution is needed to address these issues. Summary of the Invention

[0008] Purpose of the invention: In view of the fact that the existing time-varying reliability assessment has insufficient considerations and a single load model, a time-varying reliability assessment method for prestressed concrete continuous box girder bridges based on measured traffic flow is provided. This method can accurately assess the time-varying reliability of in-service prestressed concrete continuous box girder bridges during their service period and provide guidance for subsequent management and maintenance work.

[0009] Technical Solution: To achieve the above objectives, the present invention provides a time-varying reliability assessment method for prestressed concrete continuous box girder bridges based on measured traffic flow, comprising the following steps:

[0010] S1: Based on the bridge structure design drawings, establish a finite element model of the prestressed concrete continuous box girder bridge, and calculate and define the cross-sectional properties of each longitudinal section (such as cross-sectional form, cross-sectional area, bending moment of inertia, etc.);

[0011] S2: Determine the most unfavorable cross-section position of the bridge structure;

[0012] S3: Calculate the time-varying bearing capacity of the most unfavorable section using the constructed degradation model;

[0013] S4: Pre-process the data obtained by the dynamic weighing system and set a threshold to eliminate data with abnormal axle number or vehicle weight;

[0014] S5: Build the pre-processed dynamic weighing data into a numerical model to realize the construction of the traffic flow model;

[0015] S6: Combining the bridge model of step S1 with the traffic flow model of step S5 to perform bridge dynamic analysis and obtain load effects;

[0016] S7: Input the time-varying bearing capacity of step S3 and the load effect of step S6 into the reliability calculation module to calculate the time-varying reliability index of the bridge during its service life;

[0017] S8: Compare the calculated time-varying reliability index with the target reliability index in the specification to predict the service life of the bridge and determine the subsequent reinforcement and maintenance strategy.

[0018] Furthermore, in step S1, a spatial beam unit is used to simulate the box beam structure, and constraint simulation boundary conditions are imposed (the boundary conditions can be set according to the design drawings), thereby establishing a finite element model of the bridge.

[0019] Furthermore, the degradation model in step S3 includes a concrete degradation model, a steel bar corrosion degradation model, a prestressed steel bar degradation model, and a bonding performance degradation model.

[0020] In step S3, the box section is equivalent to an I-section, and the time-varying bearing capacity of the most unfavorable section is calculated by combining the concrete degradation model, the steel corrosion degradation model, the prestressed steel degradation model, and the bond performance degradation model.

[0021] Based on the various random variables involved in the time-varying bearing capacity calculation, sampling is performed according to their probability distribution characteristics. The distribution type of the bearing capacity is fitted through programming and statistical parameters (mean, standard deviation, etc.) are calculated.

[0022] Furthermore, the concrete degradation model adopts the following expression:

[0023] μ f (t) = η(t)μ f0

[0024] η(t)=1.3781exp{-0.0187[ln(t+0.0382)-1.7282] 2}

[0025] Among them, μ f0 is the mean value of concrete strength, and η(t) is the time-varying coefficient of concrete strength.

[0026] Furthermore, the steel bar corrosion degradation model adopts the following expression:

[0027]

[0028] Among them, A s (0), f y0 is the initial area and initial strength of the steel bar, t c is the initial corrosion time of steel bars, and its expression is:

[0029]

[0030] x0=4.86(-RH 2 +1.5RH-0.45)(c-5)(lnf cuk -2.30)

[0031]

[0032] Where c is the thickness of the concrete cover; x0 is the concrete residue; K is the carbonization coefficient of concrete; RH is the relative humidity of the environment; f cuk is the standard value of concrete cube compressive strength; k mc k is the random variable with uncertainty in the calculation mode; j is the position correction coefficient; is the influence coefficient of CO2 concentration; k p k is the casting surface correction coefficient; s is the working stress influence coefficient; T is the annual average temperature; m c It is the ratio of the average value of concrete cube compressive strength to the standard value;

[0033] η s is the steel corrosion rate, and its expression is:

[0034]

[0035] Where d is the diameter of the steel bar, δ e (t) is the depth of steel corrosion, which can be expressed as:

[0036] δ e1 =λ e1 (tt c )

[0037]

[0038] Among them, δ e1is the depth of steel corrosion before the protective layer cracks; e1 k is the steel corrosion rate before the protective layer cracks; l is the position correction coefficient; k m is the environmental condition correction factor; δ cr is the depth of steel corrosion when the concrete cover expands and cracks; δ e2 is the depth of steel bar corrosion after the protective layer cracks; t cr It is the time when the protective layer begins to rust and crack.

[0039] Furthermore, the prestressed steel bar degradation model is expressed as follows:

[0040] The corrosion rate of prestressed steel bars is:

[0041]

[0042] Among them, k σ is the influence coefficient of tensile stress level on the corrosion rate of prestressed tendons, and the calculation formula is:

[0043]

[0044] The strength degradation model of prestressed steel bars is:

[0045]

[0046] Among them, η p is the corrosion rate of prestressed tendons, f pu0 It is the ultimate strength of prestressed tendons in the non-corroded state.

[0047] Furthermore, the bonding performance degradation model is expressed as follows:

[0048]

[0049] Furthermore, in step S3, the concrete degradation model, the steel bar corrosion degradation model, the prestressed steel bar degradation model, and the bonding performance degradation model are combined to obtain a bending bearing capacity degradation model. The bending bearing capacity degradation model is expressed as follows:

[0050] M R (t) = f cd (t)b′ f x(t)(h0-x(t) / 2)+k′ s (t)f′ sd (t)A′ s (t)(h0-a′ s )+k′ p (t)(f′ pd (t)-σ′ p0 )A′ p(t)(h0-a′ p )

[0051] Where: f cd (t) is the design value of the time-varying compressive strength of concrete; b' f is the effective width of the compression flange; x(t) is the height of the compression zone; h0 is the effective height of the section; k' s (t) is the cooperative working coefficient between steel bars and concrete in the compression zone; k' p (t) are the cooperative working coefficients of prestressed steel bars and concrete in the tension zone. For post-tensioned precast components, take k' p (t) = 1; f' sd (t) is the design value of the time-varying tensile strength of the corroded steel bars in the compression zone, f' pd (t) is the design value of the time-varying tensile strength of the corroded prestressed reinforcement in the compression zone; σ' p0 is the stress of the prestressed steel bar when the normal stress of concrete at the resultant point of the prestressed steel bar in the compression zone is equal to zero; a' s 、a' p are the distances from the resultant force points of ordinary steel bars and prestressed steel bars in the compression zone to the edge of the compression zone; A' s (t), A' p are the time-varying effective cross-sectional areas of corroded ordinary steel bars and corroded prestressed bars in the compression zone, respectively.

[0052] Furthermore, in step S5, the vehicle type is determined by the number of axles, the load size is determined by the vehicle axle weight, and the lateral and longitudinal positions of the vehicle on the bridge are determined by information such as lane, speed, and time on the bridge, thereby realizing the construction of the traffic flow model.

[0053] Furthermore, the bridge dynamic analysis in step S6 includes: constructing a bridge dynamic equation and solving the bridge dynamic equation. The bridge dynamic equation is:

[0054]

[0055] Where: M, C and K represent the mass, damping and stiffness matrices of the bridge respectively; and y(t) are the acceleration, velocity and displacement vector of the bridge respectively; F g is the gravity load vector; f i is the load value of the i-th axle; H i (t) is the load distribution matrix of the ith load, and n is the total number of loads.

[0056] Furthermore, in step S7, a functional function Z=RGS is constructed, where R is the load capacity, G is the dead load effect, and S is the vehicle load effect. These three values ​​are set as x1, x2, and x3, respectively, and g represents the function Z.

[0057] The calculation methods of time-varying reliability indicators include:

[0058]

[0059]

[0060] Where g(·) is the functional function of the structure; X i (i=1,2,…,n) is the basic variable; is the basic variable X i The coordinate value of the verification point; is the first-order partial derivative of the performance function g(·) at the verification point The value at The basic variables X i Equivalent normalized variable X' i The mean and standard deviation of are the probability density function and probability distribution function of the basic variables respectively; Φ -1 (·) are the probability density function and the inverse function of the probability distribution function of the standard normal random variable, respectively.

[0061] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0062] 1. It integrates concrete strength degradation, steel corrosion, prestressed tendon performance loss, and bond performance degradation, comprehensively covering the key factors affecting bridge resistance degradation.

[0063] 2. Based on the load model of measured traffic flow, a random traffic flow model is constructed using the dynamic weighing system (WIM) data, combined with bridge dynamic analysis to more realistically simulate the live load effect.

[0064] 3. By combining multiple time-varying models, measured data-driven and refined numerical methods, the present invention effectively overcomes the difficulties of "staticization and single load" in the existing technology, significantly improves the accuracy and practicality of the reliability assessment of prestressed concrete continuous box girder bridges, and provides a scientific analysis tool for the full life cycle management of bridges. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 Schematic diagram of the process of the present invention;

[0066] Figure 2 is a cross-sectional equivalent schematic diagram;

[0067] Figure 3 This is a graph showing the time-varying bending bearing capacity data in an embodiment of the present invention;

[0068] Figure 4 A schematic diagram of traffic flow simulation in an embodiment of the present invention;

[0069] Figure 5 This is a graph showing vehicle weight and speed statistics of a traffic flow according to an embodiment of the present invention;

[0070] Figure 6 This is a schematic diagram of Hermite interpolation;

[0071] Figure 7 This is a diagram showing the mid-span bending moment response under traffic load in an embodiment of the present invention;

[0072] Figure 8 This is a diagram showing the time-varying reliability calculation results in an embodiment of the present invention. DETAILED DESCRIPTION

[0073] The present invention is further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, modifications of various equivalent forms of the present invention made by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0074] Example 1:

[0075] This example takes a three-span variable-section prestressed concrete continuous box girder bridge as an example. The bridge has a span of (42+70+42)m, a width of 12.75m, a support section beam height of 4.0m, a mid-span section beam height of 2.15m, and adopts a single-box single-chamber section. A time-varying reliability assessment method for prestressed concrete continuous box girder bridges based on measured traffic flow is provided. Figure 1 As shown, the following steps are included:

[0076] S1: Obtain bridge dimensions based on design drawings and calculate cross-sectional properties (such as area and moment of inertia) of each longitudinal cross-section. Use spatial beam elements in finite element software to simulate the box girder structure and apply constraints to simulate boundary conditions to establish a finite element model of the bridge.

[0077] S2: Determine the most unfavorable cross-section position of the bridge structure:

[0078] The most unfavorable position of the main beam is determined by a single moving load. For this bridge type, the most unfavorable cross-section position is at the mid-span. The cross-section shape and reinforcement form at the mid-span are determined according to the design drawings. The box section is equivalent to an I-section using the equivalent principle. The equivalent principle is equivalent beam height, area, and moment of inertia. The equivalent diagram is as follows: Figure 2 As shown;

[0079] Reason for equivalence: The analysis of box sections usually requires complex mechanical models and multi-dimensional stress distribution calculations. Many design specifications and standards have mature formulas for calculating the bearing capacity of I-shaped or T-shaped sections. By equating them to I-shaped sections, existing analysis methods and empirical formulas can be used to reduce the complexity of the calculation.

[0080] S3: The time-varying bearing capacity of the most unfavorable section is calculated by constructing the degradation model. The time-varying bearing capacity of the middle section of the bridge in this embodiment is as follows: Figure 3 As shown;

[0081] For the various random variables involved in the time-varying bearing capacity calculation (such as concrete strength, steel bar diameter and strength, concrete cover thickness, etc.), sampling is performed according to their probability distribution characteristics. The parameter distribution type can be found in the "Uniform Standard for Reliability Design of Highway Engineering Structures". A program is compiled to fit the distribution type of the bearing capacity and calculate statistical parameters (mean, standard deviation, etc.);

[0082] Degradation models include concrete degradation model, steel corrosion degradation model, prestressed steel degradation model, and bond performance degradation model.

[0083] The concrete degradation model uses the following expression:

[0084] μ f (t) = η(t)μ f0

[0085] Among them, μ f0 is the mean value of concrete strength, and η(t) is the time-varying coefficient of concrete strength.

[0086] The steel bar corrosion degradation model uses the following expression:

[0087]

[0088] Among them, A s (0), f y0 is the initial area and initial strength of the steel bar, t c is the initial corrosion time of steel bars, and its expression is:

[0089]

[0090] x0=4.86(-RH 2 +1.5RH-0.45)(c-5)(lnf cuk -2.30)

[0091]

[0092] Where c is the thickness of the concrete cover; x0 is the concrete residue; K is the carbonization coefficient of concrete; RH is the relative humidity of the environment; fcuk is the standard value of concrete cube compressive strength; k mc k is the random variable with uncertainty in the calculation mode; j is the position correction coefficient, and k is taken for the corner j =1.4, k is taken for non-corner parts j =1.0; is the influence coefficient of CO2 concentration, When CO2 concentration data is missing, Based on the following values: Urban areas of large and medium-sized cities: town: k p k is the casting surface correction coefficient, which is 1.2; s is the working stress influence coefficient, k is taken when the concrete is under compression s =1.0, k is taken when under tension s =1.1; T is the annual average temperature (℃); m c It is the ratio of the average value of concrete cube compressive strength to the standard value;

[0093] η s is the steel corrosion rate, and its expression is:

[0094]

[0095] Where d is the diameter of the steel bar, δ e (t) is the depth of steel corrosion, which can be expressed as:

[0096] δ e1 =λ e1 (tt c )

[0097]

[0098] Among them, δ e1 is the depth of steel corrosion before the protective layer cracks; e1 k is the steel corrosion rate before the protective layer cracks; l k is the position correction coefficient, which is 1.6 at the corner and 1.0 at the middle; m is the environmental condition correction coefficient, which is 3.0-4.0 for outdoor humid environment and 2.5-3.5 for outdoor dry environment; δ cr is the depth of steel corrosion when the concrete cover expands and cracks; δ e2 is the depth of steel bar corrosion after the protective layer cracks; t cr It is the time when the protective layer begins to rust and crack.

[0099] The prestressed steel bar degradation model is expressed as follows:

[0100] The area corrosion degradation model of prestressed steel bars is similar to that of ordinary steel bars. The difference is that the corrosion rate of prestressed steel bars is:

[0101]

[0102] Among them, k σ is the influence coefficient of tensile stress level on the corrosion rate of prestressed tendons, and the calculation formula is:

[0103]

[0104] The strength degradation model of prestressed steel bars is:

[0105]

[0106] Among them, η p is the corrosion rate of prestressed tendons, f pu0 It is the ultimate strength of prestressed tendons in the non-corroded state.

[0107] The bond performance degradation model is expressed as follows:

[0108]

[0109] Combining the above models, the flexural capacity degradation model is expressed as follows:

[0110] M R (t) = f cd (t)b′ f x(t)(h0-x(t) / 2)+k′ s (t)f′ sd (t)A′ s (t)(h0-a′ s )+k′ p (t)(f′ pd (t)-σ p0 )A′ p (t)(h0-a′ p )

[0111] Where: f cd (t) is the design value of the time-varying compressive strength of concrete; b′ f is the effective width of the compression flange; x(t) is the height of the compression zone; h0 is the effective height of the section; k′ s (t) is the cooperative working coefficient between steel bars and concrete in the compression zone; k' p (t) are the cooperative working coefficients of prestressed steel bars and concrete in the tension zone. For post-tensioned precast components, take k' p (t) = 1; f' sd (t) is the design value of the time-varying tensile strength of the corroded steel bars in the compression zone, f' pd(t) is the design value of the time-varying tensile strength of the corroded prestressed reinforcement in the compression zone; σ' p0 is the stress of the prestressed steel bar when the normal stress of concrete at the resultant point of the prestressed steel bar in the compression zone is equal to zero; a' s 、a' p are the distances from the resultant force points of ordinary steel bars and prestressed steel bars in the compression zone to the edge of the compression zone; A' s (t), A' p are the time-varying effective cross-sectional areas of corroded ordinary steel bars and corroded prestressed bars in the compression zone, respectively.

[0112] S4: Pre-process the data obtained by the dynamic weighing system, set a threshold to eliminate data with abnormal axle number or vehicle weight, and in this embodiment, eliminate data with a gross vehicle weight less than 1000kg. The traffic flow data in this embodiment is as follows: Figure 4 As shown;

[0113] S5: Build the pre-processed dynamic weighing data into a numerical model to realize the construction of the traffic flow model;

[0114] In this embodiment, considering the vehicle dynamic characteristics, each wheel load is simplified into a single mobile concentrated force, thus the traffic load is equivalent to a series of mobile force sequences. The bridge-entry time of each mobile force is determined according to the vehicle speed, and finally a mobile force list containing load size, load speed, load bridge-entry time, and lane information is generated. The traffic simulation is as follows: Figure 5 As shown;

[0115] S6: Combining the bridge model of step S1 with the traffic flow model of step S5 to perform bridge dynamic analysis, and obtaining the load effect by constructing and solving the bridge dynamic equation;

[0116]

[0117] Where: M, C and K represent the mass, damping and stiffness matrices of the bridge respectively; and y(t) are the acceleration, velocity and displacement vector of the bridge respectively; F g is the gravity load vector; f i is the load value of the i-th axle; H i (t) is the load distribution matrix of the ith load, and n is the total number of loads.

[0118] In this embodiment, since the position of the moving force on the bridge does not necessarily coincide with the finite element node, the Hermite interpolation theory is used to decompose the non-node load to the adjacent nodes to form the corresponding node force vector. Figure 6 Based on this, a bridge dynamic analysis model is established to calculate the dynamic response of the bridge under the action of measured traffic loads, as shown in Figure 7 As shown;

[0119] S7: Input the time-varying bearing capacity of step S3 and the load effect of step S6 into the reliability calculation module to calculate the time-varying reliability index of the bridge during its service life;

[0120] In this embodiment, a functional function Z=RGS is constructed, where R is the load capacity, G is the dead load effect, and S is the vehicle load effect. The three values ​​are x1, x2, and x3, respectively, and g represents the function Z;

[0121] The calculation methods of time-varying reliability indicators include:

[0122]

[0123]

[0124] Where g(·) is the functional function of the structure; X i (i=1,2,…,n) is the basic variable; is the basic variable X i The coordinate value of the verification point; is the first-order partial derivative of the performance function g(·) at the verification point The value at The basic variables X i Equivalent normalized variable X' i The mean and standard deviation of are the probability density function and probability distribution function of the basic variables respectively; Φ -1 (·) are the probability density function and the inverse function of the probability distribution function of the standard normal random variable, respectively.

[0125] In this embodiment, the reliability index is 5.44 at the initial stage of operation, and decreases to 3.74 after 100 years.

[0126] S8: Compare the calculated time-varying reliability index with the target reliability index in the specification. For the bridge in this embodiment, the target reliability index is 4.7. Step S7 calculates that the reliability index of the bridge is less than 4.7 after 53 years of service. Therefore, it is predicted that the service life of the bridge is 53 years and reinforcement maintenance is required. Figure 8 shown.

Claims

1. A time-varying reliability assessment method for prestressed concrete continuous box girder bridges based on measured traffic flow, characterized in that: The steps include: S1: Based on the bridge structure design drawings, a finite element model of a prestressed concrete continuous box girder bridge is established, and the cross-sectional properties of each longitudinal section are calculated and defined; S2: Determine the most unfavorable cross-section position of the bridge structure; S3: Calculate the time-varying bearing capacity of the most unfavorable section using the constructed degradation model; S4: pre-processing the data obtained by the dynamic weighing system; S5: Build the pre-processed dynamic weighing data into a numerical model to realize the construction of the traffic flow model; S6: Combining the bridge model of step S1 with the traffic flow model of step S5 to perform bridge dynamic analysis and obtain load effects; S7: Input the time-varying bearing capacity of step S3 and the load effect of step S6 into the reliability calculation module to calculate the time-varying reliability index of the bridge during its service life; S8: Compare the calculated time-varying reliability index with the target reliability index in the specification to predict the service life of the bridge and determine the subsequent reinforcement and maintenance strategy.

2. The time-varying reliability assessment method for prestressed concrete continuous box girder bridges based on measured traffic flow according to claim 1 is characterized in that: In step S1, a spatial beam unit is used to simulate the box beam structure, and constraint simulation boundary conditions are imposed to establish a finite element model of the bridge.

3. The time-varying reliability assessment method for prestressed concrete continuous box girder bridges based on measured traffic flow according to claim 1 is characterized in that: The degradation models in step S3 include a concrete degradation model, a steel bar corrosion degradation model, a prestressed steel bar degradation model, and a bonding performance degradation model.

4. The time-varying reliability assessment method for prestressed concrete continuous box girder bridges based on measured traffic flow according to claim 3 is characterized in that: The concrete degradation model uses the following expression: m f (t)=η(t)μ f0 η(t)=1.3781exp{-0.0187[ln(t+0.0382)-1.7282] 2 } Among them, μ f0 is the mean value of concrete strength, and η(t) is the time-varying coefficient of concrete strength.

5. The method for evaluating the time-varying reliability of a prestressed concrete continuous box girder bridge based on measured traffic flow according to claim 3 is characterized in that: The steel bar corrosion degradation model adopts the following expression: Among them, A s (0), f y0 is the initial area and initial strength of the steel bar, t c is the initial corrosion time of steel bars, and its expression is: x0=4.86(-RH 2 +1.5RH-0.45)(c-5)(lnf cuk -2.30) Where c is the thickness of the concrete cover; x0 is the concrete residue; K is the carbonization coefficient of concrete; RH is the relative humidity of the environment; f cuk is the standard value of concrete cube compressive strength; k mc k is the random variable with uncertainty in the calculation mode; j is the position correction coefficient; is the influence coefficient of CO2 concentration; k p k is the casting surface correction coefficient; s is the working stress influence coefficient; T is the annual average temperature; m c It is the ratio of the average value of concrete cube compressive strength to the standard value; η s is the steel corrosion rate, and its expression is: Where d is the diameter of the steel bar, δ e (t) is the depth of steel corrosion, which can be expressed as: d e1 =λ e1 (tt c ) Among them, δ e1 is the depth of steel corrosion before the protective layer cracks; e1 k is the steel corrosion rate before the protective layer cracks; l is the position correction coefficient; k m is the environmental condition correction factor; δ cr is the depth of steel corrosion when the concrete cover expands and cracks; δ e2 is the depth of steel bar corrosion after the protective layer cracks; t cr It is the time when the protective layer begins to rust and crack.

6. The method for evaluating the time-varying reliability of a prestressed concrete continuous box girder bridge based on measured traffic flow according to claim 3 is characterized in that: The prestressed steel bar degradation model is expressed as follows: The corrosion rate of prestressed steel bars is: Among them, k σ is the influence coefficient of tensile stress level on the corrosion rate of prestressed tendons, and the calculation formula is: The strength degradation model of prestressed steel bars is: Among them, η p is the corrosion rate of prestressed tendons, f pu0 It is the ultimate strength of prestressed tendons in the non-corroded state.

7. The method for evaluating the time-varying reliability of a prestressed concrete continuous box girder bridge based on measured traffic flow according to claim 3 is characterized in that: The adhesion performance degradation model is expressed as follows:

8. The method for evaluating the time-varying reliability of a prestressed concrete continuous box girder bridge based on measured traffic flow according to claim 3 is characterized in that: In step S3, the concrete degradation model, the steel bar corrosion degradation model, the prestressed steel bar degradation model, and the bonding performance degradation model are used to obtain the bending bearing capacity degradation model, which is expressed as follows: M R (t)=f cd (t)b′ f x(t)(h0-x(t) / 2)+k′ s (t)f′ sd (t)A′ s (t)(h0-a′ s ) +k′ p (t)(f′ pd (t)-σ′ p0 )A′ p (t)(h0-a′ p ) Where: f cd (t) is the design value of the time-varying compressive strength of concrete; b' f is the effective width of the compression flange; x(t) is the height of the compression zone; h0 is the effective height of the section; k' s (t) is the cooperative working coefficient between steel bars and concrete in the compression zone; k' p (t) are the cooperative working coefficients of prestressed steel bars and concrete in the tension zone; f' sd (t) is the design value of the time-varying tensile strength of the corroded steel bars in the compression zone, f' pd (t) is the design value of the time-varying tensile strength of the corroded prestressed reinforcement in the compression zone; σ' p0 is the stress of the prestressed steel bar when the normal stress of concrete at the resultant point of the prestressed steel bar in the compression zone is equal to zero; a' s 、a' p are the distances from the resultant force points of ordinary steel bars and prestressed steel bars in the compression zone to the edge of the compression zone; A' s (t), A' p are the time-varying effective cross-sectional areas of corroded ordinary steel bars and corroded prestressed bars in the compression zone, respectively.

9. The method for evaluating the time-varying reliability of a prestressed concrete continuous box girder bridge based on measured traffic flow according to claim 1, characterized in that: The bridge dynamic analysis in step S6 includes: constructing a bridge dynamic equation and solving the bridge dynamic equation; the bridge dynamic equation is: Where: M, C and K represent the mass, damping and stiffness matrices of the bridge respectively; and y(t) are the acceleration, velocity and displacement vector of the bridge respectively; F g is the gravity load vector; f i is the load value of the i-th axle; H i (t) is the load distribution matrix of the ith load, and n is the total number of loads.

10. The method for time-varying reliability assessment of prestressed concrete continuous box girder bridges based on measured traffic flow according to claim 1, characterized in that: In step S7, a function Z=RGS is constructed, where R is the load capacity, G is the dead load effect, and S is the vehicle load effect. These three values ​​are set to x1, x2, and x3, respectively, and g represents the function Z. The calculation methods of time-varying reliability indicators include: Where g(·) is the functional function of the structure; X i (i=1,2,…,n) is the basic variable; is the basic variable X i The coordinate value of the verification point; is the first-order partial derivative of the performance function g(·) at the verification point The value at The basic variables X i Equivalent normalized variable X' i The mean and standard deviation of are the probability density function and probability distribution function of the basic variables respectively; Φ -1 (·) are the probability density function and the inverse function of the probability distribution function of the standard normal random variable, respectively.

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