Method for predicting service life of reinforced concrete structure, and device

Abstract

The present application relates to a method for predicting the service life of a reinforced concrete structure, and a device, and belongs to the technical field of durability assessment for reinforced concrete structures. The method comprises: according to a relationship of influences of pore structure parameters on a diffusion coefficient, constructing a target chloride ion diffusion model for representing a variation law of chloride ion concentration distribution in a concrete structure over time; according to a relationship of influences of chloride ion concentrations on rebar corrosion and on the basis of the target chloride ion diffusion model, constructing a rebar corrosion development model for representing a variation law of rebar cross-sectional loss distribution over time; and according to a relationship between rebar cross-sectional loss and a structural performance indicator of a reinforced concrete structure, and on the basis of the rebar corrosion development model, calculating the structural performance indicator varying over time, thereby determining the service life of the reinforced concrete structure when the structural performance indicator degrades to a limit state. In the present application, the chloride ion diffusion model is constructed by considering the influences of the pore structure parameters on the diffusion coefficient, thereby effectively improving the accuracy of life prediction.

Description

A method and apparatus for predicting the lifespan of reinforced concrete structures Technical Field

[0001] This application relates to the field of durability assessment technology for reinforced concrete structures, and in particular to a method and apparatus for predicting the lifespan of reinforced concrete structures. Background Technology

[0002] Reinforced concrete structures are a widely used structural form in modern buildings. However, during their service life, they face many durability problems caused by various factors. Among them, steel corrosion caused by chloride ion erosion is one of the most common and serious problems.

[0003] In practical engineering environments, chloride ion diffusion is influenced by a variety of complex factors. On the one hand, physicochemical factors are intertwined; the ion concentration difference in the pore solution within concrete promotes chloride ion diffusion, while the complexity of the pore structure determines the chloride ion transport path. On the other hand, environmental factors are crucial to chloride ion diffusion. Temperature changes significantly affect ion movement speed according to thermodynamic principles, thus altering the chloride ion diffusion rate. Humidity differences not only affect chloride ion dissolution and transport, but alternating wet and dry environments can also cause changes in pore structure, interfering with chloride ion diffusion. Furthermore, the stress field borne by the concrete structure interacts with chloride ion diffusion; tensile stress causes pore expansion, which is conducive to chloride ion diffusion, while compressive stress has the opposite effect. Moreover, the intrusion and diffusion of chloride ions also have a reaction effect on the mechanical properties and stress distribution of concrete.

[0004] In existing technologies, the prediction of the service life of reinforced concrete structures neglects the influence of stress field, i.e. the influence of pore structure on chloride ion diffusion, when determining the chloride ion diffusion model. This results in an inaccurate and unreliable prediction of the corrosion resistance and durability of reinforced concrete structures. Summary of the Invention

[0005] In view of this, it is necessary to provide a method and apparatus for predicting the service life of reinforced concrete structures, so as to solve the problem that the existing technology ignores the influence of pore structure on chloride ion diffusion, resulting in an inaccurate and unreliable prediction of the corrosion resistance and durability of reinforced concrete structures.

[0006] To address the aforementioned problems, this application provides a method for predicting the lifespan of reinforced concrete structures, comprising:

[0007] Based on the influence of pore structure parameters on diffusion coefficient, a target chloride ion diffusion model is constructed to characterize the time-dependent variation of chloride ion concentration distribution in concrete structures.

[0008] Based on the influence of chloride ion concentration on steel corrosion, a steel corrosion development model is constructed to characterize the time-varying law of steel cross-sectional loss distribution based on the target chloride ion diffusion model.

[0009] Based on the relationship between steel reinforcement cross-sectional loss and the performance indicators of reinforced concrete structures, and based on the steel reinforcement corrosion development model, the structural performance indicators that change over time are calculated, and the life of the reinforced concrete structure when the structural performance indicators degrade to the limit state is determined.

[0010] In some possible implementations, a target chloride ion diffusion model is constructed to characterize the time-varying distribution of chloride ion concentration in concrete structures based on the influence of pore structure parameters on the diffusion coefficient. This includes:

[0011] Based on the law of conservation of mass and Fick's law, an initial chloride ion diffusion model is determined to characterize the change of chloride ion concentration distribution in concrete structures over time.

[0012] Based on the influence of pore structure parameters on the diffusion coefficient, pore structure parameters are introduced into the diffusion coefficient of the initial chloride ion diffusion model to obtain the target chloride ion diffusion model. The pore structure parameters include porosity and filling rate.

[0013] In some possible implementations, the diffusion coefficient is:

[0014] In the formula, D represents the diffusion coefficient. β represents porosity, D0 represents the initial diffusion coefficient, and β represents the filling rate. β0 represents the initial porosity, m and n represent empirical coefficients, and E represents the initial filling rate. a R represents the activation energy, R represents the gas constant, and T represents the temperature.

[0015] In some possible implementations, based on the law of conservation of mass and Fick's law, an initial chloride ion diffusion model is determined to characterize the time-varying distribution of chloride ion concentration in concrete structures, including:

[0016] Based on the law of conservation of mass, a first equation is determined to characterize the effect of the diffusion flux gradient on the change of chloride ion concentration over time.

[0017] Based on Fick's first law and the relationship between chloride ion diffusion flux and potential and temperature gradients, a second equation is constructed to characterize the effects of diffusion coefficient, chloride ion concentration gradient, potential gradient and temperature gradient on diffusion flux.

[0018] Substituting the second equation into the first equation yields the initial chloride ion diffusion model.

[0019] In some possible implementations, the second equation is:

[0020] In the formula, Ji D represents the chloride ion diffusion flux, and D represents the diffusion coefficient. Let represent the gradient operator, c represent the chloride ion concentration, z represent the chloride ion valence, F represent the Faraday constant, R represent the gas constant, and T represent the temperature. k represents electric potential. T This represents the temperature effect coefficient.

[0021] In some possible implementations, the target chloride ion diffusion model is solved using the finite difference method. When solving the target chloride ion diffusion model using the finite difference method, the concrete structure is divided into several small units, and spatial discretization is performed using the central difference scheme, while temporal discretization is performed using the forward difference scheme.

[0022] In some possible implementations, based on the influence of chloride ion concentration on steel corrosion, a steel corrosion development model is constructed using the target chloride ion diffusion model to characterize the time-varying distribution of steel cross-sectional loss, including:

[0023] Based on the target chloride ion diffusion model, a steel corrosion initiation time model is constructed to characterize the steel corrosion initiation time distribution when the chloride ion concentration reaches a critical value.

[0024] Based on the target chloride ion diffusion model, a steel corrosion rate model is constructed to characterize the effects of chloride ion concentration, oxygen concentration, humidity, and the blocking effect of steel corrosion products on concrete pores on the steel corrosion rate.

[0025] Based on the steel reinforcement corrosion initiation time model and the steel reinforcement corrosion rate model, a steel reinforcement corrosion development model is determined to characterize the change law of steel reinforcement cross-sectional loss distribution over time.

[0026] In some possible implementations, the steel reinforcement corrosion rate model is as follows:

[0027] In the formula, The value of k1 represents the rate of steel corrosion, which is related to the material properties of steel and concrete, environmental conditions, etc., C represents the chloride ion concentration, [O2] represents the oxygen concentration, RH represents the humidity, α represents the blocking effect of steel corrosion products on concrete pores, and a, b, c and d are empirical indices. a reflects the degree of influence of chloride ion concentration on steel corrosion rate, b reflects the degree of influence of oxygen concentration on steel corrosion rate, c reflects the degree of influence of humidity on steel corrosion rate, and d reflects the degree of influence of blocking effect on steel corrosion rate.

[0028] In some possible implementations, the structural performance indicators include load-bearing capacity, stiffness, and deformation, wherein the load-bearing capacity is determined by yield strength and ultimate strength, and the yield strength and ultimate strength are determined by the steel reinforcement section loss.

[0029] This application also provides a device for predicting the life of reinforced concrete structures, comprising:

[0030] The diffusion model construction unit is used to construct a target chloride ion diffusion model to characterize the time-varying law of chloride ion concentration distribution in concrete structures based on the influence relationship between pore structure parameters and diffusion coefficient.

[0031] The corrosion model construction unit is used to construct a steel corrosion development model based on the target chloride ion diffusion model, according to the influence relationship between chloride ion concentration and steel corrosion, to characterize the change law of steel cross-sectional loss distribution over time.

[0032] The structural life prediction unit is used to calculate the structural performance indicators that change over time based on the relationship between steel bar cross-sectional loss and reinforced concrete structural performance indicators, and to determine the life of the reinforced concrete structure when the structural performance indicators degrade to the limit state, based on the steel bar corrosion development model.

[0033] The beneficial effects of this application are as follows: The method for predicting the service life of reinforced concrete structures provided in this application constructs a chloride ion diffusion model by considering the influence of pore structure parameters on the diffusion coefficient, which effectively improves the accuracy of chloride ion diffusion prediction. In addition, it is combined with a steel corrosion development model to calculate the performance indicators of concrete structures, thereby comprehensively assessing the degradation of structural performance indicators, accurately predicting structural service life, providing a reliable basis for structural maintenance, repair and remaining service life assessment, effectively ensuring structural safety and economy, rationally arranging maintenance plans, reducing costs and extending service life. Attached Figure Description

[0034] Figure 1 is a schematic flowchart of an embodiment of the method for predicting the life of reinforced concrete structures provided in this application;

[0035] Figure 2 is a schematic flowchart of an embodiment of step S101 in Figure 1 of this application;

[0036] Figure 3 is a schematic flowchart of an embodiment of step S201 in Figure 2 of this application;

[0037] Figure 4 is a schematic flowchart of an embodiment of step S102 in Figure 1 of this application;

[0038] Figure 5 is a schematic diagram of an embodiment of the reinforced concrete structure life prediction device provided in this application. Detailed Implementation

[0039] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0040] It should be understood that the illustrative drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may be implemented out of order, and steps without logical contextual relationships may be reversed or performed simultaneously. Furthermore, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor systems and / or microcontroller systems.

[0041] The terms "first," "second," etc., used in the embodiments of this application are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a technical feature specified with "first" or "second" may explicitly or implicitly include at least one of those features. "And / or" describes the relationship between related objects, indicating that three relationships may exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone.

[0042] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0043] This application provides a method for predicting the life of reinforced concrete structures, which will be described in detail below.

[0044] Figure 1 is a schematic flowchart of an embodiment of the life prediction method for reinforced concrete structures provided in this application. As shown in Figure 1, the life prediction method for reinforced concrete structures includes:

[0045] S101. Based on the influence of pore structure parameters on diffusion coefficient, construct a target chloride ion diffusion model to characterize the time-dependent variation of chloride ion concentration distribution in concrete structures.

[0046] S102. Based on the influence of chloride ion concentration on steel corrosion, a steel corrosion development model is constructed based on the target chloride ion diffusion model to characterize the change law of steel cross-sectional loss distribution over time.

[0047] S103. Based on the relationship between steel section loss and the performance index of reinforced concrete structure, and based on the steel corrosion development model, calculate the structural performance index that changes over time, and determine the life of reinforced concrete structure when the structural performance index degrades to the limit state.

[0048] Compared with existing technologies, this application constructs a chloride ion diffusion model by considering the influence of pore structure parameters on the diffusion coefficient, which effectively improves the accuracy of chloride ion diffusion prediction. In addition, it calculates the performance indicators of concrete structures by combining the steel reinforcement corrosion development model, thereby comprehensively assessing the degradation of structural performance indicators, accurately predicting structural life, providing a reliable basis for structural maintenance, repair and remaining life assessment, effectively ensuring structural safety and economy, rationally arranging maintenance plans, reducing costs and extending service life.

[0049] In constructing the target chloride ion diffusion model, this application considered the following influencing factors:

[0050] 1) Coupling of physicochemical factors. In actual engineering environments, the diffusion process of chloride ions in concrete is influenced by the interaction of various physicochemical factors. The ion concentration difference in the pore solution inside the concrete is one of the driving forces for chloride ion diffusion. At the same time, the complexity of the pore structure affects the transport path of chloride ions.

[0051] 2) Environmental Factors. Environmental factors play a crucial role in chloride ion diffusion. Temperature is an important environmental parameter. According to thermodynamic principles, increased temperature increases the thermal energy of ions, thereby accelerating chloride ion diffusion. Generally, for every 10°C increase in temperature, the diffusion coefficient may increase by a certain percentage (e.g., 30%-50%, the specific percentage varying depending on the characteristics of the concrete material). Humidity also has a significant impact on chloride ion diffusion. Higher humidity increases the moisture content in the pores of concrete, which is conducive to the dissolution and transport of chloride ions. In environments with alternating wet and dry conditions, changes in humidity can lead to changes in the pore structure of concrete, thus affecting chloride ion diffusion.

[0052] 3) Interaction between stress field and chloride ion diffusion. Concrete structures are subjected to various stresses during service, such as those generated by their own weight and vehicle loads. The presence of a stress field alters the microstructure of the concrete, thereby affecting chloride ion diffusion. When concrete is subjected to tensile stress, the pores tend to expand, providing more channels for chloride ion diffusion and increasing the diffusion coefficient; conversely, compressive stress may compress the pores, reducing chloride ion diffusion channels and lowering the diffusion coefficient. This interaction between stress and chloride ion diffusion is a dynamic process. Changes in stress lead to changes in chloride ion diffusion characteristics, and conversely, chloride ion intrusion and diffusion may also affect the mechanical properties of concrete, thereby altering the stress distribution.

[0053] Based on the above considerations, in some embodiments, as shown in FIG2, step S101 specifically includes:

[0054] S201. Based on the law of conservation of mass and Fick's law, determine the initial chloride ion diffusion model to characterize the change of chloride ion concentration distribution in concrete structures over time.

[0055] S202. Based on the influence of pore structure parameters on the diffusion coefficient, pore structure parameters are introduced into the diffusion coefficient of the initial chloride ion diffusion model to obtain the target chloride ion diffusion model.

[0056] Furthermore, considering that the chloride ion diffusion flux is also affected by the potential gradient (related to the internal electrochemical environment of concrete) and the temperature gradient, in some embodiments, as shown in Figure 3, step S201 includes:

[0057] S301. Based on the law of conservation of mass, determine the first equation to characterize the effect of the diffusion flux gradient on the change of chloride ion concentration over time.

[0058] It should be noted that, according to the law of conservation of mass, considering the mass balance of chloride ions per unit volume of concrete, that is, the inflow of chloride ions minus the outflow equals the rate of change of chloride ion mass per unit volume, the first equation is obtained as follows:

[0059] In the formula, c represents the chloride ion concentration, and t represents time. J represents the gradient operator. i This represents the chloride ion diffusion flux.

[0060] S302. Based on Fick's first law and the relationship between chloride ion diffusion flux and potential and temperature gradients, a second equation is constructed to characterize the effects of diffusion coefficient, chloride ion concentration gradient, potential gradient and temperature gradient on diffusion flux.

[0061] It should also be noted that, according to Fick's first law, the chloride ion diffusion flux is directly proportional to the chloride ion concentration gradient, with the proportionality constant being the diffusion coefficient. When multiple factors are involved, this leads to the second equation:

[0062] In the formula, J i D represents the chloride ion diffusion flux, and D represents the diffusion coefficient. Let represent the gradient operator, c represent the chloride ion concentration, z represent the chloride ion valence (z = -1), F represent the Faraday constant (F = 96485 C / mol), R represent the gas constant (R = 8.314 J / (mol·K)), and T represent the temperature. k represents electric potential. T This represents the temperature influence coefficient (related to the thermophysical properties of concrete materials).

[0063] S303. Substitute the second equation into the first equation to obtain the initial chloride ion diffusion model.

[0064] It should be noted that the initial chloride ion diffusion model is as follows:

[0065] In the formula, c represents the chloride ion concentration, and t represents time. Let represent the gradient operator, D represent the diffusion coefficient, z represent the chloride ion valence, F represent the Faraday constant, R represent the gas constant, and T represent the temperature. k represents electric potential. T This represents the temperature effect coefficient.

[0066] Considering the significant impact of the internal pore structure of concrete on the diffusion coefficient, porosity reflects the proportion of pores in the total volume of concrete. Higher porosity provides more space for chloride ion diffusion, making diffusion relatively easier. Fill ratio indicates the degree to which the pores are filled by liquids or other substances; changes in fill ratio alter the transport environment of chloride ions within the pores. Temperature affects the diffusion coefficient by influencing the internal microstructure and ion mobility of concrete. Therefore, the pore structure parameters include porosity and fill ratio, and the diffusion coefficient is:

[0067] In the formula, D represents the diffusion coefficient. β represents porosity, R represents the packing density, T represents the gas constant, and D0 represents the initial diffusion coefficient (at a reference initial porosity). E represents the initial filling rate β0 and the diffusion coefficient at the initial temperature T0, where m and n are empirical coefficients (obtained by fitting experimental data; generally, m ranges from 1.5 to 3.0, and n ranges from 0.5 to 1.5). aIt represents the activation energy (related to the energy barrier of concrete material properties and diffusion process, and generally ranges from 30 to 60 kJ / mmol).

[0068] Based on the above initial chloride ion diffusion model and diffusion coefficient, the final target chloride ion diffusion model is obtained as follows:

[0069] In the formula, c represents the chloride ion concentration, and t represents time. Represents the gradient operator. β represents porosity, R represents the packing density, T represents the gas constant, and D0 represents the initial diffusion coefficient (at a reference initial porosity). (Initial filling ratio β0 and diffusion coefficient at initial temperature T0), where m and n represent empirical coefficients, E a The value represents the activation energy, z represents the valence of chloride ions, and F represents the Faraday constant. k represents electric potential. T This represents the temperature effect coefficient.

[0070] For the complex target chloride ion diffusion model described above, it can be appropriately simplified according to specific circumstances in practical applications. For example, if the potential gradient... and temperature gradient The impact is relatively small and can be ignored, so the model can be simplified to:

[0071] When solving the above target chloride ion diffusion model, numerical calculation methods, such as the finite difference method or the finite element method, can be used. In some embodiments, taking the finite difference method as an example, the concrete structure is divided into several small units, and time and space are discretized:

[0072] For spatial discretization, a central difference scheme is used to approximate the concentration gradient:

[0073] In the formula, i represents the element node number, and Δx represents the spatial step size.

[0074] For time-discrete conditions, a forward difference scheme is used:

[0075] Substituting the above difference scheme into the chloride ion diffusion model yields a set of algebraic equations concerning the chloride ion concentration at each node at different time steps. By solving this set of equations by substitution, the variation law of chloride ion concentration distribution in concrete structures over time can be obtained.

[0076] In some embodiments, as shown in FIG4, step S102 specifically includes:

[0077] S401. Based on the target chloride ion diffusion model, construct a steel corrosion initiation time model to characterize the steel corrosion initiation time distribution when the chloride ion concentration reaches a critical value.

[0078] S402. Based on the target chloride ion diffusion model, construct a steel corrosion rate model to characterize the effects of chloride ion concentration, oxygen concentration, humidity, and the blocking effect of steel corrosion products on concrete pores on the steel corrosion rate.

[0079] S403. Based on the steel reinforcement corrosion initiation time model and steel reinforcement corrosion rate model, determine the steel reinforcement corrosion development model to characterize the time-varying law of steel reinforcement cross-sectional loss distribution.

[0080] When constructing the model for the initiation time of steel reinforcement corrosion, this application considered the following influencing factors:

[0081] 1) Critical chloride ion concentration and steel reinforcement surface condition. Steel reinforcement corrosion begins when the chloride ion concentration in the concrete reaches the critical value C. cγ Closely related, when the chloride ion concentration in concrete reaches or exceeds a critical concentration in a localized area on the surface of the reinforcing steel, the passivation film on the steel surface begins to break down. Simultaneously, the electrochemical state of the steel surface also plays a crucial role. After passivation failure, localized batteries form on the steel surface. In the presence of chloride ions and water, electrochemical corrosion reactions begin, marking the onset of steel corrosion. However, the critical value C... cγ It is not a fixed value; it is affected by a variety of factors, such as the concrete mix proportion (type and proportion of cement, aggregate, and admixtures), pore structure (porosity, pore size distribution, etc.), ambient humidity, and the surface roughness of the reinforcing steel, and whether it has been pretreated.

[0082] 2) The Influence of Environmental Factors on Corrosion Initiation. Environmental factors play a crucial role in the initiation of steel corrosion. Humidity (RH) is a key factor; higher humidity provides a medium for the dissolution and transport of chloride ions, and also promotes electrochemical reactions on the steel surface. When humidity reaches a certain level (e.g., relative humidity RH > 60%), the moisture in the concrete pores is sufficient to form an electrolyte solution, accelerating the initiation of steel corrosion. Oxygen concentration [O2] is equally important; oxygen is the oxidant in the steel corrosion process, and its concentration affects the rate of the corrosion reaction. In a low-oxygen environment, the initiation of steel corrosion may be delayed, but once the oxygen supply is sufficient, the corrosion reaction will develop rapidly. In addition, temperature (T) also affects the corrosion initiation time. Higher temperatures accelerate the chemical reaction rate, causing chloride ions to reach the critical concentration more quickly and accelerating the initiation of steel corrosion. However, excessively high temperatures may cause changes in the internal structure of the concrete, affecting chloride ion diffusion and the microenvironment for steel corrosion.

[0083] 3) The effect of uneven chloride ion distribution on corrosion initiation. Due to the non-uniformity of concrete and the complexity of chloride ion diffusion, chloride ions exhibit uneven distribution in concrete.

[0084] Based on the above considerations, in some embodiments, this application introduces a chloride ion concentration distribution function C(x,y,z,t) to accurately describe the chloride ion concentration at any position (x,y,z) and time t. The steel reinforcement corrosion initiation time model can be expressed as:

[0085] C(x s ,y s ,z s ,t cγ ) = C cγ (RH,[O2],T,...)

[0086] In the formula, C represents the chloride ion concentration, (x s ,y s ,z s ) represents the coordinates of the surface position of the reinforcing bar, t cγ Indicates the time when steel reinforcement corrosion begins, C cγ The equation, where RH represents humidity, [O2] represents oxygen concentration, and T represents temperature, indicates that the onset time of steel corrosion depends on the moment when the chloride ion concentration at a specific location on the steel surface reaches a critical value, which is closely related to environmental factors (humidity, oxygen concentration, temperature, etc.).

[0087] To solve the above model of the initiation time of steel corrosion, numerical methods such as the finite difference method or the finite element method can be used. The finite difference method discretizes the space and time, transforming the partial differential equation into an algebraic equation for solution. The finite element method, based on the variational principle, divides the solution domain into multiple finite elements and approximates the true solution through element interpolation functions and nodal unknowns. In some embodiments, the finite element method is used as an example for illustration:

[0088] First, the concrete structure is divided into a finite number of elements, each containing several nodes. For the chloride ion concentration distribution C(x,y,z,t), a suitable interpolation function (such as linear interpolation, quadratic interpolation, etc.) is used to approximate it within each element. Assume that within element e, the chloride ion concentration C... c (x,y,z,t) can be represented as the node concentration C. i A linear combination of (t)(i represents the element node number): In the formula, N i The shape function of the element, n e Indicates the number of unit nodes.

[0089] Then, the equations are discretized and solved, and all elements are assembled to obtain the overall nonlinear equation system: K(C k+1 ,RH k+1 [O2] k+1 ,T k+1 ,...)C k+1 =F(C k ,RH k [O2] k ,T k In the equation, K represents the stiffness matrix, C represents the nodal chloride ion concentration vector, RH represents the humidity vector, [O2] represents the oxygen concentration vector, T represents the temperature vector, and F represents the load vector. An iterative method (such as the Newton-Raphson alternative method) is used to solve the above nonlinear equation system. In each alternative, the stiffness matrix K and the load vector F are calculated, and the nodal chloride ion concentration vector C is updated. The alternative process continues until the convergence criterion is met, such as the change in nodal chloride ion concentration between two adjacent alternatives being less than a given threshold ε. In the formula, m represents the number of iterations.

[0090] The chloride ion concentration at each node at different time steps is obtained through iterative solution. Then, based on the steel reinforcement corrosion initiation time model, the steel reinforcement corrosion initiation time t at each node is determined. cγ,i Specifically, for each node i, at each time step k+1, check whether the condition is met. If the condition is met, record the corrosion start time tc of that node. γ,i =t k+1 Ultimately, the distribution of corrosion initiation time at different locations on the entire steel reinforcement surface was obtained, thus providing a comprehensive understanding of the process and characteristics of steel reinforcement corrosion initiation. This numerical solution method based on the finite element method can consider the complexity of concrete structures and changes in environmental factors, more accurately predicting the corrosion initiation time of steel reinforcement, and providing an important foundation for subsequent analysis of steel reinforcement corrosion development and prediction of structural life.

[0091] When constructing the steel reinforcement corrosion rate model, this application considered the following influencing factors:

[0092] Steel corrosion rate The corrosion rate of steel bars is influenced by a combination of factors, with chloride ion concentration (C) being a key factor. High chloride ion concentrations accelerate the corrosion reaction because chloride ions can destroy the oxidized film on the surface of the steel bars, making them more susceptible to electrochemical corrosion. Oxygen concentration (O2) also plays an important role in the corrosion rate. Oxygen acts as an oxidant in the corrosion reaction, and the higher its concentration, the easier the corrosion reaction proceeds. Humidity (RH) affects the moisture content inside the concrete, and moisture is a necessary medium for electrochemical reactions. Suitable humidity (such as RH between 60% and 90%) promotes the corrosion reaction. In addition, the blocking effect α of steel corrosion products on concrete pores cannot be ignored. As corrosion products accumulate, concrete pores become blocked, hindering the transport of oxygen and chloride ions, thereby reducing the steel corrosion rate.

[0093] Based on the above considerations, in some embodiments, the steel reinforcement corrosion rate model is as follows:

[0094] In the formula, The values ​​represent the steel corrosion rate, k1 represents the rate constant, which is related to the material properties of the steel and concrete, environmental conditions, etc., C represents the chloride ion concentration, [O2] represents the oxygen concentration, RH represents the humidity, α represents the blocking effect of steel corrosion products on concrete pores, which can be obtained by fitting experimental data, and a, b, c and d are empirical indices. a reflects the degree of influence of chloride ion concentration on steel corrosion rate, b reflects the degree of influence of oxygen concentration on steel corrosion rate, c reflects the degree of influence of humidity on steel corrosion rate, and d reflects the degree of influence of blocking effect on steel corrosion rate.

[0095] To determine the parameters k1, a, b, c, and d in the steel corrosion rate model, a series of experiments are required. For example, while keeping other factors constant, the chloride ion concentration is varied, and the steel corrosion rate at different concentrations is measured. The value of 'a' is obtained through data fitting. Similar methods are used to experimentally determine b and c for the effects of oxygen concentration and humidity. The clogging effect coefficient 'd' can be determined by simulating the steel corrosion process and measuring the relationship between the degree of pore clogging and the steel corrosion rate. Simultaneously, the influence of ambient temperature on the rate constant k1 needs to be considered, establishing the relationship between k1 and temperature T, such as... In the formula, k0 represents the reference rate constant, E a R represents the activation energy, and R represents the gas constant. The values ​​of these parameters are obtained by fitting experimental data at different temperatures.

[0096] Based on the above steel corrosion rate model, calculate the cross-sectional loss A of the steel reinforcement at any time t. γ (t), specifically: assuming the initial cross-sectional area of ​​the reinforcing steel is A0, the approximate value of the cross-sectional loss of the reinforcing steel within a small time interval Δt is... Integrating over time, we get: Substituting the steel corrosion rate model into the above formula, we get:

[0097] In practical calculations, since C, [O2], RH, and α can all vary with time and space, calculations need to be performed based on specific environmental conditions and structural states. For example, the chloride ion concentration C can be calculated using the previously established chloride ion diffusion model to obtain the concentration values ​​at different times and locations; the oxygen concentration [O2] and humidity RH can be determined based on environmental monitoring data or empirical models; and the clogging effect α can be calculated based on the amount of corrosion products generated and the pore structure characteristics of the concrete.

[0098] Furthermore, considering the unevenness of steel reinforcement corrosion, which often occurs in unpredictable patterns in actual structures, corrosion may begin in localized areas and gradually spread. This unevenness primarily stems from factors such as the uneven distribution of chloride ions within the concrete, differences in oxygen supply, and localized variations in the surface condition of the steel reinforcement. To more accurately describe the development process of steel reinforcement corrosion, it is necessary to consider the impact of this unevenness on the cross-sectional loss of the steel reinforcement.

[0099] Specifically, the reinforcing steel is divided into several micro-units, each with different corrosion conditions. For the i-th unit, its steel corrosion rate can be determined based on the aforementioned steel corrosion rate model, combined with the chloride ion concentration C at the unit's location. i Oxygen concentration [O2] i RH humidity i The calculation also incorporates the blockage effect α. When calculating the rebar section loss, the section loss of each element is calculated separately, and then summed to obtain the total rebar section loss. At time step t... k+1 =t k +Δt, the cross-sectional loss increment ΔA of the i-th element. γ,i for: Where A 0,i Let be the initial cross-sectional area of ​​the i-th element. Let A be the total cross-sectional loss of the reinforcement at time t. γ (t) is: In the formula, n represents the total number of units into which the reinforcement is divided, and t cγ,i This indicates the corrosion start time of the i-th unit.

[0100] To ensure the accuracy of the steel corrosion development model, model validation is necessary. This involves collecting steel corrosion data from actual engineering projects under different environmental conditions, including measurements of steel cross-sectional loss and mechanical property tests. The model's calculations are then compared with the measured data. If a significant discrepancy is found between the model's predictions and the measured values, the model needs to be corrected.

[0101] When revising the model, first check whether the values ​​of the model parameters are reasonable, and adjust the parameters based on the comparison results. Simultaneously, analyze any factors not considered in the model or the irrationality of any assumptions, and improve the model accordingly. For example, if the model is found to be inaccurate in predicting the corrosion rate of steel bars under certain specific environmental conditions, it may be necessary to further study the influence mechanism of this environmental factor on the corrosion process and refine the corrosion rate model. Through continuous verification and revision, the reliability and applicability of the model are improved, enabling it to more accurately predict the development process of steel bar corrosion, providing strong support for the durability assessment and life prediction of reinforced concrete structures.

[0102] Loss of cross-sectional area in reinforcing steel leads to a decrease in its mechanical properties, which in turn affects the load-bearing capacity of the structure. Establishing the yield strength f of the reinforcing steel is crucial. y (t) and ultimate strength f u (t) Degradation model with loss of reinforcement section:

[0103] In the formula, f y0 f represents the initial yield strength of the steel reinforcement. u0 λ represents the initial ultimate strength of the steel reinforcement. y and λ u λ represents the strength degradation coefficient, which is related to factors such as the type of steel reinforcement, corrosion morphology, and structural stress state. For ordinary carbon steel reinforcement, λ y It may be between 0.1 and 0.3, λ u The values ​​are likely between 0.15 and 0.35; for high-strength steel bars, the values ​​may differ. Through experimental research and theoretical analysis, the specific values ​​of these coefficients will be determined to accurately assess the impact of steel bar corrosion on the structural mechanical properties.

[0104] In structural load-bearing capacity assessment, the load-bearing capacity P of the structure u (t) is a key indicator for measuring structural safety. As steel reinforcement corrosion progresses, the loss of cross-sectional area and degradation of mechanical properties directly affect the structure's load-bearing capacity. When calculating load-bearing capacity, the synergistic working principle of concrete and steel reinforcement must be considered. Based on structural mechanics theory, such as using finite element analysis, a mechanical model of the structure can be established to calculate the maximum load the structure can withstand at different corrosion stages. For example, for flexural members, the flexural bearing capacity can be calculated based on the yield strength and ultimate strength of the steel reinforcement after corrosion, combined with the compressive strength of the concrete; for compression members, the impact of steel reinforcement corrosion on its compressive stability should be considered to calculate its compressive bearing capacity. The degradation model of load-bearing capacity is expressed as:

[0105] In the formula, P u0 ΔP represents the initial bearing capacity of a reinforced concrete structure. u(t) represents the rate of loss of bearing capacity of reinforced concrete structure over time, which is related to factors such as the degree of steel corrosion and concrete deterioration, and can be determined through experiments and theoretical analysis.

[0106] In stiffness assessment, stiffness K(t) reflects a structure's ability to resist deformation. Steel corrosion leads to a decrease in the bond strength between the steel reinforcement and concrete, as well as damage to the concrete itself, thus reducing structural stiffness. Stiffness values ​​are calculated by measuring the deformation of the structure under different degrees of corrosion through dynamic or static loading tests. Stiffness can be calculated using the displacement method or energy method in structural mechanics, based on the relationship between structural deformation and load. The stiffness degradation model is expressed as: In the formula, K0 represents the initial stiffness of the reinforced concrete structure, and ΔK(t) represents the stiffness loss rate, which is related to factors such as the accumulation of steel corrosion products and changes in the pore structure of concrete.

[0107] In deformation assessment, deformation δ(t) is a direct representation of structural performance. Excessive deformation can affect the normal function of the structure and even lead to structural failure. Deformation can be measured using devices such as displacement sensors to monitor displacement changes in key components under structural loading or natural conditions. Deformation assessment indicators can include the maximum deflection of the structure and crack width. The deformation over time can be represented by the following model: In the formula, δ0 represents the initial deformation of the reinforced concrete structure, and Δδ(t) represents the deformation increment rate, which is related to factors such as the redistribution of internal forces caused by steel corrosion and concrete cracking.

[0108] The comprehensive evaluation function is being established to comprehensively assess the degradation of structural performance indicators.

[0109] In the formula, G(t) represents the comprehensive evaluation function, w1, w2 and w3 represent weighting coefficients, w1 represents the importance of bearing capacity in the comprehensive evaluation, w2 represents the importance of stiffness in the comprehensive evaluation, and w3 represents the importance of deformation in the comprehensive evaluation.

[0110] Based on the above definition of performance limit state, the lifetime prediction model can be expressed as:

[0111] In the formula, T L P represents the structural lifespan. u,cγ K represents the ultimate limit state of bearing capacity. allow δ represents the stiffness limit state. allow T represents the deformation limit state. L The minimum time required to satisfy any of the above limit state conditions is the structural life T. L The minimum time required to satisfy any of the above limit state conditions.

[0112] During the calculation process, for the ultimate limit state of bearing capacity, the structural bearing capacity P is continuously updated. u Calculate (t) and determine whether it reaches the critical value P. u,cγ For deformation and stiffness under normal serviceability limits, the changes in K(t) and δ(t) are also monitored in real time. At each time step t... k+1 =t k +Δt (k is the time step number), calculate the bearing capacity P at this time. u (t k+1 Stiffness K(t) k+1 ) and deformation δ(t) k+1 If P u (t k+1 )≤P u,cγ If the structure reaches its ultimate bearing capacity, then the time t is... k+1 That is, the structural life T L If K(t) k+1 )≤K allow The structure has reached its serviceability limit state (based on stiffness), t k+1 For structural lifetime; if δ(t) k+1 )≤δ allow If the structure reaches its normal serviceability limit state (based on deformation), then t k+1 It is also the structural lifespan.

[0113] In summary, the multi-factor coupled target chloride ion diffusion model, the accurate steel reinforcement corrosion initiation time model, the comprehensive steel reinforcement corrosion development model, and the integrated structural performance degradation assessment and life prediction constructed in this application can more accurately simulate the chloride ion diffusion, corrosion initiation and development process, comprehensively assess the degradation of structural performance indicators, accurately predict structural life, provide a reliable basis for structural maintenance, repair and remaining life assessment, effectively ensure structural safety and economy, rationally arrange maintenance plans, reduce costs and extend service life.

[0114] To better implement the life prediction method for reinforced concrete structures in this application embodiment, based on the method, and correspondingly as shown in Figure 5, this application embodiment also provides a life prediction device 500 for reinforced concrete structures, including:

[0115] The diffusion model construction unit 501 is used to construct a target chloride ion diffusion model to characterize the time-varying law of chloride ion concentration distribution in concrete structures based on the influence relationship between pore structure parameters and diffusion coefficient.

[0116] The corrosion model construction unit 502 is used to construct a corrosion development model for steel bars based on the target chloride ion diffusion model, according to the influence relationship between chloride ion concentration and steel bar corrosion, to characterize the change law of steel bar cross-sectional loss distribution over time.

[0117] The structural life prediction unit 503 is used to calculate the structural performance index that changes over time based on the relationship between the steel reinforcement section loss and the performance index of the reinforced concrete structure, and to determine the life of the reinforced concrete structure when the structural performance index degrades to the limit state, based on the steel reinforcement corrosion development model.

[0118] The reinforced concrete structure life prediction device 500 provided in the above embodiments can realize the technical solutions described in the above reinforced concrete structure life prediction method embodiments. The specific implementation principles of each unit can be found in the corresponding content in the above reinforced concrete structure life prediction method embodiments, and will not be repeated here.

[0119] The above provides a detailed description of the method for predicting the life of reinforced concrete structures provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and its core ideas. At the same time, those skilled in the art will recognize that there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

[0120] The above description is merely a preferred embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for predicting the lifespan of reinforced concrete structures, characterized in that, include: Based on the influence of pore structure parameters on diffusion coefficient, a target chloride ion diffusion model is constructed to characterize the time-dependent variation of chloride ion concentration distribution in concrete structures. Based on the influence of chloride ion concentration on steel corrosion, a steel corrosion development model is constructed to characterize the time-varying law of steel cross-sectional loss distribution based on the target chloride ion diffusion model. Based on the relationship between steel reinforcement cross-sectional loss and the performance indicators of reinforced concrete structures, and based on the steel reinforcement corrosion development model, the structural performance indicators that change over time are calculated, and the life of the reinforced concrete structure when the structural performance indicators degrade to the limit state is determined.

2. The method for predicting the lifespan of reinforced concrete structures according to claim 1, characterized in that, Based on the influence of pore structure parameters on the diffusion coefficient, a target chloride ion diffusion model is constructed to characterize the time-dependent distribution of chloride ion concentration in concrete structures, including: Based on the law of conservation of mass and Fick's law, an initial chloride ion diffusion model is determined to characterize the change of chloride ion concentration distribution in concrete structures over time. Based on the influence of pore structure parameters on the diffusion coefficient, pore structure parameters are introduced into the diffusion coefficient of the initial chloride ion diffusion model to obtain the target chloride ion diffusion model. The pore structure parameters include porosity and filling rate.

3. The method for predicting the lifespan of reinforced concrete structures according to claim 2, characterized in that, The diffusion coefficient is: In the formula, D represents the diffusion coefficient. β represents porosity, D0 represents the initial diffusion coefficient, and β represents the filling rate. β0 represents the initial porosity, m and n represent empirical coefficients, and E represents the initial filling rate. a R represents the activation energy, R represents the gas constant, and T represents the temperature.

4. The method for predicting the lifespan of reinforced concrete structures according to claim 2, characterized in that, Based on the law of conservation of mass and Fick's law, an initial chloride ion diffusion model is determined to characterize the time-dependent distribution of chloride ion concentration in concrete structures, including: Based on the law of conservation of mass, a first equation is determined to characterize the effect of the diffusion flux gradient on the change of chloride ion concentration over time. Based on Fick's first law and the relationship between chloride ion diffusion flux and potential and temperature gradients, a second equation is constructed to characterize the effects of diffusion coefficient, chloride ion concentration gradient, potential gradient and temperature gradient on diffusion flux. Substituting the second equation into the first equation yields the initial chloride ion diffusion model.

5. The method for predicting the service life of reinforced concrete structures according to claim 4, characterized in that, The second equation is: In the formula, J i D represents the chloride ion diffusion flux, and D represents the diffusion coefficient. Let represent the gradient operator, c represent the chloride ion concentration, z represent the chloride ion valence, F represent the Faraday constant, R represent the gas constant, and T represent the temperature. k represents electric potential. T This represents the temperature effect coefficient.

6. The method for predicting the life of reinforced concrete structures according to claim 1, characterized in that, The target chloride ion diffusion model is solved using the finite difference method. When solving the target chloride ion diffusion model using the finite difference method, the concrete structure is divided into several small units, and spatial discretization is performed using the central difference scheme, while temporal discretization is performed using the forward difference scheme.

7. The method for predicting the life of reinforced concrete structures according to claim 1, characterized in that, Based on the influence of chloride ion concentration on steel corrosion, a steel corrosion development model is constructed using the target chloride ion diffusion model to characterize the time-varying distribution of steel cross-sectional loss, including: Based on the target chloride ion diffusion model, a steel corrosion initiation time model is constructed to characterize the steel corrosion initiation time distribution when the chloride ion concentration reaches a critical value. Based on the target chloride ion diffusion model, a steel corrosion rate model is constructed to characterize the effects of chloride ion concentration, oxygen concentration, humidity, and the blocking effect of steel corrosion products on concrete pores on the steel corrosion rate. Based on the steel reinforcement corrosion initiation time model and the steel reinforcement corrosion rate model, a steel reinforcement corrosion development model is determined to characterize the change law of steel reinforcement cross-sectional loss distribution over time.

8. The method for predicting the life of reinforced concrete structures according to claim 7, characterized in that, The steel reinforcement corrosion rate model is as follows: In the formula, The value of k1 represents the rate of steel corrosion, which is related to the material properties of steel and concrete, environmental conditions, etc., C represents the chloride ion concentration, [O2] represents the oxygen concentration, RH represents the humidity, α represents the blocking effect of steel corrosion products on concrete pores, and a, b, c and d are empirical indices. a reflects the degree of influence of chloride ion concentration on steel corrosion rate, b reflects the degree of influence of oxygen concentration on steel corrosion rate, c reflects the degree of influence of humidity on steel corrosion rate, and d reflects the degree of influence of blocking effect on steel corrosion rate.

9. The method for predicting the life of reinforced concrete structures according to claim 1, characterized in that, The structural performance indicators include load-bearing capacity, stiffness, and deformation. The load-bearing capacity is determined by yield strength and ultimate strength, which are determined by the cross-sectional loss of the reinforcing steel.

10. A device for predicting the lifespan of reinforced concrete structures, characterized in that, include: The diffusion model construction unit is used to construct a target chloride ion diffusion model to characterize the time-varying law of chloride ion concentration distribution in concrete structures based on the influence relationship between pore structure parameters and diffusion coefficient. The corrosion model construction unit is used to construct a steel corrosion development model based on the target chloride ion diffusion model, according to the influence relationship between chloride ion concentration and steel corrosion, to characterize the change law of steel cross-sectional loss distribution over time. The structural life prediction unit is used to calculate the structural performance indicators that change over time based on the relationship between steel bar cross-sectional loss and reinforced concrete structural performance indicators, and to determine the life of the reinforced concrete structure when the structural performance indicators degrade to the limit state, based on the steel bar corrosion development model.

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