Time-varying prediction method and system for chloride ion erosion in concrete under marine exposure environment

Abstract

The present invention provides a time-varying prediction method and system for chloride ion erosion in concrete under marine exposure environments. The method includes: obtaining the parameters required for the concrete prediction model; determining a suitable time-varying diffusion coefficient model according to the exposure conditions and the parameters to obtain a reference diffusion coefficient and an aging factor; determining a suitable cumulative model of time-varying boundary conditions according to the cumulative property of the chloride ion adsorption capacity on the concrete surface under the exposure conditions with respect to the exposure time to obtain an initial surface chloride ion concentration and an empirical coefficient; solving the chloride ion transport model in the concrete to achieve the prediction of the chloride ion concentration under specified exposure environments and exposure times. The system is used to implement the above prediction method. The present invention can accurately predict the chloride ion concentration distribution in concrete under medium- and long-term chloride exposure conditions, and further predict the service life of the concrete structure.

Description

Technical Field

[0001] The present invention relates to the technical field of civil engineering materials, and specifically, to a time-varying prediction method and system for chloride ion erosion in concrete under a marine exposure environment. Background Art

[0002] The marine environment contains abundant chloride ions, and chloride salt erosion is one of the important factors causing the corrosion of steel bars and the reduction of durability in marine concrete structures. Chloride ions enter the concrete from the external environment and are transported within the concrete. The chloride ion concentration on the surface of the steel bars will continuously increase. When the concentration exceeds the critical value required for steel bar corrosion, the steel bars will corrode. Therefore, studying the chloride ion transport process and the chloride ion concentration distribution law inside the concrete is the key to revealing the durability deterioration mechanism of concrete materials and is also of great significance for evaluating and predicting the durability of concrete structures.

[0003] In a marine engineering environment, the essential mechanism of durability deterioration of concrete caused by chloride salt erosion is the problem of chloride ion transport. Currently, the research on the chloride ion transport process in concrete is mainly divided into two categories. The first category starts from the experimental perspective and uses steady-state and non-steady-state methods to test the chloride ion migration process in concrete, so as to evaluate the chloride ion penetration performance. Common test methods include the natural immersion method and the electro-accelerated chloride ion migration method. For example, in the patent with the application number CN201610142539.X by Guangxi University, a three-dimensional distribution test method for measuring the chloride ion concentration in concrete after natural immersion in a chloride salt solution is introduced; in addition, Beihang University has also invented a test method for quickly predicting the service life of reinforced concrete in a chloride salt environment (application number CN201310455001.0), and its key technology involves the experimental measurement method of the chloride ion concentration distribution in concrete.

[0004] The other category of methods starts from the perspective of theoretical research and numerical simulation, and predicts the chloride ion diffusion coefficient, chloride ion concentration distribution law in concrete, and the influence of various factors on the chloride ion transport performance by establishing a chloride ion transport model. For example, the patent with the application number CN202111680727.5 proposes a rapid evaluation method for the chloride ion diffusion coefficient of concrete at different curing ages; in the patent with the application number CN201210562397.4, a quantitative design method for the durability of concrete structures in a marine environment is disclosed, and its key technology involves the establishment of a calculation model for the chloride ion concentration distribution in concrete.

[0005] However, the above two types of technologies have the following problems that urgently need to be improved: 1. Although the test method is intuitive, it is time-consuming and laborious. Moreover, with the changes in test conditions and material properties, the test results will be volatile; 2. Using numerical simulation methods to study chloride ion transport problems is a suitable choice. Such methods can essentially reveal the ion transport laws and durability deterioration mechanisms. However, the above existing technologies have low computational efficiency due to their high complexity. Most importantly, the existing inventions in theoretical research rarely consider the time-varying effects of the diffusion coefficient and surface chloride ion concentration during the chloride ion transport process, which will lead to low accuracy of the concentration prediction model.

[0006] Therefore, it is necessary to provide a time-varying prediction method and system for chloride ion erosion in concrete under marine exposure environments to solve the above problems. Summary of the Invention

[0007] Aiming at the defects in the prior art, the purpose of the present invention is to provide a time-varying prediction method and system for chloride ion erosion in concrete under marine exposure environments.

[0008] According to one aspect of the present invention, there is provided a time-varying prediction method for chloride ion erosion in concrete under marine exposure environments, which includes:

[0009] Obtain the parameters required for the concrete prediction model, preprocess the parameters, and obtain the apparent diffusion coefficient and surface chloride ion concentration at different exposure times;

[0010] According to the exposure conditions and the parameters, considering the time-dependent decay of the apparent diffusion coefficient with exposure time and the degree of concrete hydration, determine the time-varying diffusion coefficient model, and obtain the reference diffusion coefficient and aging factor based on the time-varying diffusion coefficient model;

[0011] According to the cumulative property of the chloride ion adsorption capacity on the concrete surface under exposure conditions with exposure time, determine the time-varying boundary condition cumulative model, and obtain the initial surface chloride ion concentration and empirical coefficient based on the time-varying boundary condition cumulative model;

[0012] According to the reference diffusion coefficient, aging factor, initial surface chloride ion concentration and empirical coefficient, solve the chloride ion transport model in the concrete to realize the prediction of the chloride ion concentration under the specified exposure environment and exposure time.

[0013] According to another aspect of the present invention, there is provided a time-varying prediction system for chloride ion erosion in concrete under marine exposure environments, which is used to implement the above time-varying prediction method for chloride ion erosion in concrete under marine exposure environments. The system includes:

[0014] A parameter preprocessing module, which is used to obtain the parameters required for the concrete prediction model and preprocess the parameters to obtain the apparent diffusion coefficient Da and the surface chloride ion concentration Cs at different exposure times;

[0015] A calculation module, which is used to determine the time-varying diffusion coefficient model D(t) and the time-varying boundary condition model Cs(t) by considering the actual exposure conditions according to the parameters, the apparent diffusion coefficient Da and the surface chloride ion concentration Cs, and obtain the reference diffusion coefficient D ref , the aging factor m, the initial surface chloride ion concentration Cs0 and the empirical coefficient k;

[0016] A prediction module, which is used to solve the chloride ion transport model in the concrete according to the parameters obtained by the calculation module to realize the prediction of the chloride ion concentration under the specified exposure environment and exposure time.

[0017] Compared with the prior art, the present invention has at least one of the following beneficial effects:

[0018] 1. The present invention comprehensively considers various influencing factors affecting the chloride ion transport process, and then selects a suitable prediction model according to the specific working conditions, so as to efficiently obtain the distribution law of the ion concentration in the concrete under the chloride salt exposure environment, greatly improving the calculation efficiency of the ion transport process in the saturated concrete.

[0019] 2. The present invention considers the attenuation phenomenon of the chloride ion diffusion coefficient and the time dependence of the boundary conditions with the change of the exposure duration, so that the ion transport model based on Fick's second law has a wider application range, solves the problem that the traditional ion concentration calculation model has a large deviation due to ignoring the accumulation of the surface concentration with time and the decay phenomenon of the diffusion coefficient in the previous research, and further improves the prediction reliability of the subsequent concrete cover thickness design and durability performance.

[0020] 3. The present invention solves the chloride ion transport model under the same working conditions from two different perspectives of the analytical method and numerical simulation, and compares the numerical simulation and analytical calculation results with the measured results, thereby ensuring the reliability of the chloride ion concentration prediction model. Description of the Drawings

[0021] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, objects and advantages of the present invention will become more obvious:

[0022] Figure 1 It is a flowchart of the method for predicting the chloride ion concentration in the concrete in an embodiment of the present invention;

[0023] Figure 2Schematic diagram of the chloride ion concentration prediction system in concrete in an embodiment of the present invention;

[0024] Figure 3 Schematic diagram of the "skin effect" of concrete and the chloride ion concentration distribution considering the convection depth in an embodiment of the present invention;

[0025] Figure 4 Schematic diagram of the relationship between the ratio of apparent coefficients and exposure time between the Mangat model and the Tang - Guliker model under different aging factor values in an embodiment of the present invention;

[0026] Figure 5 Schematic diagram of the relationship between the ratio of apparent coefficients and exposure time between the Thomas model and the Tang - Guliker model under different aging factor values in an embodiment of the present invention. Detailed implementation manners

[0027] The present invention will be described in detail below with reference to specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can be made. These all belong to the protection scope of the present invention. In the description of the embodiments of the present invention, it should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above - mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here.

[0028] The diffusion coefficient of chloride ions in concrete in a chloride - containing environment shows obvious decay with the increase of exposure time. First, as the exposure time increases, the degree of hydration of concrete tends to be perfect, and the pore structure decreases, resulting in a decrease in the effective area of the chloride ion transmission channel. Therefore, the diffusion coefficient of ions shows obvious time - dependence. However, under long - term exposure time, as the hydration reaction is completed, this time - dependence of the diffusion coefficient will gradually disappear. Second, the concentration gradient in saturated concrete is the main driving force for ion transport. Due to the adsorption phenomenon on the concrete surface, the chloride ion concentration is high, and the corresponding ion transport rate is fast; in the interior of the concrete, due to the low ion concentration, the corresponding transport rate is relatively slow. It can be seen that generally, the diffusion coefficient involved is the average value of the diffusion coefficients throughout the concrete, rather than the actual chloride ion diffusion coefficient. Therefore, the apparent diffusion coefficient D a is used to represent it. In summary, it is necessary to develop a time - varying diffusion coefficient model for the study of the ion transport process in concrete under short - and medium - term exposure times.

[0029] Meanwhile, in the chloride ion transport model based on Fick's second law, the surface chloride ion concentration C of concrete s is generally regarded as a constant value. However, research data in recent years have shown that in the early stage of exposure, C s gradually increases with time, and then continues to increase with the exposure time. C s gradually decreases and finally equals the chloride ion concentration in the environment, reaching a dynamic equilibrium state. Also, it should be noted that due to the chloride ion adsorption effect on the concrete surface, the surface chloride ion concentration exists immediately when the concrete is exposed to a chloride salt environment. Therefore, the embodiments of the present invention propose a time-varying surface chloride ion concentration model (or time-varying boundary condition) to improve the accuracy of the chloride ion prediction model.

[0030] For this reason, the time-varying prediction method for chloride ion erosion in concrete under a marine exposure environment provided by the embodiments of the present invention refers to Figure 1 and the method includes:

[0031] S1. Obtain the parameters required for the concrete prediction model, preprocess the parameters, and obtain the apparent diffusion coefficient D a and the surface chloride ion concentration C s at different exposure times, which are respectively used to calculate the aging factor m and the reference diffusion coefficient D ref in step S2, and to calculate the initial surface chloride ion concentration C s0 and the empirical coefficient k in step S3.

[0032] As a specific implementation manner, obtain the parameters required for the concrete prediction model, where the parameters include: curing age, curing condition, exposure time, exposure environment, water-cement ratio, fly ash and slag content. The preprocessing such as fitting the above parameters to obtain the apparent diffusion coefficient and the surface chloride ion concentration specifically includes:

[0033] Obtain the apparent diffusion coefficient D a and the surface chloride ion concentration C s according to the following equation:

[0034]

[0035] where C(x, t) is the spatio-temporal distribution of the chloride ion content in the concrete (%, by weight of the concrete), x is the penetration depth of the chloride ion from the concrete surface (mm), C0 is the initial chloride ion concentration in the concrete, C s is the surface chloride ion concentration, D a is the apparent diffusion coefficient, t is the effective exposure time, and T is the integral of the time-varying diffusion coefficient over the effective exposure time.

[0036] Refer to Figure 3, if the influence of "skin effect" caused by convection on the chloride ion transport process is considered, the following analytical model is adopted:

[0037]

[0038] Where C(x,t) is the spatio-temporal distribution of the chloride ion content in the concrete (%, by weight of the concrete), x is the penetration depth of the chloride ions from the concrete surface (mm), and Δx is the depth of the convection zone near the concrete surface.

[0039] S2. According to the exposure conditions and parameters, considering the time-dependent decay of the apparent diffusion coefficient with the exposure time and the degree of concrete hydration, determine the time-varying diffusion coefficient model, and obtain the reference diffusion coefficient D ref and the aging factor m.

[0040] In S2, the first, second, and third time-varying diffusion coefficient models D(t) need to be integrated over time t, while the fourth and fifth models do not. The integral equations of the time-varying diffusion coefficient over time are as follows:

[0041]

[0042] Where, D a is the apparent diffusion coefficient, t is the effective exposure time, and T is the integral of the time-varying diffusion coefficient over the effective exposure time.

[0043] As a specific implementation manner, determining the time-varying diffusion coefficient model includes: for different parameters and exposure conditions, the time-varying diffusion coefficient model includes one of the following five models. In the following formulas, t is the effective exposure time, m is the aging factor, D(t) is the five time-varying diffusion coefficient models introduced in the embodiments of the present invention, D ref is the reference diffusion coefficient, t' is the concrete curing time, t ref is the curing time in the concrete mold, t 28 refers to the concrete curing time of 28 days, D 28 is the chloride ion reference diffusion coefficient after 28 days of concrete curing.

[0044] (1). For the chloride ion transport process where the apparent diffusion coefficient is unknown or difficult to obtain, adopt the first time-varying diffusion coefficient model, i.e., the Chalee model: D(t) = (1 / t) m ;

[0045] Furthermore:

[0046]

[0047] where m is the aging factor, which can be obtained by fitting from past chloride concentration measurement results or from the following empirical equation related to the water-cement ratio and fly ash content: where α, β, γ, and μ are all fitting factors.

[0048] This model is simple, intuitive, and the time-varying diffusion coefficient is only related to the exposure time and the aging factor, making it applicable to the chloride transport process where the apparent diffusion coefficient is unknown or difficult to obtain.

[0049] (2) For the case of considering the reference diffusion coefficient and long exposure time, the second time-varying diffusion coefficient model, i.e., the Mangat model, is adopted: D(t) = D ref (1 / t) m ;

[0050] Furthermore:

[0051]

[0052] where D ref is the reference diffusion coefficient. This model takes into account one more reference diffusion coefficient than the Chalee model. When D ref = 1, the Mangat model becomes the Chalee model. However, it should be noted that when the exposure time is short and the aging factor m is relatively large, the reference diffusion coefficient D ref value represented by the Mangat model will be relatively large, so it is relatively easy to overestimate the chloride diffusion rate. Therefore, when the exposure time is short, the Mangat model should not be used. See Figure 4 .

[0053] (3) For the case where the pre-exposure curing time and curing conditions are both known, the third time-varying diffusion coefficient model, i.e., the Tang-Guliker model, is adopted: D(t') = D ref (t ref / t') m ;

[0054] where t' is the concrete curing time, and t ref is the curing time in the concrete mold. Furthermore:

[0055]

[0056] where t is the effective exposure time, and t ex is the time elapsed from the preparation of the concrete to the exposure, and t' ≥ t > t ex ≥ t ref . In this model, D ref is the reference diffusion coefficient, which depends on the concrete curing time, t refThe values are generally 7 days, 21 days or 28 days, and the corresponding reference diffusion coefficients are D7, D 21 or D 28 . This model comprehensively considers the influence of curing time on the chloride ion diffusion rate. At the same time, it also reflects the influence of the change in pore structure on the chloride ion diffusion ability with the increase in the degree of concrete hydration and the loss of water in the concrete. This time-varying diffusion coefficient model comprehensively considers the influence of the combined action of multiple factors on the chloride ion diffusion rate and is applicable to the prediction of ion transport in concrete with known curing conditions.

[0057] (4) For the case where the pre-exposure curing time and curing conditions are unknown and to avoid the complex integration of the time-varying diffusion coefficient equation D(t) over the exposure time t, it can be assumed that the curing time of the concrete is 28 days, and the fourth time-varying diffusion coefficient model, namely the Thomas model, can be adopted: D(t) = D 28 (t 28 / t) m ;

[0058] where t 28 refers to the curing time of the concrete being 28 days. Correspondingly, D 28 is the reference chloride ion diffusion coefficient after 28 days of concrete curing. Furthermore:

[0059]

[0060] The biggest difference between this model and the previous three models is that the time-varying diffusion coefficient is directly substituted into T without performing time-related integration. Its advantage is to avoid complex integral calculations, and its disadvantage is that although this model can be used to describe the chloride ion transport performance, this operation may cause mathematical errors in the analytical model. See Figure 5 .

[0061] (5) For the case where the pre-exposure curing time and curing conditions are unknown and to avoid the complex integration of the diffusion coefficient equation D(t) over the exposure time t, it can be assumed that the curing time of the concrete is 28 days, and the fifth time-varying diffusion coefficient model, namely the Bamforth model, can be adopted: D(t) = D 28 [t 28 / (t + 28)] m ;

[0062] Furthermore:

[0063]

[0064] This model is similar to the Thomas model, directly substituting the time-varying diffusion coefficient into T without performing time-related integration. Therefore, it is applicable to the prediction of ion transport in concrete with known curing time and diffusion coefficient during the curing period.

[0065] S3. Determine the time-varying boundary condition accumulation model based on the accumulation of the chloride ion adsorption capacity on the concrete surface with the exposure time under the exposure condition. Obtain the initial surface chloride ion concentration Cs0 and the empirical coefficient k based on the time-varying boundary condition accumulation model. For example, when the concrete is completely immersed in the chloride salt solution (corresponding to the concrete structure being placed in the marine submerged area under the actual working condition), the chloride ion diffusion coefficient will decay with the passage of time and the improvement of the concrete hydration degree in the short-term exposure time, while the surface chloride concentration will accumulate with the increase of the exposure time. Therefore, in this case, it is suitable to use the time-varying chloride ion diffusion coefficient model and the time-varying boundary condition to predict the chloride ion concentration. As time goes by, the time dependence of the diffusion coefficient and the boundary condition will weaken or even disappear. Therefore, in this case, it is suitable to use the constant chloride ion diffusion coefficient model and the constant boundary condition to predict the chloride ion concentration.

[0066] As a specific implementation manner, determining the time-varying boundary condition accumulation model includes: considering the accumulation of the boundary condition with the exposure time according to the exposure condition, and the time-varying boundary condition accumulation model includes one of the following three models, where C s is the surface chloride ion concentration, C s0 is the initial surface chloride ion concentration, k is the empirical coefficient, and t is the effective exposure time.

[0067] (1). The first time-varying boundary condition accumulation model, namely the Square Root Build-up model, that is, the Square Root model in Figure 1 :

[0068]

[0069] The boundary conditions included in this model are as follows:

[0070]

[0071] And, the initial chloride ion concentration is C0(x > 0, t = 0) = 0. Therefore, the corresponding analytical model is:

[0072]

[0073] This model indicates that at the instant when the concrete is exposed to the chloride salt environment (i.e., t = 0), there is no chloride ion adsorption phenomenon on the concrete surface. As the exposure time increases, the concrete surface begins to adsorb chloride ions, and the adsorbed chloride ion concentration increases with the increase of time.

[0074] (2). The second time-varying boundary condition accumulation model, namely the Square Root Build-up with InitialConcentration model, that is, Figure 1Square Root with C s0 Model:

[0075]

[0076] The boundary conditions included in this model are as follows:

[0077]

[0078] When the initial chloride ion concentration is C0(x > 0, t = 0) = 0, the corresponding analytical model is:

[0079]

[0080] This model indicates that at the instant when the concrete is exposed to the chloride salt environment, chloride ion adsorption occurs on its surface; and as the exposure time increases, the adsorbed chloride ion concentration increases with time. This boundary condition is more in line with the actual situation, so its application range is wider.

[0081] (3) The third time-varying boundary condition cumulative model, namely the Linear Build-up with Initial Concentration model, that is Figure 1 Linear with C s0 Model:

[0082] C s = C s0 + kt;

[0083] The boundary conditions included in this model are as follows:

[0084]

[0085] When the initial chloride ion concentration is C0(x > 0, t = 0) = 0, the corresponding analytical model is:

[0086]

[0087] This model is similar to the Square Root Build-up with Initial Concentration. This model indicates that at the instant when the concrete is exposed to the chloride salt environment, chloride ion adsorption occurs on its surface; and as the exposure time increases, the adsorbed chloride ion concentration increases linearly with time. Therefore, the application ranges of models (2) and (3) are relatively wide. It should be particularly noted that through the fitting analysis in S1, a model with a large R 2 value can be selected to describe the boundary conditions to improve the accuracy of the prediction model.

[0088] In the embodiments of the present invention, the attenuation phenomenon of the chloride ion diffusion coefficient and the time-dependence of the boundary conditions are considered with the change of the exposure duration, so that the ion transport model based on Fick's second law has a broader application scope, solving the problem that the traditional ion concentration calculation model has a large deviation in previous studies due to neglecting the accumulation of surface concentration over time and the decay phenomenon of the diffusion coefficient, and further improving the reliability of the subsequent design of the concrete cover thickness and the prediction of the durability performance.

[0089] In step S3, according to the cumulative property of the chloride ion adsorption capacity on the concrete surface with the exposure time, three cumulative models of time-varying boundary conditions are proposed; starting from the actual exposure conditions of the concrete, a suitable cumulative model of time-varying boundary conditions is selected, so as to predict the distribution law of the chloride ion concentration in the concrete by using the analytical method in S4.

[0090] S4. According to the reference diffusion coefficient D ref , aging factor m, initial surface chloride ion concentration C s0 and empirical coefficient k, respectively adopt the time-varying diffusion coefficient model D(t) and time-varying boundary condition model Cs(t) proposed in S2 and S3, and use the analytical solution method listed in S3 to realize the prediction of the chloride ion concentration under the specified exposure environment and exposure time.

[0091] Meanwhile, the numerical simulation method mentioned below is used to solve the distribution law of the chloride ion concentration in the concrete to realize the prediction of the chloride ion concentration under the specified exposure environment and exposure time. And the two calculation results obtained from the analytical solution and the numerical solution are compared to verify the reliability of the analytical solution model proposed in this paper. The numerical simulation method specifically includes:

[0092] In the one-dimensional ion transport model based on Fick's second law, introduce the same time-varying diffusion coefficient model and boundary condition model as those used in S2 and S3, establish a concrete transport model in the pdetool module of the MATLAB platform, assume that the initial concentration of the concrete is 0, divide the grid, click to operate, and finally obtain the ion concentration distribution cloud map in the concrete by numerical calculation. Based on this numerical calculation result, given the exposure time, obtain the change diagram of the ion concentration with the depth, and compare it with the calculation result obtained from the analytical solution in S3 to verify the reliability of the analytical solution model in S3.

[0093] In the embodiments of the present invention, the chloride ion transport model under the same working conditions is solved from two different perspectives of the analytical method and numerical simulation, and the numerical simulation and analytical calculation results are compared with the measured results, thus ensuring the reliability of the chloride ion concentration prediction model.

[0094] In step S4, first, based on the time-varying diffusion coefficient model D(t) proposed in S2 and the time-varying boundary condition model Cs(t) proposed in S3, the analytical solution method listed in S3 is used to solve the chloride ion distribution law in the concrete. On the other hand, the numerical simulation method is used to calculate the chloride ion concentration, and the calculation results of the analytical solution are compared with the chloride ion concentration obtained by numerical simulation to verify the accuracy and reliability of the analytical solution and the ion concentration prediction model proposed in the embodiments of the present invention.

[0095] The embodiments of the present invention comprehensively consider various factors affecting the chloride ion transport process, including the curing time of the concrete, exposure conditions, water-cement ratio, depth of the convection zone, etc. Then, by selecting a suitable prediction model according to the specific working conditions, the distribution law of ion concentration in the concrete under the chloride salt exposure environment can be efficiently obtained, which greatly improves the calculation efficiency of the ion transport process in the saturated concrete.

[0096] The embodiments of the present invention provide a simple and efficient method for predicting the chloride ion concentration in concrete under the marine service environment, specifically including: fitting the apparent chloride ion diffusion coefficient and the surface chloride ion concentration, setting parameters such as the concrete curing time, exposure environment, depth of the convection zone, etc.; establishing five time-varying diffusion coefficient models and three time-varying cumulative boundary condition models according to different exposure working conditions and parameter conditions to describe the relationship between the chloride ion transport capacity and the exposure time; by considering the factors affecting the chloride ion transport capacity (curing time, exposure environment, exposure time, "skin effect", water-cement ratio, mineral admixture content such as fly ash, etc.), selecting appropriate time-varying diffusion coefficients and boundary conditions, and solving the chloride ion transport model in the concrete from the analytical method and the numerical simulation method respectively.

[0097] Another embodiment of the present invention provides a time-varying prediction system for chloride ion erosion in concrete under the marine exposure environment, which is used to implement the above-mentioned time-varying prediction method for chloride ion erosion in concrete under the marine exposure environment. Referring to Figure 2 ,the system includes:

[0098] A parameter preprocessing module, which is used to obtain the parameters required for the concrete prediction model and preprocess the parameters to obtain the apparent diffusion coefficient and the surface chloride ion concentration at different exposure times;

[0099] A calculation module, which is used to determine the time-varying diffusion coefficient model and the time-varying boundary condition model according to the parameters, the apparent diffusion coefficient and the surface chloride ion concentration by considering the actual exposure working conditions, and obtain the reference diffusion coefficient, aging factor, initial surface chloride ion concentration and empirical coefficient based on the time-varying diffusion coefficient model and the time-varying boundary condition model;

[0100] A prediction module, which is used to solve the chloride ion transport model in the concrete according to the parameters obtained by the calculation module to realize the prediction of the chloride ion concentration under the specified exposure environment and exposure time.

[0101] As a specific implementation manner, the parameter preprocessing module is based on the one-dimensional analytical solution of Fick's second law. For different exposure times, as the penetration depth increases, it fits the measured past chloride ion concentration change curve with the one-dimensional analytical solution to obtain the apparent diffusion coefficient and the surface chloride ion concentration.

[0102] As a specific implementation manner, the parameters obtained by the parameter preprocessing module include two parts: (1) the curing age, curing conditions, exposure time, exposure environment, water-cement ratio, fly ash and slag admixture content obtained from experiments or literature; (2) the apparent diffusion coefficient D obtained according to the two equations listed in step S1 of the method embodiment a and the surface chloride ion concentration C s ,

[0103] As a specific implementation manner, the functions of the calculation module include two parts, namely, determining the time-varying diffusion coefficient model D(t) and determining the time-varying boundary condition model Cs(t). Among them, the time-varying diffusion coefficient model D(t) determined by the calculation module includes one of the following five models:

[0104] For the chloride transport process where the apparent diffusion coefficient is unknown or difficult to obtain, the first time-varying diffusion coefficient model, i.e., the Chalee model, is adopted: D(t) = (1 / t) m ;

[0105] For the case considering the reference diffusion coefficient and long exposure time, the second time-varying diffusion coefficient model, i.e., the Mangat model, is adopted: D(t) = D ref (1 / t) m ;

[0106] For the case where both the pre-exposure curing time and curing conditions are known, the third time-varying diffusion coefficient model, i.e., the Tang-Guliker model, is adopted: D(t') = D ref (t ref / t') m ;

[0107] For the case where the pre-exposure curing time and curing conditions are unknown and to avoid the complex integration of the diffusion coefficient equation D(t) over the exposure time t, it can be assumed that the curing time of the concrete is 28 days, and the fourth or fifth time-varying diffusion coefficient model can be adopted. Among them, the fourth time-varying diffusion coefficient model, i.e., the Thomas model: D(t) = D 28 (t 28 / t) m , and the fifth time-varying diffusion coefficient model, i.e., the Bamforth model: D(t) = D 28 [t 28 / (t + 28)] m .

[0108] In the above formulas, t is the effective exposure time, m is the aging factor, D(t) is the time-varying diffusion coefficient model, and D ref is the reference diffusion coefficient, t' is the concrete curing time, and t ref is the curing time in the concrete mold, and t 28 refers to the concrete curing time of 28 days, and D 28 is the chloride ion reference diffusion coefficient after 28 days of concrete curing.

[0109] As a specific implementation, the time-varying boundary condition accumulation model Cs(t) determined by the calculation module includes one of the following three models:

[0110] The first time-varying boundary condition accumulation model, namely the Square Root Build-up model:

[0111]

[0112] The second time-varying boundary condition accumulation model, namely the Square Root Build-up with Initial Concentration model:

[0113]

[0114] The third time-varying boundary condition accumulation model, namely the Linear Build-up with Initial Concentration model:

[0115] C s = C s0 + kt;

[0116] In the above formulas, C s is the surface chloride ion concentration, C s0 is the initial surface chloride ion concentration, k is the empirical coefficient, and t is the effective exposure time.

[0117] The calculation module calculates the reference diffusion coefficient, aging factor, initial surface chloride ion concentration, and empirical coefficient, specifically including:

[0118] For the Chalee model, the aging factor m is obtained by fitting with the following formula:

[0119]

[0120] For the Mangat model, the reference diffusion coefficient D ref and the aging factor m are obtained by fitting with the following formula:

[0121]

[0122] or according to the following formula:

[0123] logD c = logD ref -mlogt

[0124] For the Tang-Gulikers model, the reference diffusion coefficient D ref and the aging factor m are obtained by fitting according to the following formula:

[0125]

[0126] For the Thomas model, the reference diffusion coefficient D 28 and the aging factor m are obtained by fitting according to the following formula:

[0127]

[0128] For the Banforth model, the reference diffusion coefficient D 28 and the aging factor m are obtained by fitting according to the following formula:

[0129]

[0130] As a specific implementation manner, the prediction module respectively adopts the analytical solution method and the numerical simulation method to solve the one-dimensional Fick's second law ion transport model, and predicts the chloride ion concentration under the specified exposure environment and exposure time.

[0131] Specifically, according to the concrete curing time and curing conditions under different exposure conditions, t ref 、t ex and the water-cement ratio are input into the prediction model. According to the parameters obtained by the calculation module, a suitable error analysis model is adopted to realize the prediction of the chloride ion concentration under the specified exposure environment and exposure time. On the other hand, based on the Partial Differential Equations Toolbox (pdetool) of the MATLAB platform, the time-varying diffusion coefficient and boundary conditions are adopted to solve the chloride ion transport equation.

[0132] Using the APP Designer tool, the above-mentioned parameter preprocessing module, calculation module and prediction module can be combined in a user interface to realize the integration of the time-varying chloride ion concentration prediction model, and provide a useful research tool for the durability prediction of concrete.

[0133] By using this prediction system, the influence laws of exposure conditions, curing conditions, convection zone depth, curing time, water-cement ratio, and fly ash content on the chloride ion transport performance can be studied; according to the concentration threshold required for steel bar corrosion, the time required for the failure of the reinforced concrete structure can be calculated to realize the durability prediction of the concrete structure.

[0134] The embodiments of the present invention fully consider the influence of various influencing factors on the chloride ion transport process, and can flexibly adjust parameters. Moreover, the calculation results of the analytical model and the numerical model can be compared with the test results to verify the reliability of the prediction system. This prediction system overcomes the problem of time consumption caused by long-term tests and complex finite element calculations, and can achieve a relatively accurate prediction of chloride ion concentration distribution by selecting an appropriate model, having a wide application prospect in the durability design and evaluation of concrete materials.

[0135] It should be noted that the system in the above system embodiment and the method embodiment are based on the same inventive concept. For details, please refer to the method embodiment and will not be elaborated here. Due to the above technical effects of the method for predicting the time-varying chloride ion erosion in concrete under the above marine exposure environment, the system for predicting the time-varying chloride ion erosion in concrete under the marine exposure environment in this embodiment should also have the same technical effects and will not be elaborated here either.

[0136] Combined with the method and system for predicting the time-varying chloride ion erosion in concrete under the marine exposure environment of the above embodiments, the following further elaborates on specific application examples to help further understand the technical solution of the present invention.

[0137] Embodiment 1

[0138] Place a number of concrete test blocks at the Sado Estuary Naval Shipyard in the southeastern part of the Setúbal Peninsula near the mouth of the Atlantic Ocean. Its exposure environment is a typical marine chloride tidal exposure and atmospheric exposure environment. After experiencing exposures for 0.5, 1, 2, 3, and 4 years respectively, the acid dissolution method is used to measure the chloride ion concentration in the concrete. According to the experimental results, predict the chloride ion concentration distribution in the concrete after 8 years of exposure.

[0139] First, according to the actual engineering data, obtain the relevant parameters and the parameters required for the prediction model, specifically including:

[0140] The curing time of the concrete in the mold and the curing time before the concrete is exposed are respectively: t ref = 7 and t ex = 21 days;

[0141] The exposure environment of the concrete: It experiences 22 seawater filling cycles during the 4-year monitoring period after the concrete pouring period;

[0142] The depth of the surface convection zone Δx: 15 mm;

[0143] The exposure time to be predicted: 8 years;

[0144] The water-cement ratio: 0.5;

[0145] Secondly, according to the above known conditions, it can be judged that the concrete blocks are placed in a dynamic exposure environment, and the chloride ion content in the exposure environment is not stable. Therefore, the "skin effect" must be considered. The apparent diffusion coefficient and surface chloride ion concentration are calculated using the following formula:

[0146]

[0147] According to the calculation results, it is found that the diffusion coefficient shows obvious decay, while the surface chloride ion concentration increases with the exposure time. Therefore, the following models are respectively selected to obtain the reference diffusion coefficient D ref , aging factor m, initial surface chloride ion concentration C s0 and empirical coefficient k:

[0148]

[0149] as well as

[0150]

[0151] Based on the above model parameters, the following formula is used to predict the chloride ion concentration distribution at t = 8 years:

[0152]

[0153] Meanwhile, on the MATLAB platform pdetool, the same time-varying diffusion coefficient and boundary conditions are adopted to complete the prediction of the chloride ion concentration distribution in the concrete. The results show that the ion concentration distributions obtained by the analytical solution and numerical simulation are in good agreement.

[0154] In addition, in the chloride ion prediction system, the same operations as the above method are carried out to obtain the time-varying model parameters. And according to the draft RILEM recommendation, the critical concentration required for steel corrosion is taken as 0.05 (% by mass of concrete). Assuming the thickness of the concrete cover is 50 mm, it is calculated that for ordinary concrete with a water-cement ratio of 0.45 and no fly ash added, steel corrosion will occur after 4 years of exposure in the chloride salt solution; while for concrete with a fly ash content of 25%, steel corrosion will start after 25 years of exposure in the chloride salt environment.

[0155] Example 2

[0156] Two different concrete specimens are placed in two different environments for curing for a period of time, and then placed in a chloride salt solution. After 28 days, 90 days and 180 days of exposure, the acid dissolution method is used to observe the chloride ion concentration in the concrete. Finally, according to the observation results, the chloride ion concentration at 30 mm from the concrete surface after 10 years of exposure of these two concrete specimens is predicted.

[0157] First, according to the actual engineering data, obtain the relevant parameters and the parameters required for the prediction model, specifically including:

[0158] The main differences between the two types of concrete are as follows: The first type is the concrete with water-reducing agent added, and the second type is the reference group without any admixtures;

[0159] Curing environment: A - Cured in water at 20°C for 28 days, B - Cured in air at room temperature of 20°C and humidity of 55% for 28 days;

[0160] The curing time of the concrete in the mold and the curing time before the concrete is exposed are respectively: t ref = t ex = 28 days;

[0161] The concrete is completely immersed in a static chloride salt environment. Therefore, the concrete is considered to be completely saturated without any convection phenomenon occurring. Thus, the depth of the surface convection zone Δx: 0 mm;

[0162] The predicted exposure time: 10 years

[0163] Secondly, based on the distribution of chloride ion concentration in the concrete measured in the past, obtain the apparent diffusion coefficient and the surface chloride ion concentration;

[0164] Then, according to the fitting results of the previous step, it is found that the diffusion coefficient shows an obvious time decay property. Therefore, the following two models are respectively used to obtain the reference diffusion coefficient D ref and the aging factor m. At the same time, the influence of the curing environment on the chloride ion prediction model can be studied by comparison:

[0165] Mangat:

[0166] Tang - Guliker:

[0167] In addition, since the concrete specimens are in a static chloride salt environment and according to the fitting results of the first step, the surface chloride ion concentration is relatively constant. Therefore, for the above two prediction models, the constant boundary condition is adopted to predict the chloride ion concentration of these two concrete specimens at t = 10 years and x = 30 mm:

[0168]

[0169] Finally, the prediction results obtained by the Mangat model are as follows: for the concrete containing water reducer, the chloride ion concentrations at t = 10 years and x = 30 mm are 0.297 (curing condition A) and 0.370 (curing condition B), while for the concrete without water reducer, the chloride ion concentrations at t = 10 years and x = 30 mm are 0.404 (curing condition A) and 0.631 (curing condition B); the prediction results obtained by the Tang-Guliker model are as follows: for the concrete containing water reducer, the chloride ion concentrations at t = 10 years and x = 30 mm are 0.281 (curing condition A) and 0.354 (curing condition B), while for the concrete without water reducer, the chloride ion concentrations at t = 10 years and x = 30 mm are 0.390 (curing condition A) and 0.588 (curing condition B). It can be concluded from this implementation case that, firstly, the addition of water reducer can effectively prevent the transmission of chloride ions, thus prolonging the service life of concrete; secondly, the water curing condition can promote the hydration reaction more than the air curing condition, thus improving the chloride erosion resistance of concrete.

[0170] The specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various deformations or modifications within the scope of the claims, which do not affect the essence of the present invention. The above preferred features can be used in any combination without conflict.

Claims

1. A time-varying prediction method for chloride ion erosion in concrete under marine exposure environment, characterized in that, Including: Obtain the parameters required for the concrete prediction model, preprocess the parameters, and obtain the apparent diffusion coefficient and surface chloride ion concentration at different exposure times; According to the exposure conditions and the parameters, considering the time-dependent decay of the apparent diffusion coefficient with exposure time and the degree of concrete hydration, determine the time-varying diffusion coefficient model, and obtain the reference diffusion coefficient and aging factor based on the time-varying diffusion coefficient model; According to the cumulativeness of the chloride ion adsorption capacity on the concrete surface under exposure conditions with exposure time, determine the time-varying boundary condition cumulative model, and obtain the initial surface chloride ion concentration and empirical coefficient based on the time-varying boundary condition cumulative model; According to the reference diffusion coefficient, aging factor 、 Initial surface chloride ion concentration, and empirical coefficient, solve the chloride ion transport model in concrete to achieve the prediction of chloride ion concentration under specified exposure environments and exposure times; The determination of the time-varying boundary condition cumulative model includes: according to the exposure conditions, considering the cumulativeness of the boundary conditions with exposure time, the time-varying boundary condition cumulative model Cs(t) includes one of the following three models: At the instant when the concrete is exposed to the chloride salt environment, there is no chloride ion adsorption phenomenon on the concrete surface; as the exposure time increases, the concrete surface begins to adsorb chloride ions, and the adsorbed chloride ion concentration increases with time. The first time-varying boundary condition cumulative model is adopted: ； At the instant when the concrete is exposed to the chloride salt environment, chloride ion adsorption occurs on its surface; and as the exposure time increases, the adsorbed chloride ion concentration increases with time. The second time-varying boundary condition cumulative model is adopted: ； At the instant when the concrete is exposed to the chloride salt environment, chloride ion adsorption occurs on its surface; and as the exposure time increases, the adsorbed chloride ion concentration increases linearly with time. The third time-varying boundary condition cumulative model is adopted: ； Among them, C s is the surface chloride ion concentration, C s0 is the initial surface chloride ion concentration, k is the empirical coefficient, and t is the effective exposure time.

2. The time-varying prediction method for chloride ion erosion in concrete under marine exposure environment according to claim 1, wherein The parameters required for obtaining the concrete prediction model, wherein the parameters include: curing age, curing conditions, exposure time, exposure environment, water-cement ratio, fly ash and slag content.

3. The time-varying prediction method for chloride ion erosion in concrete under marine exposure environment according to claim 1, characterized in that The determined time-varying diffusion coefficient model includes: for different parameters and exposure conditions, the time-varying diffusion coefficient model includes one of the following five models: For the chloride ion transport process where the apparent diffusion coefficient is unknown or difficult to obtain, the first time-varying diffusion coefficient model is adopted: ； For the case considering the reference diffusion coefficient and long exposure time, the second time-varying diffusion coefficient model is adopted: ； For the case where the curing time and curing conditions before exposure are both known, the third time-varying diffusion coefficient model is adopted: ； For the case where the pre-exposure curing time and curing conditions are unknown, and to avoid the complex integral of the time-varying diffusion coefficient equation with respect to the exposure time t, assume that the curing time of the concrete is 28 days and adopt the fourth or fifth time-varying diffusion coefficient model. Among them, the fourth time-varying diffusion coefficient model is , and the fifth time-varying diffusion coefficient model is ; where t is the effective exposure time, m is the aging factor, is the reference diffusion coefficient, is the concrete curing time, is the curing time of concrete in the mold, refers to the concrete curing time of 28 days, is the reference chloride ion diffusion coefficient after 28 days of concrete curing.

4. The time-varying prediction method for chloride ion erosion in concrete under marine exposure environment according to claim 1, wherein Solving the chloride ion transport model in concrete according to the reference diffusion coefficient, aging factor 、 initial surface chloride ion concentration, and empirical coefficient, including: Adopt the one-dimensional Fick's second law ion transport model, and use the analytical solution method and numerical simulation method to solve the chloride ion transport model in concrete respectively.

5. A time-varying prediction system for chloride ion erosion in concrete under marine exposure environment, which is used to implement the time-varying prediction method for chloride ion erosion in concrete under marine exposure environment described in any one of claims 1-4, and is characterized in that, Including: A parameter preprocessing module, configured to obtain the parameters required for the concrete prediction model, and preprocess the parameters to obtain the apparent diffusion coefficient Da and surface chloride ion concentration Cs at different exposure times; A calculation module, configured to determine a time-varying diffusion coefficient model D(t) and a time-varying boundary condition model Cs(t) by considering actual exposure conditions according to the parameters, the apparent diffusion coefficient Da, and the surface chloride ion concentration Cs, and obtain a reference diffusion coefficient based on the time-varying diffusion coefficient model D(t) and the time-varying boundary condition model Cs(t). , an aging factor m、 The initial surface chloride ion concentration C s0 and an empirical coefficient k; A prediction module, configured to solve the chloride ion transport model in concrete according to the parameters obtained by the calculation module, and realize the prediction of the chloride ion concentration under the specified exposure environment and exposure time.

6. The chloride ion erosion time-varying prediction system for concrete in a marine exposure environment according to claim 5, wherein The parameter preprocessing module performs fitting of the measured past chloride ion concentration change curve and the one-dimensional analytical solution based on the one-dimensional analytical solution of Fick's second law to obtain the apparent diffusion coefficient and surface chloride ion concentration.

7. The time-varying prediction system for chloride ion erosion in concrete under marine exposure environment according to claim 5, characterized in that, The parameters obtained by the calculation module include: curing age, curing conditions, exposure time, exposure environment, water-cement ratio, fly ash and slag content; The time-varying diffusion coefficient model determined by the calculation module including one of the following five models: For the chloride transport process where the apparent diffusion coefficient is unknown or difficult to obtain, the first time-varying diffusion coefficient model is adopted: ； For the case considering the reference diffusion coefficient and long exposure time, the second time-varying diffusion coefficient model is adopted: ； For the case where the pre-exposure curing time and curing conditions are both known, the third time-varying diffusion coefficient model is adopted: ； For the case where the pre-exposure curing time and curing conditions are unknown, and to avoid the complex integration of the time-dependent diffusion coefficient equation with respect to the exposure time t, assume that the curing time of the concrete is 28 days and adopt the fourth or fifth time-dependent diffusion coefficient model. Among them, the fourth time-dependent diffusion coefficient model is , and the fifth time-dependent diffusion coefficient model is ; where t is the effective exposure time, m is the aging factor, is the reference diffusion coefficient, is the concrete curing time, is the curing time of concrete in the mold, means that the concrete curing time is 28 days, is the reference chloride ion diffusion coefficient after 28 days of concrete curing.

8. The time-varying prediction system for chloride ion erosion in concrete under a marine exposure environment according to claim 7, characterized in that, The time-varying boundary condition accumulation model Cs(t) determined by the calculation module includes one of the following three models: The first time-varying boundary condition accumulation model: ； The second time-varying boundary condition accumulation model: ； The third time-varying boundary condition accumulation model: ； Among them, C s is the surface chloride ion concentration, C s0 is the initial surface chloride ion concentration, k is the empirical coefficient, and t is the effective exposure time.

9. The chloride ion erosion time-varying prediction system in concrete under marine exposure environment according to claim 5, wherein The prediction module uses the analytical solution method and the numerical simulation method respectively to solve the one-dimensional Fick's second law ion transport model and predict the chloride ion concentration under the specified exposure environment and exposure time.

Images