Prediction method for chloride ion concentration of prefabricated-cast-in-place concrete interface area

Abstract

The invention discloses a prefabricated-cast-in-place concrete interface area chloride ion concentration prediction method which combines experimental research, theoretical analysis and numerical simulation. Based on the physical and chemical binding capacity of chloride ions, the consolidated chloride ion instability condition and the double-pore theory, a concrete internal moisture and chloride ion transmission control equation in a pressed interface area under the dry-wet cycle effect is constructed, and based on the prefabricated-cast-in-place concrete interface area chloride ion concentration probability prediction based on mesoscopic simulation, the concrete internal moisture and chloride ion transmission control equation is obtained. Determining the transmission rule of chloride ions in concrete in a pressed interface area under the dry-wet cycle effect, and establishing an interface area chloride ion transmission mesoscopic model; establishing a compressed interface area concrete chloride ion transmission three-dimensional mesoscopic model under the dry-wet cycle action by combining a compressed interface area concrete internal moisture and chloride ion transmission control equation and an interface area chloride ion transmission mesoscopic model under the dry-wet cycle action; and an important theoretical basis is provided for durability evaluation and design of the structure.

Description

Technical Field

[0001] The present invention relates to the technical field of chloride ion concentration in a concrete interface zone, and in particular to a method for predicting chloride ion concentration in a precast-cast-in-situ concrete interface zone. Background Art

[0002] Existing research has largely focused on the transport and prediction of chloride ions in monolithic cast-in-place concrete. However, many challenges remain regarding the transport patterns, microscopic simulation, and prediction methods of chloride ions at the precast-cast-in-place concrete interface under dry-wet cycles and compressive loads. These challenges are summarized as follows:

[0003] (1) The mechanism of chloride ion transport in the interface zone of precast-cast-in-situ concrete under compressive loads and dry-wet cycles has not yet been clarified. Current research on chloride ion transport behavior mainly focuses on integral cast-in-situ concrete structures. There are relatively few studies on the chloride ion transport behavior in the interface zone of precast-cast-in-situ concrete under the combined action of compressive loads and dry-wet cycles. The mechanism of the influence of compressive loads and dry-wet cycles on the chloride ion transport behavior in the interface zone needs to be clarified. A quantitative method for the interface zone effect and a chloride ion diffusion coefficient distribution model considering the interface zone effect are urgently needed to be established.

[0004] (2) A microscopic model of chloride ion transport in the interface zone of precast-cast-in-situ concrete under compression under wet-dry cycles has not yet been established. The existing microscopic simulation methods of chloride ion transport in concrete ignore the material exchange between pores of different sizes and are limited to integral cast-in-situ concrete specimens with regular sizes. They cannot consider the uneven distribution of coarse aggregate and pore characteristics on both sides of the interface and cannot be directly used for the microscopic simulation of chloride ion transport in the interface zone. A microscopic model of chloride ion transport in the interface zone of concrete under compressive loads and wet-dry cycles has not yet been established.

[0005] (3) An efficient prediction method for chloride ion concentration based on mesoscopic simulation and multi-source uncertain information has not yet been proposed. Most existing chloride ion concentration prediction methods are based on macroscopic test data. They do not consider the influence of concrete mesostructure and its changes under load and environmental conditions on chloride ion transport behavior and concentration prediction results, and also ignore the role of multi-source uncertain information. At the same time, the calculation cost is high. An efficient prediction method for chloride ion concentration based on mesoscopic simulation and multi-source uncertain information has not yet been proposed.

[0006] Therefore, in order to solve the above problems, this paper proposes a method to predict the chloride ion concentration in the interface zone of precast-cast-in-situ concrete. Summary of the Invention

[0007] The purpose of the present invention is to propose a method for predicting chloride ion concentration in the interface area of ​​precast-cast-in-situ concrete, aiming to clarify the transmission law of chloride ions in the concrete of the compressed interface area under the action of dry-wet cycles, establish a mesoscopic model of chloride ion transmission in the interface area, and propose an efficient prediction method for chloride ion concentration based on mesoscopic simulation and multi-source uncertain information, providing an important theoretical basis for the durability assessment and design of such structures.

[0008] In order to achieve the above technical effects, the present invention is implemented by the following technical solutions: A method for predicting chloride ion concentration in the interface area of ​​precast-cast-in-situ concrete, characterized by comprising the following steps:

[0009] S1. Conduct chloride ion transport tests on precast-in-situ concrete specimens subjected to compressive loads and wet-dry cycles. Three-dimensional laser scanning, digital image processing (DIP), and mercury intrusion porosimetry (MIP) were used to process the specimens under compressive loads and wet-dry cycles. The true morphology of the concrete interface after roughening was obtained. The coarse aggregate area fraction and pore structure parameters at the interface and on both sides were accurately measured. The distribution characteristics of coarse aggregate and porosity in the interface region as a function of distance from the interface were analyzed. The pore size distribution was statistically analyzed, and the chloride ion concentration data inside and on the surface of the concrete were measured.

[0010] S2. Based on the measured data of chloride ion concentration inside and on the surface of concrete, the spatial distribution characteristics of the overall chloride ion concentration under different working conditions and the lateral distribution characteristics of the chloride ion concentration as a function of the distance from the interface are analyzed, and then a multivariate distribution model of the chloride ion diffusion coefficient is established;

[0011] S3. Based on the measured data of chloride ion concentration inside and on the surface of concrete, the spatial distribution characteristics of chloride ion concentration under different working conditions are analyzed in the form of exponential concrete surface chloride ion concentration. The longitudinal distribution characteristics of chloride ion concentration change with the erosion depth are established to establish a spatiotemporal characterization model of surface chloride ion concentration accumulation.

[0012] S4. Based on the spatiotemporal characterization model of surface chloride ion concentration accumulation, a Gaussian autocorrelation function was introduced to divide the concrete surface into units. The fluctuation value was selected. The measured surface chloride ion concentration, the physical and chemical binding capacity of chloride ions, the instability condition of consolidated chloride ions, and the "double porosity" theory were combined to construct the governing equations for the transport of moisture and chloride ions within the compressed interface zone of concrete under the action of dry-wet cycles.

[0013] S5. Based on the spatiotemporal characterization model of surface chloride ion concentration accumulation and the governing equations for moisture and chloride ion transport within the concrete at the compressive interface, a phased random placement strategy was used to generate aggregate at different distances from the interface. Combined with the three-dimensional true morphology of the aggregate obtained by three-dimensional laser scanning, a three-dimensional microscopic model of the concrete at the interface was established.

[0014] S6. Combining the established spatiotemporal characterization model of surface chloride ion concentration accumulation and the governing equations for the transport of moisture and chloride ions within concrete in the compressive interface zone under dry-wet cycles, and considering the influence of the three-dimensional mesoscopic composition of concrete in the interface zone, a three-dimensional mesoscopic model of chloride ion transport in concrete in the compressive interface zone under dry-wet cycles was established. Based on the chloride ion concentration test results at different locations, the rationality of the above mesoscopic model in simulating chloride ion transport was verified.

[0015] Furthermore, in S1, measuring the chloride ion concentration data inside and on the surface of the concrete includes the following steps:

[0016] S1.1. Conduct chloride ion transport tests on precast-in-situ concrete specimens under compressive loads and wet-dry cycles;

[0017] S1.2. Combined with automatic potentiometric titration, measure chloride ion concentrations at different depths on the concrete surface and on both sides of the interface area with different interface roughness, different stress levels, and different dry-wet cycle mechanisms;

[0018] S1.3. Measure and obtain the internal and surface chloride ion concentration data of the concrete in the interface area with different interface roughness, different stress levels, and different dry-wet cycle mechanisms.

[0019] Furthermore, in S2, a multivariate distribution model of chloride ion diffusion coefficient is established, specifically as follows: mathematical statistics and regression analysis methods are used to analyze the chloride ion concentration test data at different distances from the interface under different interface roughness, stress levels and dry-wet cycle mechanisms; based on the analysis results, a multivariate distribution model of chloride ion diffusion coefficient affected by lateral position is established.

[0020] Furthermore, in S3, a spatiotemporal characterization model of surface chloride ion concentration accumulation is established, specifically as follows: based on the surface chloride ion concentration test data at different spatial positions and the existing exponential accumulation model, a regression analysis method is used to obtain the initial and stabilized surface chloride ion concentrations and accumulation rate values, and a nonlinear time-varying accumulation model of chloride ion concentration on the concrete surface in the interface area is established.

[0021] Furthermore, in S4, the Gaussian autocorrelation function is as follows (1):

[0022]

[0023] Where ρ(t) is the autocorrelation function value of the chloride ion concentration at two locations, θ x and θ y In particular, they are the fluctuation values ​​of the two-dimensional random field in the x and y directions; τ x =x i -x j and τ y =yi -y j are the distances between the centroids of units i and j in the x and y directions, respectively; d x and d y are the relevant length values ​​in the x-direction and y-direction respectively.

[0024] Furthermore, in S4, the control equations for the transfer of moisture and chloride ions inside the concrete in the compressed interface area under the action of dry-wet cycles are constructed, including the following steps:

[0025] S4.1. The pores inside concrete are divided into small pores and large pores. Small pores represent small pores with a size of nanometers, while large pores represent large pores with a size of micrometers. The volume of small pores inside concrete is recorded as Ω. f1 , the macropore volume is recorded as Ω f2 , the total pore volume is Ω, then the porosity in concrete is expressed as follows (2):

[0026]

[0027] Where: φ1 represents the porosity of small pores; φ2 represents the porosity of large pores;

[0028] S4.2, based on the existing water molecule transport equation in concrete and the mass exchange rate of water between small pores and large pores R w12 The moisture transfer equation inside concrete under dry-wet cycles is obtained as follows (3):

[0029]

[0030] Where, is the water diffusion coefficient in the pores, in m 2 / s;R w12 The mass exchange rate of water; φ1 represents the porosity of small pores; ρ w Indicates the density of water, its value is 1000kg / m 3 θ i Expressed as the degree of saturation of concrete.

[0031] Water diffusion coefficient The expression is as follows (4):

[0032]

[0033] Where, α i and β i is a dimensionless constant determined based on experimental data; Ki is a constant defined as hydraulic conductivity, equal to the kinematic viscosity divided by the permeability, with the unit being m 4 / (NS);

[0034] P atm=101325P a is the standard atmospheric pressure. il With P ic Respectively expressed as Ω fi Water pressure and capillary pressure in.

[0035] The mass exchange rate of water between small pores and large pores is expressed as follows (5):

[0036] R w12 =k 12 ρ w (P2-P1) (5)

[0037] Where K 12 is a constant, the unit is m 2 / (N·s);P i (i=1,2) represents the pore Ω fi Average pore pressure.

[0038] The average pore pressure P in the pores i The expression is as follows (6):

[0039] P i =(1-θ i )P ig +P il θ i (6)

[0040] Where, P ig represents atmospheric pressure; θ i Expressed as the saturation of concrete; P il Expressed as Ω fi Water pressure in

[0041] Substituting equations (5) and (6) into equation (7), we obtain the following equation (7):

[0042]

[0043] Where, α i and β i is a dimensionless constant determined based on experimental data; P atm =101325P a is the standard atmospheric pressure; θ i Expressed as the saturation of concrete; P i (i=1,2) represents the pore Ω fi medium average pore pressure;

[0044] The initial condition of concrete is θ i =1(i=1,2), then the two boundary conditions of moisture transport can be expressed as follows (8):

[0045]

[0046] In formula (8), the flux boundary condition is used instead of the direct saturation boundary condition;

[0047] Where K si is the convection coefficient of water transfer on the concrete surface, in m / s, θ env is the equivalent saturation in the concrete exposure environment; is the water diffusion coefficient in the pores, in m 2 / s;θ i Expressed as the degree of saturation of concrete.

[0048] S4.3. Based on the physical and chemical binding capacity of chloride ions, the instability conditions of consolidated chloride ions, and the "double porosity" theory, the water and chloride ion transport equations under load-wet-dry cycles are constructed as follows:

[0049] According to the mass conservation theorem of material transport, the chloride ion transport control equations can be obtained as follows (9) and (10):

[0050]

[0051] Where C1 and C2 are the chloride ion concentrations in small pores and large pores, respectively; D1 and D2 are the diffusion coefficients of chloride ions in small pores and large pores, respectively; R C12 is the chloride ion exchange capacity between different pores, R 1S and R 2S are the binding rates of concrete with chloride ions in small pores and large pores, respectively; φ1 represents the porosity of small pores; φ2 represents the porosity of large pores.

[0052] The expression of chloride ion exchange capacity is shown in formula (11):

[0053] R c12 =k R φ1φ2(C2-C1) (11)

[0054] Where: K R is the chloride ion exchange rate between different pores;

[0055] The binding rate of concrete with chloride ions in small pores and large pores is expressed in equations (12) and (13):

[0056]

[0057] Where C3 and C4 are the concentrations of bound chloride ions in small pores and large pores, respectively; φ1 represents the porosity of small pores; φ2 represents the porosity of large pores;

[0058] The Langmuir adsorption relationship is used to describe the relationship between consolidation and free chloride ion concentration, as shown in formula (14):

[0059]

[0060] Where α and β are binding constants; C K is the free chloride ion concentration; C bK represents the solidified chloride ion concentration.

[0061] Furthermore, in S5, a three-dimensional microscopic model of chloride ion transport in concrete at the interface region is established, including the following steps:

[0062] S5.1. Determine the aggregate diameter distribution based on the concrete mix proportion and gradation curve, and calculate the number of circular aggregates of different diameters within a given space;

[0063] S5.2. Develop an aggregate placement program in Matlab to ensure that the distance between the center of the newly generated sphere and the existing spheres is greater than 1.05 times their combined diameters. The center coordinates and diameters of all aggregates must be stored during the aggregate generation process. An interface transition zone of a given thickness must be generated around each aggregate.

[0064] S5.3. Import the stored information into the finite element software to generate a spherical three-dimensional microscopic model and divide the mesh;

[0065] S5.4. Based on the three-dimensional spherical aggregate model, record the center coordinates and diameter of the spherical aggregate;

[0066] S5.5. Export the scanned aggregate model into a vector pattern file, and then import it into the finite element software to determine the position of the circular aggregate, ensuring that the centroid of the real aggregate coincides with the center of the circular aggregate;

[0067] S5.6. Repeat the above process in sequence to finally complete the modeling of the real aggregate three-dimensional concrete mesoscopic model.

[0068] The beneficial effects of the present invention are:

[0069] The present invention studies the chloride ion transmission law in the interface area of ​​compressed precast-cast-in-situ concrete under the action of dry-wet cycles, clarifies the scope of the interface effect, establishes a multivariate distribution model of chloride ion diffusion coefficient in the interface area and a spatiotemporal calculation model of surface chloride ion concentration accumulation;

[0070] The present invention analyzes the influence characteristics of non-uniform coarse aggregate and pore distribution on the diffusion of water and chloride ions, derives the "double-porosity" transmission control equations of water and chloride ions in the concrete interface, and establishes a three-dimensional mesoscopic model of chloride ion transmission in the interface of compressed precast-cast-in-situ concrete under the action of dry-wet cycles.

[0071] The present invention establishes a probabilistic prediction model for chloride ion concentration in the interface region based on microscopic scale and multi-source uncertain information, develops an efficient prediction method, and provides a theoretical basis for the durability assessment and design of such structures. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0073] Figure 1 Schematic diagram of multiple influencing factors on the precast-cast-in-situ concrete specimen of the present invention;

[0074] Figure 2 Schematic diagram of porosity change of precast-cast-in-situ concrete specimens of the present invention;

[0075] Figure 3 It is the interface zone effect of the precast-cast-in-situ concrete specimen of the present invention;

[0076] Figure 4 is the spatial distribution of chloride ions on the surface of the precast-cast-in-situ concrete specimen of the present invention;

[0077] Figure 5 It is a schematic diagram of the "double-porosity" transmission of the precast-cast-in-situ concrete specimen of the present invention;

[0078] Figure 6 It is a spherical aggregate three-dimensional concrete aggregate model of the present invention;

[0079] Figure 7 It is the real interface and real aggregate of the precast-cast-in-situ concrete specimen of the present invention;

[0080] Figure 8 Schematic diagram of the pressure load applying device of the present invention;

[0081] Figure 9 Schematic diagram of the dry-wet cycle of chloride solution in the artificial climate chamber of the present invention;

[0082] Figure 10 It is a schematic diagram of sampling for chloride ion concentration and microstructure test of precast-in-situ concrete specimens of the present invention. DETAILED DESCRIPTION

[0083] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0084] Example 1

[0085] A method for predicting chloride ion concentration in a precast-cast-in-situ concrete interface region, comprising the following steps:

[0086] Step 1: Conduct chloride ion transmission tests on precast-in-situ concrete specimens under compressive loads and dry-wet cycles:

[0087] Three-dimensional laser scanning, digital image processing (DIP) and mercury intrusion porosimetry (MIP) were used to process precast-cast-in-situ concrete specimens under compressive loads and dry-wet cycles with different interface roughness, different stress levels, and different dry-wet cycle mechanisms to obtain the true morphology of the concrete connection interface after roughening treatment (such as Figure 1 As shown, Figure 1 The dark part is precast, and the light part is cast in place), and the coarse aggregate area fraction and pore structure parameters (such as Figure 2 As shown, Figure 2 The darker part is precast, and the lighter part is cast-in-place). The distribution characteristics of coarse aggregate and porosity in the interface area as a function of distance from the interface were analyzed, and the pore size distribution was statistically analyzed. The chloride ion concentration data inside and on the surface of the concrete were measured.

[0088] The chloride ion concentrations at different depths on the surface and interface of concrete and on both sides of the interface with different interface roughness, different stress levels, and different dry-wet cycle mechanisms were measured by automatic potentiometric titration and other means (e.g. Figure 3 shown);

[0089] The measured chloride ion concentration data inside and on the surface of the concrete in the interface area with different interface roughness, different stress levels, and different dry-wet cycle mechanisms were obtained.

[0090] Step 2: Based on the measured data of chloride ion concentration inside and on the surface of concrete, mathematical statistics and regression analysis methods are used to analyze the chloride ion concentration test data at different distances from the interface under different interface roughness, stress levels, and dry-wet cycle mechanisms. Based on the analysis results, a multivariate distribution model of chloride ion diffusion coefficient affected by lateral position is established;

[0091] Step 3: Based on the surface chloride ion concentration test data at different spatial locations and the existing exponential accumulation model, the regression analysis method is used to obtain the initial and stable surface chloride ion concentrations and accumulation rate values, and a nonlinear accumulation time-varying model of chloride ion concentration on the concrete surface in the interface area is established (e.g. Figure 4 shown);

[0092] Step 4: Based on the spatiotemporal characterization model of surface chloride ion concentration accumulation, the Gaussian autocorrelation function is introduced to divide the concrete surface into units, and the fluctuation value is selected. The surface chloride ion concentration, the physical and chemical binding capacity of chloride ions, the instability condition of consolidation chloride ions and the "double porosity" theory (such as Figure 5 (As shown) Construct the governing equations for the transport of moisture and chloride ions inside the concrete in the compressed interface area under the action of dry-wet cycles;

[0093] The Gaussian autocorrelation function introduced above is as follows:

[0094]

[0095] Where ρ(t) is the autocorrelation function value of the chloride ion concentration at two locations, θ x and θ y are the fluctuation values ​​of the two-dimensional random field in the x and y directions respectively; τ x =x i -x j and τ y =y i -y j are the distances between the centroids of units i and j in the x and y directions, respectively; d x and d y are the relevant length values ​​in the x-direction and y-direction respectively.

[0096] The construction of the governing equations for the transport of moisture and chloride ions within the concrete in the compressive interface region under the action of dry-wet cycles includes the following steps:

[0097] 4.1. The pores inside concrete are divided into small pores and large pores. Small pores refer to small pores with a size of nanometers, while large pores refer to large pores with a size of micrometers. The volume of small pores inside concrete is recorded as Ω. f1 , the macropore volume is recorded as Ω f2 , the total pore volume is Ω, then the porosity in concrete is expressed as follows (2):

[0098]

[0099] Where: φ1 represents the porosity of small pores; φ2 represents the porosity of large pores;

[0100] 4.2 Based on the existing water molecule transport equation in concrete and the mass exchange rate of water between small pores and large pores R w12 The moisture transfer equation inside concrete under dry-wet cycles is obtained as follows (3):

[0101]

[0102] Where, is the water diffusion coefficient in the pores, in m 2 / s;R w12 The mass exchange rate of water; φ1 represents the porosity of small pores; ρ w Indicates the density of water, its value is 1000kg / m 3 θ i Expressed as the degree of saturation of concrete.

[0103] Water diffusion coefficient The expression is as follows (4):

[0104]

[0105] Where, α i and β i is a dimensionless constant determined based on experimental data; Ki is a constant defined as hydraulic conductivity, equal to the kinematic viscosity divided by the permeability, with the unit being m 4 / (NS);

[0106] P atm =101325P a is the standard atmospheric pressure. il With P ic Respectively expressed as Ω fi Water pressure and capillary pressure in.

[0107] The mass exchange rate of water between small pores and large pores is expressed as follows (5):

[0108] R w12 =k 12 ρ w (P2-P1) (5)

[0109] Where K 12 is a constant, the unit is m 2 / (N·s);P i (i=1,2) represents the pore Ω fi Average pore pressure.

[0110] The average pore pressure P in the pores i The expression is as follows (6):

[0111] P i =(1-θi )P ig +P il θ i (6)

[0112] Where, P ig represents atmospheric pressure; θ i Expressed as the saturation of concrete; P il Expressed as Ω fi The water pressure in.

[0113] Substituting equations (5) and (6) into equation (7), we obtain the following equation (7):

[0114]

[0115] Where, α i and β i is a dimensionless constant determined based on experimental data; P atm =101325P a is the standard atmospheric pressure; θ i Expressed as the saturation of concrete; P i (i=1,2) represents the pore Ω fi Average pore pressure.

[0116] The initial condition of concrete is θ i =1(i=1,2), then the two boundary conditions of moisture transport can be expressed as follows (8):

[0117]

[0118] In formula (8), the flux boundary condition is used instead of the direct saturation boundary condition;

[0119] Where K si is the convection coefficient of water transfer on the concrete surface, in m / s, θ env is the equivalent saturation in the concrete exposure environment; is the water diffusion coefficient in the pores, in m 2 / s;θ i Expressed as the degree of saturation of concrete.

[0120] 4.3. Based on the physical and chemical binding capacity of chloride ions, the instability conditions of consolidated chloride ions, and the "double porosity" theory, the water and chloride ion transport equations under load-wet-dry cycles are constructed. The steps are as follows:

[0121] According to the mass conservation theorem of material transport, the chloride ion transport control equations can be obtained as follows (9) and (10):

[0122]

[0123] Where C1 and C2 are the chloride ion concentrations in small pores and large pores, respectively; D1 and D2 are the diffusion coefficients of chloride ions in small pores and large pores, respectively; R C12 is the chloride ion exchange capacity between different pores, R 1S and R 2S are the binding rates of concrete with chloride ions in small pores and large pores, respectively; φ1 represents the porosity of small pores; φ2 represents the porosity of large pores.

[0124] The expression of chloride ion exchange capacity is shown in formula (11):

[0125] R c12 =k R φ1φ2(C2-C1) (11)

[0126] Where: K R is the chloride ion exchange rate between different pores;

[0127] The binding rate of concrete with chloride ions in small pores and large pores is expressed in equations (12) and (13):

[0128]

[0129] Where C3 and C4 are the concentrations of bound chloride ions in small pores and large pores, respectively; φ1 represents the porosity of small pores; φ2 represents the porosity of large pores;

[0130] The Langmuir adsorption relationship is used to describe the relationship between consolidation and free chloride ion concentration, as shown in formula (14):

[0131]

[0132] Where α and β are binding constants; C K is the free chloride ion concentration; C bK represents the solidified chloride ion concentration.

[0133] Step 5: Based on the spatiotemporal characterization model of surface chloride ion concentration accumulation and the control equations of moisture and chloride ion transmission in the concrete at the compressed interface, a phased random placement strategy is used to complete the generation of aggregates at different positions from the interface. At the same time, combined with the three-dimensional true shape of the aggregates obtained by three-dimensional laser scanning, a three-dimensional microscopic model of the concrete at the interface area is established (such as Figure 6 The establishment process is as follows:

[0134] 5.1. Determine the aggregate diameter distribution based on the concrete mix ratio and gradation curve, and calculate the number of circular aggregates of different diameters in a given space;

[0135] 5.2. Create an aggregate placement program in Matlab to ensure that the distance between the center of the newly generated sphere and the existing spheres is greater than 1.05 times their combined diameters. The center coordinates and diameters of all aggregates must be stored during the aggregate generation process, and an interface transition zone of a given thickness must be generated around each aggregate.

[0136] 5.3. Import the above stored information into the finite element software, generate a spherical three-dimensional microscopic model, and divide the mesh;

[0137] 5.4. Based on the three-dimensional spherical aggregate model, record the center coordinates and diameter of the spherical aggregate;

[0138] 5.5. Export the scanned aggregate model into a vector pattern file, then import it into the finite element software to determine the position of the circular aggregate and ensure that the centroid of the real aggregate coincides with the center of the circular aggregate;

[0139] 5.6. Repeat the above process in sequence to finally complete the modeling of the real aggregate three-dimensional concrete micro-model.

[0140] Step 6. Combining the established spatiotemporal characterization model of surface chloride ion concentration accumulation and the control equations for moisture and chloride ion transport in the concrete in the compressive interface zone under dry-wet cycles, and considering the influence of the three-dimensional mesoscopic composition of the concrete in the interface zone, a three-dimensional mesoscopic model of chloride ion transport in the concrete in the compressive interface zone under dry-wet cycles is established. Based on the chloride ion concentration test results at different positions, the rationality of the above mesoscopic model in simulating chloride ion transport is verified.

[0141] Example 2

[0142] Chloride ion transmission test of precast-in-situ concrete specimens under compression under dry-wet cycles is as follows:

[0143] Step 1. Specimen design: A batch of cubic precast concrete specimens with a side length of 150 mm were preliminarily designed. After standard curing, one surface was selected for roughening treatment, and the subsequent cast-in-place part was constructed based on the roughened surface to obtain a batch of precast-cast-in-place concrete specimens with a size of 300 mm × 150 mm × 150 mm.

[0144] Step 2: Specimen treatment: Specimen is eroded on one side by applying epoxy resin on the surface, and a self-made loading device is used (anti-rust paint is applied and then epoxy resin is applied for anti-corrosion treatment).

[0145] Step 3: Loading: Load the specimen after the above treatment (such as Figure 8 The applied compressive load is determined according to the compressive stress level and the measured compressive strength standard value, and is controlled by pressure sensors and concrete surface strain gauges.

[0146] Step 4: Environmental treatment: After loading, place it in an artificial climate chamber for a chloride ion solution dry-wet cycle corrosion test (such as Figure 9 As shown, a 3% NaCl solution was prepared to generate salt spray. The solution concentration was regularly measured to maintain a constant chloride ion concentration. Simultaneously, the artificial climate chamber was modified to increase the number of salt spray nozzles to ensure uniform salt spray and consistent concentration around all specimens.

[0147] This test was carried out under the conditions of temperature of 20℃, humidity cycling between 15% and 90%, and precast and cast-in-place concrete strength and water-cement ratio of C50 and 0.45. The main conditions were investigated, such as the dry-wet cycle mechanism (1:1 and 2:1), compressive stress level (0.1, 0.3 and 0.5), chiseling depth (0mm, 3mm, 6mm and 9mm) and erosion time (0d, 30d, 90d, 180d) on the chloride ion transport in the interface zone.

[0148] Step 7. Test items: Regularly collect data on chloride ion concentration on the concrete surface of the interface area; drill core samples from the precast-cast-in-situ concrete interface and the areas on both sides (such as Figure 10 The chloride ion concentrations at different depths and positions from the interface were tested using an automatic potentiometric titrator.

[0149] Example 3

[0150] The actual morphology and microstructure test of the interface of precast-cast-in-situ concrete specimens are as follows:

[0151] Step 1, specimen design: Before drilling, the precast-in-situ concrete specimen in Example 2 was cut along the central axis, and the cross-sectional image of the precast-in-situ concrete under different chiseling depths was obtained using a 4K high-definition digital camera, and converted into a binary image using existing image processing technology. In addition, outside the chloride ion concentration test area, three specimens with a size of 10mm×10mm×10mm and substantially the same mass were cut out longitudinally on the central axis and both sides of the concrete interface using a cutting machine. After all the specimens were placed in anhydrous ethanol and soaked for one week, they were dried in an oven set at 50°C to constant weight, and the pore structure parameters of the concrete specimens were tested using a mercury intrusion instrument; the main study was the influence of chiseling depth (0mm, 3mm, 6mm and 9mm) on the microstructure parameters of the concrete in the interface area.

[0152] Step 2. Test items: When making the specimens, use 3D laser scanning technology to obtain the three-dimensional true shape of the aggregate, and combine 3D laser scanning technology with digital reconstruction technology to obtain the true contour of the interface; based on the binary image, use digital image processing technology to obtain the distribution characteristics of the coarse aggregate area fraction on both sides of the interface under different chiseling depths; measure the initial porosity of concrete specimens at different positions from the interface under different working conditions, and use the average value of three specimens as the concrete porosity in the vertical area; measure the pore size of the interface area and the concrete bodies on both sides respectively, and calculate the pore size distribution range and corresponding proportion.

Claims

1. A method for predicting chloride ion concentration in the interface area of ​​precast-cast-in-situ concrete, characterized in that: The following steps are involved: S1. Conduct chloride ion transport tests on precast-in-situ concrete specimens subjected to compressive loads and wet-dry cycles. Three-dimensional laser scanning, digital image processing (DIP), and mercury intrusion porosimetry (MIP) were used to process the specimens under compressive loads and wet-dry cycles. The true morphology of the concrete interface after roughening was obtained. The coarse aggregate area fraction and pore structure parameters at the interface and on both sides were accurately measured. The distribution characteristics of coarse aggregate and porosity in the interface region as a function of distance from the interface were analyzed. The pore size distribution was statistically analyzed, and the chloride ion concentration data inside and on the surface of the concrete were measured. S2. Based on the measured data of chloride ion concentration inside and on the surface of concrete, the spatial distribution characteristics of the overall chloride ion concentration under different working conditions and the lateral distribution characteristics of the chloride ion concentration as a function of the distance from the interface are analyzed, and then a multivariate distribution model of the chloride ion diffusion coefficient is established; S3. Based on the measured data of chloride ion concentration inside and on the surface of concrete, the spatial distribution characteristics of chloride ion concentration under different working conditions are analyzed in the form of exponential concrete surface chloride ion concentration. The longitudinal distribution characteristics of chloride ion concentration change with the erosion depth are established to establish a spatiotemporal characterization model of surface chloride ion concentration accumulation. S4. Based on the spatiotemporal characterization model of surface chloride ion concentration accumulation, a Gaussian autocorrelation function was introduced to divide the concrete surface into units. The fluctuation value was selected. The measured surface chloride ion concentration, the physical and chemical binding capacity of chloride ions, the instability condition of consolidated chloride ions, and the "double porosity" theory were combined to construct the governing equations for the transport of moisture and chloride ions within the compressed interface zone of concrete under the action of dry-wet cycles. S5. Based on the spatiotemporal characterization model of surface chloride ion concentration accumulation and the governing equations for moisture and chloride ion transport within the concrete at the compressive interface, a phased random placement strategy was used to generate aggregate at different distances from the interface. Combined with the three-dimensional true morphology of the aggregate obtained by three-dimensional laser scanning, a three-dimensional microscopic model of the concrete at the interface was established. S6. Combining the established spatiotemporal characterization model of surface chloride ion concentration accumulation and the governing equations for the transport of moisture and chloride ions within concrete in the compressive interface zone under dry-wet cycles, and considering the influence of the three-dimensional mesoscopic composition of concrete in the interface zone, a three-dimensional mesoscopic model of chloride ion transport in concrete in the compressive interface zone under dry-wet cycles was established. Based on the chloride ion concentration test results at different locations, the rationality of the above mesoscopic model in simulating chloride ion transport was verified.

2. The method for predicting chloride ion concentration in the interface area of ​​precast-cast-in-situ concrete according to claim 1, wherein: In S1, the chloride ion concentration data inside and on the surface of concrete is measured, including the following steps: S1.

1. Conduct chloride ion transport tests on precast-in-situ concrete specimens under compressive loads and wet-dry cycles; S1.

2. Combined with automatic potentiometric titration, measure chloride ion concentrations at different depths on the concrete surface and on both sides of the interface area with different interface roughness, different stress levels, and different dry-wet cycle mechanisms; S1.

3. Measure and obtain the internal and surface chloride ion concentration data of the concrete in the interface area with different interface roughness, different stress levels, and different dry-wet cycle mechanisms.

3. The method for predicting chloride ion concentration in the interface area of ​​precast-cast-in-situ concrete according to claim 1, wherein: In S2, a multivariate distribution model of chloride ion diffusion coefficient is established, specifically as follows: mathematical statistics and regression analysis methods are used to analyze the chloride ion concentration test data at different distances from the interface under different interface roughness, stress levels and dry-wet cycle mechanisms; based on the analysis results, a multivariate distribution model of chloride ion diffusion coefficient affected by lateral position is established.

4. The method for predicting chloride ion concentration in the interface area of ​​precast-cast-in-situ concrete according to claim 1, wherein: In S3, a spatiotemporal characterization model of surface chloride ion concentration accumulation is established, as follows: Based on the surface chloride ion concentration test data at different spatial positions and the existing exponential accumulation model, the regression analysis method is used to obtain the initial and stabilized surface chloride ion concentrations and accumulation rate values, and a nonlinear time-varying accumulation model of chloride ion concentration on the concrete surface in the interface area is established.

5. The method for predicting chloride ion concentration in the interface area of ​​precast-cast-in-situ concrete according to claim 1, wherein: In S4, the Gaussian autocorrelation function is as follows (1): Where ρ(t) is the autocorrelation function value of the chloride ion concentration at two locations, θ x and θ y In particular, they are the fluctuation values ​​of the two-dimensional random field in the x and y directions; τ x =x i -x j and τ y =y i -y j are the distances between the centroids of units i and j in the x and y directions, respectively; d x and d y are the relevant length values ​​in the x-direction and y-direction respectively.

6. The method for predicting chloride ion concentration in the interface area of ​​precast-cast-in-situ concrete according to claim 1, characterized in that: In S4, the control equations for the transport of moisture and chloride ions inside the concrete in the compressed interface area under the action of dry-wet cycles are constructed, including the following steps: S4.

1. The pores inside concrete are divided into small pores and large pores. Small pores represent small pores with a size of nanometers, while large pores represent large pores with a size of micrometers. The volume of small pores inside concrete is recorded as Ω. f1 , the macropore volume is recorded as Ω f2 , the total pore volume is Ω, then the porosity in concrete is expressed as follows (2): Where: φ1 represents the porosity of small pores; φ2 represents the porosity of large pores; S4.2, based on the existing water molecule transport equation in concrete and the mass exchange rate of water between small pores and large pores R w12 The moisture transfer equation inside concrete under dry-wet cycles is obtained as follows (3): Where, is the water diffusion coefficient in the pores, in m 2 / s;R w12 Water mass exchange rate; Indicates the porosity of small pores; ρ w Indicates the density of water, its value is 1000kg / m 3 θ i Expressed as the degree of saturation of concrete; Water diffusion coefficient The expression is as follows (4): Where, α i and β i is a dimensionless constant determined based on experimental data; K i is a constant defined as hydraulic conductivity, equal to the kinematic viscosity divided by the permeability, with units of m 4 / (NS);P atm =101325P a is the standard atmospheric pressure; P il With P ic Respectively expressed as Ω fi Water pressure and capillary pressure in; The mass exchange rate of water between small pores and large pores is expressed as follows (5): R w12 =k 12 r w (P2-P1) (5) Where K 12 is a constant, the unit is m 2 / (N·s);P i (i=1,2) represents the pore Ω fi medium average pore pressure; The average pore pressure P in the pores i The expression is as follows (6): P i =(1-θ i )P ig +P il i i (6) Where, P ig represents atmospheric pressure; θ i Expressed as the saturation of concrete; P il Expressed as Ω fi Water pressure in Substituting equations (5) and (6) into equation (7), we obtain the following equation (7): Where, α i and β i is a dimensionless constant determined based on experimental data; P atm =101325P a is the standard atmospheric pressure; θ i Expressed as the saturation of concrete; P i (i=1,2) represents the pore Ω fi medium average pore pressure; The initial condition of concrete is θ i =1(i=1,2), then the two boundary conditions of moisture transport can be expressed as follows (8): In formula (8), the flux boundary condition is used instead of the direct saturation boundary condition; Where K si is the convection coefficient of water transfer on the concrete surface, in m / s, θ env is the equivalent saturation in the concrete exposure environment; is the water diffusion coefficient in the pores, in m 2 / s;θ i Expressed as the degree of saturation of concrete; S4.

3. Based on the physical and chemical binding capacity of chloride ions, the instability conditions of consolidated chloride ions, and the "double porosity" theory, the water and chloride ion transport equations under load-wet-dry cycles are constructed as follows: According to the mass conservation theorem of material transport, the chloride ion transport control equations can be obtained as follows (9) and (10): Where C1 and C2 are the chloride ion concentrations in small pores and large pores, respectively; D1 and D2 are the diffusion coefficients of chloride ions in small pores and large pores, respectively; R C12 is the chloride ion exchange capacity between different pores, R 1S and R 2S are the binding rates of concrete and chloride ions in small pores and large pores, respectively; Indicates the porosity of small pores; represents the porosity of macropores; The expression of chloride ion exchange capacity is shown in formula (11): R c12 =k R φ1φ2(C2-C1) (11) Where: K R is the chloride ion exchange rate between different pores; The binding rate of concrete with chloride ions in small pores and large pores is expressed in equations (12) and (13): Where C3 and C4 are the concentrations of bound chloride ions in small pores and large pores, respectively; φ1 represents the porosity of small pores; φ2 represents the porosity of large pores; The Langmuir adsorption relationship is used to describe the relationship between consolidation and free chloride ion concentration, as shown in formula (14): Where α and β are binding constants; C K is the free chloride ion concentration; C bK represents the solidified chloride ion concentration.

7. The method for predicting chloride ion concentration in the interface area of ​​precast-cast-in-situ concrete according to claim 1, characterized in that: In S5, a three-dimensional microscopic model of chloride ion transport in concrete at the interface region is established, including the following steps: S5.

1. Determine the aggregate diameter distribution based on the concrete mix proportion and gradation curve, and calculate the number of circular aggregates of different diameters within a given space; S5.

2. Develop an aggregate placement program in Matlab to ensure that the distance between the center of the newly generated sphere and the existing spheres is greater than 1.05 times their combined diameters. The center coordinates and diameters of all aggregates must be stored during the aggregate generation process. An interface transition zone of a given thickness must be generated around each aggregate. S5.

3. Import the stored information into the finite element software to generate a spherical three-dimensional microscopic model and divide the mesh; S5.

4. Based on the three-dimensional spherical aggregate model, record the center coordinates and diameter of the spherical aggregate; S5.

5. Export the scanned aggregate model into a vector pattern file, and then import it into the finite element software to determine the position of the circular aggregate, ensuring that the centroid of the real aggregate coincides with the center of the circular aggregate; S5.

6. Repeat the above process in sequence to finally complete the modeling of the real aggregate three-dimensional concrete mesoscopic model.

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