Method for predicting spatial and temporal distribution of chloride ion concentration based on concrete microstructure in cold region

Abstract

The application discloses a kind of based on the spatiotemporal distribution prediction method of chloride ion concentration of concrete microstructure in cold region, the method with the field drilling sample of retired bridge in cold region as experimental material, the layered chloride ion concentration of concrete is measured;Based on X-ray computed tomography and apparent three-dimensional topography scanning technology, construct concrete mortar phase, interface transition zone phase and aggregate phase three-dimensional three-phase microstructure, respectively establish concrete mortar chloride ion diffusion coefficient model and interface transition zone chloride ion diffusion coefficient model;Based on finite element simulation method, establish accurate concrete three-dimensional micro model and consider the chloride ion diffusion characteristics of each phase of concrete microstructure, can accurately simulate the diffusion law of chloride ion in concrete interior.This method can truly simulate the chloride ion diffusion behavior in concrete structure under natural service environment, and can accurately predict the spatiotemporal distribution of chloride ion concentration of bridge structure.

Description

Technical Field

[0001] This invention relates to a method for predicting the durability of reinforced concrete bridges, specifically a method for predicting the spatiotemporal distribution of chloride ion concentration based on the microstructure of concrete in cold regions. Background Technology

[0002] Chloride ion corrosion has a significant impact on the structural performance of reinforced concrete bridges in cold-climate de-icing environments, reducing their durability and service life. When chloride ions accumulate to a critical concentration, they damage the passivation layer of the reinforcing steel, making it susceptible to corrosion. This ultimately leads to concrete cracking, a significant reduction in structural durability, and a threat to the structural safety during service. Therefore, understanding the chloride ion diffusion mechanism is of great practical value for predicting the service life of reinforced concrete bridges in cold regions and promoting sustainable design.

[0003] Numerous studies have been conducted on the diffusion of chloride ions in concrete structures. Domestic and international scholars have performed extensive experimental research on variables such as erosion time, aggregate volume fraction, water-cement ratio, and the interfacial transition zone. However, some shortcomings and problems remain in the study of the microstructure of concrete, particularly regarding the interfacial transition zone: First, some studies neglect the influence of the interfacial transition zone on chloride ion diffusion, simplifying concrete into a two-phase structure of mortar and aggregate. Current research shows that the microstructure and texture of the interfacial transition zone are more porous, with higher porosity and superior chloride ion transport characteristics. Its thickness ranges from 10 to 100 μm, and its chloride ion diffusion coefficient is 1.5 to 109 times that of mortar. Therefore, the influence of the interfacial transition zone on chloride ion transport in concrete cannot be ignored. Second, most current studies, when considering the thickness of the interfacial transition zone, arbitrarily select a value within the range of 10 to 100 μm, resulting in a large error range and failing to accurately reflect the impact of the actual thickness of the interfacial transition zone on the service condition of bridges. Therefore, it is urgent to establish a model that can accurately reflect the internal microstructure of concrete and propose a method for predicting the chloride ion diffusion rate in the mortar phase and the interface transition zone phase, so as to explore the spatiotemporal distribution law of chloride ion concentration inside concrete.

[0004] Core sampling of actual bridge components to study chloride ion diffusion patterns under real-world service conditions is undoubtedly the most direct and effective method. However, conducting experiments on actual bridges to study chloride ion diffusion patterns is prohibitively expensive, technically challenging, and time-consuming. Therefore, most studies still employ accelerated diffusion methods in the laboratory, where the experimental environment and chloride ion concentration on the specimen surface are constant. However, in the actual service environment of bridges, environmental factors such as wind, rain, snow, and sunlight, as well as the chloride ion concentration on the bridge deck surface, exhibit randomness and time-varying characteristics, differing significantly from those in the laboratory. Furthermore, the size and erosion time of laboratory concrete specimens differ greatly from those of actual bridges in service, meaning the results obtained cannot be truly equivalent to actual service conditions and exhibit significant deviations when applied to practical engineering. Therefore, based on the real microstructural information and chloride ion erosion data of bridge core samples under actual service conditions, establishing a predictive model that can accurately simulate the chloride ion diffusion rate of the mortar phase and the interfacial transition zone phase, as well as the spatiotemporal distribution law of chloride ion concentration inside the concrete, can enable preventive maintenance of bridges in service, achieving the lowest maintenance cost and highest economic benefits throughout the entire life cycle. This is of great significance for the monitoring and evaluation of the durability and health status of concrete structures, and is also a problem that urgently needs to be studied. Summary of the Invention

[0005] To address the challenge that existing spatiotemporal prediction models for chloride ion concentration are not well-suited for predicting chloride ion concentration distribution in concrete bridge structures under real-world service conditions, this invention provides a method for predicting the spatiotemporal distribution of chloride ion concentration based on the microstructure of concrete in cold regions. This method accurately measures and simulates the geometric characteristics of the mortar phase and the interfacial transition zone phase within the concrete microstructure. Based on experimental data, a spatiotemporal theoretical model, and a numerical simulation method, this invention can realistically simulate the chloride ion diffusion behavior within concrete structures under natural service conditions and accurately predict the spatiotemporal distribution of chloride ion concentration in bridge structures.

[0006] The objective of this invention is achieved through the following technical solution:

[0007] A method for predicting the spatiotemporal distribution of chloride ion concentration based on the microstructure of concrete in cold regions includes the following steps:

[0008] Step 1: Core samples were taken from decommissioned concrete bridge components in cold regions on-site, and concrete powder was obtained in layers. The chloride ion concentration on the concrete surface and in each layer was measured using a chloride ion concentration testing instrument. A model of chloride ion concentration on the concrete surface was established based on the test data.

[0009] Step 2: Based on the concrete surface chloride ion concentration model established in Step 1, the process of chloride ion concentration accumulation on the concrete surface over time is first decomposed into a time series, and the total integral of surface chloride ion concentration over time remains constant. Combined with Fick's second law, the theoretical formulas of the concrete chloride spatiotemporal concentration distribution prediction model and the concrete apparent chloride ion diffusion rate prediction model are obtained. Then, the measured internal layered chloride ion concentration data of the concrete are used for fitting to obtain the analytical values ​​of the coefficients to be solved in the theoretical formula of the model.

[0010] Step 3: Measure the volume distribution of aggregates and mortar using X-ray computed tomography (CT) technology; measure the average thickness of the interface transition zone using an appearance 3D topography scanner;

[0011] Step 4: Integrate the three-dimensional three-phase spherical model and the stepwise homogeneous model, and use the concrete apparent chloride ion diffusion rate model measured in Step 2 and the three-phase volume distribution measured in Step 3 to calculate the chloride ion diffusion rate model of concrete mortar and interface transition zone.

[0012] Step 5: Based on the core sample 3D scanning data and surface morphology scanning data, generate 3D random polyhedral aggregates, interface transition zones and mortar of concrete, and construct a 3D three-phase random model of concrete.

[0013] Step 6: Based on the chloride ion concentration data of concrete, the chloride ion diffusion rate model of mortar, the chloride ion diffusion rate model of the interface transition zone, and the three-dimensional three-phase random model of concrete, construct a finite element model to predict the chloride ion concentration at any time and any depth inside the concrete.

[0014] Compared with the prior art, the present invention has the following advantages:

[0015] This invention addresses the challenge of accurately simulating the spatiotemporal distribution of chloride ion concentration in concrete bridge structures under real-world service conditions in current research. First, by drilling and sampling core samples from bridge structures under real-world service conditions, the most authentic and reliable experimental materials are obtained, overcoming the inherent biases and limitations of laboratory models. Second, addressing the lack of research on the precise measurement of interface transition zone thickness, high-precision advanced digital image surface three-dimensional morphology scanning technology is employed to measure the precise three-phase microstructure information within concrete under real-world service conditions, obtaining crucial data on the interface transition zone thickness and significantly improving the accuracy of model predictions. Finally, combining the three-phase spherical model theory and finite element three-dimensional model numerical simulation, a more effective method is provided for predicting the spatiotemporal distribution of chloride ion concentration within concrete bridge structures under real-world service conditions. Attached Figure Description

[0016] Figure 1 This is a schematic diagram showing the time-segmented concentration of surface chloride ions.

[0017] Figure 2 Comparison between empirical prediction models and measured values ​​of chloride ion concentration;

[0018] Figure 3 For the microstructure of concrete, (a) computed tomography image, (b) scanned image of the apparent three-dimensional morphology of the interface transition zone;

[0019] Figure 4 For a three-dimensional polyhedral micro-aggregate model of concrete; (a) the steps of generating a single aggregate, (b) the integral aggregate;

[0020] Figure 5 For the finite element software model, (a) solid model, (b) mesh generation;

[0021] Figure 6 Comparison of empirical prediction models, numerical simulation models and measured values ​​for chloride ion concentration;

[0022] Figure 7 This is a schematic diagram of chloride ion concentration contour lines. Detailed Implementation

[0023] The technical solution of the present invention will be further described below with reference to the accompanying drawings, but it is not limited thereto. Any modifications or equivalent substitutions to the technical solution of the present invention that do not depart from the spirit and scope of the technical solution of the present invention should be covered within the protection scope of the present invention.

[0024] This invention provides a method for predicting the spatiotemporal distribution of chloride ion concentration in concrete microstructure in cold regions. The method uses core samples from decommissioned bridges in cold regions as experimental materials to measure the stratified chloride ion concentration in the concrete. Based on X-ray computed tomography (CT) and apparent three-dimensional morphology scanning techniques, a three-dimensional three-phase microstructure of the concrete mortar phase, interface transition zone phase, and aggregate phase is constructed, and chloride ion diffusion coefficient models for the concrete mortar and interface transition zone are established respectively. Based on the finite element method, an accurate three-dimensional microstructure model of the concrete is established, considering the chloride ion diffusion characteristics of each phase in the concrete microstructure, which can accurately simulate the diffusion law of chloride ions inside the concrete. Specifically, the method includes the following steps:

[0025] Step 1: Perform core sampling on the concrete structure according to the "Technical Standard for On-site Testing of Concrete Structures" (GB / T 50784-2013). Step 2: Measure the chloride ion concentration in the concrete according to the "Technical Specification for Testing Chloride Ion Content in Concrete" (JGJ / T 322-2013). Step 3: Specific parameters of the concrete are based on relevant design drawings and the "General Specification for Concrete Structures" (GB 55008-2021). Establishing a model for the chloride ion concentration on the concrete surface requires measuring the chloride ion concentration C(x) on the concrete surface and in each layer. iThe initial chloride ion concentration of concrete is taken as the value at which the chloride ion concentration inside the concrete structure remains stable. The chloride ion concentration model on the concrete surface is as follows:

[0026]

[0027] In the formula: C s (t) represents the chloride ion concentration on the concrete surface at time t, C0 represents the initial chloride ion concentration on the concrete surface, and C a =C s -C0,C s The current measurement time represents the chloride ion concentration on the concrete surface, α is the time factor, and t0 is the time when the concrete was first exposed to the chloride ion environment.

[0028] Step 2: First, decompose the accumulation of chloride ion concentration on the concrete surface over time into a time series, ensuring that the total integral of the surface chloride ion concentration over time remains constant. Then, using Fick's second law, derive the theoretical formulas for predicting the spatiotemporal distribution of chloride ions in concrete and the apparent chloride ion diffusion rate in concrete. Finally, fit the measured data of layered chloride ion concentrations within the concrete to obtain the analytical values ​​of the coefficients to be solved in the theoretical formulas of the models. The specific steps are as follows:

[0029] Step 2.1 The apparent chloride ion diffusion rate model for concrete can be expressed as:

[0030]

[0031] In the formula: D(t) is the apparent diffusion coefficient of chloride ions in concrete at time t, D0 is the initial apparent diffusion coefficient of chloride ions, t0 is the time when concrete is first exposed to the chloride ion environment, and m is the time factor of the diffusion coefficient.

[0032] Step 22: Divide the service life into N segments, while ensuring that the total integral of the surface chloride ion concentration over time remains unchanged after dividing into N segments. This can be expressed as:

[0033]

[0034] In the formula: t i For the i-th segment time, τ i For the corresponding t i The i-th time segment, where N is the number of time segments.

[0035] Steps 2 and 3: By fitting the measured data, determine the corresponding number of time segments N and t. i Substituting the decomposed time series into Fick's second law, we obtain an empirical prediction model for the spatiotemporal concentration distribution of chloride ions, which can be expressed as:

[0036]

[0037] In the formula: C(x,t) is the chloride ion concentration at a depth of x cm in the concrete at time t, and erf is the error function.

[0038] Step 3: Perform microstructural scanning on the core sample using X-ray computed tomography (CT) to obtain the volume distribution of mortar and aggregate, and simultaneously obtain the aggregate gradation information; accurately measure the average thickness of the interface transition zone using a surface 3D topography scanner. Specific steps are as follows:

[0039] Step 3: Based on X-ray computed tomography (CT) technology, the grayscale images of the sample CT are processed using the threshold segmentation method to measure the volume distribution of aggregates and mortar.

[0040] Step 3. First, use 100-800 grit sandpaper to polish concrete samples at various depths. Each sample surface includes mortar, aggregate, and interface transition zone. Then, use a surface 3D topography scanner to measure and obtain 3D point cloud data of the sample surface topography. Set the average elevation data of the mortar and aggregate topography as a threshold. The point cloud data of the interface transition zone is the elevation data below the threshold. In this way, the average thickness information of the interface transition zone can be obtained.

[0041] Step 4: Integrate the three-dimensional three-phase spherical model and the stepwise homogeneous model. Using the apparent chloride ion diffusion rate model of concrete measured in Step 2 and the three-phase volume distribution measured in Step 3, calculate the chloride ion diffusion rate model of concrete mortar and the interface transition zone. The specific steps are as follows:

[0042] Step 41: Based on the three-dimensional three-phase sphere model, considering the aggregate dilution and distortion effects as well as the interface transition zone effect, the chloride ion diffusion rate model of concrete mortar and the interface transition zone can be expressed as:

[0043] D = D m (1-V agg ) 1.5 +D m (k-1)V itz (5)

[0044] In the formula: D is the apparent diffusion coefficient of chloride ions, D m V is the chloride ion diffusion coefficient of the mortar. agg V represents the volume fraction of coarse aggregate. itz denoted as the volume fraction of coarse aggregate, and k is a constant.

[0045] Step 4.2. Based on the theory of stepwise homogenization, the aggregate and the interface transition zone can first be homogenized into equivalent aggregate, and then the mortar and the equivalent aggregate can be homogenized into single-phase concrete. The chloride ion diffusion rate model of the concrete mortar and the interface transition zone can be expressed as:

[0046]

[0047] In the formula: D itz D is the chloride ion diffusion coefficient in the interfacial transition region. ea D is the equivalent aggregate chloride ion diffusion coefficient. itz =kD m .

[0048] Step 5: Based on the core sample 3D scanning data and surface morphology scanning data, generate 3D random polyhedral aggregates, interface transition zones, and mortar for concrete, and construct a 3D three-phase random model of concrete. The specific steps are as follows:

[0049] Step 51: Based on the 3D scanning data and surface morphology scanning data of the core sample, obtain the aggregate volume, gradation information, and thickness information of the interface transition zone. Randomly generate random spherical aggregates that meet the gradation requirements using the Range function, ensuring that the spheres do not interfere with each other. Save the coordinate information of the spherical aggregates. The formula for determining whether interference occurs is as follows:

[0050]

[0051] In the formula: x i y i z i These are the coordinates of the center of the newly generated circumcircle of the polyhedron, x and x. j y j z j These are the coordinates of the center of the sphere of the circumcircle of the generated polyhedron, r. i r j Let δ be the radius of the newly generated and the already generated circumscribed spheres of the polyhedron, respectively. i δ j These represent the thicknesses of the transition zones between the newly generated and already generated polyhedral interfaces, respectively.

[0052] Step 52: Based on the spherical aggregate, by taking a random point at each of the eight octants of the sphere and using the convex hull function, construct a random three-dimensional polyhedral aggregate that meets the aggregate volume requirements.

[0053] Step 53: Based on the center of the sphere in Step 51, generate a concentric sphere. The radius of this sphere is the original sphere radius plus the thickness of the interface transition zone. Based on this sphere, extend the original polyhedron vertices to the new sphere and use the convex hull function to construct the interface transition zone.

[0054] Step 54: Triangulate the generated random 3D polyhedral aggregate and interface transition zone using the Triangulation function, and save the coordinate information as an STL file.

[0055] Step Six: Based on concrete chloride ion concentration data, mortar chloride ion diffusion rate model, interface transition zone chloride ion diffusion rate model, and concrete three-dimensional three-phase stochastic model, construct a finite element model to predict the chloride ion concentration of concrete at any time and any depth. The specific steps are as follows:

[0056] Step 61: Based on the COMSOL Mutiphysics finite element software, input the STL file from Step 5 to construct a three-dimensional three-phase polyhedral aggregate solid model. Select the rare matter transfer module in the three-dimensional space dimension to perform transient simulation analysis on the three-dimensional three-phase polyhedral aggregate solid model.

[0057] Step 62: After defining the mortar phase, aggregate phase, and interface transition zone phase, perform mesh generation. Define one face of the cube as the chloride ion erosion face, and set the other faces to no flux.

[0058] Step 63: Construct a finite element model based on concrete chloride ion concentration data, mortar chloride ion diffusion rate model, interface transition zone chloride ion diffusion rate model, and concrete three-dimensional three-phase stochastic model;

[0059] Step 64: Input the chloride ion diffusion rate model of mortar, the chloride ion diffusion rate model of the interface transition zone, the surface chloride ion concentration model, and the initial chloride ion concentration into the finite element model in an analytical manner to assign chloride ion diffusion characteristics to each phase, thereby predicting the spatiotemporal concentration of chloride ions in concrete.

[0060] Example:

[0061] This embodiment is illustrated by combining the chloride ion concentration detection test results of an actual reinforced concrete bridge with the results of COMSOL Multiphysics finite element numerical simulation analysis.

[0062] The test subject was a decommissioned reinforced concrete simply supported slab beam from a cold region. The calculated span of the slab beam was 9.60m, with a height of 0.60m and a width of 1.01m. The slab beam used C30 grade II aggregate concrete without any air-entraining agent. The medium aggregate particle size was 20mm–31.5mm, the small aggregate particle size was 5mm–20mm, and the cement was ordinary Portland cement. The bridge's actual service life was 24 years. In the past 30 years, the region has experienced more than 180 days per year with minimum temperatures below zero degrees Celsius, and an average of 93 days of snow cover per year. Core samples were taken from different locations on the top slab of the reinforced concrete slab beam along its entire length, in accordance with the "Technical Standard for On-Site Testing of Concrete Structures" (GB / T50784-2013).

[0063] Step 1: Core samples were taken from decommissioned concrete bridges in cold regions on-site, and concrete powder was obtained layer by layer. The chloride ion concentration on the concrete surface and in each layer was measured using a chloride ion concentration testing instrument. The measured chloride ion concentration on the concrete surface was 0.32%; the initial concentration of the concrete was 0.05%; C a =C s -C0 = 0.27%; time factor α = 0.309.

[0064] Step 2: Assume the accumulation of chloride ion concentration on the concrete surface over time as a discrete process. A schematic diagram of the discrete segmentation is shown below. Figure 1 As shown, by applying the superposition principle and combining it with Fick's second law, theoretical formulas for predicting the spatiotemporal concentration distribution of chloride ions in concrete and the apparent chloride ion diffusion rate in concrete are obtained. Then, by fitting the measured data of layered chloride ion concentrations within the concrete, analytical values ​​of the coefficients to be solved in the theoretical formulas are obtained. The time of the first exposure of concrete to the chloride ion environment is t0 = 28 days, the time factor of the diffusion coefficient is m = 0.516, and the initial apparent chloride ion diffusion coefficient is D0 = 0.914 cm⁻¹. 2 / a, apparent diffusion rate of chloride ions The service life is 24 years, the number of time segments N = 10, t i = [0.244, 0.604, 1.001, 1.473, 2.014, 2.665, 3.4815, 4.579, 6.267, 10.645], the fitting result is as follows Figure 2 As shown.

[0065] Step 3: The volume distribution of aggregate, mortar, and interface transition zone was measured using X-ray computed tomography (CT) and an apparent 3D topography scanner. The CT results are shown below. Figure 3 As shown in (a), the scanning results of the apparent three-dimensional morphology of the interface transition zone are as follows: Figure 3 As shown in (b), the measured aggregate volume fraction was 50.9%, the volume fraction of the interface transition zone was 0.07%, and the average thickness of the interface transition zone was 45 μm.

[0066] Step 4: Using a three-dimensional three-phase spherical model, the chloride ion diffusion rate of the concrete mortar is calculated based on the apparent chloride ion diffusion rate model obtained in Step 2 and the three-phase volume distribution obtained in Step 3. Chloride ion diffusion rate D in the interface transition region itz =kD m k = 10.225.

[0067] Step 5: Based on the core sample 3D scanning data and surface morphology scanning data, generate 3D random polyhedral aggregates and interface transition zones for concrete. The steps for generating individual aggregates are as follows: Figure 4As shown in (a); then construct a three-dimensional three-phase random model of concrete and save it as an STL file, as follows. Figure 4 As shown in (b).

[0068] Step 6: Import the STL file and generate the solid geometry of each phase of concrete, such as... Figure 5 As shown in (a); the generated random aggregate model is meshed, as follows: Figure 5 As shown in (b), the chloride ion concentration data of concrete and the chloride ion diffusion rate model of mortar and interface transition zone were applied to the finite element model to calculate the chloride ion concentration of concrete at any time and at any depth. The results were compared with the measured values ​​and the empirical prediction model. Figure 6 As shown, the empirical prediction model agrees well with the measured values, and the numerical simulation results agree well with both the prediction model and the measured values, verifying the accuracy and rationality of the proposed method for predicting the spatiotemporal distribution of chloride ion concentration in cold-region bridges based on the microstructure of concrete. Furthermore, by extracting the chloride ion concentration isosurface from the model, Figure 7 It can be clearly observed that the isosurfaces near the coarse aggregate and the interface transition zone have a tendency to convex upwards and concave downwards, which also verifies the dilution and distortion effect of coarse aggregate on chloride ion diffusion, which reduces the chloride ion diffusion rate, while the interface transition zone increases the chloride ion diffusion rate. This indicates that the microstructure of concrete has a significant impact on chloride ion diffusion behavior, and verifies that the chloride ion concentration prediction model based on the microstructure of concrete established in this invention has good prediction effect.

Claims

1. A method for predicting the spatiotemporal distribution of chloride ion concentration based on the microstructure of concrete in cold regions, characterized in that... The method includes the following steps: Step 1: On-site core sampling of decommissioned concrete bridges in cold regions, and obtaining concrete powder in layers. The chloride ion concentration on the concrete surface and in each layer is measured using a chloride ion concentration testing instrument to establish a chloride ion concentration model on the concrete surface. Step 2: Based on the concrete surface chloride ion concentration model established in Step 1, the process of chloride ion concentration accumulation on the concrete surface over time is first decomposed into a time series, and the total integral of surface chloride ion concentration over time remains constant. Combined with Fick's second law, the theoretical formulas of the concrete chloride spatiotemporal concentration distribution prediction model and the concrete apparent chloride ion diffusion rate prediction model are obtained. Then, the measured internal layered chloride ion concentration data of the concrete are used for fitting to obtain the analytical values ​​of the coefficients to be solved in the theoretical formula of the model. Step 3: Measure the volume distribution of aggregates and mortar using X-ray computed tomography (CT) technology; measure the thickness of the interface transition zone using an appearance 3D topography scanner; Step 4: Integrate the three-dimensional three-phase spherical model and the stepwise homogeneous model. Using the concrete apparent chloride ion diffusion rate prediction model obtained in Step 2 and the three-phase volume distribution measured in Step 3, calculate the chloride ion diffusion rate model for the concrete mortar and the interface transition zone. The specific steps are as follows: Step 41: Based on the three-dimensional three-phase sphere model, considering the aggregate dilution and distortion effects as well as the interface transition zone effect, the chloride ion diffusion rate model for concrete mortar and the interface transition zone is expressed as follows: In the formula: D is the apparent diffusion coefficient of chloride ions, D m V is the chloride ion diffusion coefficient of the mortar. agg V represents the volume fraction of coarse aggregate. itz Let k be the volume fraction of the interface transition zone, where k is a constant. Step 42: Based on the stepwise homogenization theory, the aggregate and the interface transition zone can first be homogenized into equivalent aggregate, and then the mortar and the equivalent aggregate can be homogenized into single-phase concrete. The chloride ion diffusion rate model of the concrete mortar and the interface transition zone is expressed as: In the formula: D itz D is the chloride ion diffusion coefficient in the interfacial transition region. ea D is the equivalent aggregate chloride ion diffusion coefficient. itz =kD m ; Step 5: Based on the core sample 3D scanning data and surface morphology scanning data, generate 3D random polyhedral aggregates, interface transition zones and mortar of concrete, and construct a 3D three-phase random model of concrete. Step 6: Based on concrete chloride ion concentration data, mortar chloride ion diffusion rate model, interface transition zone chloride ion diffusion rate model, and concrete three-dimensional three-phase stochastic model, construct a finite element model to predict the chloride ion concentration of concrete at any time and any depth.

2. The method for predicting the spatiotemporal distribution of chloride ion concentration based on the microstructure of concrete in cold regions according to claim 1, characterized in that... In step one, the chloride ion concentration model on the concrete surface is as follows: In the formula: C s (t) represents the chloride ion concentration on the concrete surface at time t, C0 represents the initial chloride ion concentration on the concrete surface, and C a =C s - C0, C s The current measurement time represents the chloride ion concentration on the concrete surface, α is the time factor, and t0 is the time when the concrete was first exposed to the chloride ion environment.

3. The method for predicting the spatiotemporal distribution of chloride ion concentration based on the microstructure of concrete in cold regions according to claim 1, characterized in that... The specific steps of step two are as follows: Step 2.1, The apparent chloride ion diffusion rate model for concrete is expressed as: In the formula: D(t) is the apparent diffusion coefficient of chloride ions in concrete at time t, D0 is the initial apparent diffusion coefficient of chloride ions, t0 is the time when concrete is first exposed to the chloride ion environment, and m is the time factor of the diffusion coefficient. Step 22: Divide the service life into N segments, while ensuring that the total integral of the surface chloride ion concentration over time remains unchanged after dividing into N segments, expressed as: In the formula: t i For the i-th segment time, τ i For the corresponding t i The i-th time segment, where N is the number of time segments; Steps 2 and 3: By fitting the measured data, determine the corresponding number of time segments N and t. i Substituting the decomposed time series into Fick's second law, we obtain an empirical prediction model for the spatiotemporal concentration distribution of chloride ions, expressed as: In the formula: C(x, t) is the chloride ion concentration at a depth of x cm in the concrete at time t, and erf is the error function.

4. The method for predicting the spatiotemporal distribution of chloride ion concentration based on the microstructure of concrete in cold regions according to claim 1, characterized in that... The specific steps of step three are as follows: Step 3: Based on X-ray computed tomography (CT) technology, the grayscale image of the sample is processed using the threshold segmentation method to measure the volume distribution of aggregate and mortar. Step 3. First, use 100-800 grit sandpaper to polish concrete samples at various depths. Each sample surface includes mortar, aggregate, and interface transition zone. Then, use a surface 3D topography scanner to measure and obtain 3D point cloud data of the sample surface topography. Set the average elevation data of the mortar and aggregate topography as a threshold. Point cloud data with elevations below the threshold are the interface transition zone point cloud data, thereby obtaining the average thickness information of the interface transition zone.

5. The method for predicting the spatiotemporal distribution of chloride ion concentration based on the microstructure of concrete in cold regions according to claim 1, characterized in that... The specific steps of step five are as follows: Step 51: Based on the 3D scanning data and surface morphology scanning data of the core sample, obtain the aggregate volume, gradation information, and thickness information of the interface transition zone. Randomly generate random spherical aggregates that meet the gradation requirements using the Range function, ensuring that the spheres do not interfere with each other. Save the coordinate information of the spherical aggregates. The formula for determining whether interference occurs is as follows: In the formula: x i y i z i These are the coordinates of the center of the newly generated circumcircle of the polyhedron, x and x. j y j z j These are the coordinates of the center of the sphere of the circumcircle of the generated polyhedron, r. i r j Let δ be the radius of the newly generated and the already generated circumscribed spheres of the polyhedron, respectively. i δ j These represent the thicknesses of the transition zones between the newly generated and already generated polyhedron interfaces, respectively. Step 52: Based on the spherical aggregate, by taking a random point at each of the eight octants of the sphere and using the convex hull function, construct a random three-dimensional polyhedron aggregate and make it meet the aggregate volume requirements. Step 53: Based on the center of the sphere in Step 51, generate a concentric sphere. The radius of this sphere is the original sphere radius plus the thickness of the interface transition zone. Based on this sphere, extend the original polyhedron vertices to the new sphere and use the convex hull function to construct the interface transition zone. Step 54: Triangulate the generated random 3D polyhedral aggregate and interface transition zone using the Triangulation function, and save the coordinate information as an STL file.

6. The method for predicting the spatiotemporal distribution of chloride ion concentration based on the microstructure of concrete in cold regions according to claim 4, characterized in that... The specific steps of step six are as follows: Step 61: Based on the COMSOL Mutiphysics finite element software, input the STL file from Step 5 to construct a three-dimensional three-phase polyhedral aggregate solid model. Select the rare matter transfer module in the three-dimensional space dimension to perform transient simulation analysis on the three-dimensional three-phase polyhedral aggregate solid model. Step 62: After defining the mortar phase, aggregate phase, and interface transition zone phase, perform mesh generation. Define one face of the cube as the chloride ion erosion face, and set the other faces to no flux. Step 63: Construct a finite element model based on concrete chloride ion concentration data, mortar chloride ion diffusion rate model, interface transition zone chloride ion diffusion rate model, and concrete three-dimensional three-phase stochastic model; Step 64: Input the chloride ion diffusion rate model of mortar, the chloride ion diffusion rate model of the interface transition zone, the surface chloride ion concentration model, and the initial chloride ion concentration into the finite element model in an analytical manner to assign chloride ion diffusion characteristics to each phase, thereby predicting the spatiotemporal concentration of chloride ions in concrete.

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