Method and device for optimizing concrete proportion in salt-fresh water intersection area

Abstract

The invention discloses a concrete proportion optimization method and device for a salt-fresh water intersection area. The method comprises the following steps: determining an aggregate corner distribution condition based on a first morphological parameter of single-particle-size broken stone within a preset particle size range in a typical estuary environment; determining durability parameters of the concrete test piece prepared from the broken stones with the single particle size; calculating a gravel aggregate concrete chlorine salt erosion distribution rule based on the parameters; calculating a round aggregate concrete chlorine salt erosion distribution rule; determining the influence degree of the aggregate corner effect on the concrete chlorine salt erosion rule based on the circular aggregate concrete chlorine salt erosion distribution rule, the chlorine salt concentration time-varying rule in the indoor environment and the gravel aggregate concrete chlorine salt erosion distribution rule; and optimizing the initial concrete proportion based on the influence degree. According to the method, the aggregate corner effect, the aggregate form and the chlorine salt concentration rule of the indoor and salt-fresh water intersection area serve as reference factors for optimizing the concrete proportion, and the durability of the concrete is guaranteed.

Description

Technical Field

[0001] This application relates to, but is not limited to, the field of building materials technology, and in particular to a method and apparatus for optimizing concrete mix proportions in brackish water confluence areas. Background Technology

[0002] Reinforced concrete structures, due to their excellent mechanical properties and strong construction adaptability, are widely used in projects such as cross-sea bridges and wharves in brackish water areas. However, reinforced concrete structures in brackish water environments are susceptible to chloride erosion, affecting their durability. The corrosion resistance of concrete is a crucial indicator for evaluating its durability and predicting its lifespan, and it is related to raw materials and mix proportions. Current reinforced concrete mix design processes only adjust and optimize the mix proportions based on indoor chloride erosion tests to account for the impact of corrosion resistance on concrete durability.

[0003] However, the brackish water confluence area is an unsteady corrosive environment for reinforced concrete due to periodic tides and seasonal rainfall. Therefore, the concrete obtained by the existing mix design optimization method cannot adapt well to the chloride erosion pattern in the brackish water confluence area, and thus cannot guarantee the durability of the concrete. Summary of the Invention

[0004] This application provides a method and apparatus for optimizing concrete mix proportions in brackish water areas, which can ensure the durability of concrete made based on the optimized concrete mix proportions in brackish water areas.

[0005] In a first aspect, embodiments of this application provide a method for optimizing concrete mix proportions in brackish water confluence areas, including:

[0006] The study aimed to determine the time-varying patterns of chloride concentration in estuarine water and indoor environments.

[0007] The first morphological parameters of single-size crushed stone within a preset particle size range are determined in a typical estuary environment, and the aggregate edge and corner distribution of the single-size crushed stone is determined based on statistical principles and the first morphological parameters.

[0008] Concrete specimens were prepared using the single-size crushed stone described above;

[0009] The concrete specimens were immersed in a brackish water environment for a preset period of time, and the chloride distribution pattern of the concrete specimens within the preset period of time was determined. Based on Fick's second law and the chloride distribution pattern, the durability parameters of the concrete specimens were calculated.

[0010] Based on the preset particle size range, the morphological parameters, the distribution of aggregate edges and corners, and the durability parameters, a geometric model of random aggregate crushed stone for the concrete specimen is generated using Python.

[0011] The geometric model of the random aggregate was input into COMSOL Multiphysics software to construct a chloride erosion model of aggregate concrete suitable for brackish water environments, and the distribution law of chloride erosion of aggregate concrete was obtained by numerical calculation through software.

[0012] The first morphological parameter is adjusted to obtain the second morphological parameter. Based on the preset particle size range, the second morphological parameter, the distribution of the aggregate edges and corners, and the durability parameter, a geometric model of circular random aggregate concrete is generated using Python language, and the chloride erosion distribution law of circular aggregate concrete is calculated. The second morphological parameter represents the morphological parameter corresponding to adjusting the sphericity of the single-size crushed stone to meet the conditions of circular aggregate.

[0013] By comparing the first similarity between the chloride erosion distribution pattern of circular aggregate concrete and the time-varying pattern of chloride concentration in the indoor environment, and the second similarity between the chloride erosion distribution pattern of crushed stone aggregate concrete and the time-varying pattern of chloride concentration in the indoor environment, the influence of aggregate edge effect on the chloride erosion pattern of concrete in brackish water confluence area is determined.

[0014] The initial concrete mix proportion is adjusted based on the degree of influence to obtain the target concrete mix proportion.

[0015] In some embodiments, determining the time-varying pattern of chloride concentration in the estuarine water environment includes:

[0016] Statistical data on chloride concentration monitoring in typical estuary areas are collected, and a statistical data chart is generated based on the daily chloride concentration within a preset period in the chloride concentration monitoring data.

[0017] The average chloride concentration for each month in the statistical data chart is used as the representative value of the chloride concentration for the corresponding month, and the time-varying pattern of chloride concentration in the estuary water environment within the preset period is determined based on all the representative values.

[0018] In some embodiments, determining the aggregate edge distribution of the single-size crushed stone based on statistical principles and the first morphological parameter includes:

[0019] A predetermined number of gravel samples are randomly selected from all single-size gravel samples within the predetermined particle size range, and the gravel samples are cleaned to remove surface impurities.

[0020] A high-precision digital camera is used to photograph the maximum two-dimensional projection plane and the vertical projection plane of each of the cleaned gravel samples to obtain reference images. The maximum two-dimensional projection plane is the plane formed by the major axis and the middle axis of the cleaned gravel sample, and the vertical projection plane is the plane formed by the middle axis and the minor axis of the cleaned gravel sample.

[0021] The grayscale threshold of each reference image is adjusted using IPP software to separate aggregate particles from the background, thereby obtaining each intermediate image;

[0022] Extract aggregate dimensional quantization parameters from each of the intermediate images, and calculate the surface area of ​​the tangent cuboid of the smallest volume of the crushed stone sample and the point cloud coordinates of the corresponding particle surface based on the aggregate dimensional quantization parameters.

[0023] Based on the quantitative parameters of the three dimensions of each aggregate, the sphericity, particle size dispersion, elongation and shape factor of each crushed stone sample are calculated.

[0024] Based on statistical principles, the mean, standard deviation, skewness, and kurtosis of the sphericity, particle size dispersion, elongation, and shape factor of all the crushed stone samples are statistically analyzed. Based on the mean, standard deviation, skewness, and kurtosis of all the crushed stone samples, the number and sharpness of the edges and corners of the crushed stone samples are determined. Based on the number and sharpness of the edges and corners, the distribution of the aggregate edges and corners is determined.

[0025] In some embodiments, preparing concrete specimens using the single-size crushed stone includes:

[0026] The concrete specimens are obtained by molding single-size crushed stone within a preset particle size range in a typical estuary environment in an indoor environment. The concrete specimens include 6 cross-sections.

[0027] After 28 days of standard curing, five of the concrete specimens were sealed with epoxy resin, leaving one of the specimens exposed.

[0028] In some embodiments, the preset period is 1 year, and immersing the concrete specimen in a brackish water environment for the preset period includes:

[0029] The concrete specimen was placed in a pre-designed container;

[0030] Based on the representative values ​​for each month corresponding to the time-varying pattern of chloride concentration in the estuary water environment, NaCl solutions with concentrations corresponding to each representative value are prepared respectively.

[0031] According to the month corresponding to each representative value and the corresponding concentration of NaCl solution, the solution in the preset container is replaced every month to soak the concrete specimen.

[0032] In some embodiments, durability parameters include the chloride ion diffusion coefficient of concrete, the chloride ion concentration on the concrete surface, and the age decay coefficient. The method for determining the chloride distribution pattern of the concrete specimen within the preset period, and calculating the durability parameters of the concrete specimen based on Fick's second law and the chloride distribution pattern, includes:

[0033] The crushed stone concrete specimens were taken out from the preset container in different months.

[0034] The crushed stone concrete specimens taken in any month were sampled in layers by grinding to obtain multi-layer concrete powder samples, each layer of which was 1 to 2 millimeters thick.

[0035] The silver nitrate titration method was used to test the concrete powder sample of each layer, and the corresponding chloride concentration was calculated based on the volume of silver nitrate consumed for each layer of concrete powder sample.

[0036] Based on the chloride concentration of each layer of concrete powder sample, a chloride concentration distribution curve as a function of depth is plotted to obtain the chloride distribution law of the concrete specimen.

[0037] The analytical solution equation for Fick's second law is obtained based on the surface chloride ion concentration of the concrete specimen at any given time and the chloride ion concentration of the corresponding concrete powder sample. The analytical solution equation includes a reference expression, which is used to indicate the expression for the chloride ion diffusion coefficient at any given time.

[0038] The chloride ion diffusion coefficient of the concrete and the chloride ion concentration on the concrete surface are obtained by nonlinear fitting of the chloride salt concentration corresponding to the concrete powder sample at different depths at various times within a preset period with the analytical solution equation using programming software.

[0039] Based on the chloride ion diffusion coefficient of concrete at various time points, the reference expression is linearized, and the independent and dependent variables of the linearized reference expression are linearly regressed using the least squares method to obtain the age decay coefficient.

[0040] In some embodiments, based on the preset particle size range, the morphological parameters, the aggregate edge distribution, and the durability parameters, a geometric model of the crushed stone random aggregate of the concrete specimen is generated using Python, including:

[0041] A rectangular region is used to define the model boundary, and a closed polygon is determined by a sequence of vertex coordinates. The size parameters of the closed polygon are then used as the size parameters of the concrete two-dimensional geometric model.

[0042] Based on the Python language, the dimensional parameters, target aggregate filling rate, preset particle size range, dispersion, morphological parameters, aggregate corner distribution, preset boundary, preset spacing, and interface transition zone thickness of the concrete two-dimensional geometric model are obtained by using the layered noise superposition method to generate the crushed stone random aggregate geometric model.

[0043] Export the geometric model of the random aggregate crushed stone as a DXF format.

[0044] In some embodiments, the geometric model of the random aggregate crushed stone is input into COMSOL Multiphysics software to construct a chloride erosion model of crushed stone concrete suitable for brackish water environments, and the distribution law of chloride erosion of crushed stone aggregate concrete is obtained by numerical calculation using the software, including:

[0045] Transient simulations were performed using the steady convection-diffusion equation physics layer interface under the Classical Differential Equations module in COMSOL Multiphysics software.

[0046] Import the random aggregate geometry model of crushed stone as DXF format, and establish the three-phase domain of the chloride erosion model of crushed stone concrete, wherein the three-phase domain includes crushed stone aggregate, ITZ and mortar.

[0047] The crushed stone aggregate, the ITZ and the mortar are constructed into a combined body, and the aggregate portion is deducted using a Boolean algorithm.

[0048] The apparent chloride ion diffusion coefficient, age decay coefficient, chloride ion concentration boundary function of indoor brackish water environment, and initial chloride ion concentration of concrete are determined for the chloride salt erosion model of the crushed stone concrete.

[0049] The chloride erosion model of the crushed stone concrete is meshed, and the calculation step size of the chloride erosion model of the crushed stone concrete is set to perform transient calculation of the chloride erosion model of the crushed stone concrete.

[0050] The simulation data of each layer of the crushed stone concrete chloride erosion model are exported. Based on all the simulation data, a chloride concentration distribution curve as a function of depth is plotted. Based on the chloride concentration distribution curve, the chloride erosion distribution law of the crushed stone aggregate concrete is determined.

[0051] In some embodiments, calculating the chloride erosion distribution pattern of circular aggregate concrete includes:

[0052] The geometric model of the circular random aggregate concrete is input into the COMSOL Multiphysics software, and a chloride erosion model of circular crushed stone concrete suitable for brackish water environments is constructed using the COMSOL Multiphysics software.

[0053] Based on the chloride erosion model of the circular crushed stone concrete, the distribution law of chloride erosion of the circular aggregate concrete was obtained by software numerical calculation.

[0054] Secondly, embodiments of this application provide a control device, including at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enable the at least one control processor to perform the concrete mix design optimization method for brackish water confluence zones as described in the first aspect.

[0055] This application provides a method and apparatus for optimizing concrete mix proportions in brackish water areas. The method includes: determining the time-varying pattern of chloride concentration in the estuary water environment and the time-varying pattern of chloride concentration in an indoor environment; determining the first morphological parameters of single-size crushed stone within a preset particle size range in a typical estuary environment, and determining the aggregate edge distribution of the single-size crushed stone based on statistical principles and the first morphological parameters; preparing concrete specimens using the single-size crushed stone; immersing the concrete specimens in a brackish water environment for a preset period, and determining the chloride distribution pattern of the concrete specimens within the preset period, calculating the durability parameters of the concrete specimens based on Fick's second law and the chloride distribution pattern; generating a random aggregate geometric model of the concrete specimens using Python based on the preset particle size range, the morphological parameters, the aggregate edge distribution, and the durability parameters; and inputting the random aggregate geometric model into COMSOL. Multiphysics software was used to construct a chloride erosion model of crushed stone concrete suitable for brackish water environments, and the distribution law of chloride erosion of crushed stone aggregate concrete was obtained through software numerical calculation. The first morphological parameter was adjusted to obtain a second morphological parameter. Based on the preset particle size range, the second morphological parameter, the aggregate edge distribution, and the durability parameter, a geometric model of circular random aggregate concrete was generated using Python, and the chloride erosion distribution law of circular aggregate concrete was calculated. The second morphological parameter represents the morphological parameter corresponding to adjusting the sphericity of the single-size crushed stone to meet the conditions for circular aggregate. By comparing the first similarity between the chloride erosion distribution law of circular aggregate concrete and the time-varying chloride concentration law in the indoor environment, and the second similarity between the chloride erosion distribution law of crushed stone aggregate concrete and the time-varying chloride concentration law in the indoor environment, the influence of aggregate edge effect on the chloride erosion law of concrete in the brackish water confluence area was determined. Based on the influence degree, the initial concrete mix proportion was adjusted to obtain the target concrete mix proportion. According to the solution provided in the embodiments of this application, the aggregate edge effect, aggregate morphology, and chloride concentration patterns in indoor and brackish water mixing zones are used as reference factors for optimizing concrete mix proportions, which can ensure the durability of concrete made based on the optimized concrete mix proportions in brackish water mixing zones. Attached Figure Description

[0056] Figure 1 This is a flowchart of the steps of a concrete mix design optimization method for brackish water confluence areas provided in one embodiment of this application;

[0057] Figure 2 This is a structural diagram of a control device provided in another embodiment of this application;

[0058] Figure 3This is another embodiment of the present application, which provides a curve that can indicate the time-varying pattern of chloride concentration in the estuary water environment over a year;

[0059] Figure 4 This is another embodiment of the present application, which provides a curve that indicates the chloride distribution pattern of concrete specimens after one year of cyclic soaking;

[0060] Figure 5 This is a linear fitting curve provided in another embodiment of this application;

[0061] Figure 6 This is a two-dimensional geometric schematic diagram of a random aggregate geometric model of crushed stone provided in another embodiment of this application;

[0062] Figure 7 This is a two-dimensional geometric schematic diagram of a random aggregate geometric model of crushed stone provided in another embodiment of this application;

[0063] Figure 8 This is a schematic diagram of chloride concentration boundary values ​​provided in another embodiment of this application;

[0064] Figure 9 This is a two-dimensional geometric schematic diagram of a meshed random aggregate geometric model provided in another embodiment of this application;

[0065] Figure 10 This application provides a chloride concentration distribution curve that can indicate the chloride erosion distribution pattern of crushed stone aggregate concrete, according to another embodiment of the present application.

[0066] Figure 11 This is a two-dimensional geometric schematic diagram of a circular crushed stone concrete chloride erosion model provided in another embodiment of this application.

[0067] Figure 12 This is a two-dimensional geometric schematic diagram of a circular crushed stone concrete chloride erosion model provided in another embodiment of this application;

[0068] Figure 13 This is a two-dimensional geometric schematic diagram of a circular crushed stone concrete chloride erosion model after meshing, provided in another embodiment of this application;

[0069] Figure 14 This application provides another embodiment of a chloride concentration distribution curve that can indicate the chloride erosion distribution pattern of circular aggregate concrete;

[0070] Figure 15 This is a comparison chart of simulation and experimental results provided in another embodiment of this application. Detailed Implementation

[0071] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0072] It is understandable that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, or the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0073] Reinforced concrete structures, due to their excellent mechanical properties and strong construction adaptability, are widely used in projects such as cross-sea bridges and wharves in brackish water areas. However, reinforced concrete structures in brackish water environments are susceptible to chloride erosion, affecting their durability. The corrosion resistance of concrete is a crucial indicator for evaluating its durability and predicting its lifespan, and it is related to raw materials and mix proportions. Current reinforced concrete mix design processes only adjust and optimize the mix proportions based on indoor chloride erosion tests to account for the impact of corrosion resistance on concrete durability.

[0074] However, the brackish water confluence area is an unsteady corrosive environment for reinforced concrete due to periodic tides and seasonal rainfall. Therefore, the concrete obtained by the existing mix design optimization method cannot adapt well to the chloride erosion pattern in the brackish water confluence area, and thus cannot guarantee the durability of the concrete.

[0075] To address the aforementioned problems, this application provides a method and apparatus for optimizing concrete mix design in brackish water areas. The method includes: determining the time-varying pattern of chloride concentration in the estuary environment and the time-varying pattern of chloride concentration in an indoor environment; determining the first morphological parameters of single-size crushed stone within a preset particle size range in a typical estuary environment, and determining the aggregate edge distribution of the single-size crushed stone based on statistical principles and the first morphological parameters; preparing concrete specimens using the single-size crushed stone; immersing the concrete specimens in a brackish water environment for a preset period, and determining the chloride distribution pattern of the concrete specimens within the preset period, calculating the durability parameters of the concrete specimens based on Fick's second law and the chloride distribution pattern; generating a random aggregate geometric model of the concrete specimens using Python based on the preset particle size range, the morphological parameters, the aggregate edge distribution, and the durability parameters; and inputting the random aggregate geometric model into COMSOL. Multiphysics software was used to construct a chloride erosion model of crushed stone concrete suitable for brackish water environments, and the distribution law of chloride erosion of crushed stone aggregate concrete was obtained through software numerical calculation. The first morphological parameter was adjusted to obtain a second morphological parameter. Based on the preset particle size range, the second morphological parameter, the aggregate edge distribution, and the durability parameter, a geometric model of circular random aggregate concrete was generated using Python, and the chloride erosion distribution law of circular aggregate concrete was calculated. The second morphological parameter represents the morphological parameter corresponding to adjusting the sphericity of the single-size crushed stone to meet the conditions for circular aggregate. By comparing the first similarity between the chloride erosion distribution law of circular aggregate concrete and the time-varying chloride concentration law in the indoor environment, and the second similarity between the chloride erosion distribution law of crushed stone aggregate concrete and the time-varying chloride concentration law in the indoor environment, the influence of aggregate edge effect on the chloride erosion law of concrete in the brackish water confluence area was determined. Based on the influence degree, the initial concrete mix proportion was adjusted to obtain the target concrete mix proportion. According to the solution provided in the embodiments of this application, the aggregate edge effect, aggregate morphology, and chloride concentration patterns in indoor and brackish water mixing zones are used as reference factors for optimizing concrete mix proportions, which can ensure the durability of concrete made based on the optimized concrete mix proportions in brackish water mixing zones.

[0076] The embodiments of this application will be further described below with reference to the accompanying drawings.

[0077] refer to Figure 1 , Figure 1 This is a flowchart illustrating the steps of a concrete mix design optimization method for brackish water confluence areas according to an embodiment of this application. This application provides a concrete mix design optimization method for brackish water confluence areas, which includes, but is not limited to, the following steps:

[0078] Step S10: Determine the time-varying pattern of chloride concentration in the estuary water environment and the time-varying pattern of chloride concentration in the indoor environment.

[0079] Step S20: Determine the first morphological parameters of single-size crushed stone within a preset particle size range in a typical estuary environment, and determine the aggregate edge distribution of single-size crushed stone based on statistical principles and the first morphological parameters.

[0080] Step S30: Prepare concrete specimens using single-size crushed stone;

[0081] Step S40: Place the concrete specimen in a brackish water environment for a preset period of immersion, determine the chloride distribution pattern of the concrete specimen within the preset period, and calculate the durability parameters of the concrete specimen based on Fick's second law and the chloride distribution pattern.

[0082] Step S50: Based on the preset particle size range, morphological parameters, aggregate edge and corner distribution and durability parameters, use Python language to generate a geometric model of crushed stone random aggregate for concrete specimens.

[0083] Step S60: Input the geometric model of random aggregate crushed stone into COMSOL Multiphysics software to construct a chloride erosion model of crushed stone concrete suitable for brackish water environment, and obtain the distribution law of chloride erosion of crushed stone aggregate concrete through software numerical calculation.

[0084] Step S70: Adjust the first morphological parameter to obtain the second morphological parameter. Based on the preset particle size range, the second morphological parameter, the distribution of aggregate edges and corners and the durability parameter, use Python language to generate a geometric model of circular random aggregate concrete and calculate the chloride erosion distribution law of circular aggregate concrete. The second morphological parameter represents the morphological parameter corresponding to adjusting the sphericity of single-size crushed stone to meet the conditions of circular aggregate.

[0085] Step S80: By comparing the first similarity between the chloride erosion distribution pattern of circular aggregate concrete and the time-varying pattern of chloride concentration in indoor environment, and the second similarity between the chloride erosion distribution pattern of crushed stone aggregate concrete and the time-varying pattern of chloride concentration in indoor environment, the influence of aggregate edge effect on the chloride erosion pattern of concrete in brackish water confluence area is determined.

[0086] Step S90: Adjust the initial concrete mix proportion based on the degree of influence to obtain the target concrete mix proportion.

[0087] Understandably, this embodiment, based on a clear understanding of the edge effect of crushed stone, uses single-size crushed stone to prepare concrete specimens. Regarding environmental concentration, considering the periodic time-varying pattern of chloride concentration in the brackish water confluence area influenced by tides, runoff, and wind direction, and based on monitoring data of chloride concentration in the estuary area, a daily chloride concentration statistical data chart was obtained over a year to determine the chloride concentration variation pattern during the year-long indoor test, and indoor immersion cycle tests were conducted. In terms of model establishment, a chloride ion concentration boundary function based on the indoor brackish water environment was selected. According to the chloride distribution pattern of the crushed stone specimens obtained from the indoor test and Fick's second law, the durability parameters of the pebble and crushed stone specimens were calculated as important parameters for model construction. A concrete chloride erosion model considering the edge effect of coarse aggregate, suitable for brackish water confluence areas, was proposed. This model improves the simulation accuracy of chloride erosion in concrete in brackish water confluence areas, providing more accurate and refined data support for the durability design of concrete structures in this region. In other words, this application uses the aggregate edge effect, aggregate morphology, and chloride concentration patterns in indoor and brackish water areas as reference factors for optimizing concrete mix proportions, which can ensure the durability of concrete made based on the optimized concrete mix proportions in brackish water areas.

[0088] It should be noted that the preset particle size range in this embodiment is 10 to 20 millimeters.

[0089] Specifically, in some embodiments, Figure 1 Step S10, determining the time-varying pattern of chloride concentration in the estuary water environment, includes, but is not limited to, the following steps:

[0090] Step S11: Collect chloride concentration monitoring data for typical estuary areas and generate a statistical data chart based on the daily chloride concentration within a preset period in the chloride concentration monitoring data.

[0091] Step S12: The average value of chloride concentration in each month of the statistical data chart is taken as the representative value of chloride concentration in the corresponding month, and the time variation pattern of chloride concentration in the estuary water environment within the preset period is determined based on all the representative values.

[0092] It should be noted that the preset period in this embodiment is 1 year.

[0093] It is understood that this embodiment uses statistical data on chloride concentration monitoring in typical estuary areas to form a statistical data graph based on the daily chloride concentration within a preset period in the chloride concentration monitoring data. The average chloride concentration of each month in the statistical data graph is used as the representative value of the chloride concentration of the corresponding month. Based on all the representative values, the time-varying pattern of chloride concentration in the estuary water environment within the preset period is determined, thereby providing an effective data basis for subsequent optimization of concrete mix proportions suitable for brackish water confluence.

[0094] For example, refer to Figure 3 The representative values ​​were 1307 mg / L, 2832 mg / L, 5775 mg / L, 6077 mg / L, 4758 mg / L, 1452 mg / L, 41 mg / L, 18 mg / L, 21 mg / L, 11 mg / L, 12 mg / L and 48 mg / L, totaling 12 chloride concentration variation values, which enabled the determination of the time-varying pattern of chloride concentration in the estuarine water environment.

[0095] Specifically, in some embodiments, Figure 1 Step S20, which determines the aggregate edge distribution of single-size crushed stone based on statistical principles and the first morphological parameter, includes, but is not limited to, the following steps:

[0096] Step S21: Randomly select a preset number of crushed stone samples from all single-size crushed stones within the preset particle size range, and clean the crushed stone samples to remove surface impurities.

[0097] Step S22: Use a high-precision digital camera to take pictures of the maximum two-dimensional projection plane and the vertical projection plane of each cleaned gravel sample to obtain a reference image. The maximum two-dimensional projection plane is the plane formed by the major axis and the middle axis of the cleaned gravel sample, and the vertical projection plane is the plane formed by the middle axis and the minor axis of the cleaned gravel sample.

[0098] Step S23: Use IPP software to adjust the grayscale threshold of each reference image to separate aggregate particles from the background and obtain each intermediate image;

[0099] Step S24: Extract aggregate three-dimensional quantization parameters from each intermediate image, and calculate the surface area of ​​the tangent cuboid of the smallest volume of the crushed stone sample and the point cloud coordinates of the corresponding particle surface based on the aggregate three-dimensional quantization parameters.

[0100] Step S25: Based on the quantitative parameters of the three dimensions of each aggregate, calculate the sphericity, particle size dispersion, elongation and shape factor of each crushed stone sample.

[0101] Step S26: Based on statistical principles, the mean, standard deviation, skewness, and kurtosis of the sphericity, particle size dispersion, elongation, and shape factor of all crushed stone samples are statistically analyzed. Based on the mean, standard deviation, skewness, and kurtosis of all samples, the number and sharpness of the crushed stone samples are determined. Based on the number and sharpness of the edges, the distribution of aggregate edges is determined.

[0102] It should be noted that the preset quantity in this embodiment is 100 to 200 gravel samples.

[0103] It should be noted that, in this embodiment, the sphericity of the gravel sample is calculated using the following formula:

[0104]

[0105] Where S is the minor axis dimension of the cuboid corresponding to the gravel sample, I is the median axis dimension of the cuboid corresponding to the gravel sample, and L is the major axis dimension of the cuboid corresponding to the gravel sample. p It is spheric.

[0106] It should be noted that, in this embodiment, the particle size dispersion of the gravel sample is calculated according to the following formula:

[0107]

[0108] Where κ represents the particle size dispersion of the gravel sample.

[0109] It should be noted that, in this embodiment, the shape factor of the gravel sample is calculated according to the following formula:

[0110]

[0111] Where γ is the shape factor corresponding to the gravel sample, A is the particle projection area corresponding to the gravel sample, and Q is the particle projection perimeter corresponding to the gravel sample.

[0112] It should be noted that, in this embodiment, the elongation of the crushed stone sample is calculated according to the following formula:

[0113]

[0114] Where λ is the elongation of the gravel sample.

[0115] Understandably, this embodiment randomly selects a preset number of crushed stone samples to obtain reference images of the cleaned crushed stone samples at two angles: the maximum two-dimensional projection plane and the vertical projection plane. Further, IPP software is used to adjust the grayscale threshold of each reference image to separate aggregate particles from the background, resulting in intermediate images. The aggregate's three-dimensional dimensional quantification parameters are extracted from each intermediate image. Based on these parameters, the surface area of ​​the circumscribed cuboid of the smallest volume of the crushed stone sample and the point cloud coordinates of the corresponding particle surfaces are calculated. Based on these parameters, the sphericity, particle size dispersion, elongation, and shape factor of each crushed stone sample are calculated. Based on statistical principles, the mean, standard deviation, skewness, and kurtosis of the sphericity, particle size dispersion, elongation, and shape factor of all crushed stone samples are statistically analyzed. Based on the mean, standard deviation, skewness, and kurtosis, the number and sharpness of the crushed stone samples' edges are determined. Based on the number and sharpness of the edges, the distribution of aggregate corners is determined, thus providing an effective data foundation for subsequent optimization of concrete mix proportions suitable for brackish water confluence.

[0116] Specifically, in some embodiments, Figure 1 Step S30 includes, but is not limited to, the following steps:

[0117] Step S31: Single-size crushed stone with a preset particle size range in a typical estuary environment is molded in an indoor environment to obtain a concrete specimen. The concrete specimen includes 6 cross-sections.

[0118] Step S32: After 28 days of standard curing, the concrete specimen is sealed with epoxy resin on 5 cut surfaces, leaving the remaining cut surface as the exposed surface.

[0119] It is understood that the method for obtaining concrete specimens by molding single-size crushed stone in this embodiment is as follows: single-size crushed stone with a preset particle size range in a typical estuary environment is molded in an indoor environment to obtain concrete specimens. The concrete specimens include 6 cross-sections. After standard curing of the concrete specimens for 28 days, epoxy resin is applied to 5 cross-sections of the concrete specimens for sealing, and the remaining cross-section is used as the exposed surface, thereby providing effective support for subsequent tests to detect chloride distribution in the concrete specimens.

[0120] It should be noted that the calculated aggregate morphology parameters (sphericity, particle size dispersion, elongation, and shape factor) are within the range constrained by the aggregate morphology parameter value range table, which is shown in Table 1:

[0121] Table 1. Range of Aggregate Shape Parameter Values

[0122]

[0123]

[0124] It should be noted that, based on the morphological parameters calculated above, this embodiment also calculates the mean, standard deviation, skewness, and kurtosis of the morphological parameters of the gravel sample according to statistical principles, as shown in Table 2:

[0125] Table 2 Statistical values ​​of aggregate shape parameters

[0126] Morphological parameters mean Standard deviation Skewness Kudo sphericity 0.73 0.03 -0.5 2.8 shape factor 0.82 0.07 0.35 1.8 elongation 0.73 0.06 -0.28 2.5 Particle size dispersion 1.65 0.22 0.8 3.0

[0127] Specifically, the statistical data in Table 2 shows that the number of edges of the gravel samples is between 4 and 8, and the sharpness of the gravel samples is moderate.

[0128] It should be noted that in this embodiment, the method for molding single-size crushed stone within a preset particle size range in a typical estuary environment to obtain concrete specimens in an indoor environment is as follows: the concrete mix proportion of the Modaomen Channel is selected, and the concrete mix proportion is shown in Table 3.

[0129] Table 3 Mix Proportions for Concrete Structures

[0130]

[0131] Specifically, the coarse aggregate is made of crushed stone with a single particle size of 10 to 20 mm, forming 100 mm × 100 mm × 100 mm concrete cube specimens (i.e., concrete specimens). After standing for 1 day, they are placed in a curing room and taken out after 28 days.

[0132] Specifically, in some embodiments, Figure 1 Step S40, which involves immersing the concrete specimen in a brackish water environment for a predetermined period, includes, but is not limited to, the following steps:

[0133] Step S401: Place the concrete specimen in a pre-set container;

[0134] Step S402: Based on the representative values ​​for each month corresponding to the time-varying pattern of chloride concentration in the estuary water environment, prepare NaCl solutions with concentrations corresponding to each representative value.

[0135] Step S403: According to the month corresponding to each representative value and the corresponding concentration of NaCl solution, replace the solution in the preset container every month to soak the concrete specimen.

[0136] Understandably, in this embodiment, by placing concrete specimens in a preset container, and based on the representative values ​​of the time-varying chloride concentration in the estuary water environment for each month, NaCl solutions of corresponding concentrations for each representative value are prepared. The solutions in the preset container are replaced monthly according to the month corresponding to each representative value and the corresponding concentration of NaCl solution to soak the concrete specimens. This allows for the detection of chloride concentration in the concrete specimens at each stage, providing effective conditions for subsequently determining the chloride distribution pattern of the concrete specimens within a preset period.

[0137] It should be noted that the immersion test in this embodiment lasted for one year, with concrete specimens taken every two months. In this embodiment, based on the analytical solution derived from Fick's second law and the concentration data measured at different depths every two months, the chloride ion diffusion coefficient of the concrete specimens every two months can be obtained through nonlinear fitting. The chloride ion diffusion coefficients of the concrete specimens every two months are shown in Table 4.

[0138] Table 4 Chloride ion diffusion coefficient of crushed stone every two months

[0139]

[0140]

[0141] In addition, in some embodiments, durability parameters include the chloride ion diffusion coefficient of concrete, the chloride ion concentration on the concrete surface, and the age-related decay coefficient. Figure 1 Step S40 involves determining the chloride distribution pattern of the concrete specimen within a preset period and calculating the durability parameters of the concrete specimen based on Fick's second law and the chloride distribution pattern. This includes, but is not limited to, the following steps:

[0142] Step S404: Take out crushed stone concrete specimens from the preset container in different months;

[0143] Step S405: The crushed stone concrete specimen taken in any month is sampled in layers by grinding to obtain multi-layer concrete powder samples, with the thickness of each layer of concrete powder sample being 1 to 2 mm.

[0144] Step S406: Use silver nitrate titration to test each layer of concrete powder sample, and calculate the corresponding chloride concentration based on the volume of silver nitrate consumed for each layer of concrete powder sample.

[0145] Step S407: Based on the chloride concentration of each layer of concrete powder sample, plot the chloride concentration distribution curve as a function of depth to obtain the chloride distribution law of the concrete specimen.

[0146] Step S408: Based on the surface chloride ion concentration of the concrete specimen at any time and the chloride ion concentration of the corresponding concrete powder sample, obtain the analytical solution equation of Fick's second law. The analytical solution equation includes a reference expression, which is used to indicate the expression of the chloride ion diffusion coefficient at any time.

[0147] Step S409: Using programming software, the chloride concentration of concrete powder samples at different depths at various times within a preset period is nonlinearly fitted with the analytical solution equation to obtain the chloride ion diffusion coefficient of concrete and the chloride ion concentration on the concrete surface.

[0148] Step S410: Based on the chloride ion diffusion coefficient of concrete at each time point, the reference expression is linearized, and the independent and dependent variables of the linearized reference expression are linearly regressed using the least squares method to obtain the age decay coefficient.

[0149] It should be noted that the chloride concentration is calculated based on the volume of silver nitrate consumed for each layer of concrete powder, using the following formula:

[0150]

[0151] in, The percentage (%) of acid-soluble chloride ions in the cementitious material, based on... Calculate the chloride concentration. V1 is the molar concentration of the silver nitrate standard solution (mol / L), V2 is the volume of the silver nitrate standard solution used (mL), V3 is the volume of the silver nitrate standard solution used in the blank test (mL), 0.03545 is the millimolecular mass of chloride ions (g / mmol), and m is the mass of the concrete mortar sample (g). m The amount of mortar material (excluding coarse aggregate) in the concrete mix design (kg / m²) 3 ), m B The amount of cementitious materials used per cubic meter of concrete in the concrete mix design (kg / m³) 3 ).

[0152] It should be noted that the analytical solution equation of Fick's second law in this embodiment is obtained according to the following steps:

[0153] (1) Use Fick's second law to express the governing equation for chloride ion diffusion inside concrete powder:

[0154]

[0155] Where C(x,t) is the chloride ion concentration at a distance x from the surface of the concrete powder sample after exposure time t; D(t) is the chloride ion diffusion coefficient of the concrete powder sample at exposure time t.

[0156] (2) Based on the surface chloride ion concentration of the concrete specimen at any given time and the chloride ion concentration of the corresponding concrete powder sample, obtain the analytical solution equation of Fick's second law:

[0157]

[0158] Where C(x,0) is the initial chloride ion concentration (mg / L) in the concrete powder sample; C(0,t) is the surface chloride ion concentration (mg / L) of the concrete powder sample at time t; erf is the error function; D(t) is the chloride ion diffusion coefficient at time t as a function of time, i.e., the reference expression;

[0159] The expression for D(t) is as follows:

[0160]

[0161] Where D0 is the initial chloride ion diffusion coefficient (m 2 / s), t0 is the age (d) of the concrete powder sample when it is first exposed to the chloride ion environment, and m is the age decay coefficient.

[0162] It should be noted that the programming software in this embodiment can be Python, Matlab, etc., and those skilled in the art can determine the appropriate software based on the actual situation.

[0163] It should be noted that the chloride distribution pattern of the concrete specimens after one year of cyclic immersion in this embodiment can characterize the chloride distribution pattern of the concrete specimens. (Refer to...) Figure 4 .

[0164] It should be noted that, based on the data in Table 4, the reference expression was linearized, and a linear regression was performed on the independent and dependent variables using the least squares method to obtain the age-related decay coefficient *m* of the gravel. The linear expression after linearizing the reference expression is as follows:

[0165]

[0166] The linear fitting curve corresponding to the above linear expression is as follows: Figure 5 As shown, Figure 5 As shown, the age decay coefficient m of the crushed stone is approximately taken as 0.36.

[0167] It is understood that calculating the durability parameters of concrete specimens at different stages based on steps S404 to S410 in this embodiment can provide an effective data basis for subsequent adjustment and optimization of the initial concrete mix proportion to obtain a target concrete mix proportion suitable for the confluence of fresh and salt water.

[0168] Specifically, in some embodiments, Figure 1 Step S50 involves generating a geometric model of the crushed stone random aggregate for the concrete specimen using Python, based on preset particle size range, morphological parameters, aggregate edge distribution, and durability parameters. This includes, but is not limited to, the following steps:

[0169] Step S51: Define the model boundary using a rectangular region, determine the closed polygon using the vertex coordinate sequence, and use the size parameters of the closed polygon as the size parameters of the concrete two-dimensional geometric model.

[0170] Step S52: Based on the Python language, the dimensional parameters, target aggregate filling rate, preset particle size range, dispersion, morphological parameters, aggregate corner distribution, preset boundary, preset spacing and interface transition zone thickness of the concrete two-dimensional geometric model are processed using the layered noise superposition method to generate a random aggregate geometric model of crushed stone.

[0171] Step S53: Export the geometric model of the random aggregate crushed stone as a DXF format.

[0172] It can be understood that in this embodiment, by taking the size parameters of the concrete two-dimensional geometric model, the target aggregate filling rate (i.e., the area ratio of the aggregate), the preset particle size range, the dispersion degree, the shape parameters (including sphericity, elongation rate, shape factor, and particle size dispersion), the distribution of aggregate edges and corners, the preset boundary, the preset spacing (which can ensure that the aggregate does not exceed the boundary range), and the thickness of the interfacial transition zone as adjustable parameters in the Python code, an effective data basis is provided for generating a random aggregate geometric model of crushed stone.

[0173] Among them, the size parameters of the concrete two-dimensional geometric model are determined by defining the model boundary using a rectangular area, determining the closed polygon through the vertex coordinate sequence, and taking the size parameters of the closed polygon as the size parameters; determining the closed polygon through the vertex coordinate sequence, the default implementation is a square area, and the vertex coordinates are [(0,0),(L,0),(L,L),(0,L),(0,0)], where L is the side length of the model, and it is necessary to ensure that the polygon is closed to avoid the lack of boundary line type, and the model size needs to match the aggregate particle size range (the minimum particle size ≥ 0.01L to prevent excessive calculation redundancy due to too small particles).

[0174] Specifically, in this embodiment, the model boundary is a square with a side length of 100 mm, the ITZ thickness of the fixed aggregate is 40 μm, and the Walraven formula can be used to convert the volume ratio of each gradation of aggregate in the three-dimensional space into the area ratio in the two-dimensional plane space. Through calculation, the area ratio of the aggregate is 43.3%. The expression of the Walraven formula is as follows:

[0175]

[0176] Among them, P c is the probability that an inscribed circle with an aggregate diameter D < D0 appears at any point on the two-dimensional section, P k is the percentage of coarse aggregate in the total volume of concrete, D0 is the sieve hole diameter, and D max is the maximum aggregate particle size.

[0177] Among them, for the target aggregate filling rate, in this embodiment, the target filling rate η (aggregate area ratio) is first defined, and the particles are placed iteratively to iteratively increase the particle area until the current aggregate filling rate reaches the target filling rate η. The relationship between the target filling rate η, the particle area, and the model side length L is as follows:

[0178]

[0179] For the preset particle size range, a uniform function is needed to determine the particle size range, defining the minimum particle size d_min and the maximum particle size d_max of the aggregate, with the input particle size range being (d_min, d_max). The particle size range must conform to a uniform distribution, and the particle size dispersion must conform to a normal distribution to avoid mismatch between particle size and model, while controlling the uniformity of particle size.

[0180] For morphological parameters (including sphericity, elongation, shape factor, and particle size dispersion), this embodiment defines each morphological parameter using uniform functions based on the mean, standard deviation, skewness, and kurtosis of actual crushed stone aggregate. The mathematical constraints for each parameter are: Sphericity S p ≤1 / elongation λ, elongation λ≥1 / sphericity e p Shape factor γ·d min ≥0.5mm.

[0181] It should be noted that in this embodiment, step S52 uses a layered noise superposition method to generate the geometric model of random aggregate crushed stone, which is obtained according to the following steps:

[0182] (1) Basic polygon construction: Based on the convex polygon, input the range of the number of vertices, randomly generate multiple vertices at the boundary of the ellipse, add vertex spacing angle constraints (Δθ≥15°) to prevent vertices from overlapping, determine the vertex coordinates according to the angle and radius of each vertex, ensure that each vertex satisfies the ellipse equation, sort all vertices in ascending order of angle, ensure that the connection order is clockwise or counterclockwise to avoid intersection, and then use the convex hull algorithm to process the generated vertices to ensure that these vertices form a convex polygon, that is, remove points that may cause concavity;

[0183] (2) Noise layering and superposition method: The shape of the gravel is adjusted by superimposing low-frequency, mid-frequency and high-frequency noise. The specific operation is performed according to the preset gravel noise layering and superposition operation table, which is shown in Table 5:

[0184] Table 5. Operation Table for Layered Superposition of Crushed Stone Noise

[0185] Noise level Frequency characteristics Scope of application Control parameters low frequency noise 0.2~0.6 cycles / mm Overall outline of the gravel Mean sphericity Intermediate frequency noise 0.6~2.0 cycles / mm Sharpness of edges Sphericity, skewness, elongation High frequency noise 2.0~5.0 cycles / mm Surface roughness Mean shape factor

[0186] (3) Random placement of single-size aggregates: Based on the range of single aggregate sizes, a random number r between 0 and 1 is generated in the program using a uniform aggregate size distribution function. Then, the Monte Carlo method is used to generate random spatial coordinates of the single-size aggregates. To prevent aggregates from penetrating the boundary, a buffer zone of 1.2 times the aggregate radius is reserved at the edge of the model. The expression of the uniform aggregate size distribution function is as follows:

[0187] d = d min +γ(d max-d min );

[0188] Where d is the random aggregate particle size determined by the random number r, d min d represents the minimum value within the range of single aggregate particle sizes. max This represents the maximum value within the range of single aggregate particle sizes.

[0189] (4) Collision detection: Based on the Minkowski principle, fast collision detection is achieved to ensure that the minimum spacing between particles is 50% of the average particle size, while ensuring that the crushed stone aggregate does not exceed the boundary of the concrete model.

[0190] It should be noted that the method in this embodiment also includes generating the ITZ (Intense Plate Zone) by extending a region of fixed thickness on the outer boundary of each aggregate in the concrete specimen with the generated crushed stone aggregate. The ITZ thickness of ordinary concrete is 30μm to 40μm. After generating the aggregate and extending the ITZ, it is determined whether any two aggregates are in contact (by calculating the shortest distance between the aggregate boundaries). Assuming that the centers of the two aggregates are C1 and C2, and their radii are r1+δ1 and r2+δ2 (where δ1 and δ2 are their respective ITZ thicknesses), the expression for the contact condition of the two aggregates is as follows:

[0191] d(C1,C2)≤r1+r2+δ1+δ2;

[0192] Where d(C1,C2) is the distance between the centers of the aggregates. If d(C1,C2) is less than or equal to the sum of the ITZs of the two aggregates, they are considered to be in contact. If ITZ contact of aggregates is found during the inspection, the positions of the aggregates need to be randomly generated again or the size of the aggregates needs to be adjusted to ensure that there is no overlap or ITZ contact between the aggregates.

[0193] In addition, this embodiment also determines whether the area ratio of each randomly generated aggregate gradation is within the allowable error range and adjusts the parameters accordingly. Finally, the generated aggregate geometric model is exported as a DXF file for import into COMSOL Multiphysics software.

[0194] It should be noted that the geometric model of the random aggregate of crushed stone in this embodiment is as follows: Figure 6 As shown.

[0195] Specifically, in some embodiments, Figure 1 In step S60, the geometric model of the random aggregate crushed stone is input into COMSOL Multiphysics software to construct a chloride erosion model of crushed stone concrete suitable for brackish water environments. The distribution law of chloride erosion of crushed stone aggregate concrete is obtained through numerical calculation using the software, including but not limited to the following steps:

[0196] Step S61: Select the steady convection-diffusion equation physical layer interface under the classical differential equations module in COMSOL Multiphysics software to perform transient simulation;

[0197] Step S62: Import the random aggregate geometry model of crushed stone in DXF format and establish the three-phase domain of the chloride erosion model of crushed stone concrete. The three-phase domain includes crushed stone aggregate, ITZ and mortar.

[0198] Step S63: Construct the crushed stone aggregate, ITZ and mortar into a composite, and use Boolean algorithm to deduct the aggregate portion;

[0199] Step S64: Determine the apparent chloride ion diffusion coefficient, age decay coefficient, chloride ion concentration boundary function of indoor brackish water environment, and initial chloride ion concentration of concrete for the chloride salt erosion model of crushed stone concrete.

[0200] Step S65: Mesh the crushed stone concrete chloride erosion model and set the calculation step size of the crushed stone concrete chloride erosion model to perform transient calculations of the crushed stone concrete chloride erosion model.

[0201] Step S66: Export the simulation data of each layer of the crushed stone concrete chloride erosion model, plot the chloride concentration distribution curve as a function of depth based on all the simulation data, and determine the chloride erosion distribution law of crushed stone aggregate concrete based on the chloride concentration distribution curve.

[0202] It should be noted that, in this embodiment, the model after step S63 is as follows: Figure 7 As shown.

[0203] It should be noted that the apparent chloride ion diffusion coefficients, age decay coefficients, chloride ion concentration boundary functions of indoor brackish water environments, and initial chloride ion concentrations in the crushed stone concrete chloride erosion model of this embodiment are all basic parameters and initial values ​​of the model. The basic parameters and initial values ​​are shown in Table 6.

[0204] Table 6. Model Basic Parameters and Initial Values

[0205]

[0206]

[0207] Specifically, the boundary conditions (i.e., chloride concentration boundary values) in this embodiment are as follows: Figure 8 As shown.

[0208] It is understood that the initial chloride ion diffusion coefficient of ITZ is about 10 to 20 times that of the chloride ion diffusion coefficient of mortar. In this embodiment, the initial chloride ion diffusion coefficient of ITZ is 10 times that of the initial chloride ion diffusion coefficient of mortar.

[0209] It should be noted that, in this embodiment, after performing mesh generation on the chloride erosion model of crushed stone concrete in step S65, the following result is obtained: Figure 9 The division results are shown.

[0210] Specifically, in this embodiment, the calculation step of the chloride erosion model of crushed stone concrete is 1 day. Based on the calculation step, transient calculations are performed on the model to simulate the chloride erosion results of crushed stone concrete after 1 year.

[0211] Specifically, the chloride erosion distribution pattern of the crushed stone aggregate concrete obtained after performing step S66 in this embodiment is as follows: Figure 10 As shown.

[0212] Specifically, in some embodiments, Figure 1 The calculation of chloride erosion distribution in circular aggregate concrete in step S70 includes, but is not limited to, the following steps:

[0213] Step S71: Input the geometric model of circular random aggregate concrete into COMSOL Multiphysics software, and construct a chloride erosion model of circular crushed stone concrete suitable for brackish water environments using COMSOL Multiphysics software.

[0214] Step S72: Based on the chloride erosion model of circular crushed stone concrete, the distribution law of chloride erosion of circular aggregate concrete is obtained by numerical calculation using software.

[0215] It is worth noting that the principle of constructing the chloride erosion model of circular crushed stone concrete and calculating the chloride erosion distribution law of circular aggregate concrete in this embodiment is similar to the calculation method of the chloride erosion model of crushed stone concrete and the chloride erosion distribution law of crushed stone aggregate concrete mentioned above. The difference is that the sphericity of the morphological parameter corresponding to the construction of the chloride erosion model of circular crushed stone concrete is different from that of the chloride erosion model of circular crushed stone concrete. This will not be elaborated on here.

[0216] Specifically, in this implementation, the mean range of the second sphericity is (0.95-1).

[0217] Specifically, the circular crushed stone concrete chloride erosion model in this embodiment is as follows: Figure 11 As shown.

[0218] Specifically, in this embodiment, after inputting the geometric model of circular random aggregate concrete into the COMSOL Multiphysics software, the COMSOL Multiphysics software simply replaces the chloride erosion model of crushed stone concrete with the chloride erosion model of circular crushed stone concrete. The circular crushed stone concrete chloride erosion model is as follows: Figure 11 As shown, COMSOL Multiphysics software constructed a composite model of the three-phase domains of a circular crushed stone concrete chloride erosion model, and the model after subtracting the aggregate portion is as follows. Figure 12 As shown, after dividing the grid, the result is as follows: Figure 13 The division results shown, and numerical calculations yielded the chloride erosion distribution pattern of circular aggregate concrete as follows: Figure 14 As shown.

[0219] Additionally, it should be noted that, regarding step S80, in this embodiment, to facilitate comparison and obtain the first similarity and the second similarity, [the following is omitted as it is not relevant to the main point]. Figure 4 , Figure 10 and Figure 14 Organize to Figure 15 Above, a comparison chart of simulation and experimental results is generated, such as... Figure 15 As shown, the chloride ion distribution simulated by the crushed stone aggregate model considering the aggregate corner effect shows a higher degree of agreement with the experimental values ​​compared to the simulation results of circular aggregate without considering the corner effect. This comparison more accurately reflects the chloride salt penetration pattern under actual working conditions. This result demonstrates the necessity of considering the aggregate corner effect in concrete chloride ion transport simulation, thus providing effective support for determining the target concrete mix proportion suitable for brackish water confluence areas.

[0220] By comparing the first similarity between the chloride erosion distribution pattern of circular aggregate concrete and the time-varying pattern of chloride concentration in indoor environment, and the second similarity between the chloride erosion distribution pattern of crushed stone aggregate concrete and the time-varying pattern of chloride concentration in indoor environment, the influence of aggregate edge effect on the chloride erosion pattern of concrete in brackish water confluence area is determined.

[0221] like Figure 2 As shown, Figure 2 This is a structural diagram of a control device provided in one embodiment of this application. The present invention also provides a control device 200, comprising:

[0222] The processor 210 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0223] The memory 220 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 220 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 220 and called and executed by the processor 210 to execute the concrete mix optimization method for brackish water confluence areas according to the embodiments of this application.

[0224] Input / output interface 230 is used to implement information input and output;

[0225] The communication interface 240 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0226] Bus 250 transmits information between various components of the device (e.g., processor 210, memory 220, input / output interface 230, and communication interface 240);

[0227] The processor 210, memory 220, input / output interface 230 and communication interface 240 are connected to each other within the device via bus 250.

[0228] In addition, this application embodiment also provides a storage medium, which is a computer-readable storage medium, storing a computer program that, when executed by a processor, implements the above-mentioned concrete mix design optimization method for brackish water confluence areas.

[0229] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof. The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separate, and may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0230] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0231] The above provides a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A method for optimizing concrete mix proportions in brackish water confluence zones, characterized in that, include: The study aimed to determine the time-varying patterns of chloride concentration in estuarine water and indoor environments. The first morphological parameters of single-size crushed stone within a preset particle size range are determined in a typical estuary environment, and the aggregate edge and corner distribution of the single-size crushed stone is determined based on statistical principles and the first morphological parameters. Concrete specimens were prepared using the single-size crushed stone described above; The concrete specimens were immersed in a brackish water environment for a preset period of time, and the chloride distribution pattern of the concrete specimens within the preset period of time was determined. Based on Fick's second law and the chloride distribution pattern, the durability parameters of the concrete specimens were calculated. Based on the preset particle size range, the morphological parameters, the distribution of aggregate edges and corners, and the durability parameters, a geometric model of random aggregate crushed stone for the concrete specimen is generated using Python. The geometric model of the random aggregate was input into COMSOL Multiphysics software to construct a chloride erosion model of aggregate concrete suitable for brackish water environments, and the distribution law of chloride erosion of aggregate concrete was obtained by numerical calculation through software. The first morphological parameter is adjusted to obtain the second morphological parameter. Based on the preset particle size range, the second morphological parameter, the distribution of the aggregate edges and corners, and the durability parameter, a geometric model of circular random aggregate concrete is generated using Python language, and the chloride erosion distribution law of circular aggregate concrete is calculated. The second morphological parameter represents the morphological parameter corresponding to adjusting the sphericity of the single-size crushed stone to meet the conditions of circular aggregate. By comparing the first similarity between the chloride erosion distribution pattern of circular aggregate concrete and the time-varying pattern of chloride concentration in the indoor environment, and the second similarity between the chloride erosion distribution pattern of crushed stone aggregate concrete and the time-varying pattern of chloride concentration in the indoor environment, the influence of aggregate edge effect on the chloride erosion pattern of concrete in brackish water confluence area is determined. The initial concrete mix proportion is adjusted based on the degree of influence to obtain the target concrete mix proportion.

2. The method for optimizing concrete mix proportions in brackish water confluence areas according to claim 1, characterized in that, Determine the time-varying patterns of chloride concentration in the estuarine environment, including: Statistical data on chloride concentration monitoring in typical estuary areas are collected, and a statistical data chart is generated based on the daily chloride concentration within a preset period in the chloride concentration monitoring data. The average chloride concentration for each month in the statistical data chart is used as the representative value of the chloride concentration for the corresponding month, and the time-varying pattern of chloride concentration in the estuary water environment within the preset period is determined based on all the representative values.

3. The method for optimizing concrete mix proportions in brackish water confluence areas according to claim 1, characterized in that, The distribution of aggregate edges and corners of the single-size crushed stone is determined based on statistical principles and the first morphological parameter, including: A predetermined number of gravel samples are randomly selected from all single-size gravel samples within the predetermined particle size range, and the gravel samples are cleaned to remove surface impurities. A high-precision digital camera is used to photograph the maximum two-dimensional projection plane and the vertical projection plane of each of the cleaned gravel samples to obtain reference images. The maximum two-dimensional projection plane is the plane formed by the major axis and the middle axis of the cleaned gravel sample, and the vertical projection plane is the plane formed by the middle axis and the minor axis of the cleaned gravel sample. The grayscale threshold of each reference image is adjusted using IPP software to separate aggregate particles from the background, thereby obtaining each intermediate image; Extract aggregate dimensional quantization parameters from each of the intermediate images, and calculate the surface area of ​​the tangent cuboid of the smallest volume of the crushed stone sample and the point cloud coordinates of the corresponding particle surface based on the aggregate dimensional quantization parameters. Based on the quantitative parameters of the three dimensions of each aggregate, the sphericity, particle size dispersion, elongation and shape factor of each crushed stone sample are calculated. Based on statistical principles, the mean, standard deviation, skewness, and kurtosis of the sphericity, particle size dispersion, elongation, and shape factor of all the crushed stone samples are statistically analyzed. Based on the mean, standard deviation, skewness, and kurtosis of all the crushed stone samples, the number and sharpness of the edges and corners of the crushed stone samples are determined. Based on the number and sharpness of the edges and corners, the distribution of the aggregate edges and corners is determined.

4. The method for optimizing concrete mix proportions in brackish water confluence areas according to claim 1, characterized in that, The preparation of concrete specimens using the single-size crushed stone includes: The concrete specimens are obtained by molding single-size crushed stone within a preset particle size range in a typical estuary environment in an indoor environment. The concrete specimens include 6 cross-sections. After 28 days of standard curing, five of the concrete specimens were sealed with epoxy resin, leaving one of the specimens exposed.

5. The method for optimizing concrete mix proportions in brackish water confluence areas according to claim 2, characterized in that, The preset period is 1 year, and the concrete specimens are immersed in a brackish water environment for the preset period, including: The concrete specimen was placed in a pre-designed container; Based on the representative values ​​for each month corresponding to the time-varying pattern of chloride concentration in the estuary water environment, NaCl solutions with concentrations corresponding to each representative value are prepared respectively. According to the month corresponding to each representative value and the corresponding concentration of NaCl solution, the solution in the preset container is replaced every month to soak the concrete specimen.

6. The method for optimizing concrete mix proportions in brackish water confluence areas according to claim 5, characterized in that, Durability parameters include the chloride ion diffusion coefficient of concrete, the chloride ion concentration on the concrete surface, and the age-related decay coefficient. The chloride distribution pattern of the concrete specimen within the preset period is determined, and the durability parameters of the concrete specimen are calculated based on Fick's second law and the chloride distribution pattern, including: The crushed stone concrete specimens were taken out from the preset container in different months. The crushed stone concrete specimens taken in any month were sampled in layers by grinding to obtain multi-layer concrete powder samples, each layer of which was 1 to 2 millimeters thick. The silver nitrate titration method was used to test the concrete powder sample of each layer, and the corresponding chloride concentration was calculated based on the volume of silver nitrate consumed for each layer of concrete powder sample. Based on the chloride concentration of each layer of concrete powder sample, a chloride concentration distribution curve as a function of depth is plotted to obtain the chloride distribution law of the concrete specimen. The analytical solution equation for Fick's second law is obtained based on the surface chloride ion concentration of the concrete specimen at any given time and the chloride ion concentration of the corresponding concrete powder sample. The analytical solution equation includes a reference expression, which is used to indicate the expression for the chloride ion diffusion coefficient at any given time. The chloride ion diffusion coefficient of the concrete and the chloride ion concentration on the concrete surface are obtained by nonlinear fitting of the chloride salt concentration corresponding to the concrete powder sample at different depths at various times within a preset period with the analytical solution equation using programming software. Based on the chloride ion diffusion coefficient of concrete at various time points, the reference expression is linearized, and the independent and dependent variables of the linearized reference expression are linearly regressed using the least squares method to obtain the age decay coefficient.

7. The method for optimizing concrete mix proportions in brackish water confluence areas according to claim 1, characterized in that, Based on the preset particle size range, the morphological parameters, the aggregate edge distribution, and the durability parameters, a geometric model of the random aggregate of the crushed stone in the concrete specimen is generated using Python, including: A rectangular region is used to define the model boundary, and a closed polygon is determined by a sequence of vertex coordinates. The size parameters of the closed polygon are then used as the size parameters of the concrete two-dimensional geometric model. Based on the Python language, the dimensional parameters, target aggregate filling rate, preset particle size range, dispersion, morphological parameters, aggregate corner distribution, preset boundary, preset spacing, and interface transition zone thickness of the concrete two-dimensional geometric model are obtained by using the layered noise superposition method to generate the crushed stone random aggregate geometric model. Export the geometric model of the random aggregate crushed stone as a DXF format.

8. The method for optimizing concrete mix proportions in brackish water confluence areas according to claim 1 or 7, characterized in that, The geometric model of the random aggregate was input into COMSOL Multiphysics software to construct a chloride erosion model of crushed stone concrete suitable for brackish water environments. The distribution law of chloride erosion in crushed stone aggregate concrete was obtained through numerical calculations using the software, including: Transient simulations were performed using the steady convection-diffusion equation physics layer interface under the Classical Differential Equations module in COMSOL Multiphysics software. Import the random aggregate geometry model of crushed stone as DXF format, and establish the three-phase domain of the chloride erosion model of crushed stone concrete, wherein the three-phase domain includes crushed stone aggregate, ITZ and mortar. The crushed stone aggregate, the ITZ and the mortar are constructed into a combined body, and the aggregate portion is deducted using a Boolean algorithm. The apparent chloride ion diffusion coefficient, age decay coefficient, chloride ion concentration boundary function of indoor brackish water environment, and initial chloride ion concentration of concrete are determined for the chloride salt erosion model of the crushed stone concrete. The chloride erosion model of the crushed stone concrete is meshed, and the calculation step size of the chloride erosion model of the crushed stone concrete is set to perform transient calculation of the chloride erosion model of the crushed stone concrete. The simulation data of each layer of the crushed stone concrete chloride erosion model are exported. Based on all the simulation data, a chloride concentration distribution curve as a function of depth is plotted. Based on the chloride concentration distribution curve, the chloride erosion distribution law of the crushed stone aggregate concrete is determined.

9. The method for optimizing concrete mix proportions in brackish water confluence areas according to claim 1, characterized in that, Calculate the chloride erosion distribution pattern of circular aggregate concrete, including: The geometric model of the circular random aggregate concrete is input into the COMSOL Multiphysics software, and a chloride erosion model of circular crushed stone concrete suitable for brackish water environments is constructed using the COMSOL Multiphysics software. Based on the chloride erosion model of the circular crushed stone concrete, the distribution law of chloride erosion of the circular aggregate concrete was obtained by software numerical calculation.

10. A control device, characterized in that, It includes at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enables the at least one control processor to perform the concrete mix design optimization method for brackish water confluence areas as described in any one of claims 1 to 9.

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