Method and system for evaluating freeze-thaw durability of recycled concrete

Abstract

The application provides a recycled concrete anti-freezing durability performance evaluation method and system, relates to the recycled concrete anti-freezing durability performance evaluation technical field, and establishes the quantitative correlation between the original properties of aggregate and the macroscopic freeze-thaw performance of concrete, solves the problem that the difference of aggregate form and the freeze-thaw sensitivity of attached mortar are ignored in the prior art, and the anti-freezing performance evaluation is inaccurate; the Gaussian process regression model is trained by using the data in the candidate mixing amount interval, the anti-freezing durability comprehensive index, the average damage rate and the total cumulative damage are taken as the output, and the dependence on a large amount of test data is reduced; the multi-objective optimization algorithm is used for adaptive optimization in the candidate mixing amount interval, the optimal anti-freezing durability mixing amount proportion of recycled aggregate concrete is determined finally, the intelligent and accurate optimization of the recycled aggregate mixing amount proportion is realized, and the global optimal solution instead of the experiential feasible solution is obtained.

Description

Technical Field

[0001] This invention relates to the field of evaluation technology for the freeze-thaw durability performance of recycled concrete, specifically to a method and system for evaluating the freeze-thaw durability performance of recycled concrete. Background Technology

[0002] Recycled concrete refers to a new type of concrete made by crushing, cleaning, and grading waste concrete blocks, mixing them in a certain proportion to form recycled aggregates, and partially or completely replacing natural aggregates. In precast component plants, concrete poles produced using recycled aggregates are assessed for their freeze-thaw resistance or durability. Compared with natural aggregates, recycled aggregates usually have old mortar adhering to their surface, resulting in high porosity, high water absorption, and low density. This poses a severe challenge to the application of recycled concrete in cold regions. Under freeze-thaw cycles, the water in the pores freezes and expands, causing internal micro-cracks to expand and ultimately leading to structural damage.

[0003] In existing technologies, the mass loss method and dynamic modulus of elasticity method are commonly used to evaluate the freeze-thaw durability of recycled concrete. The traditional mass loss method and dynamic modulus of elasticity method mainly focus on the decay results of macroscopic properties (such as how much slag is lost and how much stiffness is reduced), but cannot explain the process of stress development inside the material. Especially for recycled concrete with complex pores, the decrease in modulus alone cannot distinguish whether it is caused by aggregate failure or by damage to the interface transition zone, nor can it quantify the impact of actual frost heave stress on the structure.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for evaluating the freeze-thaw resistance and durability of recycled concrete, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for evaluating the freeze-thaw resistance and durability of recycled concrete, comprising the following steps: Step 1: Obtain surface morphology data and three-dimensional morphology data of recycled aggregate and natural aggregate respectively. Based on the obtained surface morphology data and three-dimensional morphology data, determine the surface fractal dimension and three-dimensional morphology coefficient of the two aggregates respectively. Based on the surface fractal dimension and three-dimensional morphology coefficient, determine the aggregate structure matching degree between the two aggregates. Detect the changes in the mass and water absorption rate of recycled aggregate before and after a preset number of pre-freeze-thaw cycle tests. Calculate the freeze-thaw sensitivity coefficient of the mortar attached to the recycled aggregate based on the test results. Step 2: Set several sets of volumetric admixture ratios of recycled aggregate and natural aggregate, prepare corresponding mixed aggregate samples and concrete samples according to each set of volumetric admixture ratios, and conduct performance tests on each set of samples at multiple freeze-thaw cycle nodes to obtain relative dynamic elastic modulus and mass loss rate data. Based on the relative dynamic elastic modulus and mass loss rate, determine the comprehensive index of freeze-thaw durability, average damage rate and total cumulative damage under each admixture ratio, and establish a sample dataset between the volumetric admixture ratio and the comprehensive index of freeze-thaw durability, average damage rate and total cumulative damage. Step 3: Based on the qualified proportion segment continuously distributed on the proportion axis of the sample dataset, the candidate admixture interval is determined. The input feature vector is constructed based on the volume admixture ratio of recycled aggregate, the matching degree of aggregate structure and the freeze-thaw sensitivity coefficient. A Gaussian process regression model is trained by using the sample data in the candidate admixture interval. The Gaussian process regression model takes the input feature vector as input and takes the comprehensive index of freeze-thaw durability, the average damage rate and the total cumulative damage as output. Step 4: Based on the trained Gaussian process regression model, with the optimization objectives of optimal freeze-thaw durability, slowest freeze-thaw damage development, and lowest total cumulative damage, an adaptive optimization algorithm is used within the candidate admixture range to finally determine the optimal freeze-thaw durability admixture ratio for recycled aggregate concrete.

[0007] Furthermore, the surface morphology data includes roughness index, angularity coefficient, and surface porosity distribution. The specific method for determining the surface fractal dimension of aggregate based on its surface morphology data is as follows: the roughness index, angularity coefficient, and surface porosity distribution of aggregate are used as coordinate components in three-dimensional space to construct surface morphology feature points of aggregate, and the Euclidean distance from the surface morphology feature point to the origin of coordinates is calculated. The obtained Euclidean distance is compared with a preset reference distance to obtain the comprehensive surface morphology index of aggregate. The comprehensive surface morphology index is logarithmically transformed, and the product of the transformed value and the preset fractal calibration coefficient is used as the surface fractal dimension of aggregate.

[0008] Furthermore, the three-dimensional morphological data includes the sphericity, aspect ratio, and convexity index of the aggregate. The specific method for determining the three-dimensional morphological coefficients based on the three-dimensional morphological data of the aggregate is as follows: construct the sphericity, aspect ratio, and convexity index of the aggregate into a three-dimensional morphological vector; perform minimum-maximum normalization on each component of the obtained three-dimensional morphological vector; perform weighted summation on the normalized sphericity, aspect ratio, and convexity index; and multiply the summation result by a preset morphological adjustment coefficient as the three-dimensional morphological coefficient.

[0009] Furthermore, the specific steps to obtain the aggregate structure matching degree between the two types of aggregates are as follows: Obtain the surface fractal dimension of recycled aggregate and the surface fractal dimension of natural aggregate, as well as the three-dimensional morphological coefficients of recycled aggregate and natural aggregate; The surface fractal dimension value is obtained by calculating the ratio between the surface fractal dimension of recycled aggregate and that of natural aggregate. The three-dimensional morphology coefficient value is obtained by calculating the ratio between the three-dimensional morphology coefficient of recycled aggregate and that of natural aggregate. The aggregate structure matching degree between the two aggregates is obtained by multiplying the surface fractal dimension value with the three-dimensional morphology coefficient value.

[0010] Furthermore, the specific steps for obtaining the freeze-thaw sensitivity coefficient of the recycled aggregate-attached mortar are as follows: Representative recycled aggregate samples were placed in a saturated water state and subjected to five rapid freezing methods. After each freeze-thaw cycle, the changes in aggregate mass loss rate and water absorption rate were measured. The mass loss rate is obtained by calculating the ratio between the cumulative mass loss of aggregate after 5 freeze-thaw cycles and the initial mass. The water absorption rate is obtained by calculating the ratio between the water absorption rate of aggregate after freeze-thaw cycles and the water absorption rate of aggregate before freeze-thaw cycles. The freeze-thaw sensitivity coefficient of recycled aggregate-attached mortar is obtained by multiplying the mass loss rate by the water absorption rate ratio.

[0011] Furthermore, the specific method for setting the volumetric admixture ratio of several groups of recycled aggregates and natural aggregates is as follows: set a lower limit and an upper limit for the volumetric admixture ratio of recycled aggregates, divide the range between the lower limit and the upper limit equally into multiple sub-intervals, use the left endpoint of each sub-interval as the set value for the volumetric admixture ratio of each group of recycled aggregates, and set the volumetric admixture ratio of each group of natural aggregates as the difference between the value 1 and the set value for the volumetric admixture ratio of the group of recycled aggregates.

[0012] Furthermore, the specific steps for determining the comprehensive index of freeze-thaw durability, average damage rate, and total cumulative damage for each dosage ratio are as follows: For each volumetric admixture ratio corresponding to a concrete sample, during the freeze-thaw cycle test, the relative dynamic elastic modulus and mass loss rate of the sample are measured once at a preset number of freeze-thaw cycles. Determine the critical number of freeze-thaw cycles for each dosage ratio; The ratio of the relative dynamic elastic modulus of the specimen after each freeze-thaw cycle to the initial relative dynamic elastic modulus is calculated to obtain the elastic modulus ratio. The damage variable of each test node is obtained by subtracting the elastic modulus ratio from 1. Using the single-cycle damage variable as the integration variable, the damage variable is integrated over the interval from the initial state to the critical number of freeze-thaw cycles, and the total cumulative damage of the specimen during the entire critical freeze-thaw cycle process is finally obtained. The ratio of the critical freeze-thaw cycle count to the critical freeze-thaw cycle count of natural aggregate concrete at each admixture ratio is used to obtain the cycle count ratio. The ratio of 1 to the root mean square of the damage increment during the freeze-thaw process is used to obtain the root mean square error ratio. The cycle count ratio and the root mean square error ratio are multiplied to obtain the comprehensive index of freeze-thaw durability. The average damage rate at each dosage ratio is obtained by calculating the ratio between the damage variable at critical failure and the critical number of freeze-thaw cycles at each dosage ratio.

[0013] Furthermore, the specific steps for determining the candidate doping level range are as follows: Based on the established sample dataset, thresholds are set for the comprehensive antifreeze durability index, average damage rate, and total cumulative damage corresponding to each volume doping ratio group. Volume doping ratio groups that do not meet the preset antifreeze admission conditions are removed. The preset antifreeze admission conditions are as follows: for any sample data, if any of the following conditions are met, the comprehensive antifreeze durability index is lower than the preset lower durability threshold, the average damage rate is higher than the preset upper damage threshold, or the total cumulative damage is higher than the preset upper cumulative damage threshold, then the sample data is determined not to meet the preset antifreeze admission conditions and is removed. The remaining volumetric admixture ratio groups after screening are arranged in ascending order according to the numerical value of the volumetric admixture ratio of recycled aggregate to form an ordered ratio sequence. The continuity of the obtained ratio sequence is tested by calculating the absolute value of the difference between the volumetric admixture ratio of recycled aggregate between two adjacent volumetric admixture ratio groups. The absolute value of the difference is compared with a preset continuity threshold. For adjacent volumetric admixture ratio groups with an absolute value of the difference less than the preset continuity threshold, they are determined to be continuously distributed. For adjacent volumetric admixture ratio groups with an absolute value of the difference greater than or equal to the preset continuity threshold, a breakpoint is determined to exist between them. After completing the continuity detection of the proportional sequence, the proportional sequence is divided into multiple continuous proportional segments based on all the obtained breakpoints. The continuous proportional segment containing the most volume doping proportion groups is selected as the candidate doping range.

[0014] Furthermore, the specific steps for adaptive optimization within the candidate dosing range using a multi-objective optimization algorithm are as follows: Set the population size and maximum iteration rounds, and randomly initialize the sample group within the candidate admixture range, where each individual in the initialized sample group represents a set of recycled aggregate volume admixture ratios. Each individual and its corresponding aggregate structure matching degree and freeze-thaw sensitivity coefficient are input into the trained Gaussian process regression model to predict the comprehensive index of freeze-thaw durability and the average damage rate under this admixture ratio; with the optimal freeze-thaw durability performance, the slowest freeze-thaw damage development and the lowest total cumulative damage as the optimization objectives, the objective function vector of each individual is defined. The initial sample group is sorted non-dominated, the crowding distance of each individual in the same frontier is calculated, and samples are selected in sequence according to the order of priority of dominance level and priority of the larger crowding distance in the same level, until the number of selected samples reaches the set sample size. The selected samples are used as parent samples to form the parent sample group. The parent sample group is subjected to crossover and mutation operations in sequence to generate a child sample group. The parent sample group and the child sample group are merged. The merged sample group is then subjected to non-dominated sorting and crowding distance calculation again. Samples are selected in order of dominance level from low to high and crowding distance within the same dominance level from large to small until a set sample size is reached, generating a new parent sample group. The crossover, mutation, and merging selection operations are repeated until the maximum number of iterations is reached. The sample with the best overall performance is selected from the non-dominated solution set of the final parent sample group, and the volume doping ratio corresponding to this sample is used as the candidate optimal volume doping ratio.

[0015] The present invention also provides a system for evaluating the freeze-thaw durability of recycled concrete. This system is used to perform the aforementioned method for evaluating the freeze-thaw durability of recycled concrete, and includes: The coefficient acquisition module acquires surface morphology data and three-dimensional morphology data of recycled aggregate and natural aggregate respectively. Based on the obtained surface morphology data and three-dimensional morphology data, it determines the surface fractal dimension and three-dimensional morphology coefficient of the two aggregates respectively. Based on the surface fractal dimension and three-dimensional morphology coefficient, it determines the aggregate structure matching degree between the two aggregates. It detects the changes in the mass and water absorption rate of recycled aggregate before and after a preset number of pre-freeze-thaw cycle tests. Based on the test results, it calculates the freeze-thaw sensitivity coefficient of the mortar attached to the recycled aggregate. The sample determination module sets several sets of volumetric admixture ratios of recycled aggregate and natural aggregate. According to each set of volumetric admixture ratios, corresponding mixed aggregate samples and concrete samples are prepared. The performance of each set of samples is tested at multiple freeze-thaw cycle nodes to obtain relative dynamic elastic modulus and mass loss rate data. Based on the relative dynamic elastic modulus and mass loss rate, the comprehensive index of freeze-thaw durability, average damage rate and total cumulative damage under each admixture ratio are determined. A sample dataset is established between the volumetric admixture ratio and the comprehensive index of freeze-thaw durability, average damage rate and total cumulative damage. The model output module determines the candidate admixture range based on the qualified proportion segment continuously distributed on the proportion axis of the sample dataset. It constructs the input feature vector based on the volume admixture ratio of recycled aggregate, the matching degree of aggregate structure and the freeze-thaw sensitivity coefficient. It trains a Gaussian process regression model using the sample data in the candidate admixture range. The Gaussian process regression model takes the input feature vector as input and outputs the comprehensive index of freeze-thaw durability, the average damage rate and the total cumulative damage. The proportion determination module, based on the trained Gaussian process regression model, takes the optimal freeze-thaw durability performance, the slowest freeze-thaw damage development, and the lowest total cumulative damage as optimization objectives. It adaptively seeks optimization within the candidate dosage range through a multi-objective optimization algorithm, and finally determines the optimal freeze-thaw durability dosage ratio of recycled aggregate concrete.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention obtains surface morphology and three-dimensional morphology data of recycled and natural aggregates, calculates the aggregate structure matching degree, and detects changes in mass and water absorption rate after pre-freeze-thaw cycles to obtain the freeze-thaw sensitivity coefficient. It can quantify the key intrinsic parameters affecting freeze-thaw durability from the perspective of aggregate microstructure and attached mortar characteristics. The advantage is that it establishes a quantitative correlation between the original properties of aggregates and the macroscopic freeze-thaw performance of concrete, solving the problem of inaccurate freeze-thaw performance evaluation caused by ignoring the differences in aggregate morphology and the freeze-thaw sensitivity of attached mortar in the prior art.

[0017] This invention sets multiple sets of volumetric admixture ratios of recycled aggregate and natural aggregate, prepares corresponding samples, and conducts performance tests at multiple freeze-thaw cycle nodes. It then calculates the comprehensive index of freeze-thaw durability, average damage rate, and total cumulative damage for each admixture ratio and establishes a sample dataset. This enables a multi-dimensional quantitative characterization of the entire freeze-thaw damage process of concrete under different admixture ratios. The advantage is that it can comprehensively capture the cumulative effect, development speed, and critical threshold of freeze-thaw damage, solving the problem that traditional single evaluation indicators (such as only mass loss rate) cannot reflect the dynamic evolution process of damage and the overall durability level.

[0018] This invention determines the candidate admixture range based on the qualified proportion range of the sample dataset continuously distributed on the proportion axis. It then uses the data within this range to train a Gaussian process regression model with the volumetric admixture ratio of recycled aggregate, aggregate structure matching degree, and freeze-thaw sensitivity coefficient as inputs, and the comprehensive index of freeze-thaw durability, average damage rate, and total cumulative damage as outputs. This model can construct a high-precision nonlinear prediction mapping under limited experimental sample conditions. The advantage is that it reduces the dependence on a large amount of experimental data, while quantifying the prediction uncertainty. It solves the problem in practical engineering where it is difficult to accurately establish the complex relationship between admixture ratio and freeze-thaw performance due to high experimental costs and limited sample size.

[0019] This invention is based on a trained Gaussian process regression model. With the optimization objectives of optimal freeze-thaw durability, slowest freeze-thaw damage development, and lowest total cumulative damage, it adaptively seeks the optimal ratio of recycled aggregate in concrete within the candidate admixture range through a multi-objective optimization algorithm. It can automatically balance multiple mutually restrictive performance indicators such as the comprehensive freeze-thaw durability index, average damage rate, and total cumulative damage. The advantage is that it realizes intelligent and precise optimization of the ratio of recycled aggregate, obtains the global optimal solution rather than an empirically feasible solution, and solves the problems of traditional trial mixing methods that rely on manual experience, are inefficient, and cannot simultaneously meet multiple performance optimization requirements. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the overall method flow of the present invention; Figure 2 The figure shows the fitting curve of the comprehensive index of recycled aggregate volume content and freeze-thaw durability. Figure 3 The figure shows the fitted curve of the volumetric content of recycled aggregate versus the total cumulative damage. Figure 4 This is a schematic diagram of the overall system of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0022] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0023] Example: Please see Figures 1-3 The present invention provides a technical solution: A method for evaluating the freeze-thaw resistance and durability of recycled concrete, comprising the following steps: Step 1: Obtain surface morphology data and three-dimensional morphology data of recycled aggregate and natural aggregate respectively. Based on the obtained surface morphology data and three-dimensional morphology data, determine the surface fractal dimension and three-dimensional morphology coefficient of the two aggregates respectively. Based on the surface fractal dimension and three-dimensional morphology coefficient, determine the aggregate structure matching degree between the two aggregates. Detect the changes in the mass and water absorption rate of recycled aggregate before and after a preset number of pre-freeze-thaw cycle tests. Calculate the freeze-thaw sensitivity coefficient of the mortar with recycled aggregate based on the test results.

[0024] In this embodiment, the surface morphology data includes roughness index, edge angle coefficient, and surface porosity distribution. The specific method for determining the surface fractal dimension based on the aggregate's surface morphology data is as follows: Using the aggregate's roughness index, edge angle coefficient, and surface porosity distribution as coordinate components in three-dimensional space, the mathematical expression for the aggregate's surface morphology data vector is: In the formula, A vector representing the surface morphology data of aggregates; Represents the roughness index; Indicates the angle coefficient; Indicates the surface porosity distribution; Surface morphology feature points of the aggregate are constructed using the surface morphology data vector, and the Euclidean distance from each feature point to the origin is calculated. The ratio of the obtained Euclidean distance to a preset reference distance is then used to obtain the comprehensive surface morphology index of the aggregate. The mathematical expression for calculating the comprehensive surface morphology index is as follows: In the formula, Indicates the comprehensive index of surface morphology; This indicates the preset reference distance, which is the corresponding Euclidean distance of standard natural aggregate; A logarithmic transformation is performed on the comprehensive index of surface morphology. The product of the transformed value and the preset fractal calibration coefficient is taken as the fractal dimension of the aggregate surface. The mathematical expression for calculating the surface fractal dimension is as follows: In the formula, Represents the fractal dimension of the surface; This represents the preset fractal calibration coefficient, which is determined through calibration experiments.

[0025] In this embodiment, the roughness index reflects the microscopic unevenness of the aggregate surface. The higher the roughness, the stronger the mechanical interlocking force between the aggregate and the cement paste, and the denser the interfacial transition zone, which is beneficial for resisting the tensile stress generated by freeze-thaw cycles. However, excessive roughness may increase the water absorption surface area. Measuring this parameter helps to quantify the interfacial bonding performance. The angularity coefficient characterizes the sharpness of the aggregate particles. Aggregates with a high angularity coefficient have a large internal friction angle and low porosity when stacked. However, stress concentration is easily generated at sharp edges, which accelerates freeze-thaw damage. This parameter is used to evaluate the influence of aggregate shape on the initiation of microcracks inside concrete. The surface porosity distribution reflects the number, size, and spatial distribution of open pores on the aggregate surface. The higher the porosity, the greater the saturated water absorption rate of the aggregate. The expansion pressure generated by the freezing of pore water during freeze-thaw cycles is more significant, which is the main reason for the peeling of mortar attached to recycled aggregate. Measuring this data is the basis for calculating the freeze-thaw sensitivity coefficient.

[0026] In this embodiment, the three-dimensional morphological data includes the sphericity, aspect ratio, and convexity index of the aggregate. The specific method for determining the three-dimensional morphological coefficients based on the aggregate's three-dimensional morphological data is as follows: the sphericity, aspect ratio, and convexity index of the aggregate are constructed into a three-dimensional morphological vector. The mathematical expression for the three-dimensional morphological vector is: In the formula, Represents a three-dimensional shape vector; Indicates sphericity; Indicates the aspect ratio; Indicates the convexity index; The components of the three-dimensional shape vector are subjected to minimum-maximum normalization. The normalized sphericity, aspect ratio, and convexity index are then weighted and summed. The product of the summation and a preset shape adjustment coefficient is taken as the three-dimensional shape coefficient. The mathematical expression for calculating the three-dimensional shape coefficient is as follows: In the formula, Represents three-dimensional morphological coefficients; This represents the preset morphological adjustment coefficient, determined based on calibration tests. The weighting coefficients of the sphericity index satisfy the following conditions: Its specific value is determined by expert experience. The minimum reference value for sphericity; This represents the maximum reference value for sphericity; The weighting coefficient for the aspect ratio index is determined by expert experience. The minimum reference value indicating the aspect ratio; This indicates the maximum reference value for the aspect ratio; The weighting coefficients for the convexity index are determined by expert experience. The minimum reference value for the convexity index; This represents the maximum reference value for the convexity index.

[0027] In this embodiment, surface morphology data and three-dimensional morphology coefficients of 50 sets of natural aggregates and recycled aggregates were statistically analyzed. Some of the data are shown in Tables 1 and 2: Table 1: Surface morphology data and three-dimensional morphology coefficient data of natural aggregates Table 2: Surface morphology data and three-dimensional morphology coefficient data of recycled aggregates According to the data in Tables 1 and 2, all indicators show extremely high linear correlation, with absolute values ​​of correlation coefficients all greater than 0.98, exhibiting a clear polarization: , , , , The comprehensive indicators are highly positively correlated pairwise; when one set of indicators increases, the others also increase synchronously. , Significantly negatively correlated with all the above indicators, the larger these two values ​​are, the smaller the other indicators are, and vice versa. Group 24 is the sample with the best overall performance, while Group 23 has the weakest overall performance. The data change trends of the entire group are highly consistent and have a very strong regularity.

[0028] In this embodiment, sphericity measures the degree to which aggregates approximate a sphere. Aggregates with high sphericity have good fluidity in concrete, but the interfacial transition zone is thin and uniform, making freeze-thaw damage more likely to propagate along the weak surface. Aggregates with low sphericity (such as flakes and needles) produce more voids when stacked, but can form mechanical interlocks with cement paste, delaying crack propagation. Measuring sphericity helps assess the influence of aggregate shape on freeze-thaw stress distribution. Aspect ratio reflects the slenderness of aggregates. Aggregates with excessively large aspect ratios tend to oriented in concrete, leading to anisotropic freeze-thaw damage, and the length... The axial direction is prone to stress concentration, which accelerates the initiation and penetration of local microcracks. This parameter is used to quantify the influence of aggregate shape on non-uniform deformation during freeze-thaw cycles. The convexity index describes the degree of convexity of the aggregate surface (defined as the ratio of the actual surface area of ​​the aggregate to the surface area of ​​its convex hull). The lower the convexity index (the more surface pits), the larger the interlocking area between the aggregate and the cement paste, and the higher the interfacial bond strength. However, at the same time, the pits are prone to accumulating moisture, increasing local freeze-thaw compressive stress. Measuring this parameter helps to analyze the dual effect of aggregate surface morphology on freeze-thaw sensitivity.

[0029] In this embodiment, the specific steps for obtaining the aggregate structure matching degree between the two types of aggregates are as follows: Obtain the surface fractal dimension of recycled aggregate and the surface fractal dimension of natural aggregate, as well as the three-dimensional morphological coefficients of recycled aggregate and natural aggregate; The surface fractal dimension value is obtained by calculating the ratio between the surface fractal dimension of recycled aggregate and that of natural aggregate. The three-dimensional morphology coefficient value is obtained by calculating the ratio between the three-dimensional morphology coefficient of recycled aggregate and that of natural aggregate. Multiplying the surface fractal dimension value by the three-dimensional morphology coefficient value yields the aggregate structure matching degree between the two aggregates. The mathematical expression for calculating the aggregate structure matching degree between the two aggregates is as follows: In the formula, This indicates the degree of aggregate structure matching between the two types of aggregates. The closer the value is to 1, the more similar the surface roughness and particle shape of the two aggregates are, resulting in a denser mixture and fewer defects in the interface transition zone. At that time, recycled aggregate is coarser / more irregular than natural aggregate. On the contrary; This represents the surface fractal dimension of recycled aggregate; This represents the surface fractal dimension of natural aggregates; Represents the three-dimensional morphology coefficient of recycled aggregate; This represents the three-dimensional morphology coefficient of natural aggregates.

[0030] In this embodiment, based on the surface morphology data and three-dimensional morphology coefficient data given in Tables 1 and 2, 50 sets of aggregate structure matching degree data were statistically analyzed, and some of the data are shown in Table 3: Table 3: Aggregate Structure Matching Degree Data According to the data in Table 3, The numerical range is The overall distribution gradient is uniform, with no abnormal abrupt changes. For low value range, For the median segment, The high-value segment consists of three data points arranged alternately, with a clear gradient pattern. and , Highly positively correlated: The simultaneous increase in both types of shape factors leads to Increased size, with a simultaneous decrease in shape factor leading to ; and , Significant negative correlation: The smaller the surface fractal dimension, the more likely it is to lead to The higher the fractal dimension, the greater the surface fractal dimension, which leads to... The lower the level, the more pronounced the characteristic of one level increasing at the other; therefore It can be used as a core correlation parameter to characterize the matching degree of aggregate / interface structure, and can simultaneously reflect the changing trends of shape features and surface features.

[0031] In this embodiment, if there are multiple samples of each type of aggregate, the surface fractal dimension and three-dimensional morphological coefficient of each sample are calculated first, and then the arithmetic mean is taken.

[0032] In this embodiment, the specific steps for obtaining the freeze-thaw sensitivity coefficient of the recycled aggregate-attached mortar are as follows: Representative recycled aggregate samples were placed in a saturated water state and subjected to five rapid freezing methods. After each freeze-thaw cycle, the changes in aggregate mass loss rate and water absorption rate were measured. The mass loss rate is obtained by calculating the ratio of the cumulative mass loss of aggregate after 5 freeze-thaw cycles to the initial mass. The water absorption rate is obtained by calculating the ratio of the water absorption rate of aggregate after freeze-thaw cycles to the water absorption rate of aggregate before freeze-thaw cycles. Multiplying the mass loss rate by the water absorption rate ratio yields the freeze-thaw sensitivity coefficient of the recycled aggregate-attached mortar. The mathematical expression for calculating the freeze-thaw sensitivity coefficient of the recycled aggregate-attached mortar is as follows: In the formula, The value represents the freeze-thaw sensitivity coefficient, which is used to quantify the ability of recycled aggregate particles to resist short-term freeze-thaw damage. The higher the value, the more easily the aggregate will suffer mass loss (stripping) and internal pore deterioration (increased water absorption capacity) in a freeze-thaw environment, that is, the more sensitive it is to freeze-thaw effects. This indicates the cumulative mass loss of aggregate after 5 freeze-thaw cycles; Indicates the initial mass; Indicates the water absorption rate of aggregate after freeze-thaw cycles; This indicates the water absorption rate of the aggregate before freeze-thaw cycles.

[0033] In this embodiment, the five-times rapid freezing method is a quick and low-cost pre-test, sufficient to induce significant damage without completely destroying the aggregate. If the number of times is too many, inferior aggregates may completely disintegrate, rendering the test meaningless; if the number of times is too few, the differences will not be obvious. This value characterizes the ability of aggregates to resist surface spalling during freeze-thaw cycles. The higher the value, the looser the attached mortar and the worse the interfacial bonding, making it more prone to frost heave spalling. The ratio represents the degree of damage to the internal pore structure of aggregates caused by freeze-thaw cycles. A ratio >1 indicates that freeze-thaw cycles increase and connect pores, thus increasing water absorption capacity (deterioration). A ratio ≈1 indicates that the pore structure is stable and has good freeze resistance. A ratio <1 is very rare and may be due to blockage or disintegration causing large pores to fall off, which may actually reduce the water absorption rate.

[0034] Step 2: Set several sets of volumetric admixture ratios for recycled and natural aggregates. Prepare corresponding mixed aggregate samples and concrete samples according to each set of volumetric admixture ratios. Perform performance tests on each set of samples at multiple freeze-thaw cycle nodes to obtain relative dynamic elastic modulus and mass loss rate data. Based on the relative dynamic elastic modulus and mass loss rate, determine the comprehensive index of freeze-thaw durability, average damage rate, and total cumulative damage for each admixture ratio. Establish a sample dataset relating the volumetric admixture ratio to the comprehensive index of freeze-thaw durability, average damage rate, and total cumulative damage.

[0035] In this embodiment, the specific method for setting the volumetric admixture ratio of several groups of recycled aggregates and natural aggregates is as follows: a lower limit and an upper limit value of the volumetric admixture ratio of recycled aggregates are set, and the range between the lower limit and the upper limit value is equally divided into multiple sub-intervals. The left endpoint value of each sub-interval is used as the set value of the volumetric admixture ratio of each group of recycled aggregates, and the set value of the volumetric admixture ratio of each group of natural aggregates is the difference between the value 1 and the set value of the volumetric admixture ratio of the group of recycled aggregates.

[0036] In this embodiment, the lower limit of the recycled aggregate volumetric content ratio is 0% (i.e., pure natural aggregate), used to provide benchmark comparison data. The upper limit is determined based on the maximum recommended content of recycled aggregate in actual engineering projects, and the common range is as follows. .

[0037] In this embodiment, the specific steps for determining the comprehensive index of freeze-thaw durability, the average damage rate, and the total cumulative damage for each dosage ratio are as follows: For each volumetric admixture ratio corresponding to a concrete sample, during the freeze-thaw cycle test, the relative dynamic elastic modulus and mass loss rate of the sample are measured once at a preset number of freeze-thaw cycles. Determine the critical number of freeze-thaw cycles for each dosage ratio; The ratio of the relative dynamic elastic modulus of the specimen after each freeze-thaw cycle to the initial relative dynamic elastic modulus is calculated to obtain the elastic modulus ratio. Subtracting 1 from the elastic modulus ratio yields the damage variable at each test node. Therefore, the mathematical expression for calculating the damage variable at each test node is: In the formula, Indicates the first The damage variable at the next test node has a range of values. , Indicates no damage. This indicates a complete loss of dynamic elastic modulus; Indicates the first The relative dynamic elastic modulus of the specimen after one freeze-thaw cycle; Indicates the initial relative dynamic elastic modulus; Using the single-cycle damage variable as the integration variable, the damage variable is integrated over the interval from the initial state to the critical freeze-thaw cycle number. The total cumulative damage of the specimen during the entire critical freeze-thaw cycle is then obtained. The mathematical expression for calculating the total cumulative damage is as follows: In the formula, Indicates total cumulative damage; Indicates the critical number of freeze-thaw cycles; The ratio of the critical freeze-thaw cycle count for each admixture ratio to the critical freeze-thaw cycle count for natural aggregate concrete is calculated to obtain the cycle count ratio. The ratio of 1 to the root mean square deviation of the damage increment during freeze-thaw processes is calculated to obtain the root mean square deviation ratio. Multiplying the cycle count ratio and the root mean square deviation ratio yields the comprehensive freeze-thaw durability index. The mathematical expression for calculating the comprehensive freeze-thaw durability index is as follows: In the formula, Indicates the dosage ratio The comprehensive index of freeze resistance durability; Indicates the dosage ratio The critical number of freeze-thaw cycles; This indicates the critical number of freeze-thaw cycles for natural aggregate concrete (r=0). Indicates the first Damage variables at the next test node; The average damage rate at each dosage ratio is obtained by calculating the ratio between the damage variable at critical failure and the critical number of freeze-thaw cycles for each dosage ratio. The mathematical expression for calculating the average damage rate at each dosage ratio is then: in, In the formula, Indicates the dosage ratio The average damage rate is measured below, and the smaller the value, the slower the damage development and the better the antifreeze performance. The damage variable represents the critical failure point.

[0038] In this embodiment, data from 50 sample datasets were collected, and some of the data is shown in Table 4: Table 4: Sample Dataset According to Table 4, Figure 2 and Figure 3It can be seen that the overall index of frost resistance and durability shows a continuous linear downward trend. When the content of recycled aggregate is 0 (baseline group), the index is 1.00, which is the highest value among all groups. When the volumetric content of recycled aggregate increases to 69.36%, the index drops to 0.74. As the content of recycled aggregate gradually increases, the frost resistance and durability of concrete continues to decline. With each increase in the content, the durability index decreases slightly, without any abrupt inflection point. The average damage rate increases monotonically overall. The damage rate of the baseline group is only 0.02, which is the lowest among all groups. As the content increases, the damage rate gradually increases, eventually reaching 0.09. At that time, the damage rate stabilized at 0.02, and the recycled aggregate content... At that time, it rose to 0.03, the recycled aggregate content At that time, it rose to 0.04, the amount of recycled aggregate added. At that time, it rose to 0.05, the recycled aggregate content At that time, it rose to 0.06, the recycled aggregate content At that time, it rose to 0.07, the recycled aggregate content When the recycled aggregate content is 67.32% or higher, the value rises to 0.08, and when it is 67.32% or higher, it rises to 0.09. The higher the recycled aggregate content, the faster the internal damage of concrete develops under freeze-thaw cycles. The total cumulative damage also increases steadily. The total cumulative damage of the baseline group is 0.42, which continues to rise with the increase of the content, reaching a maximum of 0.61. The higher the recycled aggregate content, the more severe the overall cumulative damage of the concrete after freeze-thaw cycles, and the higher the degree of structural deterioration. The volumetric content of recycled aggregate is negatively correlated with the freeze-thaw resistance of concrete. The higher the content, the worse the freeze-thaw durability, the faster the freeze-thaw damage develops, and the greater the cumulative damage. There is no obvious optimal content range in the whole range. The performance deterioration develops continuously and gradually with the increase of the content. When the content is low (≤4.08%), the differences between various indicators and the baseline concrete are very small, and the impact on freeze-thaw resistance is weak. High content of recycled aggregate will significantly aggravate the freeze-thaw damage of concrete and greatly reduce the freeze-thaw service capacity of the structure.

[0039] In this embodiment, the critical number of freeze-thaw cycles is the number of freeze-thaw cycles corresponding to the first drop in relative dynamic elastic modulus to 60%. If the relative dynamic elastic modulus is always higher than 60% during the test, the final number of tests is taken as the critical number of freeze-thaw cycles.

[0040] In this embodiment, Reflects dosage The ratio of the number of freeze-thaw cycles required for concrete to reach the failure standard to that of natural concrete is the same. The larger the ratio, the longer the service life of the concrete with that admixture. The denominator is the root mean square error of the damage increment, which is the average value of the damage fluctuation at each freeze-thaw stage. The smaller this value, the more stable and uniform the damage development (without abrupt changes); the larger its reciprocal, the more stable and slower the damage development process, and therefore the higher the value. This means that concrete has a longer service life (able to withstand more freeze-thaw cycles) and exhibits a more stable and slower damage accumulation process (without sudden deterioration) in freeze-thaw cycles.

[0041] Step 3: Based on the qualified proportion segment continuously distributed on the proportion axis of the sample dataset, the candidate admixture interval is determined. The input feature vector is constructed based on the volume admixture ratio of recycled aggregate, the matching degree of aggregate structure and the freeze-thaw sensitivity coefficient. A Gaussian process regression model is trained by using the sample data in the candidate admixture interval. The Gaussian process regression model takes the input feature vector as input and outputs the comprehensive index of freeze-thaw durability, the average damage rate and the total cumulative damage.

[0042] In this embodiment, the specific steps for determining the candidate doping level range are as follows: Based on the established sample dataset, thresholds are set for the comprehensive antifreeze durability index, average damage rate, and total cumulative damage corresponding to each volume doping ratio group. Volume doping ratio groups that do not meet the preset antifreeze admission conditions are removed. The preset antifreeze admission conditions are as follows: for any sample data, if any of the following conditions are met, the comprehensive antifreeze durability index is lower than the preset lower durability threshold, the average damage rate is higher than the preset upper damage threshold, or the total cumulative damage is higher than the preset upper cumulative damage threshold, then the sample data is determined not to meet the preset antifreeze admission conditions and is removed. The remaining volumetric admixture ratio groups after screening are arranged in ascending order according to the numerical value of the volumetric admixture ratio of recycled aggregate to form an ordered ratio sequence. The continuity of the obtained ratio sequence is tested by calculating the absolute value of the difference between the volumetric admixture ratio of recycled aggregate between two adjacent volumetric admixture ratio groups. The absolute value of the difference is compared with a preset continuity threshold. For adjacent volumetric admixture ratio groups with an absolute value of the difference less than the preset continuity threshold, they are determined to be continuously distributed. For adjacent volumetric admixture ratio groups with an absolute value of the difference greater than or equal to the preset continuity threshold, a breakpoint is determined to exist between them. After completing the continuity detection of the proportional sequence, the proportional sequence is divided into multiple continuous proportional segments based on all the obtained breakpoints. The continuous proportional segment containing the most volume doping proportion groups is selected as the candidate doping range.

[0043] In this embodiment, three indicators are extracted from the sample dataset corresponding to a recycled aggregate volume content of 0% (i.e., pure natural aggregate concrete): comprehensive freeze-thaw durability index, average damage rate, and total cumulative damage at the critical freeze-thaw cycle count. At the critical freeze-thaw cycle count, the relative dynamic modulus of elasticity of the natural aggregate concrete just drops to 60%. The lower limit of durability threshold is obtained by multiplying the comprehensive index of freeze resistance durability by the lower limit coefficient of durability, the upper limit of damage rate threshold is obtained by multiplying the average damage rate by the upper limit coefficient of damage rate, and the upper limit of cumulative damage threshold is obtained by multiplying the total cumulative damage at the critical freeze-thaw cycle number by the upper limit coefficient of cumulative damage.

[0044] In this embodiment, setting a lower limit threshold for durability, an upper limit threshold for damage rate, and an upper limit threshold for total cumulative damage is intended to quickly eliminate volumetric doping ratio groups whose antifreeze performance clearly does not meet the basic engineering requirements in the first round of screening, thereby focusing the subsequent training of the Gaussian process regression model and multi-objective optimization within the candidate doping range.

[0045] In this embodiment, by setting three thresholds (durability lower limit threshold, damage rate upper limit threshold, and cumulative damage upper limit threshold), the doping ratio that cannot meet the minimum antifreeze requirements of the project even if barely used can be directly identified from the sample data. Gaussian process regression has high prediction accuracy under small sample conditions, but if the training data contains a large number of sample points with extremely poor performance (far from the feasible region), it will cause the model's local fitting ability near the optimal doping ratio to decrease. After removing the unqualified doping group, the training data is concentrated in the "candidate interval" with relatively good performance. The model can more accurately describe the performance change law with doping ratio in this interval, thereby improving the reliability and convergence speed of subsequent multi-objective optimization. If the first round of screening is not performed, the multi-objective optimization algorithm may explore those doping regions that have been judged as unqualified by the threshold during the global search, wasting computing resources. By pre-defining continuous and qualified candidate doping intervals, the optimization algorithm only needs to search within this interval, which not only ensures the search efficiency, but also ensures that the final output optimal doping ratio naturally meets the engineering access conditions.

[0046] In this embodiment, the specific steps for training the Gaussian process regression model are as follows: Let there be a total of candidate doping ranges. There are _n_ sample points, each containing an input feature vector and a corresponding output value. The input feature vector is: In the formula, This represents the input feature vector; Indicates the first The volumetric proportion of recycled aggregate in the group of test specimens; The output vector is: In the formula, Indicates the output vector; Gaussian process regression hypothesis output Each component independently follows a Gaussian process for a single output dimension. ( Corresponding to Its expression is: In the formula, This represents the mean function, usually taking the value of a constant. Estimated from training data; The covariance function (kernel function) measures the variance between two input points. and The correlation between them; Choosing the square exponent kernel, due to its infinite differentiability, allows for a good fit to the smooth variation of concrete properties. For the th... One output, the kernel function form is: In the formula, It represents the signal variance and controls the overall variation of the output value. Indicates the first Input features ( , , The feature length scale is represented by a larger value, indicating a more gradual change in the influence of the feature on the output. It represents the noise variance and is used to characterize experimental measurement errors; This represents the Kronecker delta function, when... hour Otherwise, it is 0; For each output dimension Using the training sample set within the candidate doping range ( (For the sample size), hyperparameters are estimated by maximizing the log-marginal likelihood function. ; The log-marginal likelihood function is: In the formula, Represents the input matrix ( ); Indicates the first Each output observation vector; Represents the kernel matrix, whose elements ; Maximize the likelihood function using the conjugate gradient method or the L-BFGS algorithm to obtain the optimal hyperparameters. ; Given a new input , No. The predicted distribution of each output is a Gaussian distribution: Predicted mean: In the formula, for Vector, its first The elements are ; Prediction variance: In the formula, As a form of predictive uncertainty, it can be used as a confidence interval constraint in subsequent optimization. The sample data within the candidate doping range are randomly divided into a training set (80%) and a validation set (20%) (at least 3 samples are reserved for validation). The mean absolute percentage error is calculated on the validation set. Require each output If not satisfied, then: Increase the sample density within the candidate doping range (i.e., insert new doping levels within the range to supplement the experiments), or normalize the input features (by converting the original data into a normalized data). , , Scaled to a mean of 0 and a variance of 1). In this embodiment, if The Matern5 / 2 core is preferred, and its form is as follows: in, The Matern kernel makes fewer smoothness assumptions than the SE kernel and is more adaptable to local abrupt changes in concrete properties; Finally, three well-trained Gaussian process regression models were obtained. , , They can be based on any (The left and right endpoints of the candidate doping range) and their corresponding , It can quickly predict the comprehensive index of freeze resistance durability, average damage rate and total cumulative damage, and give the prediction variance.

[0047] In this embodiment, since there is a physical correlation between the three output dimensions, this scheme adopts multi-output Gaussian process regression, that is, using the covariance matrix to model the three outputs simultaneously. To simplify the calculation, independent single outputs can also be trained for each output separately and then predicted in parallel. Given the small sample scenario, training separately is more stable.

[0048] In this embodiment, the squared exponential kernel is suitable for continuous and smooth function approximation, and the hyperparameters have clear physical meaning (the length scale can be interpreted as the decay rate of the influence of each feature on the performance). For the freeze-thaw resistance of recycled concrete, this kernel function has been verified to be effective.

[0049] Step 4: Based on the trained Gaussian process regression model, with the optimization objectives of optimal freeze-thaw durability, slowest freeze-thaw damage development, and lowest total cumulative damage, an adaptive optimization algorithm is used within the candidate admixture range to finally determine the optimal freeze-thaw durability admixture ratio for recycled aggregate concrete.

[0050] In this embodiment, the specific steps for adaptive optimization within the candidate doping level range using a multi-objective optimization algorithm are as follows: Set the population size and maximum number of iterations, and randomly initialize the sample population within the candidate dosing range. , where each individual in the initial sample group represents a set of recycled aggregate volume content ratios; Each individual aggregate and its corresponding aggregate structure matching degree and freeze-thaw sensitivity coefficient are input into a trained Gaussian process regression model to predict the comprehensive index of freeze-thaw durability and the average damage rate at that admixture ratio. With the optimization objectives of optimal freeze-thaw durability, slowest freeze-thaw damage development, and lowest total cumulative damage, the objective function vector for each individual is defined as follows: In the formula, Represent the objective function vector; The optimization problem is then: The initial sample group is non-dominated and sorted. The crowding distance of each individual within the same frontal plane is calculated. Samples are selected sequentially according to the order of dominance level priority and larger crowding distance within the same level, until the number of selected samples reaches the set sample size. The selected samples are used as parent samples to form the parent sample group. Then, for two feasible solutions... , If the following conditions are met: Then it is called Dominate ; The mathematical expression for calculating congestion distance is: In the formula, Indicates crowded distance; Represents an individual The One objective function value (original value, non-negative sign transformation); Indicates the first in this layer The maximum value of each objective; Indicates the first in this layer The minimum value of each objective; The parent sample group is subjected to crossover and mutation operations in sequence to generate a child sample group. The parent sample group and the child sample group are merged. The merged sample group is then subjected to non-dominated sorting and crowding distance calculation again. Samples are selected in order of dominance level from low to high and crowding distance within the same dominance level from large to small until a set sample size is reached, generating a new parent sample group. The crossover, mutation, and merging selection operations are repeated until the maximum number of iterations is reached. The sample with the best overall performance is selected from the non-dominated solution set of the final parent sample group, and the volume doping ratio corresponding to this sample is used as the candidate optimal volume doping ratio.

[0051] In this embodiment, for two parent individuals With probability Perform cross generation to produce two offspring Generate random numbers ,calculate: Generate offspring: If there is no crossover (probability) ),but , Finally, the offspring individuals are constrained to Inside.

[0052] In this embodiment, for each offspring individual With probability Perform mutation to generate random numbers ,calculate: The mutated individual is: The constraints are: 。

[0053] Please see Figure 4 The present invention also provides a system for evaluating the freeze-thaw durability of recycled concrete. This system is used to perform the aforementioned method for evaluating the freeze-thaw durability of recycled concrete, and includes: The coefficient acquisition module acquires surface morphology data and three-dimensional morphology data of recycled aggregate and natural aggregate respectively. Based on the obtained surface morphology data and three-dimensional morphology data, it determines the surface fractal dimension and three-dimensional morphology coefficient of the two aggregates respectively. Based on the surface fractal dimension and three-dimensional morphology coefficient, it determines the aggregate structure matching degree between the two aggregates. It detects the changes in the mass and water absorption rate of recycled aggregate before and after a preset number of pre-freeze-thaw cycle tests. Based on the test results, it calculates the freeze-thaw sensitivity coefficient of the mortar attached to the recycled aggregate. The sample determination module sets several sets of volumetric admixture ratios of recycled aggregate and natural aggregate. According to each set of volumetric admixture ratios, corresponding mixed aggregate samples and concrete samples are prepared. The performance of each set of samples is tested at multiple freeze-thaw cycle nodes to obtain relative dynamic elastic modulus and mass loss rate data. Based on the relative dynamic elastic modulus and mass loss rate, the comprehensive index of freeze-thaw durability, average damage rate and total cumulative damage under each admixture ratio are determined. A sample dataset is established between the volumetric admixture ratio and the comprehensive index of freeze-thaw durability, average damage rate and total cumulative damage. The model output module determines the candidate admixture range based on the qualified proportion segment continuously distributed on the proportion axis of the sample dataset. It constructs the input feature vector based on the volume admixture ratio of recycled aggregate, the matching degree of aggregate structure and the freeze-thaw sensitivity coefficient. It trains a Gaussian process regression model using the sample data in the candidate admixture range. The Gaussian process regression model takes the input feature vector as input and outputs the comprehensive index of freeze-thaw durability, the average damage rate and the total cumulative damage. The proportion determination module, based on the trained Gaussian process regression model, takes the optimal freeze-thaw durability performance, the slowest freeze-thaw damage development, and the lowest total cumulative damage as optimization objectives. It adaptively seeks optimization within the candidate dosage range through a multi-objective optimization algorithm, and finally determines the optimal freeze-thaw durability dosage ratio of recycled aggregate concrete.

[0054] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0055] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0056] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0057] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for evaluating the freeze-thaw resistance and durability of recycled concrete, characterized in that, The specific steps include: Step 1: Obtain surface morphology data and three-dimensional morphology data of recycled aggregate and natural aggregate respectively. Based on the obtained surface morphology data and three-dimensional morphology data, determine the surface fractal dimension and three-dimensional morphology coefficient of the two aggregates respectively. Based on the surface fractal dimension and three-dimensional morphology coefficient, determine the aggregate structure matching degree between the two aggregates. Detect the changes in the mass and water absorption rate of recycled aggregate before and after a preset number of pre-freeze-thaw cycle tests. Calculate the freeze-thaw sensitivity coefficient of the mortar attached to the recycled aggregate based on the test results. Step 2: Set several sets of volumetric admixture ratios of recycled aggregate and natural aggregate, prepare corresponding mixed aggregate samples and concrete samples according to each set of volumetric admixture ratios, and conduct performance tests on each set of samples at multiple freeze-thaw cycle nodes to obtain relative dynamic elastic modulus and mass loss rate data. Based on the relative dynamic elastic modulus and mass loss rate, determine the comprehensive index of freeze-thaw durability, average damage rate and total cumulative damage under each admixture ratio, and establish a sample dataset between the volumetric admixture ratio and the comprehensive index of freeze-thaw durability, average damage rate and total cumulative damage. Step 3: Based on the qualified proportion segment continuously distributed on the proportion axis of the sample dataset, the candidate admixture interval is determined. The input feature vector is constructed based on the volume admixture ratio of recycled aggregate, the matching degree of aggregate structure and the freeze-thaw sensitivity coefficient. A Gaussian process regression model is trained by using the sample data in the candidate admixture interval. The Gaussian process regression model takes the input feature vector as input and takes the comprehensive index of freeze-thaw durability, the average damage rate and the total cumulative damage as output. Step 4: Based on the trained Gaussian process regression model, with the optimization objectives of optimal freeze-thaw durability, slowest freeze-thaw damage development, and lowest total cumulative damage, an adaptive optimization algorithm is used within the candidate admixture range to finally determine the optimal freeze-thaw durability admixture ratio for recycled aggregate concrete.

2. The method for evaluating the freeze-thaw resistance and durability of recycled concrete according to claim 1, characterized in that: Surface morphology data includes roughness index, angularity coefficient, and surface porosity distribution. The specific method for determining the surface fractal dimension of aggregate based on surface morphology data is as follows: the roughness index, angularity coefficient, and surface porosity distribution of aggregate are used as coordinate components in three-dimensional space to construct surface morphology feature points of aggregate, and the Euclidean distance from the surface morphology feature point to the origin of coordinates is calculated. The obtained Euclidean distance is compared with a preset reference distance to obtain the comprehensive surface morphology index of aggregate. The comprehensive surface morphology index is logarithmically transformed, and the product of the transformed value and the preset fractal calibration coefficient is used as the fractal dimension of aggregate surface.

3. The method for evaluating the freeze-thaw resistance and durability of recycled concrete according to claim 1, characterized in that: The three-dimensional morphological data includes the sphericity, aspect ratio, and convexity index of the aggregate. The specific method for determining the three-dimensional morphological coefficients based on the three-dimensional morphological data of the aggregate is as follows: construct the sphericity, aspect ratio, and convexity index of the aggregate into a three-dimensional morphological vector; perform minimum-maximum normalization on each component of the obtained three-dimensional morphological vector; perform weighted summation on the normalized sphericity, aspect ratio, and convexity index; and multiply the summation result by a preset morphological adjustment coefficient as the three-dimensional morphological coefficient.

4. The method for evaluating the freeze-thaw resistance and durability of recycled concrete according to claim 1, characterized in that: The specific steps to obtain the aggregate structure matching degree between the two types of aggregates are as follows: Obtain the surface fractal dimension of recycled aggregate and the surface fractal dimension of natural aggregate, as well as the three-dimensional morphological coefficients of recycled aggregate and natural aggregate; The surface fractal dimension value is obtained by calculating the ratio between the surface fractal dimension of recycled aggregate and that of natural aggregate. The three-dimensional morphology coefficient value is obtained by calculating the ratio between the three-dimensional morphology coefficient of recycled aggregate and that of natural aggregate. The aggregate structure matching degree between the two aggregates is obtained by multiplying the surface fractal dimension value with the three-dimensional morphology coefficient value.

5. The method for evaluating the freeze-thaw resistance and durability of recycled concrete according to claim 1, characterized in that: The specific steps to obtain the freeze-thaw sensitivity coefficient of recycled aggregate-attached mortar are as follows: Representative recycled aggregate samples were placed in a saturated water state and subjected to five rapid freezing methods. After each freeze-thaw cycle, the changes in aggregate mass loss rate and water absorption rate were measured. The mass loss rate is obtained by calculating the ratio between the cumulative mass loss of aggregate after 5 freeze-thaw cycles and the initial mass. The water absorption rate is obtained by calculating the ratio between the water absorption rate of aggregate after freeze-thaw cycles and the water absorption rate of aggregate before freeze-thaw cycles. The freeze-thaw sensitivity coefficient of recycled aggregate-attached mortar is obtained by multiplying the mass loss rate by the water absorption rate ratio.

6. The method for evaluating the freeze-thaw resistance and durability of recycled concrete according to claim 1, characterized in that: The specific method for setting the volumetric admixture ratio of several groups of recycled aggregates and natural aggregates is as follows: set a lower limit and an upper limit for the volumetric admixture ratio of recycled aggregates, divide the range between the lower limit and the upper limit equally into multiple sub-intervals, use the left endpoint of each sub-interval as the set value for the volumetric admixture ratio of each group of recycled aggregates, and set the volumetric admixture ratio of each group of natural aggregates as the difference between the value 1 and the set value for the volumetric admixture ratio of the group of recycled aggregates.

7. The method for evaluating the freeze-thaw resistance and durability of recycled concrete according to claim 1, characterized in that: The specific steps for determining the comprehensive index of freeze-thaw durability, average damage rate, and total cumulative damage for each dosage ratio are as follows: For each concrete sample corresponding to a volumetric admixture ratio, during the freeze-thaw cycle test, the relative dynamic elastic modulus and mass loss rate of the sample are measured once at a preset interval of freeze-thaw cycles. Determine the critical number of freeze-thaw cycles for each dosage ratio; The ratio of the relative dynamic elastic modulus of the specimen after each freeze-thaw cycle to the initial relative dynamic elastic modulus is calculated to obtain the elastic modulus ratio. The damage variable of each test node is obtained by subtracting the elastic modulus ratio from 1. Using the single-cycle damage variable as the integration variable, the damage variable is integrated over the interval from the initial state to the critical number of freeze-thaw cycles, and the total cumulative damage of the specimen during the entire critical freeze-thaw cycle process is finally obtained. The ratio of the critical freeze-thaw cycle count to the critical freeze-thaw cycle count of natural aggregate concrete at each admixture ratio is used to obtain the cycle count ratio. The ratio of 1 to the root mean square of the damage increment during the freeze-thaw process is used to obtain the root mean square error ratio. The cycle count ratio and the root mean square error ratio are multiplied to obtain the comprehensive index of freeze-thaw durability. The average damage rate for each dosage ratio is obtained by calculating the ratio between the damage variable at critical failure and the critical number of freeze-thaw cycles for each dosage ratio.

8. The method for evaluating the freeze-thaw resistance and durability of recycled concrete according to claim 1, characterized in that: The specific steps for determining the candidate doping level range are as follows: Based on the established sample dataset, thresholds are set for the comprehensive antifreeze durability index, average damage rate, and total cumulative damage corresponding to each volume doping ratio group. Volume doping ratio groups that do not meet the preset antifreeze admission conditions are removed. The preset antifreeze admission conditions are as follows: for any sample data, if any of the following conditions are met, the comprehensive antifreeze durability index is lower than the preset lower durability threshold, the average damage rate is higher than the preset upper damage threshold, or the total cumulative damage is higher than the preset upper cumulative damage threshold, then the sample data is determined not to meet the preset antifreeze admission conditions and is removed. The remaining volumetric admixture ratio groups after screening are arranged in ascending order according to the numerical value of the volumetric admixture ratio of recycled aggregate to form an ordered ratio sequence. The continuity of the obtained ratio sequence is tested by calculating the absolute value of the difference between the volumetric admixture ratio of recycled aggregate between two adjacent volumetric admixture ratio groups. The absolute value of the difference is compared with a preset continuity threshold. For adjacent volumetric admixture ratio groups with an absolute value of the difference less than the preset continuity threshold, they are determined to be continuously distributed. For adjacent volumetric admixture ratio groups with an absolute value of the difference greater than or equal to the preset continuity threshold, a breakpoint is determined to exist between them. After completing the continuity detection of the proportional sequence, the proportional sequence is divided into multiple continuous proportional segments based on all the obtained breakpoints. The continuous proportional segment containing the most volume doping proportion groups is selected as the candidate doping range.

9. The method for evaluating the freeze-thaw resistance and durability of recycled concrete according to claim 1, characterized in that: The specific steps for adaptive optimization within the candidate doping level range using a multi-objective optimization algorithm are as follows: Set the population size and maximum iteration rounds, and randomly initialize the sample group within the candidate admixture range, where each individual in the initialized sample group represents a set of recycled aggregate volume admixture ratios. Each individual and its corresponding aggregate structure matching degree and freeze-thaw sensitivity coefficient are input into the trained Gaussian process regression model to predict the comprehensive index of freeze-thaw durability and the average damage rate under this admixture ratio; with the optimal freeze-thaw durability performance, the slowest freeze-thaw damage development and the lowest total cumulative damage as the optimization objectives, the objective function vector of each individual is defined. The initial sample group is sorted non-dominated, the crowding distance of each individual in the same frontier is calculated, and samples are selected in sequence according to the order of priority of dominance level and priority of the larger crowding distance in the same level, until the number of selected samples reaches the set sample size. The selected samples are used as parent samples to form the parent sample group. The parent sample group is subjected to crossover and mutation operations in sequence to generate a child sample group. The parent sample group and the child sample group are merged. The merged sample group is then subjected to non-dominated sorting and crowding distance calculation again. Samples are selected in order of dominance level from low to high and crowding distance within the same dominance level from large to small until a set sample size is reached, generating a new parent sample group. The crossover, mutation, and merging selection operations are repeated until the maximum number of iterations is reached. The sample with the best overall performance is selected from the non-dominated solution set of the final parent sample group, and the volume doping ratio corresponding to this sample is used as the candidate optimal volume doping ratio.

10. A system for evaluating the freeze-thaw resistance and durability of recycled concrete, characterized in that: The aforementioned system for evaluating the freeze-thaw durability of recycled concrete is used to implement the method for evaluating the freeze-thaw durability of recycled concrete as described in any one of claims 1-9, comprising: The coefficient acquisition module acquires surface morphology data and three-dimensional morphology data of recycled aggregate and natural aggregate respectively. Based on the obtained surface morphology data and three-dimensional morphology data, it determines the surface fractal dimension and three-dimensional morphology coefficient of the two aggregates respectively. Based on the surface fractal dimension and three-dimensional morphology coefficient, it determines the aggregate structure matching degree between the two aggregates. It detects the changes in the mass and water absorption rate of recycled aggregate before and after a preset number of pre-freeze-thaw cycle tests. Based on the test results, it calculates the freeze-thaw sensitivity coefficient of the mortar attached to the recycled aggregate. The sample determination module sets several sets of volumetric admixture ratios of recycled aggregate and natural aggregate. According to each set of volumetric admixture ratios, corresponding mixed aggregate samples and concrete samples are prepared. The performance of each set of samples is tested at multiple freeze-thaw cycle nodes to obtain relative dynamic elastic modulus and mass loss rate data. Based on the relative dynamic elastic modulus and mass loss rate, the comprehensive index of freeze-thaw durability, average damage rate and total cumulative damage under each admixture ratio are determined. A sample dataset is established between the volumetric admixture ratio and the comprehensive index of freeze-thaw durability, average damage rate and total cumulative damage. The model output module determines the candidate admixture range based on the qualified proportion segment continuously distributed on the proportion axis of the sample dataset. It constructs the input feature vector based on the volume admixture ratio of recycled aggregate, the matching degree of aggregate structure and the freeze-thaw sensitivity coefficient. It trains a Gaussian process regression model using the sample data in the candidate admixture range. The Gaussian process regression model takes the input feature vector as input and outputs the comprehensive index of freeze-thaw durability, the average damage rate and the total cumulative damage. The proportion determination module, based on the trained Gaussian process regression model, takes the optimal freeze-thaw durability performance, the slowest freeze-thaw damage development, and the lowest total cumulative damage as optimization objectives. It adaptively seeks optimization within the candidate dosage range through a multi-objective optimization algorithm, and finally determines the optimal freeze-thaw durability dosage ratio of recycled aggregate concrete.

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