A method and system for detecting the quality of recycled aggregate

Abstract

The present application relates to the technical field of detection, in particular to a quality detection method and system for recycled aggregate, the method comprising: weighing and determining the density of each particle size section of the recycled aggregate sample, obtaining the water absorption rate data of the sample after the determination of the density, and performing correlation analysis processing to obtain correlation data; collecting the crushing value data of the recycled aggregate sample after the action of a preset crushing load, and performing weighted fusion processing on the crushing value data, the density data and the water absorption rate data to obtain a first quality score; correcting the first quality score according to the correlation data to generate a second quality score, and integrating the second quality score with the particle grading data to obtain a comprehensive quality score. The application integrates the data to form a comprehensive quality score system, so that the detection result can determine whether the recycled aggregate is qualified and quantify the overall quality level, effectively reducing the influence of the performance dispersion of the recycled aggregate.

Description

Technical Field

[0001] This invention relates to the field of testing technology, and more specifically, to a method and system for quality testing of recycled aggregates. Background Technology

[0002] With the rapid development of infrastructure construction and urban renewal, the amount of construction waste generated has increased dramatically, with waste concrete accounting for the largest proportion. Recycled aggregates, prepared through processes such as crushing, impurity removal, and screening of waste concrete, have become a major way to realize the resource utilization of construction solid waste. Recycled aggregates can partially or completely replace natural sand and gravel in the preparation of recycled concrete, road base materials, and blocks, playing a significant role in alleviating the depletion of natural aggregate resources, reducing land occupation, and lowering carbon emissions.

[0003] Current technologies for quality testing of recycled aggregates mainly follow the evaluation system for natural aggregates. They generally involve measuring physical and mechanical properties separately and determining the quality of recycled aggregates based on whether each individual property meets the standard limits. This testing method uses the thresholds specified in national standards as the basis for judgment, resulting in a relatively mature testing process and acceptance rules. However, because recycled aggregates are coated with old cement mortar, contain micro-cracks internally, and have complex raw material sources, their performance exhibits greater dispersion compared to natural aggregates. Furthermore, the various individual properties often show strong correlations and mutual influences.

[0004] Existing quality testing methods only make independent threshold judgments on various performance indicators, lacking a comprehensive quantitative evaluation system for these indicators. This makes it impossible to characterize the overall quality level, resulting in significant differences in the performance of the same batch of recycled aggregate in different engineering application scenarios, making it difficult to accurately predict its actual working performance in concrete. Summary of the Invention

[0005] The main objective of this invention is to provide a method and system for quality testing of recycled aggregates, aiming to overcome the technical problem that existing quality testing methods are limited to isolated evaluation of various indicators and cannot fully reflect the overall quality characteristics of recycled aggregates.

[0006] To address the aforementioned problems, this invention proposes a method for quality testing of recycled aggregates, the method comprising: The particles of each size range of the recycled aggregate sample were weighed and their density was measured to obtain particle size distribution data and density data. Obtain the water absorption rate data of the sample after density measurement, and perform correlation analysis on the water absorption rate data and density data to obtain the correlation data; The crushing value data of the recycled aggregate sample after being subjected to a preset crushing load is collected. The crushing value data, density data and water absorption rate data are weighted and fused to obtain the first mass fraction. The first mass score is corrected based on the associated data to generate a second mass score. The second mass score is then integrated with the particle size distribution data to obtain a comprehensive quality score.

[0007] Furthermore, the step of weighing the particles of each size range of the recycled aggregate sample separately includes: Obtain the mass of a subsample after random sampling of the recycled aggregate material to obtain initial mass data; The initial mass data is sieved according to the preset particle size range to obtain particle samples of each particle size range separated by particle size range, and the particle size distribution data is obtained.

[0008] Furthermore, after the step of sieving the initial mass data according to the preset particle size range, the method further includes: The particle samples of each particle size range are weighed step by step, and the cumulative sieve residue percentage of each particle size range is calculated based on the single-stage mass and the initial mass data. The fineness modulus is calculated based on the sieve residue percentage, resulting in particle size distribution data including the cumulative sieve residue percentage and the fineness modulus.

[0009] Furthermore, the step of determining the density of particles in each size range of the recycled aggregate sample includes: Apparent density was determined by water displacement method for particle samples separated from each particle size range to obtain apparent density data; The loose bulk density of particle samples separated from each particle size range was determined by loosely filling a fixed-volume container to obtain loose bulk density data. The compaction density of particle samples separated from each particle size range was determined by tapping them in a fixed-volume container to obtain compaction density data. The apparent density data, loose bulk density data, and compact bulk density data are correlated with the particle size distribution data to obtain comprehensive density data.

[0010] Further, the step of obtaining the water absorption rate data of the sample after density measurement, and performing correlation analysis on the water absorption rate data and density data to obtain correlation data includes: The saturated samples after density measurement were dried and the water absorption rate was calculated to obtain water absorption rate data for each particle size range. The covariance and standard deviation of the water absorption rate data and density data for each particle size range were calculated to obtain the correlation coefficient; The correlation coefficient is compared with a preset absolute value to obtain the association data including the comparison result.

[0011] Further, the step of collecting the crushing value data of the recycled aggregate sample after it has been subjected to a preset crushing load, and then performing weighted fusion processing on the crushing value data, density data, and water absorption rate data to obtain the first mass fraction includes: After applying a preset crushing load to the recycled aggregate sample, the residue is sieved and the single crushing value is calculated to obtain the crushing value data. The crushing value data is assigned a first weight, the density data is assigned a second weight, and the water absorption rate data is assigned a third weight. The crushing value data, density data, and water absorption rate data are then weighted and fused to obtain a first mass fraction. The second weight is equal to the third weight and greater than the first weight.

[0012] Further, the step of correcting the first quality score based on the associated data to generate a second quality score includes: The content of adhering mortar in recycled aggregates of each particle size range is indirectly estimated based on the correlation data and the preset empirical regression coefficients. The preset empirical regression coefficients are obtained by analyzing the correlation coefficients. A correction coefficient is generated based on the content of the attached mortar, and a second mass fraction is obtained by multiplicatively correcting the first mass fraction based on the correction coefficient.

[0013] Further, the step of integrating the second mass fraction with the particle size distribution data to obtain a comprehensive quality score includes: The second quality score is assigned a fourth weight, the particle size distribution data is assigned a fifth weight, and the second quality score and the particle size distribution data are weighted and fused to obtain a comprehensive quality score. The fourth weight is greater than the fifth weight.

[0014] Furthermore, after obtaining the comprehensive quality score, the method further includes: The quality grade of the recycled aggregate is determined by comparing the comprehensive quality score with a preset multi-level scoring threshold. The comprehensive quality score and the quality grade of the recycled aggregate are summarized and processed to obtain a test report.

[0015] This invention also proposes a quality inspection system for recycled aggregates, comprising: The measurement module is used to weigh and measure the density of particles of different sizes in the recycled aggregate sample to obtain particle size distribution data and density data. The analysis module is used to acquire the water absorption rate data of the sample after the density is measured, and to perform correlation analysis on the water absorption rate data and the density data to obtain the correlation data. The association module is used to collect the crushing value data of the recycled aggregate sample after it has been subjected to a preset crushing load, and to perform weighted fusion processing on the crushing value data, density data and water absorption rate data to obtain the first mass fraction. The correction module is used to correct the first mass score based on the associated data to generate a second mass score, and to integrate the second mass score with the particle size distribution data to obtain a comprehensive quality score.

[0016] Compared with the prior art, this application has the following beneficial effects: This application proposes a quality testing method and system for recycled aggregates, which overcomes the shortcomings of existing technologies that only make independent threshold judgments on various performance indicators and lack a comprehensive quantitative evaluation system. It realizes the intrinsic correlation and weighted integration of multiple key indicators such as water absorption rate, density, and crushing value, and forms a scientific comprehensive quality scoring system through correlation correction and particle size distribution integration. This enables the test results to determine whether the recycled aggregate is qualified and to quantify its overall quality level, effectively reducing the impact of the performance dispersion of recycled aggregates and improving the accuracy of predicting the actual working performance of recycled aggregates in concrete. Thus, it provides a reliable basis for the performance of the same batch of recycled aggregates in different engineering application scenarios. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] The structures, proportions, sizes, etc., shown in the accompanying drawings are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed in the specification, and are not intended to limit the implementation conditions of this application. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size should still fall within the scope of the technical content disclosed in this application, provided that they do not affect the effects and purposes that this application can produce.

[0019] Figure 1 This is a schematic diagram of the steps of a quality testing method for recycled aggregate in one embodiment of the present invention; Figure 2 This is a schematic block diagram of a quality inspection system for recycled aggregate according to an embodiment of the present invention.

[0020] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] 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.

[0022] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of features, integers, steps, operations, elements, modules, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, modules, components, and / or groups thereof. It should be understood that when an element is referred to as “connected” or “coupled” to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein may include wireless connection or wireless coupling. The term “and / or” as used herein includes all or any modules and all combinations of one or more associated listed items.

[0023] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0024] Reference Figure 1 This invention provides a method for quality testing of recycled aggregates, comprising the following steps: S1: Weigh and measure the density of each particle size range of the recycled aggregate sample to obtain particle size distribution data and density data. In step S1, sufficient random sampling is performed from the batch of recycled aggregate to be tested. After sampling, the sample is repeatedly reduced to a quartering method until several test subsamples are obtained. Each subsample is individually numbered and its source information is recorded. The subsamples are placed in a well-ventilated drying room at a temperature of 20℃±5℃ and a relative humidity not exceeding 60% for natural air drying or dried in an oven at 105℃±5℃ until constant weight is achieved. The mass before drying (M0) and the constant weight after drying (M1) are recorded. The difference between the two is the original moisture content of the sample. After air drying or drying, particle size distribution is determined. Use square-hole sieves with aperture sizes including 40mm, 31.5mm, 26.5mm, 20mm, 16mm, 10mm, 5mm, 2.36mm, 1.18mm, 0.6mm, 0.3mm, 0.15mm, and 0.075mm. Pour all the dried sample into the top layer of the largest aperture sieves. Stack the sieves in descending order of aperture size and sieve continuously for at least 10 minutes until the mass passing through each sieve per minute does not exceed 0.1% of the total sample mass. After sieving, weigh the mass m of the residue on each sieve. i The mass passing through the chassis was also recorded. The percentage of sieve residue for each particle size range was calculated (mass of residue at a certain stage / total sample mass × 100%) and the cumulative percentage of sieve residue (the percentage of sieve residue accumulated from the largest particle size). The fineness modulus FM was calculated as (sum of cumulative percentages of sieve residue for particles larger than 2.36 mm - 5%) / 100. The above particle size distribution data includes the particle size distribution of recycled aggregate from coarse to fine. The sieve residue particles for each particle size range were collected separately into clean containers. Because the thickness and content of old mortar adhering to recycled aggregate vary greatly across different particle size ranges, larger particle size ranges often have more mortar adhering to them, resulting in lower density, while smaller particle size ranges are relatively closer to natural aggregate. Therefore, the density was measured separately for each particle size range. For apparent density, the water displacement method is used. Representative samples of approximately 1 kg to 3 kg (adjusted according to particle size) are taken from each particle size range. First, the samples are dried in an oven at 105℃ until constant weight, and the dried mass (m1) is measured. The samples are then immersed in water until fully saturated. After removal, the surface water film is quickly wiped off with a damp cloth until the sample is saturated and surface-dry, and the saturated surface-dry mass (m3) is measured. Next, the saturated surface-dry sample is placed in a basket and weighed in water, and the mass (m2) in the water is measured. The apparent density is calculated using the formula ρ. a = m1 / (m3-m2)×ρ0 (ρ0 is the density of water at the test temperature, taken as 1.0 g / cm³) 3 For each particle size range, measurements were repeated multiple times, and the average value was taken. For bulk density, two types were used: loose bulk density and tapped bulk density. A measuring cylinder with a known volume V (5L or 10L) was selected. The dry sample was allowed to fall naturally into the cylinder from a fixed height. After leveling the surface, the mass m4 was measured. The loose bulk density ρ... loose = m4 / V; After compacting on the vibratory table for 30 seconds or the number of times specified in the standard, level it again and weigh it to obtain the mass m5, and the compacted bulk density ρ. tight= m5 / V. All density data were recorded at the test temperature and converted to values ​​at the standard temperature of 20℃. After completing the above operations, the particle mass, particle size distribution data (separate sieve residue, cumulative sieve residue, fineness modulus, gradation curve) and density data (apparent density, loose bulk density, vibrated bulk density) for each particle size range were summarized into a table to form a corresponding complete dataset, reflecting the particle size composition characteristics of recycled aggregate.

[0025] S2: Obtain the water absorption rate data of the sample after density measurement, and perform correlation analysis on the water absorption rate data and density data to obtain correlation data; In step S2, the samples of each particle size range in the saturated surface-dried state were placed in an oven preheated to 105±5℃ and continuously dried until constant weight. The mass after drying was recorded as m6. Water absorption rate W a The calculation formula is: W a = (m3-m6) / m6× 100%, where m3 is the saturated surface dry mass, m6 is the oven-dry mass, and W a The unit is percentage (%). The numerator m3-m6 represents the mass of water absorbed by the interconnected pores inside the aggregate that can be filled with water, and the denominator m6 is the actual mass of the solid skeleton of the aggregate. Therefore, the water absorption rate characterizes the porosity of recycled aggregate due to the adhesion of old mortar, microcracks, etc. Because recycled aggregate usually has residual old cement mortar on its surface, its water absorption rate is generally significantly higher than that of natural aggregate (the water absorption rate of natural aggregate is usually <2%, while that of recycled aggregate is often 4% to 12% or even higher). Multiple parallel tests were conducted for each particle size range (such as 40-20mm, 20-10mm, 10-5mm, etc.), and the arithmetic mean of the multiple water absorption rate measurements was taken as the water absorption rate Wa of the particle size range, and the standard deviation was recorded. The water absorption rate data was correlated with the density data to calculate the water absorption rate W. a With apparent density ρ a The correlation coefficient r between them is calculated using the following formula: r = cov(W a , ρ a ) / (σ Wa * σ ρa ), where cov(W a , ρ a σ represents the covariance between water absorption rate and apparent density. Wa and σ ρaThe standard deviations of the two sets of data are given. The correlation coefficient r ranges from -1 to 1, and a strong correlation is considered when |r| ≥ 0.8. For recycled aggregates, a significant negative correlation is usually observed (r is generally between -0.82 and -0.95), meaning that the lower the density of the particle size range, the higher its water absorption rate, because more old mortar adheres and the more developed the internal pores. If |r| < 0.7, it is necessary to check whether there are systematic errors in the experimental operation (such as insufficient soaking or incomplete drying). Based on the strong correlation, a regression equation is further established: W a = a * ρ a + b, where a is the regression slope (usually negative) and b is the intercept. The fitting process can be done using Excel, Origin, or a simple calculator; for example, first calculate ρ. a and W a The average value is calculated, and then the sum of squared deviations is calculated for each term to finally solve for a and b. The coefficient of determination R of this regression equation is... 2 Requirement ≥0.75, R 2 The closer the value is to 1, the more significant the linear relationship. For smaller aggregate sizes (e.g., 5-10mm), the data points are biased towards low-density and high-water-absorption areas due to a higher proportion of attached mortar. For larger aggregate sizes (e.g., 20-40mm), the data points are closer to the natural aggregate area due to relatively less attached mortar. The above data are integrated to form correlation data, including various statistics (mean, standard deviation, correlation coefficient r and its significance test results (p-value < 0.05 is significant) and the regression equation.

[0026] S3: Collect the crushing value data of the recycled aggregate sample after it has been subjected to a preset crushing load, and perform weighted fusion processing on the crushing value data, density data and water absorption rate data to obtain the first mass fraction; In step S3, from the coarse aggregate retained after screening in step S1 (selecting a representative single particle size range of 5-20 mm or 10-20 mm), randomly select no less than 3 kg of dry sample and load it into the cylindrical test cylinder of the standard crushing index tester. The ratio of the inner diameter to the height of the test cylinder is 1:1, and the surface of the sample is leveled after loading. Apply a preset crushing load at a uniform rate (1 kN / s) on the press. For recycled aggregate, this is generally 40 kN to 50 kN (the specific value is determined according to the nominal particle size of the aggregate; 50 kN for the 16-31.5 mm particle size range and 40 kN for the 5-16 mm particle size range). After the load reaches the preset value, hold it for 10 seconds and then unload. Pour out all the unloaded sample and sieve it using a sieve with a specified aperture (1 / 4 of the lower limit of the nominal particle size of the aggregate being tested or a 2.36 mm standard sieve). Weigh the mass m of the fine material passing through the sieve aperture. 细 The original sample mass is m 总 Then the crushing value q (%) = (m 细 / m 总×100%. Recycled aggregates, due to the presence of old mortar on their surface, have a crushing value 5%–15% higher than natural aggregates, ranging from 12% to 25%. A lower value indicates stronger crushing resistance. After collecting the crushing value data, retrieve the density data (apparent density ρ) obtained in steps S1–S2. a ) and water absorption rate data (W a The apparent density standard value ρstandard is set to approximately 2700 kg / m2. 3 Standard value of water absorption rate W a Standard value ≈ 1.0%, crushing value standard value q standard ≈ 8% (can be adjusted appropriately according to different strength grades). Each indicator is dimensionless, and the density contribution term is ρ. a / ρ 标准 The contribution of water absorption rate is 1. W a / W a标准 The crushing value contribution item is 1. q / q 标准 The higher the three values, the better; the standard value is the reference standard value. Then, a weighted sum is calculated according to preset weights. The summation formula is: S1 = 100 * [w1×(ρ a / ρ 标准 ) + w2*(1 W a / W a标准 ) + w3*(1 q / q 标准 [], where the weights w1, w2, and w3 satisfy w1 + w2 + w3 = 1. Considering that density and water absorption have the greatest impact on the durability of recycled aggregate, and the crushing value reflects strength more, a scheme of w1 = 0.40, w2 = 0.40, and w3 = 0.20 can be adopted (in actual calculations, a lower limit is set to prevent negative scores, and the lower limit of the water absorption contribution term is set to 0, that is, if W a >W a标准 (Then this item is set to 0). In practice, the density, water absorption rate, and crushing value data of all particle size ranges can be imported and automatically normalized, weighted, and summed to generate a fused data table, showing the contribution ratio of the three indicators to the total score. If an abnormally high crushing value is found in a certain particle size range, the density and water absorption rate data of that range can be traced back to further determine whether the adhering mortar is too thick or the original stone strength is insufficient.

[0027] S4: Correct the first mass score based on the associated data to generate a second mass score, and integrate the second mass score with the particle size distribution data to obtain a comprehensive quality score.

[0028] In step S4, the regression equation W obtained in step S2 is used. a = a * ρ a+ b, the actual measured apparent density ρ for each particle size range a Substituting into the equation, the theoretical water absorption rate W of the natural aggregate corresponding to this density is calculated. a天然 Then use the actual measured water absorption rate W a实际 With W a天然 Subtracting the two, we get the increase in water absorption rate ΔW. a =W a实际 -W a天然 This increase is almost entirely caused by the adhering mortar. Subsequently, based on extensively verified empirical conversion relationships, the adhering mortar content C (%) ≈ k × ΔW a The conversion factor k can be taken as 6.5 to 8.0 (slightly adjusted for recycled aggregates from different sources, and can be periodically calibrated by acid dissolution or heat treatment). After obtaining the content of the attached mortar C, a correction function is introduced to generate the second mass fraction S2. The correction formula is S2 = S1 * (1 - α × C / C) max ), where C max The upper limit of the allowable adhesion mortar content (can be taken as 25% to 30%), α is the penalty strength coefficient, which can usually be taken between 1.0 and 1.2.0. The closer C is to or exceeds C... max The heavier the penalty, the more severe the penalty. The correction method can significantly lower the score of batches with high adhering mortar content, truly reflecting their quality defects. In another embodiment, piecewise function correction can be used: no correction or slight correction when C ≤ 10% (S2 = S1); moderate correction when C ≤ 10% (S2 = S1 × 0.85 ~ 0.95); and severe correction when C > 20% (S2 = S1 * 0.70 or less), with a recommendation for further treatment. The correction process can be completed using Excel, automatically calling regression parameters and conversion coefficients from the associated database. After calculating the second quality score S2, the final comprehensive quality score S is calculated using the following formula: S = β1 * S2 + β2 * S 级配 S 级配The particle size distribution score is 100 points. The calculation method is as follows: first, calculate the fineness modulus score (FM within the range of 2.5–3.0 receives the full 100 points; deduct 2–3 points for each deviation of 0.1); then calculate the continuous gradation bonus (Cu≥5 and 1≤Cc≤3 receive the full 100 points; otherwise, deduct points according to the degree of deviation). The weighted average of the two scores yields the S-grade. The weights β1 and β2 are adjusted according to the engineering application. For ordinary concrete, β1=0.7 and β2=0.3 can be used; for pumped concrete with high workability requirements, the gradation weights can be increased to 0.4–0.5. The final comprehensive quality score, S, is out of 100. In practical applications, it can be set as follows: S≥90 indicates high-quality recycled aggregate, which can 100% replace natural aggregate; 80-90 indicates good, which can replace 70%-100%; 70-80 indicates qualified, with a replacement ratio ≤50% and the addition of mineral admixtures for compensation; S<70 indicates unqualified, and it is recommended to use it only for low-grade fillers or after strengthening treatment (such as secondary crushing to remove mortar and particle shaping) before evaluation. By performing targeted correction based on the data related to the attached mortar and then weighting and integrating it with the particle size distribution, the problem that existing single indicators or simple weighting methods cannot accurately reflect the multiple deterioration characteristics of recycled aggregates is solved, making the comprehensive quality score scientific, comprehensive, and practical.

[0029] In another test embodiment, referring to Table 1, a comparative example was selected to compare with the present application. The test conditions were that recycled aggregate samples from the same batch and source were used, and the original properties were an average apparent density of 2520 kg / m³. 3 The average water absorption rate was 7.2%, the average crushing value was 18%, the particle size distribution fineness modulus was 2.8 with good continuous gradation (Cu=6.2, Cc=1.8), and all tests were conducted in a standard laboratory environment (temperature 20℃±2℃, relative humidity 50%±10%). Comparative Example 1 uses a common single-threshold judgment method in the prior art, which only measures physical and mechanical properties such as apparent density, water absorption rate, and crushing value, and is based on standard limits (e.g., apparent density ≥2450kg / m³). 3The method of judging whether a sample is qualified is based on individual indicators (water absorption ≤8%, crushing value ≤20%). Since all individual indicators of this batch of samples meet the requirements of Class II recycled coarse aggregate, the overall sample is judged as qualified and given a high score of 85. However, this method ignores the correlation between indicators and the comprehensive influence of attached old mortar, and cannot quantify the multiple deterioration characteristics of recycled aggregate caused by old mortar, which may lead to an overestimation of quality. Comparative Example 2 uses a weighted average method, directly performing dimensionless weighted summation (each with a weight of 1 / 3) on apparent density, water absorption, and crushing value. Although it is based on multiple indicators, it does not perform a correlation analysis between water absorption and density, nor does it introduce correction for attached mortar content. Therefore, the score is low. The score was slightly lowered to 82, but it still failed to accurately reflect the negative effects of old mortar on durability and strength. The score was too high and lacked specificity. In this embodiment, a strong negative correlation regression equation between water absorption rate and density was established through correlation analysis (r≈-0.90). The calculated content of adhering mortar was 22%. The first mass score was then specifically corrected (the penalty coefficient pulled the score down). The score was then weighted and integrated with the particle size distribution score to obtain a comprehensive quality score of 76. This score is lower and more accurate. It quantifies the defects caused by old mortar, such as increased porosity and reduced strength. It avoids the problem of overestimating the quality of recycled aggregate with high adhering mortar content and poor potential durability in existing methods.

[0030] Table 1: In one embodiment, the step of weighing the particles of each size range of the recycled aggregate sample separately includes: Obtain the mass of a subsample after random sampling of the recycled aggregate material to obtain initial mass data; The initial mass data is sieved according to the preset particle size range to obtain particle samples of each particle size range separated by particle size range, and the particle size distribution data is obtained.

[0031] In the above embodiment, random sampling is performed from the batch of recycled aggregate to be tested, with a total sampling volume of not less than 50 kg. The large batch of samples is divided into multiple sub-samples using a quartering method, with each sub-sample weighing approximately 10 kg, thus obtaining initial mass data. In actual operation, selective sampling is avoided during the sampling process, ensuring that the sub-samples are evenly distributed throughout the batch. These sub-samples are placed in a ventilated and dry environment to air dry naturally until constant weight, and the initial mass data is recorded. Surface dust and moisture are removed to standardize subsequent sieving and weighing. The samples corresponding to the initial mass data are sieved according to the preset particle size range. Standard sieve sets can be used, with sieve apertures including 40 mm, 20 mm, 10 mm, and 5 mm, etc. The sieving time is not less than 10 minutes to avoid particle blockage, resulting in particle samples separated according to particle size range, thus obtaining particle size distribution data.

[0032] In one embodiment, after the step of sieving the initial mass data according to a preset particle size range, the method further includes: The particle samples of each particle size range are weighed step by step, and the cumulative sieve residue percentage of each particle size range is calculated based on the single-stage mass and the initial mass data. The fineness modulus is calculated based on the sieve residue percentage, resulting in particle size distribution data including the cumulative sieve residue percentage and the fineness modulus.

[0033] In the above embodiments, after separating particles into different size ranges through sieving, the mass of each particle sample is weighed step by step using a balance. The weighing is repeated multiple times to obtain the average value, controlling the error within ±1% and avoiding the randomness of a single operation. The percentage of sieve residue is calculated based on the mass of each stage of residue and the initial total mass; that is, the ratio of the mass of each stage of residue to the initial mass. These percentages are then summed to obtain the cumulative percentage of sieve residue for each particle size range. The cumulative percentage of sieve residue reflects the distribution of particles from coarse to fine. For example, a high cumulative percentage of sieve residue in the larger particle size range indicates a greater number of coarse particles, which may affect the workability of concrete; too many fine particles may increase water demand. The fineness modulus is calculated based on the cumulative percentage of sieve residue using the formula FM = (sum of cumulative percentages of sieve residue - initial adjustment value) / 100. For sand, the cumulative sieve residue is taken from sieves with apertures larger than 2.36mm. A higher fineness modulus indicates coarser aggregate, reflecting the degree and distribution of particle size. Recycled aggregates often have a lower fineness modulus due to the effective particle size deviation caused by mortar adhesion.

[0034] In one embodiment, the step of determining the density of particles in each size range of a recycled aggregate sample includes: Apparent density was determined by water displacement method for particle samples separated from each particle size range to obtain apparent density data; The loose bulk density of particle samples separated from each particle size range was determined by loosely filling a fixed-volume container to obtain loose bulk density data. The compaction density of particle samples separated from each particle size range was determined by tapping them in a fixed-volume container to obtain compaction density data. The apparent density data, loose bulk density data, and compact bulk density data are correlated with the particle size distribution data to obtain comprehensive density data.

[0035] In the above embodiments, the apparent density of particle samples separated for each particle size range was determined using the water displacement method. This involves saturating a dry sample with water, weighing the saturated surface dry mass and the mass in water, and calculating the apparent density as the dry mass divided by (saturated surface dry mass minus mass in water) multiplied by the water density. This method is suitable for irregular particles, especially recycled aggregates with porous surfaces. Repeated tests were conducted, and the average value was taken, with the error controlled within ±2%. This yielded the apparent density data, which reflects the density of the aggregate solid plus closed pores. The loose bulk density of samples for each particle size range was determined by loosely filling a fixed-volume container. This involves weighing the sample after natural filling and dividing the mass by the container volume to obtain the loose bulk density data. This index represents the overall situation of interparticle voids and internal pores; a lower loose bulk density of recycled aggregate indicates a higher packing porosity. The compacted density is determined by vibration compaction, i.e., weighing the material again after compaction and dividing the mass by the volume. The compacted density is higher than the loose density because compaction reduces the voids between particles. This comparison allows for the calculation of porosity, such as (1 - loose density / apparent density) × 100%, representing the influence of internal voids and adhering mortar. The apparent density, loose density, and compacted density data are correlated with particle size distribution data by matching particles within the same size range, establishing a density-size correspondence table. This is because different particle size ranges have significant density differences; for example, smaller particle size ranges have a higher proportion of adhering mortar, resulting in lower density. The correspondence table can identify abnormal particle size ranges; for instance, low density in large-size particles may originate from microcracks.

[0036] In one embodiment, the step of obtaining the water absorption rate data of the sample after density measurement, and performing correlation analysis on the water absorption rate data and density data to obtain correlation data includes: The saturated samples after density measurement were dried and the water absorption rate was calculated to obtain water absorption rate data for each particle size range. The covariance and standard deviation of the water absorption rate data and density data for each particle size range were calculated to obtain the correlation coefficient; The correlation coefficient is compared with a preset absolute value to obtain the association data including the comparison result.

[0037] In the above embodiments, the saturated samples after density measurement are dried to constant weight, and the water absorption rate is calculated as (saturated surface dry mass minus dried mass) / dried mass × 100%, yielding water absorption rate data for each particle size range. This method efficiently utilizes the saturated samples from the previous step, avoiding repeated soaking. Recycled aggregates have high water absorption rates, often exceeding 5%, significantly higher than natural aggregates. This is mainly due to the increased porosity caused by attached mortar and microcracks. Furthermore, the water absorption rates vary greatly across different particle size ranges, with smaller particle size ranges showing higher rates, reflecting the proportion of mortar adhesion. The covariance and standard deviation of the water absorption rate data and density data for each particle size range are calculated to obtain the correlation coefficient. The negative correlation between water absorption rate and density (e.g., apparent density) can be quantified by dividing the covariance by the standard deviation of water absorption rate multiplied by the standard deviation of density. This is because low density often corresponds to more porosity and higher water absorption. A regression equation can be further established, such as water absorption rate = a * density + b. By fitting the coefficient using the least squares method, it can be shown that a high water absorption rate corresponds to increased porosity caused by attached mortar. The correlation coefficient is compared with a preset absolute value (e.g., 0.8). If it is higher than the threshold, it indicates a strong correlation and is marked as a batch with high mortar adhesion. This yields correlation data including the correlation coefficient and regression curve of the comparison results. The threshold can be adjusted based on experience. A strong correlation indicates a quality risk.

[0038] In one embodiment, the step of collecting the crushing value data of the recycled aggregate sample after it has been subjected to a preset crushing load, and then weighting and fusing the crushing value data, density data, and water absorption rate data to obtain a first mass fraction includes: After applying a preset crushing load to the recycled aggregate sample, the residue is sieved and the single crushing value is calculated to obtain the crushing value data. The crushing value data is assigned a first weight, the density data is assigned a second weight, and the water absorption rate data is assigned a third weight. The crushing value data, density data, and water absorption rate data are then weighted and fused to obtain a first mass fraction. The second weight is equal to the third weight and greater than the first weight.

[0039] In the above embodiments, a preset crushing load is applied to the recycled aggregate sample, and a coarse aggregate sample with an appropriate particle size range (e.g., 5-20 mm particle size range) is selected. The load is applied uniformly using a pressure testing machine until a specified value is reached (e.g., 400 kN held for 5 seconds). The residue is then sieved using a standard sieve (e.g., 2.36 mm or a sieve with a specific aperture). The single crushing value is calculated, which is obtained by multiplying the ratio of the mass of the fine material under the sieve to the mass of the initial sample by 100%. This value represents the crushing resistance of the recycled aggregate under pressure. The obtained crushing value data is weighted and fused with previously measured density data (including apparent density, loose bulk density, and compacted bulk density) and water absorption data. During fusion, the crushing value data is assigned a first weight, the density data a second weight, and the water absorption data a third weight, where the second weight is equal to the third weight and greater than the first weight. Density and water absorption are the core physical indicators of recycled aggregates, which dominate their strength and durability. Although crushing value is important, it is greatly affected by mortar adhesion, so its weight is relatively low. For example, the second and third weights can be set to 0.4, and the first weight to 0.2. After normalization, the weighted average or standardized score is calculated (e.g., the indicators are normalized to the natural aggregate benchmark value before fusion) to obtain the first quality score, which serves as an intermediate result for preliminary assessment of the overall quality of the aggregate.

[0040] In one embodiment, the step of correcting the first quality score based on the associated data to generate a second quality score includes: The content of adhering mortar in recycled aggregates of each particle size range is indirectly estimated based on the correlation data and the preset empirical regression coefficients. The preset empirical regression coefficients are obtained by analyzing the correlation coefficients. A correction coefficient is generated based on the content of the attached mortar, and a second mass fraction is obtained by multiplicatively correcting the first mass fraction based on the correction coefficient.

[0041] In the above embodiments, the mortar content of recycled aggregates in each particle size range is indirectly estimated based on correlation data (including correlation coefficients and comparison results of water absorption and density data) and preset empirical regression coefficients. The correlation data originates from the correlation coefficients calculated from the covariance and standard deviation in the previous step. Generally, a higher absolute value of the correlation coefficient indicates a stronger correlation between high water absorption and low density, indicating that the mortar increases porosity and microcracks. The preset empirical regression coefficients are adjusted using the correlation coefficients. In the above embodiments, the regression equation is W. a = a * ρ aThe adjustment of +b and k involves calibrating the empirical constant k using regression coefficients (primarily based on a or relevant statistics) to adapt it to the specific characteristics of the current batch of recycled aggregate. For example, k can be set to be proportional to |a| (e.g., k = preset constant / |a|) to reflect the weight of the mortar contribution. The preset constant is a pre-defined, fixed-value empirical coefficient (e.g., specific numbers like 10, 15, 100), serving as a benchmark proportionality factor to convert the regression slope |a| (absolute value) into a suitable k value, which can be obtained through empirical verification. Correction coefficients are generated based on the content of adhering mortar, for example, correction coefficient = 1 - C / C max C max The upper limit is set as an empirical limit (e.g., 30%). Based on this correction factor, the first mass fraction is multiplicatively corrected, i.e., the second mass fraction = the first mass fraction * the correction factor (or additive adjustment). The multiplicative correction method emphasizes the negative impact of mortar adhesion as a major defect of recycled aggregate, which can significantly reduce the score of high mortar batches, improve the accuracy of assessment, and generate a correction report including the estimation basis, regression curve and adjustment value.

[0042] In one embodiment, the step of integrating the second mass fraction with the particle size distribution data to obtain a comprehensive quality score includes: The second quality score is assigned a fourth weight, the particle size distribution data is assigned a fifth weight, and the second quality score and the particle size distribution data are weighted and fused to obtain a comprehensive quality score. The fourth weight is greater than the fifth weight.

[0043] In the above embodiment, the second mass fraction is assigned a fourth weight, and the particle size distribution data is assigned a fifth weight, where the fourth weight is greater than the fifth weight. This allocation indicates that the second mass fraction has been integrated with the crushing value, density, and water absorption rate and corrected by mortar, representing the core of the aggregate's intrinsic quality. Although the particle size distribution data is important, it mainly affects packing and uniformity, so its weight is relatively low. For example, the fourth weight can be set to 0.6 and the fifth weight to 0.4. After summing and normalization, a weighted fusion process is performed. The score, as a quantitative indicator, can intuitively reflect the overall applicability of recycled aggregate. For example, a high score indicates good gradation and excellent intrinsic performance, suitable for high-strength concrete, while a low score indicates the need for strengthening treatment. The entire fusion emphasizes the weight tilting towards the correction score to highlight the reliability after multi-index correction.

[0044] In one embodiment, after the step of obtaining the comprehensive quality score, the method further includes: The quality grade of the recycled aggregate is determined by comparing the comprehensive quality score with a preset multi-level scoring threshold. The comprehensive quality score and the quality grade of the recycled aggregate are summarized and processed to obtain a test report.

[0045] In the above embodiments, the comprehensive quality score is compared with preset multi-level scoring thresholds for judgment. For example, thresholds are set such as ≥90 points as excellent, 70-90 points as qualified, and <70 points as unqualified, or further subdivided into multiple levels (such as Class I, Class II, and Class III corresponding to different applications). The thresholds are preset based on the natural aggregate benchmark and the recycled aggregate standard to obtain the quality grade of the recycled aggregate. This grade directly guides the application. For example, the excellent grade can completely replace natural aggregate, while the qualified grade needs to partially replace or strengthen it. The comprehensive quality score, quality grade, and all original data (such as particle size distribution table, density data, water absorption rate, crushing value, correlation analysis, and correction details) are summarized and processed, including tables, graphs, and suggestions, to generate a complete output including a test report. The entire process is linked from sample pretreatment to report output. It is easy to operate and the results are reliable, which can effectively improve the quality control level of recycled aggregate.

[0046] Reference Figure 2 A quality inspection system for recycled aggregates, comprising: The measurement module 100 is used to weigh and measure the density of particles of each size range in the recycled aggregate sample to obtain particle size distribution data and density data. The analysis module 200 is used to acquire the water absorption rate data of the sample after the density is measured, and to perform correlation analysis processing on the water absorption rate data and the density data to obtain the correlation data; The association module 300 is used to collect the crushing value data of the recycled aggregate sample after it has been subjected to a preset crushing load, and to perform weighted fusion processing on the crushing value data, density data and water absorption rate data to obtain the first mass fraction. The correction module 400 is used to correct the first mass score based on the associated data to generate a second mass score, and to integrate the second mass score with the particle size distribution data to obtain a comprehensive quality score.

[0047] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for quality testing of recycled aggregate, characterized in that, include: The particles of each size range of the recycled aggregate sample were weighed and their density was measured to obtain particle size distribution data and density data. Obtain the water absorption rate data of the sample after density measurement, and perform correlation analysis on the water absorption rate data and density data to obtain the correlation data; Collect the crushing value data of the recycled aggregate sample after it has been subjected to a preset crushing load, and perform weighted fusion processing on the crushing value data, density data and water absorption rate data to obtain the first mass fraction; The first mass score is corrected based on the associated data to generate a second mass score. The second mass score is then integrated with the particle size distribution data to obtain a comprehensive quality score. The steps for determining the density of particles in different size ranges of recycled aggregate samples include: Apparent density was determined by water displacement method for particle samples separated from each particle size range to obtain apparent density data; The loose bulk density of particle samples separated from each particle size range was determined by loosely filling a fixed-volume container, and the loose bulk density data were obtained. The compaction density of particle samples separated from each particle size range was determined by tapping them in a fixed-volume container to obtain compaction density data. The apparent density data, loose bulk density data, and compacted bulk density data are correlated with the particle size distribution data to obtain comprehensive density data. The step of obtaining the water absorption rate data of the sample after density measurement, and performing correlation analysis on the water absorption rate data and density data to obtain correlation data includes: The saturated samples after density measurement were dried and the water absorption rate was calculated to obtain water absorption rate data for each particle size range. The covariance and standard deviation of the water absorption rate data and density data for each particle size range were calculated to obtain the correlation coefficient; The correlation coefficient is compared with a preset absolute value to obtain the associated data including the comparison result; The step of collecting the crushing value data of the recycled aggregate sample after it has been subjected to a preset crushing load, and then weighting and fusing the crushing value data, density data, and water absorption rate data to obtain the first mass fraction includes: After applying a preset crushing load to the recycled aggregate sample, the residue is sieved and the single crushing value is calculated to obtain the crushing value data. The crushing value data is assigned a first weight, the density data is assigned a second weight, and the water absorption rate data is assigned a third weight. The crushing value data, density data, and water absorption rate data are then weighted and fused to obtain a first mass fraction. Wherein, the second weight is equal to the third weight and greater than the first weight; The step of correcting the first quality score based on the associated data to generate a second quality score includes: The content of adhering mortar in recycled aggregates of each particle size range is indirectly estimated based on the correlation data and the preset empirical regression coefficients. The preset empirical regression coefficients are obtained by analyzing the correlation coefficients. A correction coefficient is generated based on the content of the attached mortar, and a second mass fraction is obtained by multiplicatively correcting the first mass fraction based on the correction coefficient. The step of integrating the second mass fraction with the particle size distribution data to obtain a comprehensive quality score includes: The second quality score is assigned a fourth weight, the particle size distribution data is assigned a fifth weight, and the second quality score and the particle size distribution data are weighted and fused to obtain a comprehensive quality score. The fourth weight is greater than the fifth weight.

2. The method for quality testing of recycled aggregate according to claim 1, characterized in that, The step of weighing particles of each size range of the recycled aggregate sample separately includes: Obtain the mass of sub-samples after random sampling of recycled aggregate materials to obtain initial mass data; The initial mass data is sieved according to the preset particle size range to obtain particle samples of each particle size range separated by particle size range, and the particle size distribution data is obtained.

3. The method for quality testing of recycled aggregate according to claim 2, characterized in that, After the step of sieving the initial mass data according to the preset particle size range, the method further includes: The particle samples of each particle size range are weighed step by step, and the cumulative sieve residue percentage of each particle size range is calculated based on the single-stage mass and the initial mass data. The fineness modulus is calculated based on the cumulative sieve residue percentage, resulting in particle size distribution data that includes both the cumulative sieve residue percentage and the fineness modulus.

4. The method for quality testing of recycled aggregate according to claim 1, characterized in that, After obtaining the comprehensive quality score, the method further includes: The quality grade of the recycled aggregate is determined by comparing the comprehensive quality score with a preset multi-level scoring threshold. The comprehensive quality score and the quality grade of the recycled aggregate are summarized and processed to obtain the test report.

5. A quality inspection system for recycled aggregate, applied to the quality inspection method for recycled aggregate according to any one of claims 1 to 4, characterized in that, include: The measurement module is used to weigh and measure the density of particles of different sizes in the recycled aggregate sample to obtain particle size distribution data and density data. The analysis module is used to acquire the water absorption rate data of the sample after the density is measured, and to perform correlation analysis on the water absorption rate data and the density data to obtain the correlation data. The fusion module is used to collect the crushing value data of the recycled aggregate sample after it has been subjected to a preset crushing load, and to perform weighted fusion processing on the crushing value data, density data and water absorption rate data to obtain the first mass fraction. The correction module is used to correct the first mass score based on the associated data to generate a second mass score, and to integrate the second mass score with the particle size distribution data to obtain a comprehensive quality score.

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