A method and system for controlling the stability of steel slag aggregate

Abstract

This invention provides a method and system for controlling the stability of steel slag aggregate, belonging to the technical field of steel slag aggregate stability control. The method determines the degree of specimen damage under different combinations of maximum steel slag particle size and dosage through autoclaving accelerated testing. The degree of damage is quantified into a continuous stability index according to a preset grading standard. A quadratic polynomial regression model is established between the stability index and the maximum steel slag particle size and dosage, which also includes interaction terms for particle size and dosage. Based on the model and a preset engineering acceptable threshold, the maximum safe dosage or maximum allowable particle size of steel slag that meets the threshold requirement is calculated. The mix design of road base materials is then performed based on the calculation results. This invention achieves continuous quantitative characterization of the influence of steel slag particle size and dosage on stability, overcoming the shortcomings of existing technologies that rely on single-factor empirical limits and lack engineering back-calculation methods. It can improve the resource utilization rate of steel slag while ensuring stability.

Description

Technical Field

[0001] This invention relates to the field of steel slag aggregate stability control technology, and in particular to a method and system for steel slag aggregate stability control. Background Technology

[0002] Steel slag is an important industrial solid waste generated during the iron and steel smelting process. It is characterized by high strength, rough surface, and good wear resistance, making it a valuable resource for road engineering as an aggregate substitute for natural aggregates. Especially in road base materials, steel slag can partially replace crushed stone aggregates, which not only helps reduce the consumption of natural stone but also improves the utilization rate of solid waste resources, resulting in significant economic and environmental benefits.

[0003] However, steel slag typically contains a certain amount of potentially expansive components such as free calcium oxide and free magnesium oxide. When exposed to water or under long-term service conditions, these components may continue to undergo hydration reactions, causing volume expansion and leading to stability issues in the base material, such as cracking, bulging, and strength reduction. This insufficient stability has become a key limiting factor for the large-scale, safe, and high-value application of steel slag in road base layers.

[0004] Existing methods for controlling the application of steel slag can be mainly categorized as follows: The first category is empirical control methods, such as individually limiting the amount of steel slag added, individually restricting the maximum particle size of steel slag, or reducing the risk of expansion by extending the aging time or strengthening autoclaving pretreatment. Although the above methods can alleviate the problem of steel slag volume instability to some extent, it is difficult to reflect the synergistic effect of the maximum particle size and amount of steel slag on stability using single-factor limit control. The influence of steel slag on the volume stability of the base layer is different under different particle size conditions, and precise design cannot be achieved by relying solely on empirical values ​​for control.

[0005] The second category involves experimental research methods that simultaneously consider particle size and admixture dosage. Some literature employs orthogonal or uniform experiments to examine the effects of steel slag dosage and particle size on material properties, using range analysis or variance analysis to find the optimal mix proportion. For example, some studies have used uniform experiments to analyze the effects of steel slag dosage and particle size on the properties of lime-fly ash steel slag soil, while others have used orthogonal experimental methods to conduct compaction tests, comprehensively considering three factors: steel slag dosage, aging age, and maximum particle size. However, these studies only remain at the level of factor analysis and finding the optimal mix proportion; they have not quantified the stability index and established a continuous functional relationship with particle size and dosage, nor have they revealed the interaction between particle size and dosage through regression analysis, let alone established a regression model that can be used for engineering design. Furthermore, existing research lacks methods to correlate stability evaluation results with engineering acceptable thresholds and to calculate design parameters accordingly.

[0006] The third category is the evaluation method for the soundness of steel slag. Existing standards or literature usually use the autoclaving or boiling method to evaluate the volumetric soundness of steel slag. The evaluation results are mostly qualitative descriptions (such as "qualified" or "unqualified") or single expansion rate values, lacking detailed multi-level continuous indices, making it difficult to directly use them for the establishment of quantitative models.

[0007] In summary, existing technologies lack a method for controlling the stability of steel slag aggregates that comprehensively considers the synergistic effect of steel slag particle size and admixture dosage, quantifies stability evaluation results into continuous indices, and establishes regression models to achieve quantitative calculations and engineering back-calculations. This leads to problems such as unclear safety boundaries, insufficient control precision, and weak practical design guidance in the application of steel slag in road base courses. Summary of the Invention

[0008] The purpose of this invention is to provide a method and system for controlling the stability of steel slag aggregate, which solves the problems of existing steel slag aggregate stability control relying on single-factor empirical limits, lacking continuous regression models for particle size and dosage, and lacking engineering back-calculation methods.

[0009] To achieve the above objectives, the present invention provides a method for controlling the stability of steel slag aggregate, comprising the following steps: Step S1: Determine the degree of damage to specimens under different combinations of maximum steel slag particle size and steel slag content through autoclaving accelerated test, and quantify the degree of damage into a continuous stability index according to the preset grading standard. Step S2: Establish a quadratic polynomial regression model between the stability index and the maximum particle size of steel slag and the amount of steel slag content. This model also includes the interaction term between the maximum particle size of steel slag and the amount of steel slag content. Step S3: Based on the quadratic polynomial regression model and the preset stability index engineering acceptable threshold, back-calculate the maximum safe content or maximum allowable particle size of steel slag that meets the threshold requirements. Step S4: Design the mix proportion of road base materials according to the maximum particle size of steel slag or the amount of steel slag obtained by back calculation.

[0010] The preferred expression for the quadratic polynomial regression model is as follows: ; in, The stability index is dimensionless. This represents the maximum particle size of steel slag, in mm. This represents the steel slag content, expressed in percent.

[0011] Preferably, the accelerated pressure steam test in step S1 specifically includes: After 28 days of standard curing, the molded specimens were placed in an autoclave and autoclaved for 3 hours at 215℃ and 2.0MPa saturated steam. The grading standards are as follows: Level I, stability index is 1; Level II, stability index is 0.8; Level III, stability index is 0.6; Level IV, stability index is 0.4; Level V, stability index is 0.2.

[0012] Preferably, the stability index is an acceptable threshold for engineering. .

[0013] Preferably, the specific method for inverse calculation in step S3 is as follows: When the maximum particle size of steel slag is known Find the maximum safe dosage of steel slag. When, let the quadratic polynomial regression model in ,Will Solve after substituting Take the value that satisfies the stability index being greater than or equal to The largest value; When the steel slag content is known Find the maximum allowable particle size When, let the quadratic polynomial regression model in ,Will Solve after substituting Take the value that satisfies the stability index being greater than or equal to The largest value.

[0014] Preferably, steel slag aggregate is used in road base materials to replace the 0-2.36mm, 2.36-4.75mm and 4.75-9.5mm particle sizes in natural sand and gravel aggregates.

[0015] Preferably, in step S2, the method for establishing the quadratic polynomial regression model includes: With the maximum particle size of steel slag and steel slag content As the independent variable, the stability index As the dependent variable, a quadratic polynomial regression model is fitted using the least squares method. By minimizing the sum of squared residuals between the model's predicted values ​​and the experimentally measured values, the regression coefficients are obtained. , , , , , .

[0016] Preferably, the method further includes: When the source of steel slag changes, the model is corrected and updated by supplementing the autoclave test data of the new source of steel slag, refitting the coefficients in the quadratic polynomial regression model.

[0017] Preferably, the mix design in step S4 also includes determining the cement content, optimum moisture content and maximum dry density, and comparing and verifying the design results with the control mix design without steel slag.

[0018] The present invention also provides a steel slag aggregate stability control system for executing a steel slag aggregate stability control method as described above, comprising: The stability index determination module is used to determine the degree of damage to specimens under different combinations of maximum steel slag particle size and steel slag content through autoclaving accelerated testing, and to quantify the degree of damage into a continuous stability index according to a preset grading standard. The regression model building module is used to build a quadratic polynomial regression model between the stability index and the maximum particle size of steel slag and the steel slag content. This model also includes the interaction term between the maximum particle size of steel slag and the steel slag content. The parameter back calculation module is used to back-calculate the maximum safe content or maximum allowable particle size of steel slag that meets the threshold requirements based on the quadratic polynomial regression model and the preset stability index engineering acceptable threshold. The mix design module is used to design the mix proportion of road base materials according to the maximum particle size of steel slag or the steel slag content obtained by back calculation. The model update module is used to correct and update the model by supplementing the autoclave test data of the new source of steel slag with the coefficients in the quadratic polynomial regression model.

[0019] Therefore, the present invention employs the above-mentioned method and system for controlling the stability of steel slag aggregate, and the beneficial technical effects are as follows: (1) By establishing a quadratic polynomial regression model that simultaneously includes the interaction term of the maximum particle size of steel slag and the dosage, this invention has for the first time realized the continuous quantitative characterization of the influence of particle size and dosage on stability, which solves the problem that the existing technology relies only on the empirical limit of a single factor and cannot reflect the synergistic effect of the two, and significantly improves the scientificity and accuracy of stability control.

[0020] (2) Based on the stability index grading and quantification standard and the engineering acceptable threshold, the present invention uses the regression model to back-calculate the maximum safe dosage or maximum allowable particle size that meets the stability requirements. This overcomes the shortcomings of existing experimental research methods (such as orthogonal experiments) that can only obtain discrete optimal mix proportions, lack continuous functional relationships and engineering back-calculation methods, and realizes the rapid and accurate design of steel slag aggregate mix proportions.

[0021] (3) By setting the stability index threshold to 0.6 (corresponding to Level III damage), this invention can fully tap the high utilization potential of steel slag aggregate while ensuring the volume stability of the road base material, avoid reducing the utilization rate of solid waste resources due to overly conservative control parameters, and avoid engineering hazards caused by blindly increasing the dosage. It has good economic benefits and promotion and application value. Attached Figure Description

[0022] Figure 1 The evaluation indicators for pressure-steam stability include, Figure 1 (a) in the figure represents the morphology model of a specimen with a damage level of I. Figure 1 (b) in the figure represents the morphology model of a specimen with a damage level of II. Figure 1 (c) in the figure represents the morphology model of a specimen with a damage level of III. Figure 1 (d) in the figure represents the morphology model of the specimen with damage level IV. Figure 1 (e) in the figure represents the morphology model of a specimen with a damage level of V; Figure 2 The results of the stability test of the steel slag base course; among them, Figure 2 (a) is a specimen with a steel slag particle size of 2.36 mm and an admixture content of 30%, and a stability level of III; Figure 2 (b) is a specimen with a steel slag particle size of 4.75 mm and an admixture content of 30%, with a stability class of V; Figure 2 (c) in the sample is a steel slag with a particle size of 9.5 mm and an admixture content of 30%, with a stability level of V; Figure 2 (d) in the figure represents a specimen with a steel slag particle size of 2.36 mm and an admixture content of 20%, and a stability level of II. Figure 2 (e) in the text refers to a specimen with a steel slag particle size of 4.75 mm and an admixture content of 20%, and a stability level of III. Figure 2 (f) in the figure represents a steel slag specimen with a particle size of 9.5 mm and an admixture content of 20%, with a stability level of IV. Figure 2 (g) in the sample is a steel slag with a particle size of 2.36 mm and an admixture content of 10%, with a stability level of I. Figure 2 (h) in the figure refers to a specimen with a steel slag particle size of 4.75 mm and an admixture content of 10%, and a stability level of II. Figure 2 (i) is a specimen with a steel slag particle size of 9.5 mm and an admixture content of 10%, and a stability level of III; Figure 2 (j) in the figure represents a specimen with zero steel slag content and a stability class of I. Figure 3 Evaluation of the impact of steel slag content and maximum particle size on the stability of steel slag base course; Figure 4 This is a regression relationship model. Detailed Implementation

[0023] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0024] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0025] Example 1 This embodiment provides a method for controlling the stability of steel slag aggregate based on the correlation equation between particle size and admixture content, which is specifically applied to the design and verification of cement-stabilized steel slag material for road base.

[0026] 1. Test materials and mixing ratio.

[0027] The steel slag used in the experiment was converter steel slag from a steel plant, which was crushed and screened to obtain three particle sizes: 0-2.36mm, 2.36-4.75mm, and 4.75-9.5mm. PO 42.5 ordinary Portland cement was used, with a fixed cement content of 5%. Limestone crushed stone was used as the natural sand and gravel aggregate.

[0028] The designed steel slag content is 10%, 20%, and 30% (replaced by the total mass of natural sand and gravel aggregate), and the maximum particle size of steel slag is 2.36mm, 4.75mm, and 9.5mm, respectively. A total of 10 mix proportions were designed, including a control sample (S0) without steel slag. The specific mix proportions are shown in Table 1.

[0029] Table 1. Test mix proportions showing the influence of steel slag particle size and steel slag content on the stability of pavement base course.

[0030] Specimens were molded according to the mix proportions in Table 1: cylindrical specimens with a diameter of 100×100mm were used for steel slag base courses and cement-steel slag stabilized soil; cubic specimens with a diameter of 100mm×100mm×100mm were used for cement-steel slag concrete. The specimens were cured for 28 days under standard curing conditions.

[0031] 2. Accelerated pressure steam test and stability evaluation.

[0032] After 28 days of curing, the specimens were placed in an autoclave and autoclaved for 3 hours at 215℃ and 2.0MPa saturated steam. After removal, the surface damage of the specimens was observed, including cracks, defects, bulges, and disintegration.

[0033] Based on the degree of damage, stability is quantified into a continuous stability index according to a pre-defined five-level classification standard. : Level I: The specimen is intact, with a smooth surface, no defects, no cracks, and a stability index of 1; Level II: The specimen is basically intact, with local defects and protrusions on the surface, no cracks, and a stability index of 0.8; Level III: The specimen is incomplete, with small-area defects and protrusions on the surface, but no cracks, and the stability index is 0.6; Level IV: The specimen is incomplete, with large areas of defects and protrusions on the surface, and cracks are present. The stability index is 0.4. Grade V: The specimen collapsed, with obvious cracks, and the stability index was 0.2.

[0034] Figure 1 This is a schematic diagram of the evaluation indicators for pressure-steam stability. Figure 1 The typical failure modes of Class I to Class V specimens are shown from left to right, and the corresponding stability indices (1, 0.8, 0.6, 0.4, 0.2) are marked.

[0035] Figure 2 This is a collection of photos showing the results of the stability test observation of steel slag base course. Figure 2 The actual failure morphology of specimens after autoclaving for each mix proportion is shown, and the corresponding ratings (such as III, V, II, I, etc.) are indicated. From Figure 2 It can be seen that as the amount of steel slag and the maximum particle size increase, the degree of damage to the specimens gradually increases.

[0036] Figure 3 The figure shows the evaluation results of the influence of steel slag content and maximum particle size on the stability of steel slag base course. Figure 3 Different features are displayed in the form of three-dimensional surfaces or contour lines. , Stability index under combination The distribution directly reflects the coupled effect of particle size and dosage on stability.

[0037] 3. Establishment of the regression model.

[0038] With the maximum particle size of steel slag (mm) and steel slag content (%) is the independent variable, and the stability index is used as the criterion. As the dependent variable, a quadratic polynomial regression model is fitted using the least squares method: ; By minimizing the sum of squared residuals between the model's predicted values ​​and the experimentally measured values, the regression coefficients are obtained. , , , , , The final fitted regression equation is as follows: ; in, The maximum particle size of steel slag is (mm). The percentage is the amount of steel slag added. This is a stability index (dimensionless). The model also includes... and Interaction terms (coefficients) This model can quantitatively reflect the coupling effect of particle size and doping concentration on stability. The coefficient is positive (+0.0117), indicating that the rate of decrease in the stability index gradually slows down with increasing steel slag content. The regression model is as follows: Figure 4 As shown.

[0039] 4. Acceptable thresholds and back-calculation design for engineering.

[0040] Based on engineering experience and autoclaving test results, the acceptable threshold for the stability index was determined. This corresponds to Level III damage (small surface defects or protrusions but no cracks). When the stability index is ≥0.6, the specimen is considered to meet the engineering requirements for stability.

[0041] Using the regression model described above, we can perform an inverse calculation design: Scenario 1: Given the maximum particle size of steel slag, calculate the maximum safe dosage. Assume the project requires the maximum particle size of steel slag to not exceed 4.75 mm. Let... , Substitute the values ​​into the regression equation and solve for the desired value. The largest Value, get That is, when the maximum particle size of steel slag is 4.75mm, the maximum safe dosage is 26.5%. The actual mix design can use a steel slag dosage of 26%, and after autoclaving, the stability index is 0.62, which meets the requirements.

[0042] Scenario 2: Given the steel slag content, determine the maximum allowable particle size. Assume the planned steel slag content for the project is 20%. Let... , Substitute into the regression equation and solve. Take the satisfied The largest Value, get That is, when the steel slag content is 20%, the maximum allowable particle size is 4.75mm. In actual production, the maximum particle size of the steel slag should be controlled to not exceed 4.75mm.

[0043] 5. Mix design and verification.

[0044] The mix design was carried out based on the back-calculation results. Taking Scenario 1 as an example, the final mix proportion was: 5% cement content, 26% steel slag content, with a maximum steel slag particle size of 4.75mm, and alternative particle sizes including 0-2.36mm, 2.36-4.75mm, and 4.75-9.5mm. The optimum moisture content was determined to be 5.4%, and the maximum dry density was 2.485g / cm³. 3 The design results were compared and verified with the control mix proportion S0 without steel slag. After autoclaving, the stability index was 0.62, which met the threshold requirement, and there were no defects such as cracking or bulging.

[0045] It is worth noting that all contents not described in detail in this invention are existing technologies and are well known to those skilled in the art.

[0046] Therefore, the present invention adopts the above-mentioned method and system for controlling the stability of steel slag aggregate, realizing the coordinated quantitative control of steel slag particle size and dosage, and overcoming the defects of existing technologies that rely on single-factor empirical limits.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for controlling the stability of steel slag aggregate, characterized in that, Includes the following steps: Step S1: Determine the degree of damage to specimens under different combinations of maximum steel slag particle size and steel slag content through autoclaving accelerated test, and quantify the degree of damage into a continuous stability index according to the preset grading standard. Step S2: Establish a quadratic polynomial regression model between the stability index and the maximum particle size of steel slag and the amount of steel slag content. This model also includes the interaction term between the maximum particle size of steel slag and the amount of steel slag content. Step S3: Based on the quadratic polynomial regression model and the preset stability index engineering acceptable threshold, back-calculate the maximum safe content or maximum allowable particle size of steel slag that meets the threshold requirements. Step S4: Design the mix proportion of road base materials according to the maximum particle size of steel slag or the amount of steel slag obtained by back calculation.

2. The method for controlling the stability of steel slag aggregate according to claim 1, characterized in that, The specific expression for the quadratic polynomial regression model is: ； in, The stability index is dimensionless. This represents the maximum particle size of steel slag, in mm. This represents the steel slag content, expressed in percent.

3. The method for controlling the stability of steel slag aggregate according to claim 1, characterized in that, The accelerated autoclaving test in step S1 specifically includes: After 28 days of standard curing, the molded specimens were placed in an autoclave and autoclaved for 3 hours at 215℃ and 2.0MPa saturated steam. The grading standards are as follows: Level I, stability index is 1; Level II, stability index is 0.8; Level III, stability index is 0.6; Level IV, stability index is 0.4; Level V, stability index is 0.

2.

4. The method for controlling the stability of steel slag aggregate according to claim 1, characterized in that, Stability index engineering acceptable threshold .

5. The method for controlling the stability of steel slag aggregate according to claim 1, characterized in that, The specific method for inverse calculation in step S3 is as follows: When the maximum particle size of steel slag is known Find the maximum safe dosage of steel slag. When, let the quadratic polynomial regression model in ,Will Solve after substituting Take the value that satisfies the stability index being greater than or equal to The largest value; When the steel slag content is known Find the maximum allowable particle size When, let the quadratic polynomial regression model in ,Will Solve after substituting Take the value that satisfies the stability index being greater than or equal to The largest value.

6. The method for controlling the stability of steel slag aggregate according to claim 1, characterized in that, Steel slag aggregate is used in road base materials to replace the 0-2.36mm, 2.36-4.75mm and 4.75-9.5mm particle sizes in natural sand and gravel aggregates.

7. The method for controlling the stability of steel slag aggregate according to claim 1, characterized in that, In step S2, the method for establishing the quadratic polynomial regression model includes: With the maximum particle size of steel slag and steel slag content As the independent variable, the stability index As the dependent variable, a quadratic polynomial regression model is fitted using the least squares method. By minimizing the sum of squared residuals between the model's predicted values ​​and the experimentally measured values, the regression coefficients are obtained. , , , , , .

8. The method for controlling the stability of steel slag aggregate according to claim 1, characterized in that, The method further includes: When the source of steel slag changes, the model is corrected and updated by supplementing the autoclave test data of the new source of steel slag, refitting the coefficients in the quadratic polynomial regression model.

9. The method for controlling the stability of steel slag aggregate according to claim 1, characterized in that, Step S4, the mix design, also includes determining the cement content, optimum moisture content, and maximum dry density, and comparing and verifying the design results with the control mix design without steel slag.

10. A stability control system for steel slag aggregate, characterized in that, A method for implementing the stability control of steel slag aggregate as described in any one of claims 1-9, comprising: The stability index determination module is used to determine the degree of damage to specimens under different combinations of maximum steel slag particle size and steel slag content through autoclaving accelerated testing, and to quantify the degree of damage into a continuous stability index according to a preset grading standard. The regression model building module is used to build a quadratic polynomial regression model between the stability index and the maximum particle size of steel slag and the steel slag content. This model also includes the interaction term between the maximum particle size of steel slag and the steel slag content. The parameter back-calculation module is used to back-calculate the maximum safe content or maximum allowable particle size of steel slag that meets the threshold requirements based on the quadratic polynomial regression model and the preset stability index engineering acceptable threshold. The mix design module is used to design the mix proportion of road base materials according to the maximum particle size of steel slag or the steel slag content obtained by back calculation. The model update module is used to correct and update the model by supplementing the autoclave test data of the new source of steel slag with the coefficients in the quadratic polynomial regression model.

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