Green recycled aggregate strengthening method based on multi-component composite modification

Abstract

The invention provides a green recycled aggregate strengthening method based on multi-component composite modification, and belongs to the technical field of building materials.The green recycled aggregate strengthening method comprises the steps that waste concrete is crushed and screened to obtain recycled aggregate, and a stable nano-silicon dioxide dispersion liquid and a composite cementing material are prepared for basic strengthening; deep penetration of the reinforced material is realized in a supercritical carbon dioxide environment in combination with a vibration-assisted technology, and comprehensive reinforced modification is completed through vacuum negative-pressure pretreatment, sequential soaking of a composite chemical solution and fine filling of nano-silica sol. A quantitative relation model of process parameters and penetration depth is established by using a rough set theoretical knowledge discovery method, intelligent optimization control of the penetration process is realized through calculation of a dependency function and an importance weight function, and finally, an internal pore structure is obtained to obtain the fully-filled and modified high-performance reinforced recycled aggregate. The technical problem of insufficient penetration depth of the recycled aggregate reinforced material is solved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of building materials, and in particular relates to a green recycled aggregate strengthening method based on multi-element composite modification. BACKGROUND

[0002] In the field of building engineering, the resource utilization of waste concrete is mainly realized by preparing recycled aggregate through mechanical crushing. The traditional recycled aggregate strengthening technology mainly adopts surface treatment methods such as cement mortar wrapping method, chemical solution soaking method and mineral admixture modification method to improve the physical and mechanical properties of recycled aggregate. These methods play an important role in road base materials, low-grade concrete and mortar preparation engineering applications. By forming a strengthening layer on the surface of recycled aggregate, the overall performance of the aggregate is improved, providing an effective way for the reduction and resource utilization of construction waste. However, the traditional strengthening method mainly relies on the adhesion and shallow penetration of the strengthening material on the surface of the recycled aggregate. Due to the complex porous structure and irregular pore distribution characteristics of the recycled aggregate, the strengthening material mainly exists in the form of liquid in the conventional soaking and coating process. Its flow in the micro-pore is severely restricted by factors such as capillary resistance, surface tension and poor pore connectivity, making it difficult for the strengthening material to penetrate into the deep pore structure inside the aggregate. In the current recycled aggregate strengthening engineering practice, due to the limited penetration depth of the strengthening material, it can usually only reach the surface layer of the aggregate within a few millimeters, while a large number of pores and micro-cracks inside the aggregate cannot be effectively filled and modified. These un-strengthened internal weak areas are prone to become stress concentration points and failure sources under load, seriously affecting the overall strength and durability of the recycled aggregate. That is, the existing technology has the technical problem of insufficient penetration depth of the recycled aggregate strengthening material. SUMMARY

[0003] Therefore, the present application provides a green recycled aggregate strengthening method based on multi-element composite modification, which can solve the technical problem of insufficient penetration depth of the recycled aggregate strengthening material in the prior art.

[0004] The application is implemented in the following manner: the application provides a green recycled aggregate strengthening method based on multi-element composite modification, which sequentially coarsely crushes, medium crushes and finely crushes waste concrete, screens recycled aggregate particles, analyzes particle size distribution data by using a rough set theory knowledge discovery method, prepares a nano-SiO2 dispersion liquid and performs surface modification treatment, prepares a composite cementitious material and a composite cement slurry, immerses the recycled aggregate particles in the composite cement slurry for centrifugal stirring and curing treatment to obtain a cementitious material strengthened aggregate, places the cementitious material strengthened aggregate in a supercritical fluid infiltration device, applies vibration assistance in a supercritical CO2 state for infiltration treatment to obtain a supercritical strengthened aggregate, immerses the supercritical strengthened aggregate in a composite chemical solution and a nano-SiO2 sol in sequence after vacuum treatment, processes multi-source process data by using a rough set theory knowledge discovery method and adjusts process parameters according to a strength improvement rate, performs final performance detection on the chemical strengthened aggregate and establishes a strengthened aggregate performance evaluation model, and realizes deep infiltration modification of the strengthened material into the internal pore structure of the recycled aggregate by using the supercritical fluid infiltration technology combined with the vibration assistance means.

[0005] In the step of sequentially coarsely crushing, medium crushing and finely crushing the waste concrete, the waste concrete is sequentially coarsely crushed by a jaw crusher, medium crushed by a cone crusher and finely crushed by an impact crusher, and the recycled aggregate particles with a particle size in a specified range are screened by a vibrating screen.

[0006] In the rough set theory knowledge discovery method, the dependence function between the condition attributes and the decision attributes is calculated to identify the key process parameters affecting the strengthening effect of the recycled aggregate, eliminate redundant attributes and extract a minimum decision rule set, and establish a decision rule set of the particle size and the subsequent strengthening effect.

[0007] In the step of preparing the nano-SiO2 dispersion liquid, the C 19 H 42 NBr is mixed with water, a dispersant and a stabilizer, high-energy ultrasonic dispersion and mechanical stirring are combined to establish a nano-particle dispersion stability evaluation system.

[0008] In the surface modification technology, the nano-SiO2 particles are functionalized by using the surface modification technology, the surface of the nano-SiO2 particles is chemically modified, functional groups and dispersant molecules are introduced, the chemical properties and charge distribution state of the particle surface are changed, and the electrostatic repulsion and steric hindrance effect between the particles are enhanced.

[0009] In the step of preparing the composite cementitious material, the cement, silica fume and fly ash are weighed and prepared into a composite cementitious material, water is added to the composite cementitious material to prepare a composite cement slurry, and the recycled aggregate particles are immersed in the composite cement slurry for centrifugal stirring and curing treatment.

[0010] The supercritical fluid infiltration device comprises a high-pressure reaction kettle, a CO2 supply system, a temperature control system, and a vibration generator. The high-pressure reaction kettle serves as the main container and is internally provided with multiple layers of sieve plates for placing the recycled aggregate particles. The CO2 supply system compresses and heats CO2 through a high-pressure pump to make CO2 reach a supercritical state.

[0011] The vibration-assisted infiltration step specifically applies vibration assistance through a vibration generator. The vibration generator uses an electromagnetic vibrator to generate mechanical vibrations with adjustable frequency and amplitude. The vibrations are transmitted to the inside of the reaction kettle through a vibration transmission device, generating microscopic disturbances and stress wave propagation in the recycled aggregate particles.

[0012] The vacuum treatment step specifically involves placing the supercritically reinforced aggregate into a vacuum chamber to adjust to a negative pressure state for a predetermined time. This breaks the gas resistance and liquid resistance in the pores, promotes the penetration of the reinforcing material into the deep pore structure of the aggregate, and improves the penetration efficiency and uniformity of distribution.

[0013] The composite chemical solution specifically comprises a C2H4O polymer solution and a Na2SiO3 solution. The supercritically reinforced aggregate after vacuum treatment is first immersed in the composite chemical solution for soaking, and then placed in a nano-SiO2 sol for soaking. The penetration depth of the solution and the pore filling rate are monitored during the soaking process.

[0014] The C 19 H 42 NBr is hexadecyltrimethylammonium bromide, also written as CHBrN.

[0015] The nano-silica sol treatment step specifically involves placing the aggregate treated by the composite chemical solution in a nano-silica sol for soaking. The nano-silica sol can deeply penetrate into the smallest pore structure inside the aggregate for full filling and modification, forming a comprehensive reinforcement system from the surface to the core.

[0016] The multi-source process data specifically include soaking time, solution concentration, curing temperature, penetration pressure, vibration frequency, vibration amplitude process control parameters, and solution penetration depth, pore filling rate process monitoring parameters. The multi-source process data in the reinforcement process are processed using rough set theory knowledge discovery method.

[0017] The importance weight and dependency coefficient specifically involve calculating the importance weight and dependency coefficient of each process parameter. The importance weight is obtained by calculating the influence degree of each process parameter on the reinforcement effect. The dependency coefficient is obtained by analyzing the correlation strength between the process parameters and the reinforcement results.

[0018] The step of adjusting the process parameters according to the strength improvement rate is specifically: measuring the strength improvement rate of the chemically strengthened aggregate; when the strength improvement rate is in a first interval, maintaining the current process parameters; when the strength improvement rate is in a second interval, adjusting the soaking time; and when the strength improvement rate is in a third interval, optimizing the compound chemical solution ratio.

[0019] The nanoparticle dispersion stability evaluation system is specifically: monitoring the particle size distribution change of the nano-SiO2 particles in the nano-SiO2 dispersion liquid through a laser particle size analyzer, measuring the particle surface potential by using a Zeta potential instrument, measuring the particle sedimentation velocity by using a sedimentation analyzer, calculating the aggregation index parameter, and establishing a dispersion stability evaluation model.

[0020] The step of final performance detection and establishing the strengthened aggregate performance evaluation model is specifically: performing final performance detection on the chemically strengthened aggregate, measuring the water absorption reduction amplitude and the apparent density improvement rate, establishing the strengthened aggregate performance evaluation model, and outputting the strengthened recycled aggregate meeting the engineering requirements.

[0021] The present application realizes the effective penetration of the strengthened material into the deep pore structure of the recycled aggregate by adopting the supercritical fluid penetration technology combined with the vibration auxiliary means, and solves the technical problem of limited penetration depth of the traditional strengthening method. The present application utilizes the unique physical properties of supercritical carbon dioxide, such as liquid density and gas viscosity, under certain pressure and temperature conditions, can overcome the flow resistance and mass transfer limitation encountered by conventional liquid strengthening materials in the micro-pore structure, through the high diffusivity and low viscosity characteristics of supercritical fluid, the strengthened components such as composite cementitious materials and nano-silicon dioxide can penetrate into the smallest pore structure inside the recycled aggregate. The present application applies controllable frequency and amplitude mechanical vibration in the supercritical penetration process, generates micro disturbance and stress wave propagation effect, effectively breaks the gas resistance and liquid resistance in the pore, significantly improves the penetration efficiency and penetration depth of the strengthened material inside the aggregate, and expands the strengthening treatment from the traditional surface modification to the all-around strengthening of the entire aggregate core. The present application establishes a hierarchical composite strengthening system, combines vacuum negative pressure pretreatment and sequential penetration of multiple strengthening solutions, forms a continuous strengthening area from the surface to the internal core of the aggregate, and simultaneously uses the rough set theory knowledge discovery method to establish a quantitative relationship model between the process parameters and the penetration effect, realizes the accurate control and depth optimization of the penetration process, and solves the technical problem of insufficient penetration depth of the recycled aggregate strengthening material. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 The flowchart of the method of the present application.

[0023] Figure 2 The pressure and temperature change monitoring diagram of the supercritical fluid penetration process in the embodiment.

[0024] Figure 3 Figure 2 is a comparison chart of performance improvement of the recycled aggregate in different strengthening stages in the embodiment. DETAILED DESCRIPTION

[0025] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application.

[0026] As shown in Figure 1 Figure 1 is a flow chart of a green recycled aggregate strengthening method based on multi-element composite modification provided by the present application, and the method comprises the following steps:

[0027] S01, the waste concrete is sequentially crushed by a jaw crusher, crushed by a cone crusher, and finely crushed by an impact crusher, and the recycled aggregate particles with a particle size in the range of 5-25 mm are selected by a vibrating screen, the particle size distribution data of the recycled aggregate particles are analyzed by using a rough set theory knowledge discovery method, and a decision rule set of particle size and subsequent strengthening effect is established;

[0028] S02, a nano-silica dispersion liquid is prepared, cetyltrimethylammonium bromide is mixed with water at a mass ratio of 0.1-0.3 parts to 100 parts, a dispersant and a stabilizer are added, functionalization treatment is performed on nano-silica particles by using a surface modification technology, and a nano-particle dispersion stability evaluation system is established by combining high-energy ultrasonic dispersion and mechanical stirring;

[0029] S03, 60-100 parts of cement, 5-10 parts of silica fume, and 20-40 parts of fly ash are weighed by weight to prepare a composite cementitious material, 20-50 parts of water is added to the composite cementitious material to prepare a composite cement slurry with a water-binder ratio of 0.2-0.3, the recycled aggregate particles are immersed in the composite cement slurry, centrifugal stirring is performed for 60-90 seconds every 5-15 minutes for 3 times, and the cementitious material strengthened aggregate is obtained after drying after curing in a 40-80℃ curing box for 6-8 hours;

[0030] S04, the cementitious material strengthened aggregate is placed in a supercritical fluid infiltration device, the supercritical fluid infiltration device comprises a high-pressure reaction kettle, a carbon dioxide supply system, a temperature control system, and a vibration generator, the supercritical state parameters of carbon dioxide are set as a pressure of 7.38 MPa and a temperature of 31.1℃, vibration assistance with a frequency of 20-50 Hz and an amplitude of 0.1-0.5 mm is applied by the vibration generator, and the supercritical strengthened aggregate is obtained after infiltration for 30-60 minutes;

[0031] S05, the supercritical reinforced aggregate is put into a vacuum chamber to adjust to a negative pressure of 2 kPa for 2 hours, and then is immersed in a composite chemical solution composed of polyvinyl alcohol solution and sodium silicate solution for 1-4 hours, and then is placed in a nano-silica sol for 6-8 hours, and the solution penetration depth and the pore filling rate are monitored during the soaking process to obtain a chemically reinforced aggregate;

[0032] S06, a rough set theory knowledge discovery method is used to process multi-source process data in the reinforcement process, the importance weight and the dependence coefficient of each process parameter are calculated, and the strength improvement rate of the chemically reinforced aggregate is determined; when the strength improvement rate ∈ [0, 15%], the current process parameters are maintained; when the strength improvement rate ∈ (15%, 30%], the soaking time is increased by 20%; when the strength improvement rate ∈ (30%, 50%], the composite chemical solution ratio is optimized.

[0033] S07, the chemically reinforced aggregate is subjected to final performance detection, the water absorption reduction amplitude and the apparent density improvement rate are determined, a performance evaluation model of the reinforced aggregate is established, and the reinforced recycled aggregate meeting the engineering requirements is output.

[0034] The supercritical fluid penetration device uses a high-pressure reaction kettle as a main container, a plurality of sieve plates are arranged inside for placing recycled aggregate particles, a carbon dioxide supply system compresses CO2 to 7.38 MPa through a high-pressure pump and heats it to 31.1℃ through a heater, so that CO2 reaches a supercritical state, a temperature control system uses a PID controller to accurately control the temperature of the reaction kettle, and an electromagnetic vibrator is used as a vibration generator to generate mechanical vibration with adjustable frequency and amplitude, which is transmitted to the inside of the reaction kettle through a vibration transmission device. The supercritical CO2 has the dual characteristics of liquid density and gas viscosity under high pressure, and can penetrate into the smallest pore structure inside the recycled aggregate particles.

[0035] The vibration-assisted penetration applies mechanical vibration with a certain frequency and amplitude through the vibration generator to generate micro disturbance and stress wave propagation inside the recycled aggregate particles, break the gas resistance and liquid resistance in the pores, promote the penetration of the reinforced material to the deep pore structure of the aggregate, and improve the penetration efficiency and distribution uniformity.

[0036] The nano-particle dispersion stability evaluation system monitors the particle size distribution change of nano-silica particles in the nano-silica dispersion liquid through a laser particle size analyzer, measures the particle surface potential through a Zeta potential instrument, measures the particle sedimentation velocity through a sedimentation analyzer, calculates the aggregation index parameter, establishes a dispersion stability evaluation model, and ensures that the nano-material maintains a good dispersion state during the reinforcement process.

[0037] The surface modification technology performs chemical modification treatment on the surface of nano-SiO2 particles, introduces functional groups and dispersant molecules, changes the chemical properties and charge distribution state of the particle surface, enhances the electrostatic repulsion and steric hindrance effect between particles, and prevents the occurrence of particle aggregation.

[0038] The rough set theory knowledge discovery method identifies the key process parameters affecting the strengthening effect of recycled aggregate by calculating the dependency function between the condition attributes and the decision attributes, eliminates redundant attributes and extracts the minimum decision rule set, combines expert experience with data-driven methods, and provides interpretable knowledge rules for process optimization. The reason why the rough set theory knowledge discovery method is suitable for the present scheme is that the strengthening process of recycled aggregate involves the complex coupling of multiple process parameters such as soaking time, solution concentration, curing temperature, and osmotic pressure. Traditional optimization methods are difficult to handle the nonlinear relationship between parameters and data uncertainty, while the rough set theory knowledge discovery method can mine implicit decision rules from a large amount of experimental data, effectively handle the incompleteness and fuzziness in the data through the concepts of upper approximation set and lower approximation set. The rough set theory knowledge discovery method calculates key indicators such as importance weight matrix, attribute dependency coefficient, and decision rule confidence parameters for each process parameter, identifies the parameter combination mode with the greatest impact on the strengthening effect, establishes a data-driven process optimization strategy, and improves the stability and controllability of the strengthening process. The rough set theory knowledge discovery method uses soaking time, solution concentration, curing temperature, and osmotic pressure as the condition attribute set, and the strength improvement rate, the water absorption rate reduction amplitude, and the pore filling rate as the decision attribute set. The dependency function γ(D, C) = |POS C (D)| / |U| is used to calculate the attribute dependency coefficient, where POS C (D) is the positive region of the condition attribute C to the decision attribute D, U is the universe set, the dependency function is used to measure the decision degree of the condition attribute to the decision attribute, the input includes the condition attribute set and the decision attribute set, and the output is the dependency coefficient value; the importance weight function σ(a) = γ(D, C) - γ(D, C-a) is used to calculate the importance weight of a single attribute, where a is a single condition attribute, C-a is the condition attribute set after removing the attribute a, the importance weight function is used to evaluate the importance of a single process parameter, the input includes the complete condition attribute set and the condition attribute set after removing a single attribute, the output is the importance weight value, the optimal process parameter combination and decision threshold range are obtained, and the automatic optimization and precise control functions of the strengthening process are realized.

[0039] The multi-source process data includes process control parameters such as the soaking time, the solution concentration, the curing temperature, the penetration pressure, the vibration frequency, the vibration amplitude, and process monitoring parameters such as the solution penetration depth and the pore filling rate.

[0040] The importance weight is obtained by calculating the influence degree of each process parameter on the strengthening effect, and the dependency coefficient is obtained by analyzing the correlation strength between the process parameters and the strengthening result.

[0041] The specific implementation of the above steps is described in detail below.

[0042] The specific implementation of step S01 is that the waste concrete raw material is first sent to a jaw crusher for primary coarse crushing treatment, the crushing cavity opening is set to 100-150 mm, the eccentric shaft drives the jaw plate to make periodic reciprocating motion to generate extrusion and shearing force to crush the large concrete into medium-sized blocky materials with a particle size of 50-80 mm. Then the coarsely crushed concrete block material is transported to a cone crusher for secondary medium crushing treatment, the material particle size is further refined to 20-40 mm through the extrusion action between the moving cone and the fixed cone by using the cone crushing principle. Finally, the tertiary fine crushing treatment is performed by a impact crusher, the material is further refined by the impact crushing action of the high-speed rotating plate hammer combined with the rebound effect of the impact plate. The crushed concrete material after three-stage crushing is classified and screened by a vibrating screen, the vibrating screen has a double-layer screen mesh structure, the upper layer screen hole diameter is 25 mm, and the lower layer screen hole diameter is 5 mm, and the qualified recycled aggregate particles with a particle size of 5-25 mm are screened out. The recycled aggregate particles after screening are subjected to particle size distribution statistical analysis, the particle size distribution data are processed by using the rough set theory knowledge discovery method, the method is based on the equivalence relation and indiscernibility relation principle, the particle size range is taken as the condition attribute and the subsequent strengthening effect index is taken as the decision attribute by constructing a decision table, the influence degree of each particle size interval on the strengthening effect is calculated, and a decision rule set between the particle size and the strengthening effect is established to provide data support for subsequent process parameter adjustment.

[0043] The embodiment of step S02 is to prepare a high-stability nanosilica dispersion system. First, the cationic surfactant cetyltrimethylammonium bromide is mixed with 100 parts of deionized water at a mass ratio of 0.1-0.3 parts at room temperature to form a cationic surfactant solution. Then, an appropriate amount of dispersant and stabilizer are added. The dispersant is polyvinylpyrrolidone, and the addition amount is 2-5% of the mass of nanosilica. The stabilizer is sodium citrate, and the addition amount is 1-3% of the mass of nanosilica. Surface modification technology is used to functionalize the nanosilica particles. A chemical bond is formed on the surface of the nanoparticles by the silane coupling agent molecule, changing the chemical properties and charge distribution state of the particle surface. Specifically, γ-aminopropyltriethoxysilane is used as a coupling agent to perform hydrolysis reaction in an alkaline environment with a pH value of 8-9. The formed silanol groups condense with the hydroxyl groups on the surface of nanosilica to introduce amino functional groups on the particle surface. Combined with high-energy ultrasonic dispersion technology, the ultrasonic power is set to 400-600 W, the frequency is 20-40 kHz, and the processing time is 15-30 minutes. The micro-jet and shock wave generated by the ultrasonic cavitation effect break the agglomeration structure between the particles. At the same time, a mechanical stirring system is used, the stirring speed is 800-1200 rpm, and the stirring time is 60-120 minutes to ensure uniform distribution of nanoparticles in the dispersion liquid. An evaluation system for the dispersion stability of nanoparticles is established. The particle size distribution change is monitored in real time by a laser particle size analyzer. The fluid mechanics diameter of the particles is measured by dynamic light scattering principle, and the monitoring frequency is once every 30 minutes. The particle surface potential is measured by a Zeta potential instrument to evaluate the strength of the electrostatic repulsion between particles. The absolute value of Zeta potential should be maintained above 30 mV to ensure good dispersion stability. The particle settling velocity is measured by a sedimentation analyzer, the aggregation index parameter is calculated, and a mathematical model for evaluating the dispersion stability is established.

[0044] The specific implementation of step S03 is to prepare the composite cementitious reinforcement system and perform the preliminary aggregate reinforcement treatment. Accurately weigh 60-100 parts of ordinary Portland cement as the main cementitious component, 5-10 parts of silica fume as the active admixture, and 20-40 parts of fly ash as the auxiliary cementitious material by weight ratio. The addition of silica fume utilizes its high specific surface area and pozzolanic activity to undergo secondary hydration reactions with cement hydration products, improving the density and strength of the cementitious material. The addition of fly ash is based on its latent hydraulicity and morphological effects, which undergo a pozzolanic reaction with calcium hydroxide in an alkaline environment to generate hydrated calcium silicate gel with cementitious properties. The above three materials are thoroughly mixed in a dry powder state, with a mixing time of 10-15 minutes to ensure uniform distribution of the components. Gradually add 20-50 parts of water to the composite cementitious material, strictly control the water-cement ratio within the range of 0.2-0.3, and use mechanical stirring to prepare the composite cement slurry, with a stirring speed of 150-200 rpm and a stirring time of 5-8 minutes to ensure good fluidity and uniformity of the slurry. The screened recycled aggregate particles are completely immersed in the composite cement slurry, and an intermittent centrifugal stirring reinforcement treatment process is used, with centrifugal stirring every 5-15 minutes, each stirring time of 60-90 seconds, and a centrifugal stirring speed of 300-500 rpm, repeated for 3 times. The purpose of this process is to promote the penetration of the composite cement slurry into the internal pores of the aggregate using centrifugal force, while removing air bubbles and impurities in the pores. After completing the immersion treatment, the aggregate is placed together with the slurry in a constant temperature curing box at 40-80°C for curing, with a curing time of 6-8 hours, and the relative humidity is maintained above 90% during the curing process to promote the hydration reaction and strength development of the cement. After curing, remove excess water through drying treatment, with a drying temperature of 105-110°C and a drying time of 2-4 hours, to obtain the reinforced aggregate coated with composite cementitious material on the surface.

[0045] The specific implementation of step S04 is to perform deep reinforcement treatment using supercritical fluid infiltration technology. The cementitious reinforced aggregate is loaded into a supercritical fluid infiltration device, which uses a high-pressure reaction kettle as the main container, with multiple layers of stainless steel sieve plates inside the container for placing recycled aggregate particles, with a sieve plate spacing of 30-50 mm to ensure that the supercritical fluid can fully contact the surface of the aggregate. The CO2 supply system compresses liquid CO2 to a supercritical pressure of 7.38 MPa through a high-pressure plunger pump, and simultaneously heats the temperature to 31.1°C through an electric heater to make CO2 reach the supercritical state. Supercritical CO2 has the characteristics of close to liquid density and close to gas viscosity, with a density of about 0.47-0.50 g / cm 3, the viscosity is about 0.03-0.05 mPa·s, which can deeply penetrate into the finest pore structure of the aggregate. The temperature control system uses a proportional-integral-derivative controller to achieve accurate control of the reaction kettle temperature, with a control accuracy of ±0.5°C. The temperature sensor monitors and feeds back the temperature signal in real time to ensure the stability of the supercritical state. The vibration generator uses electromagnetic vibration principles to generate adjustable frequency and amplitude mechanical vibrations. The vibration frequency is set to 20-50 Hz, and the vibration amplitude is controlled within 0.1-0.5 mm. The vibration energy is transmitted to the inside of the reaction kettle through the vibration transmission device, generating microscopic disturbances and stress wave propagation effects inside the aggregate particles, breaking the gas and liquid resistance in the pores, and significantly improving the penetration efficiency and uniformity of the supercritical fluid. The entire penetration process lasts for 30-60 minutes. After the treatment is completed, the supercritical CO2 is released by slowly reducing the pressure to avoid damage to the internal structure of the aggregate, and the supercritically strengthened aggregate is obtained.

[0046] The specific implementation of step S05 is to achieve chemical strengthening of the aggregate by vacuum negative pressure pretreatment combined with composite chemical solution immersion. The supercritically strengthened aggregate is placed in a vacuum chamber, and the pressure in the chamber is adjusted to -2 kPa by a vacuum pump and maintained for 2 hours. The purpose of this pretreatment process is to remove air and moisture from the pores of the aggregate, creating favorable conditions for subsequent chemical solution penetration. Negative pressure treatment can reduce the gas pressure in the pores, reduce liquid penetration resistance, and improve the penetration depth of the chemical strengthening material. After vacuum pretreatment, the aggregate is first immersed in a composite chemical solution composed of polyvinyl alcohol solution and sodium silicate solution. The concentration of polyvinyl alcohol solution is 3-8%, and the concentration of sodium silicate solution is 5-12%. The two solutions are mixed in a volume ratio of 1:1 to 2:1. Polyvinyl alcohol, as an organic polymer compound, can form a flexible film structure in the pores of the aggregate, improving the toughness and crack resistance of the aggregate. The sodium silicate solution reacts with the silicate components in the aggregate in an alkaline environment to form a cementitious silica gel that fills and seals the pore structure. The immersion time is 1-4 hours, and ultrasonic assistance is used during the immersion process with an ultrasonic power of 200-400 W to promote the penetration of the chemical solution into the deep pores of the aggregate. Subsequently, the aggregate is taken out of the composite chemical solution, rinsed briefly, and then placed in a nano-silica sol for secondary immersion treatment. The solid content of the nano-silica sol is 20-35%, and the pH value is adjusted to a weak alkaline range of 8-10. The nano-sized SiO2 particles in the nano-silica sol can penetrate into even finer pores and synergize with existing cementitious materials, further improving pore filling effectiveness. The secondary immersion time is 6-8 hours, and the penetration depth of the solution is monitored by optical microscopy and scanning electron microscopy during the immersion process. The mercury intrusion method is used to determine the pore filling rate to establish a real-time monitoring system to ensure the controllability of the strengthening effect.

[0047] The specific implementation of step S06 is to use the rough set theory knowledge discovery method to intelligently analyze and optimize the multi-source process data in the strengthening process. Collect multi-source process data in the strengthening process, including soaking time, solution concentration, curing temperature, osmotic pressure, vibration frequency, vibration amplitude, and other process control parameters, as well as solution penetration depth, pore filling rate, and other process monitoring parameters. The soaking time, solution concentration, curing temperature, and osmotic pressure are used as the condition attribute set, and the strength improvement rate, water absorption rate reduction amplitude, and pore filling rate are used as the decision attribute set to construct a rough set decision table. The decision degree of the condition attribute to the decision attribute is calculated by using a dependency function, which reflects the classification ability of the given condition attribute set to the decision attribute. The input parameters include the condition attribute set and the decision attribute set, and the output parameter is the dependency coefficient value, which ranges from 0 to 1. The closer the value is to 1, the higher the dependency degree. The importance degree of each process parameter is calculated by using an importance weight function, which evaluates the importance of a single attribute by comparing the dependency difference between the complete condition attribute set and the set after removing a single attribute. The input parameters include the complete condition attribute set and the condition attribute set after removing a certain attribute, and the output parameter is the importance weight value. Based on the calculation results, the key process parameter combination that has the greatest impact on the strengthening effect is identified, and a data-driven process optimization strategy is established. The strength improvement rate of the chemically strengthened aggregate is measured. When the strength improvement rate is in the range of 0-15%, the current process parameter settings are maintained, indicating that the process conditions are basically suitable. When the strength improvement rate is in the range of 15%-30%, the soaking time is increased by 20% to prolong the action time of the chemically strengthened material and the aggregate. When the strength improvement rate is in the range of 30%-50%, the mixed ratio of polyvinyl alcohol solution and sodium silicate solution is adjusted to improve the chemical reaction efficiency and strengthening effect.

[0048] The specific implementation of step S07 is to comprehensively detect the final performance and evaluate the quality of the chemically strengthened aggregate. The water absorption rate of the strengthened aggregate is measured by using a standard test method, the aggregate sample is dried at a temperature of 105°C until the weight is constant, and then the water absorption rate value is measured after soaking in water at room temperature for 24 hours, and the water absorption rate reduction rate relative to the original recycled aggregate is calculated. The apparent density of the strengthened aggregate is measured by the specific gravity bottle method or the drainage method, and the apparent density improvement rate relative to the original recycled aggregate is calculated. A mathematical model for evaluating the performance of the strengthened aggregate is established, which comprehensively considers the strength improvement rate, the water absorption rate reduction rate, the apparent density improvement rate and other performance indicators, and calculates the comprehensive performance evaluation index by the weighted average method. The input parameters of the model include the performance test data and the corresponding weight coefficients, and the output parameters are the comprehensive performance evaluation score and the judgment result of whether it meets the engineering requirements. For the strengthened recycled aggregate that meets the engineering requirements, the strength improvement rate should be more than 15%, the water absorption rate reduction rate should be more than 20%, and the apparent density improvement rate should be more than 8%. The quality control standard and the inspection process are established to ensure that the output of the strengthened recycled aggregate has stable and reliable engineering application performance.

[0049] It should be noted that the first key technical idea of the present application is to use supercritical CO2 fluid penetration combined with vibration-assisted deep strengthening technology. Supercritical CO2 has unique dual physical properties, both high density close to liquid and low viscosity close to gas, which enables it to overcome the technical limitations of traditional liquid strengthening materials that cannot penetrate into fine pores. The vibration-assisted technology further breaks down the pore resistance by generating micro disturbances and stress wave propagation, achieving efficient penetration of the strengthened material into the deep structure of the aggregate. Compared with the traditional liquid immersion strengthening method, this technology can significantly improve the penetration depth and uniformity, solving the problem of uneven strengthening caused by the complex internal pore structure of the recycled aggregate.

[0050] The second key technical idea is to establish an intelligent optimization system of process parameters based on rough set theory. The strengthening process of recycled aggregate involves complex coupling of multiple process parameters, and traditional empirical adjustment methods are difficult to handle the nonlinear relationship between parameters and data uncertainty. Rough set theory can effectively handle the incompleteness and fuzziness of process data through the concepts of upper and lower approximation sets, and can mine the implicit decision rules from a large amount of experimental data. This method identifies the key process parameters that affect the strengthening effect by calculating the attribute dependency and importance weight, establishes a data-driven adaptive optimization strategy, and significantly improves the stability and controllability of the strengthening process.

[0051] The third key technical idea is to build a multi-level synergistic reinforced composite material system. Through the multi-level processing mode of cementitious material pre-reinforcement, supercritical fluid deep penetration, and composite chemical solution surface modification, the synergistic reinforcement effect of different scales and mechanisms is realized. The pre-reinforcement of cementitious material mainly improves the surface structure and basic strength of aggregate, the supercritical fluid penetration realizes the effective filling of deep pores, and the composite chemical solution treatment further optimizes the surface performance and pore structure. This multi-level synergistic mechanism avoids the limitations of single reinforcement technology, and through the superposition and synergistic effect of different reinforcement mechanisms, the maximization of reinforcement effect is realized.

[0052] The synergistic effect of these key technical ideas shows significant comprehensive advantages compared with existing technologies. Traditional recycled aggregate reinforcement methods mainly rely on single surface treatment or simple chemical soaking, with limited reinforcement depth and unstable effect. The invention combines supercritical fluid deep penetration, rough set theory intelligent optimization, and multi-level synergistic reinforcement to build a comprehensive reinforcement technology system. The supercritical technology ensures the deep penetration of the reinforced material, the rough set theory realizes the accurate control of process parameters, and the multi-level synergistic mechanism ensures the comprehensiveness and durability of the reinforcement effect. The three technical ideas promote each other, and together solve the technical problems of insufficient reinforcement depth of recycled aggregate, difficult process control, and uneven reinforcement effect, significantly improving the comprehensive performance and engineering application reliability of recycled aggregate.

[0053] It should be noted that the present application also solves the technical problems that the lack of scientific guidance of process parameter optimization in the strengthening process of recycled aggregate leads to unstable strengthening effect. In the traditional strengthening process of recycled aggregate, there is a complex nonlinear coupling relationship between the soaking time, solution concentration, curing temperature, osmotic pressure and other process parameters. The setting of these parameters often depends on experience and trial-and-error method, lacks systematic theoretical guidance and quantitative analysis means, leading to large fluctuations in the strengthening effect of different batches of products, making it difficult to realize the standardization and reproducibility of the process. Especially when the characteristics of raw materials change, the traditional empirical parameter setting often cannot adapt to the new working conditions, affecting the stability of the quality of recycled aggregate products and the reliability of engineering applications. The present application introduces rough set theory knowledge discovery method to establish a data-driven process parameter optimization system. This method can effectively handle the uncertainty and fuzziness characteristics in the process data, quantitatively identify the key process parameters affecting the strengthening effect by calculating the dependency function between conditional attributes and decision attributes, evaluate the influence degree of each parameter by using the importance weight function, extract the minimum decision rule set and establish the parameter optimization knowledge base, realize the process optimization from experience-driven to data-driven, and through the hierarchical control strategy of strength improvement rate and the parameter self-adaptive adjustment mechanism, ensure the stable strengthening effect in different working conditions, significantly improve the controllability of the strengthening process of recycled aggregate and the consistency of product quality.

[0054] Specifically, the principle of the present application is that the core principle of the present application for solving the technical problem of insufficient penetration depth of recycled aggregate reinforced material is to break through the mass transfer limitation of traditional liquid phase penetration method in micro-pores by the unique mass transfer characteristics of supercritical fluid and the synergistic effect of multiple physical fields. Supercritical carbon dioxide is in a supercritical state at a pressure of 7.38 MPa and a temperature of 31.1℃. At this time, its density is close to that of a liquid and its viscosity is close to that of a gas. This unique physical property enables it to have extremely strong penetration ability, enabling it to quickly diffuse into nanoscale pores like a gas, while dissolving and carrying reinforcing material components like a liquid, thereby realizing an efficient mass transfer process in a complex pore network. The principle of vibration-assisted penetration technology is based on the dynamic effect of mechanical vibration in porous media. When the vibration frequency is in the range of 20-50 Hz, periodic micro-stress disturbances can be generated inside the recycled aggregate. This stress disturbance improves the pore connectivity through acoustic wave propagation mechanisms, destroys the liquid film resistance and bubble blocking effect on the pore surface, and provides more penetration channels and driving force for the reinforcing material, thereby significantly improving the penetration depth and penetration rate. The design logic of the multi-element composite reinforcement system is reflected in the synergistic penetration mechanism of different scale reinforcing materials. The gel product produced by the hydration process of silicate cement particles in the composite cementitious material can fill larger pores. The ultra-fine particles of silica fume and fly ash enter the medium pores through the pozzolanic reaction, and the surface-modified nano-silicon dioxide particles are specially designed to fill nano-scale pores, forming a multi-level penetration and filling system from macro to nano. Vacuum negative pressure pretreatment creates a negative pressure environment by removing the gas in the pores, providing a driving pressure difference for the subsequent deep penetration of the reinforcing material. The sequential chemical reinforcement treatment forms a dense protective layer on the pore wall surface through the polymerization reaction of polyvinyl alcohol and sodium silicate solution, and the final penetration of nano-silica sol realizes the fine filling of residual micropores. The rough set theory knowledge discovery method establishes a nonlinear mapping relationship between the penetration depth and the process parameters, quantitatively analyzes the influence degree of each parameter on the penetration effect by using the dependency function and the importance weight function, realizes the intelligent optimization control of the penetration process, and ensures that the reinforcing material can reach the maximum penetration depth.

[0055] A specific embodiment 1 of the present application is provided below, and the specific implementation of each step in embodiment 1 is described in detail as follows.

[0056] The specific implementation of step S01 is to sequentially perform three-stage crushing and particle size screening on the waste concrete, and to establish a decision rule set of particle size distribution and reinforcement effect by using the rough set theory knowledge discovery method. The particle size distribution probability density function is expressed as:

[0057]

[0058] In the formula, d is the particle size of the recycled aggregate, in mm; μ dis the mean value of particle size distribution, and the value range is 12-18 mm; σ d is the standard deviation of particle size distribution, and the value range is 3-6 mm. In the construction of the rough set decision table, the particle size interval division matrix is:

[0059]

[0060] where d i is the median value of the ith particle size interval; c i is the corresponding reinforcement effect classification label; and n is the total number of particle size intervals, and is usually valued at 8-12. Among them, d i is obtained through a screening experiment, and the mass fraction calculation formula is:

[0061]

[0062] where m i is the mass fraction of the ith particle size interval; M i is the aggregate mass of the ith particle size interval, and the unit is kg; and k is the particle size interval serial number. c i is obtained through a subsequent reinforcement experiment, and the strength classification function is:

[0063]

[0064] where Δf i is the strength improvement rate of the aggregate of the ith particle size interval.

[0065] The specific implementation of step S02 is to prepare a nano-SiO2 dispersion liquid and establish a dispersion stability evaluation system. The nanoparticle aggregation index calculation formula is:

[0066]

[0067] where AI is the aggregation index; D eff is the effective particle size, and the unit is nm; and D primary is the initial particle size, and the unit is nm. The dispersion stability comprehensive evaluation function is:

[0068] S stability = α1·Z potential + α2·(1-AI) + α3·T sediment ;

[0069] where S stability is the dispersion stability evaluation index; Z potential is the absolute value of Zeta potential, and the unit is mV; T sediment is the sedimentation time constant, and the unit is h; and α1, α2, α3 are weight coefficients, and are respectively valued at 0.4, 0.3, and 0.3. Among them, Z potentialDetermined by electrophoretic light scattering method, the determination temperature is 25℃, and the determination time is 120s. sediment Obtained by sedimentation analysis experiment, the sedimentation velocity calculation formula is:

[0070]

[0071] In the formula, v sediment is the particle sedimentation velocity, in m / s; p p is the particle density, in kg / m 3 ; p f is the fluid density, in kg / m 3 ; g is the acceleration of gravity, taking the value of 9.8 m / s 2 ; r is the particle radius, in m; and h is the fluid dynamic viscosity, in Pa·s. The sedimentation time constant is obtained by exponential fitting:

[0072]

[0073] In the formula, h(t) is the suspended particle concentration height at t time, in mm; h0 is the initial height, in mm; and t is the sedimentation time, in h.

[0074] The specific implementation of steps S03-S05 is the same as the foregoing, and will not be described in detail here.

[0075] The specific implementation of step S06 is to analyze multi-source process data and optimize parameters by using rough set theory. The dependency function of the condition attribute set C and the decision attribute set D is:

[0076]

[0077] In the formula, g(D, C) is the dependency coefficient; C is the condition attribute set, including process parameters such as soaking time, solution concentration, curing temperature, and osmotic pressure; D is the decision attribute set, including performance indicators such as strength improvement rate, water absorption rate reduction amplitude, and pore filling rate; POS C (D) is the positive region of the condition attribute C to the decision attribute D; and U is the universal set. The single attribute importance weight function is:

[0078] σ(a) = g(D, C) - g(D, C-{a});

[0079] In the formula, σ(a) is the importance weight of a single condition attribute a; a is any attribute in the condition attribute set C; and C-{a} is the condition attribute set after removing the attribute a. The process parameter optimization decision matrix is:

[0080]

[0081] where t soak is the soaking time, h; c solution is the solution concentration, %; T cure is the curing temperature, ℃; P penetrate is the osmotic pressure, MPa; γ i is the dependence coefficient of the i th parameter; w i is the weight coefficient of the i th parameter. The strengthening effect prediction model is:

[0082] R strengthen = β1·f(t soak )+ β2·g(c solution )+ β3·h(T cure )+ β4·k(P penetrate )+ ξ;

[0083] where R strengthen is the strength improvement rate; f, g, h, k are the nonlinear response functions of the respective parameters; β1, β2, β3, β4 are the regression coefficients; ξ is the random error term, ranging from -2% to 2%. Among them, POS C (D) is obtained by equivalent class division calculation, and the equivalent class division function is:

[0084] U / IND(C) = {[x] c : x ∈ U};

[0085] where [x] C is the equivalent class of sample x under the condition attribute C; x is any sample in the domain U; IND(C) is the indiscernible relation of the condition attribute C. The positive region calculation formula is:

[0086] POS C (D) = ∪ X∈U / IND(D) C X;

[0087] where X is the equivalent class of the decision attribute D; C X is the lower approximation set of the set X with respect to the condition attribute C, defined as:

[0088]

[0089] γ i is calculated by the cross-validation method, and the verification accuracy function is:

[0090]

[0091] where Acc i is the prediction accuracy of the i th parameter; N correct is the number of samples predicted correctly; N totalTotal sample number; error range is ±0.05.

[0092] The specific implementation of step S07 is to establish a comprehensive evaluation model of the performance of the reinforced aggregate to perform final quality detection. The comprehensive performance evaluation function is:

[0093] Q overall = w strength · I strength + w absorption · I absorption + w density · I density ;

[0094] In the formula, Q overall is the comprehensive performance evaluation index; I strength is the strength improvement rate index; I absorption is the water absorption reduction rate index; I density is the apparent density improvement rate index; w stregth , w absorption , and w density are weight coefficients, and take values of 0.5, 0.3, and 0.2, respectively. The standardization function of each performance index is:

[0095]

[0096] In the formula, I j is the standardization value of the jth performance index; j is the performance index number, and takes values of 1, 2, and 3, corresponding to strength, water absorption, and density, respectively; X j is the measured value of the jth performance index; X j，max and X j，min are the maximum and minimum values of the jth index, respectively. The quality determination threshold matrix is:

[0097]

[0098] In the formula, T strength is the strength improvement rate threshold; T absorption is the water absorption reduction rate threshold; T density is the apparent density improvement rate threshold. I strength is obtained through a compressive strength test, step 1 is to prepare a standard test block; step 2 is to perform a compression test after 28 days of standard curing; and step 3 is to calculate the strength improvement rate relative to the original aggregate. I absorption is obtained through a water absorption test, step 1 is to dry the aggregate sample to a constant weight; step 2 is to measure the water absorption after 24 hours of immersion; and step 3 is to calculate the water absorption reduction rate relative to the original aggregate.

[0099] It needs to be explained that the particle size distribution probability density function is based on the normal distribution theory, and the particle size distribution of recycled aggregate is described by mathematical statistical method.

[0100]

[0101] The function can accurately quantify the distribution probability of different particle size intervals compared with the traditional simple particle size grading method, providing accurate input data for subsequent rough set analysis, and significantly improving the accuracy and reliability of the correlation analysis between particle size and strengthening effect.

[0102] The dependency function is based on the positive region concept in rough set theory, and the influence of process parameters is quantified by calculating the decision degree of conditional attributes to decision attributes.

[0103]

[0104] Compared with the traditional correlation analysis method, the function can handle the mixed case of discrete and continuous data, effectively solve the incompleteness and uncertainty problem in process data, and realize the accurate identification and quantitative evaluation of the correlation between complex process parameters.

[0105] The importance weight function accurately calculates the importance of each process parameter by comparing the dependency difference between including and not including a specific attribute.

[0106] σ(a) = γ(D, C) - γ(D, C-{a});

[0107] Compared with the traditional expert experience judgment method, the function is based on objective analysis driven by actual data, eliminates the influence of human subjective factors, and can automatically identify the key process parameters that contribute most to the strengthening effect, providing scientific decision basis for process optimization.

[0108] The dispersion stability comprehensive evaluation function considers the Zeta potential, aggregation index and sedimentation time three key indicators, and establishes a comprehensive evaluation system through weighted summation.

[0109] S stability = α1·Z potential + α2·(1-AI) + α3·T sediment ;

[0110] Compared with the single index evaluation method, the function can comprehensively reflect the stability state of the nanoparticle dispersion system, accurately predict the stability change of the dispersion liquid in the strengthening process, and ensure the persistence and uniformity of the strengthening effect of nanomaterials.

[0111] The strengthening effect prediction model establishes the mathematical relationship between process parameters and strengthening effect through multivariate nonlinear regression.

[0112] R strengthen = β1·f(t soak )+ β2·g(c solution )+ β3·h(T cure )+ β4·k(P penetrate )+ ξ;

[0113] Compared with the traditional linear model, the model can accurately describe the complex coupling effect and nonlinear response characteristics between process parameters, realize accurate prediction of the strengthening effect and quantitative optimization of the process parameters, and significantly improve the controllability and reproducibility of the strengthening process.

[0114] The comprehensive performance evaluation function establishes a quality evaluation system of the strengthened aggregate through a multi-index weighted comprehensive method.

[0115] Q overall = w strength ·I strength + w absorption ·I absorption + w density ·I density ;

[0116] Compared with the single performance index evaluation method, the function can comprehensively reflect the comprehensive performance level of the strengthened aggregate, balance the relationship between different performance indexes, ensure the reliability and applicability of the strengthened aggregate in actual engineering application, and provide a scientific evaluation standard for engineering quality control.

[0117] In order to better understand and implement the present application, the following provides an embodiment 2 of a specific application scenario of the present application: a technical team needs to process 5000 tons of waste concrete from a demolition project of an old residential area to prepare strengthened recycled aggregate meeting the engineering requirements for a new road engineering. The batch of waste concrete mainly comes from the structure of a 30-year-old residential building, and the concrete strength grade is C25, containing part of the steel bars and other impurities.

[0118] Step one: waste concrete crushing treatment

[0119] The technical team first manually sorted the waste concrete, removed impurities such as steel bars and wood, and then sent the cleaned concrete blocks to a jaw crusher for coarse crushing. The jaw crusher crushing chamber opening was set to 120 mm, and the eccentric shaft speed of the moving jaw plate was 250 rpm. After coarse crushing, medium-sized blocky materials with a particle size of 50-75 mm were obtained. Then the coarse crushed material was sent to a cone crusher for medium crushing, and the cone crusher speed was set to 300 rpm. After processing, the material particle size was controlled within 25-35 mm. Finally, the fine crushing was carried out by the impact crusher, the plate hammer speed was set to 1450 rpm, and the gap between the impact plates was adjusted to 15 mm. After fine crushing, the concrete crushed material was screened by a double-layer vibrating screen, the vibration frequency was 50 Hz, and the vibration amplitude was 5 mm. A total of 3200 tons of qualified recycled aggregate particles with a particle size of 5-25 mm were screened out.

[0120] The technical team used a particle size distribution analyzer to measure the particle size distribution of the screened recycled aggregate, and established a decision table containing conditional attributes such as particle size range, particle shape coefficient, and surface roughness. Through rough set theory analysis, it was found that particles with a particle size of 8-15 mm were most beneficial to subsequent strengthening, with a dependency coefficient of 0.78 and an importance weight of 0.82.

[0121] Step two: preparation of nano-silica dispersion liquid

[0122] The technical team prepared a high-stability nano-SiO2 dispersion liquid system. Hexadecyl trimethyl ammonium bromide surfactant was mixed with 100 parts of deionized water at a mass ratio of 0.2 parts at 25°C for 15 minutes. Polyvinylpyrrolidone dispersant was added to the mixed solution, with an addition amount of 3% of the mass of nano-SiO2. Sodium citrate stabilizer was added in an amount of 2% of the mass of nano-SiO2. Gamma-aminopropyl triethoxysilane was used as a coupling agent for surface modification of nano-SiO2 particles, and hydrolysis and condensation reactions were carried out at a pH of 8.5 for 30 minutes. Combined with high-energy ultrasonic dispersion technology, the ultrasonic power was set to 500 W, the frequency was 25 kHz, and the treatment time was 20 minutes. At the same time, mechanical stirring was carried out at a stirring speed of 1000 rpm for 90 minutes.

[0123] The nano-particle dispersion stability evaluation system established by the technical team showed that the average particle size of the functionalized nano-SiO2 particles was 25 nm, the Zeta potential was -35 mV, and there was no obvious aggregation and sedimentation phenomenon within 72 hours, with an aggregation index of less than 0.15, indicating good dispersion stability.

[0124] Step three: strengthening treatment of composite cementitious materials

[0125] The technical team weighed ordinary Portland cement 80 parts, silica fume 8 parts, fly ash 30 parts to prepare composite cementitious material. After mixing the three materials in a dry powder mixer for 12 minutes, 35 parts of water were added to the composite cementitious material, and the water-cement ratio was controlled at 0.3. A composite cement slurry was prepared by high-speed stirring at a stirring speed of 180 revolutions per minute for 6 minutes. The 500 kg of screened recycled aggregate particles were completely immersed in the composite cement slurry, and an intermittent centrifugal stirring process was used, with centrifugal stirring every 10 minutes, each stirring time being 75 seconds, the centrifugal speed being 400 revolutions per minute, and the operation being repeated 3 times. After completing the immersion treatment, the aggregate was placed together with the slurry in a 60°C constant temperature curing box for curing for 7 hours, and the relative humidity was kept at 95%. After curing, the aggregate was dried at 108°C for 3 hours to obtain the surface-coated composite cementitious material reinforced aggregate.

[0126] Step four: supercritical fluid infiltration strengthening

[0127] The technical team loaded the cementitious material reinforced aggregate into the high-pressure reaction kettle of the supercritical fluid infiltration device, and set 5 layers of stainless steel sieve plates inside the reaction kettle with a spacing of 40 mm. Liquid CO2 was compressed to 7.38 MPa by a high-pressure plunger pump, and the temperature was raised to 31.1°C by an electric heater to make CO2 reach the supercritical state. The PID temperature controller has a control accuracy of ±0.3°C to ensure the stability of the supercritical state. The electromagnetic vibration generator generates mechanical vibration with a frequency of 35 Hz and an amplitude of 0.3 mm, and the vibration energy is transmitted to the inside of the reaction kettle through a vibration transmission device. The whole infiltration process lasts for 45 minutes, and the density of supercritical CO2 during the process is 0.48 g / cm 3 , the viscosity is 0.04 mPa·s, and it can deeply penetrate into the micro-pores of 5-20 μm inside the aggregate.

[0128] As shown in Figure 2 , the pressure and temperature change curves during the supercritical fluid infiltration process show that the system reaches a stable supercritical state within the first 10 minutes, and then remains stable until the end of the process. The vibration auxiliary action significantly improves the infiltration efficiency of the supercritical fluid, and the infiltration depth is improved by 32% compared to the non-vibration treatment.

[0129] Step five: chemical strengthening treatment

[0130] The technical team placed the supercritical reinforced aggregates into a vacuum chamber and adjusted the pressure inside the chamber to -2 kPa using a vacuum pump for 2 hours. After the vacuum pretreatment, the aggregates were immersed in a composite chemical solution prepared by mixing a 5% polyvinyl alcohol solution and an 8% sodium silicate solution at a volume ratio of 1.5:1. The immersion time was 2.5 hours, and ultrasonic assistance was applied at a power of 300 W during the immersion process. Subsequently, the aggregates were rinsed and then immersed in a nano-silica sol with a solid content of 28% and a pH of 9.2 for a second immersion time of 7 hours.

[0131] The mercury intrusion method showed that the pore filling rate of the chemically reinforced aggregates was 73%, which was 45% higher than that of the original recycled aggregates.

[0132] Step six: Process data analysis and optimization

[0133] The technical team collected multi-source process data during the reinforcement process, as shown in Table 1:

[0134] Table 1 Process parameters and corresponding reinforcement effect data table

[0135]

[0136] The data was analyzed using the rough set theory knowledge discovery method, with immersion time, solution concentration, curing temperature, and penetration pressure as condition attributes, and strength improvement rate, water absorption reduction rate, and pore filling rate as decision attributes. The importance weight of immersion time was calculated to be 0.76, the importance weight of solution concentration was calculated to be 0.68, the importance weight of curing temperature was calculated to be 0.52, and the importance weight of penetration pressure was calculated to be 0.43. The dependency function calculation result showed that the dependency coefficient of the current process parameter combination was 0.85, indicating that there was a strong decision relationship between the process parameters and the reinforcement effect.

[0137] Based on the analysis results, the technical team found that the strength improvement rate of the current batch of aggregates was 23%, which was within the range of 15% to 30%, so the immersion time was adjusted from 2.5 hours to 3.0 hours, increasing the processing time by 20%. The adjusted process parameters further improved the reinforcement effect.

[0138] Step seven: Final performance testing and evaluation

[0139] The technical team conducted a comprehensive final performance test on the chemically reinforced aggregates. The aggregate samples were dried at 105°C until constant weight, and then immersed in water at room temperature for 24 hours to measure the water absorption rate. The results showed that the water absorption rate of the reinforced aggregates was 2.8%, which was 38% lower than the water absorption rate of the original recycled aggregates, which was 4.5%. The apparent density of the reinforced aggregates was measured by the drainage method to be 2.68 g / cm 3, the apparent density of the original recycled aggregate is 2.46 g / cm 3 , the compressive strength of the strengthened aggregate reaches 78 MPa, and the strength improvement rate is 26% compared with 62 MPa of the original recycled aggregate.

[0140] As shown in Figure 3 , the performance change curve of the strengthened aggregate at different processing stages shows that the performance is preliminarily improved after the composite cementitious material treatment, the performance is significantly improved after the supercritical fluid infiltration treatment, and the chemical strengthening treatment further optimizes the performance indicators. The performance evaluation model established by the technical team has a comprehensive score of 87, which exceeds the 80-point standard required by the engineering.

[0141] Among the finally prepared 3200 tons of strengthened recycled aggregate, 3050 tons of products meet the engineering requirements, and the qualified rate is 95%. The strength improvement rate of this batch of strengthened recycled aggregate reaches 26%, the water absorption rate decreases by 38%, and the apparent density improvement rate reaches 9%, and all indicators meet the technical requirements of the aggregate for road engineering.

[0142] Compared with the traditional recycled aggregate processing method, the technical progress brought by the present application mainly embodies the following aspects. The traditional method usually only uses single physical treatment or chemical treatment, and the strengthening effect is limited and unstable. The present application adopts multi-element composite modification technology, which organically combines composite cementitious material coating, supercritical fluid infiltration and chemical strengthening treatment, and realizes the all-round strengthening of recycled aggregate from the surface to the inside. The unique physical properties of supercritical CO2 enable it to overcome the limitations of traditional liquid strengthening materials that cannot penetrate into fine pores, significantly improving the penetration depth and distribution uniformity of the strengthening material. The application of rough set theory knowledge discovery method realizes the intelligent optimization of process parameters, compared with the traditional experience adjustment method, it can more accurately identify the key process parameters and establish a data-driven optimization strategy, improving the controllability and stability of the strengthening process. The vibration-assisted infiltration technology effectively breaks down the resistance in the pores by generating micro disturbances and stress wave propagation inside the aggregate, significantly improving the strengthening efficiency compared with static soaking treatment. The multi-stage chemical strengthening system improves the strength, toughness and durability of the aggregate through the synergistic effect of organic polymer materials and inorganic silicate materials, overcoming the defects of single strengthening material performance incompleteness.

[0143] It should be noted that the variables involved in the present application are explained in detail as shown in Tables 2 and 3.

[0144] Table 2 Variable Explanation Table (First Part)

[0145]

[0146] Table 3 Variable Explanation Table (Second Part)

[0147]

[0148]

[0149] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for strengthening green recycled aggregate based on multi-element composite modification, characterized by, The waste concrete is sequentially subjected to coarse crushing, medium crushing and fine crushing, the recycled aggregate particles are screened, the particle size distribution data are analyzed by using the rough set theory knowledge discovery method, the nano-SiO2 dispersion liquid is prepared and subjected to surface modification treatment, the composite cementitious material and the composite cement slurry are prepared, the recycled aggregate particles are immersed in the composite cement slurry for centrifugal stirring and curing treatment to obtain the cementitious material reinforced aggregate, the cementitious material reinforced aggregate is placed in a supercritical fluid infiltration device, and the infiltration treatment is carried out under the supercritical CO2 state with vibration assistance to obtain the supercritical reinforced aggregate, the supercritical reinforced aggregate is subjected to vacuum treatment, and then is sequentially immersed in the composite chemical solution and the nano-SiO2 sol to obtain the chemical reinforced aggregate, the multi-source process data are processed by using the rough set theory knowledge discovery method, the process parameters are adjusted according to the strength improvement rate, the final performance of the chemical reinforced aggregate is detected, and a reinforced aggregate performance evaluation model is established, so that the deep infiltration modification of the reinforced material into the internal pore structure of the recycled aggregate is realized by using the supercritical fluid infiltration technology combined with the vibration assistance means.

2. The method for strengthening green recycled aggregate based on multi-composite modification according to claim 1, characterized in that, The waste concrete is sequentially subjected to coarse crushing, medium crushing and fine crushing, the recycled aggregate particles are screened, the particle size distribution data are analyzed by using the rough set theory knowledge discovery method, the nano-SiO2 dispersion liquid is prepared and subjected to surface modification treatment, the composite cementitious material and the composite cement slurry are prepared, the recycled aggregate particles are immersed in the composite cement slurry for centrifugal stirring and curing treatment to obtain the cementitious material reinforced aggregate, the cementitious material reinforced aggregate is placed in a supercritical fluid infiltration device, and the infiltration treatment is carried out under the supercritical CO2 state with vibration assistance to obtain the supercritical reinforced aggregate, the supercritical reinforced aggregate is subjected to vacuum treatment, and then is sequentially immersed in the composite chemical solution and the nano-SiO2 sol to obtain the chemical reinforced aggregate, the multi-source process data are processed by using the rough set theory knowledge discovery method, the process parameters are adjusted according to the strength improvement rate, the final performance of the chemical reinforced aggregate is detected, and a reinforced aggregate performance evaluation model is established, so that the deep infiltration modification of the reinforced material into the internal pore structure of the recycled aggregate is realized by using the supercritical fluid infiltration technology combined with the vibration assistance means.

3. The method for strengthening green recycled aggregate based on multi-composite modification according to claim 2, characterized in that, The rough set theory knowledge discovery method is used to identify the key process parameters affecting the reinforcement effect of the recycled aggregate, eliminate redundant attributes and extract the minimum decision rule set, and establish the decision rule set of the particle size and the subsequent reinforcement effect.

4. The method for strengthening green recycled aggregate based on multi-composite modification according to claim 3, characterized in that, The step of preparing the nano-SiO2 dispersion liquid is specifically to add C 19 H 42 NBr is mixed with water, a dispersant and a stabilizer are added, high-energy ultrasonic dispersion and mechanical stirring are combined to establish an evaluation system for the dispersion stability of nanoparticles.

5. The method for strengthening green recycled aggregate based on multi-composite modification according to claim 4, characterized in that, The surface modification technology is used to functionalize the nano-SiO2 particles, chemically modify the surface of the nano-SiO2 particles, introduce functional groups and dispersant molecules, change the chemical properties and charge distribution state of the particle surface, and enhance the electrostatic repulsion and steric hindrance effect between the particles.

6. The method for strengthening green recycled aggregate based on multi-composite modification according to claim 5, characterized in that, The step of preparing the composite cementitious material is to prepare the composite cementitious material by weighing the cement, silica fume and fly ash according to the weight, adding water to the composite cementitious material to prepare the composite cement slurry, and immersing the recycled aggregate particles in the composite cement slurry for centrifugal stirring and curing treatment.

7. The method for strengthening green recycled aggregate based on multi-composite modification according to claim 6, characterized in that, The supercritical fluid infiltration device comprises a high-pressure reaction kettle, a CO2 supply system, a temperature control system and a vibration generator, the high-pressure reaction kettle is used as the main container, a plurality of sieve plates are arranged inside the high-pressure reaction kettle for placing the recycled aggregate particles, the CO2 supply system compresses and heats CO2 to make CO2 reach the supercritical state through the high-pressure pump.

8. The method for strengthening green recycled aggregate based on multi-composite modification according to claim 7, characterized in that, The step of vibration-assisted infiltration is to apply vibration assistance through the vibration generator, the vibration generator generates mechanical vibration with adjustable frequency and amplitude by using an electromagnetic vibrator, and the vibration is transmitted to the inside of the reaction kettle through a vibration transmission device to generate micro disturbance and stress wave propagation in the recycled aggregate particles.

9. The method for strengthening green recycled aggregate based on multi-composite modification according to claim 8, characterized in that, The step of vacuum treatment is to place the supercritical reinforced aggregate into a vacuum chamber, adjust it to a negative pressure state and maintain it for a predetermined time, break the gas resistance and liquid resistance in the pores, promote the infiltration of the reinforced material into the deep pore structure of the aggregate, and improve the infiltration efficiency and distribution uniformity.

10. The method for strengthening green recycled aggregate based on multi-composite modification according to claim 9, characterized in that, The composite chemical solution is specifically a composite chemical solution composed of a C2H4O polymer solution and a Na2SiO3 solution, the supercritical reinforced aggregate after vacuum treatment is immersed in the composite chemical solution for soaking, and then is placed in a nano-SiO2 sol for soaking, and the solution penetration depth and the pore filling rate are monitored during the soaking process.

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