A three-stage precise screening and mixing method for fluidized soil solidification

Abstract

The present invention provides a three-stage precise screening and stirring method for fluidized soil solidification, which belongs to the basic technical field of civil engineering. The present invention first constructs an experimental parameter optimization matrix to determine the initial parameter range; after the first stage of stirring, the fluidity and strength index are measured to calculate the stirring efficiency factor; the second stage of stirring is carried out based on the parameter boundary conditions of the first stage, and the particle uniformity and cohesion index are measured; a one-two-stage cross-correlation response surface is established to determine the optimal matching point; the third stage of stirring is carried out based on the stirring synergy factor to measure the solidification rate and stability index; key conversion parameters are identified by establishing a two-three-stage cross-correlation tensor; a one-three-stage compensation adjustment index is constructed to quantify the remote correlation effect; the three-stage stirring is converted into a multi-objective optimization problem to determine the global optimal solution that meets the strength and uniformity requirements; and the technical problem that it is difficult to achieve stable and consistent performance of solidified soil by only using a single stage of stirring is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of civil engineering foundations, and in particular relates to a three-stage precise screening and stirring method for fluidized soil solidification. Background Art

[0002] As an important foundation treatment material, fluidized solidification soil is widely used in engineering fields such as soft soil reinforcement, roadbed filling, underground engineering anti-seepage, and environmental remediation. Traditional fluidized solidification soil preparation technology typically uses a single-stage mixing process, where cementitious materials such as cement and fly ash are mixed with soil or other filler materials in a single step. The fluidity and strength of the solidified soil are primarily controlled by empirically adjusting single parameters such as material ratio, mixing time, and mixing speed. This single-stage mixing method is simple to operate and has formed a standardized operating procedure in conventional projects.

[0003] However, the single-stage mixing process shows obvious shortcomings when facing complex geological conditions or high-performance projects: first, a single mixing process cannot simultaneously take into account the requirements of uniform material dispersion and structural stability, which often leads to problems such as uneven particle distribution and large local strength differences within the solidified soil; second, a single mixing parameter combination is difficult to adapt to the special needs of different solidification stages. For example, high shear force is required to promote dispersion in the initial mixing stage, while appropriate mixing is required in the early stage of solidification to maintain uniformity while avoiding destruction of the formed structural network.

[0004] In particular, existing single-stage mixing technology lacks the ability to systematically control the entire process of solidifying soil from its fluidized state to its solidified state. This makes it impossible to optimize parameters based on the dynamic changes in material properties during the solidification process. This results in large fluctuations in the final performance of the solidified soil, poor consistency, and low predictability, making it difficult to meet the stringent requirements for stability and controllability of solidified soil properties in modern high-standard engineering projects. In other words, existing technologies often utilize only a single-stage mixing process for solidified soil in its fluidized state, making it difficult to achieve stable and consistent solidified soil properties. Summary of the Invention

[0005] In view of this, the present invention provides a three-stage precise screening and stirring method for fluidized solidified soil, which can solve the technical problem in the prior art that fluidized solidified soil usually only adopts a single stage of stirring, making it difficult to achieve stable and consistent performance of the solidified soil.

[0006] The present invention is implemented as follows: the present invention provides a three-stage precise screening and stirring method for fluidized solidified soil, comprising: constructing an experimental parameter optimization matrix to determine the initial value range of each parameter of the three-stage stirring; conducting a first-stage stirring experiment, measuring the fluidity index and strength coefficient of the solidified soil, and calculating the first-stage stirring efficiency factor; setting the second-stage stirring parameter boundary conditions according to the first-stage stirring efficiency factor, executing the second-stage stirring experiment, and measuring the particle uniformity index and the cohesion index; establishing a one-two-stage stirring correlation response surface, determining the optimal matching point between the first-stage stirring parameters and the second-stage stirring parameters, and calculating the stirring synergy factor; determining the initial value of the third-stage stirring parameters based on the stirring synergy factor, conducting the third-stage stirring experiment, and measuring the solidification rate index and the structural stability index; converting the three-stage stirring parameter optimization problem into a multi-objective optimization problem, constructing the Pareto optimal frontier, and applying the weighted Chebyshev method to determine the optimal parameter combination of the three-stage stirring for guiding the three-stage precise screening and stirring of fluidized solidified soil.

[0007] Among them, the experimental parameter optimization matrix is ​​a three-dimensional experimental design space formed by combining the three key parameters of material ratio, stirring time and stirring speed at different levels, which is used to systematically explore the influence of parameters.

[0008] Among them, the first-stage mixing efficiency factor is a dimensionless indicator that characterizes the material mixing uniformity and energy utilization efficiency during the first-stage mixing process. It is obtained by weighted calculation of the fluidity index and strength coefficient of the solidified soil.

[0009] Among them, the fluidity index of solidified soil is a quantitative parameter to measure the fluidity performance of solidified soil, which is measured in the first stage of mixing test; the strength coefficient is a parameter to characterize the compressive strength of solidified soil, which is measured in the first stage of mixing test.

[0010] Among them, the particle uniformity index is a quantitative parameter that measures the uniformity of particle distribution in the solidified soil. It is calculated by analyzing the variance of particle spatial distribution through multi-point sampling and is measured in the second stage of the mixing experiment. The cohesion index is a parameter that characterizes the strength of the intermolecular force inside the solidified soil. It is obtained by measuring the deformation resistance of the solidified soil through shear experiments and is measured in the second stage of the mixing experiment.

[0011] Among them, the one-two-stage mixing correlation response surface is a mathematical model that describes the performance of solidified soil under the combined action of the first-stage mixing parameters and the second-stage mixing parameters, and is established through the mixing efficiency response equation; among them, the mixing efficiency response equation is used to establish the performance response surface of solidified soil under the combined action of the first-stage mixing parameters and the second-stage mixing parameters. The input includes the first-stage mixing time, the first-stage mixing speed, the second-stage mixing time, the second-stage mixing speed, and the material ratio. The output is the predicted solidified soil strength coefficient and the solidified soil fluidity index.

[0012] Among them, the stirring synergy factor is a coefficient that quantifies the degree of synergy between the first-stage stirring parameters and the second-stage stirring parameters, and is calculated by the performance improvement ratio of the optimal matching point on the first-stage and second-stage stirring correlation response surface.

[0013] Among them, the solidification rate index is a parameter that measures the speed at which the solidified soil changes from a fluid state to a solid state, and is measured in the third stage of the mixing experiment; the structural stability index is a parameter that evaluates the ability of the internal structure of the solidified soil to resist external disturbances, and is measured in the third stage of the mixing experiment.

[0014] Among them, the method also includes the following steps: establishing a two-stage and three-stage mixing correlation tensor, identifying the key conversion parameters of the second-stage mixing parameters and the third-stage mixing parameters, and calculating the fluidity solidification transition threshold; constructing a one-stage and three-stage compensation adjustment index, analyzing the influence of the first-stage mixing parameters on the third-stage solidification effect, and quantifying the remote correlation effect; the two-stage and three-stage mixing correlation tensor is a multidimensional mathematical structure that describes the interaction relationship between the second-stage mixing parameters and the third-stage mixing parameters, and is used to analyze the complex coupling mechanism of the mixing process; the fluidity solidification transition threshold is the critical parameter value for the solidified soil to transition from a fluid state to a solid state, which is determined by analyzing the two-stage and three-stage mixing correlation tensor; the one-stage and three-stage compensation adjustment index is a parameter that quantitatively describes the influence of the first-stage mixing parameters on the third-stage solidification effect, and is used to quantify the remote correlation effect; the remote correlation effect is the nonlinear influence mechanism of the first-stage mixing parameters on the final third-stage solidification effect, which is quantitatively characterized by the one-stage and three-stage compensation adjustment index.

[0015] This method overcomes the limitations of traditional single-stage mixing methods by dividing the mixing process into three distinct phases: initial mixing, particle homogenization, and structure formation. This method designs evaluation indicators and optimization strategies for each phase, establishing a comprehensive collaborative optimization system for mixing parameters.

[0016] By introducing key parameters such as the stirring efficiency factor, stirring synergy factor, and fluid-state solidification transition threshold, the method of the present invention achieves precise control of the entire process of solidified soil from fluid state to solid state. The establishment of the first and second stage stirring correlation response surface and the second and third stage stirring correlation tensor transforms the optimization of stirring parameters from a single empirical adjustment to a multi-stage scientific quantitative process. In particular, the application of the first and third stage compensation adjustment index solves the problem that traditional single-stage stirring cannot quantify the impact of initial stirring parameters on the final solidification effect.

[0017] This method transforms the optimization of three-stage mixing parameters into a solvable multi-objective optimization problem. Using the Pareto optimal frontier and weighted Chebyshev method, it finds a global optimal solution that meets both strength and uniformity requirements. This method enables high-precision control and prediction of consolidated soil properties. Compared to traditional single-stage mixing, three-stage mixing significantly improves the uniformity, strength stability, and structural integrity of consolidated soil, meeting the stringent requirements for high-performance consolidated soil in modern engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0020] like Figure 1 FIG. 1 is a flow chart of a three-stage precise screening and mixing method for fluidized soil solidification provided by the present invention, which includes the following steps:

[0021] S01. Construct an experimental parameter optimization matrix, including the three dimensions of material ratio, stirring time, and stirring speed, to determine the initial value range of each parameter of the three-stage stirring;

[0022] S02. Conduct the first stage mixing test, measure the fluidity index and strength coefficient of the solidified soil, and calculate the first stage mixing efficiency factor;

[0023] S03. According to the first-stage stirring efficiency factor, set the second-stage stirring parameter boundary conditions, perform the second-stage stirring experiment, and measure the particle uniformity index and cohesion index;

[0024] S04, establishing a correlation response surface between the first and second stirring stages, determining the optimal matching point between the stirring parameters of the first stage and the stirring parameters of the second stage, and calculating the stirring synergy factor;

[0025] S05. Based on the stirring synergy factor, determine the initial value of the third stage stirring parameter, conduct the third stage stirring experiment, and measure the curing rate index and the structural stability index;

[0026] S06. Establish the correlation tensor between the second and third stage stirring, identify the key conversion parameters between the second stage stirring parameters and the third stage stirring parameters, and calculate the flow state solidification transition threshold;

[0027] S07. Construct a three-stage compensation adjustment index to analyze the influence of the first-stage mixing parameters on the third-stage curing effect and quantify the remote correlation effect;

[0028] S08. Convert the three-stage mixing parameter optimization problem into a multi-objective optimization problem, construct the Pareto optimal frontier, apply the weighted Chebyshev method to determine the global optimal solution that meets the strength and uniformity requirements, calculate the optimization coefficient of the entire process, and determine the optimal parameter combination for the three-stage mixing to guide the three-stage precise screening and mixing of fluidized soil solidification;

[0029] S09. Optionally, it also includes verifying the optimal parameter combination, measuring the physical and mechanical properties of the solidified soil, calculating the screening accuracy index, and confirming the effectiveness of the three-stage mixing flow state soil solidification method.

[0030] Among them, the experimental parameter optimization matrix specifically refers to the three-dimensional experimental design space formed by combining the three key parameters of material ratio, stirring time and stirring speed at different levels, which is used to systematically explore the influence of parameters.

[0031] Among them, the first-stage mixing efficiency factor specifically refers to a dimensionless index that characterizes the material mixing uniformity and energy utilization efficiency during the first-stage mixing process, and is obtained by weighted calculation of the fluidity index and strength coefficient of the solidified soil.

[0032] Among them, the fluidity index of solidified soil specifically refers to a quantitative parameter for measuring the fluidity performance of solidified soil, which is measured in the first stage of the mixing experiment.

[0033] The strength coefficient specifically refers to the parameter that characterizes the compressive strength of the stabilized soil, which is measured in the first mixing experiment.

[0034] Among them, the particle uniformity index specifically refers to a quantitative parameter that measures the uniformity of particle distribution in solidified soil. It is calculated by analyzing the variance of particle spatial distribution through multi-point sampling and is measured in the second stage of the mixing experiment.

[0035] Among them, the cohesion index specifically refers to the parameter that characterizes the strength of the intermolecular force inside the solidified soil. It is obtained by measuring the solidified soil's ability to resist deformation through shear experiments and is measured in the second stage of the stirring experiment.

[0036] The first- and second-stage mixing correlation response surface specifically refers to a mathematical model that describes the performance of solidified soil under the combined effects of the first-stage mixing parameters and the second-stage mixing parameters, and is established through the mixing efficiency response equation.

[0037] The stirring synergy factor specifically refers to the coefficient that quantifies the degree of synergy between the first-stage stirring parameters and the second-stage stirring parameters, and is calculated by the performance improvement ratio of the optimal matching point on the first-stage and second-stage stirring correlation response surface.

[0038] Among them, the solidification rate index specifically refers to the parameter that measures the speed at which the solidified soil changes from a fluid state to a solid state, and is measured in the third stage of the mixing experiment.

[0039] Among them, the structural stability index specifically refers to the parameter for evaluating the ability of the internal structure of the solidified soil to resist external disturbances, which is measured in the third stage of the mixing experiment.

[0040] Among them, the second- and third-stage stirring correlation tensor specifically refers to a multidimensional mathematical structure that describes the interaction relationship between the second-stage stirring parameters and the third-stage stirring parameters, which is used to analyze the complex coupling mechanism of the stirring process.

[0041] The fluidization-solidification transition threshold specifically refers to the critical parameter value of the solidified soil transitioning from fluidization to solidification, which is determined by the correlation tensor analysis of the two- and three-stage mixing.

[0042] The first and third stage compensation adjustment index specifically refers to the parameter that quantitatively describes the influence of the first stage stirring parameters on the third stage curing effect, and is used to quantify the remote correlation effect.

[0043] Among them, the remote correlation effect specifically refers to the nonlinear influence mechanism of the first-stage stirring parameters on the final third-stage curing effect, which is quantitatively characterized by the first and third-stage compensation adjustment indexes.

[0044] Among them, the Pareto optimal front specifically refers to the solution set that indicates that each objective function cannot be further improved at the same time in the multi-objective optimization problem, and is used to find a balance between intensity requirements and uniformity requirements.

[0045] Among them, the weighted Chebyshev method specifically refers to a mathematical method that transforms multiple objectives into a single objective in a multi-objective optimization problem, which is used to determine the global optimal solution.

[0046] Among them, the full-process optimization coefficient specifically refers to a comprehensive indicator for comprehensively evaluating the efficiency and effect of the three-stage mixing process, which is obtained by weighted calculation of the contribution of parameters in each stage through a multivariate regression model.

[0047] Among them, the screening accuracy index specifically refers to a quantitative indicator for evaluating the accuracy of the three-stage mixing method in controlling the performance of solidified soil, which is calculated through statistical analysis of the deviation between the measured performance and the target performance.

[0048] Among them, the mixing efficiency response equation is used to establish the performance response surface of the solidified soil under the combined action of the first-stage mixing parameters and the second-stage mixing parameters. The input includes the first-stage mixing time, the first-stage mixing speed, the second-stage mixing time, the second-stage mixing speed, and the material ratio. The output is the predicted solidified soil strength coefficient and solidified soil fluidity index.

[0049] Among them, the parameter coupling equation is used to describe the mutual influence mechanism between the first-stage stirring parameters and the second-stage stirring parameters. The input includes the first-stage stirring efficiency factor, particle uniformity index, cohesion index, stirring energy transfer coefficient, and interfacial activity change rate. The output is the parameter coupling intensity and stirring synergy factor.

[0050] The mixing energy transfer coefficient specifically refers to a parameter that quantifies the efficiency of energy transfer from the mixing device to the solidified soil during the mixing process, and is obtained by measuring the energy consumption of the first and second mixing tests.

[0051] The interfacial activity change rate specifically refers to the rate at which the interfacial activity of different phases in the solidified soil changes with the stirring process, which is obtained by measuring the interfacial tension in the second stage of the stirring experiment.

[0052] The parameter coupling strength specifically refers to the quantitative representation of the degree of mutual influence between the first-stage stirring parameters and the second-stage stirring parameters, which is calculated by the parameter coupling equation.

[0053] Among them, the optimization objective function equation is used to comprehensively evaluate the effects of the one- and two-stage mixing combination to form a mathematical optimization problem. The input includes the strength target value, uniformity target value, cohesion target value, process constraints, and energy consumption weight coefficient. The output is the optimal mixing parameter combination and mixing synergy factor.

[0054] Among them, the strength target value specifically refers to the preset standard of strength coefficient that the solidified soil needs to achieve, which is determined according to the engineering application requirements.

[0055] Among them, the uniformity target value specifically refers to the preset standard of particle uniformity index that the solidified soil needs to achieve, which is determined according to the engineering application requirements.

[0056] Among them, the cohesion target value specifically refers to the preset standard of cohesion index that the solidified soil needs to achieve, which is determined according to the engineering application requirements.

[0057] Among them, process constraints specifically refer to the restrictions related to the mixing equipment and the process flow, including parameters such as the upper limit of the mixing time and the range of the mixing speed.

[0058] Among them, the energy consumption weight coefficient specifically refers to the quantitative representation of the importance of stirring energy consumption in the optimization objective function, which is determined according to the energy efficiency requirements.

[0059] Among them, the hardening kinetics equation is used to describe the relationship between the structural change rate and performance evolution during the transformation of solidified soil from fluid state to solid state. The input includes cement particle concentration, hydration reaction activation energy, ambient temperature, curing time, and stirring-introduced bubble rate. The output is the curing rate index and structural stability index.

[0060] The cement particle concentration specifically refers to the volume fraction of cement particles in the solidified soil, which is obtained by calculating the material ratio.

[0061] The activation energy of hydration reaction specifically refers to the minimum energy required for cement hydration reaction, which is obtained by microcalorimetry.

[0062] The ambient temperature specifically refers to the ambient temperature during the curing process, which is measured by a temperature sensor.

[0063] The curing time specifically refers to the time from the completion of mixing to the time the cured soil reaches the preset strength, which is obtained through experimental measurement.

[0064] The bubble rate introduced by mixing specifically refers to the volume percentage of bubbles introduced into the solidified soil during the mixing process, which is calculated by density measurement.

[0065] The specific implementation of the above steps is described in detail below.

[0066] The specific implementation of step S01 involves constructing an experimental parameter optimization matrix to determine the initial value ranges for the parameters in each stage of the three-stage mixing process. This matrix was established using an orthogonal experimental design method, with material ratio, mixing time, and mixing speed as the three dimensions, each dimension set to five levels. The curing agent to soil ratio was set to a range of 5% to 15%, the first stage mixing time ranged from 60 to 180 seconds, and the first stage mixing speed ranged from 40 to 100 rpm. Before the experiment, soil samples were prepared, moisture content measured, and gradation analyzed. The samples were then sealed and stored to prevent evaporation. The curing agent was tested for active ingredient content to ensure that its fineness met standards and was stored in a dry environment to prevent clumping. After the experimental equipment was calibrated, an experimental schedule was compiled according to the design plan, clearly defining the parameter settings, number of repetitions, and testing indicators for each set of experiments. This step, by systematically designing the experimental parameter space, provides a scientific basis for subsequent optimization, avoids blind testing, and improves experimental efficiency.

[0067] The specific implementation of step S02 involves conducting a first-stage mixing experiment and calculating the mixing efficiency factor. First, the soil material is added to the slurry mixer according to the specified proportions. The stirring device is activated for premixing for 30 seconds. Then, the curing agent is gradually added according to the proportion to prevent caking. The first stage of mixing is performed according to the time and speed parameters determined by the optimization matrix. During the mixing process, a torque monitor is used to record the change in the stirring resistance curve in real time to ensure uniform mixing. After the mixing is completed, a sample is immediately taken for fluidity testing using the expansion test method. The specific procedure is as follows: a 10 cm diameter and 7 cm high truncated cone is placed on a clean flat surface, filled with the mixed solidified soil sample, vibrated to release air, and the surface is leveled. The truncated cone is lifted vertically, and the sample expansion diameter is measured. The average of two measurements taken in mutually perpendicular directions is taken to calculate the fluidity index, with an ideal range of 1.8 to 2.4. Simultaneously, a standard test block (100 mm × 100 mm × 100 mm) is prepared and cured for 7 days under standard curing conditions (temperature 20 ± 2°C, relative humidity above 95%). The compressive strength is tested and the strength coefficient is calculated. The first stage mixing efficiency factor is calculated based on the weighted flow index and strength coefficient. The ideal value of the efficiency factor should be greater than 0.75. This step provides a quantitative basis for subsequent parameter optimization by evaluating the effect of the first stage mixing.

[0068] The specific implementation of step S03 involves setting the boundary conditions for the second-stage mixing parameters based on the first-stage mixing efficiency factor and executing the experiment. First, parameter combinations with a first-stage mixing efficiency factor greater than 0.75 are selected as input conditions for the second-stage mixing. The boundary conditions for the second-stage mixing parameters are determined, with a mixing time range of 40 to 120 seconds and a mixing speed range of 60 to 150 rpm. The material, after the first-stage mixing, is discharged from the mud mixer discharge port and fed through a chute to a drum screen for screening. The drum screen is set to an adjustable speed of 50 to 200 rpm, with a sieve aperture of 25 mm, to dynamically remove stones and debris larger than 25 mm. The qualified material after screening is conveyed through a diversion pipe to the secondary mixer for second-stage mixing according to the set parameters. During the mixing process, the motor power curve is recorded to calculate energy consumption. After mixing is complete, samples are collected from multiple locations (at least nine locations) and the particle size distribution at each location is measured using a laser particle size analyzer. The standard deviation of the spatial distribution of the particles is calculated, and the particle uniformity index is derived. The ideal range is 0.85 to 0.95. Samples are also collected for direct shear testing to measure the cohesion of the solidified soil and calculate the cohesion index. The ideal range is 12 to 18 kPa. This step improves the uniformity and cohesion of the solidified soil by finely controlling the parameters of the second mixing stage.

[0069] The specific implementation of step S04 involves establishing a response surface for the correlation between the first and second mixing stages and determining the optimal matching point. A neural network algorithm is used to construct a mixing efficiency response equation, with the first and second mixing parameters as input variables and the stabilized soil performance indicators as output variables. The specific experimental method involves selecting 10 different parameter combinations for comparative experiments, with each experiment repeated three times to eliminate random errors. Energy consumption during the mixing process is recorded. High-speed video is used to analyze fluid motion characteristics during the mixing process and measure changes in interfacial tension. Nondestructive testing methods (such as ultrasound) are used to monitor the homogenization progress during the mixing process. Based on the experimental data, a parameter coupling equation is established to describe the mutual influence mechanism between the first and second mixing parameters. The optimal matching point between the first and second mixing stages is determined by calculating the parameter coupling strength and the mixing synergy factor. The optimal matching point is typically characterized by a first-stage mixing time of 110-130 seconds and a mixing speed of 75-85 rpm; a second-stage mixing time of 85-95 seconds and a mixing speed of 95-105 rpm. This step uses mathematical modeling to reveal the complex relationships between mixing parameters and identify the optimal combination for synergistic efficiency. The specific implementation of step S05 involves determining the initial values ​​of the third-stage mixing parameters based on the mixing synergy factor and conducting experiments. The initial values ​​for the third-stage mixing parameters are derived from the parameters of the first two stages, with a mixing time range of 30 to 90 seconds and a mixing speed range of 50 to 120 rpm. The specific experimental process involves transferring the material after the second-stage mixing to a planetary secondary mixer, which is set to a dual-speed mode for the third-stage mixing. The first half of the mixing process uses a high speed (100 to 120 rpm) to fully disperse the aggregates, while the second half uses a medium speed (60 to 80 rpm) to reduce air bubble introduction. During the mixing process, a water-reducing agent (0.5% to 1.5% of the curing agent's mass) is added to optimize fluidity. After mixing, samples are taken to measure the slump, which is controlled between 180 and 240 mm. The rheological properties of the solidified soil are tested using a rotational viscometer, recording the viscosity change over time and calculating the curing rate index, with an ideal value ranging from 0.05 to 0.15 / min. At the same time, standard specimens were prepared and placed on a vibration table for disturbance testing. Changes in physical properties before and after the disturbance were measured, and the structural stability index was calculated, with an ideal value range of 0.8 to 0.9. This step optimizes the solidification performance and structural stability of the solidified soil by precisely controlling the mixing parameters in the third stage.

[0070] The specific implementation of step S06 involves establishing the correlation tensor between the second and third mixing stages and determining the fluidization-solidification transition threshold. First, a 5×5×5 three-dimensional parameter matrix was designed to systematically investigate the effects of the second and third mixing parameter combinations on the curing performance. The experimental method involved preparing 25 sets of solidified soil samples with different parameter combinations. These samples were cured under standard conditions (temperature 20±2°C, relative humidity above 95%), and mechanical property changes were measured periodically. An electronic universal testing machine was used to measure the strength development curves at different ages (3 hours, 6 hours, 12 hours, 24 hours, 3 days, and 7 days). A high-precision data acquisition system was used to record changes in temperature, conductivity, and pH during the curing process. A multidimensional mathematical structure was established based on the experimental data. The core influencing factors were analyzed using tensor decomposition methods to identify the key transition parameters between the second and third mixing stages. Based on the key transition parameters and the curing rate index, the fluidization-solidification transition threshold was calculated. This threshold represents the critical time point at which the solidified soil transitions from a fluidized state to a solid state, typically within the range of 40 to 60 minutes. This step reveals the complex parameter coupling mechanism in the stirring process through high-dimensional mathematical analysis, providing a theoretical basis for precise control of the curing process.

[0071] The specific implementation of step S07 involves constructing a three-stage compensation adjustment index and quantifying the remote correlation effect. A crossover experiment was designed using time series analysis to systematically investigate the impact of first-stage mixing parameters on third-stage curing performance. The specific experimental method involved selecting three typical first-stage mixing parameter combinations (low efficiency, medium efficiency, and high efficiency) and pairing them with three typical third-stage mixing parameter combinations (slow curing, medium curing, and fast curing), creating nine combinations. Standard specimens were prepared for each combination and placed in an environmental chamber to simulate different field conditions (temperature range 10-30°C, relative humidity range 60%-90%). The performance evolution was monitored over 28 days. X-ray tomography was used to analyze the internal structural development of the cured soil, and microcalorimetry was used to determine the hydration heat release pattern. Based on the experimental data, a transfer function was established between the first-stage mixing parameters and the third-stage curing performance. A three-stage compensation adjustment index was calculated to quantify the remote correlation effect. The remote correlation effect value typically ranges from 0.4 to 0.7, indicating the degree of influence of the first-stage mixing on the final curing performance. This step provides a scientific basis for optimizing the overall mixing process by quantitatively analyzing the long-range correlations between each mixing stage.

[0072] The specific implementation of step S08 involves transforming the three-stage mixing parameter optimization problem into a multi-objective optimization problem and determining the global optimal solution. Three objective functions are constructed: strength requirements, uniformity requirements, and energy consumption requirements. The specific method is to determine target values ​​for strength (typically 0.8-1.2 MPa), uniformity (typically greater than 0.9), and cohesion (typically 15-18 kPa) based on the engineering application requirements. Process constraints are set, including a total mixing time of no more than 5 minutes, a mixing speed exceeding the equipment rating, and energy consumption no more than 80% of that of conventional processes. A non-dominated sorting genetic algorithm is used for multi-objective optimization, with a population size of 100, 500 iterations, a crossover probability of 0.8, and a mutation probability of 0.1. The algorithm iteratively calculates the Pareto optimal frontier, representing the set of solutions where each objective function cannot be further improved simultaneously. The weighted Chebyshev method is applied to transform the multi-objective problem into a single-objective problem, and the global optimal solution is obtained. Calculate the optimization coefficients for the entire process and determine the optimal parameter combination for the three-stage mixing: the first stage is 120 seconds and a stirring speed of 80 rpm; the second stage is 90 seconds and a stirring speed of 100 rpm; and the third stage is 60 seconds and a stirring speed of 90 rpm. This step uses advanced optimization algorithms to find the global optimal solution while meeting multiple requirements, achieving overall optimization of the mixing process.

[0073] Step S09 is an optional step, and its specific implementation method is to verify the optimal parameter combination and confirm the effectiveness of the method. According to the determined optimal parameter combination of the three-stage mixing, a verification experiment is carried out to measure the physical and mechanical properties of the solidified soil. The specific verification method is: prepare no less than 30 groups of samples according to the optimal parameter combination, and divide them into three categories: strength group, permeability group and deformation group. The strength group samples are subjected to unconfined compressive strength tests at different ages (3 days, 7 days, 28 days) to determine the strength development law. The permeability group samples are subjected to variable head permeability tests to determine the permeability coefficient, which is required to be less than cm / s. Consolidation tests were conducted on the deformed group samples to determine the compression modulus and consolidation coefficient. At the same time, control group samples were prepared using the traditional mixing method, and parallel tests were conducted under the same conditions. The differences in strength, uniformity, impermeability and stability of the solidified soil prepared by the two methods were compared, and the percentage of performance improvement was calculated. The screening accuracy index was calculated. When the index was greater than 0.9, it indicated that the accuracy of the three-stage mixing method met the engineering requirements. Through verification in a small-scale on-site test section, the actual engineering application effect was evaluated, and the effectiveness and practical value of the three-stage precise screening and mixing fluidized soil solidification method were confirmed.

[0074] The mathematical model or calculation process involved in the present invention is described in detail below.

[0075] In step S01, the orthogonal experimental design method is used to construct the experimental parameter optimization matrix, which is specifically expressed as follows:

[0076] ;

[0077] Where, Optimize the matrix for three-dimensional parameters; is the material ratio level, ; is the stirring time level, ; is the stirring speed level, ; is the experimental unit of the corresponding parameter combination.

[0078] The experimental parameter range is set as follows: material ratio (ratio of curing agent to soil material) ; First stirring time ; First stage stirring speed .

[0079] In step S02, the formula for calculating the first stage stirring efficiency factor is as follows:

[0080] ;

[0081] Where, is the first stage stirring efficiency factor; is the fluidity index; is the strength coefficient; and are weight coefficients, which are 0.6 and 0.4 respectively.

[0082] The parameter acquisition method is:

[0083] It is obtained by experimental method, specifically the expansion test method, and the calculation formula is: Where, is the diameter after expansion; The experimental steps include: Step 1: Place a truncated cone with a diameter of 10 cm and a height of 7 cm on a clean flat surface; Step 2: Fill the surface with the stirred solidified soil sample, vibrate to release the air, and then level the surface; Step 3: Lift the truncated cone vertically; Step 4: Measure the expanded diameter of the sample and take the average of two measurements taken in perpendicular directions. The ideal value range is 1.8 to 2.4.

[0084] It is obtained by experimental method and the calculation formula is: Where, is the measured compressive strength; The reference strength value (0.8 MPa) is obtained. The experimental steps include: Step 1: Preparation of standard test blocks (100 mm × 100 mm × 100 mm); Step 2: Curing under standard curing conditions (temperature 20 ± 2°C, relative humidity above 95%) for 7 days; Step 3: Testing the compressive strength using a press at a loading rate of 2 kN / s.

[0085] In step S03, the formula for calculating the particle uniformity index is as follows:

[0086] ;

[0087] Where, is the particle uniformity index; is the standard deviation of particle spatial distribution; is the reference standard deviation.

[0088] The parameter acquisition method is:

[0089] It is obtained by experimental method and the calculation formula is: Where, For the Particle characteristic parameters at each sampling point (such as median particle size); is the average value of the particle characteristic parameters of all sampling points; The experimental steps include: Step 1: Take multiple sampling points from the solidified soil after mixing using the grid method; Step 2: Use a laser particle size analyzer to measure the particle size distribution at each point; Step 3: Calculate the standard deviation of the particle size distribution. The ideal range is 0.85 to 0.95.

[0090] In step S04, a parameter coupling equation is established to describe the mutual influence mechanism between the stirring parameters of the first stage and the second stage, which is specifically expressed as follows:

[0091] Where, is the parameter coupling strength; is the coupling coefficient, ranging from 0.8 to 1.2; is the coupling energy in joules; is the first stage stirring efficiency factor; is the particle uniformity index; is the total stirring energy in joules; is the interfacial activity coefficient, ranging from 0.05 to 0.15; is the rate of change of interfacial activity, in units of .

[0092] The parameter acquisition method is:

[0093] The calculation formula is Where, is the stirring power curve, in watts; is the first stirring time; The experimental steps include: Step 1: Installing a power monitoring device on the mixer motor; Step 2: Recording real-time power data during the mixing process; Step 3: Integrating and calculating energy consumption.

[0094] The experimental method is used to obtain the data. The experimental steps include: Step 1: using a surface tension meter to measure the interfacial tension of the solidified soil during the mixing process; Step 2: recording the interfacial tension values ​​at different time points; Step 3: calculating the rate of change of the interfacial tension with time.

[0095] The calculation formula of stirring synergy factor is as follows:

[0096] ;

[0097] Where, is the stirring synergistic factor; is the particle uniformity index; is the cohesion index; is the first stage stirring efficiency factor; is the stirring energy consumption, in kilowatt-hours.

[0098] The parameter acquisition method is:

[0099] It is obtained by experimental method and the calculation formula is: Where, is the measured cohesion value in kPa; For reference cohesion, 15 kPa was used. The experimental steps include: Step 1: Collecting a solidified soil sample; Step 2: Conducting a shear test using a direct shear apparatus at different normal stresses; Step 3: Plotting a shear stress-normal stress curve; the intercept is the cohesion. The ideal range is 12 to 18 kPa.

[0100] The calculation formula is Where, is the stirring power curve, in watts; 、 、 There are three stirring times respectively.

[0101] In step S05, the formula for calculating the curing rate index is as follows:

[0102] ;

[0103] Where, is the curing rate index, in units of ; is the initial viscosity, in Pa·s; is the rate of change of viscosity in Pa·s / min.

[0104] The parameter acquisition method is:

[0105] The experimental method is used to obtain the viscosity of the solidified soil. The experimental steps include: Step 1: Collecting the solidified soil sample after stirring; Step 2: Using a rotational viscometer to measure the viscosity of the sample at different time points; Step 3: Drawing a viscosity-time curve and calculating the viscosity change rate. The ideal value range is 0.05~0.15 .

[0106] The calculation formula of the structural stability index is as follows:

[0107] ;

[0108] Where, is the structural stability index; is the intensity after disturbance, in kPa; is the intensity before disturbance, in kilopascals.

[0109] The parameter acquisition method is:

[0110] and The experimental method is used to obtain the initial strength. The experimental steps include: Step 1: Preparation of standard specimens; Step 2: Determination of initial strength ; Step 3: Place the sample on a vibration table and vibrate it for 60 seconds at the specified amplitude and frequency; Step 4: Measure the intensity after vibration . The ideal value range is 0.8 to 0.9.

[0111] In step S06, the mathematical expression of the correlation tensor between the second and third stirring stages is established as follows:

[0112] ;

[0113] Where, is the cross-correlation tensor; is the core tensor; 、 、 is a factor matrix; Indicates that along -dimensional tensor-matrix product.

[0114] The specific implementation method is Tucker decomposition, and the following steps are required to obtain parameters: Step 1: Construct the original data tensor, which includes three dimensions: the second-stage stirring parameters, the third-stage stirring parameters, and the curing performance index; Step 2: Standardize the original tensor; Step 3: Use the alternating least squares method to perform Tucker decomposition to obtain the core tensor and factor matrix.

[0115] The calculation formula for the fluidity-solidification transition threshold is as follows:

[0116] ;

[0117] Where, is the flow-solidification transition threshold, in minutes; As the benchmark transition time, take 50 minutes; It is the key conversion parameter between the second and third stage stirring parameters; is the curing rate index; is the exponential coefficient, and its value is 0.6; is the temperature sensitivity coefficient, which is 500; is the ambient temperature in Kelvin.

[0118] The parameter acquisition method is:

[0119] By analyzing the core tensor The main elements of are identified and the calculation formula is Where, is the maximum element value of the core tensor; is the average of the core tensor elements. The typical value range is 1.5 to 2.5.

[0120] In step S07, the calculation formula of the three-stage compensation adjustment index is as follows:

[0121] ;

[0122] Where, It is a three-stage compensation adjustment index; is the first stage stirring efficiency factor; is the curing rate index; is the structural stability index; 、 、 are weight coefficients, which are 0.3, 0.4, and 0.3 respectively.

[0123] The calculation formula of remote correlation effect is as follows:

[0124] ;

[0125] Where, It is a remote correlation effect; is the first stage stirring efficiency factor and the third stage curing effect ( and )’s partial correlation coefficient; is the stirring interval time, in minutes; is the characteristic time, which is 10 minutes.

[0126] The parameter acquisition method is:

[0127] The statistical method is used for calculation. The experimental steps include: Step 1: Conduct a series of comparative experiments, fix the second stage stirring parameters, and change the first stage stirring parameters and the third stage stirring parameters; Step 2: Measure the 、 and Step 3: Calculate the correlation coefficient using partial correlation analysis method. The value range is usually between 0.4 and 0.7.

[0128] In step S08, the three-stage mixing parameter optimization problem is transformed into a multi-objective optimization problem, and the objective function is expressed as follows:

[0129] ;

[0130] ;

[0131] ;

[0132] Where, is the decision variable vector, which includes the time and speed parameters of the three-stage stirring; represents the strength requirement objective function; represents the uniformity requirement objective function; represents the energy consumption requirement objective function; For parameter combination Strength coefficient under ; is the intensity target value; For parameter combination The uniformity index under is the uniformity target value; For parameter combination Energy consumption under For reference energy consumption.

[0133] Apply the weighted Chebyshev method to transform the multi-objective problem into a single-objective problem:

[0134] ;

[0135] Where, is the comprehensive objective function; 、 、 is the ideal value of each target; 、 、 is the weight coefficient, which is determined according to engineering requirements. The typical value is , , .

[0136] The optimization problem can be expressed as:

[0137] ;

[0138] ;

[0139] Where, and are the lower and upper bounds of the decision variable, respectively.

[0140] The calculation formula of the full process optimization coefficient is as follows:

[0141] ;

[0142] Where, Optimize the coefficient for the entire process; is the strength coefficient; is the uniformity index; is the structural stability index; 、 、 It is the sum of the three stirring times, in seconds.

[0143] This coefficient comprehensively evaluates the balance between stirring effect and efficiency. The larger the value, the higher the efficiency while ensuring performance.

[0144] In step S09, the calculation formula of the screening accuracy index is as follows:

[0145] ;

[0146] Where, is the screening accuracy index; is the measured strength in MPa; is the target intensity in MPa.

[0147] This index indicates how close the actual intensity achieved is to the target intensity. When the value is greater than 0.9, it indicates that the accuracy of the three-stage mixing method meets the engineering requirements.

[0148] The construction of the above equations takes into account the physical nature of the mixing process and the actual needs of the project. The formula for the first stage mixing efficiency factor adopts a linear weighted form, which reflects the comprehensive balance of the two key indicators of fluidity and strength. The weight coefficient setting reflects the actual situation that fluidity is usually more important than strength in engineering. The particle uniformity index formula is based on the standard deviation principle. By comparing with the reference value, a dimensionless index is obtained, which is convenient for comparison under different conditions. The parameter coupling equation introduces energy transfer and interfacial activity factors, and comprehensively considers the energy utilization efficiency and material phase change characteristics during the mixing process, which is of great significance for reflecting the actual effect of mixing. The flow state solidification transition threshold formula combines the reaction kinetics principle, takes into account the temperature effect and the coupling effect of mixing parameters, and more accurately predicts the solidification time. The first and third stage compensation adjustment index formulas adopt a weighted average form, which quantifies the remote correlation effect concisely and effectively. The full process optimization coefficient formula is constructed in the form of a ratio of performance index to time, reflecting the balance between effect and efficiency, and providing a simple and practical evaluation standard for engineering applications.

[0149] Specifically, the principle of the present invention is: the core principle of the technical solution of the present invention is based on the theory of "multi-stage sequence optimization and precise control", which deconstructs the transformation process of solidified soil from fluid state to solid state into three functional stages, and meets the needs of each stage through targeted parameter control, and finally realizes the precise regulation of the performance of solidified soil.

[0150] In the first phase, the present invention focuses on the thorough mixing and dispersion of the initial materials. By measuring the fluidity index and strength coefficient of the solidified soil, the mixing efficiency factor is calculated. Based on the principles of fluid mechanics and particle mechanics, this phase requires appropriate high shear forces to promote particle dispersion and initial mixing. The experimental parameter optimization matrix helps the system explore the optimal combination of three key dimensions: material ratio, mixing time, and mixing speed, laying the foundation for subsequent phases.

[0151] The second phase sets boundary conditions based on the stirring efficiency factors of the first phase, focusing on optimizing particle uniformity and cohesion. According to colloid chemistry theory, the primary function of stirring in this phase is to promote uniform distribution of the cementitious material and form a preliminary network structure. By establishing a stirring efficiency response equation and a parameter coupling equation, the interaction mechanism between the parameters of the first and second phases is precisely described, ensuring the optimal transfer of stirring parameters between the different phases.

[0152] The third stage, based on the optimization results of the first two stages, controls the curing rate and structural stability through the hardening kinetics equation. This stage corresponds to the critical transition period of the solidified soil from a fluid state to a solid state. It requires precise control of mixing intensity and time to avoid excessive mixing that disrupts the established structural network while ensuring sufficient uniformity. The establishment of the second and third stage mixing correlation tensors and the first and third stage compensation adjustment indices creates a closed-loop optimization system for the entire process.

[0153] From a materials science perspective, the method described in this paper adheres to the physicochemical mechanisms of solidified soil formation: the first stage ensures uniform initial mixing of the materials, the second stage promotes the formation of a cementitious network, and the third stage controls the solidification reaction rate and structural stability. This multi-stage, progressive optimization approach fully considers the characteristics and parameter transfer mechanisms of each stage, overcoming the limitations of traditional single-stage mixing methods that cannot meet the needs of the entire process, thereby achieving precise control and consistent performance of solidified soil.

[0154] A specific embodiment 1 of the present invention is provided below. The specific implementation of each step in this embodiment 1 is described in detail as follows.

[0155] The specific implementation of step S01 is to construct an experimental parameter optimization matrix to determine the initial value range of each parameter of the three-stage mixing. This matrix is ​​established using the orthogonal experimental design method, with material ratio, mixing time and mixing speed as three dimensions, and each dimension set to 5 levels. Specifically expressed as:

[0156] ;

[0157] Where, Optimize the matrix for three-dimensional parameters; is the material ratio level, ; is the stirring time level, ; is the stirring speed level, ; is the experimental unit of the corresponding parameter combination.

[0158] The ratio of curing agent to soil is set in the range of 5% to 15%, the first stage stirring time range is 60 to 180 seconds, and the first stage stirring speed range is 40 to 100 rpm. Before the experiment, soil samples need to be prepared, moisture content and gradation analysis need to be carried out, and the samples need to be sealed and stored to prevent water evaporation. The curing agent needs to be tested for active ingredient content to ensure that the fineness meets the standard and is stored in a dry environment to avoid agglomeration. By using the response surface methodology to discretely sample the parameter space, an initial set of experimental points is generated to ensure that the experimental points are evenly distributed and representative. Experimental design software is used to generate the experimental plan to clarify the parameter settings, number of repetitions and test indicators for each group of experiments. This step provides a scientific basis for subsequent optimization by systematically designing the experimental parameter space, avoiding blind experiments and improving experimental efficiency.

[0159] The specific implementation method of step S02 is to conduct the first stage stirring experiment and calculate the stirring efficiency factor. First, put the soil material into the mud mixer according to the prescribed proportion, start the stirring device to pre-mix for 30 seconds, and then gradually add the curing agent according to the ratio to prevent caking. The first stage of stirring is carried out according to the time and speed parameters determined by the optimization matrix. During the stirring process, a torque monitor is used to record the stirring resistance change curve in real time to ensure uniform stirring. After the stirring is completed, samples are immediately taken for fluidity test and strength test, and the first stage stirring efficiency factor is calculated. The formula is as follows:

[0160] ;

[0161] Where, is the first stage stirring efficiency factor; is the fluidity index, and the calculation formula is , is the diameter after expansion, is the original diameter (10 cm); is the strength coefficient, and the calculation formula is , is the measured compressive strength, is the reference strength value (0.8 MPa); and are weight coefficients, which are 0.6 and 0.4 respectively.

[0162] The fluidity test uses the expansion test method. Specifically, a 10 cm diameter, 7 cm high truncated cone is placed on a clean flat surface. The sample is filled with the mixed, solidified soil. After vibrating to release the air, the surface is leveled. The cone is then lifted vertically and the expanded diameter of the sample is measured. The average of two measurements taken in mutually perpendicular directions is taken. A standard test block (100 mm × 100 mm × 100 mm) is also prepared and cured for 7 days under standard curing conditions (temperature 20 ± 2°C, relative humidity above 95%). The compressive strength is then tested. The ideal fluidity index range is 1.8 to 2.4, and the ideal mixing efficiency factor should be greater than 0.75. This step evaluates the effectiveness of the initial mixing phase and provides a quantitative basis for subsequent parameter optimization.

[0163] The specific implementation method of step S03 is to set the boundary conditions of the second-stage stirring parameters according to the first-stage stirring efficiency factor and perform the experiment. First, the parameter combination with the first-stage stirring efficiency factor value greater than 0.75 is screened as the input condition for the second-stage stirring. The boundary conditions of the second-stage stirring parameters are determined, and the stirring time range is 40 to 120 seconds, and the stirring speed range is 60 to 150 rpm. The material after the first-stage stirring is discharged from the discharge port of the mud mixer and sent to the drum screen through the chute for screening. The drum screen is set to a speed of 50 to 200 rpm (adjustable), and the sieve hole diameter is 25 mm, which is used to dynamically screen out stones and debris with a particle size greater than 25 mm. The qualified material after screening is transported to the secondary mixer through the diversion pipe and the second stage of stirring is carried out according to the set parameters. After the stirring is completed, multi-point samples are immediately collected to measure the particle uniformity index and cohesion index. The particle uniformity index calculation formula is:

[0164] ;

[0165] Where, is the particle uniformity index; is the standard deviation of particle spatial distribution, and the calculation formula is: , For the The particle characteristic parameters of each sampling point, is the average value of the particle characteristic parameters of all sampling points, is the number of sampling points; is the reference standard deviation.

[0166] The sampling method involves taking samples from at least nine points in the solidified soil using a grid method. The particle size distribution at each point is measured using a laser particle size analyzer. Samples are also collected for direct shear testing to determine the cohesion of the solidified soil and calculate the cohesion index. The ideal range for the particle uniformity index is 0.85 to 0.95, and the ideal range for the cohesion index is 12 to 18 kPa. This step improves the uniformity and cohesion of the solidified soil by finely controlling the parameters of the second mixing stage.

[0167] The specific implementation of step S04 is to establish a response surface for the correlation between the first and second mixing stages and determine the optimal matching point. A neural network algorithm is used to construct a mixing efficiency response equation, with the first and second mixing parameters as input variables and the solidified soil performance index as the output variable. A parameter coupling equation is also established to describe the interaction mechanism between the first and second mixing parameters, as shown below: ;

[0168] Where, is the parameter coupling strength; is the coupling coefficient, ranging from 0.8 to 1.2; is the coupling energy, and the calculation formula is , is the stirring power curve, The first stirring time, The second stirring time; is the first stage stirring efficiency factor; is the particle uniformity index; is the total stirring energy; is the interfacial activity coefficient, ranging from 0.05 to 0.15; is the rate of change of interfacial activity.

[0169] The calculation formula of stirring synergy factor is:

[0170] ;

[0171] Where, is the stirring synergistic factor; is the particle uniformity index; is the cohesion index, and the calculation formula is , is the measured cohesion value, is the reference cohesion value; is the first stage stirring efficiency factor; The energy consumption for stirring.

[0172] The specific experimental method is to select 10 groups of different parameter combinations for comparative experiments. Each group of experiments is repeated 3 times to eliminate random errors, and the energy consumption during the stirring process is recorded. The fluid motion characteristics during the stirring process are analyzed by high-speed video, and the changes in interfacial tension are measured. The homogenization process during the stirring process is monitored by non-destructive testing methods. The parameter combination point that maximizes the stirring synergy factor is found on the response surface through a multivariable optimization algorithm. This point is the optimal matching point for the first and second stage stirring. The optimal matching point is usually as follows: the first stage stirring time is 110 to 130 seconds, and the stirring speed is 75 to 85 rpm; the second stage stirring time is 85 to 95 seconds, and the stirring speed is 95 to 105 rpm. This step reveals the complex relationship between stirring parameters through mathematical modeling, and finds the optimal combination of parameters for synergistic effect.

[0173] The specific implementation method of step S05 is to determine the initial value of the third-stage stirring parameter based on the stirring synergy factor and conduct experiments. The initial value of the third-stage stirring parameter is derived based on the stirring parameters of the first two stages, the stirring time range is 30 to 90 seconds, and the stirring speed range is 50 to 120 rpm. The material after the second stage of stirring is transported to a planetary secondary mixer, and a dual-speed mode is set for the third stage of stirring. The first half uses high speed to fully disperse the aggregates, and the second half uses medium speed to reduce the introduction of bubbles. During the stirring process, a water reducer is added simultaneously (the amount is 0.5% to 1.5% of the mass of the curing agent) to optimize fluidity. After the stirring is completed, the curing rate index and the structural stability index are measured. The curing rate index calculation formula is:

[0174] ;

[0175] Where, is the curing rate index, in units of ; is the initial viscosity, in Pa·s; is the rate of change of viscosity in Pa·s / min.

[0176] The calculation formula of the structural stability index is:

[0177] ;

[0178] Where, is the structural stability index; is the intensity after disturbance, in kPa; is the intensity before disturbance, in kilopascals.

[0179] The measurement method involves testing the rheological properties of the solidified soil using a rotational viscometer and recording the viscosity curve over time. Standard specimens are prepared and placed on a vibration table for a disturbance test, measuring changes in physical properties before and after the disturbance. The ideal range for the solidification rate index is 0.05 to 0.15 / minute, and the ideal range for the structural stability index is 0.8 to 0.9. This step optimizes the solidification performance and structural stability of the solidified soil by precisely controlling the third-stage mixing parameters.

[0180] The specific implementation of step S06 is to establish the correlation tensor between the second and third stirring stages and determine the flow-state solidification transition threshold. First, a 5×5×5 three-dimensional parameter matrix is ​​designed to systematically explore the impact of the combination of second and third stirring parameters on solidification performance. The mathematical expression for establishing the correlation tensor between the second and third stirring stages is as follows:

[0181] ;

[0182] Where, is the cross-correlation tensor; is the core tensor; 、 、 is a factor matrix; Indicates that along -dimensional tensor-matrix product.

[0183] The specific implementation method is Tucker decomposition. First, the original data tensor is constructed, which includes the second-stage stirring parameters, the third-stage stirring parameters and the curing performance index. Then the original tensor is normalized and finally the alternating least squares method is used to perform Tucker decomposition to obtain the core tensor and factor matrix. By analyzing the main elements of the core tensor, the key conversion parameters between the second-stage stirring parameters and the third-stage stirring parameters are identified. The calculation formula for the fluidity-solidification transition threshold is:

[0184] ;

[0185] Where, is the flow-solidification transition threshold, in minutes; As the benchmark transition time, take 50 minutes; is the key conversion parameter between the second and third stage stirring parameters, and the calculation formula is: , is the maximum element value of the core tensor, is the average value of the core tensor elements; is the curing rate index; is the exponential coefficient, and its value is 0.6; is the temperature sensitivity coefficient, which is 500; is the ambient temperature in Kelvin.

[0186] The experimental method includes: preparing 25 groups of solidified soil samples with different parameter combinations, curing them under standard conditions, and regularly measuring changes in mechanical properties; using an electronic universal testing machine to determine the strength development curves at different ages; and using a high-precision data acquisition system to record changes in temperature, conductivity, and pH during the solidification process. The typical value range of is 1.5-2.5, and the fluidity-solidification transition threshold is usually in the range of 40-60 minutes. This step reveals the complex parameter coupling mechanism during the stirring process through high-dimensional mathematical analysis, providing a theoretical basis for precise control of the solidification process.

[0187] The specific implementation of step S07 is to construct a three-stage compensation adjustment index and quantify the remote correlation effect. A crossover experiment is designed using the time series analysis method to systematically study the effect of the first stage stirring parameters on the third stage curing effect. The calculation formula of the three-stage compensation adjustment index is:

[0188] ;

[0189] Where, It is a three-stage compensation adjustment index; is the first stage stirring efficiency factor; is the curing rate index; is the structural stability index; 、 、 are weight coefficients, which are 0.3, 0.4, and 0.3 respectively.

[0190] The calculation formula of remote correlation effect is:

[0191] ;

[0192] Where, It is a remote correlation effect; is the first stage stirring efficiency factor and the third stage curing effect ( and )’s partial correlation coefficient; is the stirring interval time, in minutes; is the characteristic time, which is 10 minutes.

[0193] The specific experimental method is to select three typical parameter combinations for the first mixing stage and pair them with three typical parameter combinations for the third mixing stage, forming nine groups of combinations. Standard specimens are prepared for each combination and placed in an environmental chamber to simulate different field conditions, monitoring the performance evolution over 28 days. X-ray tomography is used to analyze the internal structural development of the solidified soil, and microcalorimetry is used to determine the hydration heat release pattern. Correlation coefficients are calculated using partial correlation analysis. The remote correlation effect value typically ranges from 0.4 to 0.7, indicating the degree of influence of the first mixing stage on the final solidification effect. This step provides a scientific basis for optimizing the overall mixing process by quantitatively analyzing the remote correlation between the various mixing stages.

[0194] The specific implementation of step S08 is to transform the three-stage mixing parameter optimization problem into a multi-objective optimization problem and determine the global optimal solution. Three objective functions are constructed: strength requirement, uniformity requirement and energy consumption requirement, which are specifically expressed as follows:

[0195] ;

[0196] ;

[0197] ;

[0198] Where, is the decision variable vector, which includes the time and speed parameters of the three-stage stirring; represents the strength requirement objective function; represents the uniformity requirement objective function; represents the energy consumption requirement objective function; For parameter combination Strength coefficient under ; is the intensity target value; For parameter combination The uniformity index under is the uniformity target value; For parameter combination Energy consumption under For reference energy consumption.

[0199] Apply the weighted Chebyshev method to transform the multi-objective problem into a single-objective problem:

[0200] ;

[0201] Where, is the comprehensive objective function; 、 、 is the ideal value of each target; 、 、 is the weight coefficient, which is determined according to engineering requirements. The typical value is , , .

[0202] The optimization problem can be formulated as minimizing , where the constraints are the upper and lower limits of the decision variables. A non-dominated sorting genetic algorithm is used for multi-objective optimization, with a population size of 100, 500 iterations, a crossover probability of 0.8, and a mutation probability of 0.1. The algorithm iteratively calculates the Pareto optimal frontier, representing the set of solutions where each objective function cannot be further improved simultaneously. The formula for calculating the full-process optimization coefficient is:

[0203] ;

[0204] Where, Optimize the coefficient for the entire process; is the strength coefficient; is the uniformity index; is the structural stability index; 、 、 It is the sum of the three stirring times, in seconds.

[0205] Based on the calculation results, the optimal parameter combination for the three-stage mixing process was determined: the first stage had a mixing time of 120 seconds and a mixing speed of 80 rpm; the second stage had a mixing time of 90 seconds and a mixing speed of 100 rpm; and the third stage had a mixing time of 60 seconds and a mixing speed of 90 rpm. This step uses an advanced optimization algorithm to find the global optimal solution while meeting multiple requirements, achieving overall optimization of the mixing process.

[0206] In this embodiment, step S09 is an optional step, and its specific implementation is to verify the optimal parameter combination and confirm the effectiveness of the method. Based on the determined optimal parameter combination of the three-stage mixing, a verification experiment is carried out to measure the physical and mechanical properties of the solidified soil. The calculation formula for the screening accuracy index is:

[0207] ;

[0208] Where, is the screening accuracy index; is the measured strength in MPa; is the target intensity in MPa.

[0209] The specific verification method is: prepare no less than 30 groups of specimens according to the optimal parameter combination, and divide them into three categories: strength group, permeability group and deformation group; conduct unconfined compressive strength test on the specimens in the strength group at different ages to determine the strength development law; conduct variable head permeability test on the specimens in the permeability group to determine the permeability coefficient, which is required to be less than cm / s; consolidation tests were conducted on the deformed samples to determine the compression modulus and consolidation coefficient. Simultaneously, control samples were prepared using a traditional mixing method and tested in parallel under the same conditions. The strength, uniformity, impermeability, and stability of the solidified soils prepared by the two methods were compared, and the percentage improvement in performance was calculated. When the screening accuracy index is greater than 0.9, it indicates that the three-stage mixing method meets the engineering requirements. Through verification in a small-scale on-site test section and evaluation of the actual engineering application results, the effectiveness and practical value of the three-stage precision screening and mixing fluidized soil solidification method were confirmed.

[0210] To better understand and implement the present invention, Example 2 of a specific application scenario of the present invention is provided below: Researchers conducted an optimization study on the fluidized solidification soil mixing process for a municipal pipeline trench backfill project. The total length of the project is approximately 2.5 kilometers, the trench width is 1.2 meters, the depth is 2.5 meters, and the volume of fluidized solidification soil required for backfill is approximately 7,500 cubic meters. Traditional mixing processes have problems such as poor uniformity, unstable strength, and high energy consumption. Researchers applied a three-stage precise screening and mixing method for fluidized solidification soil to improve it.

[0211] First, the researchers constructed an experimental parameter optimization matrix to determine the initial range of parameters for each stage of the three-stage mixing process. Sampling and analysis of excavated soil on site revealed a moisture content of 21.3%, an organic matter content of 1.7%, and a particle size distribution as shown in Table 1:

[0212] Table 1 Particle gradation distribution of excavated soil on site

[0213]

[0214] The researchers selected cement as the curing agent and determined the experimental parameter matrix. The curing agent to soil ratio ranged from 6% to 14%, the first mixing time ranged from 70 to 170 seconds, and the mixing speed ranged from 45 to 95 rpm. An orthogonal experimental design was implemented using a 5×5×5 three-dimensional parameter matrix.

[0215] The results of the first stirring experiment selected 10 typical parameter combinations, as shown in Table 2:

[0216] Table 2 Results of the first stage stirring experiment

[0217]

[0218] Based on the parameter combinations in Table 2 that achieved a stirring efficiency factor greater than 0.78, a second stage stirring experiment was conducted to determine the boundary conditions for the second stage stirring parameters. The material after the first stage stirring was fed to a rotary screen for screening, removing approximately 4.7% of the total particles with a diameter greater than 25 mm. The qualified material after screening was fed to a secondary mixer for the second stage stirring, with a stirring time range of 50 to 110 seconds and a stirring speed range of 70 to 130 rpm. The particle uniformity index and cohesion index, measured by collecting samples at multiple points, are shown in Table 3:

[0219] Table 3 Results of the second stage stirring experiment

[0220]

[0221] Using the experimental data in Tables 2 and 3, the researchers constructed a response surface for the correlation between the first and second stirring stages, calculating the parameter coupling strength and the stirring synergy factor. A neural network algorithm was used to construct the stirring efficiency response equation, employing a 5-8-2 three-layer BP neural network structure with a training error of less than 0.05. Analysis and calculations revealed that parameter combination No. 7 (first stage: 12%, 120 seconds, 85 rpm; second stage: 90 seconds, 110 rpm) had the highest stirring synergy factor of 0.62.

[0222] Based on the optimal parameter combination of the first two stages, the third stage of stirring experiment was carried out, with a stirring time range of 40 to 80 seconds, a stirring speed range of 60 to 110 rpm, and a water reducer added at a rate of 1.2% of the curing agent mass. The results of the measured curing rate index and structural stability index are shown in Table 4:

[0223] Table 4 Results of the third stage stirring experiment

[0224]

[0225] Based on experimental data, researchers established a two- and three-stage stirring correlation tensor, used Tucker decomposition to obtain the core tensor, and calculated the key conversion parameters. The calculated flow-solidification transition threshold is 47 minutes at an ambient temperature of 25°C.

[0226] By calculating the compensation adjustment index and remote correlation effect of the first and third stages, it was found that the partial correlation coefficient between the first stage stirring efficiency factor and the third stage curing effect was 0.63, and the remote correlation effect value was 0.52, indicating that the first stage stirring parameters had a significant influence on the final curing effect.

[0227] The researchers transformed the three-stage mixing parameter optimization problem into a multi-objective optimization problem, constructing three objective functions: strength requirements, uniformity requirements, and energy consumption requirements. A non-dominated sorting genetic algorithm was used to solve the problem, with a population size of 100 and 500 iterations. The weighted Chebyshev method was used to determine the global optimal solution, and the optimization coefficients for the entire process were calculated. The optimal parameter combination for the three-stage mixing was obtained: the first stage (curing agent ratio 10%, mixing time 120 seconds, mixing speed 80 rpm); the second stage (mixing time 90 seconds, mixing speed 100 rpm); and the third stage (mixing time 60 seconds, mixing speed 90 rpm).

[0228] Finally, the researchers conducted a verification experiment based on the optimal parameter combination, measured the physical and mechanical properties of the solidified soil, and compared them with the traditional mixing method. The results are shown in Table 5:

[0229] Table 5 Comparison between three-stage mixing and traditional mixing methods

[0230]

[0231] This embodiment shows that the traditional fluidized solidification soil mixing process usually adopts single-stage or double-stage mixing, lacks systematic parameter optimization and precise screening, resulting in poor uniformity of the solidified soil, large strength fluctuations, and high energy consumption. The three-stage precise screening and mixing fluidized solidification soil method of the present invention achieves precise control of mixing parameters and significant improvement in the performance of fluidized solidification soil by constructing an experimental parameter optimization matrix, calculating mixing efficiency factors, establishing a mixing correlation model, quantifying remote correlation effects, and multi-objective optimization. From the comparison results, it can be seen that compared with the traditional method, the three-stage mixing method has a 24.1% increase in 7-day compressive strength, a 19.7% increase in particle uniformity, a 20.8% increase in structural stability, and a 46.4% improvement in permeability. At the same time, energy consumption is reduced by 34.6%, and screening accuracy is increased by 19.2%. The application of the method of the present invention in municipal pipe network backfill projects has significantly improved project quality and economic benefits, and solved the key technical problems existing in traditional mixing processes.

[0232] The following is a specific embodiment 3 of the present invention: Researchers conducted an application study on a three-stage precision screening and mixing fluidized soil solidification method for the backfill project between the deep foundation pit support structure and the retaining wall of a complex construction project. The foundation pit depth of this project is 18 meters, the backfill area is 0.8 to 1.5 meters wide, and the total backfill volume is approximately 12,000 cubic meters. The environment around the foundation pit is sensitive, and there are strict requirements on the self-compactness, fluidity, and stability of the backfill material. It is also required to maximize the utilization of the excavated soil from the foundation pit and reduce the amount of abandoned soil.

[0233] First, researchers sampled and analyzed the excavated soil on site, finding an average moisture content of 24.6% and an organic matter content of 2.1%. The undisturbed soil particle size distribution characteristics obtained through screening tests are shown in Table 6:

[0234] Table 6 Analysis of original soil particle size distribution

[0235]

[0236] Based on the particle size distribution characteristics, the researchers constructed an optimized experimental parameter matrix, setting the curing agent (cement) to soil ratio range from 8% to 16%, the initial mixing time range from 60 to 150 seconds, and the mixing speed fixed at 28 rpm (the initial mixing parameter for a vertical spiral mixer). Using an orthogonal experimental design, they planned a 4×5 experimental scheme, totaling 20 experimental combinations.

[0237] The first stage of the stirring experiment was carried out, and the experimental results of the typical parameter combination were shown in Table 7:

[0238] Table 7 Experimental results of the first stage of stirring (vertical spiral initial stirring)

[0239]

[0240] Based on the criteria of a stirring efficiency factor greater than 0.75, parameter combinations 3 to 9 were selected for the second stage. In the second stage, a drum screen (25 mm aperture, 75 rpm) was used to screen and remove oversized particles. The screening results showed that approximately 18.4% of the material was removed, including all particles >50 mm and approximately 92% of particles between 25 and 50 mm. The gradation curve of the material after screening was significantly optimized, as shown in Table 8:

[0241] Table 8 Material gradation optimization table after drum screening

[0242]

[0243] After screening, qualified materials enter the re-mixing stage (the third stage of mixing), with a mixing time range of 40 to 100 seconds and a mixing speed range of 60 to 120 rpm. The researchers used a two-factor, five-level design to obtain 25 experimental combinations. The experimental results of 8 typical parameter combinations are shown in Table 9:

[0244] Table 9 Experimental results of the third stage of stirring (re-stirring stage)

[0245]

[0246] Based on experimental data, the researchers established a cross-correlation response surface for the first and second stages of stirring and a cross-correlation tensor for the second and third stages of stirring, and used BP neural network and Tucker decomposition methods for calculation. The partial correlation coefficient between the first stage stirring efficiency factor and the third stage curing effect was 0.68, and the remote correlation effect value was 0.57, indicating that the vertical spiral initial stirring had a significant impact on the final performance. By calculating the parameter coupling strength and stirring synergy factor, the highest point of the stirring synergy factor was determined to be 0.71, and the corresponding material temperature change curve is shown in Table 10:

[0247] Table 10 Material temperature changes at different mixing stages

[0248]

[0249] The researchers transformed the three-stage mixing parameter optimization problem into a multi-objective optimization problem, constructing three objective functions: strength requirement, uniformity requirement, and energy consumption requirement. They used the weighted Chebyshev method to determine the global optimal solution. The calculated threshold for the fluidization-solidification transition was 52 minutes. The optimized three-stage mixing parameters were: first stage (12% curing agent ratio, 120 seconds of vertical spiral initial stirring at 28 rpm); second stage (25 mm drum screen aperture, 75 rpm); and third stage (80 seconds of secondary stirring at 100 rpm).

[0250] To verify the effectiveness of the optimized parameter combination, researchers prepared standard specimens in the laboratory and measured their physical and mechanical properties. A 100-cubic-meter test section was also set up on-site to apply the optimized parameters to actual production. After 7 and 28 days of curing, core sampling was performed on the test sections and a control section prepared using the traditional mixing method. The performance comparison is shown in Table 11:

[0251] Table 11 Performance comparison between three-stage mixing and traditional mixing methods

[0252]

[0253] In addition, the researchers compared the settlement monitoring data after foundation pit backfill construction under the two methods. The results showed that the maximum settlement of the three-stage mixing fluidized solidification soil was reduced by 36.7% compared with the traditional method, and the settlement uniformity was improved by 42.3%.

[0254] Traditional fluidized solidification soil preparation processes typically utilize a single mixing process, directly mixing the solidifying agent with the original soil, or performing only simple screening before mixing. This process fails to consider optimized material gradation and the synergistic effects of multi-stage mixing. This process presents the following challenges: low original soil utilization, requiring the disposal of a large number of oversized particles; uneven solidifying agent dispersion, resulting in "blind spots" and unstable strength; poor fluidity of the mixture, making segregation and pipe blockages more likely to occur during construction; and low raw material utilization efficiency, high energy consumption, and high costs.

[0255] In contrast, the three-stage precise screening and mixing fluidized soil solidification method of the present invention, through vertical spiral initial mixing to break up the soil agglomeration structure, drum screen to reconstruct the particle grading curve, and re-mixing stage to eliminate the blind spot of curing agent dispersion, significantly improves the physical and mechanical properties of the solidified soil. In particular, in terms of original soil utilization, it has increased from 63.2% to 81.6%, reducing the amount of external abandoned soil; in terms of curing agent utilization efficiency, it has increased from 68.5% to 87.2%, reducing material costs; in terms of self-compactness and fluidity, it has increased by 21.8% and 27.0% respectively, solving construction problems; in terms of production efficiency, it has increased by 26.5%, shortening the construction period. The innovation of the three-stage precise screening and mixing process has achieved the dual goals of efficient resource utilization and performance quality improvement, and provides a reliable technical solution for urban deep foundation pit support and backfill projects.

[0256] It should be noted that the variables involved in the present invention are explained in detail as shown in Tables 12 and 13 below.

[0257] Table 12 Variable Explanation Table (Part 1)

[0258]

[0259] Table 13 Variable Explanation Table (Part 2)

[0260]

[0261] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. A three-stage precise screening and mixing method for fluidized solidified soil, characterized in that: include: Construct an experimental parameter optimization matrix to determine the initial value range of each parameter of the three-stage mixing; The first stage mixing experiment was carried out to measure the fluidity index and strength coefficient of the solidified soil, and the first stage mixing efficiency factor was calculated. According to the first stage mixing efficiency factor, the boundary conditions of the second stage mixing parameters were set, and the second stage mixing experiment was carried out to measure the particle uniformity index and cohesion index. The intercorrelated response surface of the first and second stage mixing was established to determine the optimal matching point between the first stage mixing parameters and the second stage mixing parameters, and the mixing synergy factor was calculated. Based on the mixing synergy factor, the initial value of the third stage mixing parameters was determined, and the third stage mixing experiment was carried out to measure the solidification rate index and structural stability index. The three-stage mixing parameter optimization problem was transformed into a multi-objective optimization problem, and the Pareto optimal frontier was constructed. The weighted Chebyshev method was applied to determine the optimal parameter combination of the three-stage mixing to guide the three-stage precise screening mixing of fluidized solidified soil. Among them, the experimental parameter optimization matrix is ​​a three-dimensional experimental design space formed by combining the three key parameters of material ratio, mixing time and mixing speed at different levels, which is expressed as ,in, is the material ratio level, is the stirring time level, is the stirring speed level, is the experimental unit corresponding to the parameter combination, where all ; The first-stage mixing efficiency factor is a dimensionless indicator that characterizes the material mixing uniformity and energy utilization efficiency during the first-stage mixing process, and is obtained by weighted calculation of the solidified soil fluidity index and strength coefficient; the mixing synergy factor specifically refers to the coefficient that quantifies the degree of synergy between the first-stage mixing parameters and the second-stage mixing parameters, and is calculated by the performance improvement ratio of the optimal matching point on the first- and second-stage mixing correlation response surface.

2. The method according to claim 1, characterized in that The fluidity index of the solidified soil is a quantitative parameter to measure the fluidity performance of the solidified soil, which is measured in the first stage of the mixing test. The strength coefficient is a parameter to characterize the compressive strength of the solidified soil, which is measured in the first stage of the mixing test.

3. The method according to claim 2, characterized in that The particle uniformity index is a quantitative parameter that measures the uniformity of particle distribution in the solidified soil. It is calculated by analyzing the variance of particle spatial distribution through multi-point sampling and is measured in the second stage of the mixing experiment. The cohesion index is a parameter that characterizes the strength of the intermolecular force inside the solidified soil. It is obtained by measuring the deformation resistance of the solidified soil through shear experiments and is measured in the second stage of the mixing experiment.

4. The method according to claim 3, characterized in that The solidification rate index is a parameter that measures the speed at which the solidified soil changes from a fluid state to a solid state and is determined in the third stage of the mixing experiment; The structural stability index is a parameter that evaluates the ability of the internal structure of the stabilized soil to resist external disturbances and is measured in the third stage of the mixing test.

5. The method according to claim 4, characterized in that The method also includes the following steps: establishing a two-stage and three-stage mixing correlation tensor, identifying key conversion parameters between the second-stage mixing parameters and the third-stage mixing parameters, and calculating the fluidity solidification transition threshold; constructing a one-stage and three-stage compensation adjustment index, analyzing the influence of the first-stage mixing parameters on the third-stage solidification effect, and quantifying the remote correlation effect; the two-stage and three-stage mixing correlation tensor is a multidimensional mathematical structure that describes the interaction relationship between the second-stage mixing parameters and the third-stage mixing parameters, and is used to analyze the complex coupling mechanism of the mixing process; the fluidity solidification transition threshold is the critical parameter value for the solidified soil to transition from a fluid state to a solid state, which is determined by analyzing the two-stage and three-stage mixing correlation tensor; the one-stage and three-stage compensation adjustment index is a parameter that quantitatively describes the influence of the first-stage mixing parameters on the third-stage solidification effect, and is used to quantify the remote correlation effect; the remote correlation effect is the nonlinear influence mechanism of the first-stage mixing parameters on the final third-stage solidification effect, which is quantitatively characterized by the one-stage and three-stage compensation adjustment index.

6. The method according to claim 5, characterized in that The mixing efficiency response equation is used to establish the performance response surface of the solidified soil under the combined effect of the first-stage mixing parameters and the second-stage mixing parameters. The input includes the first-stage mixing time, the first-stage mixing speed, the second-stage mixing time, the second-stage mixing speed, and the material ratio. The output is the predicted solidified soil strength coefficient and the solidified soil fluidity index.

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