Flow-state solidified soil mixing construction method based on automatic adjustment of proportioning parameters

Abstract

The invention provides a flow-state solidified soil mixing construction method based on automatic adjustment of proportioning parameters, belongs to the technical field of flow-state solidified soil, and realizes intelligent control of a flow-state solidified soil mixing process by establishing a three-section type precise screening and stirring system and a high-precision automatic control system. According to the method, firstly, target performance parameters are input, and an optimal matching scheme is automatically selected through a multi-target optimization algorithm; starting an original soil treatment unit to perform soil material dispersion treatment and measuring the physical characteristics of the soil material; adding the raw materials according to the accurate measurement, and fully mixing; when it is detected that the performance of the mixture deviates from a target value, the system automatically triggers a parameter compensation mechanism for adjustment; and meanwhile, a material ratio fluctuation early warning system is established, so that uneven mixture quality caused by starting and stopping of equipment or material conveying delay is avoided, and the technical problem that in the prior art, matching parameters are difficult to accurately regulate and control in real time in the mixing process of the flow-state solidified soil is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of fluidized solidified soil, and in particular relates to a fluidized solidified soil mixing construction method based on automatic adjustment of mix ratio parameters. Background Art

[0002] As a new type of foundation treatment material, fluidized solidification soil is widely used in soft soil reinforcement, trench backfilling, and underground engineering support. Traditional fluidized solidification soil mixing technology relies primarily on empirical proportioning and manual adjustments, employing simple mixing equipment and fixed proportions. While this method has accumulated considerable experience in engineering practice, it is plagued by issues such as inaccurate raw material metering, poor mixing uniformity, and difficulty in quality control.

[0003] Existing fluidized soil construction methods suffer from significant fluctuations in mix quality due to the complex and variable nature of the original soil and the influence of environmental factors. Traditional construction methods only allow for pre-construction mix testing, failing to promptly adjust for changes in soil moisture content and particle size distribution that may occur during construction. This often leads to quality issues such as insufficient or excessive fluidity and unstable strength development, impacting project safety and service life.

[0004] Currently, engineering practice lacks a method for mixing fluidized soil that can automatically adjust mix parameters based on changes in the original soil properties and target performance requirements. Existing technologies struggle to achieve real-time monitoring and precise control of the mixing process. Adjustment of mix parameters relies primarily on manual experience, which introduces lag and uncertainty, making it difficult to ensure the stability and consistency of fluidized soil quality. This has become a key bottleneck restricting the further development and application of fluidized soil technology. In other words, existing technologies present a technical challenge: the difficulty in accurately controlling mix parameters in real time during the fluidized soil mixing process. Summary of the Invention

[0005] In view of this, the present invention provides a fluidized solidified soil mixing construction method based on automatic adjustment of mix ratio parameters, which can solve the technical problem in the prior art that the mix ratio parameters are difficult to accurately control in real time during the fluidized solidified soil mixing process.

[0006] The present invention is implemented as follows: The present invention provides a fluidized solidified soil mixing construction method based on automatic adjustment of mixing parameters, including: establishing a three-stage precise screening and mixing system; building a high-precision automated control system; inputting target performance parameters of the fluidized solidified soil; starting the raw soil processing unit; automatically adjusting the mixing parameters based on a multi-objective optimization algorithm, constructing a constrained optimization model with fluidity value, strength value, and cost as objective functions and the amount of each component as a decision variable, solving the optimal solution set through a non-dominated sorting genetic algorithm, and selecting the optimal mixing ratio scheme from it according to engineering requirements; executing a mixing and stirring program; when it is detected that the fluidity value deviates from the target value, the control system automatically triggers a parameter compensation mechanism; establishing a material ratio fluctuation early warning system; and performing quality inspection after mixing is completed.

[0007] Among them, the three-stage precision screening and mixing system specifically refers to a mixing system consisting of three continuous processes: preliminary mixing, drum screening and secondary enhanced mixing. In the initial mixing stage, a vertical spiral mixer with a rotation speed of 28 rpm is used to disperse the raw soil. In the rolling screening stage, a drum screening machine with a sieve diameter of 2.5 cm is used to remove oversized impurities. In the re-mixing stage, the mixture is efficiently homogenized through a secondary mixer.

[0008] Among them, the target performance parameters of the fluidized solidified soil include fluidity value, setting time, early strength value and final strength value, and the control system automatically calls the corresponding mix ratio according to the input parameters.

[0009] The mixing and stirring procedure involves adding each component raw material according to precise measurement, fully mixing them through a secondary stirring system, and monitoring the fluidity value of the mixture in real time.

[0010] Among them, the material ratio fluctuation warning system monitors the start and stop of equipment or material transportation delays, calculates the shutdown boundary position and automatically triggers the compensation program to avoid uneven quality of the mixture due to sudden changes in material ratio; the material ratio fluctuation warning system specifically refers to real-time monitoring of the material transportation status by setting up multi-point sensors. When the start and stop of equipment or transportation delay is detected, the impact range is automatically calculated and the abnormal material is temporarily stored in a preset area to avoid unstable mixture performance due to inaccurate proportions.

[0011] The quality inspection measures the fluidity value and initial setting time by sampling, and records the production parameters to form a digital production record, providing data support for subsequent process optimization.

[0012] The fluidity value specifically refers to the fluidity index of the fluidized solidified soil mixture measured by the drop cylinder method. The flowability is evaluated by measuring the diffusion diameter of the mixture after the standard size cylinder is lifted. Generally, the diffusion diameter is required to be between 18cm and 22cm for the best state.

[0013] Among them, the curing agent specifically refers to an additive used to enhance the early strength and final strength performance of fluidized solidified soil. The main components include cement, lime, fly ash and special silicate materials, and the proportions are designed according to different geological conditions and strength requirements; the admixture specifically refers to a functional additive used to adjust the working performance of fluidized solidified soil, including water reducers, retarders, and early strength agents, and the fluidity, setting time and strength development law of the mixture are controlled by adjusting the dosage.

[0014] The range of fluidized solidification soil ratio parameters specifically refers to the dosage range of each component determined according to the engineering requirements and the original soil properties. The cement dosage range is 150 to 250 kg / m 3 The dosage of curing agent ranges from 3% to 8% of the total volume, the water-cement ratio is controlled between 0.5 and 0.8, the original soil content accounts for 65% to 80% of the total volume, and the initial mix ratio is usually cement 200kg / m 3 , 5% curing agent, 0.65 water-cement ratio, and 70% original soil content are used as the benchmark formula, and dynamic adjustments are made based on the measured parameters.

[0015] Among them, the adaptive control function specifically refers to a mathematical model designed based on fuzzy control theory for real-time adjustment of the proportion parameters of the fluidized solidified soil mixture. The adaptive control function determines the amplitude and direction of the adjustment amount by analyzing the deviation size and change rate of the fluidity value. On the one hand, it ensures that the performance of the mixture quickly returns to the target value, and on the other hand, it avoids excessive adjustment leading to system oscillation.

[0016] This paper proposes a fluidized solidified soil mixing construction method based on automatic adjustment of mix parameters. By establishing a three-stage precision screening and mixing system and a high-precision automated control system, combined with a multi-objective optimization algorithm, this method achieves intelligent control of mix parameters during the fluidized solidified soil mixing process. This method automatically selects the optimal mix ratio based on input target performance parameters and monitors the mixture's performance in real time during the mixing process. If performance deviates from the target, a parameter compensation mechanism is automatically triggered to adjust the mix.

[0017] This invention addresses the difficulty in precisely controlling mix parameters in traditional fluidized solidified soil mixing. By real-time monitoring of both raw soil properties and mixture performance, combined with adaptive control functions and hydration reaction kinetics, the system dynamically optimizes and adjusts mix parameters, ensuring that mixture performance remains within the target range. Furthermore, a material ratio fluctuation warning system effectively prevents uneven mixture quality caused by equipment startup and shutdown or material delivery delays.

[0018] Compared to traditional technologies, this invention achieves intelligent, digital, and precise mixing of fluidized solidified soil, significantly improving the stability and consistency of the mixture quality. By precisely controlling the dosage of each component and mixing process parameters, it effectively solves the core technical problem of the difficulty in accurately regulating the mixing parameters during the fluidized solidified soil mixing process, providing reliable support for the widespread application of fluidized solidified soil technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0020] 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.

[0021] like Figure 1 FIG. 1 is a flow chart of a fluidized solidified soil mixing construction method based on automatic adjustment of mix ratio parameters provided by the present invention. The method comprises the following steps:

[0022] S01. Establish a three-stage precision screening and mixing system, including a vertical spiral mixer to disperse the raw soil in the initial mixing stage, a drum screen to remove oversized particles in the rolling screening stage, and a secondary mixer to enhance the uniformity of the mixture in the re-mixing stage;

[0023] S02. Build a high-precision automated control system that integrates a numerical control panel, sensor network, and execution unit to achieve precise metering of components such as curing agent, admixture, cement, and water. The system control accuracy error does not exceed 0.5%;

[0024] S03. Input target performance parameters of fluidized solidified soil, including fluidity, setting time, early strength, and final strength. The control system automatically calls the corresponding mix ratio based on the input parameters.

[0025] S04. Start the original soil processing unit, perform shearing and dispersion processing on the soil material through the initial mixing process, break up the aggregated soil into a loose state, and simultaneously measure the moisture content, density and particle size distribution of the original soil;

[0026] S05. Automatically adjust the mix parameters based on a multi-objective optimization algorithm, construct a constrained optimization model with fluidity, strength, and cost as objective functions and the amounts of each component as decision variables, and use a non-dominated sorting genetic algorithm to find the optimal solution set. Select the optimal mix solution based on project requirements.

[0027] S06. Execute the mixing and stirring procedure, add each component raw material according to precise measurement, fully mix it through the secondary stirring system, and monitor the fluidity value of the mixture in real time;

[0028] S07. When the fluidity value is detected to deviate from the target value, the control system automatically triggers the parameter compensation mechanism to return the mixture performance to the target range by adjusting the amount of water or admixture;

[0029] S08. Establish a material ratio fluctuation early warning system to monitor equipment start-up and shutdown or material delivery delays, calculate the shutdown boundary position and automatically trigger the compensation program to avoid uneven mixture quality due to sudden changes in material ratio;

[0030] S09. Optionally, it also includes quality inspection after mixing is completed, measuring the fluidity value and initial setting time by sampling, recording the production parameters to form a digital production record, and providing data support for subsequent process optimization.

[0031] Among them, the three-stage precision screening and mixing system specifically refers to a mixing system consisting of three continuous processes: preliminary mixing, drum screening and secondary enhanced mixing. In the initial mixing stage, a vertical spiral mixer with a speed of 28rpm is used to disperse the raw soil. In the roller screening stage, a drum screening machine with a sieve diameter of 2.5cm is used to remove oversized impurities. In the re-mixing stage, the mixture is efficiently homogenized through a secondary mixer.

[0032] Among them, the fluidity value specifically refers to the fluidity index of the fluidized solidified soil mixture measured by the falling cylinder method. The flowability is evaluated by measuring the diffusion diameter of the mixture after the standard size cylinder is lifted. Generally, the diffusion diameter is required to be between 18cm and 22cm for the best state.

[0033] Among them, the curing agent specifically refers to the additive used to enhance the early strength and final strength performance of fluidized solidified soil. The main components include cement, lime, fly ash and special silicate materials, and the proportions are designed according to different geological conditions and strength requirements.

[0034] Among them, admixtures specifically refer to functional additives used to adjust the working performance of fluidized solidified soil, including water reducers, retarders, and early strength agents. By adjusting the dosage, the fluidity, setting time and strength development of the mixture can be controlled.

[0035] Among them, the material ratio fluctuation warning system specifically refers to real-time monitoring of the material conveying status by setting up multi-point sensors. When the equipment start-up or stop or conveying delay is detected, the affected range is automatically calculated and the abnormal material is temporarily stored in the preset area to avoid unstable mixture performance due to inaccurate ratios.

[0036] The range of fluidized solidification soil mix parameters specifically refers to the dosage range of each component determined according to the project requirements and the original soil properties. The cement dosage range is 150 to 250 kg / m 3The dosage of curing agent ranges from 3% to 8% of the total volume, the water-cement ratio is controlled between 0.5 and 0.8, the original soil content accounts for 65% to 80% of the total volume, and the initial mix ratio is usually 200kg / 3 , 5% curing agent, 0.65 water-cement ratio, and 70% original soil content are used as the benchmark formula, and dynamic adjustments are made based on the measured parameters.

[0037] The hydration reaction kinetics equation specifically refers to a mathematical model that describes the chemical reaction process of each component in fluidized solidified soil. It is used to predict the strength development and rheological property changes of solidified soil under different mix ratios. The hydration reaction kinetics equation uses cement dosage, curing agent content, water-cement ratio, original soil moisture content, and ambient temperature as input parameters. These parameters are measured by a metering system, determined by mix ratio design, and measured by humidity and temperature sensors. Output parameters include initial setting time, final setting time, compressive strength values at different ages, and fluidity values. These are used to guide on-site construction schedule scheduling, determine formwork support and dismantling time, assess load-bearing capacity, and set pumping process parameters.

[0038] Among them, the adaptive control function specifically refers to a mathematical model designed based on fuzzy control theory for real-time adjustment of the mix parameters of fluidized solidified soil mixtures. The input parameters of the adaptive control function include the measured fluidity value, the target fluidity value, the current water-cement ratio, the current admixture dosage and the mixture temperature. These parameters are respectively obtained through the fluidity tester, the construction plan, the mix ratio calculation, the metering system record, and the temperature sensor measurement; the output parameters are the water adjustment amount and the admixture adjustment amount, which are used to guide the automatic control system to accurately adjust the metering device to ensure that the mixture performance remains within the target range. The adaptive control function determines the amplitude and direction of the adjustment amount by analyzing the deviation size and change rate of the fluidity value. On the one hand, it ensures that the mixture performance quickly returns to the target value, and on the other hand, it avoids excessive adjustment leading to system oscillation.

[0039] The specific implementation of the above steps is described in detail below.

[0040] The specific implementation method of step S01 is to achieve efficient processing and particle classification of the original soil by establishing a three-stage precise screening and mixing system. This step first uses a vertical spiral mixer with a rotation speed of 28rpm to perform preliminary mixing on the original soil. This process mainly relies on the shear stress principle to deagglomerate the granular soil into a loose state. The mixing duration is controlled between 120 and 180 seconds to ensure that the soil is fully dispersed but not excessively disturbed. Then, in the rolling screening stage, the original soil is accurately graded through a drum screening machine with a sieve diameter of 2.5cm. The speed of the screening machine is controlled within the range of 15 to 20rpm, and the screening efficiency reaches more than 95%, effectively removing oversized impurities and unfavorable particles such as sand and gravel. Finally, in the re-mixing stage, the qualified materials enter the secondary mixer for intensive mixing. A double-blade staggered mixing structure is adopted, and the mixing energy index is controlled at 0.8 to 1.2kW·h / m 3 The three-stage process effectively solves the problems of residual agglomerates and uneven particle distribution that are easily generated in traditional single-stage mixing, laying a material foundation for subsequent ratio optimization.

[0041] The specific implementation of step S02 involves building a high-precision automated control system centered around data acquisition and precise control. The system first establishes a central digital control panel as the information processing center. A 32-bit ARM microprocessor is used to construct a real-time computing platform, running a PID control algorithm with a sampling frequency of 100Hz, ensuring a system response time of no more than 50ms. The system integrates a distributed sensor network, including load cells, flow meters, pressure sensors, and displacement sensors, to form a multi-point monitoring system. The sensor accuracy level is no less than 0.2, and the sampling frequency is 10-50Hz. The execution unit utilizes a high-precision metering pump system driven by a servo motor, with a metering accuracy error controlled within 0.5% and a minimum adjustment range of 0.1% of the total flow rate. By establishing a material property database and a recipe management system, the quantitative dosage of various components, such as curing agent, admixture, cement, and water, is achieved, ensuring mix stability. The entire control system utilizes a layered architecture, with the bottom layer executing real-time control logic, the middle layer performing data processing and parameter optimization, and the upper layer implementing human-computer interaction and decision support. The system stability index exceeds 99.9%.

[0042] The specific implementation method of step S03 is to realize the target-oriented mix ratio call by establishing a performance parameter and mix ratio relationship model. This step first inputs the target performance parameters of the fluidized solidified soil into the system, including fluidity value, setting time, early strength value and final strength value. The fluidity value is usually set in the range of 18 to 22 cm, the setting time is set according to the construction requirements, the initial setting time is between 4 and 8 hours, the early strength value is the 3-day compressive strength is not less than 0.3 MPa, and the final strength value is the 28-day compressive strength reaches 1.0 to 2.5 MPa. After receiving the input parameters, the system calls the mix ratio prediction model based on multivariate regression analysis and support vector machine algorithm. The model is trained through more than 1,000 sets of historical data, and the prediction accuracy R 2 The model calculates the optimal mix ratio range based on the input target performance parameters, automatically screening out key parameters such as cement dosage, curing agent content, water-cement ratio and admixture dosage. The initial mix ratio usually uses cement 200kg / m 3 , curing agent 5%, water-cement ratio 0.65, and original soil content 70% are used as the benchmark. The mix ratio scheme generated by the system enters the execution state after verification.

[0043] The specific implementation method of step S04 is to start the original soil processing unit to pre-treat the soil material and measure its properties. This step first uses a vertical spiral mixer to shear and disperse the original soil. The mixer speed is controlled at 28rpm, the processing time is 3 to 5 minutes, the shear stress intensity reaches 3 to 5kPa, and the dispersion coefficient of the aggregate soil reaches above 0.9. During the processing, the system uses an integrated rapid measurement module to measure the key parameters of the original soil in real time, including moisture content, density and particle size distribution. The moisture content is quickly determined by microwave drying method, the measurement time is controlled within 180 seconds, and the measurement accuracy error does not exceed 0.5%. The density is determined by volumetric method, and the measurement accuracy is controlled at ±0.02g / cm 3 Particle size distribution is measured online using a laser particle size analyzer, with a detection range of 0.1 to 2000 μm and a resolution of 128 particle size intervals. The system compares the measured parameters with the preset parameter range. If the original soil moisture content deviates by more than 3% or the particle size distribution anomaly exceeds 0.1, an early warning mechanism is triggered and a parameter compensation process is initiated to ensure the accuracy of subsequent mix ratio calculations.

[0044] The specific implementation of step S05 is to use a multi-objective optimization algorithm to accurately adjust the fluidized solidification soil ratio. This step is based on the measured original soil characteristic parameters, and constructs a mathematical optimization model with fluidity value, strength value, and cost as the objective function and the amount of each component as the decision variable. The objective function is set to ensure that the deviation between the fluidity value and the target value does not exceed 1 cm, the strength value meets the design requirements and the cost is minimized. The constraints include cement dosage between 150 and 250 kg / m 3The curing agent dosage is within the range of 3% to 8% of the total volume, the water-cement ratio is controlled between 0.5 and 0.8, and the original soil content accounts for 65% to 80% of the total volume. The optimization algorithm uses a non-dominated sorting genetic algorithm (NSGA-II), with a population size of 50, an evolutionary number of 100, a crossover probability of 0.8, and a mutation probability of 0.1. The optimal solution set is obtained through fast non-dominated sorting and crowding distance calculation. The system automatically selects the optimal mix ratio from the optimal solution set based on project requirements, including cement dosage, curing agent dosage, admixture dosage, and water dosage. This generates a precise mix ratio instruction and sends it to the control system. The relevant parameters are displayed on the operation interface and recorded in the database.

[0045] The specific implementation method of step S06 is to execute the mixing and stirring program to achieve accurate metering and sufficient mixing of each component. This step first adds each component through a precision metering system according to the optimized ratio parameters. Cement metering adopts a combination of a screw feeder and a high-precision weighing sensor, and the metering accuracy is controlled at ±0.5%. Water metering adopts a combination of an electromagnetic flowmeter and a proportional control valve, and the accuracy is controlled at ±0.3%. The curing agent and admixture are metered by a micro-pumping system, and the accuracy is controlled at ±0.2%. Each component is put into the secondary mixing system according to the preset order. The mixing system adopts a double-shaft forced mixer, and the stirring energy is controlled at 1.0~1.5kW·h / m 3 The mixing time is 120 to 180 seconds. During the mixing process, the system monitors the fluidity of the mixture in real time using an online fluidity detection device. Image recognition technology is used to measure the diffusion diameter every 30 seconds with an accuracy of ±0.5 cm. If uneven particle dispersion or agglomeration is detected during mixing, the system automatically extends the mixing time by 15 to 30 seconds to ensure a uniformity coefficient of at least 0.95.

[0046] The specific implementation of step S07 involves establishing a real-time fluidity monitoring and automatic correction mechanism. This step involves installing an online fluidity detection system at the mixer discharge port, which monitors the flow state of the mixture using computer vision technology. When the fluidity value deviates from the target, the system triggers a parameter compensation mechanism, automatically adjusting the mix parameters using a fuzzy control algorithm. Fluidity deviations within ±1 cm are considered normal. Deviations within ±1-2 cm trigger a mild adjustment mechanism, and deviations exceeding ±2 cm trigger a forced adjustment mechanism. Based on the direction and magnitude of the fluidity deviation, the system calculates the amount of water or admixture to be adjusted. This calculation is based on a water-cement ratio sensitivity coefficient of 0.3-0.5 cm / 0.01 and an admixture sensitivity coefficient of 0.5-1.0 cm / 0.1%. The adjustment mechanism employs a gradual strategy, with the initial adjustment being 70% of the theoretical calculated value. After observing the effect, a secondary fine-tuning is performed to avoid over-adjustment that may cause system oscillation. All adjustment processes are recorded in the system database, providing data support for subsequent process optimization and developing adaptive learning capabilities to improve the system's adaptability to raw material variability.

[0047] The specific implementation of step S08 involves establishing a material ratio fluctuation early warning system to ensure production continuity and quality stability. This step involves deploying multiple sensors, including flow sensors, pressure sensors, and displacement sensors, at key nodes in the material conveying system to form a comprehensive monitoring network. The early warning system uses a time series analysis algorithm to monitor material flow parameters in real time and identify equipment startup and shutdown or material conveying delays. If unstable material conveying is detected during equipment startup, the system automatically calculates the shutdown boundary, i.e., the unstable material ratio region, based on the flow rate change rate and the conveying delay time. For mixtures with ratio deviations exceeding a set threshold (cement deviation >3%, water deviation >2%, curing agent deviation >1%), the system automatically triggers an isolation procedure, diverting the unqualified material to a pre-set temporary storage area. Simultaneously, a compensation procedure is initiated, rapidly restoring the system to a stable state by briefly adjusting the target ratio parameters. The compensation time is typically controlled within 60 to 90 seconds. The system also establishes an equipment failure prediction model that, based on equipment operating parameters and material conveying status, provides early warning of potential failure risks with a lead time of 5 to 10 minutes, providing operators with ample response time.

[0048] Step S09 is an optional step, and its specific implementation method is to perform quality inspection after mixing is completed and form a digital production record. This step first obtains mixture samples through an automatic sampling system, with sampling at no less than 3 points per batch and a total sample volume of no less than 5kg. The fluidity of the obtained samples is measured using a standard drop cylinder with a diameter of 10 cm and a height of 20 cm to measure the diffusion diameter of the mixture, with a measurement accuracy of ±0.2 cm. At the same time, the initial setting time is measured using a Vicat instrument with a measurement accuracy of ±5 minutes. The measurement results are uploaded to the central database in real time via a wireless transmission module. The system automatically compares and analyzes them with the target parameters and issues a quality evaluation report. Key parameters of the entire production process, including original soil properties, component dosage, fluidity change curve, mixing energy consumption, ambient temperature and humidity, etc., are recorded in the database to form a traceable digital production record. Based on accumulated production data, the system uses machine learning algorithms to continuously optimize the ratio prediction model and control parameters to improve the system's intelligence level. The data analysis cycle is set to update the model every 10 batches to ensure continuous improvement and optimization of the system.

[0049] The mathematical model or calculation process involved in the present invention is described in detail below.

[0050] In step S02, building a high-precision automatic control system involves a PID control algorithm, the mathematical expression of which is as follows:

[0051]

[0052] Where u(t) is the output value of the control system; e(t) is the current error, that is, the difference between the target value and the actual value; K p is the proportional coefficient, ranging from 0.5 to 2.0; K i is the integral coefficient, ranging from 0.05 to 0.5; K d is the differential coefficient, ranging from 0.01 to 0.1; t is the current time; τ is the integral variable.

[0053] The parameter acquisition method is as follows: e(t) is obtained through real-time measurement of the sensor, and the calculation formula is e(t) = r(t) - y(t), where r(t) is the target value and y(t) is the actual measurement value; K p , K i , K d Parameters are acquired through a self-tuning algorithm, initially calculated using the Ziegler-Nichols method, and then optimized online using a fuzzy adaptive algorithm. This control equation comprehensively considers the present value, historical accumulation, and trend of the error to achieve precise control of the system. Compared to traditional single proportional control, it can effectively reduce overshoot and steady-state error.

[0054] In step S05, the objective function and constraints used by the multi-objective optimization algorithm are expressed as follows:

[0055] Objective function:

[0056] minF(X)=[minf1(X), minf2(X), minf3(X)];

[0057] Where F(X) is the multi-objective function vector; X is the decision variable vector, X = [x1, x2, x3, x4, x5], where x1 is the cement dosage (kg / m 3 ), x2 is the amount of curing agent (%), x3 is the water-cement ratio, x4 is the original soil content (%), and x5 is the amount of admixture (%); f1(X) is the fluidity deviation function, f2(X) is the strength deviation function, and f3(X) is the cost function.

[0058] Each sub-objective function is specifically expressed as:

[0059] f1(X)=|FD(X)-FD target |;

[0060] f2(X)=|CS(X)-CS target |;

[0061] f3(X)=c1x1+c2x2+c3x5;

[0062] Where FD(X) is the mobility prediction function; FD target is the target fluidity value, ranging from 18 to 22 cm; CS(X) is the compressive strength prediction function; CS target is the target strength value, ranging from 1.0 to 2.5 MPa; c1 is the unit price coefficient of cement, ranging from 0.5 to 0.8 yuan / kg; c2 is the unit price coefficient of curing agent, ranging from 2.0 to 3.5 yuan / kg; c3 is the unit price coefficient of admixture, ranging from 5.0 to 8.0 yuan / kg.

[0063] The fluidity prediction function FD(X) is expressed as:

[0064]

[0065] Where a0, a1, ..., a8 are regression coefficients obtained through historical data training; ε1 is the error term, which obeys the normal distribution The value range of σ1 is 0.2~0.5.

[0066] The intensity prediction function CS(X) is expressed as:

[0067] CS(X)=b0+b1x1+b2x2+b3x3+b4x4+b5x5+b6x1x2+b7x1 / x3+b8t 0.5 +ε2;

[0068] Where b0, b1, ..., b8 are regression coefficients obtained through historical data training; t is age (days); ε2 is the error term, which obeys the normal distribution The value range of σ2 is 0.1~0.3.

[0069] Constraints:

[0070] 150≤x1≤250;

[0071] 3≤x2≤8;

[0072] 0.5≤x3≤0.8;

[0073] 65≤x4≤80;

[0074] 0.1≤x5≤2.0;

[0075] x1+x2+x4+x1·x3≤1000;

[0076] The multi-objective optimization model is solved by non-dominated sorting genetic algorithm (NSGA-II). The algorithm flow is expressed as:

[0077] represents the t-th generation population;

[0078] Represents the offspring population generated by crossover and mutation operations;

[0079] R t =R t ∪Q t represents the combined population;

[0080] The non-dominated sorting process is defined as:

[0081] F={F1,F2,...,F k}, where F i represents the i-th non-dominated frontier;

[0082] For solutions p and q, if f j (p)≤f j (q) and f j (p) <f j (q), then p is said to dominate q, which can be expressed as

[0083] Crowding distance calculation:

[0084]

[0085] Where, d i is the crowding distance of individual i; f m (i+1) and f m (i-1) are the objective function values of the two individuals adjacent to individual i on target m; and are the maximum and minimum values of the target m, respectively.

[0086] This multi-objective optimization model fully considers three key performance indicators: fluidity, strength, and cost. It captures the complex interactions between these parameters by establishing a nonlinear prediction model. The fluidity prediction model incorporates a quadratic term for the water-cement ratio, reflecting its nonlinear effect on fluidity. The strength prediction model incorporates an interaction term between cement and water-cement ratio, as well as a square root term for cement age, reflecting the kinetics of the hydration reaction.

[0087] In step S07, the fuzzy control algorithm is used to automatically compensate for the fluidity deviation, and its mathematical expression is as follows:

[0088]

[0089] Where ΔW is the water adjustment amount (kg / m 3 ); ΔA is the amount of admixture adjustment (%); e FD is the fluidity deviation (cm), calculated as e FD =FD measured -FD target ; is the fluidity deviation change rate (cm / min); λ1 is the water adjustment coefficient, ranging from 1.5 to 2.5; λ2 is the admixture adjustment coefficient, ranging from 0.8 to 1.2; μ1(e FD ) and μ2(e FD ) is the membership function; f1 and f2 are the adjustment calculation functions.

[0090] The membership function is defined as:

[0091]

[0092] The adjustment calculation function is expressed as:

[0093]

[0094] In the formula, K1 is the water volume proportional coefficient, ranging from 2.0 to 3.0; K2 is the water volume differential coefficient, ranging from 0.5 to 1.0; K3 is the admixture proportional coefficient, ranging from 0.2 to 0.4; K4 is the admixture differential coefficient, ranging from 0.05 to 0.1.

[0095] The core concept of this fuzzy control algorithm is to adaptively adjust the water or admixture dosage based on the magnitude and trend of fluidity deviation. The membership function achieves a smooth transition between adjustments, avoiding the oscillation issues that can occur with traditional PID control. When the fluidity deviation is small, the system makes no adjustments; when the deviation is moderate, the system primarily adjusts the water dosage; when the deviation is large, both the water and admixture dosages are adjusted.

[0096] In step S09, the hydration reaction kinetic equation of fluidized solidified soil is used to predict the strength development law, which is expressed as follows:

[0097]

[0098] Where, S(t) is the compressive strength at age t (MPa); S ∞ is the ultimate compressive strength (MPa); k is the reaction rate constant; n is the time index; t is the age (days).

[0099] Ultimate compressive strength S ∞ Expressed as:

[0100]

[0101] Where C is the cement dosage (kg / m 3 ), ranging from 150 to 250 kg / m 3 ; A is the amount of curing agent (%), ranging from 3 to 8%; W is the amount of water (kg / m 3 ); M is the original soil moisture content (%); T is the ambient temperature (℃); α1, α2, ..., α6 are regression coefficients; ε3 is the error term, which obeys the normal distribution The value range of σ3 is 0.05~0.15.

[0102] The reaction rate constant k is expressed as:

[0103]

[0104] Where β1, β2, β3, and β4 are regression coefficients obtained by fitting experimental data.

[0105] The time index n is expressed as:

[0106]

[0107] Where γ1, γ2, γ3, and γ4 are regression coefficients obtained by fitting experimental data.

[0108] This hydration reaction kinetics equation is based on the modified Jander equation, which takes into account the diffusion control mechanism of cement hydration reaction. The exponential term is introduced into the equation Describes the nonlinear characteristics of strength development, that is, the process of rapid growth at the beginning and then gradually becoming flat. Ultimate strength S ∞ The model considers multiple factors, including cement content, curing agent content, water-cement ratio, soil moisture content, and ambient temperature. The interaction term between cement content and curing agent content reflects the synergistic effect between the two. The reaction rate constant k adopts an Arrhenius form, reflecting the exponential effect of temperature on the reaction rate, while also accounting for the effects of water-cement ratio and curing agent content on the reaction kinetics. The time exponent n is designed as a variable rather than a constant to more accurately describe the changes in the hydration reaction kinetics under different mix conditions.

[0109] Optionally, in step S08, the time series analysis algorithm of the material ratio fluctuation warning system can be expressed as:

[0110]

[0111] Where V(t) is the material flow rate change rate at time t (% / min); V(ti) is the material flow rate change rate at time ti; ε(tj) is the random disturbance term at time tj; w i is the autoregressive coefficient, i=1,2,…,m;v j is the moving average coefficient, j = 1, 2, ..., n; δ is a constant term; m and n are the autoregressive order and the moving average order, respectively, and usually range from 1 to 3.

[0112] The parameter acquisition method is: V(t) is obtained by real-time measurement of the flow sensor, and the calculation formula is: Where Q(t) is the flow rate value at time t (kg / min), Δt is the sampling time interval (s); w i 、v j and δ are estimated from historical data using the maximum likelihood estimation method.

[0113] The calculation formula for the shutdown boundary position is:

[0114]

[0115] Where, L boundary is the shutdown boundary position (m); V pipe is the pipeline flow velocity (m / s); Q(t) is the flow rate at time t (kg / s); A is the cross-sectional area of the pipeline (m 2); ρ is the material density (kg / m 3 ); t0 is the shutdown start time; t1 is the system restabilization time.

[0116] The equipment failure prediction model is expressed as:

[0117]

[0118] Where P(F|X) is the probability of equipment failure under given equipment parameters X; X = [x1, x2, ..., x p ] is the equipment operation parameter vector, including temperature, pressure, vibration, current, etc.; c0, c1, ..., c p is the logistic regression coefficient, obtained through training of historical fault data.

[0119] Warning trigger conditions:

[0120] When P(F|X)>P threshold When the warning is triggered, P threshold is the warning threshold, ranging from 0.6 to 0.8.

[0121] This time series analysis algorithm utilizes the ARIMA (Autoregressive Integrated Moving Average) model, effectively capturing cyclical fluctuations and short-term trends in material flow, enabling timely detection of abnormal fluctuations. The formula for calculating the shutdown boundary, based on fluid mechanics, considers factors such as pipeline flow velocity, flow rate variations, and material density to accurately locate areas of unstable ratios. The equipment failure prediction model utilizes logistic regression to analyze multidimensional equipment operating parameters, providing early warning of potential failures and improving the reliability and continuity of the material ratio fluctuation warning system.

[0122] Specifically, the principle of the present invention is: the present invention realizes precise automatic control of the ratio parameters through system integration and algorithm optimization, and its principle is mainly reflected in the following aspects:

[0123] First, the three-stage precision screening and mixing system addresses the issue of raw soil quality control from a material handling perspective. In the initial mixing stage, a vertical spiral mixer shears and disperses the raw soil, breaking down its structure and loosening it. A drum screen removes oversized particles, preventing the negative impact of large particles on mixing uniformity and fluidity. The remixing stage uses a secondary mixing step to enhance mixing uniformity. The integrated integration of these three processes ensures the consistent quality of the raw soil, the primary component, and lays the material foundation for subsequent precise proportioning.

[0124] Secondly, a multi-objective optimization algorithm provides a theoretical basis for adjusting mix parameters. This algorithm uses fluidity, strength, and cost as objective functions, and the amounts of each component as decision variables, constructing a constrained optimization model. Using a non-dominated sorting genetic algorithm to find the optimal solution set, the system selects the most appropriate mix solution based on project requirements. This computationally intelligent decision-making approach overcomes the limitations of traditional empirical mix design and makes mix design more scientific and rational.

[0125] Thirdly, the adaptive control function enables real-time dynamic adjustment of mix parameters. Designed based on fuzzy control theory, this function takes as input key indicators such as measured fluidity, target fluidity, and the current water-cement ratio. Its output parameters are the adjustments to the water and admixture levels. When the mixture's performance deviates from the target, the control system analyzes the magnitude and rate of change, determining the magnitude and direction of the adjustments, ensuring that the mixture's performance quickly returns to the target range without oscillation.

[0126] Finally, the material ratio fluctuation warning system ensures the stability of mixture quality from a process control perspective. Using multiple sensors, the system monitors material conveying status in real time. When it detects equipment start-up / stoppage or conveying delays, it automatically calculates the impact range and takes preventative measures to avoid uneven mixture quality caused by sudden changes in the mix ratio.

[0127] These technical principles support each other and are organically combined to form a complete intelligent mixing solution for fluidized solidified soil. From raw material processing, ratio optimization, process control to quality monitoring, the entire process has achieved automated and intelligent management, effectively solving the core technical problem that the ratio parameters are difficult to accurately control.

[0128] 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.

[0129] The specific implementation of step S01 is the same as above and will not be described in detail here.

[0130] The specific implementation of step S02 is to build a high-precision automated control system with data acquisition and precise control as its core. The system first establishes a central numerical control panel as the information processing center, uses a 32-bit ARM microprocessor to build a real-time computing platform, and runs a PID control algorithm with a sampling frequency of 100Hz to ensure that the system response time does not exceed 50ms. The mathematical expression of the PID control algorithm is:

[0131]

[0132] Where u(t) is the output value of the control system; e(t) is the current error, that is, the difference between the target value and the actual value; K p is the proportional coefficient, ranging from 0.5 to 2.0; Ki is the integral coefficient, ranging from 0.05 to 0.5; K d is the differential coefficient, ranging from 0.01 to 0.1; t is the current time; τ is the integral variable.

[0133] The system integrates a distributed sensor network, including weighing sensors, flow meters, pressure sensors, displacement sensors, and more, forming a multi-point monitoring system. The sensor accuracy level is no less than 0.2, and the sampling frequency is 10 to 50 Hz. The execution unit uses a high-precision metering pump system driven by a servo motor. The metering accuracy error is controlled within 0.5%, and the minimum adjustment range is 0.1% of the total flow rate. By establishing a material property database and recipe management system, the quantitative dosage of various components such as curing agent, admixture, cement, and water is achieved to ensure the stability of the ratio. The entire control system adopts a layered architecture design, with the bottom layer executing real-time control logic, the middle layer performing data processing and parameter optimization, and the upper layer implementing human-computer interaction and decision support. The system stability index exceeds 99.9%.

[0134] The specific implementation of steps S03-S04 is the same as above and will not be repeated here.

[0135] The specific implementation of step S05 is to use a multi-objective optimization algorithm to accurately adjust the fluidized solidification soil ratio. This step is based on the measured original soil characteristic parameters and constructs a mathematical optimization model with fluidity, strength, and cost as the objective function and the amount of each component as the decision variable. The objective function is expressed as:

[0136] minF(X)=[minf1(X), minf2(X), minf3(X)];

[0137] Where F(X) is the multi-objective function vector; X is the decision variable vector, X = [x1, x2, x3, x4, x5], where x1 is the cement dosage (kg / m 3 ), x2 is the amount of curing agent (%), x3 is the water-cement ratio, x4 is the original soil content (%), and x5 is the amount of admixture (%); f1(X) is the fluidity deviation function, f2(X) is the strength deviation function, and f3(X) is the cost function.

[0138] Each sub-objective function is specifically expressed as:

[0139] f1(X)=|FD(X)-FD target |;

[0140] f2(X)=|CS(X)-CS target |;

[0141] f3(X)=c1x1+c2x2+c3x5;

[0142] Where FD(X) is the mobility prediction function; FD target is the target fluidity value, ranging from 18 to 22 cm; CS(X) is the compressive strength prediction function; CS target is the target strength value, ranging from 1.0 to 2.5 MPa; c1 is the unit price coefficient of cement, ranging from 0.5 to 0.8 yuan / kg; c2 is the unit price coefficient of curing agent, ranging from 2.0 to 3.5 yuan / kg; c3 is the unit price coefficient of admixture, ranging from 5.0 to 8.0 yuan / kg.

[0143] The fluidity prediction function and strength prediction function are expressed as:

[0144]

[0145] CS(X)=b0+b1x1+b2x2+b3x3+b4x4+b5x5+b6x1x2+b7x1 / x3+b8t 0.5 +ε2;

[0146] Constraints include cement usage between 150 and 250 kg / m 3 The curing agent dosage is within the range of 3% to 8% of the total volume, the water-cement ratio is controlled between 0.5 and 0.8, and the original soil content accounts for 65% to 80% of the total volume. The optimization algorithm uses the non-dominated sorting genetic algorithm (NSGA-II) to obtain the optimal solution set through fast non-dominated sorting and crowding distance calculation. The crowding distance calculation formula is:

[0147]

[0148] Where, d i is the crowding distance of individual i; f m (i+1) and f m (i-1) are the objective function values of the two individuals adjacent to individual i on target m; and are the maximum and minimum values of the target m, respectively. The system automatically selects the optimal mix ratio from the optimal solution set based on the project requirements, including cement dosage, curing agent dosage, admixture dosage, and water dosage, and generates a precise mix ratio instruction that is sent to the control system.

[0149] The specific implementation of step S06 is the same as above and will not be described in detail here.

[0150] The specific implementation of step S07 is to establish a real-time fluidity monitoring and automatic correction mechanism. This step involves installing an online fluidity detection system at the mixer outlet to monitor the flow state of the mixture based on computer vision technology. When the fluidity value deviates from the target value, the system triggers a parameter compensation mechanism and automatically adjusts the mixing parameters using a fuzzy control algorithm. The mathematical expression of the fuzzy control algorithm is:

[0151]

[0152]

[0153] Where ΔW is the water adjustment amount (kg / m 3 ); ΔA is the amount of admixture adjustment (%); e FD is the fluidity deviation (cm); is the fluidity deviation change rate (cm / min); λ1 is the water adjustment coefficient, ranging from 1.5 to 2.5; λ2 is the admixture adjustment coefficient, ranging from 0.8 to 1.2; μ1(e FD ) and μ2(e FD ) is the membership function; f1 and f2 are the adjustment calculation functions.

[0154] The membership function is defined as:

[0155]

[0156] A fluidity deviation within ±1cm is considered normal. A deviation within the range of ±1-2cm triggers a mild adjustment mechanism, and a deviation exceeding ±2cm triggers a forced adjustment mechanism. The system calculates the amount of water or admixture adjustment based on the direction and magnitude of the fluidity deviation. The calculation is based on the water-cement ratio sensitivity coefficient of 0.3-0.5cm / 0.01 and the admixture sensitivity coefficient of 0.5-1.0cm / 0.1%. The adjustment mechanism adopts a progressive strategy. The first adjustment amount is 70% of the theoretical calculated value. After observing the effect, a second fine-tuning is performed to avoid excessive adjustment and system oscillation. All adjustment processes are recorded in the system database to provide data support for subsequent process optimization.

[0157] The specific implementation of step S08 is to establish a material ratio fluctuation early warning system to ensure production continuity and quality stability. This step forms a full-coverage monitoring network by deploying multiple sensors at key nodes in the material conveying system. The system uses a time series analysis algorithm to monitor material flow parameters in real time. The time series analysis algorithm is expressed as:

[0158]

[0159] Where V(t) is the material flow rate change rate at time t (% / min); V(ti) is the material flow rate change rate at time ti; ε(tj) is the random disturbance term at time tj; w i is the autoregressive coefficient, i=1,2,…,m;v j is the moving average coefficient, j = 1, 2, ..., n; δ is a constant term; m and n are the autoregressive order and the moving average order, respectively, and usually range from 1 to 3.

[0160] When it is detected that the material transportation is unstable during the equipment startup phase, the system automatically calculates the shutdown boundary position, that is, the unstable material ratio area. The calculation formula is:

[0161]

[0162] Where, L boundary is the shutdown boundary position (m); V pipe is the pipeline flow velocity (m / s); Q(t) is the flow rate at time t (kg / s); A is the cross-sectional area of the pipeline (m 2 ); ρ is the material density (kg / m 3 ); t0 is the shutdown start time; t1 is the system restabilization time.

[0163] For mixtures with ratio deviations exceeding set thresholds (cement deviation > 3%, water deviation > 2%, curing agent deviation > 1%), the system automatically triggers an isolation procedure and directs unqualified materials to a preset temporary storage area. The system also establishes an equipment failure prediction model, which is expressed as:

[0164]

[0165] Where P(F|X) is the probability of equipment failure under given equipment parameters X; X = [x1, x2, ..., x p ] is the equipment operation parameter vector, including temperature, pressure, vibration, current, etc.; c0, c1, ..., c p is the logistic regression coefficient, obtained through training of historical fault data. threshold When the warning is triggered, P threshold is the warning threshold, ranging from 0.6 to 0.8. The warning lead time is 5 to 10 minutes, providing operators with sufficient response time.

[0166] For the optional step S09, its specific implementation method is to conduct quality inspection after the mixing is completed and form a digital production record. This step first obtains the mixture sample through the automatic sampling system, and each batch of samples is sampled at no less than 3 points, and the total amount of the sample is no less than 5kg. The fluidity of the obtained sample is measured by using a standard drop cylinder measurement method with a diameter of 10 cm and a height of 20 cm to measure the diffusion diameter of the mixture, and the measurement accuracy is controlled within ±0.2cm. At the same time, the initial setting time is measured by using a Vicat instrument measurement method, and the measurement accuracy is controlled within ±5 minutes. In addition, the system also uses the hydration reaction kinetics equation to predict the strength development law of the solidified soil. The hydration reaction kinetics equation is expressed as:

[0167]

[0168] Where, S(t) is the compressive strength at age t (MPa); S ∞ is the ultimate compressive strength (MPa); k is the reaction rate constant; n is the time index; t is the age (days).

[0169] Ultimate compressive strength S ∞ Expressed as:

[0170]

[0171] Where C is the cement dosage (kg / m 3 ), ranging from 150 to 250 kg / m 3 ; A is the amount of curing agent (%), ranging from 3 to 8%; W is the amount of water (kg / m 3 ); M is the original soil moisture content (%); T is the ambient temperature (℃); α1, α2, ..., α6 are regression coefficients; ε3 is the error term, which obeys the normal distribution The value range of σ3 is 0.05~0.15.

[0172] The reaction rate constant k is expressed as:

[0173]

[0174] The time index n is expressed as:

[0175]

[0176] Based on accumulated production data, the system employs machine learning algorithms to continuously optimize mix prediction models and control parameters, enhancing its intelligence. Model updates are performed every 10 batches to ensure continuous improvement and optimization. Measurement results are uploaded to a central database in real time via a wireless transmission module. The system automatically compares and analyzes these results against target parameters and issues a quality evaluation report. Key parameters throughout the production process, including raw soil properties, component dosages, fluidity curves, mixing energy consumption, and ambient temperature and humidity, are recorded in the database, forming a traceable digital production record.

[0177] This embodiment realizes the automatic control of the whole process from raw soil treatment to final quality inspection through the implementation of the above nine steps. This method comprehensively utilizes multidisciplinary knowledge such as control theory, optimization theory, fuzzy logic and chemical kinetics, and establishes key technologies such as a three-stage precision screening and mixing system, a multi-objective optimization ratio model, a fuzzy control adjustment mechanism, and a material ratio fluctuation early warning system. It effectively solves the problems of insufficient raw soil treatment, low ratio accuracy, and large quality fluctuations in the traditional fluidized solidified soil mixing process, improves the quality stability and construction efficiency of fluidized solidified soil, reduces production costs, and has significant technical innovation and practical value.

[0178] To better understand and implement the present invention, Example 2 of a specific application scenario of the present invention is provided below: A large number of underground pipeline corridor backfill projects are required in a certain engineering project. Due to the narrow construction site and dense surrounding buildings, the traditional backfill method has problems such as large vibration and long construction period. Therefore, it was decided to use fluidized solidified soil for backfill construction. The research team adopted a fluidized solidified soil mixing construction method based on automatic adjustment of the mixing ratio parameters to ensure the stable quality, cost-effectiveness and high construction efficiency of the produced fluidized solidified soil.

[0179] First, the research team established a complete three-stage precision screening and mixing system. In the initial mixing stage, a vertical spiral mixer with a rotation speed of 28 rpm was used to process the original soil. The mixing time was controlled at 150 seconds. The shear stress intensity was measured to be 4.2 kPa, and the dispersion coefficient of the aggregate soil reached 0.93. In the rolling screening stage, a drum screening machine with a mesh diameter of 2.5 cm was used for particle classification. The rotation speed was set to 18 rpm, and the screening efficiency reached 97.2%. In the re-mixing stage, a double-blade staggered mixing structure was used, and the mixing energy was controlled at 1.0 kW·h / m 3 , the uniformity coefficient of mixture particle distribution reaches 0.94.

[0180] The high-precision automatic control system built by the team uses a 32-bit ARM microprocessor as the core computing unit and runs a 100Hz PID control algorithm. The PID parameters are determined to be K after self-tuning. p =1.2, K i=0.15, K d = 0.05, and the measured system response time is 35ms. The sensor network includes a 0.1-level accuracy weighing sensor, flowmeter, and pressure sensor, with a sampling frequency of 30Hz. The actuator unit uses a servo motor-driven metering pump system with a metering accuracy error of 0.3% and a minimum adjustment range of 0.08% of the total flow rate.

[0181] Based on the project requirements, the team set the target performance parameters as follows: fluidity of 20 cm, initial setting time of 6 hours, 3-day compressive strength of 0.4 MPa, and 28-day compressive strength of 1.5 MPa. The system calculated the optimal mix ratio parameters using the support vector machine algorithm, as shown in Table 1:

[0182] Table 1 Optimal mix parameters of fluidized solidified soil

[0183] Parameter name Numerical <![CDATA[Cement consumption (kg / m 3 )]]> 180 Curing agent dosage (%) 4.5 Water-cement ratio 0.68 Original soil content (%) 72 Amount of admixture (%) 0.6

[0184] When the raw soil processing unit pre-processed the soil, the moisture content of the raw soil was measured to be 21.5% and the density was 1.62g / cm 3 , the particle size distribution is shown in Table 2:

[0185] Table 2 Original soil particle size distribution

[0186] Particle size range (μm) content(%) 0.1-10 12.3 10-50 25.7 50-100 18.5 100-500 32.8 500-2000 10.7

[0187] In the mathematical model constructed by the multi-objective optimization algorithm, the objective function weight coefficients are set as follows: the fluidity deviation function weight is 0.4, the strength deviation function weight is 0.4, and the cost function weight is 0.2. The regression coefficients of the fluidity prediction function and the strength prediction function are shown in Table 3:

[0188] Table 3 Prediction function regression coefficients

[0189] coefficient Mobility prediction function Intensity prediction function <![CDATA[a0 / b0]]> 15.3 0.28 <![CDATA[a1 / b1]]> 0.035 0.004 <![CDATA[a2 / b2]]> 0.42 0.07 <![CDATA[a3 / b3]]> 18.2 -0.35 <![CDATA[a4 / b4]]> -0.08 -0.01 <![CDATA[a5 / b5]]> 2.5 0.15 <![CDATA[a6 / b6]]> 0.012 0.003 <![CDATA[a7 / b7]]> 0.18 1.2 <![CDATA[a8 / b8]]> -12.5 0.3

[0190] The parameters of the non-dominated sorting genetic algorithm were set as follows: population size 50, evolutionary generations 100, crossover probability 0.8, and mutation probability 0.1. Through fast non-dominated sorting and crowding distance calculation, five non-dominated solutions were ultimately obtained. Based on the comprehensive scores, the system selected the optimal solution (Table 1).

[0191] During the mixing process, the actual metering accuracy of each component is shown in Table 4:

[0192] Table 4 Measuring accuracy of each component

[0193] Components Measurement method Actual error (%) cement Screw feeder + weighing sensor ±0.38 water Electromagnetic flowmeter + proportional control valve ±0.25 curing agent Micro pumping system ±0.17 admixtures Micro pumping system ±0.15

[0194] During the mixing process, the system measured the diffusion diameter every 30 seconds using an online fluidity detection device. The measured initial fluidity value was 18.5 cm, which was 20 cm lower than the target value. The system automatically triggered the fuzzy control algorithm to perform parameter compensation and calculated the water output adjustment amount to be 5.2 kg / m 3 , the admixture adjustment amount is 0.05%. After adjustment, the fluidity value is stabilized at 19.8cm, which meets the engineering requirements.

[0195] During the production process, the material ratio fluctuation warning system detected a mixer start-stop event. The system automatically calculated the stop boundary position as 4.8m and stopped the unqualified mixed material (about 1.2m 3 ) was imported into a temporary storage area, preventing the impact of unstable material ratios on overall quality. The equipment failure prediction model did not trigger any warning signals during the production process, and the equipment was operating well.

[0196] During the quality inspection phase, 6 kg of the mixture was sampled from three locations. The average fluidity was measured to be 19.8 cm and the initial setting time was 5.8 hours, meeting the design requirements. The strength development curve predicted by the hydration reaction kinetic equation is shown in Table 5:

[0197] Table 5 Prediction of intensity development

[0198] Age (days) Compressive strength (MPa) 1 0.12 3 0.42 7 0.86 14 1.18 28 1.52

[0199] The actual 28-day compressive strength test result was 1.48 MPa, which was 2.6% different from the predicted value, verifying the accuracy of the hydration reaction kinetics equation. The material consumption during the entire production process is shown in Table 6:

[0200] Table 6 Statistics of material consumption

[0201] materials Planned usage Actual usage deviation(%) Cement (t) 32.4 32.9 +1.5 Curing agent (t) 8.1 8.2 +1.2 Admixture(t) 1.08 1.12 +3.7 <![CDATA[Water (m 3 )]]> 22.5 23.1 +2.7 <![CDATA[Original soil (m 3 )]]> 129.6 129.9 +0.2

[0202] This example successfully completed 180m 3 The entire project, produced and constructed using fluidized solidified soil, was completed within six days, saving approximately 40% of the time required by traditional methods. Post-completion backfill quality testing revealed settlement within 5mm, good uniformity, and no cracking.

[0203] The traditional fluidized solidified soil mixing construction method mainly relies on manual experience for mix design and quality control, and has many technical defects: first, the original soil is not thoroughly processed, and there are often residual agglomerates and oversized impurities mixed in, resulting in poor uniformity of the mixture; second, the determination of mix parameters is mainly based on empirical formulas, which are difficult to adapt to changes in the properties of raw materials; third, quality control mainly relies on manual sampling and testing, which has strong lag and is difficult to adjust in time; fourth, the material ratio fluctuations caused by the start and stop of equipment during the production process are difficult to control, resulting in unstable quality.

[0204] In contrast, the fluidized solidified soil mixing construction method of the present embodiment, which automatically adjusts the proportioning parameters, has significant advantages: by establishing a three-stage precise screening and mixing system, efficient processing and particle classification of the raw soil are achieved, and the uniformity coefficient of the mixture is improved by about 15%; a multi-objective optimization algorithm is used to automatically adjust the proportioning parameters, which has strong adaptability and can accurately calculate the optimal proportion according to the properties of different raw materials, and the proportioning accuracy is improved by about 30%; a real-time monitoring and automatic correction mechanism for fluidity is established, which makes the product quality more stable and the fluidity control accuracy is improved by about 50%; the material ratio fluctuation early warning system effectively solves the quality fluctuation problem caused by equipment start-up and shutdown, and the production continuity is improved by about 25%. Through these technological innovations, the present invention effectively improves the quality stability and construction efficiency of fluidized solidified soil, reduces production costs, and has significant practical value.

[0205] It should be noted that the variables involved in the present invention are explained in detail as shown in Tables 7 and 8 below.

[0206] Table 7 Variable Explanation Table (Part 1)

[0207]

[0208]

[0209] Table 8 Variable Explanation Table (Part 2)

[0210]

[0211] 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 fluidized solidified soil mixing construction method based on automatic adjustment of mix ratio parameters, characterized in that: include: Establish a three-stage precise screening and mixing system; Build a high-precision automated control system; Input target performance parameters of fluidized solidified soil; Start the raw soil processing unit; Based on the multi-objective optimization algorithm, the proportioning parameters are automatically adjusted, and a constrained optimization model is constructed with fluidity value, strength value, and cost as objective functions and the amount of each component as the decision variable. The optimal solution set is solved by the non-dominated sorting genetic algorithm, and the optimal proportioning scheme is selected from it according to engineering requirements; the mixing program is executed; when the fluidity value is detected to deviate from the target value, the control system automatically triggers the parameter compensation mechanism; a material ratio fluctuation early warning system is established; and quality inspection is carried out after the mixing is completed.

2. The fluidized solidified soil mixing construction method based on automatic adjustment of mix ratio parameters according to claim 1 is characterized in that: The three-stage precision screening and mixing system specifically refers to a mixing system consisting of three continuous processes: preliminary mixing, drum screening and secondary enhanced mixing. In the initial mixing stage, a vertical spiral mixer with a rotation speed of 28 rpm is used to disperse the raw soil. In the roller screening stage, a drum screening machine with a mesh diameter of 2.5 cm is used to remove oversized impurities. In the re-mixing stage, the mixture is efficiently homogenized by a secondary mixer.

3. The fluidized solidified soil mixing construction method based on automatic adjustment of mix ratio parameters according to claim 2 is characterized in that: The target performance parameters of the fluidized solidified soil include fluidity value, setting time, early strength value and final strength value. The control system automatically calls the corresponding mix ratio according to the input parameters.

4. The fluidized solidified soil mixing construction method based on automatic adjustment of mix ratio parameters according to claim 3 is characterized in that: The mixing and stirring procedure involves adding each component raw material according to precise measurement, fully mixing the components through a secondary stirring system, and monitoring the fluidity value of the mixture in real time.

5. The fluidized solidified soil mixing construction method based on automatic adjustment of mix ratio parameters according to claim 4 is characterized in that: The material ratio fluctuation warning system monitors the start and stop of equipment or material transportation delays, calculates the shutdown boundary position and automatically triggers the compensation program to avoid uneven quality of the mixture due to sudden changes in the material ratio; the material ratio fluctuation warning system specifically refers to real-time monitoring of the material transportation status by setting up multi-point sensors. When the start and stop of equipment or transportation delays are detected, the affected range is automatically calculated and the abnormal material is temporarily stored in a preset area to avoid unstable mixture performance due to inaccurate proportions.

6. The fluidized solidified soil mixing construction method based on automatic adjustment of mix ratio parameters according to claim 5 is characterized in that: The quality inspection measures the fluidity value and initial setting time by sampling, and records the production parameters to form a digital production record, providing data support for subsequent process optimization.

7. The fluidized solidified soil mixing construction method based on automatic adjustment of mix ratio parameters according to claim 6 is characterized in that: The fluidity value specifically refers to the fluidity index of the fluidized solidified soil mixture measured by the drop cylinder method. The flowability is evaluated by measuring the diffusion diameter of the mixture after the standard size cylinder is lifted. Generally, the diffusion diameter is required to be between 18cm and 22cm for the best state.

8. The fluidized solidified soil mixing construction method based on automatic adjustment of mix ratio parameters according to claim 7 is characterized in that: The curing agent specifically refers to an additive used to enhance the early strength and final strength performance of fluidized solidified soil. The main components include cement, lime, fly ash and special silicate materials, and the proportions are designed according to different geological conditions and strength requirements; the admixture specifically refers to a functional additive used to adjust the working performance of fluidized solidified soil, including water reducers, retarders, and early strength agents, and the fluidity, setting time and strength development of the mixture are controlled by adjusting the dosage.

9. The fluidized solidified soil mixing construction method based on automatic adjustment of mix ratio parameters according to claim 8, characterized in that: The fluidized solidification soil mix parameter range specifically refers to the dosage range of each component determined according to the project requirements and the original soil properties. The cement dosage range is 150 to 250 kg / m 3 The dosage of curing agent ranges from 3% to 8% of the total volume, the water-cement ratio is controlled between 0.5 and 0.8, the original soil content accounts for 65% to 80% of the total volume, and the initial mix ratio is usually cement 200kg / m 3 , 5% curing agent, 0.65 water-cement ratio, and 70% original soil content are used as the benchmark formula, and dynamic adjustments are made based on the measured parameters.

10. The fluidized solidified soil mixing construction method based on automatic adjustment of mix ratio parameters according to claim 9, characterized in that: The adaptive control function specifically refers to a mathematical model designed based on fuzzy control theory for real-time adjustment of the mix parameters of fluidized solidified soil mixture. The adaptive control function determines the amplitude and direction of the adjustment by analyzing the deviation and change rate of the fluidity value. On the one hand, it ensures that the performance of the mixture quickly returns to the target value, and on the other hand, it avoids excessive adjustment that causes system oscillation.

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