Synchronous grouting double-liquid slurry based on shield waste slurry and preparation method of synchronous grouting double-liquid slurry

Abstract

The invention discloses synchronous grouting double-liquid slurry based on shield waste slurry and a preparation method thereof, and relates to the technical field of environmental protection engineering, the synchronous grouting double-liquid slurry comprises the following raw materials by weight: 50-70% of waste slurry, 10-20% of Portland cement, 3-6% of water glass, 1-3% of an additive, 5-15% of shield tail water, 5-10% of blast furnace slag, 5-15% of fly ash and 1-3% of an alkali-activated material. The double-component slurry is prepared by taking the shield waste slurry and the filter-press tail water as main raw materials, the characteristics of the waste slurry and the shield tail water are fully utilized, and efficient utilization of resources and environmental protection are realized; components in the waste slurry not only ensure the stability of the slurry, but also promote the hydration process of the cement and improve the strength performance of the slurry; meanwhile, the components in the shield tail water create favorable conditions for cement hydration, and the early strength and the later strength of the slurry are enhanced; the environmental pollution is obviously reduced, the treatment cost is reduced, and meanwhile, the high performance of the grouting material is ensured.

Description

Technical Field

[0001] The invention relates to the technical field of environmental protection engineering, and in particular to a synchronous grouting double-liquid slurry based on shield waste mud and a preparation method thereof. Background Art

[0002] The shield method is a widely used construction method in modern tunnel engineering, especially in underground projects such as urban subways. However, a large amount of waste slurry is generated during the construction of large-diameter slurry shields, and the treatment of this waste has always been a major challenge facing the industry.

[0003] Waste mud is a complex mixed system composed of water, clay, silt, sand, etc., usually in the form of a mixture of suspension and colloidal solution, with good stability and not easy to settle naturally. Its density is about 1.1-1.2 g / cm 3 , the water content is as high as 70-80%. This characteristic makes the treatment of waste mud particularly difficult.

[0004] At present, there are mainly the following methods for treating shield waste mud:

[0005] 1. Direct discharge by external transport: This traditional method is not only costly, including transportation costs, labor costs, management fees, etc., but also very likely to cause environmental pollution, water pollution, and municipal pipe network blockage. With the continuous improvement of environmental protection requirements, many cities have banned this treatment method.

[0006] 2. Natural sedimentation: The mud is stored by digging a mud sedimentation tank and allowed to settle naturally. This method not only occupies a large amount of land resources for a long time, but also easily causes secondary pollution.

[0007] 3. Centrifugal dehydration: Centrifuges are used to dehydrate mud, but there are problems such as difficulty in quickly achieving mud-water separation, high energy consumption, and relatively low processing capacity.

[0008] 4. Plate and frame filter press dehydration: Usually, it is necessary to add conditioning agents for physical and chemical conditioning, and then use a plate and frame filter press for deep dehydration. Although the dehydration effect is good, the processing cost is high, and the mud cake produced still needs to be transported to landfill or further processed.

[0009] The above existing treatment methods either occupy a large amount of land resources or are costly, and cannot meet the dual needs of economic benefits and environmental protection requirements. Therefore, it is of great practical significance to develop a new method that can realize the resource utilization of waste mud. Synchronous grouting technology, as an important auxiliary means in shield construction, can effectively control stratum deformation and settlement. However, the commonly used synchronous grouting materials such as cement slurry and chemical slurry also have many problems, such as long gelation time, high cost, environmental risks, etc. Summary of the invention

[0010] Based on this, it is necessary to provide a dual-liquid slurry for synchronous grouting based on shield waste mud and a preparation method thereof to address the above technical problems.

[0011] In a first aspect, the present invention provides a synchronous grouting double-liquid slurry based on shield waste mud, and the synchronous grouting double-liquid slurry comprises the following raw materials in weight percentage:

[0012] Waste mud 50-70%, Portland cement 10-20%, water glass 3-6%, admixture 1-3%, shield tail water 5-15%, blast furnace slag 5-10%, fly ash 5-15% and alkali-activated material 1-3%.

[0013] Furthermore, the water content of the waste mud is 65-90%, and the specific gravity of the waste mud is 1.1-1.3 g / cm 3 ;

[0014] The Baume degree of water glass is 35 to 45;

[0015] The strength of silicate cement is greater than 42.5;

[0016] The pH value of shield tailwater is greater than 11;

[0017] The specific surface area of ​​blast furnace slag is greater than 500m 2 / kg.

[0018] Further, the admixture includes the following raw materials in weight percentage:

[0019] Sodium gluconate 15-25%, tartaric acid 15-25%, carboxypropyl cellulose 15-25%, methyl amyl alcohol 20-30% and guar gum 20-30%.

[0020] Furthermore, the alkali-activated material includes at least one of metakaolin, sodium silicate, alunite, and red mud.

[0021] In a second aspect, the present invention further provides a method for preparing a dual-liquid slurry for synchronous grouting based on waste slurry from a shield tunnel, the preparation method comprising the following steps:

[0022] S1. Detect the property data of each raw material through raw material component property detection, and select silicate cement, shield tail water, blast furnace slag, fly ash and alkali-activated material as primary mixed raw materials;

[0023] S2. Based on the back propagation neural network model, a ratio prediction model is established to associate the raw material ratio with the performance parameters of the synchronous grouting dual-liquid slurry, and then the ratio prediction model is used to match the optimal ratio of all raw materials that meet the preset process requirements;

[0024] S3, weighing each raw material according to the optimal ratio, preferentially adding the primary mixed raw material into the mixer in sequence for stirring, and during the stirring process, detecting the viscosity and fluidity of the mixture in real time;

[0025] S4. Add waste mud and admixtures in stages according to the viscosity of the mixture, and then dynamically adjust by detecting the stirring progress until it is completely mixed to obtain a mixed slurry;

[0026] S5. Add the weighed water glass into the mixed slurry, mix them evenly by stirring to obtain the synchronous grouting double-liquid slurry, and detect and verify the actual performance parameters of the synchronous grouting double-liquid slurry.

[0027] Furthermore, based on the back propagation neural network model, a ratio prediction model is established to associate the raw material ratio with the performance parameters of the synchronous grouting dual-liquid slurry, and then the ratio prediction model is used to match the optimal ratio of all raw materials that meet the preset process requirements, including the following steps:

[0028] S21. By acquiring historical sample data, the ratio of raw materials under different property data conditions is defined as input factor, the performance parameters of the synchronous grouting dual-liquid slurry are located as output factors, and then data cleaning and normalization are performed. The performance parameters include slurry consistency, gel time, water bleeding rate, slump and compressive strength;

[0029] S22, defining a hidden layer of a back propagation neural network, and calculating the number of nodes in the hidden layer according to the number of nodes in the input layer and the output layer, and obtaining a matching prediction model by establishing a network topology structure;

[0030] S23. Using historical sample data to establish a data training set and a data test set, perform model training and evaluation on the ratio prediction, and adjust the model parameters by cross-validation evaluation;

[0031] S24, setting the current process requirements for preparing the synchronous grouting dual-liquid slurry, the process requirements including the values ​​of various types of performance parameters in the synchronous grouting dual-liquid slurry and the demand for the synchronous grouting dual-liquid slurry;

[0032] S25. Improve the grey wolf algorithm based on the population memory elimination mechanism, and use the improved grey wolf algorithm in combination with the ratio prediction model to calculate the optimal ratio that meets the process requirements.

[0033] Furthermore, the gray wolf algorithm is improved based on the population memory elimination mechanism. The improved gray wolf algorithm is combined with the ratio prediction model to calculate the optimal ratio that meets the process requirements, including the following steps:

[0034] S251, setting the ratio of raw materials to the individual positions in the wolf pack, setting the performance parameters of the synchronous grouting dual-liquid slurry to the value of the fitness function, then setting the wolf pack size, the maximum number of iterations and the elimination ratio, and initializing the gray wolf population, randomly generating the raw material ratio as the initial solution;

[0035] S252, calculating the fitness values ​​of all gray wolf individuals in the population, sorting them according to the fitness values, selecting the top three gray wolves with the highest fitness values ​​and marking them as first-level individuals, second-level individuals and third-level individuals respectively, and marking the remaining gray wolves as candidate individuals;

[0036] S253, by searching for prey and gradually approaching the prey, after surrounding the prey, the positions of all gray wolf individuals are updated according to the positions of the first-level individual, the second-level individual, and the third-level individual;

[0037] S254, updating the fitness values ​​of all individuals according to the position of each gray wolf individual;

[0038] S255, replacing the three gray wolves with the highest fitness in the previous iteration with the three gray wolves with the worst fitness after the current iteration;

[0039] S256. When the error is less than the set error or reaches the maximum number of iterations, exit the loop and output the optimal position and optimal error as the optimal ratio; when the error is greater than the set error or does not reach the maximum number of iterations, return to step S252 and perform an iterative loop.

[0040] Furthermore, the calculation formula for updating the positions of all gray wolf individuals is:

[0041] D=|2r2·X p (t)-X(t)|

[0042]

[0043] Where D is the distance between the wolf and its prey; t is the current iteration deployment; maxt is the maximum number of iteration steps; X p represents the position of the prey; X(t) represents the current position of the gray wolf; X(t+1) represents the position of the gray wolf in the next iteration; r1 and r2 represent random numbers.

[0044] Further, according to the viscosity of the mixture, the waste mud and the admixture are added in stages, and then the stirring progress is dynamically adjusted until it is completely mixed and uniform, and the mixed slurry is obtained, which includes the following steps:

[0045] S41, obtaining the viscosity of the mixture measured by a viscosity detector, recording the initial viscosity before mixing and stirring, and recording in a time series order;

[0046] S42, setting a staged mixing strategy, adding different doses of waste mud and admixtures in sequence according to the staged mixing strategy, and monitoring the stirring rate of the agitator in real time;

[0047] S43, setting viscosity thresholds at different stages, comparing the real-time viscosity with the viscosity threshold values, if the viscosity of the mixture in the current stage exceeds the upper limit of the viscosity threshold, increasing the speed of the agitator, if the viscosity of the mixture in the current stage is lower than the lower limit of the viscosity value, reducing the speed of the agitator, and adding the waste mud and admixture of the next stage in advance;

[0048] S44. When all the raw materials have been added, continue to stir the mixture until the viscosity reaches a preset standard, then stop stirring to obtain a mixed slurry.

[0049] Further, the weighed water glass is added to the mixed slurry, and the mixture is evenly mixed by stirring to obtain the synchronous grouting double-liquid slurry, and the actual performance parameters of the synchronous grouting double-liquid slurry are detected, which includes the following steps:

[0050] S51, slowly adding the weighed water glass into the mixer, driving the agitator to stir, and setting the viscosity range of the mixed slurry until a synchronous grouting double-liquid slurry is obtained;

[0051] S52. Extract the finished product of the synchronous grouting double-liquid slurry, detect the actual performance parameters of the synchronous grouting double-liquid slurry through finished product testing, and compare them with the preset process requirements. Evaluate the quality of the finished product of the synchronous grouting double-liquid slurry by calculating the ratio of the actual performance parameters to the performance parameters of the process requirements.

[0052] The beneficial effects of the present invention are:

[0053] 1. By using shield waste mud and filter press tail water as the main raw materials to prepare the two-component slurry, the characteristics of waste mud and shield tail water are fully utilized, and efficient resource utilization and environmental protection are achieved; the components in the waste mud not only ensure the stability of the slurry, but also promote the hydration process of cement and improve the strength performance of the slurry; at the same time, the components in the shield tail water create favorable conditions for cement hydration, further enhancing the early and late strength of the slurry; thereby significantly reducing environmental pollution and processing costs, while ensuring the high performance of the grouting material.

[0054] 2. The present invention can be directly applied to shield engineering sites, greatly improving the utilization efficiency of waste; by reducing the use of traditional additives, it not only reduces material costs but also simplifies the production process; in addition, the present invention also uses industrial by-products such as fly ash and blast furnace slag to achieve the comprehensive utilization of various waste resources, greatly improving the overall resource utilization efficiency and showing significant economic and environmental benefits.

[0055] 3. By designing admixtures, which are composed of sodium gluconate, tartaric acid, carboxypropyl cellulose, methyl amyl alcohol and gur gum, the admixtures make the double liquid slurry have excellent fluidity, stability, pumpability, water retention and anti-segregation properties. At the same time, it can effectively adjust the gelation time and slurry density, ensure good working performance during the grouting process and rapid coagulation and hardening after grouting, so as to more comprehensively meet the complex engineering needs of shield synchronous grouting.

[0056] 4. Through the application of the back propagation neural network model, the raw material ratio is optimized, the material performance is improved, and the production cost is reduced; by dynamically adjusting the amount of waste mud and admixture added, the viscosity and fluidity of the slurry can be effectively controlled to ensure that the quality and performance of the final product meet the construction requirements, reduce problems and losses in the later construction, and thus improve construction efficiency; in addition, real-time monitoring technology is used in the process to ensure precise control of each step, reduce the impact of human factors on the quality of the finished product, improve the uniformity and stability of the slurry, and also help to improve the safety and reliability of construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0058] Figure 1 The present invention is a flowchart of a method for preparing dual-liquid slurry for synchronous grouting based on waste slurry from shield tunneling according to an embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0060] The present invention provides a synchronous grouting double-liquid slurry based on shield waste mud, and the synchronous grouting double-liquid slurry comprises the following raw materials in percentage by weight:

[0061] Waste mud 50-70%, Portland cement 10-20%, water glass 3-6%, admixture 1-3%, shield tail water 5-15%, blast furnace slag 5-10%, fly ash 5-15% and alkali-activated material 1-3%.

[0062] In the description of the present invention, the water content of the waste mud is 65-90%, and the specific gravity of the waste mud is 1.1-1.3 g / cm 3; The Baume degree of water glass is 35-45; The strength of silicate cement is greater than 42.5; The pH value of shield tail water is greater than 11; The specific surface area of ​​blast furnace slag is greater than 500m 2 / kg.

[0063] In the description of the present invention, the admixture includes the following raw materials in weight percentage:

[0064] Sodium gluconate 15-25%, tartaric acid 15-25%, carboxypropyl cellulose 15-25%, methyl amyl alcohol 20-30% and guar gum 20-30%.

[0065] Specifically, the synergistic effect of the five ingredients significantly improves the comprehensive performance of the dual-liquid slurry. Sodium gluconate, as a high-efficiency water reducer and retarder, improves the fluidity and pumpability of the slurry, and at the same time delays the cement hydration process; tartaric acid mainly adjusts the gel time, prevents the slurry from coagulating prematurely, and ensures fluidity during pumping; carboxypropyl cellulose, as a thickener and water-retaining agent, increases the viscosity of the slurry, improves stability and uniformity, and improves water retention and thixotropy. Dimethylpentanol, as a foaming agent, can form stable micro-bubbles in the slurry, reduce the density of the slurry, and improve fluidity and pumpability; gul gum, as a thickener and stabilizer, further increases the viscosity and stability of the slurry and prevents segregation.

[0066] In the description of the present invention, the alkali-activated material includes at least one of metakaolin, sodium silicate, alunite and red mud.

[0067] See also Figure 1 The present invention also provides a method for preparing a dual-liquid slurry for synchronous grouting based on waste slurry from shield tunneling, the method comprising the following steps:

[0068] S1. Through the detection of raw material component properties, the property data of each raw material is detected, and silicate cement, shield tail water, blast furnace slag, fly ash and alkali-activated material are selected as the primary mixed raw materials.

[0069] Among them, silicate cement: choose cement with strength ≥42.5 to ensure the strength after curing.

[0070] Shield tail water: Selecting tail water with a pH value ≥ 11 will help enhance the fluidity and stability of the slurry.

[0071] Blast furnace slag: select slag with a specific surface area greater than 500m 2 / kg of slag to increase reaction activity.

[0072] Fly ash: Choose fly ash with finer particle size and higher activity to improve the performance of the slurry.

[0073] Alkali-activated material: Select materials with good reaction activity according to their properties (such as kaolin, sodium silicate, etc.).

[0074] S2. Based on the back propagation neural network model, a ratio prediction model is established to associate the raw material ratio with the performance parameters of the synchronous grouting dual-liquid slurry. The ratio prediction model is then used to match the optimal ratio of all raw materials that meet the preset process requirements.

[0075] Specifically, the raw material ratio prediction method based on the back propagation neural network (BPNN) model aims to achieve accurate prediction of the optimal ratio by constructing the correlation between the raw material ratio and the performance parameters of the simultaneous grouting dual-liquid slurry (such as consistency, gel time, water bleeding rate, etc.).

[0076] First, we collect data on different raw material ratios and their corresponding performance parameters, and build a neural network consisting of an input layer, a hidden layer, and an output layer. The input layer contains the weight percentage of each raw material, and the output layer is the corresponding performance parameter. Through the back-propagation algorithm, the network continuously adjusts the weights to minimize the error between the predicted value and the actual value, thereby improving the accuracy of the model.

[0077] During operation, the newly measured raw material ratio is input into the model, and the trained network is used for prediction to obtain the corresponding performance parameters. Afterwards, by comparing these performance parameters with the preset process requirements, the optimization algorithm (such as the Gray Wolf Algorithm) is used to adjust the raw material ratio to find the optimal ratio that meets the process requirements. This method not only improves the accuracy and effectiveness of the ratio, but also reduces the experimental cost and ensures the performance stability of the synchronous grouting dual-liquid slurry. Through continuous optimization and feedback, an intelligent ratio decision-making system is eventually formed, making the preparation process more efficient and flexible, and adapting to the needs of different construction conditions.

[0078] In the description of the present invention, based on the back propagation neural network model, a ratio prediction model is established that associates the raw material ratio with the performance parameters of the synchronous grouting dual-liquid slurry, and then the ratio prediction model is used to match the optimal ratio of all raw materials that meet the preset process requirements, including the following steps:

[0079] S21. By acquiring historical sample data, the ratio of raw materials under different property data conditions is defined as input factor, and the performance parameters of simultaneous grouting dual-liquid slurry are located as output factors. Then data cleaning and normalization are performed. The performance parameters include slurry consistency, gel time, water seepage rate, slump and compressive strength.

[0080] S22. Define the hidden layer of the back propagation neural network, and calculate the number of nodes in the hidden layer according to the number of nodes in the input layer and the output layer, and obtain the ratio prediction model by establishing the network topology structure.

[0081] Specifically, the number of input layer nodes is determined by the number of input factors. In the present invention, the input factors are the property data of raw materials, such as the proportion of silicate cement, shield tail water, blast furnace slag, fly ash and alkali-activated material. Assuming that the properties of these raw materials have 5 characteristic parameters, the number of input layer nodes is 5.

[0082] The number of output layer nodes is determined by the number of output factors. In the present invention, the output factors are the performance parameters of the synchronous grouting dual-liquid slurry, such as slurry consistency, gel time, water bleeding rate, slump and compressive strength. Assuming that there are 5 of these performance parameters, the number of output layer nodes is 5.

[0083] The number of nodes in the hidden layer is usually the average of the number of nodes in the input layer and the output layer, or between the number of nodes in the input layer and the output layer. The network structure is determined as: input layer (5 nodes) -> hidden layer (5 or 8 nodes) -> output layer (5 nodes). Weights need to be set between each node from the input layer to the hidden layer, and between each node from the hidden layer to the output layer, and they can be randomly generated initially.

[0084] S23. Use historical sample data to establish data training sets and data test sets, conduct model training and evaluation on the ratio prediction, and use cross-validation evaluation to adjust model parameters.

[0085] S24, setting the current process requirements for preparing the synchronous grouting dual-liquid slurry, the process requirements including the values ​​of various types of performance parameters in the synchronous grouting dual-liquid slurry and the demand for the synchronous grouting dual-liquid slurry.

[0086] S25. Improve the grey wolf algorithm based on the population memory elimination mechanism, and use the improved grey wolf algorithm in combination with the ratio prediction model to calculate the optimal ratio that meets the process requirements.

[0087] Specifically, the Gray Wolf Optimizer (GWO) is a swarm intelligence optimization algorithm that imitates the hunting behavior of gray wolves. Traditional GWO may encounter problems such as local optimality and slow convergence when dealing with complex optimization problems.

[0088] By introducing a population memory elimination mechanism, the algorithm's exploration ability can be enhanced and the global search efficiency can be improved. By recording the historical optimal solution or important search information, a "memory" is formed to enhance the algorithm's memory of the search space. This mechanism can help the algorithm avoid repeated exploration of similar areas during the search process. In each generation, by evaluating the fitness of individuals, individuals with poor performance are eliminated, and individuals with good performance are retained to improve the quality of the entire population.

[0089] In the description of the present invention, the gray wolf algorithm is improved based on the population memory elimination mechanism, and the improved gray wolf algorithm is used in combination with the ratio prediction model to calculate the optimal ratio that meets the process requirements, including the following steps:

[0090] S251. Set the ratio of raw materials as the individual position in the wolf pack, set the performance parameters of the synchronous grouting dual-liquid slurry as the value of the fitness function, then set the wolf pack size, the maximum number of iterations and the elimination ratio, initialize the gray wolf population, and randomly generate the raw material ratio as the initial solution.

[0091] The ratio of raw materials is used as the position of individuals in the gray wolf population. For example, each individual can be represented as a vector containing the ratios of different raw materials. The performance parameters of the synchronous grouting dual-liquid slurry (such as consistency, gel time, etc.) are used as the input of the fitness function, and the performance of each ratio is calculated through the ratio prediction model.

[0092] S252. Calculate the fitness values ​​of all gray wolf individuals in the population, sort them according to the fitness values, select the top three gray wolves with the highest fitness values ​​and mark them as primary individuals, secondary individuals and tertiary individuals respectively, and mark the remaining gray wolves as candidate individuals.

[0093] Specifically, the current position of the gray wolf individual (i.e., the raw material ratio) is used to calculate the performance parameters of each ratio through the ratio prediction model. A fitness function is defined, which can be designed as the weighted sum of the target performance parameters, and the fitness value of each gray wolf is stored in a list. The fitness values ​​are sorted in descending order, and the individual with the highest fitness value is selected.

[0094] S253. By searching for prey and gradually approaching the prey, after surrounding the prey, the positions of all gray wolf individuals are updated according to the positions of the first-level individuals, the second-level individuals and the third-level individuals.

[0095] In the description of the present invention, the calculation formula for updating the positions of all gray wolf individuals is:

[0096] D=|2r2·X p (t)-X(t)|

[0097]

[0098] Where D is the distance between the wolf and its prey; t is the current iteration deployment; maxt is the maximum number of iteration steps; X p represents the position of the prey; X(t) represents the current position of the gray wolf; X(t+1) represents the position of the gray wolf in the next iteration; r1 and r2 represent random numbers.

[0099] In the present invention, the linear decrease of the convergence factor in the traditional gray wolf algorithm is replaced by the decrease of the cosine function, that is, The a value before and after the improvement changes with the increase of the number of iterations. The decreasing form of the cosine function can increase the global search degree of the algorithm and accelerate the local convergence speed, thereby avoiding the problem of increased error due to the long operation process in the local search stage.

[0100] S254. Update the fitness values ​​of all individuals according to the position of each gray wolf individual.

[0101] S255. Replace the three gray wolves with the highest fitness in the previous iteration with the three gray wolves with the worst fitness after the current iteration.

[0102] S256: When the error is less than the set error or reaches the maximum number of iterations, the loop is exited and the optimal position and the optimal error are output as the optimal ratio. When the error is greater than the set error or does not reach the maximum number of iterations, the loop is returned to step S252 for an iterative loop.

[0103] S3. Weigh each raw material according to the optimal ratio, and add the primary mixed raw material into the mixer in sequence for stirring. During the stirring process, the viscosity and fluidity of the mixture are detected in real time.

[0104] S4. Add waste mud and admixtures in stages according to the viscosity of the mixture, and then dynamically adjust by detecting the stirring progress until it is completely mixed to obtain a mixed slurry.

[0105] In the description of the present invention, according to the viscosity of the mixture, the waste mud and the admixture are added in stages, and then the stirring progress is dynamically adjusted until it is completely mixed and uniform, and the mixed slurry is obtained, which includes the following steps:

[0106] S41, obtaining the viscosity of the mixture measured by a viscosity detector, recording the initial viscosity before mixing and stirring, and recording in a time series order.

[0107] S42, setting a staged mixing strategy, adding different doses of waste mud and admixtures in sequence according to the staged mixing strategy, and monitoring the stirring rate of the agitator in real time.

[0108] Specifically, the appropriate viscosity range can be set according to the expected mixing effect and the performance requirements of the final product. Generally, it can be set as an upper limit and a lower limit, for example:

[0109] Initial stage threshold: 100-200 mPa·s;

[0110] Intermediate stage threshold: 150-300 mPa·s;

[0111] Final stage threshold: 200-400mPa·s.

[0112] S43, setting viscosity thresholds at different stages, comparing the real-time viscosity with the viscosity threshold values, if the viscosity of the mixture in the current stage exceeds the upper limit of the viscosity threshold, increasing the speed of the agitator, if the viscosity of the mixture in the current stage is lower than the lower limit of the viscosity value, reducing the speed of the agitator, and adding the waste mud and admixture of the next stage in advance.

[0113] Specifically, the amount of waste mud to be added is determined according to the initial viscosity value. Usually, 30% to 50% of the waste mud can be added in the initial stage. At the same time, the amount of admixture added is determined according to the fluidity and viscosity of the mixture. These admixtures can be adjusted according to their functions (such as improving fluidity or enhancing strength). The selected waste mud and admixtures are gradually added to the agitator, stirring while adding. It is recommended to stir for 1 to 2 minutes to ensure that the materials are fully mixed.

[0114] After the initial stirring, the viscosity of the mixture is measured again using a viscometer. Set the viscosity threshold, for example, an upper limit of 300 mPa·s and a lower limit of 150 mPa·s. s。 If the viscosity of the mixture is detected to exceed the upper limit, the speed of the agitator needs to be increased to improve the mixing efficiency and reduce the viscosity; if the viscosity is lower than the lower limit, the stirring rate can be appropriately reduced to prevent excessive stirring from causing material separation. On this basis, the amount of subsequent waste mud and admixture added is dynamically adjusted according to the change of real-time viscosity value. For example, if the viscosity is too high, the amount of subsequent waste mud added can be reduced, otherwise it can be increased.

[0115] Continue to add the remaining waste mud and admixtures in stages, and perform the corresponding viscosity test after each addition. The viscosity of the mixture after each addition will affect the next operation, so it is necessary to monitor in real time to ensure that the viscosity of each stage is within the ideal range. After each addition stage, test the viscosity of the mixture again to ensure that the adjusted stirring rate and addition amount meet the requirements.

[0116] In addition, for PLC (Programmable Logic Controller) or embedded control system, the above decision logic is implemented by programming. Combined with PID (Proportional-Integral-Derivative) control algorithm, the stirring rate can be adjusted more accurately. The PID controller dynamically adjusts the speed of the stirrer according to the change of real-time viscosity data to ensure that the viscosity remains within the set range.

[0117] S44. When all the raw materials have been added, continue to stir the mixture until the viscosity reaches a preset standard, then stop stirring to obtain a mixed slurry.

[0118] S5. Add the weighed water glass into the mixed slurry, mix them evenly by stirring to obtain the synchronous grouting double-liquid slurry, and detect and verify the actual performance parameters of the synchronous grouting double-liquid slurry.

[0119] In the description of the present invention, the weighed water glass is added to the mixed slurry, and the mixture is stirred and mixed to obtain the synchronous grouting double-liquid slurry, and the actual performance parameters of the synchronous grouting double-liquid slurry are detected, which includes the following steps:

[0120] S51, slowly adding the weighed water glass into the mixer, driving the agitator to stir, and setting the viscosity range of the mixed slurry until a synchronous grouting double-liquid slurry is obtained.

[0121] S52. Extract the finished product of the synchronous grouting double-liquid slurry, detect the actual performance parameters of the synchronous grouting double-liquid slurry through finished product testing, and compare them with the preset process requirements. Evaluate the quality of the finished product of the synchronous grouting double-liquid slurry by calculating the ratio of the actual performance parameters to the performance parameters of the process requirements.

[0122] Specifically, a certain amount of slurry samples are randomly selected from the preparation process of the dual-liquid slurry for simultaneous grouting to ensure the representativeness of the samples.

[0123] The performance parameters that need to be tested include but are not limited to: 1. Slurry consistency: measure the fluidity of the slurry, generally using a rotational viscometer or a cup viscometer. 2. Gelation time: use the inverted cup method to measure the time required for the slurry to change from a flowing state to a solidified state. 3. Water seepage rate: measure the ratio of the volume of the upper water phase to the initial slurry volume after a certain period of time. 4. Slump: use the standard slump test method to measure the fluidity of the slurry. 5. Compressive strength: prepare test blocks under standard conditions and conduct compressive strength tests after a certain curing period.

[0124] Embodiment 1

[0125] Raw material ratio (taking 100g as an example): waste mud 60g, silicate cement 15g, water glass 4g, admixture 1g, shield tail water 8g, blast furnace slag 5g, fly ash 5g, alkali-activated material 2g.

[0126] The performance parameters are as follows

[0127] Slurry consistency: 350mPa·s;

[0128] Gel time: 23 seconds;

[0129] Water seepage rate: 3%;

[0130] Slump: 180mm;

[0131] 28d compressive strength: 1.6MPa.

[0132] Comparative Example 1

[0133] Raw material ratio (taking 100g as an example): 15g bentonite, 15g Portland cement, 4g water glass, and 66g water.

[0134] The performance parameters are as follows

[0135] Slurry consistency: 352mPa·s;

[0136] Gel time: 37 seconds;

[0137] Water seepage rate: 6%;

[0138] Slump: 169mm;

[0139] 28d compressive strength: 1.35MPa.

[0140] Embodiment 2

[0141] Raw material ratio (taking 100g as an example): waste mud 50g, silicate cement 20g, water glass 5g, admixture 1g, shield tail water 10g, blast furnace slag 5g, fly ash 7g, alkali-activated material 2g.

[0142] The performance parameters are as follows

[0143] Slurry consistency: 400mPa·s;

[0144] Gel time: 20 seconds;

[0145] Water seepage rate: 4%;

[0146] Slump: 160mm;

[0147] Compressive strength: 1.9MPa.

[0148] Comparative Example 2

[0149] Raw material ratio (taking 100g as an example): 15g bentonite, 20g Portland cement, 5g water glass, and 60g water.

[0150] The performance parameters are as follows

[0151] Slurry consistency: 405mPa·s;

[0152] Gel time: 35 seconds;

[0153] Water seepage rate: 7%;

[0154] Slump: 150mm;

[0155] Compressive strength: 1.5MPa.

[0156] Embodiment 3

[0157] Raw material ratio (taking 100g as an example): waste mud 70g, silicate cement 10g, water glass 3g, admixture 3g, shield tail water 5g, blast furnace slag 5g, fly ash 2g, alkali-activated material 2g.

[0158] The performance parameters are as follows

[0159] Slurry consistency: 320mPa·s;

[0160] Gel time: 30 seconds;

[0161] Water bleeding rate: 2.5%;

[0162] Slump: 200mm;

[0163] 28d compressive strength: 1.4MPa.

[0164] Comparative Example 3

[0165] Raw material ratio (taking 100g as an example): 20g bentonite, 10g Portland cement, 3g water glass, and 67g water.

[0166] The performance parameters are as follows

[0167] Slurry consistency: 325mPa·s;

[0168] Gel time: 46 seconds;

[0169] Water seepage rate: 5.4%;

[0170] Slump: 184mm;

[0171] 28d compressive strength: 1.25MPa.

[0172] Embodiment 4

[0173] Raw material ratio (taking 100g as an example): waste mud 55g, silicate cement 15g, water glass 6g, admixture 1g, shield tail water 10g, blast furnace slag 7g, fly ash 5g, alkali-activated material 1g.

[0174] The performance parameters are as follows

[0175] Slurry consistency: 360mPa·s;

[0176] Gel time: 18 seconds;

[0177] Water seepage rate: 3.5%;

[0178] Slump: 170mm;

[0179] Compressive strength: 1.65MPa.

[0180] Comparative Example 4

[0181] Raw material ratio (taking 100g as an example): waste mud 17g, silicate cement 15g, water glass 6g, water 62g.

[0182] The performance parameters are as follows

[0183] Slurry consistency: 371mPa·s;

[0184] Gel time: 32 seconds;

[0185] Water seepage rate: 5.9%;

[0186] Slump: 159mm;

[0187] Compressive strength: 1.47MPa.

[0188] By adjusting the ratio of different raw materials, the performance parameters of the dual-liquid slurry for simultaneous grouting can be significantly affected. The appropriate ratio can optimize the slurry's consistency, gel time, water bleeding rate, slump and compressive strength to meet specific engineering requirements. These sample data can provide a reference for subsequent experiments and optimization.

[0189] In summary, by means of the above technical scheme of the present invention, by using shield waste mud and filter press tail water as the main raw materials to prepare a two-component slurry, the characteristics of waste mud and shield tail water are fully utilized, and efficient resource utilization and environmental protection are achieved; the components in the waste mud not only ensure the stability of the slurry, but also promote the hydration process of cement and improve the strength performance of the slurry; at the same time, the components in the shield tail water create favorable conditions for cement hydration, further enhancing the early and late strength of the slurry; thereby significantly reducing environmental pollution, reducing processing costs, and ensuring the high performance of grouting materials. The present invention can be directly applied to the shield engineering site, greatly improving the utilization efficiency of waste; by reducing the use of traditional additives, not only the material cost is reduced, but also the production process is simplified; in addition, the present invention also uses industrial by-products such as fly ash and blast furnace slag, realizing the comprehensive utilization of a variety of waste resources, greatly improving the overall resource utilization efficiency, and showing significant economic and environmental benefits.

[0190] By designing admixtures composed of sodium gluconate, tartaric acid, carboxypropyl cellulose, methyl amyl alcohol and gur gum, the admixtures enable the dual-liquid slurry to have excellent fluidity, stability, pumpability, water retention and anti-segregation properties. At the same time, it can effectively adjust the gelation time and slurry density, ensure good working performance during the grouting process and rapid coagulation and hardening after grouting, thereby more comprehensively meeting the complex engineering needs of shield synchronous grouting.

[0191] Through the application of the back-propagation neural network model, the raw material ratio was optimized, which improved the material performance and reduced the production cost. By dynamically adjusting the amount of waste mud and admixture added, the viscosity and fluidity of the slurry can be effectively controlled to ensure that the quality and performance of the final product meet the construction requirements, reduce problems and losses in the later construction, and thus improve construction efficiency. In addition, real-time monitoring technology is used in the process to ensure precise control of each step, reduce the impact of human factors on the quality of the finished product, improve the uniformity and stability of the slurry, and help improve the safety and reliability of construction.

[0192] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

Claims

1. A dual-liquid slurry for synchronous grouting based on shield waste mud, characterized in that: The synchronous grouting double-liquid slurry comprises the following raw materials in weight percentage: Waste mud 50-70%, Portland cement 10-20%, water glass 3-6%, admixture 1-3%, shield tail water 5-15%, blast furnace slag 5-10%, fly ash 5-15% and alkali-activated material 1-3%.

2. The method of synchronous grouting double-liquid slurry based on shield waste mud according to claim 1 is characterized in that: The water content of the waste mud is 65-90%, and the specific gravity of the waste mud is 1.1-1.3 g / cm 3 ; The Baume degree of the water glass is 35 to 45; The strength of the silicate cement is greater than 42.5; The pH value of the shield tail water is greater than 11; The specific surface area of ​​the blast furnace slag is greater than 500m 2 / kg.

3. The method of synchronous grouting double-liquid slurry based on shield waste mud according to claim 1 is characterized in that: The admixture includes the following raw materials in weight percentage: Sodium gluconate 15-25%, tartaric acid 15-25%, carboxypropyl cellulose 15-25%, methyl amyl alcohol 20-30% and guar gum 20-30%.

4. The method of synchronous grouting double-liquid slurry based on shield waste slurry according to claim 1 is characterized in that: The alkali-activated material includes at least one of metakaolin, sodium silicate, alunite and red mud.

5. A method for preparing a dual-liquid slurry for synchronous grouting based on waste slurry from shield tunneling, used for preparing a dual-liquid slurry for synchronous grouting based on waste slurry from shield tunneling as claimed in any one of claims 1 to 4, characterized in that: The preparation method comprises the following steps: S1. Detect the property data of each raw material through raw material component property detection, and select silicate cement, shield tail water, blast furnace slag, fly ash and alkali-activated material as primary mixed raw materials; S2. Based on the back propagation neural network model, a ratio prediction model is established that relates the raw material ratio to the performance parameters of the dual-liquid slurry for synchronous grouting, and then the ratio prediction model is used to match the optimal ratio of all raw materials that meet the preset process requirements; S3, weighing each raw material according to the optimal ratio, preferentially adding the primary mixed raw material into the mixer in sequence for stirring, and during the stirring process, detecting the viscosity and fluidity of the mixture in real time; S4. Add waste mud and admixtures in stages according to the viscosity of the mixture, and then dynamically adjust by detecting the stirring progress until it is completely mixed to obtain a mixed slurry; S5. Add the weighed water glass into the mixed slurry, mix them evenly by stirring to obtain the synchronous grouting double-liquid slurry, and detect and verify the actual performance parameters of the synchronous grouting double-liquid slurry.

6. The method for preparing dual-liquid slurry for synchronous grouting based on waste slurry from shield tunneling according to claim 5, characterized in that: The method of establishing a ratio prediction model that associates raw material ratios with performance parameters of dual-liquid slurry for synchronous grouting based on a back propagation neural network model, and then using the ratio prediction model to match the optimal ratio of all raw materials that meet preset process requirements includes the following steps: S21. By acquiring historical sample data, the ratio of raw materials under different property data conditions is defined as input factor, the performance parameters of the synchronous grouting dual-liquid slurry are located as output factors, and then data cleaning and normalization are performed. The performance parameters include slurry consistency, gel time, water bleeding rate, slump and compressive strength; S22, defining a hidden layer of a back propagation neural network, and calculating the number of nodes in the hidden layer according to the number of nodes in the input layer and the output layer, and obtaining a matching prediction model by establishing a network topology structure; S23, using historical sample data to establish a data training set and a data test set, perform model training and evaluation on the ratio prediction, and adjust model parameters by cross-validation evaluation; S24, setting the current process requirements for preparing the synchronous grouting dual-liquid slurry, wherein the process requirements include the values ​​of various types of performance parameters in the synchronous grouting dual-liquid slurry and the required amount of the synchronous grouting dual-liquid slurry; S25. The gray wolf algorithm is improved based on the population memory elimination mechanism, and the improved gray wolf algorithm is used in combination with the ratio prediction model to calculate the optimal ratio that meets the process requirements.

7. The method for preparing dual-liquid slurry for synchronous grouting based on waste slurry from shield tunneling according to claim 6, characterized in that: The improved gray wolf algorithm based on population memory elimination mechanism, using the improved gray wolf algorithm in combination with the ratio prediction model to calculate the optimal ratio that meets the process requirements includes the following steps: S251, setting the ratio of raw materials to the individual positions in the wolf pack, setting the performance parameters of the synchronous grouting dual-liquid slurry to the value of the fitness function, then setting the wolf pack size, the maximum number of iterations and the elimination ratio, and initializing the gray wolf population, randomly generating the raw material ratio as the initial solution; S252, calculating the fitness values ​​of all gray wolf individuals in the population, sorting them according to the fitness values, selecting the top three gray wolves with the highest fitness values ​​and marking them as first-level individuals, second-level individuals and third-level individuals respectively, and marking the remaining gray wolves as candidate individuals; S253, by searching for prey and gradually approaching the prey, after surrounding the prey, the positions of all gray wolf individuals are updated according to the positions of the first-level individual, the second-level individual, and the third-level individual; S254, updating the fitness values ​​of all individuals according to the position of each gray wolf individual; S255, replacing the three gray wolves with the highest fitness in the previous iteration with the three gray wolves with the worst fitness after the current iteration; S256. When the error is less than the set error or reaches the maximum number of iterations, exit the loop and output the optimal position and optimal error as the optimal ratio; when the error is greater than the set error or does not reach the maximum number of iterations, return to step S252 and perform an iterative loop.

8. The method for preparing dual-liquid slurry for synchronous grouting based on waste slurry from shield tunneling according to claim 7, characterized in that: The calculation formula for updating the positions of all gray wolf individuals is: D=|2r2·X p (t)-X(t)| In the formula, D represents the distance between the gray wolf and the prey; t represents the current iteration deployment; maxt represents the maximum number of iteration steps; X p Indicates the location of prey; X(t) represents the current position of the gray wolf; X(t+1) represents the position of the gray wolf in the next iteration; r1 and r2 represent random numbers.

9. The method for preparing dual-liquid slurry for synchronous grouting based on waste slurry from shield tunneling according to claim 6, characterized in that: The waste mud and the admixture are added in stages according to the viscosity of the mixture, and then the stirring progress is detected and dynamically adjusted until the mixture is completely mixed to obtain a mixed slurry, which includes the following steps: S41, obtaining the viscosity of the mixture measured by a viscosity detector, recording the initial viscosity before mixing and stirring, and recording in a time series order; S42, setting a staged mixing strategy, adding different doses of waste mud and admixtures in sequence according to the staged mixing strategy, and monitoring the stirring rate of the stirrer in real time; S43, setting viscosity thresholds at different stages, comparing the real-time viscosity with the viscosity threshold values, if the viscosity of the mixture in the current stage exceeds the upper limit of the viscosity threshold, increasing the speed of the agitator, if the viscosity of the mixture in the current stage is lower than the lower limit of the viscosity value, reducing the speed of the agitator, and adding the waste mud and admixture of the next stage in advance; S44. When all the raw materials have been added, continue to stir the mixture until the viscosity reaches a preset standard, then stop stirring to obtain a mixed slurry.

10. The method for preparing dual-liquid slurry for synchronous grouting based on waste slurry from shield tunneling according to claim 6, characterized in that: The weighed water glass is added to the mixed slurry, and the mixture is stirred and mixed to obtain the synchronous grouting double-liquid slurry, and the actual performance parameters of the synchronous grouting double-liquid slurry are detected, which includes the following steps: S51, slowly adding the weighed water glass into the mixer, driving the agitator to stir, and setting the viscosity range of the mixed slurry until a synchronous grouting double-liquid slurry is obtained; S52. Extract the finished product of the synchronous grouting double-liquid slurry, detect the actual performance parameters of the synchronous grouting double-liquid slurry through finished product testing, and compare them with the preset process requirements. Evaluate the quality of the finished product of the synchronous grouting double-liquid slurry by calculating the ratio of the actual performance parameters to the performance parameters of the process requirements.

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