A prefabricated structure sinking and rectifying method based on a substitute model and a flow direction optimization algorithm

Abstract

The application provides a kind of sinking rectification method of fabricated structure based on alternative model and flow optimization algorithm, belongs to engineering construction method technical field, comprising: establishing sinking construction numerical model of fabricated structure, sampling in the feasible region of to be identified variable and substituting into numerical model to solve, then using back propagation neural network method to construct alternative model of numerical model;Install monocular image sensor and stress sensor, and transmit the sensor signal wirelessly to the central control computer during the sinking process of fabricated structure;The central control computer solves the construction parameter optimization model based on the alternative model and the flow optimization algorithm, and sends the optimal construction parameters to the operation console to correct the construction parameters in real time;Repeat the above steps to complete the whole process of fabricated structure sinking rectification.The application provides an intelligent, information-based and efficient solution for fabricated structure sinking construction, avoiding the construction risks caused by human construction parameter setting.

Description

Technical Field

[0001] This invention belongs to the technical field of engineering construction methods, and in particular relates to a method for correcting the sinking of prefabricated structures based on a substitution model and flow direction optimization algorithm. Background Technology

[0002] Currently, large-scale prefabricated underground structures are usually constructed using the open-cut method, but its drawbacks, such as long construction period, large amount of excavated soil, and small working space, are still quite obvious.

[0003] The current technical solution employs a chain cutter excavation method to excavate the soil and rock at the bottom of the structure, with a hanging device assisting in the overall sinking of the prefabricated structure. The main construction process involves first assembling the prefabricated components of each unit of the prefabricated structure on the ground using horizontal and vertical joints. Then, the chain cutter equipment is wrapped around the main structure, and the chain cutter is used to excavate the soil and rock at the bottom of the main structure, allowing the prefabricated structure to sink as a whole under its own gravity. At the same time, the excavated soil and rock are brought to the ground through the upward movement of the chain cutter. This technology has many advantages, such as high construction efficiency, no need for foundation pits, no need for support, and no need for dewatering.

[0004] Although large prefabricated structures are heavy and not easily deflected, the sinking construction conditions are extremely complex. Various factors, such as the vertical force of the steel strand suspension, the speed of the chain cutter movement, and the excavation into uneven strata, can significantly affect the stability of the prefabricated structure during the sinking construction. If the prefabricated structure experiences excessive lateral displacement or serious abnormal posture, it may have a huge impact on the construction quality, efficiency, and even the life and property safety of relevant personnel.

[0005] Currently, the process of correcting the sinking of prefabricated structures mainly relies on on-site construction personnel to periodically extract and analyze monitoring data to determine whether construction parameters need to be adjusted. However, structural sinking is a dynamic process, and relying entirely on manual operation is very likely to cause construction risks due to unreasonable or untimely combinations of construction parameters. Therefore, there is an urgent need for an auxiliary decision-making method that can adjust and optimize construction parameters in real time under different sinking conditions for prefabricated structures to correct the sinking, so as to ensure the safe progress of construction. Summary of the Invention

[0006] The purpose of this invention is to provide a method for correcting the sinking of prefabricated structures based on an alternative model and flow direction optimization algorithm, characterized by the following steps:

[0007] S1: Establish a numerical model for the sinking construction of prefabricated structures, take the parameters that significantly affect the output of the model as variables to be identified and sample them in their feasible domain, substitute them into the numerical model to solve and obtain the sample dataset, and then use the backpropagation neural network method to construct an alternative model of the numerical model.

[0008] S2: Install the monocular image sensor on both sides of the prefabricated structure hanging device, and install the stress sensor at the prestressed inclined bolt position of the prefabricated structure joint;

[0009] S3: Real-time and accurate capture of structural attitude and displacement trend during the sinking process of prefabricated structure, and wireless transmission of the collected sensor signals to the central control computer.

[0010] S4: The central control calculator identifies and processes the received sensor signals, and then uses the flow optimization algorithm combined with the substitution model to solve the construction parameter optimization problem, thereby calculating the optimal range of construction parameters such as the chain cutter rotation speed, the vertical force of the steel strand suspension, and the arc length of the steel strand rotation in real time.

[0011] S5: The central control calculator filters the calculated optimal construction parameter range and sends it to the operation control console of the chain cutter drive device and the steel strand lifting jack. The operation control console changes the voltage and current required for the chain cutter drive and the steel strand lifting jack to load, and corrects the construction parameters in real time.

[0012] S6: Repeat the above process from S3 to S5 until the entire process of correcting the sinking of the prefabricated structure is completed.

[0013] Furthermore, S1 specifically involves: the variables to be identified are selected through sensitivity analysis; an adaptive sampling method is used to sample within their feasible region; the sample dataset obtained by substituting them into the numerical model is the corresponding input-output sample dataset; the alternative model is constructed using the backpropagation neural network method to replace the input-output response relationship of the numerical model, thereby obtaining results similar to numerical computation with lower computational cost and time.

[0014] Furthermore, in S3, monocular image sensors are installed on both sides of the prefabricated structure hanging device. The number of monocular image sensors is set according to the longitudinal length of the prefabricated structure, specifically 6-8 on each side. Stress sensors are installed at the prestressed bolt positions where the joints of the prefabricated structure are inclined. The monocular image sensors and stress sensors achieve real-time data transmission with the central control computer through a wireless data transmitter.

[0015] Furthermore, in S3, the image signal parameters transmitted by the monocular image sensor include the tilt angle, horizontal offset, sinking speed, and acceleration of each segment of the prefabricated structure in the X, Y, and Z directions, and the stress sensor signal parameters include the stress of the prestressed bolts of the prefabricated structure joints.

[0016] Furthermore, in S4, the flow direction optimization algorithm achieves optimal construction parameter decision-making for sensor signal tracking control by simulating the outlet at the lowest height of the runoff basin. Specifically, this includes the following steps:

[0017] S51: The initial parameters of the flow direction optimization algorithm include the number of runoffs m, the number of neighbors n for each runoff, and the neighborhood radius d. First, the initial runoff matrix Runoff is generated. X The rows and columns of the matrix represent the random values ​​of different decision variables for runoff within their upper and lower limits, as expressed by Formula 1:

[0018]

[0019] In Formula 1, p represents the problem dimension of a single runoff;

[0020] S52: For different random decision variables, an alternative model is invoked to solve the problem. The objective function of the construction parameter optimization model is evaluated, and the optimal objective function is regarded as the runoff outlet point, which is expressed by Equation 2. Equation 2 is expressed as follows:

[0021]

[0022]

[0023] In Formula 2: Runoff obj Let E be the objective function, and F, w, v, and σ be decision variables. k This represents the tilt measurement value of segment k in the prefabricated structure. The tilting amount of segment k in the prefabricated structure is replaced by the calculated value of the substitute model, C. k Let D be the tilt angle of segment k. k Let F be the horizontal offset of segment k, w be the vertical force suspending the steel strand, u be the rotation arc length of the steel strand, σ be the rotation speed of the chain cutter, σ be the stress of the joint connecting bolt, and Limit_E be the control value of the deviation variance. L F U These represent the upper and lower limits of the vertical force suspending the steel strand, v l v u These are the upper and lower limits of the chain cutter device's rotation speed, σ l σ u These are the upper and lower limits of stress for the joint connection bolts, respectively.

[0024] S53: Create n neighborhoods with radius d for each runoff in the population, specify the objective function value for each of the n neighborhoods and determine the optimal neighborhood;

[0025] S54: If the objective function value of the best neighborhood is better than the current runoff, proceed to S55; otherwise, proceed to S56.

[0026] S55: Since runoff has a velocity V that is directly related to the slope, the runoff velocity vector V is updated according to Formula 3, and the new runoff position Runoff is generated according to Formula 4. newX(i), Formula 3 is expressed as:

[0027]

[0028] In Formula 3, randn represents a random value from a standard normal distribution, S0 represents the slope vector between the current location of the runoff and its neighborhood, and Runoff... obj(i) and Runofff obj(j) Let i and j represent the target values ​​of the i-th runoff and the j-th neighborhood, respectively; Formula 4 is expressed as:

[0029]

[0030] S56: Update the location of runoff according to Formula 5, which is expressed as:

[0031]

[0032] S57: Calculate the objective function of the new runoff. When the objective function of the new runoff is better than the old objective function, update the objective function and location of the runoff. If the termination control condition is met, return the optimal solution. If the termination control condition is not met, repeat S53 to S56. After multiple iterations, the optimal solution of the prefabricated structure sinking construction parameter optimization model can be obtained.

[0033] Furthermore, the prefabricated structure is assembled from multiple segments. Each segment consists of an upper top plate-side wall module and a lower middle plate-bottom plate module. The joint connection nodes between the upper top plate-side wall module and the lower middle plate-bottom plate module are mortise and tenon structures and are connected by inclined prestressed bolts. The joint joints formed after connection are waterproofed by a combination of joint grouting, elastic sealing gaskets and internal caulking. Multiple segments are assembled into a whole by tensioning.

[0034] Furthermore, the prefabricated structure construction device includes a hanging device and a chain cutter drive device. The hanging device includes steel strands and steel strand lifting jacks. The chain cutter drive device is fixed to the top of the uppermost single segment. The chain cutter structure envelops the main structure of the prefabricated structure and excavates the soil at the bottom of the structure by rotating the chain cutter. While the chain cutter rotates, it brings the excavated soil to the ground, thereby carrying out the sinking construction of the prefabricated structure.

[0035] Furthermore, during the sinking and correction process of the prefabricated structure, when the central control computer determines, based on the collected real-time data, that the prefabricated structure has excessive lateral deviation and serious abnormal posture, the central control computer issues an instruction to the operation console to stop the operation of the chain cutter drive device, steel strand, and steel strand lifting jack.

[0036] Compared with the prior art, the beneficial effects of the present invention are mainly reflected in:

[0037] 1. This invention provides an intelligent, information-based, and highly efficient construction solution by introducing an alternative model and flow direction optimization algorithm to optimize construction parameters and assist decision-making during the sinking and correction process of prefabricated structures, thereby realizing the automated sinking and correction function of prefabricated structures.

[0038] 2. The method provided by this invention greatly improves construction efficiency and effectively avoids construction risks that may be caused by unreasonable or untimely setting of construction parameters. Attached Figure Description

[0039] Figure 1 This is the overall flowchart of the present invention.

[0040] Figure 2 A schematic diagram of the prefabricated subway station structure and construction device provided by the present invention.

[0041] Figure 3 for Figure 2 Enlarged schematic diagram of the joint connection node of the prefabricated subway station structure.

[0042] Figure 4 A three-dimensional schematic diagram of the prefabricated subway station structure and construction device provided by the present invention.

[0043] Figure 5 The finite element calculation cloud diagram of stress and displacement distribution in a single segment of a prefabricated subway station structure provided by this invention.

[0044] Figure 6 The flowchart of the flow direction optimization algorithm provided by this invention.

[0045] In the diagram, 1. Top plate-side wall module; 2. Middle plate-bottom plate module; 3. Joint connection node; 4. Steel strand; 5. Steel strand lifting jack; 6. Monocular image sensor; 7. Chain cutter drive device; 8. Chain cutter structure; 9. Hanging device; 10. Central control calculator; 11. Operation control console; 12. Elastic sealing gasket; 13. Internal caulking; 14. Joint grouting; 15. Prestressed inclined bolt; 16. Stress sensor. Detailed Implementation

[0046] The following will describe in more detail, with reference to the schematic diagram, a prefabricated structure sinking correction method based on the substitution model and flow direction optimization algorithm of the present invention, which illustrates the preferred embodiment of the present invention. It should be understood that those skilled in the art can modify the present invention described herein while still achieving the advantageous effects of the present invention. Therefore, the following description should be understood as being of broad knowledge to those skilled in the art and is not intended to limit the present invention.

[0047] A method for correcting the sinking of prefabricated structures based on an alternative model and flow direction optimization algorithm is used for, for example Figure 2-4 The prefabricated double-layer single-span subway station structure shown is composed of several segments. Each segment consists of an upper top plate-side wall module 1 and a lower middle plate-bottom plate module 2. The joint connection node 3 between the upper and lower modules is in the form of a tenon and groove and is connected by inclined prestressed bolts (hereinafter referred to as prestressed inclined bolts 15). The joint joint is equipped with a combination of joint grouting 14, elastic sealing gasket 12 and internal caulking 13 for waterproofing. Several segments are assembled into a whole by tensioning.

[0048] The prefabricated structure construction device includes a suspension device 9 and a chain cutter drive device 7. The suspension device 9 includes a steel strand 4 and a steel strand lifting jack 5. The steel strand 4 and the steel strand lifting jack 5 adopt a through-type hydraulic lifter, which can realize the overall sinking of the prefabricated structure in a suspended state. The chain cutter drive device 7 is installed on the top of the uppermost single segment. The chain cutter structure 8 encloses the main structure of the prefabricated structure. The rotation of the chain cutter excavates the soil at the bottom of the structure. While the chain cutter is rotating, it brings the excavated soil to the ground, thereby carrying out the sinking construction of the prefabricated structure.

[0049] like Figure 1 As shown, a method for correcting the sinking of prefabricated structures based on an alternative model and flow direction optimization algorithm includes the following steps:

[0050] Step 1: As Figure 5 As shown, a numerical model for the sinking construction of prefabricated structures is established. Parameters that significantly affect the output of the model are taken as variables to be identified and sampled within their feasible region. They are substituted into the numerical model to obtain the sample dataset. Then, the backpropagation neural network method is used to construct an alternative model of the numerical model.

[0051] The variables to be identified are selected through sensitivity analysis; an adaptive sampling method is used to sample within their feasible region; the sample dataset obtained by substituting them into the numerical model is the corresponding input-output sample dataset; the alternative model is constructed through backpropagation neural network method to replace the input-output response relationship of the numerical model, so as to obtain results similar to numerical calculation with less computational cost and time.

[0052] Step 2: Install the monocular image sensor 6 on both sides of the prefabricated structure hanging device 9, and install the stress sensor 16 at the position of the prestressed inclined bolt 15 of the prefabricated structure joint.

[0053] Among them, six to eight monocular image sensors 6 are respectively installed on both sides of the prefabricated structure hanging device 9 according to the longitudinal length of the structure, to realize all-round monitoring of the attitude and displacement trend of the prefabricated structure. The stress sensor 16 is installed at the position of the prestressed inclined bolt 15 of the prefabricated structure joint. The monocular image sensor 6 and the stress sensor 16 realize real-time data transmission with the central control computer 10 through a wireless data transmitter.

[0054] The image signal parameters transmitted by the monocular image sensor 6 include the tilt angle, horizontal offset, sinking speed and acceleration of each segment of the prefabricated structure in the X, Y and Z directions, and the signal parameters of the stress sensor 16 include the stress of the prestressed inclined bolt 15 of the prefabricated structure joint.

[0055] Step 3: During the sinking process of the prefabricated structure, the structural attitude and displacement trend are captured in real time and accurately, and the collected sensor signals are wirelessly transmitted to the central control computer 10.

[0056] Step 4: The central control calculator 10 identifies and organizes the received sensor signals, and then uses the flow direction optimization algorithm combined with the substitution model to solve the construction parameter optimization problem, thereby calculating the optimal construction parameter range in real time, such as the chain cutter rotation speed, the suspension vertical force of the steel strand, and the rotation arc length of the steel strand.

[0057] The core idea of ​​the flow direction optimization algorithm is to simulate the outlet at the lowest height of the runoff basin to achieve optimal construction parameter decision-making for sensor signal tracking control, referencing... Figure 6 Specifically, it includes the following steps:

[0058] Step 4.1: The initial parameters of the flow direction optimization algorithm include the number of runoffs m, the number of neighbors n for each runoff, and the neighborhood radius d. First, the initial runoff matrix Runoff is generated. X Each row and column of the matrix represents the random values ​​of different decision variables for runoff within their upper and lower limits;

[0059]

[0060] In equation (1): p is the problem dimension of a single runoff.

[0061] Step 4.2: Solve using alternative models for different random decision variables, evaluate the objective function of the construction parameter optimization model, and regard the optimal objective function as the runoff outlet point;

[0062]

[0063]

[0064] In equation (2): Runeff objLet E be the objective function, and F, w, v, σ be the decision variables, where E is the objective function. k This represents the tilt measurement value of segment k in the prefabricated structure. The numerical model calculation value of the tilt amount of prefabricated structural segment k, C k Let D be the tilt angle of segment k. k Let F be the horizontal offset of segment k, w be the vertical force suspending the steel strand, u be the rotation arc length of the steel strand, σ be the rotation speed of the chain cutter, σ be the stress of the joint connecting bolt, and Limit_E be the control value of the deviation variance. L F U These represent the upper and lower limits of the vertical force suspending the steel strand, v l u u These are the upper and lower limits of the chain cutter device's rotation speed, σ l σ u These are the upper and lower limits of stress for the connecting bolts of the joint.

[0065] Step 4.3: Create n neighborhoods with radius d for each runoff in the population, assign objective function values ​​to each neighborhood and determine the optimal neighborhood.

[0066] Step 4.4: If the objective function value of the best neighborhood is better than the current runoff, then proceed to step 4.5; otherwise, proceed to step 4.6.

[0067] Step 4.5: Since the runoff has a velocity V and is directly related to the slope, update the runoff velocity vector V according to formula (3), and generate the new runoff position Runoff according to formula (4). newX(i) ;

[0068]

[0069]

[0070] In equations (3) and (4): randn represents a random value from a standard normal distribution, S0 represents the slope vector between the current location of the runoff and its neighborhood, and Runoff... obj(i) and Runofff obj(j) Let i and j represent the target values ​​of the i-th runoff and the j-th neighborhood, respectively.

[0071] Step 4.6: Update the location of the runoff according to formula (5);

[0072]

[0073] Step 4.7: Calculate the objective function of the new runoff. If it is better than the old objective function, update the objective function and location of the runoff. If the termination control condition is met, return the optimal solution. Otherwise, repeat steps 4.3 to 4.6. Repeat this process multiple times to obtain the optimal solution of the prefabricated structure sinking construction parameter optimization model.

[0074] Step 5: The central control calculator 10 filters the calculated optimal construction parameter range and sends it to the operation control console 11 of the chain cutter drive device 7, steel strand 4, and steel strand lifting jack 5. The operation control console 11 changes the voltage and current required for loading the chain cutter drive device 7, steel strand 4, and steel strand lifting jack 5, and corrects the construction parameters in real time.

[0075] Step 6: Repeat steps 3-5 to complete the entire process of correcting the sinking of the prefabricated structure.

[0076] During the sinking and correction process of the above prefabricated structure, if the central control computer 10 determines that the prefabricated structure has excessive lateral displacement and serious abnormal posture based on the collected real-time data, the central control computer 10 issues an instruction to the operation control console 11 to stop the operation of the chain cutter drive device 7, the steel strand 4, and the steel strand lifting jack 5.

[0077] The above are merely preferred embodiments of the present invention and do not constitute any limitation on the present invention. Any equivalent substitutions or modifications made by those skilled in the art to the technical solutions and content disclosed in the present invention without departing from the scope of the present invention shall be deemed to have remained within the scope of protection of the present invention.

Claims

1. A method for correcting the sinking of prefabricated structures based on an alternative model and flow direction optimization algorithm, characterized in that, Includes the following steps: S1: Establish a numerical model for the sinking construction of prefabricated structures, take the parameters that significantly affect the output of the model as variables to be identified and sample them in their feasible domain, substitute them into the numerical model to solve and obtain the sample dataset, and then use the backpropagation neural network method to construct an alternative model of the numerical model. S2: Install the monocular image sensor on both sides of the prefabricated structure hanging device, and install the stress sensor at the prestressed inclined bolt position of the prefabricated structure joint; S3: Real-time and accurate capture of structural attitude and displacement trend during the sinking process of prefabricated structure, and wireless transmission of the collected sensor signals to the central control computer. S4: The central control calculator identifies and processes the received sensor signals, and then uses a flow optimization algorithm combined with a substitution model to solve the construction parameter optimization problem, thereby calculating the optimal range of construction parameters in real time. S5: The central control calculator filters the calculated optimal construction parameter range and sends it to the operation control console of the chain cutter drive device and the steel strand lifting jack. The operation control console changes the voltage and current required for the chain cutter drive and the steel strand lifting jack to load, and corrects the construction parameters in real time. S6: Repeat the above process from S3 to S5 until the entire process of correcting the sinking of the prefabricated structure is completed. S1 specifically involves: the variable to be identified is selected through sensitivity analysis; an adaptive sampling method is used to sample within its feasible region; the sample dataset obtained by substituting it into the numerical model is the corresponding input-output sample dataset; the alternative model is constructed through the backpropagation neural network method to replace the input-output response relationship of the numerical model, thereby reducing the computational cost and time to obtain the computational result; In S3, the monocular image sensor is located on both sides of the prefabricated structure hanging device. The number of monocular image sensors is set according to the longitudinal length of the prefabricated structure, specifically 6-8 on each side. The stress sensor is located at the position of the prestressed bolt at the inclined joint of the prefabricated structure. The monocular image sensor and the stress sensor realize real-time data transmission with the central control computer through a wireless data transmitter. The prefabricated structure is assembled from multiple segments. Each segment consists of a top slab-side wall module and a middle slab-bottom slab module. The joints between the top slab-side wall module and the middle slab-bottom slab module are mortise and tenon joints and are connected by inclined prestressed bolts. The joints formed after connection are waterproofed by a combination of joint grouting, elastic sealing gaskets and internal caulking. Multiple segments are assembled into a whole by tensioning.

2. The prefabricated structure sinking correction method based on the substitution model and flow direction optimization algorithm according to claim 1, characterized in that, In S3, the image signal parameters transmitted by the monocular image sensor include the tilt angle, horizontal offset, sinking speed and acceleration of each segment of the prefabricated structure in the X, Y and Z directions, and the stress sensor signal parameters include the stress of the prestressed bolts of the prefabricated structure joint.

3. The prefabricated structure sinking correction method based on the substitution model and flow direction optimization algorithm according to claim 1, characterized in that, In step S4, the flow direction optimization algorithm achieves optimal construction parameter decision-making for sensor signal tracking control by simulating the outlet at the lowest height of the runoff basin. Specifically, it includes the following steps: S51: Initial parameters for the flow direction optimization algorithm include runoff quantity. m The number of neighborhoods for each runoff n and neighborhood radius d First, generate the initial runoff matrix. Each row and column of the matrix represents the random values ​​of different decision variables for runoff within their upper and lower limits, as expressed by Formula 1, which is: ； In Formula 1, p The problem dimension for a single runoff; S52: For different random decision variables, an alternative model is invoked to solve the problem, the objective function of the construction parameter optimization model is evaluated, and the optimal objective function is regarded as the runoff outlet point, expressed by Formula 2, which is as follows: ； ； In Formula 2: Let be the objective function. All are decision variables, among which For prefabricated structural segments The tilt measurement value, Prefabricated structural segments The tilt amount is used to replace the calculated value of the model. For segments The tilt angle, For segments Horizontal offset, To suspend the vertical force of the steel strand, The arc length of the steel strand during rotation. The chain cutter rotation speed, For the stress of the joint connection bolts, This is the control value for the variance of the skewness. These represent the upper and lower limits of the vertical force suspending the steel strand. These are the upper and lower limits of the chain cutter device's rotation speed. These are the upper and lower limits of stress for the joint connection bolts, respectively. S53: Create a radius of [missing value] for each runoff in the population. d of n Each neighborhood is a n Each neighborhood is assigned an objective function value, and the optimal neighborhood is determined. S54: If the objective function value of the best neighborhood is better than the current runoff, proceed to S55; otherwise, proceed to S56. S55: Because runoff has velocity V Furthermore, it is directly related to the slope, and the runoff velocity vector is updated according to Formula 3. And generate new locations for runoff according to Formula 4. Formula 3 is expressed as follows: ； In formula three, Represents a random value from a standard normal distribution. This represents the slope vector between the current location of the runoff and its neighborhood. and They represent the first The runoff and the first The target value of each neighborhood; Formula 4 is expressed as: ； S56: Update the location of runoff according to Formula 5, which is expressed as: ； S57: Calculate the objective function of the new runoff. When the objective function of the new runoff is better than the old objective function, update the objective function and location of the runoff. If the termination control condition is met, return the optimal solution. If the termination control condition is not met, repeat S53 to S56. After multiple iterations, the optimal solution of the prefabricated structure sinking construction parameter optimization model can be obtained.

4. The prefabricated structure sinking correction method based on the substitution model and flow direction optimization algorithm according to claim 1, characterized in that, The prefabricated structure construction device includes a hanging device and a chain cutter drive device. The hanging device includes steel strands and steel strand lifting jacks. The chain cutter drive device is fixed to the top of the uppermost single segment. The chain cutter structure envelops the main structure of the prefabricated structure and excavates the soil at the bottom of the structure by rotating the chain cutter. While the chain cutter is rotating, it brings the excavated soil to the ground, thereby carrying out the sinking construction of the prefabricated structure.

5. The prefabricated structure sinking correction method based on the substitution model and flow direction optimization algorithm according to claim 1, characterized in that, During the sinking and correction process of the prefabricated structure, when the central control computer determines that the prefabricated structure has excessive lateral displacement and serious abnormal posture based on the collected real-time data, the central control computer issues an instruction to the operation console to stop the operation of the chain cutter drive device, steel strand and steel strand lifting jack.

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