Method for dynamically correcting precast process parameters of concrete structural members based on simulation results

Abstract

The application discloses a method for dynamically correcting prefabrication process parameters of a concrete structural member based on simulation results, which comprises the following steps: reversely optimizing the prefabrication process parameters of the concrete structural member through a set of measured parameters collected from the concrete structure, obtaining the prefabrication process parameters through simulation, and adjusting the threshold process through the parameters after continuous reverse optimization until the prefabrication of the concrete structural member is completed. The dynamic parameter inversion method based on the set of Kalman filters can integrate the measured data of the embedded sensor into the simulation model in real time; the reverse optimization function of the process can automatically adjust the pouring rate, the cooling strategy and other parameters according to the prediction results, so that the closed-loop intelligent decision is formed.

Description

Technical Field

[0001] This invention relates to the field of concrete structural component simulation technology, specifically a method for dynamically correcting the prefabrication process parameters of concrete structural components based on simulation results. Background Technology

[0002] For concrete structures with multiple chambers, such as immersed tunnels, box culverts, and dock floor slabs, the structures are complex. These concrete structures typically undergo process simulation to predict cracking risks and provide early warnings. These warnings include determining the demolding and tensioning times of the concrete structure, ensuring timely demolding and tensioning to prevent cracking.

[0003] For simulation of concrete structures, existing technologies mainly include empirical formula-based methods for estimating thermal temperature rise, independent finite element analysis of hydration heat and temperature fields, and simplified flow simulations. Technological approaches such as temperature sequence coupling simulation have been explored. However, current technologies still have the following shortcomings in achieving low crack rate prefabrication of complex multi-chamber concrete structural components: There is a lag in the interaction between process parameters and simulation models. Adjustable process boundary conditions such as pouring sequence, pouring rate, cooling water pipe layout, and ambient temperature and humidity often deviate from the design in actual construction. Existing simulation methods cannot dynamically embed measured data and adaptively correct model parameters, resulting in a continuous accumulation of deviations between simulation predictions and actual conditions. Process reverse optimization lacks closed-loop data support. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the purpose of this invention is to provide a method for dynamically correcting the prefabrication process parameters of concrete structural components based on simulation results, which can solve the problems described in the background art.

[0005] The technical solution to achieve the purpose of this invention is: a method for dynamically correcting the prefabrication process parameters of concrete structural components based on simulation results, including back-optimizing the prefabrication process parameters of concrete structural components by collecting a set of measured parameters of the concrete structure. The prefabrication process parameters are obtained through simulation, and the threshold process is adjusted by continuously back-optimizing the parameters until the prefabrication of the concrete structural components is completed.

[0006] Furthermore, the method of back-optimizing the prefabrication process parameters of concrete structural components by collecting measured parameters from the concrete structure, wherein the prefabrication process parameters are obtained through simulation, and the threshold process is adjusted by continuously back-optimizing the parameters until the prefabrication of the concrete structural components is completed, includes the following steps: Step 31: Determine the prefabrication process parameters for concrete structural components. These parameters include pouring parameters, temperature control parameters, and environmental parameters.

[0007] Step 32: Collect parameters of the concrete structural components to obtain a set of measured parameters, which includes the temperature, strain, and environmental parameters of the concrete structural components; Step 33: Dynamically correct the prefabrication process parameters based on the ensemble Kalman filter algorithm to obtain the corrected prefabrication process parameters; Step 34: Simulate the prefabrication process of concrete structural components. This prefabrication process includes multiple stages that are carried out sequentially. Before the start of each stage, the prefabrication process parameters for the next stage are optimized based on the current cumulative measured data set. The optimized prefabrication process parameters serve as executable construction instructions, and the concrete structural components are prefabricated according to the construction instructions.

[0008] Further, in step 31, a parameter model is established for the prefabrication process parameters to obtain the prefabrication process parameter model, and the prefabrication process parameters used to determine the prefabrication process boundary are set as input parameters that can be dynamically adjusted on the model.

[0009] Furthermore, in step 32, temperature sensors, strain gauges, and rebar gauges are embedded inside and on the surface of the concrete structure to collect a set of measured parameters.

[0010] Furthermore, in step 32, the temperature, strain, and environmental parameters of the concrete structural components are collected in real time at a sampling frequency of not less than 1 Hz, thereby forming a set of measured data.

[0011] Furthermore, the Kalman filter algorithm is used to dynamically correct the prefabrication process parameters in the prefabrication process parameter model, including the following steps: Step 331: Update the parameters in the prediction and analysis steps of the Kalman filter algorithm according to the following formulas:

[0012] In the formula, This represents the parameter vector to be inverted. This includes the thermal conductivity, specific heat capacity, hydration activation energy, yield stress and plastic viscosity of non-Newtonian fluids, and creep coefficient of concrete. This represents the posterior estimate vector of the parameters to be inverted, obtained after correction with measured data during the k-th parameter update. This represents the posterior estimate vector of the parameters to be inverted, obtained after correction with measured data during the (k-1)th parameter update. This represents the process noise or model perturbation vector during the k-th parameter prediction process. This represents a normal distribution with zero mean and covariance matrix Q. Indicates Kalman gain, This represents the measured temperature and strain vector. Represents the observation matrix. Indicates process noise It follows a normal distribution with zero mean and covariance matrix Q. This represents the error covariance of the prefabrication process parameter model. Specifically, parameters are updated once for each layer of a poured concrete structural component or at a preset time interval.

[0013] Furthermore, the prefabrication process of concrete structural components is simulated through a continuous coupled simulation in a time-step manner, including the use of time-step measurements to sequentially simulate each stage of the prefabrication process.

[0014] Furthermore, the prefabrication process includes four stages in sequence: building filling, hydration heating, cooling shrinkage, and stress release.

[0015] Furthermore, the optimization of the prefabrication process parameters for the next stage based on the currently accumulated measured data set includes: The crack risk index (CRI) is calculated based on temperature T. The CRI characterizes the degree of risk of cracking in a concrete structural member at location x at time t. If the CRI predicted by numerical simulation is ≥0.9, then adjust the subsequent pouring rate, cooling water flow rate and interval time, and recalculate the CRI until the newly calculated CRI meets the safety margin. If the measured peak temperature is less than the peak temperature predicted by numerical simulation, and the difference between the two peak temperatures is ≥5℃, then the pouring rate can be appropriately increased or the amount of cooling water used can be reduced.

[0016] Furthermore, the pouring parameters include pouring sequence, single-layer thickness, pouring rate, and interval time. Temperature control parameters include the spatial arrangement of cooling water pipes, pipe diameter, inlet water temperature and flow rate time history, as well as the insulation layer material, thickness, and laying range; environmental parameters include the time history curves of ambient temperature, relative humidity, and wind speed.

[0017] The beneficial effects of the present invention are as follows: Adaptive correction and reverse optimization of process parameters: The dynamic parameter inversion method based on ensemble Kalman filtering can integrate the measured data of pre-embedded sensors into the simulation model in real time; the process reverse optimization function can automatically adjust parameters such as pouring rate and cooling strategy according to the prediction results, forming a closed-loop intelligent decision. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a preferred embodiment of the method of the present invention; Figure 2 This is a flowchart illustrating step 1; Figure 3This is a flowchart illustrating step 2; Figure 4 This is a flowchart of process 3. Detailed Implementation

[0019] The present invention will be further described below with reference to the accompanying drawings and specific embodiments: like Figures 1-4 As shown, the method for dynamically correcting the prefabrication process parameters of concrete structural components based on simulation results is implemented based on an early warning method for the prefabrication process of concrete structural components based on numerical simulation. This early warning method for the prefabrication process of concrete structural components based on numerical simulation includes the following steps: Step 1: Construct a full-size three-dimensional geometric model for simulating concrete structural components. The full-size three-dimensional geometric model includes multi-chamber constraints and governing equations that couple non-Newtonian flow, heat of hydration, and thermal stress. Solve for the temperature T at any time t in the governing equations of the full-size three-dimensional geometric model.

[0020] It is understandable that the purpose of constructing the above full-size three-dimensional geometric model is to solve the prediction deviation problem caused by the decoupling of concrete flow, temperature and stress sequence, so as to construct a fully coupled driving model that can solve the above three parameters simultaneously.

[0021] For example, a full-size three-dimensional geometric model is constructed to simulate concrete structural members. The full-size three-dimensional geometric model includes multi-chamber constraints and governing equations that couple non-Newtonian flow, heat of hydration, and thermal stress. The temperature T at any time t in the governing equations of the full-size three-dimensional geometric model is solved. The specific implementation process includes the following steps: Step 11: Establish an initial full-size 3D geometric model, which includes partition walls, chamfers, dense steel reinforcement bundles, and prestressed corrugated pipe ducts. The space of the simulated concrete structural components in the full-size 3D geometric model is discretized using a hybrid mesh of eight-node hexahedral elements and tetrahedral elements. Local refinement is also applied to the chamfers, dense steel reinforcement bundles, and prestressed corrugated pipe ducts to obtain the final full-size 3D geometric model.

[0022] Among them, the transition ratio of the localized mesh size is controlled (limited) to within 1.5, that is, the transition ratio of the mesh size is ≤1.5, in order to ensure the analytical accuracy of non-Newtonian flow field and temperature field gradient.

[0023] It is understandable that an eight-node hexahedral element represents a concrete structural member.

[0024] Step 12: Establish the governing equations for coupling non-Newtonian flow, heat of hydration, and thermal stress, as shown in the following formula:

[0025] In the formula, Let represent the gradient operator, u represent the velocity vector, P represent pressure, and t represent time. The apparent viscosity of a non-Newtonian fluid is... This represents the equivalent density of the concrete mixture that forms the concrete structural member. Represents gravitational acceleration. This represents the source of flow resistance, specifically the source of flow resistance within the internal components of a concrete structure. These internal components include the reinforcing bars in densely packed steel bundles and the prestressed corrugated pipe ducts. This indicates the specific heat capacity at constant pressure of concrete. Indicates temperature. Indicates the cooling rate. This represents the equivalent thermal conductivity of concrete. Indicates the degree of hydration. Indicates about and hydration heat release efficiency, Represents the stress tensor. Indicates about t and The time-varying elastic matrix, This represents the total strain tensor. Indicates thermal strain, It indicates creep and change.

[0026] Step 13: Introduce the nonlinear coupling effect of partition walls, chamfers, dense steel reinforcement bundles, and prestressed corrugated pipe ducts on concrete flow obstruction, hydration heat accumulation, uneven heat dissipation, and constraint force concentration. This effect factor serves as a multi-chamber constraint condition. The effect factor includes a flow resistance source term. Corrected thermal conductivity The third type of convection boundary condition and the local displacement boundary condition. The third type of convection boundary condition is used to describe the hydration heat accumulation and uneven heat dissipation characteristics on the inner surface of the prestressed corrugated pipe duct. The local displacement boundary condition is used in thermal stress calculation based on the bond-slip relationship between steel bars and concrete and the abrupt change in stiffness around the duct to reflect the influence of constraint stress concentration.

[0027] Among them, the flow resistance source term The formula is as follows:

[0028] In the formula, This represents the tensor of the drag coefficient, calibrated based on the steel density in a densely packed steel bundle. This represents the drag coefficient tensor calibrated based on the arrangement of the prestressed corrugated pipe ducts. and Used to simulate flow retardation effects.

[0029] Corrected thermal conductivity The formula is as follows:

[0030] In the formula, This indicates that the reference thermal conductivity of the concrete material itself was not taken into account when the reinforcement layout was modified. This represents the reinforcement density correction factor determined based on the volume fraction of reinforcement, reinforcement spacing, reinforcement direction, and reinforcement density.

[0031] Step 14: Using a partitioned strongly coupled iterative algorithm based on the finite element method, the flow subproblem, energy subproblem, and stress subproblem are solved sequentially in each time step, and the convergence is accelerated by a relaxation factor, thereby solving for the temperature T at any time t in the governing equation.

[0032] Understandably, in this step, the governing equations are solved at each time step, and the solution process of the partitioned strongly coupled iterative algorithm based on the finite element method is accelerated to converge and eventually reach convergence by setting a relaxation factor, thereby calculating the temperature T at any time t in the governing equations.

[0033] Step 2: Calculate the cracking risk index (CRI) based on temperature T. The cracking risk index (CRI) characterizes the risk of cracking in the concrete structure at position x at time t. Based on the magnitude of the cracking risk index (CRI), issue early warning measures for the prefabrication process of the concrete structure. The early warning measures include whether demolding and tensioning are currently possible.

[0034] Understandably, the purpose of this step is to establish a dynamic quantification of cracking risk based on temperature history (i.e., temperature changes over time) and to correlate it with early warning measures for prefabrication processes. By analyzing and constructing this correlation, the aim is to solve the problems of real-time quantification of cracking risk and scientific determination of demolding / tensioning timing, and to construct a dynamic safety margin index based on simulated temperature history.

[0035] For example, the crack risk index (CRI) is calculated based on temperature T. The CRI characterizes the risk of cracking in a concrete structural member at location x at time t. Based on the magnitude of the CRI, early warning measures are issued for the prefabrication process of the concrete structural member. These early warning measures include whether demolding and tensioning are currently possible. The specific implementation process includes the following steps: Step 21: Calculate the temperature of each eight-node hexahedral element at any time t from the temperature T. Temperature gradient and cooling rate And the peak temperature time of the concrete structural member was calculated. Maximum temperature difference between inside and outside Maximum temperature difference between inside and outside The location and the cooling rate of each section of the concrete structure. maximum value .

[0036] Step 22: Solve for the tensile stress in the concrete structural member at time t. And shrinkage stress, and calculate the tensile strength of the concrete structural member at time t according to the following formula. and elastic modulus :

[0037]

[0038] In the formula, This indicates the tensile strength of a precast concrete structural component 28 days after its initial fabrication. This represents a coefficient related to the type of cement. This represents the modulus of elasticity of a precast concrete structural component 28 days after its initial fabrication. This represents a coefficient related to the type of cement and curing conditions.

[0039] It is understandable that the above formula is based on a material early mechanical property evolution model for concrete structural members, which can calculate the tensile strength at any time t. and elastic modulus .

[0040] Among them, tensile stress It is obtained by superimposing thermal stress field and shrinkage stress, and shrinkage stress is calculated by equivalent temperature drop method.

[0041] Step 23: Calculate the cracking risk index (CRI) using the following formula. The cracking risk index at position x at time t is denoted as... :

[0042] In the formula, This represents the tensile stress at position x in a concrete structural member at time t. This represents the tensile strength of a concrete structural member at position x at time t.

[0043] when If the value is ≥1.0 (or other preset thresholds), then a crack is determined to have occurred at position x; when... When the value is set to a certain value, it indicates a high risk of cracking at location x of the concrete structural member, meaning location x is in a high-risk warning zone; when... When x is the location of the concrete structural member, it indicates that the risk of cracking is low and that location x is in the safe zone.

[0044] Step 24: Based on the spatial distribution and temporal evolution of the Crack Risk Index (CRI) in concrete structures, generate early warning measures, including: When the CRI of all eight-node hexahedral elements on all sections of a concrete structural member is less than 0.7, and the maximum internal and external temperature difference is... When the temperature drops continuously to below the allowable value specified in the standard, a signal indicating that the concrete structural member can be demolded is issued; otherwise, a warning signal indicating delayed demolding or enhanced thermal insulation curing is issued. Tensioning is permitted when the CRI of the prestressed tensioning anchorage zone of the concrete structural member is <0.85 (other values ​​can also be used) and the strength of the concrete structural member reaches more than 90% of the design strength; otherwise, a recommendation to postpone the tensioning for a specified number of days is issued. An alarm is issued when the cooling rate exceeds the limit, and when the local cooling rate of the concrete structural member exceeds the limit... When the cooling rate is greater than 3℃ / h (this cooling rate threshold can be adjusted according to the actual situation, that is, other values ​​can be used), an alarm will be triggered, and it is recommended to adjust the cooling water flow or cover the insulation layer.

[0045] It is understandable that step 2 enables numerical simulation of concrete structural components, and based on the simulation results, process warnings and early warning measures are generated for the prefabrication of concrete structural components. Performing these steps can solve the first and second problems mentioned in the background section.

[0046] Step 3: Optimize the prefabrication process parameters of concrete structural components by back-optimizing the set of measured parameters collected from the concrete structure. The prefabrication process parameters are obtained through simulation, and the threshold process is adjusted by continuously back-optimizing the parameters until the prefabrication of the concrete structural components is completed.

[0047] Understandably, the purpose of step 3 is to address the third problem mentioned in the background section, in order to solve the problem of lag between process parameters and simulation model interaction, and to achieve dynamic model correction and process reverse optimization based on measured data.

[0048] For example, the method of back-optimizing the prefabrication process parameters of concrete structural components by collecting measured parameters from the concrete structure, wherein the prefabrication process parameters are obtained through simulation, and the threshold process is adjusted by continuously back-optimizing the parameters until the prefabrication of the concrete structural components is completed, includes the following steps: Step 31: Determine the prefabrication process parameters for concrete structural components. These parameters include pouring parameters, temperature control parameters, and environmental parameters.

[0049] For example, the pouring parameters include pouring sequence, single-layer thickness, pouring rate, and interval time. Temperature control parameters include cooling water pipe spatial arrangement, pipe diameter, inlet water temperature and flow rate time history, as well as insulation layer material, thickness and laying range; environmental parameters include time history curves of ambient temperature, relative humidity, and wind speed.

[0050] Understandably, a parameter model can be established for the prefabrication process parameters to obtain the prefabrication process parameter model. The prefabrication process parameters used to determine the boundaries of the prefabrication process can be set as input parameters that can be dynamically adjusted on the model. The input parameters include casting parameters, temperature control parameters, and environmental parameters.

[0051] Step 32: Collect parameters of the concrete structural components to obtain a set of measured parameters, including the temperature, strain, and environmental parameters of the concrete structural components.

[0052] The collection of all measured parameters constitutes the set of measured parameters, which is a very large set that includes all the measured parameters.

[0053] For example, temperature sensors, strain gauges, and rebar gauges can be embedded inside and on the surface of concrete structures to collect measured parameters. Temperature sensors can collect temperature data, strain gauges can collect strain parameters, and rebar gauges can collect strain and stress changes.

[0054] Temperature, strain, and environmental parameters of concrete structural components can be collected in real time at a sampling frequency of not less than 1Hz, thereby forming a set of measured data, which is the set of measured parameters.

[0055] Step 33: Dynamically correct the prefabrication process parameters based on the ensemble Kalman filter algorithm to obtain the corrected prefabrication process parameters.

[0056] Understandably, the purpose of this step is to address the problems of poor noise immunity and unquantified parameter uncertainty in existing technologies. Therefore, the Kalman filter algorithm is specifically proposed for parameter inversion correction.

[0057] For example, the Kalman filter algorithm is used to dynamically correct the prefabrication process parameters of the prefabrication process parameter model, including the following steps: Step 331: Update the parameters in the prediction and analysis steps of the Kalman filter algorithm according to the following formulas:

[0058] In the formula, This represents the parameter vector to be inverted. The parameters include concrete thermal conductivity, specific heat capacity, hydration activation energy, non-Newtonian fluid yield stress, plastic viscosity, and creep coefficient. These parameters form a vector, which is the parameter vector to be inverted. , This represents the posterior estimate vector of the parameters to be inverted, obtained after correction with measured data during the k-th parameter update. This represents the posterior estimate vector of the parameters to be inverted, obtained after correction with measured data during the (k-1)th parameter update. This represents the process noise or model perturbation vector during the k-th parameter prediction process. This represents a normal distribution with zero mean and covariance matrix Q. Indicates Kalman gain, This represents the measured temperature and strain vector. Represents the observation matrix, Indicates process noise It follows a normal distribution with zero mean and covariance matrix Q. This represents the error covariance of the prefabrication process parameter model.

[0059] The parameters can be updated once for each layer of the concrete structure being poured or at preset intervals (e.g., 4 hours). The updated parameters are then used for simulation in subsequent time steps.

[0060] Step 34: Simulate the prefabrication process of concrete structural components. This prefabrication process includes multiple stages that are carried out sequentially. Before the start of each stage, the prefabrication process parameters for the next stage are optimized based on the current cumulative measured data set. The optimized prefabrication process parameters serve as executable construction instructions, and the concrete structural components are prefabricated according to the construction instructions.

[0061] It is understandable that the simulation of the prefabrication process of concrete structural components is a continuous coupled simulation performed in a time-step manner, including the use of time-step measurements to simulate each stage of the prefabrication process in sequence.

[0062] For example, the prefabrication process includes four stages in sequence: building filling, hydration heating, cooling shrinkage, and stress release.

[0063] For example, the step of optimizing the prefabrication process parameters for the next stage based on the currently accumulated measured data set includes: If the numerical simulation predicts a CRI ≥ 0.9 (or other values), then adjust the subsequent pouring rate, cooling water flow rate, and interval time, and recalculate the CRI until the newly calculated CRI meets the safety margin.

[0064] If the measured peak temperature is less than the peak temperature predicted by the numerical simulation, and the difference between the two peak temperatures is ≥5℃ (or other values), that is, the measured peak temperature is more than 5℃ lower than the peak temperature predicted by the numerical simulation, then the pouring rate can be appropriately increased or the amount of cooling water used can be reduced to save time.

[0065] The optimized prefabrication process parameters are output as executable construction instructions to form a closed-loop iteration of "simulation prediction - actual measurement feedback - parameter correction - process adjustment" until the prefabrication of concrete structural components is completed.

[0066] Understandably, the prefabrication process for concrete structural components also includes implementing closed-loop management of process data throughout its entire lifecycle, enabling data management before, during, and after prefabrication. Before prefabrication, a fully coupled simulation is performed based on initial parameters to determine the baseline process scheme. During prefabrication, the model is continuously corrected through dynamic parameter embedding and adaptive correction algorithms, providing real-time crack risk warnings and triggering process adjustments. After prefabrication, the simulation data, measured data, and final process scheme are archived together for subsequent optimization of process design and calibration of prior parameters for similar components.

[0067] The embodiments disclosed in this specification are merely illustrative of one aspect of the invention, and the scope of protection of the invention is not limited to these embodiments. Any other functionally equivalent embodiments fall within the scope of protection of the invention. Those skilled in the art can make various other corresponding changes and modifications based on the technical solutions and concepts described above, and all such changes and modifications should fall within the scope of protection of the claims of this invention.

Claims

1. A method for dynamically correcting the prefabrication process parameters of concrete structural components based on simulation results, characterized in that, This includes back-optimizing the prefabrication process parameters of concrete structural components by collecting a set of measured parameters from the concrete structure. The prefabrication process parameters are obtained through simulation, and the threshold process is adjusted by continuously back-optimizing the parameters until the prefabrication of the concrete structural components is completed.

2. The method for dynamically correcting the prefabrication process parameters of concrete structural members based on simulation results according to claim 1, characterized in that, The process of back-optimizing the prefabrication process parameters of concrete structural components by collecting measured parameters from the concrete structure, where the prefabrication process parameters are obtained through simulation and the threshold process is adjusted by continuously back-optimizing the parameters until the prefabrication of the concrete structural components is completed, includes the following steps: Step 31: Determine the prefabrication process parameters for concrete structural components. These parameters include pouring parameters, temperature control parameters, and environmental parameters. Step 32: Collect parameters of the concrete structural components to obtain a set of measured parameters, which includes the temperature, strain, and environmental parameters of the concrete structural components; Step 33: Dynamically correct the prefabrication process parameters based on the ensemble Kalman filter algorithm to obtain the corrected prefabrication process parameters; Step 34: Simulate the prefabrication process of concrete structural components. This prefabrication process includes multiple stages that are carried out sequentially. Before the start of each stage, the prefabrication process parameters for the next stage are optimized based on the current cumulative measured data set. The optimized prefabrication process parameters serve as executable construction instructions, and the concrete structural components are prefabricated according to the construction instructions.

3. The method for dynamically correcting the prefabrication process parameters of concrete structural members based on simulation results according to claim 2, characterized in that, In step 31, a parameter model is established for the prefabrication process parameters to obtain the prefabrication process parameter model, and the prefabrication process parameters used to determine the prefabrication process boundary are set as input parameters that can be dynamically adjusted on the model.

4. The method for dynamically correcting the prefabrication process parameters of concrete structural members based on simulation results according to claim 2, characterized in that, In step 32, temperature sensors, strain gauges, and rebar gauges are embedded inside and on the surface of the concrete structure to collect a set of measured parameters.

5. The method for dynamically correcting the prefabrication process parameters of concrete structural members based on simulation results according to claim 2, characterized in that, In step 32, the temperature, strain, and environmental parameters of the concrete structural components are collected in real time at a sampling frequency of not less than 1 Hz, thereby forming a set of measured data.

6. The method for dynamically correcting the prefabrication process parameters of concrete structural members based on simulation results according to claim 2, characterized in that, The Kalman filter algorithm is used to dynamically correct the prefabrication process parameters of the prefabrication process parameter model, including the following steps: Step 331: Update the parameters in the prediction and analysis steps of the Kalman filter algorithm according to the following formulas: In the formula, This represents the parameter vector to be inverted. This includes the thermal conductivity, specific heat capacity, hydration activation energy, yield stress and plastic viscosity of non-Newtonian fluids, and creep coefficient of concrete. This represents the posterior estimate vector of the parameters to be inverted, obtained after correction with measured data during the k-th parameter update. This represents the posterior estimate vector of the parameters to be inverted, obtained after correction with measured data during the (k-1)th parameter update. This represents the process noise or model perturbation vector during the k-th parameter prediction process. This represents a normal distribution with zero mean and covariance matrix Q. Indicates Kalman gain, This represents the measured temperature and strain vector. Represents the observation matrix. Indicates process noise It follows a normal distribution with zero mean and covariance matrix Q. This represents the error covariance of the prefabrication process parameter model. Specifically, parameters are updated once for each layer of a poured concrete structural component or at a preset time interval.

7. The method for dynamically correcting the prefabrication process parameters of concrete structural members based on simulation results according to claim 6, characterized in that, The simulation of the prefabrication process of concrete structural components is a continuous coupled simulation performed in a time-step manner, including the use of time-step measurements to simulate each stage of the prefabrication process in sequence.

8. The method for dynamically correcting the prefabrication process parameters of concrete structural members based on simulation results according to claim 7, characterized in that, The prefabrication process includes four stages in sequence: building filling, hydration and heating, cooling and shrinkage, and stress release.

9. The method for dynamically correcting the prefabrication process parameters of concrete structural members based on simulation results according to claim 8, characterized in that, The optimization of prefabrication process parameters for the next stage based on the current cumulative measured data set includes: The crack risk index (CRI) is calculated based on temperature T. The CRI characterizes the degree of risk of cracking in a concrete structural member at location x at time t. If the CRI predicted by numerical simulation is ≥0.9, then adjust the subsequent pouring rate, cooling water flow rate and interval time, and recalculate the CRI until the newly calculated CRI meets the safety margin. If the measured peak temperature is less than the peak temperature predicted by numerical simulation, and the difference between the two peak temperatures is ≥5℃, then the pouring rate can be appropriately increased or the amount of cooling water used can be reduced.

10. The method for dynamically correcting the prefabrication process parameters of concrete structural members based on simulation results according to claim 9, characterized in that, Pouring parameters include pouring sequence, single-layer thickness, pouring rate, and interval time. Temperature control parameters include cooling water pipe spatial arrangement, pipe diameter, inlet water temperature and flow rate time history, as well as insulation layer material, thickness, and laying range; environmental parameters include time history curves of ambient temperature, relative humidity, and wind speed.

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