A mass concrete layered pouring process monitoring and regulation method

Abstract

The application discloses a mass concrete layered pouring process monitoring and regulation method, and belongs to the technical field of mass concrete construction monitoring and regulation. The method acquires temperature time sequence signals of a temperature sensor array in each pouring layer and strain time sequence signals of a fiber strain sensor array; for each completed pouring layer, a layered thermal profile tensor is constructed according to the temperature time sequence, and a layered strain profile tensor is constructed according to the strain time sequence; the thermal profile tensors and the strain profile tensors of all the completed pouring layers are stacked into an overall thermal profile tensor and an overall strain profile tensor along the pouring direction; the overall thermal profile tensor and the overall strain profile tensor are used as boundary constraints to solve a transient heat conduction equation to obtain thermal mechanical potential field gradients of each layer; according to the thermal mechanical potential field gradients, a pseudo-elastic restoring force function value of an upper surface of each layer and a self-densification index of a poured body are calculated; and the current layer potential field gradient and the densification index are used as feedback to calculate a cooling medium flow regulation value and adjust a valve opening degree. The application realizes self-adaptive adjustment of concrete pouring temperature control.

Description

Technical Field

[0001] This invention relates to the field of monitoring and control technology for large-volume concrete construction, specifically a method for monitoring and controlling the layered pouring process of large-volume concrete. Background Technology

[0002] During the layered pouring of large-volume concrete, the temperature and strain fields continuously change as each layer is poured. Differences in temperature gradient, shrinkage strain, and constraint conditions can cause interlayer cracks. Existing technologies typically embed temperature and strain sensors within the pouring layers, acquiring time-series temperature and strain data at each measuring point through a data acquisition system. The flow rate of the cooling medium in the cooling pipes is then controlled based on preset temperature thresholds, internal and external temperature differences, or strain rate limits. Some methods also incorporate finite element simulation, using discrete measuring point data as boundary conditions to invert the temperature distribution of the poured body.

[0003] In existing technologies, data collected by temperature and strain sensors are mostly in the form of single points or discrete points, lacking a means to organize the horizontal temperature and strain distributions as a whole according to a spatial grid and temporal sequence, making it difficult to form a complete description of the thermo-mechanical state of the cast layer. During layered casting, completed layers are stacked one after another, with significant differences in temperature history and strain state between layers. Existing methods cannot characterize the changes in free energy density and gradient distribution of the completed layers from the perspective of thermodynamic potential field. Cooling control strategies often rely on local temperature thresholds or temperature differences at single measuring points, failing to incorporate the compaction state and thermodynamic gradient deviation of the completed layers as feedback quantities, causing cooling pipe adjustments to lag behind the actual thermo-mechanical state changes of the layered cast body. Existing methods lack a process for obtaining the thermodynamic potential field gradient from the overall thermal and strain profiles, and also fail to incorporate the restoring force characteristics and compaction index of the completed layers into the cooling flow feedback, making it difficult to effectively control the temperature gradient deviation and inter-layer state mismatch during layered casting. Summary of the Invention

[0004] To address the problems of existing monitoring data for large-volume concrete layered pouring processes, which mainly rely on isolated measuring points, depend on local temperature thresholds for cooling control, and fail to reflect the thermodynamic state and gradient deviation of completed pouring layers, this invention provides a method for monitoring and controlling the layered pouring process of large-volume concrete. This method utilizes temperature sensor arrays and fiber optic strain sensor arrays embedded in each pouring layer to acquire time-series signals, constructs overall thermal profile tensors and strain profile tensors, solves for the thermodynamic potential gradient, and generates cooling flow feedback through quasi-elastic restoring force function values ​​and self-compacting density index to adjust the opening of regulating valves in the cooling pipes within the current pouring layer.

[0005] To achieve the above objectives, the present invention provides the following technical solution: The present invention provides a method for monitoring and controlling the layered pouring process of large-volume concrete, comprising: acquiring temperature time-series signals collected by temperature sensor arrays embedded in each pouring layer and strain time-series signals collected by fiber optic strain sensor arrays; for each completed pouring layer, constructing a layered thermal profile tensor of the pouring layer based on the temperature time-series signal of the pouring layer, and constructing a layered strain profile tensor of the pouring layer based on the strain time-series signal of the pouring layer; stacking the layered thermal profile tensors of all completed pouring layers along the pouring direction to form an overall thermal profile tensor, and stacking the layered strain profile tensors of all completed pouring layers along the pouring direction to form an overall strain profile tensor; and so on. Using the overall thermal profile tensor and the overall strain profile tensor as boundary constraints, the pre-established transient heat conduction partial differential equations of the cast body are solved to obtain the thermodynamic potential field gradient of each completed cast layer. Based on the thermodynamic potential field gradient of each completed cast layer, the quasi-elastic restoring force function value at the upper surface of each completed cast layer is calculated, and the self-compacting density index of the cast body is calculated based on the quasi-elastic restoring force function value of each completed cast layer. Using the thermodynamic potential field gradient of the current cast layer and the self-compacting density index of the cast body as feedback inputs, the cooling medium flow rate control value of the cooling pipe inside the current cast layer is calculated, and the opening of the regulating valve of the cooling pipe inside the current cast layer is adjusted according to the cooling medium flow rate control value. This method uses the temperature field and strain field as overall boundary constraints, obtains the thermodynamic potential gradient by solving the transient heat conduction partial differential equations, and uses the quasi-elastic restoring force and self-compacting density index as feedback inputs. It can sense the changes in the internal thermodynamic state of concrete in real time during the layered pouring process and perform closed-loop regulation of the cooling pipeline to reduce the risk of cracking of large-volume concrete caused by temperature gradient.

[0006] In one embodiment of the present invention, when constructing the layered thermal profile tensor, the data acquisition and monitoring control system discretizes each completed pouring layer into multiple thermal profile grid cells in the horizontal direction according to a preset spatial sampling interval. For each thermal profile grid cell, the arithmetic mean of the temperature time-series signals of all temperature sensors within the thermal profile grid cell at the same acquisition time is taken as the instantaneous thermal response value of the thermal profile grid cell. The instantaneous thermal response values ​​of all thermal profile grid cells at the same acquisition time are arranged according to their horizontal coordinates to form a two-dimensional thermal response matrix. The two-dimensional thermal response matrices of multiple consecutive acquisition times are stacked in chronological order to form the layered thermal profile tensor of the completed pouring layer. The layered strain profile tensor can be formed using appropriate spatial discretization and temporal stacking methods. Through gridding and temporal stacking processing, discrete sensor time-series signals can be transformed into tensor boundary conditions that can participate in the solution of partial differential equations, while reducing the impact of local sensor abnormal fluctuations on the overall thermal state judgment.

[0007] In one embodiment of the present invention, when solving the transient heat conduction partial differential equations of the cast-in-place body using the overall thermal profile tensor and the overall strain profile tensor as boundary constraints, the values ​​of each element in the overall thermal profile tensor are assigned to the corresponding elements in the boundary temperature constraint matrix; the values ​​of each element in the overall strain profile tensor are assigned to the corresponding elements in the boundary strain constraint matrix; the assigned transient heat conduction partial differential equations of the cast-in-place body are numerically discretized using an alternating direction implicit difference scheme, and the discretized tridiagonal equations are solved using the chasing method to obtain the numerical solutions of the temperature field at each node within each completed cast-in-place layer; based on the numerical solutions of the temperature field at each node, the thermodynamic potential gradient of each completed cast-in-place layer is calculated using the thermoelastic constitutive equation. The alternating direction implicit difference scheme combined with the chasing method can improve the calculation speed while ensuring numerical stability, adapting to the needs of real-time monitoring and control during the layered casting of large-volume concrete.

[0008] Furthermore, when calculating the thermodynamic potential gradient based on the numerical solutions of the temperature field at each node, for each completed pouring layer, the numerical solutions of the temperature field at all nodes within that pouring layer are substituted into the pre-calibrated thermoelastic constitutive equation to calculate the free energy density value at each node. The partial derivatives of the free energy density value at each node within that pouring layer in the vertical direction are calculated using the central difference scheme. These partial derivatives in the vertical direction of all nodes within that pouring layer are then arranged according to their node coordinates to form a three-dimensional gradient matrix, which serves as the thermodynamic potential gradient of that completed pouring layer. This three-dimensional gradient matrix can accurately reflect the gradient of thermodynamic potential changes along the vertical direction within each pouring layer, providing a basis for determining the restoring force state of the upper surface of the pouring layer.

[0009] In one embodiment of the present invention, when calculating the pseudoelastic restoring force function value of each completed pouring layer on its upper surface based on the thermodynamic potential field gradient of each completed pouring layer, for each completed pouring layer, the thermodynamic potential field gradient values ​​of all nodes on the upper surface of the pouring layer are extracted from the thermodynamic potential field gradient of the completed pouring layer; the Frobenius norm of the thermodynamic potential field gradient values ​​of all nodes on the upper surface of the pouring layer is calculated to obtain the gradient comprehensive amplitude of the completed pouring layer; the gradient comprehensive amplitude is subtracted from the gradient comprehensive amplitude of the completed pouring layer at the previous acquisition time to obtain the gradient change rate of the completed pouring layer; the gradient comprehensive amplitude is used as the pseudoelastic modulus factor, the gradient change rate is used as the viscous damping factor, and the product of the pseudoelastic modulus factor and the viscous damping factor is used as the pseudoelastic restoring force function value of the completed pouring layer on its upper surface. By combining the pseudo-elastic modulus factor and the viscous damping factor, the influence of the thermodynamic potential field amplitude and its rate of change on the restoring force of the upper surface of the cast layer can be considered simultaneously, making the evaluation of the restoring force more closely resemble the viscoelastic mechanical behavior of concrete.

[0010] In one embodiment of the present invention, when calculating the self-compacting density index of the cast body based on the pseudo-elastic restoring force function values ​​of each completed pouring layer, the pseudo-elastic restoring force function values ​​of each completed pouring layer are weighted and summed according to the thickness of the completed pouring layer to obtain the weighted restoring force sum of the cast body; the sum of the thicknesses of each completed pouring layer is divided by the total height of the cast body to obtain the height normalization coefficient of the cast body; the weighted restoring force sum is multiplied by the height normalization coefficient to obtain the average restoring force density of the cast body; the ratio of the average restoring force density of the cast body at the current acquisition time to the average restoring force density of the cast body at the initial acquisition time is used as the self-compacting density index of the cast body. This index, through thickness weighting and height normalization processing, can more accurately reflect the overall density change of the cast body under thermodynamic action, providing a stable feedback quantity for cooling control.

[0011] In one embodiment of the present invention, when calculating the cooling medium flow rate control value of the cooling pipes inside the current cast layer, the thermodynamic potential field gradient values ​​of the central region nodes and edge region nodes of the current cast layer are extracted from the thermodynamic potential field gradient of the current cast layer. The average thermodynamic potential field gradient values ​​of the central region nodes are taken to obtain the central gradient mean, and the average thermodynamic potential field gradient values ​​of the edge region nodes are taken to obtain the edge gradient mean. The central gradient mean is subtracted from the edge gradient mean to obtain the intra-layer gradient deviation of the current cast layer. The intra-layer gradient deviation is multiplied by a preset gradient deviation gain coefficient to obtain the first flow rate control component. The self-compacting density index of the cast body is subtracted from the preset density target value to obtain the density deviation. The density deviation is multiplied by a preset density gain coefficient to obtain the second flow rate control component. The first flow rate control component and the second flow rate control component are added to obtain the cooling medium flow rate control value of the cooling pipes inside the current cast layer. By simultaneously introducing the gradient deviation and density deviation within the layer, it is possible to balance the temperature uniformity control of the current pouring layer with the overall density control of the poured body, thereby improving the targeting and stability of the cooling pipeline adjustment.

[0012] In one embodiment of the present invention, when performing numerical discretization of the transient heat conduction partial differential equations of the cast body after assignment using an alternating direction implicit difference scheme, the time step and spatial step of the alternating direction implicit difference scheme are determined as follows: the data acquisition and monitoring control system obtains the sampling interval of the time dimension in the layered thermal profile tensor of the currently completed cast layer, and uses the sampling interval as the initial value of the time step of the alternating direction implicit difference scheme; the horizontal grid spacing and the vertical interlayer spacing in the layered thermal profile tensor of the currently completed cast layer are obtained, and the grid spacing is used as the horizontal spatial step of the alternating direction implicit difference scheme, and the interlayer spacing is used as the vertical spatial step of the alternating direction implicit difference scheme; the squares of all element values ​​of the thermal profile time change rate tensor of the currently completed cast layer are summed and then squared to obtain the thermal profile fluctuation intensity of the currently completed cast layer; the initial value of the time step is scaled according to the thermal profile fluctuation intensity, and the scaled time step value is used as the final time step of the alternating direction implicit difference scheme. The time step can be adaptively adjusted according to the intensity of thermal profile fluctuations, which can improve the time resolution when the thermal state changes drastically and improve the computational efficiency when the thermal state is stable, thus ensuring the accuracy and stability of numerical solutions.

[0013] Preferably, the material's thermal expansion coefficient and elastic modulus parameters in the thermoelastic constitutive equation are obtained from a pre-stored age-parameter relationship curve based on the real-time age of the currently completed pouring layer. By using material parameters that vary with age, the evolution of the early-age thermodynamic properties of concrete can be simulated more accurately, improving the reliability of the calculated thermodynamic potential gradient.

[0014] Preferably, when weighting and summing the pseudo-elastic restoring force function values ​​of each completed pouring layer according to the thickness of the completed pouring layer, the thickness weight of each completed pouring layer is also multiplied by a solidification degree correction coefficient related to the current age of the pouring layer. The solidification degree correction coefficient monotonically increases with age and tends to 1. Introducing the solidification degree correction coefficient can take into account the differences in solidification degree caused by different pouring layers due to different ages, so that the self-compacting density index more reasonably reflects the actual contribution of each layer to the overall density.

[0015] The technical effects and advantages provided by the present invention in the above technical solution are as follows: Each completed pouring layer is constructed into a layered thermal profile tensor based on its temperature time-series signal, and a layered strain profile tensor based on its strain time-series signal. These are then stacked along the pouring direction to form a global thermal profile tensor and a global strain profile tensor. These are then used as boundary constraints to solve the transient heat conduction partial differential equations of the poured body, yielding the thermodynamic potential gradient of each completed pouring layer. This method organizes the horizontally discrete sensor array data into tensors according to spatial grids, time series, and vertical layers, creating continuous constraints on the temperature and strain fields in space and time, rather than relying on isolated measurement points. The resulting thermodynamic potential gradient reflects the changes in free energy density within and between layers, directly characterizing the thermo-mechanical coupling state of the layered poured body. Cooling control can directly utilize the gradient quantities associated with the thermodynamic state. Compared to using only single-point temperature or temperature difference as control, this thermodynamic potential gradient, derived from the global tensor constraints of the temperature and strain fields, more completely reflects the gradient distribution and free energy density changes within the poured layer, reducing the interference of local anomalies on the control process.

[0016] Using the thermodynamic potential gradient of the current cast layer and the self-compacting density index of the cast body as feedback inputs, the cooling medium flow rate control value is calculated and the regulating valve opening is adjusted. Specifically, the quasi-elastic restoring force function value is obtained by multiplying the comprehensive amplitude of the thermodynamic potential gradient by the gradient change rate. The self-compacting density index is obtained by comparing the quasi-elastic restoring force function values ​​of each completed cast layer after thickness weighting and height normalization with the initial average restoring force density. The difference between the mean gradient at the center and the mean gradient at the edge of the current cast layer forms the intra-layer gradient deviation, and the density deviation is obtained by comparing the self-compacting density index with the target value. The cooling medium flow rate control value is jointly determined by the intra-layer gradient deviation and the density deviation, enabling the cooling pipe adjustment to simultaneously respond to the gradient distribution within the current layer and changes in the compaction state of the completed cast layers. Compared to control methods that rely solely on temperature thresholds, this feedback regulation process incorporates the thermodynamic state of the completed cast layers into the current layer's cooling control, which helps reduce temperature gradient concentration and inter-layer state mismatch during layered casting, thus reducing the likelihood of temperature cracks. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0018] Figure 1 This is a flowchart of the monitoring and control method for the layered pouring process of large-volume concrete. Figure 2 It is a flowchart of cooling medium flow rate regulation based on thermodynamic potential gradient and self-compacting density; Figure 3 It is the time-series variation curve of the instantaneous thermal response value of the thermal profile mesh element; Figure 4 It is a curve showing the change of the comprehensive gradient amplitude of each completed pouring layer with the time of data collection; Figure 5 It is the curve showing the change of the current cooling medium flow rate control value of the casting layer, the average gradient value within the layer, and the gradient of the thermodynamic potential field. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] See Figure 1 This invention provides a method for monitoring and controlling the layered pouring process of large-volume concrete, comprising the following steps: Step S1: Obtain the temperature time-series signal collected by the temperature sensor array embedded in each pouring layer and the strain time-series signal collected by the fiber optic strain sensor array. Step S2: For each completed pouring layer, construct the layered thermal profile tensor of the pouring layer based on the temperature time series signal of the pouring layer, and construct the layered strain profile tensor of the pouring layer based on the strain time series signal of the pouring layer. Step S3: Stack the layered thermal profile tensors of all completed pouring layers along the pouring direction to form an overall thermal profile tensor, and stack the layered strain profile tensors of all completed pouring layers along the pouring direction to form an overall strain profile tensor. Step S4: Using the overall thermal profile tensor and the overall strain profile tensor as boundary constraints, solve the pre-established set of transient heat conduction partial differential equations of the cast body to obtain the thermodynamic potential field gradient of each completed cast layer. Step S5: Calculate the pseudo-elastic restoring force function value of each completed pouring layer on its upper surface based on the thermodynamic potential field gradient of each completed pouring layer, and calculate the self-compacting density index of the poured body based on the pseudo-elastic restoring force function value of each completed pouring layer. Step S6: Using the thermodynamic potential gradient of the current pouring layer and the self-compacting density index of the poured body as feedback inputs, calculate the cooling medium flow rate control value of the cooling pipe inside the current pouring layer, and adjust the opening of the regulating valve of the cooling pipe inside the current pouring layer according to the cooling medium flow rate control value.

[0021] In practical implementation, the data acquisition and monitoring control system discretizes each completed pouring layer into several thermal profile grid units in the horizontal direction according to a preset spatial sampling interval. The preset spatial sampling interval is consistent with the horizontal arrangement spacing of the temperature sensor array within the completed pouring layer. Based on the length of the completed pouring layer in the first horizontal direction and the length in the second horizontal direction, and combined with the preset spatial sampling interval, the data acquisition and monitoring control system divides the completed pouring layer into several first-direction intervals along the first horizontal direction and several second-direction intervals along the second horizontal direction. Each first-direction interval and each second-direction interval together define a thermal profile grid unit. The number of the thermal profile grid unit in the first horizontal direction is the sequence number of the first-direction interval, and the number of the thermal profile grid unit in the second horizontal direction is the sequence number of the second-direction interval.

[0022] For each thermal profile grid cell, the data acquisition and monitoring control system acquires the temperature time-series signal samples from all temperature sensors within the range of that thermal profile grid cell at each acquisition time. The system sums the temperature time-series signal samples from all temperature sensors within the thermal profile grid cell at the same acquisition time, divides the sum by the number of temperature sensors within that thermal profile grid cell, and obtains the arithmetic mean, which is used as the instantaneous thermal response value of that thermal profile grid cell. The data acquisition and monitoring control system performs the above arithmetic mean calculation process at each acquisition time, thereby obtaining the instantaneous thermal response value of each thermal profile grid cell at multiple consecutive acquisition times.

[0023] The instantaneous thermal response values ​​of all thermal profile grid cells at the same acquisition time are arranged into a two-dimensional thermal response matrix according to their horizontal coordinates. Specifically, the data acquisition and monitoring control system uses the grid cell numbers in the first horizontal direction as the row indices and the grid cell numbers in the second horizontal direction as the column indices. The data acquisition and monitoring control system then writes the instantaneous thermal response values ​​of the corresponding thermal profile grid cells into the corresponding matrix element positions in the two-dimensional thermal response matrix based on the row and column indices. The row indices of the two-dimensional thermal response matrix are arranged in ascending order according to the coordinates in the first horizontal direction, and the column indices are arranged in ascending order according to the coordinates in the second horizontal direction.

[0024] Two-dimensional thermal response matrices from multiple consecutive acquisition times are stacked sequentially to form a layered thermal profile tensor for the completed pouring layer. During stacking, the data acquisition and monitoring control system uses the two-dimensional thermal response matrix generated at the first acquisition time as the first slice of the layered thermal profile tensor along the time dimension, the two-dimensional thermal response matrix generated at the second acquisition time as the second slice, and so on, until the two-dimensional thermal response matrix generated at the last acquisition time is used as the last slice. The layered thermal profile tensor contains three dimensions: the first horizontal grid number dimension, the second horizontal grid number dimension, and the acquisition time dimension. The data acquisition and monitoring control system stores the layered thermal profile tensor as a multidimensional array for subsequent steps.

[0025] See Figure 3 The figure shows the instantaneous thermal response values ​​of each thermal profile grid cell during continuous data acquisition. The horizontal axis represents the acquisition time in minutes, ranging from 0 to 120 minutes; the vertical axis represents the instantaneous thermal response value in degrees Celsius (°C), with an initial value of approximately 20°C. The curves correspond to different thermal profile grid cells, labeled as (1,1), (1,2), (2,1), (2,2), (3,1), (3,2), (4,1), (4,2), (5,1), and (5,2) respectively.

[0026] Each curve exhibits a typical trend of rising temperature followed by a slow decline. Specifically, the temperature of all grid cells rises rapidly within the first 20 minutes after data acquisition begins, with grid cell (5,2) showing the largest temperature increase, reaching a peak temperature of approximately 38.5°C, representing the highest thermal response intensity. Cells (5,1) and (4,2) follow closely, with peak values ​​of approximately 36.0°C and 35.5°C, respectively. In contrast, the temperature peaks near cells (1,1) and (1,2) are lower, around 27.5°C, indicating spatial differences in the thermal response distribution.

[0027] After the peak temperature appeared, the temperature of each thermal profile grid cell gradually decreased, and the rate of decrease varied. Grid cells near the top edge, such as (5,2) and (5,1), maintained a higher temperature, remaining above approximately 28°C after 120 minutes, while grid cells near the bottom or center, such as (1,1) and (1,2), saw their temperatures drop to approximately 21.5°C to 22.5°C. The overall curve variation reflects the heat conduction and dissipation characteristics at different spatial locations within the large-volume concrete pouring layer.

[0028] In practical implementation, the data acquisition and monitoring control system obtains the overall thermal profile tensor, which contains element values ​​arranged along the grid number dimensions of the first horizontal direction, the grid number dimensions of the second horizontal direction, and the acquisition time dimension. The data acquisition and monitoring control system establishes a boundary temperature constraint matrix. The number of rows in the boundary temperature constraint matrix is ​​the same as the total number of grid numbers in the first horizontal direction of the overall thermal profile tensor, and the number of columns in the boundary temperature constraint matrix is ​​the same as the total number of grid numbers in the second horizontal direction of the overall thermal profile tensor. The data acquisition and monitoring control system assigns each element value in the overall thermal profile tensor to the corresponding element positions in the boundary temperature constraint matrix according to the grid number dimensions of the first horizontal direction and the second horizontal direction. The boundary temperature constraint matrix is ​​used to limit the temperature conditions of the transient heat conduction partial differential equations of the cast body at the boundary of the completed cast layer.

[0029] In practical implementation, the data acquisition and monitoring control system obtains the overall strain profile tensor, which contains element values ​​arranged along the grid number dimensions of the first horizontal direction, the grid number dimensions of the second horizontal direction, and the acquisition time dimension. The data acquisition and monitoring control system establishes a boundary strain constraint matrix. The number of rows in the boundary strain constraint matrix is ​​the same as the total number of grid numbers in the first horizontal direction of the overall strain profile tensor, and the number of columns in the boundary strain constraint matrix is ​​the same as the total number of grid numbers in the second horizontal direction of the overall strain profile tensor. The data acquisition and monitoring control system assigns each element value in the overall strain profile tensor to the corresponding element positions in the boundary strain constraint matrix according to the grid number dimensions of the first horizontal direction and the second horizontal direction. The boundary strain constraint matrix is ​​used to limit the strain conditions of the transient heat conduction partial differential equations of the cast body at the boundary of the completed cast layer.

[0030] In practical implementation, the data acquisition and monitoring control system employs an alternating direction implicit difference scheme to numerically discretize the transient heat conduction partial differential equations of the cast-in-place body after assignment. The transient heat conduction partial differential equations of the cast-in-place body include the first-order partial derivatives of the temperature field with respect to time, the second-order partial derivatives of the temperature field with respect to the first horizontal coordinate, the second-order partial derivatives of the temperature field with respect to the second horizontal coordinate, and the second-order partial derivatives of the temperature field with respect to the vertical coordinate. The alternating direction implicit difference scheme is divided into first-horizontal-direction implicit processing, second-horizontal-direction implicit processing, and vertical-direction implicit processing within each time step. In the first-horizontal-direction implicit processing, the second-order partial derivatives of the temperature field with respect to the first horizontal coordinate are discretized using the unknown temperature value of the current time step, while the second-order partial derivatives of the temperature field with respect to the second horizontal coordinate and the second-order partial derivatives of the temperature field with respect to the vertical coordinate are discretized using the known temperature value of the previous time step, forming the first tridiagonal equation system. In the implicit processing in the second horizontal direction, the second-order partial derivative of the temperature field with respect to the second horizontal direction coordinates is discretized using the unknown temperature value of the current time step. The second-order partial derivative of the temperature field with respect to the first horizontal direction coordinates is discretized using the intermediate temperature value obtained from the implicit processing in the first horizontal direction. The second-order partial derivative of the temperature field with respect to the vertical direction coordinates is discretized using the known temperature value of the previous time step, forming the second tridiagonal equation system. In the implicit processing in the vertical direction, the second-order partial derivative of the temperature field with respect to the vertical direction coordinates is discretized using the unknown temperature value of the current time step. The second-order partial derivatives of the temperature field with respect to the first and second horizontal direction coordinates are discretized using the intermediate temperature values ​​obtained from the implicit processing in the second horizontal direction, forming the third tridiagonal equation system.

[0031] In practice, the data acquisition and monitoring control system solves the first, second, and third tridiagonal equation systems using a chasing method. The chasing method includes a forward elimination process and a backward substitution process. The forward elimination process calculates the elimination coefficients line by line, starting from the first line of the tridiagonal equation system, while the backward substitution process calculates the unknown temperature values ​​line by line, starting from the last line of the tridiagonal equation system. The data acquisition and monitoring control system uses the unknown temperature values ​​obtained from the forward elimination and backward substitution processes as the numerical solutions of the temperature field at each node within each completed pouring layer. The nodes are located at the intersections of the first horizontal grid line, the second horizontal grid line, and the vertical grid line.

[0032] In practice, the data acquisition and monitoring control system substitutes the numerical solutions of the temperature field at each node into the pre-calibrated thermoelastic constitutive equation to obtain the free energy density value of each node. Based on the free energy density value of each node, the data acquisition and monitoring control system calculates the thermodynamic potential gradient of each completed pouring layer. The thermodynamic potential gradient is a three-dimensional gradient matrix formed by arranging the partial derivatives of the free energy density values ​​of each node in the vertical direction according to the node coordinates.

[0033] In practical implementation, the data acquisition and monitoring control system determines the time step and spatial step of the alternating direction implicit difference scheme. The system acquires the sampling interval of the time dimension in the tensor of the layered thermal profile of the currently completed layer, using this interval as the initial value for the time step of the alternating direction implicit difference scheme. The system also acquires the horizontal grid spacing in the tensor of the layered thermal profile of the currently completed layer, using this horizontal grid spacing as the horizontal spatial step of the alternating direction implicit difference scheme. Finally, the system acquires the vertical interlayer spacing in the tensor of the layered thermal profile of the currently completed layer, using this vertical interlayer spacing as the vertical spatial step of the alternating direction implicit difference scheme.

[0034] In practical implementation, the data acquisition and monitoring control system performs a first-order backward difference along the time dimension on the layered thermal profile tensor of the currently completed pouring layer to obtain the thermal profile time-rate-of-change tensor. The time dimension index in the thermal profile time-rate-of-change tensor is... The first horizontal grid number index is The second horizontal grid number index is The element value is the difference between the corresponding element value in the layered thermal profile tensor at the current acquisition time and the corresponding element value in the layered thermal profile tensor at the previous acquisition time, divided by the sampling interval. The data acquisition and monitoring control system squares all element values ​​of the thermal profile time change rate tensor, sums them, and then takes the square root to obtain the thermal profile fluctuation intensity of the currently completed pouring layer. The thermal profile fluctuation intensity is calculated according to the following formula:

[0035] in, Indicates the intensity of thermal profile fluctuations; This represents the total number of time dimension indices in the thermal profile rate of change tensor; This represents the total number of grid number indices in the first horizontal direction of the thermal profile time-varying rate tensor; This represents the total number of grid number indices in the second horizontal direction of the thermal profile time-varying rate tensor; This represents the index of the time dimension in the thermal profile rate of change tensor; This represents the grid number index in the first horizontal direction of the thermal profile time-varying rate tensor; This represents the grid number index in the second horizontal direction of the thermal profile time-varying rate tensor; The index of the time dimension in the thermal profile time-rate-of-change tensor is... The first horizontal grid number index is The second horizontal grid number index is The element value.

[0036] In practice, the data acquisition and monitoring control system scales the initial time step value based on the thermal profile fluctuation intensity. The system divides the initial time step value by the ratio of the thermal profile fluctuation intensity to a preset fluctuation intensity reference value to obtain the scaled time step value. This scaled time step value is then used as the final time step in the alternating direction implicit difference scheme. The preset fluctuation intensity reference value is the thermal profile fluctuation intensity calculated by the data acquisition and monitoring control system at the initial acquisition time based on the thermal profile time-varying rate tensor of the initially completed pouring layer.

[0037] In practice, for each completed pouring layer, the data acquisition and monitoring control system obtains the numerical solutions of the temperature field at all nodes within that layer. Each node corresponds to the intersection of the first horizontal grid line, the second horizontal grid line, and the vertical grid line within the completed pouring layer. The data acquisition and monitoring control system substitutes the numerical solutions of the temperature field at each node into a pre-calibrated thermoelastic constitutive equation. The pre-calibrated thermoelastic constitutive equation represents the functional relationship between the free energy density and the numerical solutions of the temperature field. After substituting the numerical solutions of the temperature field, the data acquisition and monitoring control system calculates the free energy density value of each node using the thermoelastic constitutive equation.

[0038] In practice, the material thermal expansion coefficient and elastic modulus parameters in the thermoelastic constitutive equation are obtained from a pre-stored age-parameter relationship curve based on the real-time age of the currently completed pouring layer. The data acquisition and monitoring control system records the time from the completion time of pouring to the current acquisition time of the currently completed pouring layer, which is taken as the real-time age of the currently completed pouring layer. The data acquisition and monitoring control system reads the pre-stored age-material thermal expansion coefficient relationship curve and age-elastic modulus parameter relationship curve. The data acquisition and monitoring control system uses the ordinate value corresponding to the real-time age of the currently completed pouring layer on the age-material thermal expansion coefficient relationship curve as the material thermal expansion coefficient in the thermoelastic constitutive equation. The data acquisition and monitoring control system uses the ordinate value corresponding to the real-time age of the currently completed pouring layer on the age-elastic modulus parameter relationship curve as the elastic modulus parameter in the thermoelastic constitutive equation. The age-material thermal expansion coefficient relationship curve and the age-elastic modulus parameter relationship curve are calibrated by the data acquisition and monitoring control system through age tests on concrete specimens with the same mix proportion before pouring construction.

[0039] In practical implementation, the data acquisition and monitoring control system calculates the free energy density value of each node based on the numerical solution of the temperature field, the material's thermal expansion coefficient, and the elastic modulus parameters, according to the pre-calibrated thermoelastic constitutive equation. The free energy density value is calculated using the following formula:

[0040] in, The first horizontal grid number is The second horizontal grid number is The vertical grid number is The free energy density value of the nodes; This indicates the grid number of the node in the first horizontal direction; This indicates the grid number of the node in the second horizontal direction; Indicates the grid number of the node in the vertical direction; The vertical grid number is indicated as The elastic modulus parameters of the completed pouring layer at the node; The vertical grid number is indicated as The coefficient of thermal expansion of the material in the completed pouring layer at the node; This represents the temperature difference between the node temperature and the initial reference temperature, not the absolute temperature; when multiplying the parameters, and First multiply to obtain the thermal strain, then square it and multiply by . and .

[0041] In practical implementation, for each completed pouring layer, the data acquisition and monitoring control system uses a central difference scheme to calculate the partial derivative of the free energy density value of each node within that pouring layer in the vertical direction. The system acquires the free energy density values ​​of the nodes above and below the node in the vertical direction. It then subtracts the free energy density value of the node below from the value of the node above, divides the difference by twice the vertical spatial step size, and obtains the partial derivative of the node's free energy density value in the vertical direction. For nodes located on the top surface of the completed pouring layer in the vertical direction, the system uses a forward difference scheme to calculate the partial derivative of the free energy density value in the vertical direction. For nodes located on the bottom surface of the completed pouring layer in the vertical direction, the system uses a backward difference scheme to calculate the partial derivative of the free energy density value in the vertical direction.

[0042] In practice, for each completed pouring layer, the data acquisition and monitoring control system arranges the partial derivatives of all nodes within that layer in the vertical direction into a three-dimensional gradient matrix based on node coordinates. The three-dimensional gradient matrix includes dimensions for the first horizontal grid number, the second horizontal grid number, and the vertical grid number. The index of the first horizontal grid number corresponds to the first horizontal grid number of the node, the index of the second horizontal grid number corresponds to the second horizontal grid number of the node, and the index of the vertical grid number corresponds to the vertical grid number of the node. The data acquisition and monitoring control system writes the partial derivative of each node in the vertical direction into the element positions corresponding to the first horizontal grid number, the second horizontal grid number, and the vertical grid number in the three-dimensional gradient matrix. The data acquisition and monitoring control system uses the arranged three-dimensional gradient matrix as the thermodynamic potential gradient of the completed pouring layer.

[0043] In practice, for each completed pouring layer, the data acquisition and monitoring control system extracts the thermodynamic potential gradient values ​​of all nodes on the upper surface of the completed pouring layer from the thermodynamic potential gradient of that layer. The nodes on the upper surface of the completed pouring layer are those nodes whose vertical grid number equals the top surface grid number. The data acquisition and monitoring control system then constructs a two-dimensional upper surface gradient matrix from the thermodynamic potential gradient values ​​of all nodes on the upper surface of the completed pouring layer according to the order of the first horizontal grid number and the second horizontal grid number.

[0044] In practical implementation, for each completed pouring layer, the data acquisition and monitoring control system calculates the Frobenius norm of the thermodynamic potential field gradient values ​​at all nodes on the upper surface of the completed pouring layer, thus obtaining the comprehensive gradient amplitude of the completed pouring layer. The comprehensive gradient amplitude is calculated according to the following formula:

[0045] in, Indicates the first The gradient comprehensive amplitude of each completed pouring layer; This indicates the number of the layer that has been poured. This indicates the numbering of the nodes on the upper surface of the completed pouring layer in the first horizontal direction; This indicates the numbering of the nodes on the upper surface of the completed pouring layer in the second horizontal direction; Indicates the first The total number of nodes on the upper surface of the completed pouring layer in the first horizontal direction; Indicates the first The total number of nodes on the upper surface of the completed pouring layer in the second horizontal direction; Indicates the first The first horizontal direction number of the upper surface of the completed pouring layer is as follows: The second horizontal direction is numbered as follows The thermodynamic potential gradient value of the node. The data acquisition and monitoring control system will... All of the completed pouring layer on the upper surface Square the result, sum the results, and then take the square root of the sum to get the first square root. The gradient comprehensive amplitude of the completed pouring layer.

[0046] In practical implementation, for each completed pouring layer, the data acquisition and monitoring control system subtracts the comprehensive gradient amplitude of the completed pouring layer from the comprehensive gradient amplitude of the completed pouring layer at the previous acquisition time to obtain the gradient change rate of the completed pouring layer. The data acquisition and monitoring control system obtains the gradient change rate of the completed pouring layer at the previous acquisition time. The gradient comprehensive amplitude of the completed pouring layer will be the current acquisition time of the first layer. The gradient comprehensive amplitude of the completed pouring layer minus the value of the previous data acquisition time. The gradient comprehensive amplitude of the completed pouring layer is obtained to obtain the first... The gradient change rate of the completed pouring layer. For the first acquisition moment, the data acquisition and monitoring control system will... The gradient change rate of each completed pouring layer is set to zero.

[0047] In practical implementation, for each completed pouring layer, the data acquisition and monitoring control system uses the comprehensive gradient amplitude of the completed pouring layer as the pseudo-elastic modulus factor and the gradient change rate of the completed pouring layer as the viscous damping factor. Multiplying the pseudo-elastic modulus factor and the viscous damping factor yields the pseudo-elastic restoring force function value of the completed pouring layer at its upper surface. The pseudoelastic restoring force function value of the completed pouring layer at its upper surface is equal to that of the first... The gradient comprehensive amplitude of the completed pouring layer and the first The product of the gradient change rates of each completed pouring layer.

[0048] In practical implementation, the data acquisition and monitoring control system weights and sums the pseudo-elastic recovery force function values ​​of each completed pouring layer according to the thickness of that layer, obtaining the weighted sum of the recovery forces of the poured body. During the weighted summation, the system acquires the thickness value and the solidification correction coefficient for each completed pouring layer. The solidification correction coefficient for each layer is obtained by looking up a pre-stored age-solidification correction coefficient curve based on the current age of the layer. The solidification correction coefficient ranges from greater than 0 to less than or equal to 1, and monotonically increases with age, tending towards 1. The system then multiplies the thickness value of each completed pouring layer by the solidification correction coefficient to obtain the corrected thickness value. Finally, the system multiplies the pseudo-elastic recovery force function value of each completed pouring layer by the corrected thickness value to obtain the weighted recovery force contribution value of each layer. The data acquisition and monitoring control system sums up the weighted restoring force contribution values ​​of all completed pouring layers to obtain the total weighted restoring force of the poured body.

[0049] In practice, the data acquisition and monitoring control system divides the sum of the thicknesses of each completed pouring layer by the total height of the poured body to obtain the height normalization coefficient of the poured body. The system acquires the thickness value of each completed pouring layer and sums them to obtain the total thickness of the poured body. The system then acquires the total height of the poured body, which is the sum of the thicknesses of all completed pouring layers. Finally, the system divides the sum of the thicknesses of the poured body by the total height of the poured body to obtain the height normalization coefficient. When the sum of the thicknesses of all completed pouring layers equals the total height of the poured body, the height normalization coefficient is 1.

[0050] In practical implementation, the data acquisition and monitoring control system multiplies the weighted sum of the restoring forces of the cast-in-place body by the height normalization coefficient to obtain the average restoring force density of the cast-in-place body. The system then compares the average restoring force density of the cast-in-place body at the current acquisition time with that at the initial acquisition time to obtain the self-compacting density index. The system obtains the average restoring force density of the cast-in-place body at the initial acquisition time, divides the average restoring force density of the cast-in-place body at the current acquisition time by the same value, and uses this ratio as the self-compacting density index. When the average restoring force density of the cast-in-place body at the initial acquisition time is zero, the system sets the self-compacting density index to 1.

[0051] See Figure 4 In the graph, the horizontal axis represents the time of data collection in minutes, ranging from 0 to 200 minutes; the vertical axis represents the gradient comprehensive amplitude, ranging from 0 to 50. Five curves are plotted in the graph, corresponding to the changing trends of the gradient comprehensive amplitude over time for the completed pouring layers 1 to 5.

[0052] The curves generally exhibit a rapid initial rise followed by a period of stabilization, reflecting the evolution of the thermodynamic potential gradient of each completed pouring layer after pouring. Specifically, all curves show a relatively steep upward trend within the first 50 minutes. Among them, the gradient amplitude of completed pouring layer 5 increases the fastest, reaching a maximum of nearly 50; followed by completed pouring layers 4 and 3, with final amplitudes exceeding 40; completed pouring layer 2 is next, approaching 40; and completed pouring layer 1 has the lowest amplitude, around 30.

[0053] After 100 minutes, the growth rate of each curve slowed significantly and stabilized, exhibiting a slow rise or plateau phase, indicating that the thermodynamic potential gradient gradually reached a relatively stable state. A certain hierarchy was maintained between the comprehensive gradient amplitudes of each layer, and as the pouring layer number increased, the overall comprehensive gradient amplitude showed an increasing trend, reflecting the influence of changes in pouring layer thickness and age on the gradient amplitude.

[0054] In specific implementation, please refer to Figure 2 The data acquisition and monitoring control system extracts the thermodynamic potential field gradient values ​​of the central and edge nodes of the current poured layer from the thermodynamic potential field gradient. The thermodynamic potential field gradient of the current poured layer is a three-dimensional gradient matrix, which includes the first horizontal grid number dimension, the second horizontal grid number dimension, and the vertical grid number dimension. The data acquisition and monitoring control system obtains the thermodynamic potential field gradient values ​​of all nodes on the upper surface of the current poured layer. The nodes on the upper surface of the current poured layer are those nodes whose vertical grid number is equal to the top surface grid number.

[0055] In practical implementation, the data acquisition and monitoring control system divides the nodes on the upper surface of the current poured layer into central region nodes and edge region nodes based on the first and second horizontal coordinates of the nodes. A central region node is a node where the absolute value of the difference between the first horizontal coordinate of the current poured layer's upper surface and the first horizontal coordinate of the geometric center of the current poured layer's upper surface is less than or equal to a preset central region radius, and the absolute value of the difference between the second horizontal coordinate of the current poured layer's upper surface and the second horizontal coordinate of the geometric center of the current poured layer's upper surface is less than or equal to the preset central region radius. An edge region node is a node where the absolute value of the difference between the first horizontal coordinate of the current poured layer's upper surface and the first horizontal boundary coordinate of the current poured layer's upper surface is less than or equal to a preset edge region width, or the absolute value of the difference between the second horizontal coordinate of the current poured layer's upper surface and the second horizontal boundary coordinate of the current poured layer's upper surface is less than or equal to a preset edge region width. The preset central region radius and preset edge region width are set by the data acquisition and monitoring control system during the system parameter configuration phase based on the horizontal dimensions of the current poured layer.

[0056] In practice, the data acquisition and monitoring control system sums the thermodynamic potential gradient values ​​of the nodes in the central region, divides the sum by the total number of nodes in the central region, and obtains the mean gradient value of the central region. Similarly, the data acquisition and monitoring control system sums the thermodynamic potential gradient values ​​of the nodes in the edge regions, divides the sum by the total number of nodes in the edge regions, and obtains the mean gradient value of the edge regions.

[0057] In practice, the data acquisition and monitoring control system subtracts the mean gradient at the center from the mean gradient at the edges to obtain the intra-layer gradient deviation of the current poured layer. The intra-layer gradient deviation is the difference between the mean gradient at the center and the mean gradient at the edges.

[0058] In practical implementation, the data acquisition and monitoring control system obtains a preset gradient deviation gain coefficient. The intra-layer gradient deviation of the current pouring layer is multiplied by this preset gradient deviation gain coefficient to obtain the first flow control component. The preset gradient deviation gain coefficient ranges from 0.1 to 10.0 and is set by the data acquisition and monitoring control system during the system parameter configuration phase. The gradient deviation gain coefficient is obtained by determining it through a cooling medium flow rate regulation calibration test based on the response sensitivity of the regulating valve in the cooling pipes within the current pouring layer and the allowable range of the intra-layer gradient deviation of the current pouring layer. The preset gradient deviation gain coefficient is stored as an internal parameter of the data acquisition and monitoring control system.

[0059] In practice, the data acquisition and monitoring control system obtains the self-compacting density index and the preset density target value of the poured concrete. The system subtracts the preset density target value from the self-compacting density index to obtain the density deviation. The preset density target value ranges from 0.8 to 1.2 and is set by the data acquisition and monitoring control system during the system parameter configuration phase. The density target value is obtained based on the density standard value determined during the concrete mix design phase. When the average restoring force density of the poured concrete is zero at the initial acquisition time, the data acquisition and monitoring control system sets the self-compacting density index to 1, and the preset density target value is set to 1.0.

[0060] In practical implementation, the data acquisition and monitoring control system obtains a preset density gain coefficient, multiplies the density deviation by the preset density gain coefficient, and obtains the second flow control component. The preset density gain coefficient ranges from 0.1 to 10.0 and is set by the data acquisition and monitoring control system during the system parameter configuration stage. The density gain coefficient is obtained by determining it through a cooling medium flow rate adjustment calibration test based on the degree of influence of changes in cooling medium flow rate on the self-compacting density index of the cast body. The preset density gain coefficient is stored as an internal parameter of the data acquisition and monitoring control system.

[0061] In practice, the data acquisition and monitoring control system adds the first flow control component and the second flow control component to obtain the cooling medium flow control value of the cooling pipes inside the current casting layer. The cooling medium flow control value is calculated according to the following formula:

[0062] in, This indicates the current cooling medium flow rate control value of the cooling pipes inside the cast layer; the preset gradient deviation gain coefficient. The dimension is [cooling medium flow rate] / [thermodynamic potential gradient], and its value is determined through a cooling medium flow rate adjustment calibration test, with a range of values ​​of [missing value]. ; This represents the mean of the central gradient, with dimensions of . ; This represents the mean of the marginal gradient, with dimensions of . Preset density gain coefficient The dimension of the density index is the same as that of the cooling medium flow rate. Its value is determined through calibration tests based on the influence of changes in the cooling medium flow rate on the density index, and its range is [value missing]. ; The self-compacting density index of the poured concrete is dimensionless. This represents the preset density target value, which is dimensionless.

[0063] In practice, the data acquisition and monitoring control system adjusts the opening of the regulating valve in the cooling pipeline within the current pouring layer based on the cooling medium flow rate control value. The system converts the cooling medium flow rate control value into a regulating valve opening control signal, which can be a current or voltage signal. This signal is then sent to the regulating valve actuator in the cooling pipeline within the current pouring layer. The actuator changes the valve core position according to the control signal, thereby regulating the cooling medium flow rate. When the cooling medium flow rate control value is positive, the regulating valve opening increases; when it is negative, the opening decreases; and when it is zero, the opening remains unchanged.

[0064] See Figure 5 In the graph, the horizontal axis represents the data acquisition time in minutes, the left side of the vertical axis represents the cooling medium flow rate control value in cubic meters per hour, and the right side of the vertical axis represents the thermodynamic potential gradient value. The graph includes three curves: the red solid line represents the cooling medium flow rate control value, the blue dashed line represents the mean central gradient value, and the green dotted line represents the mean edge gradient value.

[0065] As shown in the figure, the cooling medium flow rate regulation value is approximately 20 m³ / h at the beginning, gradually increasing to a peak of approximately 35 m³ / h over time, around 125 minutes. Afterward, the flow rate regulation value shows a downward trend, dropping to a minimum of approximately 15 m³ / h around 250 minutes, before slightly rebounding to approximately 20 m³ / h. This trend corresponds to the process of calculating the cooling medium flow rate regulation value based on the intralayer gradient deviation and the self-compacting density index, where the regulation value is dynamically adjusted according to the change in the average gradient of the thermodynamic potential field.

[0066] The mean gradient values ​​at the center and edges exhibit similar periodic fluctuations. The mean gradient at the center is generally higher than that at the edges, with the peak of the mean gradient at the center occurring between approximately 60 and 100 minutes, reaching a high of around 185, before slowly declining to a low of around 120 and then slowly recovering. The mean gradient at the edges is approximately 21 at the beginning, slowly fluctuating and decreasing over time to a low of around 15, before gradually recovering to around 20. The difference between the mean gradients at the center and edges, i.e., the intralayer gradient deviation, persists throughout the entire process.

[0067] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for monitoring and controlling the layered pouring process of large-volume concrete, characterized in that, include: Acquire the temperature timing signals collected by the temperature sensor array embedded in each pouring layer and the strain timing signals collected by the fiber optic strain sensor array; For each completed pouring layer, construct the layered thermal profile tensor of the pouring layer based on the temperature time series signal of the pouring layer, and construct the layered strain profile tensor of the pouring layer based on the strain time series signal of the pouring layer. All the layered thermal profile tensors of the completed pouring layers are stacked along the pouring direction to form an overall thermal profile tensor, and all the layered strain profile tensors of the completed pouring layers are stacked along the pouring direction to form an overall strain profile tensor. Using the overall thermal profile tensor and the overall strain profile tensor as boundary constraints, the pre-established transient heat conduction partial differential equations of the cast body are solved to obtain the thermodynamic potential field gradient of each completed cast layer. The pseudo-elastic restoring force function value of each completed pouring layer at its upper surface is calculated based on the thermodynamic potential field gradient of each completed pouring layer, and the self-compacting density index of the poured body is calculated based on the pseudo-elastic restoring force function value of each completed pouring layer. Using the thermodynamic potential gradient of the current casting layer and the self-compacting density index of the cast body as feedback inputs, the cooling medium flow rate control value of the cooling pipe inside the current casting layer is calculated, and the opening of the regulating valve of the cooling pipe inside the current casting layer is adjusted according to the cooling medium flow rate control value.

2. The method for monitoring and controlling the layered pouring process of large-volume concrete according to claim 1, characterized in that, The specific implementation method for constructing the layered thermal profile tensor of each completed pouring layer based on the temperature time series signal of that pouring layer is as follows: The data acquisition and monitoring control system discretizes each completed pouring layer into several thermal profile grid units in the horizontal direction according to the preset spatial sampling interval. For each thermal profile grid cell, the arithmetic mean of the temperature time-series signals of all temperature sensors within the thermal profile grid cell at the same acquisition time is taken as the instantaneous thermal response value of the thermal profile grid cell. Arrange the instantaneous thermal response values ​​of all thermal profile grid elements at the same acquisition time into a two-dimensional thermal response matrix according to their horizontal coordinates; The two-dimensional thermal response matrices from multiple consecutive acquisition times are stacked in chronological order to form the layered thermal profile tensor of the completed pouring layer.

3. The method for monitoring and controlling the layered pouring process of large-volume concrete according to claim 1, characterized in that, The specific implementation method for solving the pre-established set of transient heat conduction partial differential equations of the cast-in-place body using the overall thermal profile tensor and the overall strain profile tensor as boundary constraints is as follows: The values ​​of each element in the overall thermal profile tensor are assigned to the corresponding elements in the boundary temperature constraint matrix of the transient heat conduction partial differential equation system of the casting body. The values ​​of each element in the overall strain profile tensor are assigned to the corresponding elements in the boundary strain constraint matrix of the transient heat conduction partial differential equation system of the cast body. The transient heat conduction partial differential equations of the cast body after assignment are numerically discretized by an alternating direction implicit difference scheme, and the discretized tridiagonal equations are solved by the chasing method to obtain the numerical solution of the temperature field at each node in each completed cast layer. Based on the numerical solutions of the temperature field at each node, the thermodynamic potential gradient of each completed pouring layer is calculated using the thermoelastic constitutive equation.

4. The method for monitoring and controlling the layered pouring process of large-volume concrete according to claim 3, characterized in that, The specific implementation method for calculating the thermodynamic potential gradient of each completed pouring layer based on the numerical solution of the temperature field at each node using the thermoelastic constitutive equation is as follows: For each completed pouring layer, the numerical solution of the temperature field of all nodes in the pouring layer is substituted into the pre-calibrated thermoelastic constitutive equation to calculate the free energy density value of each node. For each completed pouring layer, the partial derivatives of the free energy density values ​​of each node in the pouring layer in the vertical direction are calculated using the central difference scheme. For each completed pouring layer, the partial derivatives of all nodes in the vertical direction within the pouring layer are arranged into a three-dimensional gradient matrix according to the node coordinates, which serves as the thermodynamic potential gradient of the completed pouring layer.

5. The method for monitoring and controlling the layered pouring process of large-volume concrete according to claim 1, characterized in that, The specific implementation method for calculating the pseudo-elastic restoring force function value of each completed pouring layer at its upper surface based on the thermodynamic potential field gradient of each completed pouring layer is as follows: For each completed pouring layer, extract the thermodynamic potential gradient values ​​of all nodes on the upper surface of the pouring layer from the thermodynamic potential gradient of the completed pouring layer. For each completed pouring layer, calculate the Frobenius norm of the thermodynamic potential field gradient values ​​of all nodes at the upper surface of the pouring layer, and obtain the comprehensive gradient amplitude of the completed pouring layer. For each completed pouring layer, the gradient comprehensive amplitude of the completed pouring layer is subtracted from the gradient comprehensive amplitude of the completed pouring layer at the previous acquisition time to obtain the gradient change rate of the completed pouring layer. For each completed pouring layer, the gradient comprehensive amplitude of the completed pouring layer is used as the pseudo-elastic modulus factor, the gradient change rate of the completed pouring layer is used as the viscous damping factor, and the product of the pseudo-elastic modulus factor and the viscous damping factor is used as the pseudo-elastic restoring force function value of the completed pouring layer at its upper surface.

6. The method for monitoring and controlling the layered pouring process of large-volume concrete according to claim 5, characterized in that, The specific implementation method for calculating the self-compacting density index of the poured body based on the pseudo-elastic restoring force function value of each completed pouring layer is as follows: The pseudo-elastic restoring force function values ​​of each completed pouring layer are weighted and summed according to the thickness of the completed pouring layer to obtain the weighted restoring force of the poured body; Divide the sum of the thicknesses of each completed pouring layer by the total height of the poured body to obtain the height normalization coefficient of the poured body; Multiply the weighted sum of the restoring forces of the cast-in-place body by the height normalization coefficient of the cast-in-place body to obtain the average restoring force density of the cast-in-place body; The ratio of the average restoring force density of the cast body at the current sampling time to the average restoring force density of the cast body at the initial sampling time is used as the self-compacting density index of the cast body.

7. The method for monitoring and controlling the layered pouring process of large-volume concrete according to claim 1, characterized in that, The specific implementation method for calculating the cooling medium flow rate control value of the cooling pipes inside the current casting layer, using the thermodynamic potential field gradient of the current casting layer and the self-compacting density index of the cast body as feedback inputs, is as follows: Extract the thermodynamic potential gradient values ​​of the central and edge nodes of the current pouring layer from the thermodynamic potential gradient of the current pouring layer. Take the average of the thermodynamic potential gradient values ​​of the central nodes to obtain the central gradient mean, and take the average of the thermodynamic potential gradient values ​​of the edge nodes to obtain the edge gradient mean. Subtracting the mean gradient at the center from the mean gradient at the edge yields the gradient deviation within the current poured layer. Multiply the intralayer gradient deviation of the current pouring layer by the preset gradient deviation gain coefficient to obtain the first flow control component; Subtract the preset density target value from the self-compacting density index of the poured body to obtain the density deviation. Multiply the density deviation by the preset density gain coefficient to obtain the second flow control component. The first flow control component and the second flow control component are added together to obtain the flow control value of the cooling medium in the cooling pipes inside the current casting layer.

8. The method for monitoring and controlling the layered pouring process of large-volume concrete according to claim 3, characterized in that, In the step of numerically discretizing the transient heat conduction partial differential equations of the cast body after assignment using the alternating direction implicit difference scheme, the time step and spatial step of the alternating direction implicit difference scheme are determined as follows: The data acquisition and monitoring control system obtains the sampling interval of the time dimension in the layered thermal profile tensor of the currently completed pouring layer, and uses the sampling interval as the initial value of the time step of the alternating direction implicit difference format. The data acquisition and monitoring control system acquires the horizontal grid spacing and vertical interlayer spacing in the layered thermal profile tensor of the currently completed pouring layer, and uses the grid spacing as the horizontal spatial step size of the alternating direction implicit difference scheme, and the interlayer spacing as the vertical spatial step size of the alternating direction implicit difference scheme. The data acquisition and monitoring control system squares all the element values ​​of the thermal profile time change rate tensor of the currently completed pouring layer, sums them, and then takes the square root to obtain the thermal profile fluctuation intensity of the currently completed pouring layer. The data acquisition and monitoring control system scales the initial value of the time step according to the intensity of the thermal profile fluctuation, and uses the scaled time step value as the final time step of the alternating direction implicit difference scheme.

9. The method for monitoring and controlling the layered pouring process of large-volume concrete according to claim 4, characterized in that, The thermal expansion coefficient and elastic modulus parameters of the material in the thermoelastic constitutive equation are obtained by looking up the pre-stored age-parameter relationship curves based on the real-time age of the currently completed pouring layer.

10. A method for monitoring and controlling the layered pouring process of large-volume concrete according to claim 6, characterized in that, When the pseudo-elastic restoring force function values ​​of each completed pouring layer are weighted and summed according to the thickness of the completed pouring layer, the thickness weight of each completed pouring layer is also multiplied by a solidification degree correction coefficient related to the current age of the pouring layer. The solidification degree correction coefficient increases monotonically with the age and tends to 1.

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