Real-time regulation and control method for pouring temperature field of concrete panel during construction period

Abstract

The invention discloses a real-time regulation and control method for a pouring temperature field of a concrete panel during a construction period. The real-time regulation and control method comprises the following steps: step 1, establishing a temperature-crystallization-crack multi-physics field coupling model; 2, outputting a regulation and control instruction according to the coupling model, and adjusting the temperature field distribution; 3, optimizing a temperature regulation and control strategy based on model iteration; 4, outputting a temperature regulation and control result and generating an intensity state evaluation report; according to the method, the strength of the concrete panel is analyzed through the multi-physical coupling model, the distribution proportion of the cooling water flow and the heating power is dynamically adjusted, and real-time control over the pouring temperature field is achieved; a temperature gradient model is constructed through physical parameters of concrete, ambient temperature fluctuation and an exponential decay function, and the gradient temperature of each level of the concrete panel is predicted and calculated according to the surface temperature of the concrete panel, so that the temperature difference between the levels is controlled, cracks in the concrete panel are reduced, and the quality of the concrete panel is improved. And the strength of the concrete panel is effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of concrete pouring, and in particular to a method for real-time regulation of a pouring temperature field of a concrete panel during construction. BACKGROUND

[0002] In the field of concrete engineering, especially in the construction of mass concrete or concrete panels in high-cold regions, temperature field control is a key link to ensure structural quality and durability. During the pouring process of concrete, a large amount of heat is released due to hydration reactions, leading to uneven internal temperature field distribution, and thus causing problems such as temperature stress, crack propagation, and structural performance degradation. In traditional construction, temperature regulation relies on empirical methods or static models, which are difficult to adapt to complex and variable construction environments, resulting in defects such as early cracks, uneven strength, and insufficient durability in concrete panels, seriously affecting the quality and service life of the project.

[0003] Traditional temperature regulation methods are usually based on empirical formulas or artificial experience, such as rough control by presetting cooling water flow or heating power parameters. However, such methods lack real-time response capability to the dynamic changes of concrete material properties, environmental parameters, and construction, making it difficult to accurately match the temperature requirements at different construction stages. In addition, empirical regulation is easily disturbed by human factors, leading to inconsistent temperature field control, and thus causing crack propagation rate fluctuations or uneven crystal growth rates, affecting the final strength and crack resistance of the concrete.

[0004] Some studies use static thermodynamic models to predict temperature field distribution, but such models usually ignore real-time changes in environmental parameters during construction, making the regulation strategy unable to dynamically adapt to construction conditions. For example, when the ambient temperature drops suddenly or the internal temperature gradient of the concrete changes abruptly, static models cannot adjust the cooling water flow or heating power in time, which may exacerbate crack propagation or reduce crystal growth efficiency, ultimately affecting the structural stability of the concrete.

[0005] The present application dynamically predicts the interaction between crack propagation rate and crystal growth rate through a multi-physical field coupling model, and optimizes the distribution ratio of cooling water flow and heating device power using a genetic algorithm, achieving precise control of crack and crystal regulation and multi-objective optimization. SUMMARY

[0006] The main purpose of the present application is to provide a method for real-time regulation of a pouring temperature field of a concrete panel during construction, to solve the problem in the prior art that temperature regulation relies on empirical methods or static models, which are difficult to adapt to complex and variable construction environments, resulting in defects such as early cracks, uneven strength, and insufficient durability in concrete panels, seriously affecting the quality and service life of the project.

[0007] To achieve the above-mentioned purpose, the present application provides the following technical solutions: The construction period concrete panel pouring temperature field real-time regulation method comprises the following steps: Step one: establish a temperature-crystal-crack multi-physical field coupling model: based on temperature gradient, crystal growth rate, crystal growth direction angle, crystal density and crack connectivity index, a multi-physical field coupling model is constructed; Step two: output control instructions according to the coupling model and adjust the temperature field distribution: through the control instructions output by the coupling model, the temperature field distribution of the concrete panel is dynamically adjusted, and the control instructions include cooling water flow and heating device power; Step three: iteratively optimize the temperature regulation strategy based on the model: the temperature regulation strategy is iteratively optimized based on the numerical interaction relationship between the crack propagation rate and the crystal growth rate, and the dynamic distribution ratio of the cooling water flow and the heating device power is adjusted; Step four: output the temperature regulation result and generate the strength state evaluation report: integrate the temperature regulation result with the crystal state parameters and the crack propagation rate data to generate the concrete panel strength state evaluation report, and the evaluation report contains the strength prediction value and the strength fluctuation rate.

[0008] As a further improvement of the present application, the multi-physical field coupling model in step one describes the numerical interaction relationship between the parameters through a nonlinear regression equation; The nonlinear regression equation is:

[0009] Wherein, represents the temperature gradient, represents the crystal growth rate, represents the crystal growth direction angle, reflecting the anisotropy characteristics of crystal growth, represents the crystal density, represents the crack connectivity index, , , , , represents the model coefficient, represents the crack propagation rate, and represents the crack parameter caused by the temperature gradient ratio, represents the crystal growth at temperature, represents the crystal density at temperature; the model coefficient is calibrated by experimental data to ensure that the temperature gradient and the crack propagation rate are linearly negatively correlated, and the crystal growth rate and the crack propagation rate are nonlinearly positively correlated.

[0010] As a further improvement of the present application, the processing process of the temperature gradient, the crystal growth rate, the crystal growth direction angle, the crystal density and the crack connectivity index: The temperature gradient, crystal growth rate, crystal growth direction angle, crystal density, and crack connectivity index are used to construct the input time series vector. Furthermore, for the actual values ​​at the time series nodes, interpolation is used to fill in the gaps, thus completing the integrity of each parameter at the time series nodes; The interpolation is filled by the mean of each parameter, and the formula for calculating the mean is as follows: ;in, Expressed as mean, This represents the total number of data points for each parameter. Represented as the first of each parameter Data.

[0011] As a further improvement to this application, the steps for generating the control command in step two are as follows: The control command is based on the crack propagation rate output by the model and sets a threshold for the crack propagation rate; The threshold for crack propagation rate is obtained by calculating historical data or under specified conditions. If historical data shows that cracks are prone to propagation at a rate of 0.05 mm / h, then a threshold of 0.05 mm / h is set, and a machine learning model is used to predict the threshold range under the current construction conditions. Calculations were performed under the following conditions: a temperature gradient of 2°C was set, meaning the temperature difference between each gradient in a multi-gradient system was 2°C. At this temperature, the crystal growth rate of the concrete panel was highest, the crystal density was densest, and cracks in the concrete panel were less likely to propagate. In other words, keeping the gradient temperatures within a consistent range would prevent crack formation. The threshold for crack propagation rate was then determined. ; When the crack propagation rate is uncontrollable, the temperature field can be stratified and controlled by jointly optimizing the cooling water flow rate and the power of the heating device.

[0012] As a further improvement of this application, the dynamic allocation ratio in step three is solved by a genetic algorithm to minimize the crack propagation rate and maximize the crystal growth rate, thereby ensuring the multi-objective convergence of the temperature control strategy. The specific steps for dynamically allocating the ratio are as follows: First, based on the experimental data, we obtain the target value for minimizing the crack propagation rate and the target value for maximizing the crystal growth rate. Set constraints: the cooling water flow rate must meet the maximum and minimum values ​​of the equipment; the heating device power must meet the maximum and minimum values ​​of the equipment. The crack propagation rate and the crystal growth rate are correspondingly divided in time sequence, and both are set to 20 time nodes; The node range of the crack propagation rate and the crystal growth rate is converted into a set, and the crack propagation rate set and the crystal growth rate set are correspondingly matched to generate a mapping parameter pair; Then the mapping parameter pair is input into the optimization objective function to calculate the fitness.

[0013] As a further improvement of the present application, the strength prediction value in step four is calculated by a thermo-chemical reaction kinetics model, and the strength fluctuation rate is based on the statistical analysis results of the crystal growth direction angle and the crack network connectivity, ensuring the quantifiability of the evaluation report; Specifically: the fitness, the crystal growth rate, the crystalline density and the crack propagation rate are input into the thermo-chemical reaction kinetics model; The strength fluctuation rate is determined by the consistency of the crystal growth direction angle and the crack propagation direction: If the crystal growth direction is inconsistent with the crack propagation direction, the crack resistance effect of the crystal to the crack is weakened, the crack propagation is intensified, leading to the increase of the crack connectivity, and thus the strength fluctuation rate is increased.

[0014] As a further improvement of the present application, the temperature gradient acquisition step is as follows: A simulation test is set, and a plurality of gradient temperatures of the concrete panel in the thickness direction are collected in the concrete panel of the simulation test; The thickness range of the concrete panel is determined, and the thickness of the concrete panel is calculated as the depth from the surface to the bottom; Sensors are arranged at different depth positions of the concrete panel, including collecting temperature by thermocouple or collecting temperature by infrared thermometer; And at the thickness change of the concrete panel, the temperature is collected every 5cm interval; And the temperature data of the concrete panel is collected every hour interval, and a multidimensional matrix is established by the temperature data, the thickness data and the time data; The multidimensional matrix is input into the temperature gradient model to obtain how the internal temperature of the concrete panel changes with the depth.

[0015] As a further improvement of the present application, the crystal growth rate, the crystal growth direction angle and the crystalline density constitute the crystallization state parameter, and in the pouring process of the concrete panel, the three-dimensional structure information is collected by X-ray computed tomography to generate a crystal diffraction spectrum and calculate the crystallization state parameter; X-ray computed tomography scans the three-dimensional structure information inside the concrete, and identifies the crystal region in the three-dimensional structure through threshold segmentation or deep learning algorithm, obtains the crystal diffraction pattern, and the crystal diffraction pattern extracts the crystal growth rate, the crystal growth direction angle and the crystallization density through the image processing algorithm, wherein the crystallization density is calculated by the crystal volume fraction.

[0016] As a further improvement of the present application, the specific steps of collecting and processing the crystal diffraction pattern are as follows: X-ray computed tomography device deployment: during the pouring process of the concrete panel, the X-ray computed tomography device is used to scan the concrete panel layer by layer, and the two-dimensional projection image in the thickness direction is collected every 2 hours; Image reconstruction algorithm: the two-dimensional projection image is reconstructed into a three-dimensional crystal distribution image through an iterative reconstruction algorithm to ensure the visualization analysis of the crystal structure, and then the crystal region in the three-dimensional structure is identified through threshold segmentation or deep learning algorithm to obtain the crystal diffraction pattern; Crystal growth rate calculation: the crystal diffraction pattern is subjected to integral intensity analysis, and the crystal growth rate is determined by calculating the rate of change of the crystal diffraction peak area with time; Crystal growth direction angle extraction: the crystal diffraction peak direction in the crystal diffraction pattern is identified through an image processing algorithm to calculate the crystal growth direction angle; Crystallization density: the crystallization density is calculated based on the ratio of the integral intensity of the crystal diffraction peak in the crystal diffraction pattern to the volume of the concrete.

[0017] As a further improvement of the present application, the crack connectivity index is obtained by non-contact scanning of the interior of the concrete through X-ray computed tomography technology, and the crack distribution image is obtained, and the crack connectivity parameters including the crack branch number and the crack connectivity index are extracted based on the image processing algorithm.

[0018] The beneficial effects of the present application are as follows: The present application realizes the multi-physical analysis of the strength of the concrete panel by establishing a multi-physical coupling model of temperature, crystallization and cracks, realizes the coordinated control of the temperature field, the crystallization state and the crack propagation during the pouring process of the concrete panel, significantly reduces the crack propagation rate and improves the crystal growth rate, thereby improving the structural stability and durability of the concrete panel; the dynamic distribution ratio is solved by a genetic algorithm, the distribution ratio of the cooling water flow and the heating power is dynamically adjusted, the multi-objective convergence of the crack propagation rate and the crystal growth rate is realized, the strength prediction value is calculated through a thermal-chemical reaction kinetics model, and the strength fluctuation rate is quantified based on the statistical analysis results of the crystal growth direction angle and the crack connectivity, thereby solving the problems of strong subjectivity and lack of quantitative basis in the traditional method of strength evaluation; And by setting the dynamic allocation ratio, for the case that the temperature gradient change in the thickness direction of the concrete panel is small, the minimum crack propagation rate is maintained, and the maximum crystal growth rate is maintained, so that the crystal can grow rapidly, the generation of cracks in the concrete panel caused by temperature difference is inhibited, the strength of the concrete panel is improved, the temperature is higher in the case that the temperature gradient change in the thickness direction of the concrete panel is small, so that the growth efficiency of the crystal is higher, through the rapid growth of the crystal, the generation of cracks in the concrete panel can be inhibited, and the crystal growth direction is maintained with the crack propagation direction, the crack resistance effect of the crystal is enhanced, the crack propagation is hindered, resulting in reduced crack connectivity, thereby reducing the strength fluctuation rate, so that the stability of the concrete panel will not generate a large number of cracks; The present application constructs the temperature gradient in the thickness direction of the concrete panel through test simulation, that is, constructs a temperature gradient model through the physical parameters of the concrete, environmental temperature fluctuation and exponential decay function, so as to facilitate the prediction and calculation of the gradient temperature of each level of the concrete panel according to the surface temperature of the concrete panel during the field pouring process of the concrete panel, and then control the temperature difference between each level, so that the temperature difference between each level is small, which can effectively eliminate the cracks in the concrete panel caused by the temperature difference between the levels, reduce the cracks in the concrete panel, and effectively improve the strength of the concrete panel. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 The figure is a schematic diagram of the step flow of the concrete panel pouring temperature field real-time regulation method during the construction period of the present application; Figure 2 The figure is a schematic diagram of the specific step flow of the dynamic allocation ratio of the concrete panel pouring temperature field real-time regulation method during the construction period of the present application; Figure 3 The figure is a schematic diagram of the temperature gradient acquisition step flow of the concrete panel pouring temperature field real-time regulation method during the construction period of the present application; Figure 4 The figure is a schematic diagram of the specific step flow of the crystal diffraction pattern acquisition and processing of the concrete panel pouring temperature field real-time regulation method during the construction period of the present application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0021] As Figures 1-4As shown, the embodiment provides an embodiment of a construction period concrete panel pouring temperature field real-time regulation method, in the embodiment, Step one: build a temperature gradient model: by detecting the temperature of the concrete panel in the thickness direction, and constructing a temperature gradient model according to the temperature in the thickness direction, the temperature gradient is obtained; By constructing the temperature gradient module, the temperature gradient difference of the interior of the concrete panel can be realized when the temperature of the surface of the concrete panel is detected, which is convenient for subsequent temperature regulation, so that the temperature difference in the concrete panel is small, the high generation rate of crystallization is maintained, and the generation of concrete panel cracks is reduced.

[0022] S101, set up a simulation test, collect multiple gradient temperatures of the concrete panel in the thickness direction in the simulation test concrete panel; S102, determine the thickness range of the concrete panel, and the thickness of the concrete panel is calculated as the depth from the surface to the bottom; S103, arrange sensors at different depth positions of the concrete panel, including collecting temperature by thermocouple or collecting temperature by infrared thermometer; And on the thickness change of the concrete panel, collect temperature every 5cm thickness interval; And collect temperature data every hour for the concrete panel, and establish a multidimensional matrix of temperature data, thickness data and time data; S104, input the multidimensional matrix into the temperature gradient model to obtain how the internal temperature of the concrete panel changes with depth; By setting up experiments to collect the temperature change of the concrete panel, the distribution function of the internal temperature of the concrete panel changing with depth is calculated, that is, the temperature gradient of the concrete panel, which is convenient for control and adjustment to keep the temperature difference of the whole concrete panel small, and will not harm the integrity of the site cast concrete panel, and the temperature data is constructed into a multidimensional matrix, which is convenient for forming time domain parameters, and convenient for subsequent real-time temperature change adjustment control.

[0023] The specific calculation formula is as follows: The temperature distribution is combined with the physical parameters of concrete (such as thermal conductivity , heat flux density , thermal diffusivity , environmental temperature fluctuation and exponential decay function ) to establish a more comprehensive model:

[0024] Among them, a distribution function representing the internal temperature of the concrete panel as a function of depth, a surface temperature of the concrete panel, a heat flux density, representing the heat input per unit area, a thermal conductivity, representing the ability of a material to conduct heat, the thermal conductivity of concrete is in the range of 1.7-2.5 W / (m·K), a thermal diffusivity, representing the speed at which heat spreads in the concrete, calculated by where is the thermal conductivity, is the density, is the specific heat capacity, a depth of the concrete panel, measured from the surface towards the interior, an ambient temperature fluctuation, representing the influence of the external environment on the temperature of the concrete, estimated by environmental temperature monitoring or experimental data; a depth-dependent correction function, an exponential decay function where is the thermal penetration depth, fitted by experimental data or derived based on heat conduction theory; The combination of thermal conductivity , heat flux density , thermal diffusivity , ambient temperature fluctuation and exponential decay function effectively analyzes the influence of heat source intensity, material thermal conductivity and environmental temperature on concrete, facilitating the overall construction of temperature difference changes in the thickness gradient of concrete panels, facilitating subsequent temperature regulation control, and providing strong data support for the growth of crystals and the generation of cracks; and heat flux density can come from external heat sources (such as solar radiation) or internal heat sources (such as cement hydration heat), measured by experiments or calculated by thermodynamic models; thermal conductivity is affected by material composition and humidity, measured by thermal conductivity experiments (such as heat flow meter method, thermal plate method); specific heat capacity represents the heat required to raise the temperature of unit mass of material by unit temperature, the specific heat capacity of concrete is about 880-1000 J / (kg·K), affected by temperature and humidity, measured by experiments or literature data; density represents the mass per unit volume of material, the density of concrete is usually 2300-2500 kg / m 3 , measured by material density test or literature data; ambient temperature fluctuation can be caused by environmental factors such as solar radiation, wind speed, humidity; depth-dependent correction function describes the law that the influence of ambient temperature on the interior of concrete decreases with depth.

[0025] Step two, obtain the crystallization state parameters: during the pouring process of the concrete panel, three-dimensional structure information is collected by X-ray computed tomography to generate a crystal diffraction spectrum and calculate the crystallization state parameters; The crystallization state parameters include crystal growth rate, crystal growth direction angle, and crystalline density; The X-ray computed tomography scans the three-dimensional structure information inside the concrete and identifies the crystal regions in the three-dimensional structure through threshold segmentation or deep learning algorithm to obtain the crystal diffraction spectrum. The crystal diffraction spectrum combines with the image processing algorithm to extract the crystal growth rate, crystal growth direction angle, and crystalline density. The crystalline density is calculated by the crystal volume fraction to ensure the layered quantification of the crystallization state parameters; The X-ray computed tomography scans the concrete panel to obtain the three-dimensional structure information of the concrete panel, effectively identifies the state of the crystal, and does not cause damage to the concrete panel, maintaining the integrity of the concrete panel. The threshold segmentation or deep learning algorithm is used to identify the crystal to obtain the crystal diffraction spectrum, and then the image processing algorithm is used to extract the crystallization state parameters.

[0026] The specific steps of collecting and processing the crystal diffraction spectrum are as follows: S201, X-ray computed tomography device deployment: during the pouring process of the concrete panel, the X-ray computed tomography device is used to scan the concrete panel layer by layer, and two-dimensional projection images in the thickness direction are collected every 2 hours; S202, image reconstruction algorithm: the two-dimensional projection images are reconstructed into three-dimensional crystal distribution images by iterative reconstruction algorithm to ensure the visualization analysis of the crystal structure. The threshold segmentation or deep learning algorithm is used to identify the crystal regions in the three-dimensional structure to obtain the crystal diffraction spectrum; Threshold segmentation: according to the gray value distribution of the three-dimensional crystal distribution image, the gray threshold of the crystal region is set (such as 150-250HU), and the crystal region is extracted by the threshold segmentation algorithm; Deep learning segmentation: a pre-trained U-Net model is used to identify the crystal in the three-dimensional image. The model input is the three-dimensional crystal distribution image, and the output is the binary mask of the crystal region (0 for non-crystal and 1 for crystal); S203, crystal growth rate calculation: the integral intensity analysis is performed on the crystal diffraction spectrum to determine the crystal growth rate by calculating the rate of change of the crystal diffraction peak area over time; First, the crystal diffraction spectrum collected by the X-ray computed tomography device is constructed according to the time sequence; The crystal diffraction spectrum is optimized by fitting and determining the abnormal values of the time sequence crystal data collected by the X-ray computed tomography device; Firstly, the first collected crystal data is taken as the reference data value, and the first and second crystal data are calculated for the first mean value, and then the curves of the first and second crystal data are fitted; If the first mean value is outside the fitting line of the first and second crystal data, it is determined that the second crystal data is abnormal data for rejection; Then the third crystal data is continuously collected, and the second mean value of the first and third crystal data is calculated, and the curve fitting is performed again, and it is determined whether the second mean value is on the fitting line, if the second mean value is on the fitting line, the second mean value is taken as the second crystal data; Then the integral intensity analysis is performed on the fitted crystal diffraction pattern, and the crystal growth rate is determined by calculating the change rate of the crystal diffraction peak area with time; If the diffraction peak area at a certain depth position increases by 10% within 1 hour; The crystal growth rate is calculated as: Wherein, The crystal growth rate is represented as, The diffraction peak area change amount is represented as, The time interval is represented as, The initial diffraction peak area is represented as; S204, crystal growth direction angle extraction: the crystal diffraction peak direction in the crystal diffraction pattern is identified by image processing algorithm, and the crystal growth direction angle is calculated; If the angle between the main direction of the diffraction peak and the thickness direction of the concrete is 30°, the direction angle is 30°, reflecting the anisotropy characteristics of the crystal growth; S205, crystalline density: based on the ratio of the integral intensity of the crystal diffraction peak in the crystal diffraction pattern to the volume of the concrete, the crystalline density is calculated; If the crystal volume integral intensity at a certain depth position is 50000, and the concrete volume is 1000cm 3 , The crystalline density is:

[0027] Wherein, The crystalline density is represented as the ratio of the mass and volume of the crystal in the concrete, reflecting the compactness of the crystallization process, The crystalline density is calculated by the integral intensity of the crystal diffraction peak in the crystal diffraction pattern, reflecting the number or growth degree of the crystal, The volume of the concrete panel at a certain depth layer is represented as, which is determined by three-dimensional scanning or layered volume calculation, The crystal density calibration coefficient is represented as, which is determined by experimental data; ​By fitting the time series crystal data, the abnormal values can be accurately determined, and the fitting state can be determined by interpolation, so as to realize the detection of abnormal values, maintain the smoothness of the fitting line, and calculate the mean value directly after fitting the graph, that is, the calculated mean value is on the curve between the first crystal data and the second crystal data, if not, it is an abnormal value and needs to be removed, and the mean value is used as the removed data for filling, ensuring the integrity of the data and the smoothness of the fitting line, and then the crystal growth rate, crystal growth direction angle and crystallization density are calculated through the crystal diffraction pattern, which can improve the accuracy of the crystal growth rate, crystal growth direction angle and crystallization density, and prevent abnormal data from causing disturbance.

[0028] Step three, the internal part of the concrete is scanned by X-ray computed tomography technology to obtain a crack distribution image, and crack connectivity parameters are extracted based on an image processing algorithm, the crack connectivity parameters including crack branch number and crack connectivity index; The crack distribution image is collected by X-ray computed tomography technology, which can realize non-contact collection, reduce the influence on the concrete panel, and maintain the integrity of the concrete panel, and the crack connectivity parameters are extracted by the image processing algorithm, which is convenient for subsequent calculation and analysis.

[0029] The collection steps of the crack are as follows: S301, X-ray computed tomography device deployment: during the pouring process of the concrete panel, the X-ray computed tomography device is fixed outside the concrete formwork to ensure that the scanning area covers the full depth range in the thickness direction of the panel; the scanning frequency of the X-ray computed tomography device is set to once every 2 hours to ensure real-time monitoring of crack propagation and avoid regulation errors caused by data lag; S302, crack distribution image collection: the X-ray computed tomography device is used to scan the concrete panel in three dimensions to generate a tomographic image in the thickness direction, and the image resolution is not less than 0.1mm / pixel to capture microcracks (such as cracks less than 1mm); a low-dose X-ray source (such as 100kV voltage and 50mA current) is used to reduce the interference of radiation on the concrete material while ensuring that the image quality meets the crack identification requirements; S303, image preprocessing and noise elimination: the collected tomographic image is filtered to eliminate image blur caused by X-ray scattering or device noise; the crack area is separated from the concrete matrix by binary processing (threshold set to 150HU) to ensure the accuracy of crack identification; S304, Crack Branch Number Extraction: Use image processing algorithms (such as watershed segmentation algorithm) to segment the crack image, identify independent branches in the crack network, and count the number of crack branches at each depth layer; If a layer at a certain depth has 3 crack branches, it is marked as a crack characteristic parameter of the layer for subsequent connectivity analysis. S305. Crack Connectivity Index Calculation: Based on the crack branch number and crack length distribution, a graph theory algorithm (such as the minimum spanning tree algorithm) is used to calculate the crack connectivity index. The formula is: ; in, Represented as the first The length of each crack branch Represented as the total length of all cracks. This is expressed as the number of crack branches. Represented as the crack connectivity index; Data is collected at the same time, and image blurring caused by X-ray scattering or equipment noise is eliminated by filtering. Then, the crack area and concrete matrix are separated by binarization to facilitate crack identification. The number of crack branches is then counted, and the connectivity index of the crack is calculated by combining the total length of the crack. The connectivity index is calculated by the minimum spanning tree algorithm to ensure the operability of crack network analysis and avoid the impracticality caused by theoretical derivation. Furthermore, if the crystal density increases, the crack connectivity index decreases due to the crystal filling the pores; if the crystal density decreases, the crack connectivity index may increase due to the obstruction of crystal growth. If the crack connectivity index increases, the crack propagation rate may be accelerated due to the enhanced crack network connectivity; if the crack connectivity index decreases, the crack propagation rate may be reduced due to the fracture network breakdown. Step 4: Establish a temperature-crystallization-crack multiphysics coupling model: Based on temperature gradient, crystal growth rate, crystal growth direction angle, crystal density and crack connectivity index, a multiphysics coupling model is constructed, and the numerical interaction relationship between parameters is described by nonlinear regression equations; The nonlinear regression equation is:

[0030] in, Represented as a temperature gradient, Expressed as crystal growth rate, This is expressed as the crystal growth direction angle, reflecting the anisotropic characteristics of crystal growth. Expressed as crystal density, Represented as the crack connectivity index, , , is expressed as a model coefficient, is expressed as a crack propagation rate, and is expressed as a crack parameter caused by a temperature gradient ratio, is expressed as a crystal growth at a temperature, is expressed as a density of the crystal at a temperature; the model coefficient is calibrated by experimental data to ensure that the temperature gradient is negatively correlated with the crack propagation rate, and the crystal growth rate is positively correlated with the crack propagation rate in a non-linear manner; Only the physical meaning and numerical interaction logic of each parameter in the crack propagation rate formula are discussed to determine the supplementary and limiting nature of the technical features, ensure a progressive relationship with the model construction, and reflect the scientific correlation between parameters through specific formulas and experimental calibration, ensuring the hierarchical quantification of crack propagation rate and avoiding local control failure due to overall parameters; is a contribution coefficient of the temperature gradient to the crack propagation rate, and its numerical range is determined by experimental calibration, set to 0.5-1.2; is an inhibition coefficient of the crystal growth rate to the crack propagation rate, and its numerical range is determined by experimental calibration, set to -0.8 to -1.5; is a correction coefficient of the crystal growth direction angle to the crack propagation rate, and its numerical range is determined by experimental calibration, set to -0.3 to -0.7; is a correction coefficient of the crystal density to the crack propagation rate, and its numerical range is determined by experimental calibration, set to -0.2 to -0.4; is a constant term, representing the comprehensive influence of other environmental factors (such as humidity, types of additives) on the crack propagation rate.

[0031] The processing process of the above parameters is as follows: The temperature gradient, crystal growth rate, crystal growth direction angle, crystal density, and crack connectivity index are constructed into input time series vectors in time sequence; And for the actual numerical value on the time sequence node, interpolation is used to fill in the completeness of each parameter on the time sequence node; The interpolation is filled in by the mean value of each parameter, and the calculation formula of the mean value is as follows: ; wherein, is expressed as a mean value, is expressed as the total number of data of each parameter, is expressed as the th data of each parameter; ​​The correspondence of the digital nodes of each parameter in the time domain is determined by interpolation filling, and the integrity of each parameter can be determined.

[0032] Step five: output control instructions according to the coupling model and adjust the temperature field distribution: through the control instructions output by the coupling model, the temperature field distribution of the concrete panel is dynamically adjusted, and the control instructions include cooling water flow and heating device power; By adjusting the temperature field distribution of the concrete panel, and regulating the temperature field distribution through the cooling water flow and the heating device power, it is convenient to realize the control of the temperature difference between the thickness levels of the concrete panel, so that the concrete panel basically maintains the same temperature in thickness; The steps of generating the control instructions are as follows: The control instructions are based on the crack propagation rate output by the model, and the threshold value of the crack propagation rate is set; The threshold value of the crack propagation rate is calculated through historical data values or in a set environment; If the historical data shows that the crack propagation rate is easy to expand at 0.05mm / h, the threshold value is set to 0.05mm / h, and the threshold value range under the current construction condition is predicted through the machine learning model; The calculation in the set environment: that is, the temperature gradient is set to 2℃, that is, the temperature difference between each gradient of the multi-gradient temperature is 2℃, at this time the crystal growth rate of the concrete panel is the highest and the crystal density is the most dense, and the crack in the concrete panel is not easy to expand, that is, the gradient temperature is kept in the same range, and the crack will not be caused;

[0033] And keep 0, Indicated as the threshold value of the crack propagation rate; Then compare And Value; If the value of < The value, it indicates that the crack propagation rate in the concrete panel is in the controllable range, If the value of > The value, it indicates that the crack propagation rate in the concrete panel is not in the controllable range; When the crack propagation rate is uncontrollable, the layered regulation of the temperature field is realized through the joint optimization of the cooling water flow and the heating device power; Cooling water flow adjustment: If the crack propagation rate exceeds the threshold value, increase the cooling water flow to reduce the temperature gradient and slow down the crack propagation.

[0034] Heating device power adjustment: If the crack propagation rate is lower than the threshold value, reduce the heating device power to reduce thermal stress and optimize the crystal growth rate; Set the threshold value through historical data, and obtain the threshold value by calculating the nonlinear regression equation under the constraint condition, and determine the controllability of the crack in the concrete panel by comparing the crack propagation rate and the threshold value, and then realize the hierarchical regulation of the temperature field by the joint optimization of the cooling water flow and the heating device power.

[0035] Step six: iterative optimization of temperature control strategy based on model: the numerical interaction between crack propagation rate and crystal growth rate is used to optimize the temperature control strategy to adjust the dynamic allocation ratio of cooling water flow and heating device power; The dynamic allocation ratio is solved by genetic algorithm to minimize the crack propagation rate (target value ≤0.5mm / h) and maximize the crystal growth rate (target value ≥2mm / h) to ensure the multi-objective convergence of the temperature control strategy; By establishing the numerical interaction between crack propagation rate and crystal growth rate, the dynamic allocation ratio of cooling water flow and heating device power is analyzed, and the minimization of crack propagation rate and the maximization of crystal growth rate are used as constraint conditions to ensure the multi-objective convergence of the temperature control strategy.

[0036] The specific steps of dynamic allocation ratio are as follows: First, according to the test data, the target value of minimizing crack propagation rate is ≤0.5mm / h; the target value of maximizing crystal growth rate is ≥2mm / h; Set the constraint conditions, cooling water flow should meet the maximum value ≤10L / min and the minimum value ≥2L / min; heating device power should meet the maximum value ≤20kW and the minimum value ≥5kW; Divide the crack propagation rate and crystal growth rate in time series, i.e. divide the crack propagation rate into the range of 0.4mm / h to 0.6mm / h, and divide the crystal growth rate into the range of 1mm / h to 3mm / h, and set 20 time nodes for each, the change value of crack propagation rate is 0.01mm / h, and the change value of crystal growth rate is 0.1mm / h; Convert the node range of crack propagation rate and crystal growth rate into sets, and correspond the crack propagation rate set and crystal growth rate set to generate mapping parameter pairs; Then input the mapping parameter pairs into the optimization objective function to calculate the fitness: Objective function: ; Where, and These are weighting coefficients, representing the priority of crack propagation rate and crystal growth rate, respectively. Represented as fitness; like =-1, =1, which means minimizing the crack rate and maximizing the crystal rate; By intersecting the set of crack propagation rates and the set of crystal growth rates, offspring individuals are generated; The values ​​of the crack propagation rate set and the crystal growth rate set are randomly perturbed and adjusted within ±1%. The calculation is based on the objective function as follows: The optimization goal is to minimize the crack propagation rate (≤0.5mm / h) and maximize the crystal growth rate (≥2mm / h) to achieve structural stability and durability of the concrete panel. like If the value reaches the preset optimal range (e.g., ≥2.0), the control strategy is deemed effective; if If the value is below the threshold (e.g., <1.5), the distribution ratio of cooling water flow and heating power needs to be adjusted. By dividing the crack propagation rate and crystal growth rate into corresponding time series, it is convenient to select numerical information near the optimal value, effectively realize the analysis and processing of multiple values, and prevent the fitness from being unadjustable due to a single value. A fixed and single value cannot effectively control the distribution ratio of cooling water flow and heating power. Step 7: Output temperature control results and generate strength condition assessment report: Integrate the temperature control results with crystallization state parameters and crack propagation rate data to generate a concrete panel strength condition assessment report. The assessment report includes the predicted strength value and strength fluctuation rate. The intensity prediction value is calculated using a thermo-chemical reaction kinetic model, and the intensity fluctuation rate is based on the statistical analysis results of crystal growth direction angle and crack network connectivity, ensuring the quantifiability of the assessment report; By integrating the results of temperature control with crystallization state parameters and crack propagation rate data, an assessment report on strength prediction and strength fluctuation rate can be generated. This enables comprehensive analysis and processing of concrete panels, ensuring the quantifiability of the assessment report and providing a scientific basis for subsequent construction decisions. Specifically: Fitness, crystal growth rate, crystal density, and crack propagation rate are input into the thermo-chemical reaction kinetics model; Its core formula is: ; in, Predicted strength prediction value, Strength of the reference concrete panel, Influence of the stable temperature gradient on the strength development, Indicates the strengthening effect of crystal growth on strength; Wherein, the strength of the reference concrete panel is calculated based on the mix proportion of the concrete material, including the amount of cement, aggregate type, admixture type and curing time, and is determined by laboratory test; The influence of temperature gradient on strength reflects the damage or repair effect of thermal stress on concrete structure; Formula: ; Wherein, Indicates the correction coefficient of temperature gradient on strength, which is determined by experimental calibration, indicating that the higher the temperature gradient, the more significant the loss of strength; Indicates the curing time, which is used to calculate the cumulative effect of temperature on strength; Crystal growth rate and crystalline density enhance the strength: Formula: ; Indicates the correction coefficient of crystal growth on strength, which is determined by experimental calibration, indicating that the higher the crystalline density and rate, the more significant the strength enhancement; Indicates the crystalline density; Indicates the crystal growth rate; Strength fluctuation rate is determined by the consistency of crystal growth direction angle and crack propagation direction: If the crystal growth direction is inconsistent with the crack propagation direction, the crack resistance effect of crystal on crack is weakened, the crack propagation is intensified, leading to the increase of crack connectivity, thus increasing the strength fluctuation rate; The strength fluctuation rate needs to be calculated by statistical analysis to quantify the relevance of crystal growth direction and crack propagation direction; By adjusting the distribution ratio of cooling water flow and heating power, the fluctuation of crack propagation rate is reduced; If the crystal growth direction is inconsistent with the crack propagation direction, the crystal growth direction needs to be optimized to match the crack propagation direction by optimizing the temperature field distribution, so as to reduce the fluctuation rate; Calibration of the values of and by laboratory test (such as =-0.1、 0.5), to ensure the accuracy of the strength prediction value; Optimization direction of strength fluctuation rate: Reduce the fluctuation rate: by adjusting the distribution ratio of cooling water flow and heating power, reduce the fluctuation of crack propagation rate; Enhance the consistency of crystal growth: if the crystal growth direction angle is inconsistent with the crack propagation direction, the temperature field distribution needs to be optimized, such as adjusting the heating power, so that the crystal growth direction is more matched with the crack propagation direction, and the fluctuation rate is reduced; Evaluate the quantifiability of the report: Quantify the intensity prediction value: the intensity prediction value calculated by the thermo-chemical model needs to be compared with the laboratory test data to ensure the implementability of the model; Quantify the fluctuation rate: the intensity fluctuation rate needs to be quantified through statistical analysis (such as the correlation between the crack connectivity index and the direction angle) to avoid errors caused by subjective judgment.

[0037] The storage medium of the present application can store program instructions for all the methods described above, wherein the program instructions can be stored in the storage medium in the form of a software product, including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes, or a computer, a server, a mobile phone, a tablet, and other terminal devices.

[0038] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are only schematic, and the division of units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0039] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The above integrated unit can be realized in the form of hardware or in the form of a software functional unit. The above is only an embodiment of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process transformation using the content of the specification and drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

[0040] The above detailed description of the application is merely exemplary in nature and, thus, does not limit the application. Any modification and the like that do not depart from the spirit and principles of the application are intended to be within the scope of the application.

Claims

1. A method for real-time regulation of the temperature field of a concrete face during construction, characterized in that, The method comprises the following steps: Step 1: Establish a temperature-crystallization-crack multi-physics field coupling model: based on the temperature gradient, crystal growth rate, crystal growth direction angle, crystallization density and crack connectivity index, a multi-physics field coupling model is constructed; Step 2: Adjust the temperature field distribution according to the output control instruction of the coupling model: the temperature field distribution of the concrete panel is dynamically adjusted through the output control instruction of the coupling model, and the control instruction includes cooling water flow and heating device power; Step 3: Iteratively optimize the temperature control strategy based on the model: the temperature control strategy is iteratively optimized based on the numerical interaction between the crack propagation rate and the crystal growth rate, and the dynamic distribution ratio of the cooling water flow and the heating device power is adjusted; Step 4: Output the temperature control result and generate a strength state evaluation report: integrate the temperature control result with the crystallization state parameters and the crack propagation rate data to generate a concrete panel strength state evaluation report, which includes strength prediction value and strength fluctuation rate.

2. The construction period concrete face panel pouring temperature field real-time regulation and control method according to claim 1, characterized in that, The multi-physics field coupling model in step 1 describes the numerical interaction between parameters through a nonlinear regression equation; The nonlinear regression equation is: wherein, is represented as a temperature gradient, is represented as a crystal growth rate, is represented as a crystal growth direction angle, reflecting the anisotropic characteristics of crystal growth, is represented as a crystalline density, is represented as a crack connectivity index, , , , , is represented as a model coefficient, is represented as a crack propagation rate, and is represented as a crack parameter induced at a temperature gradient ratio, is represented as a crystal growth at a temperature, is represented as a density of crystals at a temperature; the model coefficient is calibrated by experimental data, ensuring that the temperature gradient is negatively linearly correlated with the crack propagation rate, and the crystal growth rate is positively nonlinearly correlated with the crack propagation rate.

3. The construction period concrete face panel pouring temperature field real-time regulation and control method according to claim 2, characterized in that, The processing process of the temperature gradient, crystal growth rate, crystal growth direction angle, crystallization density and crack connectivity index is as follows: The temperature gradient, crystal growth rate, crystal growth direction angle, crystallization density and crack connectivity index are constructed into input time series vectors according to time sequence; And for the exact numerical value at the time sequence node, interpolation is used to fill in the completeness of each parameter at the time sequence node; The interpolation is filled in by the mean value of each parameter, and the calculation formula of the mean value is as follows: ; wherein, is expressed as the mean, is expressed as the total number of data for each parameter, is expressed as the first data for each parameter.

4. The construction period concrete face panel pouring temperature field real-time regulation and control method according to claim 1, characterized in that, The steps of generating the control instruction in step 2 are as follows: The control instruction is based on the crack propagation rate output by the model, and the threshold value of the crack propagation rate is set; The threshold value of the crack propagation rate is calculated through historical data or in a set environment to obtain the threshold value of the crack propagation rate; If the historical data shows that the crack propagation rate is easy to expand at 0.05mm / h, the threshold value is set to 0.05mm / h, and the threshold value range under the current construction condition is predicted through a machine learning model; Carrying out the calculation under the set environment: that is, the temperature gradient is set at 2℃, that is, the temperature difference between each gradient of the multi-gradient temperature is 2℃, at which time the crystal growth rate of the concrete panel is the highest, and the crystal density is the most dense, and the cracks in the concrete panel are not easy to expand, that is, the gradient temperature is kept within the same range, and will not cause the generation of cracks; the threshold value of the crack expansion rate is obtained ; When the crack propagation rate is uncontrollable, the temperature field is controlled by joint optimization of cooling water flow and heating device power.

5. The construction period concrete face panel pouring temperature field real-time regulation and control method according to claim 1, characterized in that, The dynamic distribution ratio in step 3 is solved by genetic algorithm to minimize the crack propagation rate and maximize the crystal growth rate, ensuring the multi-objective convergence of the temperature control strategy; The specific steps of the dynamic distribution ratio are as follows: First, according to the test data, the target value of minimizing the crack propagation rate is obtained; the target value of maximizing the crystal growth rate is obtained; Set the constraint condition, the cooling water flow must meet the maximum and minimum values of the equipment; The heating device power must meet the maximum and minimum values of the equipment; Divide the crack propagation rate and the crystal growth rate into 20 time nodes in time sequence; Convert the node range of the crack propagation rate and the crystal growth rate into a set, and correspondingly interact the crack propagation rate set and the crystal growth rate set to generate a mapping parameter pair; The mapping parameter pair is then input into the optimization objective function to calculate the fitness.

6. The construction period concrete panel pouring temperature field real-time regulation and control method according to claim 1, characterized in that, The strength prediction value in the fourth step is calculated by a thermo-chemical reaction kinetics model, and the strength fluctuation rate is based on the statistical analysis results of the crystal growth direction angle and the crack network connectivity, to ensure the quantifiability of the evaluation report; Specifically, the fitness, the crystal growth rate, the crystalline density, and the crack propagation rate are input into the thermo-chemical reaction kinetics model; The strength fluctuation rate is determined by the consistency of the crystal growth direction angle and the crack propagation direction: If the crystal growth direction is inconsistent with the crack propagation direction, the crack resistance effect of the crystal is weakened, the crack propagation is intensified, the crack connectivity is increased, and thus the strength fluctuation rate is increased.

7. The construction period concrete panel pouring temperature field real-time regulation and control method according to claim 1, characterized in that, The temperature gradient is obtained by the following steps: An analog test is set up, and a plurality of gradient temperatures of the concrete panel in the thickness direction are collected in the concrete panel of the analog test; The thickness range of the concrete panel is determined, and the thickness of the concrete panel is calculated as the depth from the surface to the bottom; Sensors are arranged at different depth positions of the concrete panel, including collecting temperature by thermocouples or collecting temperature by infrared thermometers; And every 5 cm of thickness on the thickness change of the concrete panel is collected for temperature collection; And the temperature data, thickness data, and time data are established into a multidimensional matrix; The multidimensional matrix is input into the temperature gradient model to obtain how the internal temperature of the concrete panel changes with the depth.

8. The construction period concrete panel pouring temperature field real-time regulation and control method according to claim 1, characterized in that, The crystal growth rate, the crystal growth direction angle, and the crystalline density constitute the crystallization state parameters, and the three-dimensional structure information is collected by X-ray computed tomography during the pouring process of the concrete panel, the crystal diffraction spectrum is generated, and the crystallization state parameters are calculated; The X-ray computed tomography scans the three-dimensional structure information inside the concrete, and identifies the crystal regions in the three-dimensional structure by threshold segmentation or deep learning algorithm to obtain the crystal diffraction spectrum, and the crystal diffraction spectrum extracts the crystal growth rate, the crystal growth direction angle, and the crystalline density by combining the image processing algorithm, wherein the crystalline density is calculated by the crystal volume fraction.

9. The construction period concrete panel pouring temperature field real-time regulation method according to claim 8, characterized in that, The specific steps of the collection and processing of the crystal diffraction spectrum are as follows: X-ray computed tomography equipment deployment: during the pouring process of the concrete panel, the X-ray computed tomography equipment is used to scan the concrete panel layer by layer, and the two-dimensional projection image in the thickness direction is collected once every 2 hours; Image reconstruction algorithm: the two-dimensional projection image is reconstructed into a three-dimensional crystal distribution image by an iterative reconstruction algorithm to ensure the visualization analysis of the crystal structure, and the crystal regions in the three-dimensional structure are identified by threshold segmentation or deep learning algorithm to obtain the crystal diffraction spectrum; Crystal growth rate calculation: the crystal diffraction spectrum is subjected to integral intensity analysis, the crystal growth rate is determined by calculating the change rate of the crystal diffraction peak area with time; Crystal growth direction angle extraction: the crystal diffraction peak direction in the crystal diffraction spectrum is identified by the image processing algorithm to calculate the crystal growth direction angle; Crystallization density: the crystallization density is calculated based on the ratio of the integral intensity of the crystal diffraction peak in the crystal diffraction spectrum to the volume of the concrete.

10. The method of claim 1, wherein the method further comprises: The crack connectivity index is obtained by non-contact scanning of the inside of the concrete through an X-ray computed tomography technology, obtaining a crack distribution image, and extracting a crack connectivity parameter based on an image processing algorithm, the crack connectivity parameter including a crack branch number and a crack connectivity index.

Images