Mass concrete curing method based on intelligent temperature control system

Abstract

The mass concrete curing method based on the intelligent temperature control system comprises the following steps that concrete is divided into a top layer, a bottom layer and a middle layer, each layer is provided with an independent temperature control pipeline, and temperature monitoring points are designed; constructing a three-dimensional thermal field finite element model containing a hydration heat release function, temperature control pipeline heat sink and boundary conditions, and generating data by using a calculation result of the finite element model to train a neural network prediction model; a temperature control pipeline and a temperature monitoring point are installed before mass concrete is poured, and heat preservation is covered after pouring; data is collected in real time and input into the prediction model, the predicted temperature of each temperature monitoring point is output, if the temperature difference control threshold value or the temperature reduction control threshold value is exceeded, temperature control pipeline parameters and heat preservation measures of the corresponding layer are adjusted, and a temperature control strategy is executed after calculation is repeated till the requirement is met. The concrete temperature field is accurately controlled, temperature difference over-limit is effectively avoided, and the mass concrete curing quality is improved.

Description

Technical Field

[0001] The present invention relates to the field of concrete curing. More specifically, the present invention relates to a method for curing mass concrete based on an intelligent temperature control system. Background Art

[0002] In the construction of mass concrete structures, the heat released during the hydration process of concrete can easily cause a significant increase in the internal temperature. When the internal and external temperature difference is too large, temperature stress will be induced, which may cause structural cracking. To control the influence of hydration heat, the technical means of burying circulating condensate pipes inside the concrete is often adopted in engineering. The heat is carried away by the cooling medium flowing inside the pipes, reducing the internal temperature rise of the concrete. By adjusting parameters such as the flow rate and temperature of the cooling medium, this method can control the temperature change rate of the concrete to a certain extent, and it is one of the mainstream measures for curing mass concrete at present.

[0003] However, there are several difficult-to-control problems in the actual application of the traditional condensate pipe temperature control system. First of all, the existing condensate pipe temperature control strategies are mostly based on empirical parameters or fixed control modes, and it is difficult to dynamically adjust according to the real-time temperature field evolution of the concrete. The traditional system usually presets the inlet water temperature and flow rate of the cooling medium, lacking the accurate prediction ability of the internal temperature field of the concrete. When the meteorological conditions or the concrete covering measures change, it cannot perceive and adjust the temperature control parameters in time, which may lead to insufficient cooling or overcooling, especially in environments with low and large temperature differences, it is even more difficult to regulate.

[0004] Secondly, the layered pouring process is often adopted in the construction of mass concrete. The pouring time of each layer is usually 6 - 10 hours. There are differences in the initial setting time and the hydration heat release process of each concrete layer. Due to the different pouring times of each layer of concrete, the time when the internal hydration heat peak appears and the continuous process are different, resulting in uneven temperature distribution between layers. The traditional condensate pipe system mostly adopts an integral pipeline layout, without considering the independent temperature control design for the hydration heat characteristics of different concrete layers, and it cannot effectively adapt to and solve the problem of asynchronous hydration heat caused by layered construction. The bottom concrete poured first may have entered the hydration heat attenuation stage, while the newly poured top concrete is in the rapid hydration heat release period. If the same cooling parameters are adopted, it may lead to too low temperature at the bottom layer and insufficient temperature control at the top layer, and the temperature difference between layers exceeds the allowable range.

[0005] The existing temperature control system lacks the ability to integrate and analyze multi-source data, and cannot form a closed-loop control of "prediction - adjustment - feedback". The formulation of temperature control strategies still relies on manual experience, and it is difficult to meet the demand for high-precision temperature difference control in the curing of mass concrete. Therefore, it is urgent to propose a new curing method that can accurately and effectively control the temperature during the curing of mass concrete. Summary of the Invention

[0006] An object of the present invention is to provide a method for curing mass concrete based on an intelligent temperature control system, which can accurately and effectively control the temperature during the curing of mass concrete.

[0007] To achieve these and other advantages in accordance with the present invention, the present invention provides a method for curing mass concrete based on an intelligent temperature control system, comprising the following steps: S1. According to the mass concrete pouring plan, the mass concrete is divided into a top layer, a bottom layer and more than one intermediate layer. There is at least one set of independent temperature control pipelines in each layer, and temperature monitoring points are designed in the mass concrete. S2. Based on the concrete mix proportion parameters and adiabatic temperature rise test data, a three-dimensional thermal field finite element model including the hydration heat release curve, the heat transfer effect of the temperature control pipelines and the concrete environmental boundary conditions is constructed. The finite element model outputs the temperature data of each point in the model after the calculation under various initial conditions. S3. Use the temperature data generated by the finite element model to train a temperature field prediction model. The input parameters include the temperatures of each temperature monitoring point in the concrete at the current moment, the inlet water temperature and flow rate of the temperature control pipelines in each layer of concrete, and the predicted amount of hydration heat generated in each layer of concrete between the current moment and the predicted moment. The output value of the temperature field prediction model is the predicted temperature value of each temperature monitoring point in the mass concrete structure at the predicted moment. S4. Before the mass concrete structure is poured, install each set of temperature control pipelines as designed and set temperature sensors at the temperature monitoring points. After each temperature control pipeline is inspected for water passing, complete the pouring of the mass concrete structure. After the pouring is completed, cover the concrete surface for heat preservation. S5. Input the measured temperature values of each current temperature monitoring point, the parameters of each layer of temperature control pipelines, the predicted amount of hydration heat of each layer of concrete, meteorological parameters and the parameters related to the covering measures into the temperature field prediction model, and output the predicted temperature values of each temperature monitoring point at the next predicted moment. If there is a situation where the predicted temperature value exceeds the temperature difference control requirement, adjust the inlet water temperature and flow rate of the temperature control pipelines in the corresponding concrete layer, and adjust the covering measures of the mass concrete structure. Input the adjusted temperature control pipeline parameters, the adjusted parameters related to the covering measures and other parameters into the temperature field prediction model, and recalculate the predicted temperature values of each temperature monitoring point at the next predicted moment until the temperature difference control requirement is met, and execute the temperature control strategy with the parameters adopted in this prediction. S6. Repeat step S5 at regular intervals until the concrete structure reaches the curing age.

[0008] Preferably, the temperature control pipeline is an independent circulating water path, and a variable frequency water pump, a temperature adjustment module and a flow direction change module are arranged outside the mass concrete structure.

[0009] Preferably, step S2 includes: S21. Obtain the heat of hydration release curve according to the concrete mix proportion and test data, and obtain the top surface heat transfer coefficient and the bottom surface heat transfer coefficient based on the covering measures at the top and bottom of the mass concrete structure; S22. According to the layering of the concrete structure, establish a concrete structure model with layering and considering the heat transfer effect of the internal temperature control pipeline in the finite element calculation software, where the concrete material properties include the heat of hydration release curve, and the top surface heat transfer coefficient and the bottom surface heat transfer coefficient are used as the interface boundary conditions of the concrete structure model; S23. Take different top surface heat transfer coefficients, bottom surface heat transfer coefficients, initial setting times of each layer, initial temperatures of each layer, and temperature control pipeline parameters of each layer as loading conditions, and the output result is the temperature data of each temperature monitoring point after a certain period of time obtained based on the loading conditions.

[0010] Preferably, in step S21, the heat of hydration release curve is , where Q 0 is the initial heat of hydration peak value, k is the attenuation coefficient obtained through tests of the same material, t 0 is the initial setting time of the concrete; the top surface heat transfer coefficient , the bottom surface heat transfer coefficient , where is the atmospheric composite heat dissipation coefficient, is the thermal conductivity of the covering, is the thickness of the covering, is the exposure coefficient, and its value range is 0.1 - 1, is the thermal conductivity of the concrete cushion, is the thickness of the cushion, is the equivalent heat dissipation coefficient of the foundation.

[0011] Preferably, in step S22, the method for establishing a concrete structure model with layering and considering the heat dissipation of the internal temperature control pipeline is to equivalent the heat transfer effect of the temperature control pipeline to a distributed heat sink, and the heat sink intensity calculation formula is , where s is the pipeline spacing of the temperature control pipeline, D is the inner diameter of the temperature control pipeline, L is the length of the temperature control pipeline, v is the designed water flow velocity, α w is the thermal diffusivity of water, T w is the initial temperature of water, T cis the real-time temperature of the concrete calculation unit around the temperature control pipeline, which is updated in real time through finite element iterative calculation. After the mesh generation is completed, the heat sink intensity is applied as a volumetric heat source to the concrete elements around the temperature control pipeline.

[0012] Preferably, the atmospheric composite heat dissipation coefficient , where w is the wind speed, is the Stefan-Boltzmann constant, is the emissivity of the concrete surface, T a is the atmospheric temperature, T s is the real-time temperature of the concrete top surface calculation unit, which is updated in real time through finite element iterative calculation.

[0013] Preferably, when there is no covering on the concrete surface, the exposure coefficient , when the covering on the concrete surface does not completely cover , when the covering on the concrete surface covers tightly .

[0014] Preferably, the temperature field prediction model adopts a BP neural network model including an input layer, a hidden layer and an output layer. The input layer of the BP neural network model includes multiple input neuron nodes, and multiple hidden neuron nodes are arranged on the hidden layer. Each of the input neuron nodes is respectively connected to each of the hidden neuron nodes; the hidden neuron nodes are all connected to the output layer. Meteorological parameters, parameters related to covering measures, initial temperature data of temperature monitoring points, predicted values of hydration heat of each layer of concrete, inlet water parameters of each layer of temperature control pipelines, etc. are used as input neuron node input values, and the output result is compared with the temperature data of the temperature monitoring points obtained by finite element calculation, and the BP neural network model is optimized by the algorithm to obtain the temperature field prediction model.

[0015] Preferably, step S5 includes the following steps: S51. Real-time collect the measured temperature values of each temperature monitoring point; S52. Input the measured temperature values of each temperature monitoring point, meteorological parameters obtained according to meteorological forecasts, parameters related to on-site covering measures, predicted values of hydration heat of each layer of concrete, and inlet water parameters of each layer of temperature control pipelines into the trained temperature field prediction model to predict the temperature prediction values of each temperature monitoring point at the next moment; S53. Calculate the same-point temperature difference between each temperature prediction value and the measured temperature value of the corresponding temperature monitoring point and compare it with the cooling control threshold, calculate the two-point temperature difference with the temperature prediction values of any two temperature monitoring points, and compare each two-point temperature difference with the temperature difference control threshold; S54. If the temperature difference at the same point does not exceed the cooling control threshold and the temperature difference between two points does not exceed the temperature difference control threshold, the existing water inlet parameters of the temperature control pipeline and the covering measures shall be maintained. If the temperature difference at the same point exceeds the cooling control threshold or the temperature difference between two points exceeds the temperature difference control threshold, the water inlet parameters of the corresponding layer and the covering measures on the concrete surface shall be adjusted. Using the new water inlet parameters and the relevant parameters of the covering measures as input values, repeat steps S52 - S53 until the temperature differences at the same points and all the temperature differences between two points in the new plan meet the requirements, and this plan shall be immediately implemented as the execution plan.

[0016] Preferably, the temperature difference control threshold is 20°C, and the cooling control threshold is 2°C / d.

[0017] The present invention has at least the following beneficial effects: First, by constructing a three-dimensional thermal field finite element model, combining the hydration heat release curve, heat dissipation of the temperature control pipeline, and boundary conditions, the present invention accurately simulates the evolution law of the internal temperature field of concrete. Based on the data of the finite element model, a neural network prediction model is trained. With multi-source information such as real-time temperature data, temperature control pipeline parameters, and meteorological parameters as input, the temperature distribution at future moments is dynamically predicted. When the prediction result exceeds the threshold, the water inlet temperature, flow rate, and covering measures of the corresponding layer are adjusted to form a closed-loop control of "monitoring - prediction - regulation - verification", reducing the risk of shrinkage cracks caused by internal and external temperature differences and improving the integrity and durability of the structure.

[0018] Second, aiming at the reality that the mass concrete structure is huge and cannot be integrally cast based on the existing casting technology, the present invention proposes to design the independent temperature control pipeline in layers. During the regulation stage, the pipeline parameters can be adjusted separately for the abnormal temperature difference areas of specific layers instead of unified global operation, making the temperature gradient distribution more uniform, avoiding local overheating or overcooling, and significantly reducing the crack generation rate.

[0019] Third, the present invention introduces a multi-physical field coupling mechanism in finite element modeling and uses the massive temperature field data generated by the finite element model to train the BP neural network, breaking through the limitations of traditional empirical formulas for single influencing factors and being able to fuse multi-source data to output the temperature values of each temperature monitoring point at the prediction moment.

[0020] Other advantages, objectives, and features of the present invention will be partially reflected by the following description and partially understood by those skilled in the art through the research and practice of the present invention. Description of the Drawings

[0021] Figure 1 It is the flow chart of the curing method in a technical solution of the present invention; Figure 2 It is the flow chart of the on-site temperature adjustment process in a technical solution of the present invention. Detailed Embodiments

[0022] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments, so that those skilled in the art can implement it with reference to the text of the specification.

[0023] It should be understood that the terms such as "having", "comprising" and "including" used herein do not exclude the presence or addition of one or more other elements or combinations thereof.

[0024] It should be noted that, unless otherwise specified, the experimental methods described in the following embodiments are all conventional methods, and the reagents and materials, unless otherwise specified, can all be obtained from commercial channels; in the description of the present invention, it should be noted that, unless otherwise clearly defined and limited, the terms "installed", "connected" and "set" should be understood in a broad sense. For example, they can be fixedly connected and set, or detachably connected and set, or integrally connected and set. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations. The orientation or positional relationship indicated by the terms "lateral", "longitudinal", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention.

[0025] As Figure 1-2 shown, the present invention provides a method for curing mass concrete based on an intelligent temperature control system, comprising the following steps: S1. According to the mass concrete pouring plan, the mass concrete is divided into a top layer, a bottom layer and more than one intermediate layer. There is at least one set of independent temperature control pipelines in each layer, and temperature monitoring points are designed in the mass concrete. Specifically, in the mass concrete pouring operation, due to the existing concrete pouring process, material scheduling and pouring speed, it is difficult to be poured into a whole form at one time, so the site will be divided into several layers for pouring operation. The overall pouring time is generally more than 24 hours. During this period, the hydration heat release time of each actual pouring layer cannot be unified. Therefore, according to this situation, the first batch of poured concrete can be regarded as the bottom layer, and the last batch of poured concrete can be regarded as the top layer. The intermediate layer can be divided into multiple layers according to the pouring time. Since the hydration heat of each layer is different due to the pouring time, and the heat absorption and dissipation environment faced by the bottom layer and the top layer is affected by external factors, independent temperature control pipelines are set in the bottom layer, the top layer and each intermediate layer. For the position design of the temperature monitoring points, it should be ensured that the full temperature field of the concrete structure can be fully covered, especially in the key parts such as the layer interface, around the temperature control pipelines and the geometric mutation of the structure where large temperature differences are expected to occur, so as to realize the comprehensive and effective monitoring of the temperature field evolution.

[0026] S2. Based on the concrete mix proportion parameters and adiabatic temperature rise test data, a three-dimensional thermal field finite element model including the hydration heat release curve, the heat transfer effect of the temperature control pipeline and the concrete environmental boundary conditions is constructed. The finite element model outputs the temperature data of each point in the model after the calculation under various initial conditions. Specifically, the finite element model is a thermodynamic model established by using finite element analysis software such as ABAQUS and ANAYS. In the finite element analysis software, the material properties of each layer of concrete in the concrete structure are added based on the designed gradation and the thermodynamic parameters obtained through tests. After applying conditions to the boundary of the concrete structure, the concrete structure is discretized into multiple finite elements, and the heat conduction equation of each element under the combined action of hydration heat release, heat transfer of the temperature control pipeline and environmental boundary conditions is solved by mathematical methods, so as to simulate the temperature distribution and change process of the concrete from pouring to curing.

[0027] S3. Use the temperature data generated by the finite element model to train the temperature field prediction model. The input parameters include the temperatures of each temperature monitoring point in the concrete at the current moment, the inlet water temperature and flow rate of the temperature control pipelines in each layer of concrete, and the predicted hydration heat generation amount in each layer of concrete between the current moment and the prediction moment. The output value of the temperature field prediction model is the predicted temperature value of the temperature monitoring points of the mass concrete structure at the prediction moment. Specifically, the temperature field data output by the finite element model under various initial conditions is processed into training samples. Based on the positions of the temperature monitoring points in the design, the temperature data of each temperature monitoring point is extracted from the finite element data. Each sample includes the temperatures of each temperature monitoring point at the current moment, the inlet water temperature and flow rate of each layer of temperature control pipelines, and the predicted hydration heat generation amount in each layer of concrete between the current moment and the prediction moment as input parameters, and the temperature data of the temperature monitoring points at the corresponding prediction moment as the output label. Through the learning of the above samples, the model captures the internal relationship between the input parameters and the evolution of the temperature field, forms the fitting ability for the time-varying law of temperature under the coupling action of multiple parameters. During the training process, the model parameters are adjusted through an optimization algorithm to minimize the error between the predicted output and the true output of the finite element, so as to construct a mapping model that can predict the temperature data of future temperature monitoring points according to real-time input parameters. This model quantifies the comprehensive influence of multiple factors on the temperature field through data fitting, provides a real-time and accurate temperature prediction basis for the subsequent dynamic adjustment of the temperature control strategy, and effectively solves the problem that it is difficult to accurately predict the temperature field under complex working conditions through empirical formulas.

[0028] S4. Install each set of temperature control pipelines according to the design and set temperature sensors at the temperature monitoring points before the pouring of the mass concrete structure. After the water passing inspection of each temperature control pipeline is completed, the pouring of the mass concrete structure is completed. After the pouring is completed, covering measures are taken on the concrete surface for heat preservation. Specifically, after the installation of the temperature control pipelines is completed, a water passing test needs to be carried out to check the pipeline fluidity and sealing performance to ensure that there are no abnormal conditions such as blockage and leakage. Check the instruments at the temperature monitoring points. After the above preparatory work is completed, the concrete pouring operation is carried out according to the layered pouring plan. After the pouring is completed, covering measures are promptly taken on the concrete surface for heat preservation to reduce surface heat dissipation and maintain a suitable curing temperature environment. The covering measures can use materials such as felt, geotextile, and plastic film.

[0029] S5. Input the measured temperature values of the current temperature monitoring points, the temperature control pipeline parameters of each layer, the predicted values of the hydration heat of the concrete in each layer, the meteorological parameters, and the parameters related to the covering measures into the temperature field prediction model, and output the predicted temperature values of each temperature monitoring point at the next prediction moment. If there are cases where the predicted temperature values exceed the temperature difference control requirements, adjust the inlet water temperature and flow rate of the temperature control pipeline in the corresponding concrete layer, and adjust the covering measures of the mass concrete structure. Input the adjusted temperature control pipeline parameters, the parameters related to the adjusted covering measures, and other parameters into the temperature field prediction model, and recalculate the predicted temperature values of each temperature monitoring point at the next prediction moment until the temperature difference control requirements are met. Execute the temperature control strategy with the parameters adopted in this prediction. Specifically, during the curing process of the mass concrete, the measured temperature data of each temperature monitoring point are collected in real time according to a preset cycle, and the current inlet water temperature, flow rate and other operating parameters of the temperature control pipeline of each layer are obtained synchronously. In addition, the predicted values of the hydration heat of the concrete in each layer calculated based on the concrete mix ratio and pouring time are obtained. The meteorological parameters include the atmospheric temperature and wind speed. The parameters related to the covering measures include the thickness of the covering material and the exposure coefficient obtained according to the covering situation of the covering material. Input the above parameters into the trained neural network prediction model, and output the predicted temperature values of each temperature monitoring point at the next prediction moment. Subsequently, compare the difference between any two predicted temperature values with the preset temperature difference control threshold, and at the same time compare the temperature drop rate at the same position with the temperature drop control threshold. The temperature difference control threshold is the difference between any two predicted temperature values, which can be set to 25°C according to the specifications and standards. The temperature drop control threshold is 2°C / d. If there are unsatisfied situations, it means that the temperature control strategy needs to be adjusted immediately.

[0030] Re-input the temperature control pipeline parameters, covering measure parameters and other real-time parameters of each adjusted concrete layer into the prediction model, recalculate the predicted temperature again and verify whether it meets the control requirements. Through the above iterative process, until the predicted temperature values of all temperature monitoring points meet the temperature difference control standard, finally execute the temperature control strategy according to the optimized parameter combination, including operating such as driving a variable frequency water pump to adjust the flow rate, starting a temperature adjustment module to control the inlet water temperature, and adjusting the arrangement of the covering material.

[0031] This process realizes the dynamic optimization control of the temperature field of the layered concrete through a real-time data-driven closed-loop feedback mechanism, ensuring that the temperature rise, temperature drop rate and interlayer temperature difference of each layer of concrete during the curing period are always within the safe range until the design curing age is reached.

[0032] S6. Repeat step S5 according to a cycle until the concrete structure reaches the curing age.

[0033] In another technical solution, the temperature control pipeline is an independent circulating waterway, and a variable-frequency water pump, a temperature regulation module and a flow direction change module are arranged outside the mass concrete structure. Specifically, the temperature control pipeline adopts an independent circulating pipeline design, and a variable-frequency water pump, a temperature regulation module and a flow direction change module are configured outside the concrete structure to achieve precise temperature control: the variable-frequency water pump can dynamically adjust the flow rate of the cooling medium in the pipeline according to the real-time temperature monitoring data and the temperature control strategy. When the temperature of a certain layer of concrete is abnormal, the heat dissipation is enhanced or reduced by increasing or decreasing the flow rate; the temperature regulation module is used to accurately control the inlet temperature of the cooling medium. Combining the predicted temperature trend, the water temperature is reduced to strengthen the cooling when the risk of excessive temperature rise exceeds the standard, or the water temperature is increased to avoid excessive cooling when the temperature is too low; the flow direction change module can periodically or as needed change the flow direction of the cooling medium to improve the uniformity of heat exchange inside the concrete. Especially for areas with uneven layered heat of hydration or large local temperature differences, the heat dissipation efficiency of each part is balanced by adjusting the flow direction. The three work together, enabling the temperature control system to flexibly adjust the flow rate, temperature and flow direction of the cooling medium according to the real-time temperature field of the concrete, the process of heat of hydration and the environmental conditions, realizing the refined dynamic control of the temperature of each layer of concrete, effectively avoiding the overlimit of temperature difference and temperature cracks, and improving the maintenance quality of mass concrete.

[0034] In another technical solution, step S2 includes: S21. Obtain the heat release curve of heat of hydration according to the concrete mix proportion and test data, and obtain the top surface heat transfer coefficient and the bottom surface heat transfer coefficient based on the covering measures at the top and bottom of the mass concrete structure. Specifically, according to the designed mix proportion of the concrete, before the formal pouring, the heat release data of the concrete hydration process is measured through a small-scale adiabatic temperature rise test, and this data is used as the material property element of the subsequent finite element model, accurately reflecting the real hydration process of a specific concrete. The top surface heat transfer coefficient and the bottom surface heat transfer coefficient are used as the interface boundary conditions of the finite element model.

[0035] S22. According to the layering of the concrete structure, establish a concrete structure model with layering and considering the heat transfer of the internal temperature control pipeline in the finite element calculation software. The concrete material properties include the hydration heat release curve, and the top surface heat transfer coefficient and the bottom surface heat transfer coefficient are used as the interface boundary conditions of the concrete structure model. Specifically, according to the pouring scheme, the concrete is divided into the top layer, the bottom layer and the middle layer. Each layer independently constructs a three-dimensional solid model, correctly sets the contact relationship between each layer, and applies material properties in combination with factors such as pouring time, hydration heat release, and concrete mix ratio to match the concrete layers poured at different times. The temperature control pipeline can be established as a line model according to the design drawings and bound to the concrete model. Optionally, the thermal effect of the temperature control pipeline is simplified as a distributed line heat source, and the heat sink intensity is obtained based on the pipe diameter, spacing and water flow velocity, and is applied as a volume heat source to the concrete elements around the pipeline after the concrete is meshed, and the coverage range is extended to the area 3 to 5 times the pipe diameter to ensure the accuracy of the heat dissipation field. For the interface conditions of the concrete surfaces of the top layer and the bottom layer, the top surface heat transfer coefficient obtained according to the covering measures, the atmospheric temperature and the wind speed is adopted, and the bottom surface heat transfer coefficient obtained according to the thermal conductivity coefficient and thickness of the cushion under the mass concrete and the thermal conductivity coefficient of the foundation is adopted. In the finite element software, the top surface and the bottom surface of the concrete structure are respectively specified as the convective heat transfer boundary and the corresponding heat transfer coefficient is input.

[0036] S23. With different top surface heat transfer coefficients, bottom surface heat transfer coefficients, initial setting times of each layer, initial temperatures of each layer, and temperature control pipeline parameters of each layer as the loading conditions, the output result is the temperature data of each design temperature monitoring point after a certain time obtained based on the loading conditions. Specifically, the meteorological parameters, the initial setting times, the initial temperatures of each layer of concrete and the temperature control pipeline parameters are defined as variable input parameters, and are batch imported through scripts or software interfaces to form multiple groups of condition combinations. When performing the thermal analysis, an adaptive time step can be adopted to automatically solve the evolution process of the temperature field, and the temperature time history data of the preset monitoring points are extracted through the post-processing module and output as a structured table containing time, position and temperature values.

[0037] In another technical solution, in step S21, the hydration heat release curve is , where Q 0 is the initial hydration heat peak value, k is the attenuation coefficient obtained through the same material test, t 0 is the initial setting time of the concrete; the top surface heat transfer coefficient , the bottom surface heat transfer coefficient , where is the atmospheric composite heat transfer coefficient, is the thermal conductivity coefficient of the covering, is the thickness of the covering, is the exposure coefficient, with a value range of 0.1 to 1, is the thermal conductivity of the concrete cushion layer, is the thickness of the cushion layer, is the equivalent heat dissipation coefficient of the foundation. In this technical solution, the hydration heat release curve is obtained by fitting the adiabatic temperature rise test of the same material, which truly reflects the time-varying characteristics of heat release after the initial setting of concrete, avoiding the deviation of traditional theoretical assumptions. The top surface heat transfer coefficient integrates the heat conduction of the covering and the heat dissipation of the atmosphere, quantifies the surface exposure state, and accurately simulates the top surface heat exchange under different covering measures; the bottom surface heat transfer coefficient considers the thermal conductivity, thickness of the cushion layer and the equivalent heat dissipation coefficient of the foundation, and quantifies the thermal resistance distribution between the cushion layer and the foundation through a fractional structure, accurately reflecting the heat conduction effect between the bottom layer of concrete and the foundation. This technical solution enables the finite element model to accurately capture the heat exchange dynamics between the internal hydration heat source of concrete and the external environment, providing a reliable basis for the layered temperature control strategy. By differentiating and quantifying the top surface exposure degree and the influence of the bottom cushion layer, it supports the refined thermal field analysis for different concrete layers and different construction scenarios, effectively improving the scientificity and accuracy of temperature control in the maintenance of mass concrete.

[0038] In another technical solution, in step S22, the method for establishing a concrete structure model with stratification and considering the heat dissipation of the internal temperature control pipeline is to equivalently convert the heat transfer effect of the temperature control pipeline into a distributed heat sink. The calculation formula for the heat sink intensity is , where s is the pipeline spacing of the temperature control pipeline, D is the inner diameter of the temperature control pipeline, L is the length of the temperature control pipeline, v is the designed water flow velocity, α w is the thermal diffusivity of water, T w is the initial temperature of water, T c is the real-time temperature of the concrete calculation unit around the temperature control pipeline, which is updated in real time through finite element iterative calculation. After the mesh generation is completed, the heat sink intensity is applied as a volume heat source to the concrete elements around the temperature control pipeline. In this technical solution, the heat transfer effect of the temperature control pipeline is equivalently converted into a distributed heat sink inside the concrete through the heat sink intensity formula, accurately quantifying the influence of pipeline heat dissipation on the temperature field. Among them, the heat sink intensity describes the heat dissipation rate of the temperature control pipeline in the unit volume of concrete. By integrating the principles of fluid mechanics and heat transfer, the heat dissipation effect of the temperature control pipeline is converted into a volume heat source that can be applied to the finite element model, solving the problem that it is difficult to accurately model the heat dissipation of the embedded pipeline in the finite element model in the traditional method, and providing reliable heat dissipation data support for the intelligent prediction model. The equivalent processing method of the distributed heat sink is convenient for combining with the concrete layered design, supports independent adjustment of the temperature control pipelines in each layer, and realizes refined control.

[0039] In another technical solution, the atmospheric composite heat dissipation coefficient , where w is the wind speed,[[]] is the Stefan-Boltzmann constant,[[]] is the emissivity value of the concrete surface, which is 0.9,[[]] T a is the atmospheric temperature,[[]] T s is the real-time temperature of the calculation unit on the top surface of the concrete, which is updated in real time through finite element iteration. In this technical solution, this formula dynamically reflects the real-time influence of the atmospheric environment on the heat dissipation of the concrete surface by coupling the wind speed, surface state and temperature difference. For example, when the wind speed increases, the surface heat dissipation is accelerated; when the difference between the surface temperature and the atmospheric temperature is significant, the radiation effect is amplified by the product of the temperature terms, accurately capturing the heat exchange characteristics under complex meteorological conditions.[[]]

[0040] In another technical solution, when there is no covering on the concrete surface, the exposure coefficient When the covering on the concrete surface is not completely covered When the covering on the concrete surface is tightly covered Specifically, during the concrete curing process, especially in an environment with a relatively low temperature, the covering can extremely effectively play the role of wind and temperature insulation. However, experiments and engineering experience show that when there are gaps in the covering, the wind and heat insulation effects will be significantly reduced. Therefore, η is divided into three typical working condition values, which can conveniently determine the boundary conditions according to the on-site covering situation, avoiding complex real-time measurements, and at the same time ensuring that the finite element model can truly reflect the surface heat exchange characteristics under different covering states. This technical solution solves the problem of difficult quantification of the top surface covering measures through standardized exposure coefficient values, enabling the finite element model to flexibly adapt to different construction scenarios.[[]]

[0041] In another technical solution, the temperature field prediction model adopts a BP neural network model including an input layer, a hidden layer and an output layer. The input layer of the BP neural network model includes multiple input neuron nodes, and multiple hidden neuron nodes are arranged on the hidden layer. Each of the input neuron nodes is respectively connected to each of the hidden neuron nodes; the hidden neuron nodes are all connected to the output layer. The meteorological parameters, parameters related to the covering measures, the initial temperature data of the temperature monitoring points, the predicted values of the hydration heat of each layer of concrete, the water inlet parameters of each layer of temperature control pipelines, etc. are used as the input values of the input neuron nodes, and the output result is compared with the temperature data of the temperature monitoring points obtained by finite element calculation, and the BP neural network model is optimized by the algorithm to obtain the temperature field prediction model.[[]]

[0042] The method for optimizing the BP neural network by the algorithm includes the following steps: B1: Taking meteorological parameters, covering parameters, initial temperatures of each temperature monitoring point, predicted values of concrete hydration heat of each layer, and water inlet parameters of each temperature control pipeline as influencing factors, taking the total amount of parameter types as the number of neuron nodes m, and taking the temperature prediction values of each temperature monitoring point as the output value c, the number of hidden layer nodes c1 is , where a is a random constant between 1 and 10; B2. Perform normalization processing on the sample set data, and its mathematical expression is: Among them, represents the sample data of the influencing factor, , are respectively the minimum value and the maximum value in the sample data, is the dimensionless processed influencing factor data; B3. Initialize the mapping relationship between the population particles and the weights and thresholds of the BP neural network, including particle dimension, initial velocity, population size, learning factor, and inertia weight; B4. Input the normalized input variables and output variables into the BP neural network model, calculate the fitness function value of the particle, and obtain the historical optimal fitness and global fitness of the particle. The fitness function value of the particle is the mean square error of the calculation result, and its function expression is: Among them, represents the predicted value of the i-th sample, is the true value of the i-th sample, and n is the total number of calculation results of the neural network; B5. Perform iterative calculation on the particle fitness, update the historical optimal fitness and the global fitness according to the preset update conditions until the preset iteration end condition is met; B6. Update the weights and thresholds of the BP neural network model to obtain an optimized BP neural network model for calculating temperature prediction values.

[0043] In another technical solution, step S5 includes the following steps: S51. Real-time collect the measured temperature values of each temperature monitoring point; S52. Input the measured temperature values of each temperature monitoring point, the meteorological parameters obtained from meteorological forecasts, the parameters related to on-site covering measures, the predicted hydration heat of each layer of concrete, and the water inlet parameters of the temperature control pipelines of each layer into the trained temperature field prediction model to predict the temperature prediction values of each temperature monitoring point at the next moment. Specifically, first, the measured temperature data of each temperature monitoring point are collected in real time through pre-buried temperature sensors to ensure the real-time temperature status of the key positions inside the concrete is obtained. At the same time, real-time meteorological parameters, including air temperature, wind speed, and humidity, are obtained from the meteorological forecast system. The on-site covering parameters include the thermal conductivity, thickness corresponding to the current covering, and the exposure coefficient η determined by the tightness of the covering. The predicted hydration heat of each layer of concrete is calculated by combining the hydration heat release curve obtained from the adiabatic temperature rise test and the pouring time. The water inlet parameters of the temperature control pipelines of each layer include the real-time water inlet temperature and flow rate. After the above multi-source data are normalized, they are input into the trained BP neural network prediction model in a preset format to obtain the temperature prediction values of each temperature monitoring point at the predicted time, and the predicted time can be 1 to 24 hours from the current time.

[0044] S53. Calculate the same-point temperature difference between each temperature prediction value and the measured temperature value of the corresponding temperature monitoring point and compare it with the cooling control threshold, and calculate the two-point temperature difference with the temperature prediction values of any two temperature monitoring points, and compare each two-point temperature difference with the temperature difference control threshold. Specifically, the temperature prediction values of each temperature monitoring point output in step S52 are matched point by point with the measured temperature values of the corresponding positions. The same-point temperature difference is the difference between the temperature prediction value and the measured temperature value in the same temperature monitoring point. Calculate all the two-point temperature differences by calculating the temperature prediction values of any two temperature monitoring points. The two-point temperature difference represents the temperature difference between different positions at the predicted moment to identify whether there is a risk of excessive interlayer or local temperature difference in the mass concrete structure. Among them, the temperature difference control threshold and the cooling control threshold are formulated based on the basic principles of mass concrete temperature crack control and engineering practice standards.

[0045] S54. If the temperature difference at the same point does not exceed the cooling control threshold and the temperature difference between two points does not exceed the temperature difference control threshold, the existing water inlet parameters of the temperature control pipeline and the covering measures shall be maintained. If the temperature difference at the same point exceeds the cooling control threshold or the temperature difference between two points exceeds the temperature difference control threshold, the water inlet parameters of the corresponding layer and the covering measures on the concrete surface shall be adjusted. Using the new water inlet parameters and the relevant parameters of the covering measures as input values, repeat steps S52 - S53 until the temperature differences at the same point and all the temperature differences between two points in the new plan meet the requirements. Then, take this plan as the implementation plan and execute it immediately. Specifically, first make a decision based on the comparison result in step S53. If the temperature differences at the same point of all temperature monitoring points do not exceed the cooling control threshold and the temperature differences between any two temperature monitoring points do not exceed the temperature difference control threshold, it is determined that the current temperature control strategy is effective, and the operating parameters such as the water inlet temperature and flow rate of the temperature control pipeline for each layer are maintained unchanged, while keeping the existing covering arrangement plan. If the temperature difference at the same point causes the cooling rate to exceed the limit or the temperature difference between two points exceeds the allowable range, parameter adjustment shall be implemented for the corresponding concrete layer with abnormal temperature. In the corresponding concrete layer, the flow rate of the water in the temperature control pipeline is increased or decreased for the abnormal layer through a variable frequency water pump, and at the same time, the water inlet temperature is finely adjusted through a temperature adjustment module. For the covering arrangement, the heat preservation measures are adjusted according to the temperature difference direction. Re - input the new water inlet parameters, covering parameters and other real - time data into the temperature field prediction model, and repeat the prediction and comparison process of steps S52 - S53 to form a closed - loop iteration of "adjustment - prediction - verification" until the temperature differences at the same point and the temperature differences between two points of all temperature monitoring points meet the threshold requirements. Finally, take the verified parameter combination as the implementation plan, immediately drive equipment such as variable frequency water pumps and temperature adjustment modules to execute, and synchronously adjust the on - site covering measures to ensure that the temperature fields of each layer of mass concrete continuously meet the safety standards during dynamic regulation until the curing period is completed.

[0046] In another technical solution, the temperature difference control threshold is 20°C and the cooling control threshold is 2°C / d. Specifically, the temperature difference control threshold and the cooling control threshold are formulated according to the "Standard for Mass Concrete Construction" and the balance relationship between the tensile strength of concrete and temperature stress.

[0047] It should be noted that although the above steps are described in a specific order, it does not mean that the steps must be executed in the above - mentioned specific order. In fact, some of these steps can be executed concurrently or even in a different order, as long as the required functions can be achieved. The number of devices and the processing scale described here are used to simplify the description of the present invention, and it is obvious to those skilled in the art for the application, modification and variation of the present invention.

[0048] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and embodiments. It can be fully applied to various fields suitable for the present invention. For those skilled in the art, additional modifications can be easily achieved. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and the examples shown and described herein.

Claims

1. A method for curing mass concrete based on an intelligent temperature control system, characterized in that, Including the following steps: S1. According to the mass concrete pouring plan, divide the mass concrete into a top layer, a bottom layer and more than one intermediate layer. There is at least one set of independent temperature control pipelines in each layer, and temperature monitoring points are designed in the mass concrete; S2. Based on the concrete mix proportion parameters and adiabatic temperature rise test data, construct a three-dimensional thermal field finite element model including the hydration heat release curve, the heat transfer effect of the temperature control pipelines and the concrete environmental boundary conditions. The finite element model outputs the temperature data of each point in the model after calculation under various initial conditions; S3. Use the temperature data generated by the finite element model to train the temperature field prediction model. The input parameters include the temperatures of each temperature monitoring point at the current moment in the concrete, the inlet water temperature and flow rate of the temperature control pipelines in each layer of concrete, and the predicted amount of hydration heat generated in each layer of concrete between the current moment and the prediction moment. The output value of the temperature field prediction model is the predicted temperature value of each temperature monitoring point in the mass concrete structure at the prediction moment; S4. Install each set of temperature control pipelines according to the design before pouring the mass concrete structure and set temperature sensors at the temperature monitoring points. After each temperature control pipeline is inspected for water passing, complete the pouring of the mass concrete structure. After pouring, cover the concrete surface for heat preservation; S5. Input the measured temperature values of each current temperature monitoring point, the parameters of each layer of temperature control pipelines, the predicted amount of hydration heat of each layer of concrete, meteorological parameters and the parameters related to the covering measures into the temperature field prediction model, and output the predicted temperature values of each temperature monitoring point at the next prediction moment. If there is a situation where the predicted temperature value exceeds the temperature difference control requirement, adjust the inlet water temperature and flow rate of the temperature control pipelines in the corresponding concrete layer, and adjust the covering measures of the mass concrete structure. Input the adjusted temperature control pipeline parameters, the adjusted parameters related to the covering measures and other parameters into the temperature field prediction model, and recalculate the predicted temperature values of each temperature monitoring point at the next prediction moment until the temperature difference control requirement is met, and execute the temperature control strategy with the parameters adopted in this prediction; S6. Repeat step S5 periodically until the concrete structure reaches the curing age.

2. The method for curing mass concrete based on an intelligent temperature control system according to claim 1, wherein, The temperature control pipeline is an independent circulating water path, and a variable frequency water pump, a temperature regulation module and a flow direction change module are arranged outside the mass concrete structure.

3. The large-volume concrete curing method based on the intelligent temperature control system according to claim 1, characterized in that, Step S2 includes: S21. Obtain the hydration heat release curve according to the concrete mix proportion and test data, and obtain the top surface heat transfer coefficient and the bottom surface heat transfer coefficient based on the covering measures at the top and bottom of the mass concrete structure; S22. According to the layering situation of the concrete structure, establish a concrete structure model with layering and considering the heat transfer effect of the internal temperature control pipelines in the finite element calculation software. The concrete material properties include the hydration heat release curve, and the top surface heat transfer coefficient and the bottom surface heat transfer coefficient are used as the interface boundary conditions of the concrete structure model; S23. Use different top surface heat transfer coefficients, bottom surface heat transfer coefficients, initial setting times of each layer, initial temperatures of each layer, and temperature control pipeline parameters of each layer as loading conditions, and the output result is the temperature data of each temperature monitoring point after a certain time obtained based on the loading conditions.

4. The method for curing mass concrete based on an intelligent temperature control system according to claim 3, wherein, In step S21, the heat of hydration release curve is , where Q 0 is the initial peak heat of hydration, k is the attenuation coefficient obtained from the same material test, t 0 is the initial setting time of the concrete; the top surface heat transfer coefficient , the bottom surface heat transfer coefficient , where is the atmospheric composite heat dissipation coefficient, is the thermal conductivity of the covering, is the thickness of the covering, is the exposure coefficient with a value range of 0.1 to 1, is the thermal conductivity of the concrete cushion, is the thickness of the cushion, is the equivalent heat dissipation coefficient of the foundation.

5. The method for curing mass concrete based on an intelligent temperature control system according to claim 3, characterized in that, In step S22, the method for establishing a concrete structure model with layering and considering the heat dissipation of the internal temperature control pipeline is to equivalent the heat transfer effect of the temperature control pipeline to a distributed heat sink, and the calculation formula for the heat sink intensity is , where s is the pipeline spacing of the temperature control pipeline, D is the inner diameter of the temperature control pipeline, L is the length of the temperature control pipeline, v is the designed water flow velocity, α w is the thermal diffusivity of water, T w is the initial temperature of water, T c The real-time temperature of the concrete calculation unit around the temperature control pipeline is updated in real time through finite element iteration. After the mesh division is completed, the heat sink intensity is applied as a volume heat source to the concrete elements around the temperature control pipeline.

6. The method for curing mass concrete based on an intelligent temperature control system according to claim 4, wherein The atmospheric composite heat dissipation coefficient , where w is the wind speed,[[]] is the Stefan-Boltzmann constant,[[]] is the emissivity of the concrete surface,[[]] T a is the atmospheric temperature,[[]] T s is the real-time temperature of the calculation unit on the top surface of the concrete, which is updated in real time through finite element iterative calculation.[[]] 7. The method for curing mass concrete based on an intelligent temperature control system according to claim 4, characterized in that, The exposure factor when there is no covering on the concrete surface , when the covering on the concrete surface does not completely cover , when the covering on the concrete surface is tightly covered .

8. The large-volume concrete curing method based on an intelligent temperature control system according to claim 1, characterized in that, The temperature field prediction model adopts a BP neural network model including an input layer, a hidden layer and an output layer. The input layer of the BP neural network model includes a plurality of input neuron nodes. A plurality of hidden neuron nodes are arranged on the hidden layer. Each of the input neuron nodes is respectively connected to each of the hidden neuron nodes. The hidden neuron nodes are all connected to the output layer. Meteorological parameters, parameters related to covering measures, initial temperature data of temperature monitoring points, predicted values of concrete hydration heat of each layer, water inlet parameters of temperature control pipelines of each layer, etc. are used as input values of the input neuron nodes. The output result is compared with the temperature data of the temperature monitoring points obtained by finite element calculation, and the BP neural network model is optimized by the algorithm to obtain the temperature field prediction model.

9. The method for curing mass concrete based on an intelligent temperature control system according to claim 8, characterized in that Step S5 includes the following steps: S51. Real-time collect the measured temperature values of each temperature monitoring point; S52. Input the measured temperature values of each temperature monitoring point, meteorological parameters obtained according to meteorological forecasts, parameters related to on-site covering measures, predicted values of concrete hydration heat of each layer, and water inlet parameters of temperature control pipelines of each layer into the trained temperature field prediction model to predict the temperature prediction values of each temperature monitoring point at the next moment; S53. Calculate the same-point temperature difference between each temperature prediction value and the measured temperature value of the corresponding temperature monitoring point and compare it with the cooling control threshold. Calculate the two-point temperature difference with the temperature prediction values of any two temperature monitoring points and compare each two-point temperature difference with the temperature difference control threshold; S54. If there is no case where the same-point temperature difference exceeds the cooling control threshold and no two-point temperature difference exceeds the temperature difference control threshold, then maintain the existing water inlet parameters of the temperature control pipeline and the covering measures. If there is a case where the same-point temperature difference exceeds the cooling control threshold or a two-point temperature difference exceeds the temperature difference control threshold, adjust the water inlet parameters of the corresponding layer and the covering measures on the concrete surface. Use the new water inlet parameters and parameters related to the covering measures as input values, and repeat steps S52 to S53 until all the same-point temperature differences and all two-point temperature differences in the new plan meet the requirements, and immediately execute this plan as the implementation plan.

10. The method for curing mass concrete based on an intelligent temperature control system according to claim 9, wherein, The temperature difference control threshold is 20°C, and the cooling control threshold is 2°C / d.

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