Concrete temperature field prediction method and storage medium

Abstract

This application relates to the field of concrete engineering technology and provides a method and storage medium for predicting concrete temperature fields. The method includes: first, acquiring basic information and measured temperature information of the poured concrete; second, establishing a three-dimensional heat conduction numerical model including a hydration heat source term based on the basic information, wherein the early window parameters in the three-dimensional heat conduction numerical model are determined based on historical data; third, outputting predicted concrete temperature field information based on the early window parameters; fourth, providing the measured temperature to the three-dimensional heat conduction numerical model, which can update the early window parameters; and finally, outputting updated predicted concrete temperature field information based on the updated early window parameters. The concrete temperature field prediction method described in this application helps improve the accuracy of concrete temperature field prediction.

Description

Technical Field

[0001] This application relates to the field of concrete engineering technology, and in particular to a method for predicting the temperature field of concrete and a storage medium. Background Technology

[0002] Large-volume concrete experiences a significant temperature rise in its early years after pouring due to the concentrated release of heat of hydration. If the internal peak temperature is too high or the temperature difference between the inside and outside is too large, temperature cracks can easily be induced, thereby affecting the structural durability and safety. Therefore, it is usually necessary to predict the concrete temperature field before pouring in order to formulate construction measures in advance. However, traditional methods for predicting concrete temperature fields cannot achieve dynamic prediction during construction, which is not conducive to further improving the accuracy of concrete temperature field prediction. Summary of the Invention

[0003] In view of this, this application aims to propose a method for predicting the temperature field of concrete to improve the accuracy of concrete temperature field prediction.

[0004] To achieve the above objectives, the technical solution of this application is implemented as follows:

[0005] A method for predicting the temperature field of concrete includes:

[0006] S1. Obtain the foundation information and measured temperature information of the poured concrete;

[0007] S2. Based on the aforementioned basic information, establish a three-dimensional heat conduction numerical model that includes a hydration heat source term. The early window parameters in the three-dimensional heat conduction numerical model are determined based on historical data.

[0008] S3. Based on the early window parameters, the three-dimensional heat conduction numerical model outputs the early predicted temperature field information of the concrete.

[0009] S4. The measured temperature information is provided to the three-dimensional heat conduction numerical model, which can update the early window parameters.

[0010] S5. Based on the updated early window parameters and the measured temperature information, the three-dimensional heat conduction numerical model outputs updated predicted temperature field information of the concrete.

[0011] Furthermore, in step S1, the basic information includes at least one of the following: structural dimensions, concrete material parameters and mix proportions, subbase material parameters, insulation layer configuration, construction and maintenance plan information, and ambient temperature scenario information.

[0012] Furthermore, in step S1, the measured temperature information includes multiple ambient temperature information and multiple actual temperature information of the concrete after pouring.

[0013] Furthermore, in step S2, the early window parameters include at least one of the following: hydration heat parameters, top surface heat transfer parameters, side surface heat transfer parameters, bottom surface interface contact thermal resistance parameters, and bottom surface reference temperature parameters; and / or, in step S2, the historical data includes at least one of the following: material test data, historical engineering data, simulation data, and empirical data.

[0014] Furthermore, in step S2, establishing a three-dimensional heat conduction numerical model including hydration heat source terms includes: establishing three-dimensional heat conduction numerical models including hydration heat source terms for the top boundary, side boundary and bottom boundary of the concrete respectively.

[0015] Furthermore, a three-dimensional heat conduction numerical model including the hydration heat source term is established for the bottom boundary. The model is constructed using the equivalent boundary modeling method of series thermal resistance, and the bottom thermal resistance includes at least the thermal resistance of the insulation layer and the thermal resistance of the concrete cushion layer.

[0016] Furthermore, the bottom surface thermal resistance also includes at least one of the interface contact thermal resistance and the foundation equivalent thermal resistance; and / or, the bottom surface boundary is defined using the Robin boundary condition.

[0017] Furthermore, in steps S2 and S5, the temperature field prediction information includes at least one of the following: temperature field prediction result, temperature field prediction range, upper limit of peak temperature prediction, and prediction range of maximum temperature difference between the core and surface.

[0018] Furthermore, within the prediction period, steps S4 and S5 are repeated once every preset time interval; and / or, in step S4, the three-dimensional heat conduction numerical model can update the early window parameters by: the three-dimensional heat conduction numerical model updates the early window parameters according to a genetic algorithm, wherein the improvement strategy of the genetic algorithm includes at least one of the following strategies: individuals in the initial population are randomly generated from a prior parameter set, adaptive crossover probability and adaptive mutation probability are adopted, constraint repair operators are set, and parameters that are out of bounds or do not conform to physical constraints are repaired to the feasible region.

[0019] Compared with related technologies, this application has the following advantages:

[0020] (1) The concrete temperature field prediction method of this application obtains the basic information and measured temperature information of the poured concrete. Based on the basic information, a three-dimensional heat conduction numerical model including the hydration heat source is established. The early window parameters in the three-dimensional heat conduction numerical model are determined based on historical data. Based on the early window parameters, the three-dimensional heat conduction numerical model outputs the early predicted concrete temperature field information. The measured temperature information is provided to the three-dimensional heat conduction numerical model, which can update the early window parameters. Based on the updated early window parameters, the three-dimensional heat conduction numerical model outputs the updated predicted concrete temperature field information. This method can predict the concrete temperature field before pouring and update the predicted parameters based on the measured parameters after pouring, forming a closed-loop prediction mechanism that is available before construction and updatable during construction, thereby improving the accuracy of concrete temperature field prediction.

[0021] (2) By collecting at least one of the following: structural dimensions, concrete material parameters and mix proportion information, subbase material parameters, insulation layer settings, construction and maintenance scheme information and ambient temperature scenario information, the model can be provided with complete input that fits the actual project, which helps to avoid prediction deviations caused by missing data or rough parameters, and helps to ensure the accuracy of concrete temperature field prediction.

[0022] (3) The measured temperature information includes multiple ambient temperature information and multiple actual temperature information of the concrete after pouring. It can obtain temperature information and ambient temperature information at different locations of the concrete after pouring, providing sufficient measured basis for early window parameter updates, which is conducive to improving the accuracy of early window parameter updates, and thus conducive to improving the accuracy of concrete temperature field prediction.

[0023] (4) The early window parameters include at least one of the following: hydration heat parameters, top surface heat transfer parameters, side surface heat transfer parameters, bottom surface interface contact thermal resistance parameters, and bottom surface reference temperature parameters. These parameters can describe the heat transfer parameters of different parts of the concrete and improve the accuracy of concrete temperature field prediction.

[0024] The historical data includes at least one of material test data, historical engineering data, simulation data, and empirical data, which can avoid prediction bias caused by purely empirical parameter calculations and improve the accuracy of concrete temperature field prediction.

[0025] (5) By establishing three-dimensional heat conduction numerical models containing hydration heat source terms for the top, side and bottom boundaries of concrete, it is possible to match the differences in heat dissipation characteristics in different regions and avoid the prediction error caused by the simplification of the unified boundary, thereby improving the accuracy of concrete temperature field prediction.

[0026] (6) Establish a three-dimensional heat conduction numerical model for the bottom boundary that includes the hydration heat source term. The model is modeled using the equivalent boundary modeling method of series thermal resistance. The bottom thermal resistance includes at least the thermal resistance of the insulation layer and the thermal resistance of the concrete cushion layer. This can restore the multi-layer heat transfer path of the concrete bottom surface, which is conducive to improving the simulation accuracy of bottom surface heat transfer and thus improving the accuracy of concrete temperature field prediction.

[0027] (7) The bottom surface thermal resistance also includes at least one of the interface contact thermal resistance and the foundation equivalent thermal resistance, which is conducive to further improving the simulation accuracy of bottom surface heat transfer, thereby improving the accuracy of concrete temperature field prediction.

[0028] The bottom boundary is defined using Robin boundary conditions, which helps to further improve the accuracy of concrete temperature field prediction.

[0029] (8) In steps S2 and S5, the temperature field prediction information includes at least one of the following: temperature field prediction result, temperature field prediction range, upper limit of peak temperature prediction, and maximum temperature difference prediction range between core and surface. This facilitates the selection of temperature control measures by construction personnel and helps ensure the quality of the project.

[0030] (9) During the prediction period, steps S4 and S5 are repeated once every preset time, which can update the temperature field prediction information of concrete in a rolling manner, which is conducive to improving the accuracy of the temperature field prediction information of concrete.

[0031] The three-dimensional heat conduction numerical model can update early window parameters by: updating early window parameters based on a genetic algorithm. The improvement strategy of the genetic algorithm includes at least one of the following strategies: individuals in the initial population are randomly generated from the prior parameter set; adaptive crossover probability and adaptive mutation probability are adopted; constraint repair operators are set; parameters that are out of bounds or do not conform to physical constraints are repaired to the feasible region. This can avoid problems such as unidentifiable early window parameters, unstable prediction results, overfitting, and non-physical problems under the condition of a small amount of measured data. It adjusts the early window parameters to the actual engineering state and improves the accuracy of concrete temperature field prediction information.

[0032] Another objective of this application is to provide a storage medium for storing a computer program that, when executed by a processor, enables the concrete temperature field prediction method described above.

[0033] The storage medium described in this application has the same beneficial effects as the prior art and the concrete temperature field prediction method described above, so it will not be described in detail here. Attached Figure Description

[0034] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0035] Figure 1 This is a flowchart of the concrete temperature field prediction method described in the embodiments of this application;

[0036] Figure 2 This is a flowchart illustrating the updating of early window parameters based on a genetic algorithm, as described in an embodiment of this application. Detailed Implementation

[0037] To make the technical solution and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0038] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0039] Furthermore, it should be noted that in the description of this application, if terms such as "upper," "lower," "inner," or "outer" appear, indicating orientation or positional relationship, these are based on the orientation or positional relationship shown in the accompanying drawings and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on this application. In addition, if terms such as "first" or "second" appear, they are also used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0040] Furthermore, in the description of this application, unless otherwise expressly defined, the terms "installation," "connection," "joining," and "connector" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application in light of the specific circumstances.

[0041] In this application, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0042] The present application will now be described in detail through exemplary embodiments. However, it should be understood that, without further description, elements, structures, and features in one embodiment may be advantageously incorporated into other embodiments.

[0043] The embodiments of the first aspect of this application provide a method for predicting the temperature field of concrete, which is typically applied in the construction of large-volume concrete. Furthermore, through methodological improvements, this application can enhance the accuracy of concrete temperature field prediction.

[0044] In related technologies, large-volume concrete experiences a significant temperature rise in its early years after pouring due to the concentrated release of hydration heat. If the internal peak temperature is too high or the temperature difference between the inside and outside is too large, temperature cracks can easily be induced, thereby affecting the structural durability and safety. Therefore, it is usually necessary to predict the concrete temperature field before pouring in order to formulate construction measures in advance. However, traditional methods for predicting concrete temperature fields cannot achieve dynamic prediction during construction, which is not conducive to further improving the accuracy of concrete temperature field prediction.

[0045] In view of this, in order to overcome the shortcomings of related technologies, the concrete temperature field prediction method in this embodiment combines... Figure 1 In terms of overall design, it includes:

[0046] S1. Obtain the foundation information and measured temperature information of the poured concrete;

[0047] S2. Based on the basic information, establish a three-dimensional heat conduction numerical model that includes the hydration heat source term. The early window parameters in the three-dimensional heat conduction numerical model are determined based on historical data.

[0048] S3. Based on the early window parameters, the three-dimensional heat conduction numerical model outputs the early predicted temperature field information of the concrete.

[0049] S4. Provide the measured temperature information to the three-dimensional heat conduction numerical model, which can update the early window parameters.

[0050] S5. Based on the updated early window parameters, the three-dimensional heat conduction numerical model outputs updated predicted temperature field information for concrete.

[0051] Therefore, by acquiring the basic information and measured temperature information of the poured concrete, and based on the basic information, a three-dimensional heat conduction numerical model including a hydration heat source is established. The early window parameters in the three-dimensional heat conduction numerical model are determined based on historical data. Based on the early window parameters, the three-dimensional heat conduction numerical model outputs early predicted concrete temperature field information. Providing the measured temperature information to the three-dimensional heat conduction numerical model allows it to update the early window parameters. Based on the updated early window parameters, the three-dimensional heat conduction numerical model outputs updated predicted concrete temperature field information. This enables prediction of the concrete temperature field before pouring and updating the prediction parameters after pouring based on measured parameters, forming a closed-loop prediction mechanism that is usable before construction and updatable during construction, thereby improving the accuracy of concrete temperature field prediction. Furthermore, improving the accuracy of concrete temperature field prediction also facilitates pre-construction scheme comparison, early warning threshold setting, and sensor placement optimization, ultimately contributing to improved construction quality.

[0052] Based on the above method, in specific implementation, let the spatial coordinates of any point in the concrete be (x, y, z), and the temperature field be represented as T(x, y, z, t), where t is the age after pouring. To facilitate the establishment of a computable model, the following basic assumptions are made: the concrete is continuous, homogeneous, or zone-homogeneous within the computational region; heat transfer within the concrete is mainly by conduction; the heat released during cement hydration enters the governing equations as a volumetric heat source; equivalent convective heat transfer boundaries are used between the top and sides and the environment; and an equivalent thermal resistance boundary consisting of a series of insulation layers, a concrete cushion layer, and contact thermal resistance is used between the bottom and the outside environment. The material thermophysical parameters are corrected according to age or temperature.

[0053] As shown above, it may include establishing a three-dimensional heat conduction numerical model for the hydration heat source term:

[0054]

[0055] Where T(x,y,z,t) represents the concrete temperature field in °C, ρ represents the concrete density in kg / m³, c represents the concrete specific heat capacity in °C, λ represents the concrete thermal conductivity in °C, q_h(t;α) represents the concrete hydration heat volumetric heat source term in W / m³, α represents the hydration heat model parameter set, and ∇ represents the spatial gradient operator.

[0056] The aforementioned hydration heat source term is determined by the first time derivative of the adiabatic temperature rise function. The parameters of the adiabatic temperature rise function include the maximum adiabatic temperature rise and the hydration heat reaction rate parameter, and its calculation formula is as follows:

[0057]

[0058] Where m represents the maximum adiabatic temperature rise; and m and n represent the hydration heat reaction rate parameters. This represents the heat source term for hydration per unit volume.

[0059] In some exemplary embodiments, in step S1, the basic information includes at least one of the following: structural dimensions, concrete material parameters and mix proportions, subbase material parameters, insulation layer configuration, construction and maintenance plan information, and ambient temperature scenario information.

[0060] This setup provides the model with complete input that closely matches the actual engineering situation, which helps to avoid prediction deviations caused by missing data or coarse parameters, and helps to ensure the accuracy of concrete temperature field prediction.

[0061] In specific implementation, the basic information in this embodiment includes at least one of the following: structural dimensions, concrete material parameters and mix proportions, subbase material parameters, insulation layer configuration, construction and curing plan information, and ambient temperature scenario information. That is, it may include structural dimensions, concrete material parameters, or only structural dimensions, or one of the other basic information items. Of course, it may also include all of the aforementioned basic information items. Preferably, the basic information in this embodiment includes all of the aforementioned items to improve the accuracy of concrete temperature field prediction.

[0062] Furthermore, in this embodiment, the insulation layer, specifically, can be a polystyrene board insulation layer well-known to those skilled in the art. This achieves good insulation performance while maintaining low operating costs. Of course, it can also be adjusted according to actual needs, as long as it meets the usage requirements.

[0063] In some exemplary implementations, step S1 includes multiple ambient temperature data points and multiple actual temperature data points of the poured concrete. This setup allows for the acquisition of temperature information at different locations of the poured concrete, as well as ambient temperature information, providing sufficient measured data for early window parameter updates. This improves the accuracy of early window parameter updates and consequently enhances the accuracy of concrete temperature field prediction.

[0064] In practical implementation, the actual temperature information of the poured concrete in this embodiment is detected by pre-embedded temperature sensors after the start of pouring. Specifically, the temperature sensors are placed on the surface layer, middle layer, and bottom layer of the concrete test block during testing.

[0065] In some exemplary embodiments, in step S2, the early window parameters include at least one of the following: hydration heat parameters, top surface heat transfer parameters, side surface heat transfer parameters, bottom surface interface contact thermal resistance parameters, and bottom surface reference temperature parameters. This configuration allows for the description of heat transfer parameters in different parts of the concrete, improving the accuracy of concrete temperature field prediction.

[0066] In specific implementation, at least one of the early window parameters in this embodiment may include hydration heat parameters, top surface heat transfer parameters, or only hydration heat parameters, or one of other historical data. Alternatively, it may include hydration heat parameters, top surface heat transfer parameters, side surface heat transfer parameters, bottom surface interface contact thermal resistance parameters, and bottom surface reference temperature parameters. Preferably, the early window parameters in this embodiment include all of the above information to improve the accuracy of concrete temperature field prediction.

[0067] In some exemplary embodiments, in step S2, the historical data includes at least one of material test data, historical engineering data, simulation data, and empirical data. This avoids prediction biases caused by purely empirical parameter calculations and improves the accuracy of concrete temperature field prediction.

[0068] In specific implementation, at least one of the historical data in this embodiment may include material test data, historical engineering data, or only material test data, or one of other historical data. Of course, it may also include hydration heat parameters, top surface heat transfer parameters, side surface heat transfer parameters, bottom surface interface contact thermal resistance parameters, and bottom surface reference temperature parameters. Preferably, the historical data in this embodiment includes all of the above information to improve the accuracy of concrete temperature field prediction.

[0069] In some exemplary embodiments, step S2, establishing a three-dimensional heat conduction numerical model including hydration heat source terms, includes establishing three-dimensional heat conduction numerical models including hydration heat source terms for the top boundary, side boundary, and bottom boundary of the concrete, respectively.

[0070] The advantage of this setup is that it can match the differences in heat dissipation characteristics in different areas, avoid prediction errors caused by simplification of uniform boundaries, and thus help improve the accuracy of concrete temperature field prediction.

[0071] In specific implementation, this embodiment uses an equivalent convective heat transfer boundary between the top surface and the external environment, and the boundary formula is as follows:

[0072]

[0073] in, The equivalent heat transfer coefficient of the top surface; Ambient temperature; This is the top surface boundary.

[0074] In this embodiment, the side boundary adopts a partitioned equivalent convection boundary, and the side boundary formula is as follows:

[0075]

[0076] in, The equivalent heat transfer coefficient on the side; is the ambient temperature; n is the normal direction outside the boundary.

[0077] Furthermore, it is worth mentioning that the above-mentioned , It can also be set as a piecewise function, that is:

[0078]

[0079]

[0080] Furthermore, the aforementioned heat transfer parameters can also be expressed in a piecewise linear form, such as:

[0081] .

[0082] In some exemplary embodiments, a three-dimensional heat conduction numerical model including hydration heat source terms is established for the bottom boundary, and the model is modeled using the equivalent boundary modeling method of series thermal resistance, and the bottom thermal resistance includes at least the thermal resistance of the insulation layer and the thermal resistance of the concrete cushion layer.

[0083] This setup can recreate the multi-layered heat transfer paths of the concrete bottom surface, which helps improve the simulation accuracy of heat transfer on the bottom surface, thereby improving the accuracy of concrete temperature field prediction.

[0084] In some exemplary embodiments, the bottom surface thermal resistance also includes at least one of the interfacial contact thermal resistance and the equivalent thermal resistance of the foundation. This configuration helps to further improve the simulation accuracy of heat transfer at the bottom surface, thereby enhancing the accuracy of concrete temperature field prediction.

[0085] In specific implementation, at least one of the bottom surface thermal resistances in this embodiment may include one of the interfacial contact thermal resistance and the equivalent thermal resistance of the foundation, or only one of the interfacial contact thermal resistance or the equivalent thermal resistance of the foundation. Of course, it may also include both interfacial contact thermal resistance and the equivalent thermal resistance of the foundation. Preferably, the bottom surface thermal resistance in this embodiment includes both interfacial contact thermal resistance and the equivalent thermal resistance of the foundation to improve the accuracy of concrete temperature field prediction.

[0086] In some exemplary implementations, the bottom boundary is defined using Robin boundary conditions. This approach has the advantage of further improving the accuracy of concrete temperature field prediction.

[0087] In specific implementation, the bottom thermal resistance of this embodiment includes the thermal resistance of the insulation layer and the concrete cushion layer, the interface contact thermal resistance and the equivalent thermal resistance of the foundation, and is defined using the equivalent Robin boundary condition of series thermal resistance, the formula of which is:

[0088]

[0089] Furthermore, its total thermal resistance formula is:

[0090]

[0091] in, The equivalent heat transfer coefficient of the bottom surface; The total thermal resistance of the bottom series connection; The interfacial thermal resistance between the concrete bottom surface and the insulation layer; This refers to the thickness of the insulation layer; The equivalent thermal conductivity of the insulation layer; Concrete cushion layer thickness; Thermal conductivity of concrete subbase; External or foundation equivalent thermal resistance; This is the reference temperature for the bottom surface.

[0092] When polystyrene board insulation is used as the insulation layer, the above total thermal resistance formula can be written as:

[0093]

[0094] in, The value refers to the thickness of the polystyrene insulation layer. All other related meanings not mentioned above can be found in the total thermal resistance formula above, and will not be elaborated upon here.

[0095] In some exemplary implementations, in steps S2 and S5, the temperature field prediction information includes at least one of the following: temperature field prediction result, temperature field prediction range, upper limit of peak temperature prediction, and prediction range of maximum temperature difference between the core and surface. This facilitates the selection of temperature control measures by construction personnel and helps ensure project quality.

[0096] In some exemplary implementations, steps S4 and S5 are repeated once at preset intervals within the prediction period. The advantage of this setup is that it allows for continuous updating of the concrete temperature field prediction information, which helps improve the accuracy of the predicted concrete temperature field.

[0097] In specific implementation, the prediction period in this embodiment can be within 6-72 hours after concrete pouring. Furthermore, the preset time in this embodiment is preferably within 24-48 hours. Of course, it can be adjusted adaptively according to actual conditions, as long as it falls within 6-72 hours.

[0098] The aforementioned preset time can be, for example, 4 hours. This means that the early window parameters are updated every 4 hours based on the measured temperature, and the three-dimensional heat conduction numerical model outputs updated predicted temperature field information for the concrete based on the updated early window parameters. Of course, the preset time can also be adaptively adjusted according to actual conditions, such as selecting 5 hours or 6 hours, as long as the requirements are met.

[0099] In some exemplary embodiments, step S4, where the three-dimensional heat conduction numerical model can update the early window parameters, includes: the three-dimensional heat conduction numerical model updates the early window parameters according to a genetic algorithm, wherein the improvement strategy of the genetic algorithm includes at least one of the following strategies: the initial population is randomly generated from the prior parameter set, adaptive crossover probability and adaptive mutation probability are adopted, constraint repair operators are set, and parameters that are out of bounds or do not conform to physical constraints are repaired to the feasible region.

[0100] In this way, problems such as unidentifiable early window parameters, unstable prediction results, overfitting, and non-physical problems can be avoided when there is a small amount of measured data. The early window parameters can be adjusted to reflect the actual engineering conditions, thereby improving the accuracy of concrete temperature field prediction information.

[0101] In specific implementation, at least one of the improvement strategies of the genetic algorithm in this embodiment may include two of the above-mentioned improvement strategies, or only one of the above-mentioned improvement strategies. Of course, it may also include all of the above-mentioned improvement strategies simultaneously. Preferably, the improvement strategy of the genetic algorithm in this embodiment includes randomly generating individuals in the initial population from the prior parameter set, using adaptive crossover probability and adaptive mutation probability, setting constraint repair operators, and repairing parameters that are out of bounds or do not conform to physical constraints to the feasible region.

[0102] In practical application, the specific steps for updating early window parameters based on the genetic algorithm are as follows:

[0103] S4.1. The initial population was determined based on the foundation information of the poured concrete and historical data;

[0104] S4.2. Substitute each individual in the initial population into the three-dimensional heat conduction numerical model to calculate a temperature curve. Obtain the individual fitness value by calculating the temperature error with the sensor measurement. Then, use the selection operator of the embedded elite strategy to select two individuals from the parent population to be crossovered.

[0105] S4.3. Encode the selected individuals in binary form and perform multi-point crossover using adaptive crossover probability;

[0106] S4.4. Adaptive mutation probability is adopted, and perturbation is performed using a perturbation formula:

[0107] S4.5. Calculate the fitness value of the individual after mutation;

[0108] S4.6. Set constraint repair operators to repair parameters that are out of bounds or do not conform to physical constraints to the feasible region;

[0109] S4.7. Select individuals that have undergone mutation to form a new maternal population;

[0110] S4.8 The program terminates when the difference in average fitness values ​​among all individuals in the offspring population across multiple consecutive generations is less than a set value. If the program terminates, the best individual in the offspring population is output as the final result; otherwise, the offspring population is used as the initial population for further iteration.

[0111] The above perturbation formula is as follows:

[0112] ;

[0113] ;

[0114] ;

[0115] in, The parameter is the unperturbed value; To represent the value of the parameter after perturbation; This represents the upper limit of parameter i; This represents the lower limit value of parameter i; The disturbance coefficient; is the sign function; u is a random number uniformly distributed on [0,1]. This represents the temperature value of the system in the kth generation; The initial temperature; Indicates the temperature decay factor; This represents the number of generations in the genetic algorithm.

[0116] The concrete temperature field prediction method in this embodiment adopts the design described above, acquiring the basic information and measured temperature information of the poured concrete. Based on the basic information, a three-dimensional heat conduction numerical model including hydration heat source terms is established. The early window parameters in the three-dimensional heat conduction numerical model are determined based on historical data. According to the early window parameters, the three-dimensional heat conduction numerical model outputs early predicted concrete temperature field information. When the measured temperature information is provided to the three-dimensional heat conduction numerical model, the model can update the early window parameters. Based on the updated early window parameters, the three-dimensional heat conduction numerical model outputs updated predicted concrete temperature field information. This method enables prediction of the concrete temperature field before pouring and updates to the prediction parameters based on measured parameters after pouring, forming a closed-loop prediction mechanism that is available before construction and updatable during construction, thereby improving the accuracy of concrete temperature field prediction.

[0117] An embodiment of the second aspect of this application provides a storage medium for storing a computer program that, when executed by a processor, enables the concrete temperature field prediction method as described in the first aspect embodiment.

[0118] In this embodiment, the storage medium is generally exemplified by a memory. This storage medium includes permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology.

[0119] Furthermore, the aforementioned information may be computer-readable instructions, data structures, program modules, or other data. Examples of storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory, or other memory technologies, CD-ROM, digital versatile optical disc (DVD), or other optical storage, magnetic tape, magnetic magnetic tape, disk storage, or other magnetic storage devices, or any other non-transfer medium that can be used to store information that can be accessed by a computing device.

[0120] The above descriptions are merely some embodiments of this application and are not intended to limit this application. The technical features or structures in the foregoing different embodiments can be arbitrarily combined to form other specific technical solutions as needed. For those skilled in the art, this application can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of the claims of this application.

Claims

1. A method for predicting the temperature field of concrete, characterized in that, The method includes: S1. Obtain the foundation information and measured temperature information of the poured concrete; S2. Based on the aforementioned basic information, establish a three-dimensional heat conduction numerical model that includes a hydration heat source term. The early window parameters in the three-dimensional heat conduction numerical model are determined based on historical data. S3. Based on the early window parameters, the three-dimensional heat conduction numerical model outputs the early predicted temperature field information of the concrete. S4. The measured temperature information is provided to the three-dimensional heat conduction numerical model, which can update the early window parameters. S5. Based on the updated early window parameters, the three-dimensional heat conduction numerical model outputs updated predicted temperature field information for the concrete.

2. The method for predicting the temperature field of concrete according to claim 1, characterized in that: In step S1, the basic information includes at least one of the following: structural dimensions, concrete material parameters and mix proportions, subbase material parameters, insulation layer configuration, construction and maintenance plan information, and ambient temperature scenario information.

3. The method for predicting the temperature field of concrete according to claim 1, characterized in that: In step S1, the measured temperature information includes multiple ambient temperature information and multiple actual temperature information of the concrete after pouring.

4. The method for predicting the temperature field of concrete according to claim 1, characterized in that: In step S2, the early window parameters include at least one of the following: hydration heat parameters, top surface heat transfer parameters, side surface heat transfer parameters, bottom surface interface contact thermal resistance parameters, and bottom surface reference temperature parameters; and / or, In step S2, the historical data includes at least one of material test data, historical engineering data, simulation data, and empirical data.

5. The method for predicting the temperature field of concrete according to claim 1, characterized in that, In step S2, establishing a three-dimensional heat conduction numerical model including a hydration heat source term includes: Three-dimensional heat conduction numerical models, including hydration heat source terms, are established for the top, side, and bottom boundaries of the concrete.

6. The method for predicting the temperature field of concrete according to claim 5, characterized in that: A three-dimensional heat conduction numerical model including the hydration heat source term is established for the bottom boundary. The model is modeled using the equivalent boundary modeling method of series thermal resistance, and the bottom thermal resistance includes at least the thermal resistance of the insulation layer and the thermal resistance of the concrete cushion layer.

7. The method for predicting the temperature field of concrete according to claim 6, characterized in that: The bottom surface thermal resistance also includes at least one of the interfacial contact thermal resistance and the foundation equivalent thermal resistance; and / or, The bottom boundary is defined using Robin boundary conditions.

8. The method for predicting the temperature field of concrete according to claim 1, characterized in that: In steps S2 and S5, the temperature field prediction information includes at least one of the following: temperature field prediction result, temperature field prediction range, upper limit of peak temperature prediction, and prediction range of maximum temperature difference between the core and surface.

9. The method for predicting the temperature field of concrete according to any one of claims 1-8, characterized in that: Within the prediction period, steps S4 and S5 are repeated once every preset time interval; and / or, In step S4, the three-dimensional heat conduction numerical model can update the early window parameters by: the three-dimensional heat conduction numerical model updates the early window parameters according to a genetic algorithm, and the improvement strategy of the genetic algorithm includes at least one of the following strategies: individuals in the initial population are randomly generated from a prior parameter set, adaptive crossover probability and adaptive mutation probability are adopted, constraint repair operators are set, and parameters that are out of bounds or do not conform to physical constraints are repaired to the feasible region.

10. A storage medium, characterized in that: The storage medium is used to store a computer program, which, when executed by a processor, enables the concrete temperature field prediction method as described in any one of claims 1-9.

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