Dam body internal temperature control method and device

Abstract

The invention discloses a dam body internal temperature control method which comprises the following steps: S1, according to the elastic strain of concrete on the surface of a dam body and the internal temperature of the dam body, determining the incidence relation of the elastic strain and the internal temperature through a machine learning method; s2, according to the incidence relation between the elastic strain of the concrete on the surface of the dam body and the internal temperature of the dam body, the internal temperature of the dam body is controlled, so that the elastic strain of the concrete on the surface of the dam body is not higher than an elastic strain allowable value; wherein the elastic strain allowable value is equal to the value obtained by dividing the ultimate tensile value of the concrete on the surface of the dam body by the safety coefficient, and the safety coefficient is larger than or equal to 1.5. Due to the adoption of the technical scheme, compared with the prior art, the internal temperature of the dam body is regulated and controlled in real time through the existing embedded cooling water pipe in the dam body, so that the safety coefficient of surface concrete can reach the standard, comprehensive temperature control and anti-cracking control of the concrete dam is facilitated, and the method is more effective than an existing measure for reinforcing surface heat preservation; and the cost is lower.

Description

Technical Field

[0001] The present invention relates to the technical field of hydropower engineering, and in particular to a method and device for controlling the internal temperature of a dam body. Background Art

[0002] During the construction period, concrete gravity dams require strict temperature control measures to prevent temperature cracks. Internal concrete crack prevention typically involves water circulation. An appropriate cooling process for the internal concrete is determined based on concrete material parameters, environmental conditions, and construction progress, ultimately reducing the temperature to a specific target. Surface concrete crack prevention typically involves applying insulation to the dam's surface. This reduces the internal and external temperature differentials, ensuring that concrete surface stresses do not exceed design values, ultimately preventing cracks.

[0003] Because gravity dams have larger cross-sectional dimensions than cast-in-place arch dams, controlling surface stresses caused by internal and external temperature differences is more challenging. Current gravity dam designs often overestimate internal safety margins while underestimating surface safety margins. To address this, measures such as enhanced surface insulation are often adopted. This requires thicker insulation materials at the construction site, making on-site implementation more difficult and increasing dam construction costs. Even more seriously, in some cases, simply strengthening surface insulation alone is insufficient to achieve the required surface concrete safety margin. Summary of the Invention

[0004] The present invention provides a method and device for controlling the internal temperature of a dam body to solve the technical problems that the existing method of strengthening surface insulation requires thickening of insulation materials at the construction site, making on-site implementation more difficult, increasing the construction cost of the dam, and in some cases making it difficult to ensure that the safety factor of the surface concrete meets the standard.

[0005] To achieve the above-mentioned purpose, the present invention adopts the following technical solutions.

[0006] In one aspect, a method for controlling the internal temperature of a dam body is provided, comprising the following steps:

[0007] S1. Determine the correlation between the elastic strain of the concrete on the dam surface and the temperature inside the dam using a machine learning method.

[0008] S2. Based on the correlation between the elastic strain of the concrete on the dam body surface and the temperature inside the dam body, the internal temperature of the dam body is controlled so that the elastic strain of the concrete on the dam body surface does not exceed the allowable elastic strain value;

[0009] The allowable value of elastic strain is equal to the ultimate tensile value of the concrete on the dam surface divided by the safety factor, and the safety factor is greater than or equal to 1.5.

[0010] Compared to elastic modulus, elastic strain can be measured directly, while elastic modulus requires indirect calculation from stress and strain obtained through laboratory experiments. Therefore, elastic strain is a more effective indicator of concrete's real-time performance. More importantly, concrete cracking is essentially caused by elastic strain exceeding the ultimate tensile value. Therefore, elastic strain, a direct indicator of concrete cracking risk, is a better indicator than elastic modulus. In the above method, the ultimate tensile value can be measured experimentally.

[0011] The above method can achieve the safety factor of surface concrete by regulating the internal temperature of the dam body in real time through the existing pre-buried cooling water pipes inside the dam body, which is beneficial to the comprehensive temperature control and crack prevention of concrete dams. It is not only more effective than the existing measures to strengthen surface insulation, but also less costly.

[0012] In some embodiments, the method for obtaining the elastic strain of the concrete on the dam surface includes:

[0013] Measure the free deformation ε of the dam concrete fr ;

[0014] The elastic strain of the concrete on the dam surface is calculated according to the following formula: e =-(1+ψ)γ R ε fr ;

[0015] Among them, ε e is the elastic strain of the concrete on the dam surface, ψ is the relaxation coefficient of the dam concrete, γ R The degree of restraint of the dam concrete.

[0016] In some embodiments, determining the association between the two using a machine learning method includes the following steps:

[0017] Pre-process the elastic strain of the concrete on the dam surface and the temperature inside the dam;

[0018] The preprocessed dataset is divided into a training set and a test set. The internal temperature of the dam body is used as the input feature and the elastic strain of the concrete on the dam body surface is used as the output label to train the machine learning model and obtain a trained machine learning model.

[0019] In some embodiments, the preprocessing includes the following steps: using a Kalman filter algorithm to remove noise interference from the collected elastic strain of the concrete on the dam surface and the temperature inside the dam;

[0020] The moving average method is used to smooth the data after removing noise interference;

[0021] Normalize the smoothed data.

[0022] In some embodiments, the machine learning model is a long short-term memory network algorithm.

[0023] On the other hand, a device for controlling the internal temperature of a dam body is provided, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0024] On the other hand, a computer-readable storage medium is provided, on which a computer program / instruction is stored, and when the computer program / instruction is executed by a processor, the steps of the above method are implemented.

[0025] In yet another aspect, a computer program product is provided, comprising a computer program / instruction, which implements the steps of the above method when executed by a processor.

[0026] The present invention has at least the following technical effects or advantages: the surface concrete safety factor can be made to meet the standard by real-time regulation of the internal temperature of the dam body through the existing pre-buried cooling water pipes inside the dam body, which is beneficial to the comprehensive temperature control and crack prevention of the concrete dam. It is not only more effective than the existing measures to strengthen surface insulation, but also has lower cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of a method for controlling the internal temperature of a dam body according to an embodiment of the present invention;

[0028] Figure 2 This is a schematic diagram of a pre-processing process in one embodiment of the present invention;

[0029] Figure 3 Schematic diagram of the process of a machine learning method in one embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0031] Example 1

[0032] See also Figure 1 A method for controlling the internal temperature of a dam body comprises the following steps:

[0033] S1. Determine the correlation between the elastic strain of the concrete on the dam surface and the temperature inside the dam using a machine learning method.

[0034] S2. Based on the correlation between the elastic strain of the concrete on the dam body surface and the temperature inside the dam body, the internal temperature of the dam body is controlled so that the elastic strain of the concrete on the dam body surface does not exceed the allowable elastic strain value;

[0035] The allowable value of elastic strain is equal to the ultimate tensile value of the concrete on the dam surface divided by the safety factor, and the safety factor is greater than or equal to 1.5.

[0036] The method for obtaining the elastic strain of the concrete on the dam surface includes:

[0037] Measure the free deformation ε of the dam concrete fr ;

[0038] The elastic strain of the concrete on the dam surface is calculated according to the following formula: e =-(1+ψ)γ R ε fr ;

[0039] Among them, ε e is the elastic strain of the concrete on the dam surface, ψ is the relaxation coefficient of the dam concrete, γ R The degree of restraint of the dam concrete.

[0040] The actual deformation of concrete ε res It includes the elastic strain, creep strain and free strain of concrete, that is,

[0041] ε res =ε e +ε cr +ε fr ;

[0042] Where, ε e is the elastic strain of concrete, ε cr is the creep strain of concrete, ε fr is the free strain of concrete.

[0043] The relaxation coefficient ψ of concrete can be expressed as: Where, γ R is the degree to which the concrete is confined.

[0044] The actual deformation of concrete can also be expressed as the relationship between the free strain of concrete and the degree of constraint, that is,

[0045] ε res =(1-γ R )ε fr ;

[0046] The elastic strain of concrete can be calculated according to the following formula:

[0047] ε e =-(1+ψ)γ R ε fr ;

[0048] The relaxation coefficient of concrete ψ is a material parameter, γ R= 1.0 (considering the strong confinement effect near the dam surface). fr The elastic strain ε of the dam concrete can be obtained in real time e .

[0049] Determining the correlation between the two through machine learning methods includes the following steps:

[0050] S11, preprocessing the collected temperature data and elastic strain data, specifically including the following steps:

[0051] 1. Place temperature sensors at key locations in the water flow area inside the dam to collect temperature data Ti (i is the time series number) during the water flow process; at the same time, calculate the elastic strain data ε of the dam surface concrete at the corresponding time point using the above method. e ,i.

[0052] 2. The Kalman filter algorithm is used to remove noise interference from the collected temperature data and elastic strain data. Its state equation and observation equation are:

[0053] Equation of state: x k =F k x k-1 +B k u k +w k-1 ;

[0054] Observation equation: z k =H k x k +v k ;

[0055] Among them, k represents the observation time, x k is the state vector, z k is the observation vector, F k is the state transfer matrix, B k is the control input matrix, H k is the observation matrix, w k is the process noise, v k is the observation noise, u k For control input.

[0056] 3. Use the moving average method to smooth the data;

[0057] 4. Normalize the data to the interval [0,1]: Normalization formula: yi′=ymax-yminyi-ymin; where ymin and y max are the minimum and maximum values ​​of the smoothed data, respectively.

[0058] S12. Select the long short-term memory network algorithm to establish the correlation model between internal temperature and surface elastic strain.

[0059] 1. The input gate, forget gate, output gate and cell state of the long short-term memory neural network, and its unit calculation formula are as follows:

[0060] Input gate: i t =σ(W xi x t +W hi h t-1 +W cict-1 +b i );

[0061] Forget gate: f t =σ(W xf x t +W hf h t-1 +W cfct-1 +b f );

[0062] Output gate: o t =σ(W xo x t +W ho h t-1 +W coct-1 +b o );

[0063] Cell status update: c t =f t ⊙c t-1 +i t ⊙tanh(W xc x t +W hc h t-1 +b c );

[0064] Hidden state update: h t =o t ⊙tanh(c t );

[0065] Among them, x t is the input, h t is the hidden state, c t is the cell state, W is the weight matrix, b is the bias term, σ is the activation function, and ⊙ represents element-by-element multiplication.

[0066] 2. The preprocessed data set is divided into a training set (70%) and a test set (30%). The internal temperature data Ti is used as the input feature, and the corresponding surface elastic strain data ε e ,i is used as the output label to train the long short-term memory network model.

[0067] 3. During the training process, the model performance is optimized by adjusting the model's hyperparameters (such as the number of network layers, number of neurons, learning rate, etc.). The mean square error (MSE) is used as the loss function:

[0068]

[0069] in, To predict elastic strain data, yi is the actual elastic strain data, and n is the number of samples. The gradient descent method is used to optimize the model parameters, and the update formula is:

[0070]

[0071] Where η is the learning rate.

[0072] S13. Establishing Relationships and Model Verification

[0073] 1. The trained long short-term memory network model can be used as the correlation model between internal temperature and dam surface elastic strain. The input of the model is the internal temperature T, and the output is the corresponding surface elastic strain ε e , the association relationship can be expressed as:

[0074] ε e =f(T;θ);

[0075] Among them, θ is the model parameter.

[0076] 2. Validate the model using an independent validation data set. Input the actual measured internal temperature data into the model to obtain the predicted surface elastic strain value. Compared with the actual measured surface elastic strain data ε e,i Perform comparative analysis. Calculate the relative error between the predicted value and the actual value:

[0077] Relative error: and the coefficient of determination (R 2 ):

[0078]

[0079] in, is the mean of the true elastic strain data. If the relative error is within an acceptable range (e.g., less than 10%) and R² is close to 1 (e.g., greater than 0.9), then the established correlation model between internal temperature and dam surface elastic strain has high prediction accuracy and generalization ability and can be used as a trained machine learning model.

[0080] Example 2

[0081] A device for controlling the internal temperature of a dam body comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0082] Example 3

[0083] A computer-readable storage medium stores a computer program / instruction thereon, which implements the steps of the above method when executed by a processor.

[0084] Example 4

[0085] A computer program product comprises a computer program / instruction, which implements the steps of the above method when executed by a processor.

[0086] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0087] Similarly, it should be understood that in order to streamline the present disclosure and aid understanding of one or more of the various inventive aspects, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the claims, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Accordingly, the claims that follow the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of the invention.

[0088] Those skilled in the art will appreciate that the modules, units, or groups of devices in the examples disclosed herein may be arranged in the device described in the embodiment, or alternatively may be located in one or more devices different from the devices in the examples. The modules in the aforementioned examples may be combined into one module or further divided into multiple submodules.

[0089] It will be appreciated by those skilled in the art that the modules in the devices of the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or groups in the embodiments may be combined into one module or unit or group, and furthermore they may be divided into a plurality of submodules or subunits or subgroups. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed herein may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0090] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features and not other features included in other embodiments, the combination of features from different embodiments is intended to be within the scope of the invention and to form different embodiments.

[0091] In addition, some of the embodiments are described herein as methods or combinations of method elements that can be implemented by a processor of a computer system or by other devices that perform the functions described. Thus, a processor having the necessary instructions for implementing the method or method element forms a device for implementing the method or method element. Furthermore, the elements described herein of the device embodiments are examples of devices for implementing the functions performed by the elements for the purpose of implementing the invention.

[0092] The various techniques described herein may be implemented in conjunction with hardware or software, or a combination thereof. Thus, the methods and apparatus of the present invention, or certain aspects or portions of the methods and apparatus of the present invention, may take the form of program code (i.e., instructions) embedded in a tangible medium, such as a floppy disk, CD-ROM, hard drive, or any other machine-readable storage medium, wherein when the program is loaded into a machine such as a computer and executed by the machine, the machine becomes an apparatus for practicing the present invention.

[0093] When the program code is executed on a programmable computer, the computing device generally includes a processor, a storage medium readable by the processor (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device. The memory is configured to store the program code; the processor is configured to execute the method of the present invention according to the instructions in the program code stored in the memory.

[0094] By way of example and not limitation, computer-readable media include computer storage media and communication media. Computer-readable media include computer storage media and communication media. Computer storage media stores information such as computer-readable instructions, data structures, program modules, or other data. Communication media generally embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and includes any information delivery media. Combinations of any of the above are also included within the scope of computer-readable media.

[0095] As used herein, unless otherwise specified, the use of ordinal numbers "first," "second," "third," etc. to describe common objects merely indicates that different instances of similar objects are involved and are not intended to imply that the objects so described must have a given order in time, space, ranking, or in any other manner.

[0096] Although the present invention has been described with respect to a limited number of embodiments, it will be apparent to those skilled in the art, having benefit of the foregoing description, that other embodiments are contemplated within the scope of the invention thus described. Furthermore, it should be noted that the language used in this specification has been selected primarily for readability and didactic purposes, rather than for the purpose of explaining or limiting the subject matter of the present invention. Consequently, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is intended to be illustrative rather than restrictive of the scope of the invention, which is defined by the appended claims.

[0097] Finally, it should be noted that the present invention does not explain in detail the common knowledge recognized by technicians in this field. The above is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for controlling the internal temperature of a dam body, characterized in that: The method comprises the following steps: S1. Determine the correlation between the elastic strain of the concrete on the dam surface and the temperature inside the dam using a machine learning method. S2. Based on the correlation between the elastic strain of the concrete on the dam body surface and the temperature inside the dam body, the internal temperature of the dam body is controlled so that the elastic strain of the concrete on the dam body surface does not exceed the allowable elastic strain value; The allowable value of elastic strain is equal to the ultimate tensile value of the concrete on the dam surface divided by the safety factor, and the safety factor is greater than or equal to 1.

5.

2. The method for controlling the internal temperature of a dam body according to claim 1, wherein: The method for obtaining the elastic strain of the concrete on the dam surface includes: Measure the free deformation ε of the dam concrete fr ; The elastic strain of the concrete on the dam surface is calculated according to the following formula: e =-(1+ψ)γ R ε fr ; Among them, ε e is the elastic strain of the concrete on the dam surface, ψ is the relaxation coefficient of the dam concrete, γ R The degree of restraint of the dam concrete.

3. The method for controlling the internal temperature of a dam body according to claim 1 or 2, characterized in that: Determining the correlation between the two through machine learning methods includes the following steps: Pre-process the elastic strain of the concrete on the dam surface and the temperature inside the dam; The preprocessed dataset is divided into a training set and a test set. The internal temperature of the dam body is used as the input feature and the elastic strain of the concrete on the dam body surface is used as the output label to train the machine learning model and obtain a trained machine learning model.

4. The method for controlling the internal temperature of a dam body according to claim 3, wherein: The preprocessing includes the following steps: using a Kalman filter algorithm to remove noise interference from the collected elastic strain of the dam surface concrete and the temperature inside the dam; The moving average method is used to smooth the data after removing noise interference; Normalize the smoothed data.

5. The method for controlling the internal temperature of a dam body according to claim 3, wherein: The machine learning model is a long short-term memory network algorithm.

6. A dam body internal temperature control device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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