Variable temperature curing method for shotcrete specimens in high-temperature tunnels

Abstract

This invention discloses a variable-temperature curing method for shotcrete specimens in high-temperature tunnels, relating to the field of concrete construction technology. The method includes the following steps: First, concrete specimens with different water-cement ratios, aggregate types, and additive contents are acquired, their hydration heat data are recorded and normalized to generate a training sample dataset. Second, a neural network prediction model is established based on this dataset, taking concrete characteristics as input and outputting predicted hydration heat values. Third, the hydration heat influence index is calculated by combining thickness and surface roughness, while simultaneously acquiring environmental parameters within the tunnel, including temperature, humidity, and wind speed, to calculate an environmental influence index. Finally, using the thermal conductivity of the tunnel surface structure, and considering both the environmental and hydration heat influence indices, the curing temperature of the concrete specimens under test is dynamically adjusted to ensure accurate and effective temperature control, significantly improving the overall efficiency and quality of construction.

Description

Technical Field

[0001] This invention relates to the field of concrete construction technology, specifically to a variable temperature curing method for shotcrete specimens in high-temperature tunnels. Background Technology

[0002] High-temperature tunnel construction is a crucial technology in modern infrastructure development. The complex environmental conditions and high temperatures make concrete curing a critical issue. In high-temperature environments, the hydration rate of concrete accelerates, and if not effectively controlled, this can lead to a decrease in concrete strength and durability. Traditional curing methods often rely on manual experience, which is difficult to adapt to environmental changes, resulting in uneven concrete quality and structural safety hazards. In high-temperature tunnels, environmental parameters such as temperature, humidity, and wind speed fluctuate frequently, posing a significant challenge to the concrete hydration process. Due to a lack of effective monitoring and control methods, construction workers often struggle to achieve precise control of the concrete curing temperature.

[0003] In the early stages of hydration, the heat of hydration can accelerate the strength development of concrete and improve its early strength. However, excessive heat of hydration may lead to a decrease in strength later on. In the construction of large-volume concrete, the heat of hydration can cause the internal temperature of the concrete to rise, easily leading to temperature cracks. Appropriate temperature control measures, such as cooling water pipes and admixtures, can slow down the release rate of heat of hydration. The effects of heat of hydration also delay the curing time of concrete, affecting its impermeability and durability. Proper control of heat of hydration ensures that concrete is cured at optimal humidity and temperature, thereby improving its durability. Therefore, the heat of hydration reaction affects the performance of concrete in many ways and is an important factor that cannot be ignored.

[0004] Currently, many studies have explored the characteristics of concrete hydration heat and its impact on concrete performance. However, in the application of high-temperature tunnels, how to scientifically and in real-time monitor and regulate the curing temperature of concrete through hydration heat remains a pressing technical challenge. Furthermore, existing technologies often rely on static data analysis, lacking dynamic prediction and adjustment capabilities, and cannot respond to environmental changes in real time, thus limiting the efficiency and effectiveness of concrete curing. Therefore, it is necessary to develop a new temperature control method that utilizes advanced data processing technology and artificial intelligence models to achieve accurate prediction of concrete hydration heat and dynamic regulation of curing temperature.

[0005] In the prior art, CN116680782A discloses a temperature control method for large-volume concrete construction based on temperature rise regulation. The specific scheme includes: establishing an experimental model of cement hydration and concrete heat transfer; establishing a refined temperature field numerical calculation model based on hydration degree according to structural geometric parameters, boundary heat transfer conditions, and construction and curing measures; extracting the highest internal temperature and the maximum temperature difference between the inner and outer surfaces as temperature control indicators, and comparing them with design requirements while meeting specification requirements; those meeting the requirements can be packaged and output as temperature control technology. However, while the "highest temperature" and "maximum temperature difference between the inner and outer surfaces" mentioned in this scheme can reflect the temperature rise of the concrete, they may not fully reflect the actual performance and state of the concrete. The lack of a comprehensive assessment of other influencing factors (such as environmental impact and roughness risk) may lead to incomplete temperature control of the concrete. Furthermore, this scheme relies solely on model calculations and lacks the ability to monitor changes in the concrete temperature field in real time. In actual construction, environmental conditions may change at any time; if temperature control measures are not adjusted in real time, the concrete temperature may exceed the standard or become uneven. Therefore, the accuracy and effectiveness of the temperature control method are reduced.

[0006] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] The purpose of this invention is to provide a variable temperature curing method for shotcrete specimens in high-temperature tunnels, so as to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A variable-temperature curing method for shotcrete specimens in high-temperature tunnels, comprising the following steps:

[0010] Several concrete specimen samples with different water-cement ratios, aggregate types, and additive contents were obtained. Hydration heat data of different concrete specimen samples under the same mass were obtained through experiments. The obtained hydration heat data were normalized and preprocessed. The preprocessed hydration heat data were mapped one-to-one with the water-cement ratio, aggregate type, and additive content data of the corresponding concrete specimen samples to generate a training sample dataset.

[0011] Based on the data in the training sample dataset, a neural network prediction model is established. The water-cement ratio, aggregate type and additive content data of concrete specimens in the training sample dataset are used as input to the neural network prediction model, and the corresponding heat of hydration data are used as labels to train the neural network prediction model and obtain the heat of hydration prediction model.

[0012] Data on the water-cement ratio, aggregate type, and additive content of the concrete specimen to be tested are obtained and input into the trained hydration heat prediction model to obtain the predicted hydration heat value of the concrete specimen to be tested. Based on the obtained predicted hydration heat value of the concrete specimen to be tested, combined with the surface roughness and thickness of the concrete specimen to be tested, the hydration heat influence index is calculated.

[0013] The environmental parameters inside the high-temperature tunnel are obtained, and the environmental impact index is calculated based on the obtained environmental parameters inside the high-temperature tunnel. The environmental parameters inside the high-temperature tunnel include the real-time temperature of the tunnel surface, the real-time humidity inside the tunnel, and the real-time wind speed inside the tunnel.

[0014] The thermal conductivity of the tunnel surface structure inside the high-temperature tunnel is collected. Based on the obtained thermal conductivity of the tunnel surface structure, combined with the environmental impact index and the hydration heat impact index, the curing temperature of the concrete specimen to be tested is dynamically corrected to obtain the accurate real-time curing temperature value. Based on the obtained accurate real-time curing temperature value, the curing temperature of the concrete specimen to be tested is adjusted to complete the temperature control of concrete specimen curing.

[0015] Furthermore, the obtained heat of hydration data is preprocessed by normalization, and the specific formula used for normalization is as follows:

[0016]

[0017] In the formula, The data represents the normalized heat of hydration for the i-th concrete specimen. For the heat of hydration data of the i-th concrete specimen, This represents the minimum heat of hydration data for the concrete specimen. This represents the maximum heat of hydration data in the concrete specimen sample, where i is the index of the concrete specimen sample. ,in The total number of concrete specimen samples;

[0018] The method for generating the training sample dataset is as follows: the preprocessed heat of hydration data is mapped one-to-one with the water-cement ratio, aggregate type and additive content data of the corresponding concrete specimen samples to form a corresponding grid, and the resulting grid is recorded as the training sample dataset.

[0019] Furthermore, based on the data in the training sample dataset, a neural network prediction model is established. Specifically, a hydration heat prediction model is established using a Long Short-Term Memory (LSTM) network model. For the LSTM model, an activation function and optimization algorithm are selected, with the Tanh function chosen as the activation function and Adam as the optimization algorithm. The formula for the Tanh function is:

[0020]

[0021] In the formula, Represents the Tanh function, with the independent variable... This represents the weighted sum of the neuron's inputs, that is, the result of the weighted sum of the inputs received by the neuron from the previous layer;

[0022] Simultaneously, the hyperparameters of the LSTM model are set, including: number of network layers, number of iterations, learning rate, batch size, number of training iterations, number of batches, and number of hidden layer neurons.

[0023] The network is set to a 4-layer network structure, the number of iterations is set to 100, the learning rate is set to 0.001, the batch size is set to 32, the number of training iterations is set to 100, the batch size is set to 128, and the number of hidden layer neurons is set to 32.

[0024] The input to the trained hydration heat prediction model is the water-cement ratio, aggregate type, and additive content data of the concrete specimen, and the output is the corresponding predicted hydration heat value.

[0025] Furthermore, based on the predicted hydration heat of the concrete specimen to be tested, and combined with the surface roughness and thickness of the concrete specimen, the hydration heat influence index is calculated. The specific formula used to calculate the hydration heat influence index is as follows:

[0026]

[0027] In the formula, The heat of hydration influence index, The surface roughness of the concrete specimen to be tested. This is the predicted value for heat of hydration. The thickness of the concrete specimen to be tested. This represents the volume of the concrete specimen to be tested.

[0028] Furthermore, the surface roughness of the concrete specimen to be tested... The specific method for obtaining it is as follows:

[0029] The surface roughness measurement equipment for the concrete specimen to be tested is a stylus profilometer. Multiple sampling areas are randomly selected from the surface region of the concrete specimen, and the stylus profilometer is used to measure and analyze these areas to obtain the surface roughness of each sampling area. The average surface roughness of all sampling areas is then calculated and used as the surface roughness of the concrete specimen to be tested. The calculation formula is as follows:

[0030]

[0031]

[0032] In the formula, This represents the sampling length of the stylus profilometer in the k-th sampling region, where k is the index of the sampling region, and K is the number of sampling regions. Let q be the height of the sampling point at position q within the sampling length of the k-th sampling region, deviating from the center line, where q is the coordinate of the sampling point within the sampling length. Let be the surface roughness of the k-th sampling region. For the surface roughness of the concrete specimen to be tested, the centerline and Data is obtained through data processing using the built-in software of a stylus-type profilometer.

[0033] Furthermore, environmental parameters within the high-temperature tunnel are obtained. Based on these parameters, an environmental impact index is calculated. The specific formula used to calculate the environmental impact index is as follows:

[0034]

[0035] In the formula, For environmental impact index, Real-time temperature of the tunnel surface. Real-time humidity inside the tunnel. This represents the real-time wind speed inside the tunnel. The set reference humidity, This is the set reference temperature.

[0036] Furthermore, based on the obtained thermal conductivity of the tunnel surface structure, combined with the environmental impact index and the hydration heat impact index, the curing temperature of the concrete specimen to be tested is dynamically corrected to obtain the accurate real-time curing temperature value. The specific formula used to calculate the accurate real-time curing temperature value is as follows:

[0037]

[0038] In the formula, To ensure accurate real-time maintenance temperature values, The initial curing temperature of the concrete specimen to be tested is [value missing]. Thermal conductivity of the tunnel surface structure These are the weighting coefficients for the environmental impact index. The weighting coefficients for the influence of heat of hydration are given. The weighting coefficient for the thermal conductivity of the tunnel surface structure is denoted as , where and , and All are greater than 0.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] First, by establishing a hydration heat prediction model and dynamically adjusting the curing temperature in conjunction with environmental parameters, the curing process of concrete can be precisely controlled in real time in response to changes in temperature and humidity within the tunnel. This real-time dynamic control mechanism avoids the problem of improper temperature control due to insufficient experience in traditional curing methods, ensuring that the concrete is always in an optimal hydration state, significantly improving its strength and durability. Second, machine learning technology is used to predict the hydration heat of concrete, which not only improves the accuracy of predictions but also helps to quickly adapt to and optimize the concrete mix proportions under different conditions. Furthermore, the combination of real-time environmental parameter monitoring and dynamic adjustment strategies makes the construction process of high-temperature tunnels more flexible. This not only improves construction safety and reduces potential risks caused by temperature changes but also increases the work efficiency of construction personnel. Construction personnel can more intuitively understand the hydration state of the concrete, thereby better organizing the construction schedule and avoiding subsequent problems caused by improper curing. The application of this method in the construction of high-temperature tunnels can significantly improve the overall efficiency and quality of construction, promoting the advancement of tunnel construction technology. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the overall method flow of the present invention;

[0042] Figure 2 This is a fitted curve of surface roughness versus hydration heat effect index;

[0043] Figure 3 The fitted curve of the predicted heat of hydration and the influence index of heat of hydration;

[0044] Figure 4 Statistical charts of environmental parameters and environmental impact indices;

[0045] Figure 5 This is a fitted curve of surface temperature versus environmental impact index.

[0046] Figure 6 This is a curve showing the real-time wind speed versus the environmental impact index.

[0047] Figure 7 Comparison chart for calculating precise values ​​of curing temperature;

[0048] Figure 8 This is a comparison chart of concrete performance parameters under different curing temperatures. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0050] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0051] Example:

[0052] Please see Figures 1-8 The present invention provides a technical solution:

[0053] A variable-temperature curing method for shotcrete specimens in high-temperature tunnels, comprising the following steps:

[0054] Step 1: Obtain several concrete specimen samples with different water-cement ratios, aggregate types, and additive contents. Obtain hydration heat data of different concrete specimen samples under the same mass through experiments. Normalize the obtained hydration heat data and map the preprocessed hydration heat data one-to-one with the water-cement ratio, aggregate type, and additive content data of the corresponding concrete specimen samples to generate a training sample dataset.

[0055] The water-cement ratio is the ratio of the mass of water to the mass of cement (binder) in a concrete mix design. It is a crucial component of concrete, directly affecting its strength, durability, and workability. Different water-cement ratios are specifically defined as follows: High water-cement ratio: typically greater than 0.5. This type of concrete has good fluidity but lower strength and durability, and is prone to cracking; Medium water-cement ratio: generally between 0.4 and 0.5, achieving a balance between workability and strength; Low water-cement ratio: less than 0.4, usually used for concrete requiring high strength and low permeability, but may reduce its fluidity.

[0056] Aggregates are the main component of concrete, including coarse aggregates and fine aggregates. Different aggregate types affect the workability, strength, and durability of concrete. Specifically, aggregate types refer to: Coarse aggregates: typically stone with a particle size greater than 5mm, such as crushed stone and pebbles. Different types of coarse aggregates (such as granite, limestone, and basalt) affect the strength and density of concrete; Fine aggregates: typically sand with a particle size less than 5mm. The type of fine aggregate (such as natural sand and manufactured sand) affects the fluidity and workability of concrete; Special aggregates: such as lightweight aggregates (used to reduce the weight of concrete) and heavy aggregates (used to increase the density of concrete), the selection of which affects specific properties of concrete.

[0057] Additives are materials added to improve the performance of concrete. Different types of additives and their contents can significantly alter the properties of concrete. Specifically, the types and contents of additives refer to the proportions of additives such as water-reducing agents, retarders, accelerators, and antifreeze agents.

[0058] Hydration heat data of different concrete specimens of equal mass were obtained through experiments. The specific method involved preparing concrete samples, using a DSC (Digital Substances Computational Calorimeter) device, and setting it according to the manufacturer's instructions, including temperature range and heating rate. The mixed concrete was poured into the calorimeter's measuring chamber, with insulation material provided to reduce interference from ambient heat. The calorimeter was then activated, and temperature changes during hydration were recorded. The hydration heat was calculated from these temperature changes. Higher hydration heat can cause temperature variations in the concrete, leading to drying shrinkage and temperature stress, thus increasing the risk of cracking. The effects of hydration heat also delay the curing time of concrete, affecting its impermeability and durability. Proper control of hydration heat ensures that concrete is cured at optimal humidity and temperature, thereby improving its durability. In the early hydration reaction, hydration heat can accelerate the strength development of concrete and improve early strength. However, excessively high hydration heat may lead to a decrease in later strength.

[0059] In conclusion, the characteristics of the heat of hydration in concrete have a significant impact on its strength, crack resistance, durability, and construction control. By analyzing different factors related to the heat of hydration and taking corresponding effective curing temperature control measures, the advantages of the heat of hydration can be fully utilized, while its negative impact on concrete performance can be reduced.

[0060] The obtained heat of hydration data were preprocessed using normalization. The specific formula used for this normalization preprocessing was as follows:

[0061]

[0062] In the formula, The data represents the normalized heat of hydration for the i-th concrete specimen. For the heat of hydration data of the i-th concrete specimen, This represents the minimum heat of hydration data for the concrete specimen. This represents the maximum heat of hydration data in the concrete specimen sample, where i is the index of the concrete specimen sample. ,in The total number of concrete specimen samples;

[0063] The method for generating the training sample dataset is as follows: the preprocessed heat of hydration data is mapped one-to-one with the water-cement ratio, aggregate type and additive content data of the corresponding concrete specimen samples to form a corresponding grid, and the resulting grid is recorded as the training sample dataset.

[0064] Step 2: Based on the data in the training sample dataset, establish a neural network prediction model. Use the water-cement ratio, aggregate type, and additive content data of the concrete specimens in the training sample dataset as input to the neural network prediction model, and use the corresponding heat of hydration data as labels to train the neural network prediction model to obtain the heat of hydration prediction model.

[0065] Based on the data in the training sample dataset, a neural network prediction model is established. Specifically, a hydration heat prediction model is built using a Long Short-Term Memory (LSTM) network model. For the LSTM model, an activation function and optimization algorithm are selected, with the Tanh function chosen as the activation function and Adam as the optimization algorithm. The formula for the Tanh function is:

[0066]

[0067] In the formula, Represents the Tanh function, with the independent variable... This represents the weighted sum of the neuron's inputs, that is, the result of the weighted sum of the inputs received by the neuron from the previous layer;

[0068] Simultaneously, the hyperparameters of the LSTM model are set, including: number of network layers, number of iterations, learning rate, batch size, number of training iterations, number of batches, and number of hidden layer neurons.

[0069] The network is set to a 4-layer network structure, the number of iterations is set to 100, the learning rate is set to 0.001, the batch size is set to 32, the number of training iterations is set to 100, the batch size is set to 128, and the number of hidden layer neurons is set to 32.

[0070] The input to the trained hydration heat prediction model is the water-cement ratio, aggregate type, and additive content data of the concrete specimen, and the output is the corresponding predicted hydration heat value.

[0071] LSTM networks can capture long-term dependencies in time series data, which is especially important for predicting the cement hydration process, because the heat of hydration is a time-varying process, and the early hydration state will affect the subsequent hydration reaction.

[0072] LSTM can dynamically adjust its internal state based on the input sequence to adapt to the time-varying heat of hydration. This adaptability allows the model to maintain high prediction accuracy under different construction conditions and environmental factors. The generation of heat of hydration is not only related to factors such as water-cement ratio, temperature, and humidity, but also affected by cement type and other materials. LSTM can effectively capture these complex nonlinear relationships, and compared with traditional linear models, LSTM networks have a greater advantage in handling nonlinear features.

[0073] Step 3: Obtain the water-cement ratio, aggregate type, and additive content data of the concrete specimen to be tested, and input them into the trained hydration heat prediction model to obtain the hydration heat prediction value of the concrete specimen to be tested. Based on the obtained hydration heat prediction value of the concrete specimen to be tested, combined with the surface roughness and thickness of the concrete specimen to be tested, calculate the hydration heat influence index.

[0074] Based on the predicted heat of hydration of the concrete specimen to be tested, and combined with the surface roughness and thickness of the concrete specimen, the heat of hydration influence index is calculated. The specific formula used to calculate the heat of hydration influence index is as follows:

[0075]

[0076] In the formula, The heat of hydration influence index, The surface roughness of the concrete specimen to be tested. This is the predicted value for heat of hydration. The thickness of the concrete specimen to be tested. The volume of the concrete specimen to be tested;

[0077] It should be noted that the hydration heat impact index The heat of hydration effect index is used to represent the influence of heat released during the hydration reaction of concrete specimens. The higher the value, the more heat is released during the hydration reaction of the concrete specimen, and the more necessary it is to lower the curing temperature of the concrete specimen.

[0078] The heat of hydration is the heat generated during the hardening process of concrete. Excessive heat of hydration can lead to an increase in the internal temperature of the concrete, increasing the risk of cracking. This can be predicted using the heat of hydration value. Quantifying the potential risks of heat of hydration to concrete performance, therefore, the predicted value of heat of hydration. With hydration heat influence index Proportional, through This indicates that the higher the heat of hydration, the faster the heat may accumulate in the concrete, which may lead to significant effects of the heat of hydration, such as the formation of cracks.

[0079] The surface roughness of concrete specimens under test is typically used to assess the unevenness of the concrete surface. Variations in curing temperature affect the capillary structure in concrete; excessively high temperatures may lead to the formation of capillaries, increasing surface roughness and consequently increasing the probability of surface drying and cracking, ultimately affecting the molding quality of the concrete. Therefore, the surface roughness of concrete specimens under test is crucial. With hydration heat influence index Proportional, through an exponential function The denominator indicates the roughness. The effect is logarithmic. The effect of surface roughness on concrete structures can be nonlinear, especially when... When the value is small, the properties of the logarithm can better reflect the impact of its growth.

[0080] Under the same water-cement ratio, aggregate type, and additive content, the heat of hydration released by concrete is directly proportional to its volume. That is, the larger the specimen volume, the higher the total heat of hydration. This is because more cement and other components participate in the hydration reaction, naturally increasing the released heat; therefore, the volume of the concrete specimen to be tested... With hydration heat influence index Proportional.

[0081] The thickness of the concrete specimen being tested affects heat conduction and dissipation, thus influencing the accumulation and release of hydration heat. Concrete thickness affects its heat storage and dissipation capacity. Thicker concrete typically has a higher capacity for hydration heat accumulation, but may also experience slower heat dissipation, leading to temperature concentration and potentially affecting the quality of concrete molding. Therefore, the thickness of the concrete specimen being tested is crucial. With hydration heat influence index Proportional, in the form of square root This indicates that increasing the thickness leads to a greater impact, but the increase is not linear. At the same time, it avoids excessively large diameter values ​​from having a disproportionate impact on the calculation results, ensuring that the calculation results are smooth and reasonable.

[0082] The parameters for calculating the heat of hydration influence index are shown in Table 1.

[0083] Table 1

[0084]

[0085] Analysis of the data revealed certain correlations among different characteristic parameters. For example, the data showed a negative correlation between surface roughness and the heat of hydration effect index. As the sample number increased, the surface roughness gradually decreased (from 2.00 to 1.55), and the heat of hydration effect index also decreased accordingly (from 6.120 to 5.085). This indicates that under rougher surface conditions, the heat of hydration effect of concrete is more pronounced, potentially leading to higher heat release.

[0086] In analyzing the relationship between the predicted heat of hydration and the thickness of the concrete specimen, we found that the predicted heat of hydration gradually decreases with increasing thickness. This may be because thicker concrete specimens have greater difficulty in conducting heat to the external environment during hydration, thus affecting the efficiency of heat release. For example, the concrete thickness of sample number 5 is 36 mm, corresponding to a predicted heat of hydration value of 460, while the thickness of sample number 1 is 40 mm, with a predicted value of 500. The combined effect of thickness and heat of hydration should be considered to optimize the concrete mix design and construction process.

[0087] Furthermore, a correlation was observed between the volume of the concrete specimens tested and the heat effect index (HAI). Larger concrete volumes were generally accompanied by higher HAIs. Sample 1 had a volume of 1.50 m³ and an HAI of 6.120, while sample 6 had a volume of 1.25 m³ and an HAI of 5.475. This indicates that in larger concrete specimens, the accumulation of heat of hydration may lead to higher temperature variations, affecting the strength and durability of the concrete.

[0088] Therefore, surface roughness, predicted heat of hydration, concrete thickness and volume are important factors affecting the heat of hydration influence index.

[0089] Apply an appropriate coupling agent, such as oil or water, to the surface of the concrete specimen. Place the ultrasonic probe close to the surface of the specimen, and the instrument will emit ultrasonic waves. Measure the time it takes for the waves to travel through the concrete and calculate the thickness. Similarly, measurements can be taken at multiple locations, and the average value can be taken as the thickness of the concrete specimen to be tested.

[0090] The concrete specimen is completely immersed in a water tank of known volume. The initial water level is recorded. After the specimen is placed in the tank, the water level is measured again. The volume of water corresponding to the rise in water level is calculated. The volume of water is the volume of the concrete specimen.

[0091] The surface roughness of the concrete specimen to be tested The specific method for obtaining it is as follows:

[0092] The surface roughness measurement equipment for the concrete specimen to be tested is a stylus profilometer. Multiple sampling areas are randomly selected from the surface region of the concrete specimen, and the stylus profilometer is used to measure and analyze these areas to obtain the surface roughness of each sampling area. The average surface roughness of all sampling areas is then calculated and used as the surface roughness of the concrete specimen to be tested. The calculation formula is as follows:

[0093]

[0094]

[0095] In the formula, This represents the sampling length of the stylus profilometer in the k-th sampling region, where k is the index of the sampling region, and K is the number of sampling regions. Let q be the height of the sampling point at position q within the sampling length of the k-th sampling region, deviating from the center line, where q is the coordinate of the sampling point within the sampling length. Let be the surface roughness of the k-th sampling region. For the surface roughness of the concrete specimen to be tested, the centerline and Data is obtained through data processing using the built-in software of a stylus-type profilometer.

[0096] Step 4: Obtain environmental parameters inside the high-temperature tunnel. Based on the obtained environmental parameters inside the high-temperature tunnel, calculate the environmental impact index. The environmental parameters inside the high-temperature tunnel include the real-time temperature of the tunnel surface, the real-time humidity inside the tunnel, and the real-time wind speed inside the tunnel.

[0097] Environmental parameters inside the high-temperature tunnel were obtained, and the environmental impact index was calculated based on these parameters. The specific formula used to calculate the environmental impact index is as follows:

[0098]

[0099] In the formula, For environmental impact index, Real-time temperature of the tunnel surface. Real-time humidity inside the tunnel. This represents the real-time wind speed inside the tunnel. The set reference humidity, This is the set reference temperature.

[0100] It should be noted that the environmental impact index The environmental impact index is used to characterize the influence of the tunnel environment on the curing temperature of the concrete specimens to be tested. The higher the value, the higher the temperature inside the tunnel, and the curing temperature of the concrete specimen to be tested should be reduced.

[0101] High humidity can slow down the evaporation of moisture from the concrete surface, delaying curing. Simultaneously, the heat capacity of air increases under high humidity conditions because moist air has a higher specific heat capacity than dry air. This means that in a high-humidity environment, air can store more heat, leading to reduced heat dissipation efficiency. When humidity is high, the water vapor content in the air increases, which affects heat transfer. Moist air has lower thermal conductivity, limiting heat dissipation and potentially causing excessive heat buildup in concrete specimens, affecting concrete quality. Conversely, low humidity may accelerate moisture evaporation, triggering drying shrinkage cracks. Therefore, both excessively high and low humidity are detrimental to concrete performance and construction quality. Thus, the real-time humidity inside the tunnel is compared with the set reference humidity. The difference between the environmental impact index and the environmental impact index Proportional, calculated in square form This means that the greater the deviation of humidity from the reference humidity, the greater its contribution to the environmental impact index. When the humidity is higher or lower than the reference value, the impact is positive, especially when the deviation is large, the environmental impact index will increase significantly.

[0102] High temperatures can accelerate the hydration reaction of concrete, but they can also lead to excessively rapid moisture evaporation, affecting strength development and increasing the risk of cracking, especially in high-temperature tunnels. Therefore, the real-time surface temperature of the tunnel and its environmental impact index are crucial factors. Proportional, through an exponential function This indicates that its impact on the environmental impact index is non-linear. As the temperature deviates more from the reference temperature, the impact index rises rapidly.

[0103] Increased wind speed enhances the rate of moisture evaporation from the concrete surface, leading to lower humidity, accelerated curing, and improved heat dissipation. Higher wind speeds also carry away generated heat, preventing excessively high curing temperatures; therefore, real-time wind speed inside the tunnel is crucial. Environmental Impact Index Inversely proportional, through the square and through the logarithmic function The standardization process indicates that wind speed has a relatively small impact on the environmental impact index and does not produce drastic changes like humidity and temperature.

[0104] The reference humidity mentioned above is the set humidity. The setting can be based on expert experience and the environment inside high-temperature tunnels, and is generally set to a value of [value missing]. to Reference temperature The setup method is the same. Specific data for some sample parameters and the environmental impact index are shown in Table 2.

[0105] Table 2

[0106]

[0107] In high-temperature tunnels, environmental changes have a more significant impact on curing temperature. Therefore, adjusting the curing temperature according to the unique environmental parameters inside high-temperature tunnels can improve their durability and crack resistance.

[0108] Step 5: Collect the thermal conductivity of the tunnel surface structure inside the high-temperature tunnel. Based on the obtained thermal conductivity of the tunnel surface structure, combined with the environmental impact index and the hydration heat impact index, dynamically correct the curing temperature of the concrete specimen to be tested to obtain the accurate real-time curing temperature value. Adjust the curing temperature of the concrete specimen to be tested according to the obtained accurate real-time curing temperature value to complete the temperature control of concrete specimen curing.

[0109] Based on the obtained thermal conductivity of the tunnel surface structure, combined with the environmental impact index and the hydration heat impact index, the curing temperature of the concrete specimen to be tested is dynamically corrected to obtain the accurate real-time curing temperature value. The specific formula used to calculate the accurate real-time curing temperature value is as follows:

[0110]

[0111] In the formula, To ensure accurate real-time maintenance temperature values, The initial curing temperature of the concrete specimen to be tested is [value missing]. Thermal conductivity of the tunnel surface structure These are the weighting coefficients for the environmental impact index. The weighting coefficients for the influence of heat of hydration are given. The weighting coefficient for the thermal conductivity of the tunnel surface structure is denoted as , where and , and All are greater than 0.

[0112] Since the above explanation already illustrates the relative relationship between the environmental impact index and the heat of hydration impact index on curing temperature, it will not be repeated here. It should be noted that the square form... This indicates the nonlinear characteristics of environmental impact, meaning that the correction effect will significantly increase when the environmental impact is large, emphasizing the greater need for adjustment of curing temperature under adverse environmental conditions; through logarithmic functions... This indicates the importance of heat of hydration in affecting curing temperature, and is expressed as a logarithmic function. The effect on the accuracy of real-time maintenance temperature is significant within a small range, but the effect becomes less pronounced at larger values.

[0113] thermal conductivity of tunnel surface structure This reflects the heat absorption and dissipation capacity of the concrete specimen. Higher thermal conductivity indicates a stronger heat dissipation capacity, which may require higher curing temperatures to prevent insufficient concrete strength due to excessively low temperatures. Therefore, the thermal conductivity of the tunnel surface structure... With real-time maintenance temperature precision value Proportional, in the form of square root To reduce its impact and avoid excessive thermal conductivity from having a disproportionate effect on the calculation results, we must ensure that the calculation results are smooth and reasonable.

[0114] Among these factors, environmental conditions (such as temperature, humidity, and wind speed) have the most significant impact on the hydration process and strength development of concrete during construction, especially for high-temperature tunnels. Adverse environmental conditions directly lead to reduced concrete strength and increased cracking. Heat of hydration, the heat generated during concrete hydration, significantly affects the hydration rate and strength development. However, compared to the influence of the external environment, the impact of heat of hydration, while important, is often secondary. The thermal conductivity of the tunnel surface structure affects the concrete's heat conduction capacity, and although it influences heat dissipation to some extent, its impact is usually smaller than that of the environment and heat of hydration. Therefore, [the following is omitted as it is not directly related to the preceding text]. and , and All values ​​are greater than 0. Some precise real-time maintenance temperature values ​​are shown in Table 3.

[0115] Table 3

[0116]

[0117] The performance parameters of the concrete specimens under the precise real-time curing temperature are shown in Table 4.

[0118] Table 4

[0119]

[0120] Analysis of the data revealed that variations between the initial and precise curing temperatures significantly impact the strength and number of cracks in concrete. For example, sample 1, with an initial curing temperature of 30°C, achieved a strength of 31 MPa. At the corrected temperature, the strength increased to 31 MPa, while the number of cracks remained at 1. These results demonstrate that temperature stability during curing is crucial for maintaining concrete performance.

[0121] In most samples, the strength of the concrete was generally improved under the modified curing temperature. For example, the strength of sample number 5 increased from 32 MPa to 35 MPa, showing that the modified curing temperature can promote the hydration reaction of concrete, thereby enhancing its overall strength.

[0122] The modified curing temperature also significantly affected the number of cracks. In multiple samples, the number of cracks decreased after the modification, indicating that appropriate temperature adjustment can effectively reduce shrinkage stress in concrete during hydration, thereby reducing crack formation. For example, in sample number 5, the number of cracks decreased from 4 to 2 while the strength increased, demonstrating that optimizing the curing temperature can improve the overall durability of concrete.

[0123] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0124] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0125] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0126] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A variable-temperature curing method for shotcrete specimens in high-temperature tunnels, characterized in that, The specific steps include: Several concrete specimen samples with different water-cement ratios, aggregate types, and additive contents were obtained. Hydration heat data of different concrete specimen samples under the same mass were obtained through experiments. The obtained hydration heat data were normalized and preprocessed. The preprocessed hydration heat data were mapped one-to-one with the water-cement ratio, aggregate type, and additive content data of the corresponding concrete specimen samples to generate a training sample dataset. Based on the data in the training sample dataset, a neural network prediction model is established. The water-cement ratio, aggregate type and additive content data of concrete specimens in the training sample dataset are used as input to the neural network prediction model, and the corresponding heat of hydration data are used as labels to train the neural network prediction model and obtain the heat of hydration prediction model. Data on the water-cement ratio, aggregate type, and additive content of the concrete specimen to be tested are obtained and input into the trained hydration heat prediction model to obtain the predicted hydration heat value of the concrete specimen to be tested. Based on the obtained predicted hydration heat value of the concrete specimen to be tested, combined with the surface roughness and thickness of the concrete specimen to be tested, the hydration heat influence index is calculated. The environmental parameters inside the high-temperature tunnel are obtained, and the environmental impact index is calculated based on the obtained environmental parameters inside the high-temperature tunnel. The environmental parameters inside the high-temperature tunnel include the real-time temperature of the tunnel surface, the real-time humidity inside the tunnel, and the real-time wind speed inside the tunnel. The thermal conductivity of the surface structure of the tunnel in the high-temperature tunnel is collected. Based on the obtained thermal conductivity of the tunnel surface structure, combined with the environmental impact index and the hydration heat impact index, the curing temperature of the concrete specimen to be tested is dynamically corrected to obtain the real-time accurate curing temperature value. Based on the obtained real-time accurate curing temperature value, the curing temperature of the concrete specimen to be tested is adjusted to complete the temperature control of the concrete specimen curing. Based on the predicted heat of hydration of the concrete specimen to be tested, and combined with the surface roughness and thickness of the concrete specimen, the heat of hydration influence index is calculated. The specific formula used to calculate the heat of hydration influence index is as follows: In the formula, The heat of hydration influence index, The surface roughness of the concrete specimen to be tested. This is the predicted value for heat of hydration. The thickness of the concrete specimen to be tested. The volume of the concrete specimen to be tested; Environmental parameters inside the high-temperature tunnel were obtained, and the environmental impact index was calculated based on these parameters. The specific formula used to calculate the environmental impact index is as follows: In the formula, For environmental impact index, Real-time temperature of the tunnel surface. Real-time humidity inside the tunnel. This represents the real-time wind speed inside the tunnel. The set reference humidity, This is the set reference temperature.

2. The variable-temperature curing method for high-temperature tunnel shotcrete specimens according to claim 1, characterized in that: The obtained heat of hydration data were preprocessed using normalization. The specific formula used for this normalization preprocessing was as follows: In the formula, The data represents the normalized heat of hydration for the i-th concrete specimen. For the heat of hydration data of the i-th concrete specimen, This represents the minimum heat of hydration data for the concrete specimen. This represents the maximum heat of hydration data in the concrete specimen sample, where i is the index of the concrete specimen sample. ,in The total number of concrete specimen samples; The method for generating the training sample dataset is as follows: the preprocessed heat of hydration data is mapped one-to-one with the water-cement ratio, aggregate type and additive content data of the corresponding concrete specimen samples to form a corresponding grid, and the resulting grid is recorded as the training sample dataset.

3. The variable-temperature curing method for high-temperature tunnel shotcrete specimens according to claim 2, characterized in that: Based on the data in the training sample dataset, a neural network prediction model is established. Specifically, a hydration heat prediction model is built using a Long Short-Term Memory (LSTM) network model. For the LSTM model, an activation function and optimization algorithm are selected, with the Tanh function chosen as the activation function and Adam as the optimization algorithm. The formula for the Tanh function is: In the formula, Represents the Tanh function, with the independent variable... This represents the weighted sum of the neuron's inputs, that is, the result of the weighted sum of the inputs received by the neuron from the previous layer; Simultaneously, the hyperparameters of the LSTM model are set, including: number of network layers, number of iterations, learning rate, batch size, number of training iterations, number of batches, and number of hidden layer neurons. The network is set to a 4-layer network structure, the number of iterations is set to 100, the learning rate is set to 0.001, the batch size is set to 32, the number of training iterations is set to 100, the batch size is set to 128, and the number of hidden layer neurons is set to 32. The input to the trained hydration heat prediction model is the water-cement ratio, aggregate type, and additive content data of the concrete specimen, and the output is the corresponding predicted hydration heat value.

4. The variable-temperature curing method for high-temperature tunnel shotcrete specimens according to claim 1, characterized in that: The surface roughness of the concrete specimen to be tested The specific method for obtaining it is as follows: The surface roughness measurement equipment for the concrete specimen to be tested is a stylus profilometer. Multiple sampling areas are randomly selected from the surface region of the concrete specimen, and the stylus profilometer is used to measure and analyze these areas to obtain the surface roughness of each sampling area. The average surface roughness of all sampling areas is then calculated and used as the surface roughness of the concrete specimen to be tested. The calculation formula is as follows: In the formula, This represents the sampling length of the stylus profilometer in the k-th sampling region, where k is the index of the sampling region, and K is the number of sampling regions. Let q be the height of the sampling point at position q within the sampling length of the k-th sampling region, deviating from the center line, where q is the coordinate of the sampling point within the sampling length. Let be the surface roughness of the k-th sampling region. For the surface roughness of the concrete specimen to be tested, the centerline and Data is obtained through data processing using the built-in software of a stylus-type profilometer.

5. The variable-temperature curing method for high-temperature tunnel shotcrete specimens according to claim 4, characterized in that: Based on the obtained thermal conductivity of the tunnel surface structure, combined with the environmental impact index and the hydration heat impact index, the curing temperature of the concrete specimen to be tested is dynamically corrected to obtain the accurate real-time curing temperature value. The specific formula used to calculate the accurate real-time curing temperature value is as follows: In the formula, To ensure accurate real-time maintenance temperature values, The initial curing temperature of the concrete specimen to be tested is [value missing]. Thermal conductivity of the tunnel surface structure These are the weighting coefficients for the environmental impact index. The weighting coefficients for the influence of heat of hydration are given. The weighting coefficient for the thermal conductivity of the tunnel surface structure is denoted as , where and , and All are greater than 0.

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