Mass concrete pouring temperature intelligent monitoring method

Abstract

The invention relates to the technical field of mass concrete pouring, in particular to an intelligent mass concrete pouring temperature monitoring method which comprises the steps that a transient heat balance equation is established after concrete pouring is completed, and the global instantaneous heat dissipation coefficient is determined by combining the temperature gradient of the core temperature and the surface temperature; constructing a thermal inertia-stress associated equation based on the core temperature and stress data in the stable temperature time interval to determine a global thermal inertia index; the global thermal inertia index and the reference core temperature construct a linear model to determine a temperature rise inertia item, and the reference core temperature, the global instantaneous heat dissipation coefficient and the environment temperature predicted value construct an index attenuation model to calculate a heat dissipation attenuation item; and determining a core temperature prediction value of the concrete at the future target moment according to the temperature rise inertia item and the heat dissipation attenuation item, and adjusting the laying thickness of the thermal insulation layer or determining the spraying time according to the core temperature prediction value. According to the invention, temperature cracks after large-volume concrete pouring can be avoided.

Description

Technical Field

[0001] This invention relates to the field of large-volume concrete pouring technology, and in particular to an intelligent monitoring method for the temperature of large-volume concrete pouring. Background Technology

[0002] With the rapid development of building technology towards super high-rise, long-span, and heavy structures, large-volume concrete, due to its excellent overall performance and load-bearing capacity, is widely used in core engineering projects such as high-rise building foundations, bridge abutments, and large equipment foundations. However, due to its large volume, concentrated release of cement hydration heat, and slow heat dissipation, large-volume concrete is prone to excessive temperature differences between the interior and surface, and between the surface and the environment, leading to temperature cracks that seriously threaten the integrity, durability, and safety stability of the structure.

[0003] Currently, the industry mainly controls temperature through technologies such as optimizing concrete mix proportions, improving pouring processes, adopting active temperature control measures, and passive heat preservation and curing. However, existing technologies still have problems such as insufficient temperature control accuracy, high energy consumption, and low level of intelligence in high-temperature, cold environments and ultra-large volume concrete structures, making it difficult to fully meet the higher requirements of modern engineering for the construction quality and efficiency of large volume concrete.

[0004] Existing technology provides a method for monitoring concrete temperature during vehicle-mounted concrete pouring, including the following steps: S1, determining the boundary conditions during concrete pouring: determining the concrete placement location and the paver's location, collecting and calculating the initial concrete temperature upon placement, determining the concrete surface temperature and locating the machine's movement trajectory, and monitoring the air temperature inside the placement area; S2, establishing a concrete temperature analysis model during pouring: calculating the heat transfer flux of the air inside the placement area and establishing temperature models for concrete at different depths. Although this method can predict the temperature of concrete of different thicknesses under different meteorological conditions and analyze the concrete temperature changes during pouring, providing support for concrete pouring temperature control, the model is established in the dynamic stage where the concrete is still flowing and has not yet formed a stable solid structure, and cannot cover the core heat release stage of hydration after pouring.

[0005] Therefore, there is an urgent need for an intelligent monitoring method for the temperature of large-volume concrete pouring, so as to realize the forward-looking intelligent prediction of temperature changes during the core curing period after the completion of large-volume concrete pouring. Summary of the Invention

[0006] To address this issue, the present invention provides an intelligent monitoring method for the temperature of large-volume concrete pouring, which overcomes the problem that existing technologies lack high-precision and adaptive prediction capabilities for temperature changes during the core curing period after large-volume concrete pouring.

[0007] To achieve the above objectives, the present invention provides an intelligent monitoring method for the temperature of large-volume concrete pouring, comprising: after the concrete pouring is completed, synchronously collecting core temperature and stress data at preset measurement points inside the concrete at preset sampling time intervals, and collecting surface temperature, ambient temperature and ambient wind speed at measurement points outside the concrete. A transient thermal balance equation is established based on the core temperature, ambient temperature, ambient wind speed, stress data, and insulation layer thickness. The global instantaneous heat dissipation coefficient is then determined based on the transient thermal balance equation. The stable temperature time interval is determined based on the core temperature. A thermal inertia-stress correlation equation is constructed based on the core temperature and stress data within the stable temperature time interval. The global thermal inertia index is determined by solving the thermal inertia-stress correlation equation. A linear model is constructed based on the global thermal inertia index and the reference core temperature to determine the temperature rise inertia term. An exponential decay model is constructed based on the reference core temperature, the global instantaneous heat dissipation coefficient, and the predicted ambient temperature at the future target time to calculate the heat dissipation decay term. The reference core temperature is determined based on the core temperature after the concrete is poured. The temperature rise inertia term and the heat dissipation attenuation term are weighted and summed to determine the predicted core temperature of the concrete at the future target time. The thickness of the insulation layer is adjusted or the spraying timing is determined based on the predicted core temperature.

[0008] As a preferred technical solution for intelligent monitoring of temperature during large-volume concrete pouring, the overall core temperature is determined based on the core temperature at the preset measurement point at the same sampling time point, and the overall surface temperature is determined based on the surface temperature at the external measurement point. The equivalent thermal resistance of the insulation layer is determined based on the thermal conductivity of the insulation layer and the thickness of the insulation layer. A transient thermal balance equation is established based on the overall core temperature, the overall surface temperature, the ambient wind speed, the stress data, and the equivalent thermal resistance.

[0009] As a preferred technical solution for intelligent temperature monitoring of large-volume concrete pouring, the global instantaneous heat dissipation coefficient is determined, including: Acquire current data, including overall core temperature, overall surface temperature, ambient temperature, and ambient wind speed, and determine the current equivalent thermal resistance based on the current insulation layer thickness. The current time data and the current equivalent thermal resistance are input into the transient thermal balance equation to determine the global instantaneous heat dissipation coefficient.

[0010] As a preferred technical solution for intelligent temperature monitoring of large-volume concrete pouring, the stable temperature time interval is determined, including: The temperature change rate is calculated based on the core temperature. The stabilization start time point is determined based on the temperature change rate and a first change rate threshold. The stabilization end time point is determined based on the temperature change rate and a second change rate threshold. The stabilization start time point and the stabilization end time point are then used to determine the stabilization temperature time interval. Wherein, the first rate of change threshold is less than the second rate of change threshold.

[0011] As a preferred technical solution for intelligent temperature monitoring of large-volume concrete pouring, the global thermal inertia index is determined, including: Extract the core temperature and corresponding stress data within the stable temperature time interval; Using the first derivative of the core temperature as the independent variable and the first derivative of the stress data as the dependent variable, the thermal inertia-stress correlation equation is constructed. The global thermal inertia index is determined based on the slope of the thermal inertia-stress correlation equation.

[0012] As a preferred technical solution for intelligent monitoring of temperature during large-volume concrete pouring, the average ambient temperature within the stable temperature time interval is calculated, the initial temperature difference is determined based on the average ambient temperature and the reference core temperature, and the temperature rise inertia term is determined based on the initial temperature difference and the global thermal inertia index.

[0013] As a preferred technical solution for intelligent temperature monitoring of large-volume concrete pouring, the heat dissipation attenuation term is determined, including: Determine the predicted ambient temperature value at the target future time based on historical ambient temperature data and current ambient temperature. The extreme temperature difference is determined based on the baseline core temperature and the predicted ambient temperature. An exponential decay model is constructed based on the extreme temperature difference, the time difference between the future target time and the current time, and the global instantaneous heat dissipation coefficient to calculate the heat dissipation decay term.

[0014] As a preferred technical solution for intelligent temperature monitoring of large-volume concrete pouring, the predicted core temperature is compared with a preset critical temperature threshold and a target curing temperature. Based on the comparison results, the thickness of the insulation layer is adjusted or the spraying timing is determined, including: If the predicted core temperature value is greater than or equal to the preset critical temperature threshold, the thickness of the insulation layer is increased or the spraying is activated. If the predicted core temperature is less than the preset critical temperature threshold but greater than the target curing temperature, the thickness of the insulation layer is maintained. If the predicted core temperature is lower than the target curing temperature and the duration exceeds a preset time, the thickness of the insulation layer will be reduced.

[0015] As a preferred technical solution for intelligent monitoring of temperature during large-volume concrete pouring, the adjustment amount of the laying thickness is determined based on the initial laying thickness of the insulation layer, the predicted core temperature, the target curing temperature, the duration, and the preset time.

[0016] As a preferred technical solution for intelligent temperature monitoring of large-volume concrete pouring, the preset sampling time interval is dynamically updated based on the core temperature under the pre-divided pouring stages, including: Calculate the temperature change amplitude of the core temperature per unit time during each pouring stage, and adjust the preset sampling time interval based on the comparison result of the temperature change amplitude with the preset amplitude threshold of the corresponding stage.

[0017] Compared with existing technologies, the beneficial effects of this invention are as follows: By accurately collecting multi-dimensional data such as the internal and external temperatures of concrete, stress, ambient temperature, and wind speed parameters, this invention constructs transient thermal balance equations and thermal inertia-stress correlation equations, extracting the global instantaneous heat dissipation coefficient and global thermal inertia exponent respectively, thus achieving the decomposition and quantification of the heat release from hydration inside the concrete and the heat dissipation from the external environment. The temperature rise inertia term and heat dissipation decay term are calculated using linear and exponential decay models, and superimposed to obtain the core temperature prediction value, allowing for early prediction of temperature risks. Adjusting the insulation layer thickness or determining the spraying timing based on the core temperature prediction value allows for precise intervention before cracks occur, avoiding lag issues, effectively controlling the internal and external temperature differences and the risk of core overheating, improving the accuracy and efficiency of temperature control for large-volume concrete, and ensuring structural quality.

[0018] In particular, this invention combines multidimensional data to establish a transient thermal balance equation, fully restoring the dynamic heat dissipation mechanism of concrete, avoiding calculation gaps caused by missing key boundary conditions, thereby providing reliable support for subsequent heat dissipation coefficient calculation and core temperature prediction, helping to accurately control the temperature of large-volume concrete, effectively preventing cracks, and ensuring the integrity and durability of the structure.

[0019] In particular, this invention acquires multi-dimensional integrated data at the current moment, taking into account both immediacy and overall data. By using the transient thermal balance equation and heat transfer principles, it calculates the global instantaneous heat dissipation coefficient, accurately quantifies the true heat dissipation capacity of concrete under the coupling of multiple factors, solves the problem that heat dissipation characteristics in dynamic environments are difficult to quantify with fixed formulas, provides reliable parameter support for subsequent heat dissipation attenuation term calculation, makes core temperature prediction more accurate, and thus helps large-volume concrete achieve precise temperature control, effectively reduces the risk of cracks caused by temperature deviation, and ensures the integrity, stability and durability of concrete structures.

[0020] In particular, to avoid interference from fluctuating data during the temperature rise phase on the calculation results, this invention first selects data within a stable temperature time interval to ensure that the global thermal inertia index truly reflects the inherent characteristics of concrete materials and improves parameter accuracy. Secondly, it transforms the abstract thermal inertia into a concrete index, making the material's ability to resist temperature changes quantifiable and applicable, providing a reliable basis for subsequent calculations of the temperature rise inertia term, thereby improving the accuracy of future core temperature predictions.

[0021] In particular, this invention combines the initial temperature difference with the thermal inertia index to quantify the temperature rise inertia term, avoiding prediction deviations caused by ignoring internal inertia or data instability, ensuring that temperature prediction simultaneously covers both internal maintenance and external heat dissipation factors, and improving the completeness and accuracy of future core temperature prediction.

[0022] In particular, this invention provides a precise and efficient basis for temperature control of large-volume concrete through a graded response mechanism. On the one hand, by comparing the predicted core temperature value with the preset critical temperature threshold in advance, proactive intervention is made before crack risk or strength development is affected, significantly reducing the crack incidence rate and ensuring the integrity and durability of the concrete structure. On the other hand, differentiated measures are corresponding to different temperature ranges, which not only avoids the cost waste caused by excessive adjustments when the temperature is safe, but also enables precise measures to be taken when the temperature is abnormal, ensuring that the concrete is always in a state without crack risk and with normal strength, significantly improving the controllability of the curing process. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating the steps of the intelligent monitoring method for large-volume concrete pouring temperature according to an embodiment of the present invention. Figure 2 A flowchart illustrating the steps for establishing the transient thermal balance equation in an embodiment of the present invention; Figure 3 A flowchart illustrating the steps for determining the temperature rise inertia term in an embodiment of the present invention; Figure 4 A flowchart illustrating the steps for determining the heat dissipation attenuation term in an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0025] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0026] Please see Figure 1The diagram illustrates the steps of an intelligent monitoring method for the temperature of large-volume concrete pouring according to an embodiment of the present invention. To prevent temperature cracks from occurring after the large-volume concrete pouring is completed, the present invention provides an intelligent monitoring method for the temperature of large-volume concrete pouring, comprising: Step S1: After the concrete is poured, core temperature and stress data at preset measurement points inside the concrete are collected synchronously at preset sampling time intervals, and surface temperature, ambient temperature and ambient wind speed at measurement points outside the concrete are collected. Step S2: Establish a transient thermal balance equation based on the core temperature, ambient temperature, ambient wind speed, stress data, and the thickness of the insulation layer, and determine the global instantaneous heat dissipation coefficient based on the transient thermal balance equation. Step S3: Determine the stable temperature time interval based on the core temperature, construct the thermal inertia-stress correlation equation based on the core temperature and stress data within the stable temperature time interval, and determine the global thermal inertia index by solving the thermal inertia-stress correlation equation. Step S4: Based on the global thermal inertia index and the reference core temperature, a linear model is constructed to determine the temperature rise inertia term. Based on the reference core temperature, the global instantaneous heat dissipation coefficient, and the predicted ambient temperature at the future target time, an exponential decay model is constructed to calculate the heat dissipation decay term. The reference core temperature is determined based on the core temperature after the concrete pouring is completed. Step S5: The temperature rise inertia term and the heat dissipation attenuation term are weighted and summed to determine the predicted core temperature of the concrete at the future target time. The thickness of the insulation layer is adjusted or the spraying timing is determined based on the predicted core temperature.

[0027] In detail, the temperature change after the pouring of large-volume concrete is essentially the result of the combined effects of internal hydration heat release and external environmental heat dissipation. The internal cement hydration heat release exhibits an initial strong and then weak inertial characteristic, and stress is generated due to volume changes during the heat release process. This stress, in turn, affects the heat transfer efficiency. Therefore, collecting core temperature and stress data that reflect the intensity of the internal heat source in the concrete is crucial. On the other hand, the external environment drives heat exchange through temperature differences and wind speed. The thickness of the insulation layer directly determines the heat dissipation resistance of the concrete surface. Therefore, collecting ambient temperature, ambient wind speed, and the thickness of the insulation layer are essential. These parameters together construct a transient thermal balance equation, accurately quantifying the global instantaneous heat dissipation coefficient that characterizes the external heat dissipation capacity.

[0028] In detail, because a stable temperature time interval can eliminate interference from drastic temperature fluctuations and allow the core temperature and stress data to closely match the stable state of internal hydration heat release, a thermal inertia-stress correlation equation is constructed to determine the global thermal inertia index. This global thermal inertia index is used to characterize the initial strong and subsequent weak inertia of concrete hydration heat release. Simultaneously, to decompose the two core driving factors of concrete temperature change—internal heat generation and external heat dissipation—and accurately quantify and superimpose them to reconstruct the concrete temperature change, on the one hand, environmental heat dissipation is disregarded, and a linear model is constructed based on the global thermal inertia index and the benchmark core temperature to determine the temperature rise inertia term. This temperature rise inertia term is the temperature trend component determined by the material's thermal inertia. On the other hand, only the influence of the external environment is considered, and an exponential decay model is constructed based on the benchmark core temperature, the global instantaneous heat dissipation coefficient, and the predicted environmental temperature to calculate the heat dissipation decay term. This heat dissipation decay term is the temperature decrease trend component determined by the heat dissipation conditions. By weighted summing the temperature rise inertia term and the heat dissipation decay term, the internal temperature of the concrete at a future target time is predicted. This allows for advance thickening of insulation and prediction of spraying timing before temperature cracks occur, avoiding temperature control failure due to lag.

[0029] In implementation, embedded temperature and stress sensors are installed at pre-set measurement points inside the concrete, while surface-adhesive temperature sensors are installed at external measurement points. The pre-set measurement points are located at the geometric center of the concrete component and on both sides of the center, with 5 to 8 points, preferably 6. External measurement points are located at the center and edge of the concrete component surface (0.3m from the edge), with 8 to 14 points, preferably 10. Environmental measurement points are located 1m beside the concrete component, 1.2m above the ground, to detect ambient temperature and wind speed.

[0030] In practice, the preset sampling time interval ranges from 30 min to 60 min, preferably 40 min, to account for the temperature rise inertia term. With heat dissipation attenuation A weighted summation is performed to determine the predicted core temperature of the concrete at the target future time. ,as follows:

[0031] in, .

[0032] The value ranges from 0.3 to 0.4, and preferably, the value is 0.35. The value ranges from 0.6 to 0.7, and preferably, the value is 0.65.

[0033] Building upon the aforementioned embodiments, this invention precisely collects multi-dimensional data such as internal and external concrete temperatures, stress, ambient temperature, and wind speed parameters to construct transient thermal balance equations and thermal inertia-stress correlation equations. It then extracts the global instantaneous heat dissipation coefficient and global thermal inertia exponent, achieving a quantified breakdown of the heat release from hydration within the concrete and the heat dissipation from the external environment. By calculating the temperature rise inertia term and heat dissipation decay term using linear and exponential decay models, and superimposing them, a core temperature prediction value is obtained, allowing for early assessment of temperature risks. Adjusting the insulation layer thickness or determining the spraying timing based on the core temperature prediction value enables precise intervention before cracks occur, avoiding lag issues, effectively controlling the internal and external temperature differences and the risk of core overheating, improving the accuracy and efficiency of temperature control for large-volume concrete, and ensuring structural quality.

[0034] Please see Figure 2 The diagram illustrates the steps for establishing the transient thermal equilibrium equation according to an embodiment of the present invention. Specifically, in step S2, establishing the transient thermal equilibrium equation includes: Step S21: At the same sampling time point, determine the overall core temperature based on the core temperature at the preset measurement point, and determine the overall surface temperature based on the surface temperature at the external measurement point. Step S22: Determine the equivalent thermal resistance of the insulation layer based on the thermal conductivity of the insulation layer and the laying thickness; Step S23: Establish a transient thermal balance equation based on the overall core temperature, the overall surface temperature, the ambient wind speed, the stress data, and the equivalent thermal resistance.

[0035] In detail, the temperature at a single measurement point cannot represent the overall thermal state of the concrete structure. Therefore, data from multiple measurement points are integrated to obtain the overall core temperature and the overall surface temperature, so that the transient thermal balance equation can be constructed based on the actual global thermal state of the structure. Furthermore, insulation materials with low thermal conductivity (such as rock wool) or larger laying thickness will significantly increase the equivalent thermal resistance and slow down the outward transfer of heat; conversely, they will accelerate heat dissipation. If the equivalent thermal resistance of the insulation layer is ignored, the impact of insulation measures on heat dissipation cannot be reflected, and the theoretical heat flow value and global instantaneous heat dissipation coefficient calculated subsequently will be seriously inconsistent with reality.

[0036] In detail, the transient thermal balance equation is established because the heat dissipation of concrete is a dynamic equilibrium process of internal heat generation, internal conduction, and surface heat dissipation. The transient thermal balance equation is needed to relate multiple factors such as overall temperature, wind speed, stress, and equivalent thermal resistance in order to fully describe the heat dissipation mechanism.

[0037] In practice, at the same sampling time point, the overall core temperature is the sum of the average core temperatures at the preset measurement points, and the overall surface temperature is the sum of the average surface temperatures at the external measurement points. The equivalent thermal resistance of the insulation layer... The calculation formula is as follows:

[0038] in, The thickness of the insulation layer is as follows. The thermal conductivity of the insulation layer is denoted as .

[0039] Based on the law of conservation of energy, the heat lost from the concrete should be equal to the heat dissipated into the environment through the insulation layer. Considering the effects of wind speed and internal stress on heat dissipation, the transient thermal balance equation is constructed as follows:

[0040] in, For the overall core temperature, The overall surface temperature. For ambient wind speed, The average stress data at the preset measurement points, For ambient wind speed and the average stress data at the preset measurement points relational functions, The ambient temperature.

[0041] Based on the above embodiments, this invention combines multidimensional data to establish a transient thermal balance equation, fully restoring the dynamic heat dissipation mechanism of concrete, avoiding calculation gaps caused by missing key boundary conditions, thereby providing reliable support for subsequent heat dissipation coefficient calculation and core temperature prediction, assisting in precise temperature control of large-volume concrete, effectively preventing cracks, and ensuring structural integrity and durability.

[0042] Specifically, in step S2, determining the global instantaneous heat dissipation coefficient includes: Acquire current data, including overall core temperature, overall surface temperature, ambient temperature, and ambient wind speed, and determine the current equivalent thermal resistance based on the current insulation layer thickness. The current time data and the current equivalent thermal resistance are input into the transient thermal balance equation to determine the global instantaneous heat dissipation coefficient.

[0043] In detail, changes in wind speed, temperature, and insulation layer thickness all alter the heat dissipation resistance. Calculating the coefficient using historical data would be inaccurate due to a disconnect from the current actual heat dissipation capacity, leading to distorted temperature predictions. Therefore, it is crucial to obtain current data and determine the current equivalent thermal resistance. Based on the current data, the current equivalent thermal resistance, and the transient thermal balance equation, the global instantaneous heat dissipation coefficient is determined. This global instantaneous heat dissipation coefficient characterizes the overall heat dissipation capacity of the concrete structure under the current wind speed and stress.

[0044] During implementation, current-time data is used, including overall core temperature, overall surface temperature, ambient temperature, and ambient wind speed. The current equivalent thermal resistance is determined based on the current insulation layer thickness. The current-time data and the current equivalent thermal resistance are then input into the transient thermal balance equation to determine the global instantaneous heat dissipation coefficient, as follows:

[0045] Based on the above embodiments, this invention acquires multi-dimensional integrated data at the current moment, taking into account both immediacy and overall data. By using the transient thermal balance equation and heat transfer principles, it calculates the global instantaneous heat dissipation coefficient, accurately quantifies the true heat dissipation capacity of concrete under the coupling of multiple factors, solves the problem that heat dissipation characteristics in dynamic environments are difficult to quantify with fixed formulas, provides reliable parameter support for subsequent heat dissipation attenuation term calculation, makes core temperature prediction more accurate, and thus helps large-volume concrete achieve precise temperature control, effectively reduces the risk of cracks caused by temperature deviation, and ensures the integrity, stability and durability of concrete structures.

[0046] Specifically, in step S3, determining the stable temperature time interval includes: The temperature change rate is calculated based on the core temperature. The stabilization start time point is determined based on the temperature change rate and a first change rate threshold. The stabilization end time point is determined based on the temperature change rate and a second change rate threshold. The stabilization start time point and the stabilization end time point are then used to determine the stabilization temperature time interval. Wherein, the first rate of change threshold is less than the second rate of change threshold.

[0047] In implementation, because it is necessary to accurately capture the transition from rapid temperature rise to gradual temperature stabilization in concrete, the first rate of change threshold is set in the range of 0.1–0.2℃ / h, preferably 0.15℃ / h. Since it is necessary to distinguish between small fluctuations during the concrete stabilization period and significant changes during the cooling period, the second rate of change threshold is slightly higher than the first, and its range is 0.2–0.3℃ / h, preferably 0.25℃ / h.

[0048] In implementation, if the temperature change rate is less than or equal to a first change rate threshold and the duration exceeds a preset first time, the end of the last time period that meets the condition is taken as the stable start time point. If the temperature change rate is greater than or equal to a second change rate threshold and the duration exceeds a preset second time, the end of the last time period that meets the condition is taken as the stable termination time point. Preferably, to eliminate short-term random fluctuations and ensure that the start point corresponds to a true stable state, the preset first time is set to 3 hours, and the preset second time is set to 2 hours. The interval between the stable start time point and the stable termination time point is taken as the stable temperature time interval.

[0049] Specifically, in step S3, determining the global thermal inertia index includes: Extract the core temperature and corresponding stress data within the stable temperature time interval; Using the first derivative of the core temperature as the independent variable and the first derivative of the stress data as the dependent variable, the thermal inertia-stress correlation equation is constructed. The global thermal inertia index is determined based on the slope of the thermal inertia-stress correlation equation.

[0050] In detail, temperature changes directly induce stress changes. The faster the temperature change rate, the higher the stress change rate, indicating a linear correlation between temperature and stress. However, during the temperature rise phase of concrete, the heat of hydration is released rapidly, and the core temperature and stress fluctuate frequently and significantly, resulting in poor data stability and making it difficult to establish a reliable correlation. In contrast, within the stable temperature time interval, the heat of hydration release slows down, and the temperature and stress changes become more gradual, providing more representative data and ensuring the accuracy of the subsequent thermal inertia-stress correlation equation. Therefore, the core temperature and corresponding stress data within the stable temperature time interval are extracted. A correlation equation is constructed using the first derivative of the core temperature as the independent variable and the first derivative of the stress as the dependent variable. The slope of the equation can intuitively reflect the strength of this linear relationship, thereby transforming the abstract thermal inertia into a calculable global thermal inertia exponent. The global thermal inertia exponent characterizes the inherent ability of concrete to resist temperature changes, providing characteristic support for subsequent calculation of the temperature rise inertia term and the formulation of temperature control strategies.

[0051] During implementation, the core temperature and corresponding stress data within the stable temperature time interval are extracted to form a core temperature sequence. ， ,.., Extract stress data sequence , ,.., The first derivative of the core temperature As the independent variable, the first derivative of the stress data is used. With the thermal inertia-stress correlation equation as the dependent variable, the following equation is constructed:

[0052] in, i =1, 2, ..., n 1. G is the global thermal inertia index. This is the error term.

[0053] The global thermal inertia index is solved by minimizing the sum of squared errors using the least squares method. G as follows:

[0054]

[0055] Building upon the above embodiments, this invention firstly selects stable temperature time interval data to avoid interference from temperature rise fluctuations in the calculation results, ensuring that the global thermal inertia index truly reflects the inherent characteristics of concrete materials and improving parameter accuracy. Secondly, it transforms the abstract thermal inertia into a concrete index, making the material's ability to resist temperature changes quantifiable and applicable, providing a reliable basis for subsequent temperature rise inertia term calculations, thereby improving the accuracy of future core temperature predictions.

[0056] like Figure 3 The diagram shows the steps for determining the temperature rise inertia term in an embodiment of the present invention; specifically, step S4 includes: Step S411: Calculate the average ambient temperature within the stable temperature time interval; Step S412: Determine the initial temperature difference based on the average ambient temperature and the reference core temperature; Step S413: Determine the temperature rise inertia term based on the initial temperature difference and the global thermal inertia index.

[0057] In detail, diurnal temperature fluctuations can interfere with inertial analysis. Using the average ambient temperature within a stable range eliminates the influence of random fluctuations and ensures the stability of the calculation basis data. The reference core temperature is the initial thermal reference after concrete pouring. Its initial temperature difference from the average ambient temperature reflects the initial state of internal and external thermal imbalance and is the driving source of inertial action. The global thermal inertia index determines the material's temperature maintenance ability. The initial temperature difference and the global thermal inertia index combined can reasonably quantify the temperature rise inertia term. By calculating the temperature rise inertia term, the temperature continuation component resulting from the concrete's own material thermal inertia characteristics' ability to maintain the current temperature state is quantified, essentially reflecting the hindering effect of the concrete's internal hydration thermal inertia on temperature changes.

[0058] In practice, the reference core temperature is determined based on the core temperature after the concrete is poured. The reference core temperature is the average of the core temperatures at the preset measurement points at the first sampling time point after the concrete is poured.

[0059] The average ambient temperature during the stable temperature time interval is Based on average ambient temperature The initial temperature difference is determined by the reference core temperature. , The reference core temperature. Based on the initial temperature difference. and global thermal inertia index G Determine the temperature rise inertia term. .

[0060] Based on the above embodiments, the present invention combines the initial temperature difference with the thermal inertia index to quantify the temperature rise inertia term, avoiding prediction deviations caused by ignoring internal inertia or data instability, ensuring that temperature prediction simultaneously covers both internal maintenance and external heat dissipation factors, and improving the completeness and accuracy of future core temperature prediction.

[0061] like Figure 4 The diagram illustrates the steps for determining the heat dissipation attenuation term in an embodiment of the present invention. Specifically, in step S4, determining the heat dissipation attenuation term includes: Step S421: Determine the predicted ambient temperature value at the future target time based on historical ambient temperature data and the current ambient temperature; Step S422: Determine the extreme temperature difference based on the reference core temperature and the predicted ambient temperature. Step S423: Construct an exponential decay model based on the extreme temperature difference, the time difference between the future target time and the current time, and the global instantaneous heat dissipation coefficient to calculate the heat dissipation decay term.

[0062] In detail, since the temperature of large-volume concrete is affected by both internal hydration heat inertia and external heat dissipation, internal inertia alone cannot fully predict the temperature trend. Therefore, it is necessary to first combine historical and current ambient temperatures to determine the predicted ambient temperature at the target time in the future, thereby clarifying the external benchmark for heat dissipation. Then, based on the benchmark core temperature and the predicted ambient temperature, the limit temperature difference is determined, and the theoretical maximum heat dissipation potential limit temperature difference of concrete under current conditions is clarified. The larger the limit temperature difference, the higher the degree of thermal imbalance between concrete and the environment, and the larger the space for heat dissipation.

[0063] In detail, since the temperature difference between the core and the environment gradually decreases over time during the heat dissipation process of concrete, and the heat dissipation rate decays exponentially, an exponential decay model is constructed based on the extreme temperature difference, the time difference between the future target time and the current time, and the global instantaneous heat dissipation coefficient that can reflect the current heat dissipation capacity. This model accurately simulates the degree of heat dissipation decay at different times, ensuring that the quantification of the temperature drop trend caused by external heat dissipation is scientific and in line with reality.

[0064] In practice, the time difference between the future target time and the current time is... Historical ambient temperature data is used to train the model using time series analysis methods (such as ARIMA models and LSTM neural networks) to fit the pattern of temperature change over time, thereby determining the rate of temperature change in the historical data. Using the current ambient temperature as a starting point, the rate of temperature change output by the trained model is used as the basis for calculation to predict the ambient temperature at the target future time, obtaining the predicted ambient temperature value. .

[0065] Based on the reference core temperature and predicted ambient temperature Determine the extreme temperature difference ,as follows:

[0066] An exponential decay model is constructed based on the extreme temperature difference, the time difference between the future target time and the current time, and the global instantaneous heat dissipation coefficient to calculate the heat dissipation decay term. ,as follows:

[0067] in, It is the natural constant (approximately 2.718). Characterizes the decay of temperature over time.

[0068] Specifically, in step S5, the predicted core temperature is compared with a preset critical temperature threshold and a target curing temperature, and the thickness of the insulation layer is adjusted or the spraying timing is determined based on the comparison results, including: If the predicted core temperature value is greater than or equal to the preset critical temperature threshold, the thickness of the insulation layer is increased or the spraying is activated. If the predicted core temperature is less than the preset critical temperature threshold but greater than the target curing temperature, the thickness of the insulation layer is maintained. If the predicted core temperature is lower than the target curing temperature and the duration exceeds a preset time, the thickness of the insulation layer will be reduced.

[0069] In detail, the preset critical temperature threshold is the upper limit of the core temperature corresponding to the tensile strength of concrete. If the predicted core temperature reaches or exceeds this threshold, it indicates that too much heat of hydration has accumulated inside the concrete, which can easily lead to a large temperature difference between the inside and outside, causing excessive temperature stress and cracks. Therefore, it is necessary to add an insulation layer to reduce heat dissipation and reduce the temperature difference, or to start spraying to reduce the surface temperature and relieve stress. When the predicted core temperature is between the preset critical temperature threshold and the target curing temperature, the temperature will not cause cracks and can meet the strength development requirements. The structure is in a safe state and no adjustment measures are required.

[0070] In detail, the target curing temperature is the lower limit for the normal development of concrete strength. If the predicted core temperature is lower than the target curing temperature and the duration is too long, the cement hydration reaction will slow down and affect the formation of concrete strength. It is necessary to reduce the insulation layer and accelerate heat dissipation to increase the temperature.

[0071] In implementation, the preset critical temperature threshold ranges from 55 to 70°C. To balance the tensile strength of concrete with the risk of temperature-induced cracking and to adapt to common summer construction conditions of C30-C50 ordinary large-volume concrete, the preset critical temperature threshold is preferably set at 65°C. The target curing temperature ranges from 30 to 40°C. To meet the efficiency of concrete strength development while taking into account the prevention and control of temperature cracks, the target curing temperature is preferably set at 35°C. The preset time ranges from 4 hours to 7 hours. To avoid misjudgment due to short-term temperature fluctuations and to balance monitoring efficiency with timely adjustment, the preset time is preferably set at 6 hours.

[0072] In practice, the spray volume ranges from 0.10 to 0.2 L / m²·min, and preferably, the spray volume ranges from 0.15 L / m²·min.

[0073] Building upon the above embodiments, this invention provides precise and efficient execution guidelines for temperature control of large-volume concrete through a graded response mechanism. On one hand, by comparing the predicted core temperature value with the preset critical temperature threshold, proactive intervention is made before crack risk or strength development is affected, significantly reducing the crack incidence rate and ensuring the integrity and durability of the concrete structure. On the other hand, differentiated measures correspond to different temperature ranges, avoiding cost waste caused by excessive adjustments when the temperature is safe, while enabling precise measures to be taken when the temperature is abnormal, ensuring that the concrete is always in a state without crack risk and with normal strength, significantly improving the controllability of the curing process.

[0074] Specifically, in step S5, the thickness adjustment amount is determined based on the initial laying thickness of the insulation layer, the predicted core temperature, the target curing temperature, the duration, and the preset time.

[0075] In implementation, if the predicted core temperature value is greater than or equal to the preset critical temperature threshold, The increased thickness of the insulation layer is equal to the thickness adjustment plus the initial thickness. If the predicted core temperature is lower than the target curing temperature, the insulation layer thickness is reduced. The thickness of the insulation layer is reduced by the initial thickness minus the thickness adjustment.

[0076] Specifically, in step S1, the preset sampling time interval is dynamically updated based on the core temperature under the pre-defined pouring stages, including: Calculate the temperature change amplitude of the core temperature per unit time during each pouring stage, and adjust the preset sampling time interval based on the comparison result of the temperature change amplitude with the preset amplitude threshold of the corresponding stage.

[0077] In detail, the core temperature changes significantly differ at different stages after the pouring of large-volume concrete: during the heating stage, the core temperature rises rapidly and fluctuates frequently due to the concentrated release of hydration heat; during the isothermal stage, the temperature change tends to be gradual; and during the cooling stage, the temperature decreases slowly with a gradually decreasing amplitude of change. Using a fixed preset sampling time interval leads to two problems: first, in the initial heating stage, an excessively long interval may miss key temperature fluctuation data, potentially increasing the risk of cracking; second, in the later cooling stage, an excessively short interval will generate a large amount of redundant data, increasing the pressure on data storage and transmission.

[0078] In implementation, the period from 0 to 48 hours after pouring is defined as the heating phase, 48 to 96 hours as the isothermal phase, and 96 hours after pouring is defined as the cooling phase. Preferably, the preset threshold values ​​for the heating, isothermal, and cooling phases are 2℃ / h, 0.5℃ / h, and 0.3℃ / h, respectively. During the heating phase, when the core temperature rises by ≥2℃ / h, the preset sampling interval is set to 10–20 minutes, preferably 15 minutes; when the rise is <2℃ / h, the preset sampling interval is set to 20–30 minutes, preferably 25 minutes. During the isothermal phase, when the core temperature rises by ≥0.5℃ / h, the preset sampling interval is set to 30–45 minutes, preferably 40 minutes; when the fluctuation is <0.5℃ / h, the preset sampling interval is set to 45–60 minutes, preferably 50 minutes. During the cooling phase, when the core temperature drops by ≥0.3℃ / h, the preset sampling time interval is set to 60-90 minutes, preferably 75 minutes; when the temperature drops by <0.3℃ / h, the preset sampling time interval is set to 90-120 minutes, preferably 100 minutes.

[0079] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0080] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for intelligent monitoring of temperature during large-volume concrete pouring, characterized in that, include: After the concrete is poured, core temperature and stress data at preset measurement points inside the concrete are collected synchronously at preset sampling time intervals, and surface temperature, ambient temperature and wind speed at measurement points outside the concrete are collected. A transient thermal balance equation is established based on the core temperature, ambient temperature, ambient wind speed, stress data, and insulation layer thickness. The global instantaneous heat dissipation coefficient is then determined based on the transient thermal balance equation. The stable temperature time interval is determined based on the core temperature. A thermal inertia-stress correlation equation is constructed based on the core temperature and stress data within the stable temperature time interval. The global thermal inertia index is determined by solving the thermal inertia-stress correlation equation. A linear model is constructed based on the global thermal inertia index and the reference core temperature to determine the temperature rise inertia term. An exponential decay model is constructed based on the reference core temperature, the global instantaneous heat dissipation coefficient, and the predicted ambient temperature at the future target time to calculate the heat dissipation decay term. The reference core temperature is determined based on the core temperature after the concrete is poured. The temperature rise inertia term and the heat dissipation attenuation term are weighted and summed to determine the predicted core temperature of the concrete at the future target time. The thickness of the insulation layer is adjusted or the spraying timing is determined based on the predicted core temperature.

2. The intelligent monitoring method for the pouring temperature of large-volume concrete according to claim 1, characterized in that, The transient thermal equilibrium equation is established by: At the same sampling time point, the overall core temperature is determined based on the core temperature at the preset measurement point, and the overall surface temperature is determined based on the surface temperature at the external measurement point. The equivalent thermal resistance of the insulation layer is determined based on the thermal conductivity of the insulation layer and the thickness of the insulation layer. A transient thermal balance equation is established based on the overall core temperature, the overall surface temperature, the ambient wind speed, the stress data, and the equivalent thermal resistance.

3. The intelligent monitoring method for the pouring temperature of large-volume concrete according to claim 2, characterized in that, Determining the global instantaneous heat dissipation coefficient includes: Acquire current data, including overall core temperature, overall surface temperature, ambient temperature, and ambient wind speed, and determine the current equivalent thermal resistance based on the current insulation layer thickness. The current time data and the current equivalent thermal resistance are input into the transient thermal balance equation to determine the global instantaneous heat dissipation coefficient.

4. The intelligent monitoring method for the pouring temperature of large-volume concrete according to claim 1, characterized in that, Determine the time interval for stabilizing the temperature, including: The temperature change rate is calculated based on the core temperature. The stabilization start time point is determined based on the temperature change rate and a first change rate threshold. The stabilization end time point is determined based on the temperature change rate and a second change rate threshold. The stabilization start time point and the stabilization end time point are then used to determine the stabilization temperature time interval. Wherein, the first rate of change threshold is less than the second rate of change threshold.

5. The intelligent monitoring method for the pouring temperature of large-volume concrete according to claim 4, characterized in that, Determine the global thermal inertia index, including: Extract the core temperature and corresponding stress data within the stable temperature time interval; Using the first derivative of the core temperature as the independent variable and the first derivative of the stress data as the dependent variable, the thermal inertia-stress correlation equation is constructed. The global thermal inertia index is determined based on the slope of the thermal inertia-stress correlation equation.

6. The intelligent monitoring method for the pouring temperature of large-volume concrete according to claim 5, characterized in that, Calculate the average ambient temperature within the stable temperature time interval, determine the initial temperature difference based on the average ambient temperature and the reference core temperature, and determine the temperature rise inertia term based on the initial temperature difference and the global thermal inertia index.

7. The intelligent monitoring method for the pouring temperature of large-volume concrete according to claim 1, characterized in that, Determine the heat dissipation attenuation terms, including: Determine the predicted ambient temperature value at the target future time based on historical ambient temperature data and current ambient temperature. The extreme temperature difference is determined based on the baseline core temperature and the predicted ambient temperature. An exponential decay model is constructed based on the extreme temperature difference, the time difference between the future target time and the current time, and the global instantaneous heat dissipation coefficient to calculate the heat dissipation decay term.

8. The intelligent monitoring method for the pouring temperature of large-volume concrete according to claim 1, characterized in that, The predicted core temperature is compared with the preset critical temperature threshold and the target curing temperature, and the thickness of the insulation layer is adjusted or the spraying timing is determined based on the comparison results, including: If the predicted core temperature value is greater than or equal to the preset critical temperature threshold, the thickness of the insulation layer is increased or the spraying is activated. If the predicted core temperature is less than the preset critical temperature threshold but greater than the target curing temperature, the thickness of the insulation layer is maintained. If the predicted core temperature is lower than the target curing temperature and the duration exceeds a preset time, the thickness of the insulation layer will be reduced.

9. The intelligent monitoring method for the pouring temperature of large-volume concrete according to claim 8, characterized in that, The thickness adjustment is determined based on the initial thickness of the insulation layer, the predicted core temperature, the target curing temperature, the duration, and the preset time.

10. The intelligent monitoring method for the pouring temperature of large-volume concrete according to claim 1, characterized in that, The preset sampling time interval is dynamically updated based on the core temperature under the pre-defined pouring stages, including: Calculate the temperature change amplitude of the core temperature per unit time during each pouring stage, and adjust the preset sampling time interval based on the comparison result of the temperature change amplitude with the preset amplitude threshold of the corresponding stage.

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