High-performance mass concrete regulation and control method based on hydration heat and shrinkage control

Abstract

The invention discloses a high-performance mass concrete regulation and control method based on hydration heat and shrinkage control, and relates to the technical field of concrete regulation and control, and the method comprises the following steps: inputting a start response phase difference into a constructed collaborative entropy mapping model of a hydration heat release and volume shrinkage rhythm relationship based on a determined hydration start behavior asynchronous phenomenon, and obtaining a hydration heat release and volume shrinkage rhythm relationship; generating a synchronization parameter between the surface hydration heat release curve and the internal volume shrinkage curve to judge whether the concrete surface hydration heat release is matched with the internal shrinkage control rhythm or not; and according to a judgment result, calling a threshold hierarchical model to divide the concrete state into rhythm matching, moderate mismatching or serious mismatching, and obtaining a state grade result corresponding to the synchronization parameter. According to the method, the problem that concrete hydration starting behaviors are asynchronous and difficult to identify and regulate due to surface ventilation treatment is solved, and accurate matching and dynamic regulation and control of surface hydration heat and internal shrinkage rhythm are realized.

Description

Technical Field

[0001] This invention relates to the field of concrete regulation technology, specifically to a method for regulating high-performance mass concrete based on hydration heat and shrinkage control. Background Technology

[0002] High-performance mass concrete regulation based on hydration heat and shrinkage control is a systematic approach to achieve early strength, crack resistance, and volume stability in concrete under high-volume conditions by optimizing material systems and construction processes. The core of this regulation method lies in systematically optimizing the entire process from material design and internal / external curing to prestressing, focusing on the thermo-mechanical-volume changes during the release of concrete's hydration heat and combining this with shrinkage control mechanisms. Existing technologies in this field primarily reduce the peak hydration heat and enhance early strength through a "low-heat cementitious material system + early-strength functional component." CSH crystal seeding and non-chloride salt early-strength agents are introduced within the first 8–24 hours of concrete development to ensure sufficient tensile strength when the temperature stress peak occurs. Simultaneously, a dual-time-sequence expansion compensation system consisting of "microencapsulated CSA + medium-slow-reacting MgO" is employed. The former releases expansion stress within 2–6 hours to offset plastic shrinkage, while the latter continuously releases expansion stress over 3–14 days to control mid-to-late-stage temperature and drying shrinkage effects. Based on this, SAP or saturated lightweight aggregates are used to achieve internal slow-release supply. Water, combined with SRA shrinkage-reducing agent, lowers capillary stress and further inhibits autogenous shrinkage. During construction, traditional cooling water pipes are abandoned; instead, comprehensive external insulation control is achieved through double-layer insulation felt, plastic film, and insulated formwork. This ensures a stable cooling rate of 1.0–1.5℃ / h and a temperature difference between the inside and outside within 25℃, preventing cracks caused by sudden cooling. Finally, an early-strength system ensures that the concrete reaches a compressive strength of 15–20MPa and a tensile strength of 2.0–2.5MPa within 24 hours, meeting prestressing requirements and preventing cracking during prestressing, thus improving structural integrity and durability. This control technology system encompasses multiple collaborative aspects, including material optimization, thermal control, shrinkage compensation, internal and external curing, and prestressed construction, achieving systematic control of the performance of large-volume concrete throughout its entire lifespan, from early to late stages.

[0003] The existing technology has the following shortcomings: In the process of controlling high-performance mass concrete based on hydration heat and shrinkage, if a brief ventilation treatment is applied to the concrete surface before insulation covering to reduce the overall temperature control difficulty during construction, in concrete systems using high-fineness cementitious materials, the surface layer of the concrete is prone to prematurely initiating the hydration reaction due to localized dehydration, releasing hydration heat. This premature reaction causes the release rhythm of surface hydration heat to be asynchronous with that of the internal concrete, disrupting the original synergistic relationship between hydration heat release and volume shrinkage control, easily leading to misalignment of hydration peaks and uncontrolled localized shrinkage. However, existing high-performance mass concrete control technologies based on hydration heat and shrinkage control cannot determine whether the release of hydration heat on the concrete surface still matches the internal shrinkage control rhythm based on the asynchronous phenomenon of hydration initiation behavior under localized dehydration caused by surface ventilation treatment. This makes it difficult to adjust insulation measures and shrinkage compensation strategies in a timely manner, leading to an aggravated surface temperature gradient, increased risk of early cracking, and in severe cases, the formation of potential weak interfaces during the structural service stage, affecting the integrity and durability of the mass concrete structure.

[0004] 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

[0005] The purpose of this invention is to provide a high-performance, large-volume concrete control method based on hydration heat and shrinkage control, so as to solve the problems in the background art mentioned above.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for controlling high-performance mass concrete based on hydration heat and shrinkage, specifically including the following steps: S1. Collect data on the surface temperature change rate and surface moisture response change after concrete pouring, construct the surface water film dissipation rate curve, and determine whether the instantaneous slope of the surface water film dissipation rate curve exceeds the preset threshold during the ventilation treatment stage by analyzing whether local dehydration caused by surface ventilation treatment has occurred. S2. In the case of local dehydration caused by surface ventilation treatment, collect the hydration heat release rate curve of the local dehydration area and the strain response curve of the internal area, establish a heat-strain time series comparison matrix, calculate the start-up response phase difference, and determine the asynchronous phenomenon of hydration start-up behavior under the local dehydration caused by surface ventilation treatment. S3. Based on the determined asynchronous phenomenon of hydration initiation behavior, the collaborative entropy mapping model of the relationship between hydration heat release and volume shrinkage rhythm is constructed by inputting the phase difference of the initiation response. The synchronicity parameter between the surface hydration heat release curve and the internal volume shrinkage curve is generated to determine whether the surface hydration heat release of concrete matches the internal shrinkage control rhythm. S4. Based on the judgment results, call the threshold stratification model to classify the concrete state into rhythm matching, moderate mismatch or severe mismatch, and obtain the state level results corresponding to the synchronicity parameters. S5. Based on the state level results, execute a recursive adjustment algorithm to adjust the surface insulation sequence, internal humidity compensation rate, and shrinkage compensator release time, so as to achieve dynamic control of the release of hydration heat of concrete surface and the rhythm of internal shrinkage control.

[0007] Preferably, S1 is as follows: After the concrete pouring is completed, temperature and humidity sensors are installed in the monitoring area. Surface temperature and humidity data are collected at fixed time intervals. The derivatives of each set of time series data are calculated to obtain surface temperature change rate data and surface humidity response change data. The surface temperature change rate data and the surface moisture response change data were normalized respectively, and the weighted superposition algorithm was used to calculate the joint change value of the temperature change rate and the moisture response change rate. The surface water film dissipation rate curve was constructed with time as the horizontal axis and the joint change value as the vertical axis. Extract the time interval corresponding to the ventilation treatment stage from the surface water film dissipation rate curve, calculate the instantaneous slope change value of the curve in the time interval, and compare the instantaneous slope change value with a preset threshold. When the slope change value of the curve is continuously higher than the preset threshold, it is determined that local dehydration caused by surface ventilation treatment has occurred.

[0008] Preferably, S2 is as follows: In the case of localized dehydration caused by surface ventilation, temperature sensors installed in the localized dehydration area collect internal concrete temperature data at fixed time intervals, and obtain the hydration heat release rate curve of the localized dehydration area by dividing the temperature difference between adjacent time points by the time interval; strain sensors are installed in the internal area unaffected by ventilation, and the data sequence of strain changes over time is recorded at the same sampling frequency, and the strain response curve of the internal area is generated by calculating the time derivative. Based on the hydration heat release rate curve of the local dehydration region and the strain response curve of the internal region, data are paired according to the same time node to establish a heat-strain time series comparison matrix with time as the horizontal axis and heat release rate and strain response as the vertical data pairs. In the thermal-strain time series comparison matrix, the starting point of the initial rising segment of each of the two curves is identified. The phase difference of the start-up response is obtained by calculating the time difference between the two starting points. The phase difference of the start-up response is compared with the preset synchronous response judgment threshold. When the phase difference of the start-up response exceeds the preset synchronous response judgment threshold, it is determined to be an asynchronous phenomenon of hydration start-up behavior under local dehydration caused by surface ventilation treatment.

[0009] Preferably, S3 specifically includes the following steps: S301. Based on the determined asynchronous phenomenon of hydration initiation behavior, a collaborative entropy mapping model is constructed by inputting the phase difference of the initiation response to establish the relationship between the hydration heat release and the volume contraction rhythm. The collaborative entropy mapping model aggregates multiple sets of historical hydration heat release curves and volume contraction curve samples, uses an improved mutual information evaluation mechanism to extract the coupling features of the historical hydration heat release curves and volume contraction curves in terms of rhythm consistency, and establishes a mapping channel between the initiation response phase difference and the coupling features. S302. In the completed collaborative entropy mapping model, based on the input start-up response phase difference, the set of coupling indicators corresponding to the start-up response phase difference is extracted by the segmented entropy weight matching algorithm, and the synchronization parameter is calculated. This synchronization parameter is used to quantify the temporal synchronization degree between the surface hydration heat release curve and the internal volume contraction curve. S303. Compare the synchronicity parameter with the rhythm tolerance range in the constructed synchronicity assessment benchmark model. If the synchronicity parameter falls within the rhythm tolerance range, it is determined that the release of hydration heat on the concrete surface matches the rhythm of internal shrinkage control. If it exceeds the range, it is determined that the rhythm does not match, which is used for subsequent classification and control decisions.

[0010] Preferably, S302 is as follows: In the completed collaborative entropy mapping model, the phase difference of the initiation response is discretized into time segment intervals, and the local entropy weight is calculated for the surface hydration heat release curve and the internal volume contraction curve in each interval to obtain the coupling weight value of each time segment. Based on the coupling weight values ​​of each time segment, the mutual information intensity and rhythmic synergy index within each segment are calculated by the segmented entropy weight matching algorithm, and weighted fusion is performed across all segments to generate a set of coupling indicators corresponding to the phase difference between the input start response, which is used to characterize the overall rhythmic coupling features. After obtaining the set of coupling indicators, the weighted average synergy value among the indicators is calculated and standardized to obtain the synchronization parameter. This synchronization parameter is used to quantify the temporal synchronization between the surface hydration heat release curve and the internal volume contraction curve.

[0011] Preferably, S4 is as follows: A threshold hierarchical model for classifying concrete condition levels is constructed. Multidimensional cluster analysis is performed on historical data of synchronicity parameters obtained under different construction conditions. Based on the distribution characteristics and cluster boundaries of synchronicity parameters in numerical space, the threshold intervals of synchronicity parameters corresponding to three conditions—rhythm matching, moderate mismatch, and severe mismatch—are defined to form a grade hierarchical mapping table. Based on the judgment result, the current synchronization parameter value is obtained, and the synchronization parameter is input into the threshold hierarchical model. The synchronization parameter is compared with the three threshold intervals of rhythm matching, moderate mismatch and severe mismatch in the hierarchical mapping table to identify the interval to which the synchronization parameter belongs in the threshold structure. Based on the assignment of the synchronicity parameter in the rhythm level range, the current state level of the concrete is output, and this state level result is used as the classification basis to transmit to the subsequent dynamic control stage.

[0012] Preferably, S5 is as follows: Based on the state level results corresponding to the synchronization parameters, the initial adjustment template matching the level is retrieved from the preset dynamic control command database. The current temperature field and humidity field data are used as environmental boundary conditions to input the recursive adjustment algorithm to initialize the reference adjustment range of the surface insulation sequence, internal humidity compensation rate and shrinkage compensator release time point. Based on the initial adjustment range, the recursive adjustment algorithm adjusts the insulation timing offset, humidity compensation rate curve gradient, and delay window of shrinkage compensator release time according to the degree of rhythm mismatch. It also introduces a multivariate feedback backtracking mechanism to evaluate the sensitivity of the adjustment strategy to the synchronicity parameter, forming a multi-round iterative optimization path. The synchronicity parameter change trend obtained after each round of optimization is compared with the rhythm matching interval. When the synchronicity parameter converges to the rhythm matching interval or meets the minimum change rate condition, the recursive adjustment algorithm is terminated, and the final heat preservation timing adjustment command, humidity compensation rate value and shrinkage compensator release time point are output to complete the dynamic regulation of the release of hydration heat of concrete surface and the internal shrinkage control rhythm.

[0013] The technical effects and advantages provided by the present invention in the above technical solution are as follows: 1. This invention, by constructing a surface water film dissipation rate curve and combining it with the slope change characteristics during the ventilation stage, can accurately identify premature hydration induced by localized surface dehydration. Furthermore, by constructing a heat-strain time-series comparison matrix using the hydration heat release curve and strain response curve, it extracts the initiation response phase difference and determines asynchronous hydration initiation behavior, effectively achieving dynamic capture of the asynchronous initiation state of surface hydration reaction. Based on the phase difference, a collaborative entropy mapping model is constructed, which can transform the coupling relationship between different hydration heats and volume contraction rhythms into quantifiable synchronicity parameters, providing strong mathematical support for judging the degree of rhythm matching. The overall identification process combines multi-layer data fusion and response feature extraction, possessing technical advantages of accurate identification, rapid response, and strong adaptability.

[0014] 2. This invention introduces a synchronicity parameter level classification model and a recursive adjustment algorithm to construct a dynamic control closed-loop mechanism. This mechanism can dynamically adjust the surface insulation sequence, internal humidity compensation rate, and shrinkage compensator release time based on the difference in rhythm mismatch levels, ensuring precise coordination between surface hydration heat release and internal volume shrinkage rhythm under different curing and construction conditions. By constructing a rhythm tolerance range and threshold hierarchical mapping table, the synchronicity parameters are made operable and have evaluation standards, ensuring the control strategy has high controllability and a basis for judgment. Combined with a multivariate feedback backtracking mechanism and a minimum change rate judgment strategy, iterative optimization of the control response path is achieved, ultimately achieving automatic convergence of the control target. This technology can not only effectively reduce the risk of cracks caused by surface heat release mismatch but also improve the overall stability and service durability of large-volume concrete structures, making it particularly suitable for high-performance concrete engineering applications in complex construction environments. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0016] Figure 1 This is a schematic flowchart of the high-performance mass concrete control method based on hydration heat and shrinkage control according to the present invention. Detailed Implementation

[0017] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0018] This invention provides, for example Figure 1 The high-performance mass concrete control method based on hydration heat and shrinkage control, as shown, specifically includes the following steps: S1. Collect data on the surface temperature change rate and surface moisture response change after concrete pouring, construct the surface water film dissipation rate curve, and determine whether the instantaneous slope of the surface water film dissipation rate curve exceeds the preset threshold during the ventilation treatment stage by analyzing whether local dehydration caused by surface ventilation treatment has occurred. In this embodiment, S1 specifically refers to: After the concrete pouring is completed, temperature and humidity sensors are installed in the monitoring area. Surface temperature and humidity data are collected at fixed time intervals. The derivatives of each set of time series data are calculated to obtain surface temperature change rate data and surface humidity response change data. After concrete pouring, to obtain real-time data on the surface temperature change rate and moisture response, high-precision digital temperature and humidity sensors can be deployed at predetermined locations on the concrete surface. Temperature sensors can be thermistors or infrared non-contact types, and humidity sensors can be capacitive surface moisture probes; both must have remote data transmission capabilities. The acquisition frequency is set to record data every minute, and the continuously recorded temperature and moisture data are arranged in a time series. To obtain the surface temperature change rate and moisture response, the first derivative of each continuously acquired data set needs to be processed. That is, the temperature change rate for each time period is obtained by dividing the temperature difference between two adjacent time points by the time difference; similarly, the moisture response data can be obtained. For example, if the temperature at one time point is 26.3℃ and the next time point is 26.6℃, with an interval of 1 minute, the temperature change rate is 0.3℃ / minute. This method forms a complete temperature change rate sequence and moisture response change sequence, laying the foundation for subsequent analysis of thermo-humidity coupling behavior. This is done in order to promptly capture the impact of drastic changes in temperature and humidity during surface ventilation on the initial hydration behavior of concrete, thereby enabling early identification of abnormal areas.

[0019] The purpose of deploying temperature and humidity sensors is to conduct high-frequency, real-time monitoring of the concrete surface environment, accurately sensing the changing trends of temperature and humidity during the early curing process. Collecting "surface temperature data" and "surface moisture content data" is to obtain dynamic information on the thermal and moisture state of the concrete surface under specific working conditions, especially during critical stages such as heat release fluctuations and water film dissipation after ventilation disturbance. The setting of "fixed time intervals" ensures the continuity and uniformity of the data, making the changing trends traceable over time. The action of "calculating derivatives separately" for each set of time-series data is essentially performing data differentiation processing to extract the rate of change of the original variables, thereby reflecting the surface response speed of the concrete. "Surface temperature change rate data" reveals the characteristics of the speed of temperature rise or fall, while "surface moisture content response change data" describes the degree of water film or capillary water loss. Both constitute the basic response signals of the early hydrothermal behavior of concrete and are indispensable original parameter sources for subsequently constructing the surface water film dissipation rate curve. These technical features together achieve real-time sensing and quantitative expression of fluctuations in the early hydration environment.

[0020] The surface temperature change rate data and the surface moisture response change data were normalized respectively, and the weighted superposition algorithm was used to calculate the joint change value of the temperature change rate and the moisture response change rate. The surface water film dissipation rate curve was constructed with time as the horizontal axis and the joint change value as the vertical axis. To jointly analyze surface temperature change rate data and surface moisture response change data, the two different physical quantities need to be normalized first. Normalization can be achieved by subtracting the minimum value of each data set from the set's range and then dividing by the set's range, thus limiting the value range to between zero and one. This eliminates the differences in dimensions and orders of magnitude between temperature and humidity, preventing one variable from dominating the results in subsequent calculations. After normalization, a weighted superposition algorithm is used to merge the two normalized sequences into a single sequence. The weighting coefficients can be set based on the experimental weights of the influence of temperature change rate and moisture response change rate on surface dehydration; for example, a temperature weight of 0.6 and a moisture weight of 0.4. The joint change values ​​obtained after weighted superposition constitute a new data sequence. By visualizing this sequence chronologically with time as the horizontal axis and the joint change values ​​as the vertical axis, the surface water film dissipation rate curve is obtained. For example, when the normalized value of temperature change is 0.75 and the normalized value of moisture response is 0.65, after multiplying them by their respective weights and adding them together, the combined change value is 0.71, corresponding to a specific time point. This curve is used to reflect the trend of water film loss on the concrete surface and is an important parameter carrier for identifying the state of ventilation-induced dehydration.

[0021] Normalization refers to the process of transforming two variables with different dimensions to the same scale range, avoiding bias caused by differences in physical dimensions during subsequent fusion. Through normalization, the rate of temperature change and the rate of change in humidity response can participate in joint analysis under a unified dimension. The weighted superposition algorithm is a calculation method that assigns weight values ​​to multiple input variables and performs linear combination. This method has significant advantages in data fusion analysis. Setting different weights reflects the relative importance of the two variables to the target phenomenon, and assigning greater weight to factors with higher influence can enhance the model's sensitivity. The joint change value is a single quantitative parameter obtained by this algorithm to describe the comprehensive response of multiple factors, which integrates the driving effect of temperature change and reflects the dehydration trend of humidity response. The surface water film dissipation rate curve plotted with time on the horizontal axis and joint change value on the vertical axis can reflect the drastic fluctuation range in the water film change process in the form of a continuous curve, providing mathematical basis and visualization support for subsequent judgment of whether there is local dehydration. This group of technical features constitutes the key analytical foundation for quantitatively characterizing the early surface water loss trend under ventilation interference.

[0022] Extract the time interval corresponding to the ventilation treatment stage from the surface water film dissipation rate curve, calculate the instantaneous slope change value of the curve in the time interval, and compare the instantaneous slope change value with a preset threshold. When the slope change value of the curve is continuously higher than the preset threshold, it is determined that local dehydration caused by surface ventilation treatment has occurred.

[0023] To identify whether ventilation treatment causes localized dehydration of the concrete surface, the time interval from the start to the end of the ventilation treatment needs to be located on the surface water film dissipation rate curve. This can be achieved by synchronously recording the start and end times of ventilation during construction and marking them on the water film dissipation rate curve to form a clear time window. Within this time interval, the combined change values ​​of each set of adjacent time points are differentially calculated to obtain a sequence of instantaneous slope changes. The slope reflects the rate of change in the degree of water film dissipation; a higher value indicates a faster rate of water evaporation. Subsequently, each instantaneous slope value is compared with a preset threshold. When the slope values ​​at multiple consecutive time points exceed the preset threshold and remain there for a certain duration, such as several minutes, it can be determined that severe dehydration has occurred on the concrete surface during that time period, thus confirming the existence of localized dehydration. For example, if the slope value is higher than 0.12 for five consecutive minutes and is much higher than the background value before ventilation, it can be identified as dehydration induced by water film rupture. This judgment logic allows the impact of ventilation to be expressed in a quantifiable and traceable manner at the data level.

[0024] The time interval for the ventilation treatment stage is the actual time window determined by the on-site ventilation operation control behavior, and it is the core boundary condition for this judgment step. The instantaneous slope change value is a derivative data obtained by performing a first-order difference on the surface water film dissipation rate curve. It is used to reflect the rate of change of the combined change value at each moment, revealing the dynamic characteristics of the water film dissipation rate. Through the continuous increase of the slope, it can be revealed whether the moisture loss is concentrated and explosive, distinguishing between normal evaporation and abnormal dehydration processes. The preset threshold is a numerical limit set based on experience extracted from a large number of engineering samples or training models. It is used as a standard for judging the critical state. Its selection needs to consider parameters such as concrete type, ambient temperature, and wind speed, and can be adapted and adjusted according to the actual situation in different projects. The process of comparing the instantaneous slope with the preset threshold realizes the transformation from qualitative observation to quantitative judgment, enabling the water film rupture state to be accurately identified. The synergy of these technical features provides a data expression and logical judgment basis for the ventilation-induced dehydration state of the concrete surface, which is a prerequisite for realizing subsequent thermal and moisture synchronization analysis.

[0025] S2. In the case of local dehydration caused by surface ventilation treatment, collect the hydration heat release rate curve of the local dehydration area and the strain response curve of the internal area, establish a heat-strain time series comparison matrix, calculate the start-up response phase difference, and determine the asynchronous phenomenon of hydration start-up behavior under the local dehydration caused by surface ventilation treatment. In this embodiment, S2 specifically refers to: In the case of localized dehydration caused by surface ventilation, temperature sensors installed in the localized dehydration area collect internal concrete temperature data at fixed time intervals, and obtain the hydration heat release rate curve of the localized dehydration area by dividing the temperature difference between adjacent time points by the time interval; strain sensors are installed in the internal area unaffected by ventilation, and the data sequence of strain changes over time is recorded at the same sampling frequency, and the strain response curve of the internal area is generated by calculating the time derivative. After concrete pouring, to capture the hydration heat response characteristics under localized dehydration caused by surface ventilation, a highly sensitive temperature acquisition device, such as a distributed fiber optic temperature sensor or a high-resolution thermocouple, can be pre-embedded in the surface area expected to be affected by ventilation. The data acquisition system records a continuous sequence of temperature values ​​at fixed time intervals (e.g., once per minute). To extract the hydration heat release rate of this area, the temperature difference at consecutive time points is divided by the time interval to form a hydration heat release rate curve, reflecting the trend of heat change per unit time. Simultaneously, strain sensors, such as fiber optic gratings or resistance strain gauges, are deployed in the core area of ​​the concrete to ensure it is unaffected by ventilation. The acquired raw strain data is recorded at the same time frequency as the temperature data, and a strain response rate sequence is generated by dividing the strain change at adjacent time points by the time interval, representing the volume change trend of the internal structure. For example, when heat release induced by ventilation dehydration occurs prematurely on the surface, the surface temperature rate curve will show a rapid upward trend earlier, while the strain response rate curve in the internal area may start later. The time difference between the two can be used to subsequently determine whether the response is synchronized.

[0026] The temperature change rate curve reflects the rhythm and intensity of heat release from the hydration reaction in the localized dehydration region, while the strain response curve reflects the actual feedback from the internal volume shrinkage control of the structure. Together, they constitute a key physical indicator of thermo-mechanical linkage. The independent deployment of temperature and strain sensors ensures spatial separation, enabling the separate analysis of the independent behavioral characteristics of the surface and core regions. Fixed time interval data acquisition ensures temporal comparability, forming the basis for constructing a time-series comparison matrix. The rate of change curve obtained through derivative calculation avoids interference from direct temperature or strain values, more accurately reflecting the dynamic response trend. This technical step realizes the transformation from basic data acquisition to dynamic response extraction, a prerequisite for identifying whether the hydration heat release rhythm is abnormal, and provides a precise data foundation for subsequent phase difference and synchronicity analysis.

[0027] Based on the hydration heat release rate curve of the local dehydration region and the strain response curve of the internal region, data are paired according to the same time node to establish a heat-strain time series comparison matrix with time as the horizontal axis and heat release rate and strain response as the vertical data pairs. To establish the temporal correspondence between heat release rate and strain response, the hydration heat release rate curve from the localized dehydration region and the strain response curve from the internal region need to be time-synchronized. A unified timestamp can be used to calibrate the data, ensuring that the sampling frequency and time nodes of the two curves are completely consistent. Then, at each time point, the heat release rate value and the corresponding strain response value are paired into ordered data pairs. These data pairs are arranged chronologically to construct a two-dimensional heat-strain time-series comparison matrix with time as the horizontal axis and heat release rate and strain response as the vertical axes. For example, if the heat release rate is measured at a certain value 10 minutes later in the surface region, and the strain response value in the internal region is a different value, then these two constitute a data pair for the tenth minute. By continuously pairing these pairs, a time-series matrix containing multiple time nodes can be obtained, allowing observation of the coupling between the two physical processes in the time domain and providing fundamental data for phase difference analysis.

[0028] The construction of this thermal-strain time-series comparison matrix fully reflects the dynamic coupling characteristics between thermal and force responses, and is a crucial step in determining whether the hydration heat release rhythm matches the volumetric shrinkage control rhythm. Time serves as a unified reference axis, ensuring horizontal consistency in subsequent analyses; while the two dimensions on the vertical axis—heat release rate and strain response value—originate from different sensing channels in the surface and internal regions, respectively, representing independent physical processes of thermal excitation and volumetric feedback behavior. Data pairing emphasizes synchronous acquisition at the same time point, avoiding misjudgments caused by time misalignment. The matrix structure integrates the changing rhythms of the two physical curves, providing a formalized and computable framework for subsequent phase difference extraction and synchronicity determination, thus supporting the subsequent determination step of identifying asynchronous phenomena in hydration initiation behavior based on rhythm deviation.

[0029] In the thermal-strain time series comparison matrix, the starting point of the initial rising segment of each of the two curves is identified. The phase difference of the start-up response is obtained by calculating the time difference between the two starting points. The phase difference of the start-up response is compared with the preset synchronous response judgment threshold. When the phase difference of the start-up response exceeds the preset synchronous response judgment threshold, it is determined to be an asynchronous phenomenon of hydration start-up behavior under local dehydration caused by surface ventilation treatment.

[0030] To identify whether there is a rhythmic misalignment between the surface hydration heat release behavior and the internal volume response, it is first necessary to extract the initial rising points of the two time series from the thermo-strain time series comparison matrix. The inflection point of the first continuous rise in the heat release rate curve can be identified using first-order derivative analysis and marked as the thermal response start-up time; similarly, the moment of the first continuous increase in the corresponding strain response curve is considered the strain response start-up time. The interval between these two time points is calculated as the start-up response phase difference. For example, if the hydration heat release rate starts to rise at 15 minutes, while the strain response only begins to change at 20 minutes, the phase difference is 5 minutes. This value is then compared with a preset synchronization response judgment threshold, which can be set based on historical engineering data or theoretical models. If the phase difference exceeds this judgment threshold, it indicates that the thermal response start-up is significantly earlier than the strain response, signifying a loss of synchronization in the thermo-mechanical process. This is identified as an asynchronous phenomenon of hydration start-up behavior induced by local dehydration, providing a basis for control strategies.

[0031] The initiation response phase difference is a key parameter for measuring the synchronicity between thermal and mechanical responses. Physically, it represents the time lag between the heat release behavior at the structural surface and the contraction response within the structure. The preset synchronization response judgment threshold is a dynamic standard developed based on extensive experimental data. It can be intelligently calibrated by incorporating environmental conditions, material properties, and historical project data. Its set value is not fixed but adaptive, ensuring the relevance and effectiveness of the assessment. The introduction of this judgment threshold maps continuously changing phase differences into determinable discrete judgment results, solving the identification challenge of complex time-response relationships in practical applications and providing a clear classification basis for the subsequent execution of different strategies. This step essentially achieves a cross-dimensional transformation from continuous behavior to judgment logic, and is a key computational mechanism for controlling the hydration rhythm of high-performance, large-volume concrete.

[0032] S3. Based on the determined asynchronous phenomenon of hydration initiation behavior, the collaborative entropy mapping model of the relationship between hydration heat release and volume shrinkage rhythm is constructed by inputting the phase difference of the initiation response. The synchronicity parameter between the surface hydration heat release curve and the internal volume shrinkage curve is generated to determine whether the surface hydration heat release of concrete matches the internal shrinkage control rhythm. In this embodiment, S3 specifically includes the following steps: S301. Based on the determined asynchronous phenomenon of hydration initiation behavior, a collaborative entropy mapping model is constructed by inputting the phase difference of the initiation response to establish the relationship between the hydration heat release and the volume contraction rhythm. The collaborative entropy mapping model aggregates multiple sets of historical hydration heat release curves and volume contraction curve samples, uses an improved mutual information evaluation mechanism to extract the coupling features of the historical hydration heat release curves and volume contraction curves in terms of rhythm consistency, and establishes a mapping channel between the initiation response phase difference and the coupling features. After identifying the asynchronous hydration initiation behavior in concrete, a collaborative entropy mapping model of the rhythmic relationship between the surface hydration heat release process and the internal volume shrinkage process needs to be constructed to accurately describe the relationship. This model can be established by collecting thermo-mechanical monitoring data from multiple historical pouring projects, including hydration heat release curves for the surface area and volume shrinkage curves for the internal area. First, these time-series samples are standardized to ensure comparability of data from different scales and material systems within the same dimension. Then, an improved mutual information evaluation mechanism is used to calculate the temporal correlation and information coupling strength between each set of hydration heat release curves and volume shrinkage curves, thereby extracting the coupling characteristics of the rhythmic consistency between the two physical processes. The actual detected initiation response phase difference is then input into the model, and a mapping function is used to determine the corresponding coupling characteristic position in the model space. For example, when the input phase difference is small, the model output coupling characteristic values ​​tend to have high consistency, indicating strong synchronicity between the heat and shrinkage processes; when the phase difference is large, the output coupling characteristic values ​​decrease, reflecting weakened synchronicity. This method allows complex thermo-mechanical rhythm relationships to be digitally mapped, providing a quantitative basis for subsequent synchronicity judgments.

[0033] The synergistic entropy mapping model of the relationship between hydration heat release and volume shrinkage rhythm is a time-series correlation modeling structure based on statistical entropy theory. Its core lies in quantifying the information coupling strength between two nonlinear processes. Historical hydration heat release and volume shrinkage curve samples represent typical distributions of concrete thermo-mechanical behavior under different working conditions, used to train the model to identify the regular characteristics of rhythm matching and deviation. The improved mutual information evaluation mechanism, by introducing a time delay window and weight allocation coefficient into traditional mutual information calculation, not only considers the synchronicity of data points but also enhances the responsiveness to rhythm change trends, thus more accurately capturing the dynamic consistency of heat release and shrinkage changes. The coupling characteristics in terms of rhythm consistency are the core results of the model output, used to characterize the synchronization level of the thermo-mechanical system during time evolution; its magnitude reflects the degree of coordination between thermal and shrinkage responses in terms of rhythm. In this way, complex multi-time-series thermo-mechanical response relationships can be transformed into a computable entropy space mapping structure, enabling the unified characterization and analysis of concrete hydration behavior characteristics under different environments and material systems.

[0034] S302. In the completed collaborative entropy mapping model, based on the input start-up response phase difference, the set of coupling indicators corresponding to the start-up response phase difference is extracted by the segmented entropy weight matching algorithm, and the synchronization parameter is calculated. This synchronization parameter is used to quantify the temporal synchronization degree between the surface hydration heat release curve and the internal volume contraction curve. S303. Compare the synchronicity parameter with the rhythm tolerance range in the constructed synchronicity assessment benchmark model. If the synchronicity parameter falls within the rhythm tolerance range, it is determined that the release of hydration heat on the concrete surface matches the rhythm of internal shrinkage control. If it exceeds the range, it is determined that the rhythm does not match, which is used for subsequent classification and control decisions.

[0035] After obtaining the synchronicity parameters, to achieve automated determination of rhythm matching, the synchronicity parameters need to be compared with the rhythm tolerance range in the synchronicity assessment benchmark model. The synchronicity assessment benchmark model can be constructed through historical experimental data and simulation analysis. The model records the distribution range of reference parameters under the synchronous state of hydration heat release and volume shrinkage rhythm under different material systems, construction conditions, and temperature environments. In specific implementation, the system inputs the currently calculated synchronicity parameters into the model and matches them with the rhythm tolerance range under the corresponding working conditions in the model. When the synchronicity parameter value falls within the tolerance range, it indicates that the hydration heat release of the concrete surface layer and the internal volume shrinkage control maintain a coordinated rhythm, and the hydration-shrinkage process is well synchronized. If the synchronicity parameter exceeds the upper limit of the range or falls below the lower limit, it indicates that there is a lag or advance between the thermal response and the volume response, i.e., rhythm mismatch occurs. For example, in low-temperature construction in winter, if the synchronicity parameter is lower than the lower tolerance limit, it means that the surface hydration is delayed, which may cause shrinkage to occur before the hydration heat release, thus indicating an increased risk of cracking. This result will be transmitted to the control decision module to trigger thermal insulation strengthening or shrinkage compensation strategies.

[0036] The synchronicity assessment benchmark model is a judgment model based on multi-dimensional feature samples. Its structure integrates statistical experience and machine learning recognition mechanisms. Through clustering and regression analysis of a large amount of concrete thermo-mechanical coupling experimental data, it extracts the characteristic distribution patterns of synchronicity parameters under different synchronization states. Each set of sample data in the model includes concrete mix proportions, environmental conditions, temperature control history, and response rhythm results, thus forming a synchronicity standard system applicable to different application scenarios. The rhythm tolerance range is a key output of the model, reflecting the numerical boundary of the normal fluctuation range of synchronicity parameters. It is typically centered on the mean of the synchronicity parameters, with upper and lower limits set by combining the standard deviation and actual tolerance. This range reflects the system's tolerance level when judging the coordination of thermo-mechanical response rhythm; too narrow a range may lead to misjudgment, while too wide a range reduces sensitivity. By comparing the synchronicity parameters with the rhythm tolerance range, the model transforms the continuous response signal into a discrete rhythm matching judgment result, achieving a logical closed loop from physical data to control decisions. This ensures that hydration heat and shrinkage control remain dynamically coordinated throughout the entire lifespan, guaranteeing the overall stability and structural safety of large-volume concrete.

[0037] In this embodiment, S302 specifically refers to: In the completed collaborative entropy mapping model, the phase difference of the initiation response is discretized into time segment intervals, and the local entropy weight is calculated for the surface hydration heat release curve and the internal volume contraction curve in each interval to obtain the coupling weight value of each time segment, so as to reflect the relative information contribution of the thermo-mechanical response in different time periods. In the constructed synergistic entropy mapping model, to improve the ability to distinguish the rhythmic changes of the thermo-mechanical process, the initiation response phase difference is first discretized along the time dimension, that is, the entire response time axis is divided into several continuous, non-overlapping time segments. Within each time segment, subsequences of the surface hydration heat release curve and the internal volume contraction curve are extracted for that time period, and local entropy weights are calculated based on information entropy theory. Local entropy weight calculation assesses the information content within that time period by measuring the uncertainty of data fluctuations within each curve segment. Combined with the joint entropy or mutual information of the two curves in that interval, a coupling weight value is calculated to reflect the degree of synergy of the thermo-mechanical response within that time period. In this way, each time segment not only retains temporal information but also forms a distribution map of local coupling degree in space. For example, high coupling may be observed in the early hydration stage, while synchronicity decreases in the middle and later stages; the local entropy weight calculation results can intuitively reflect this trend. This process effectively avoids the loss of detail caused by averaging the entire data segment, making the rhythmic relationship analysis more resolution and providing a high-quality data foundation for the subsequent generation and judgment of rhythmic synergy parameters. This method has advantages such as high dynamic response sensitivity and strong local discrimination ability, and is an important extension of the traditional overall coupling evaluation method.

[0038] Based on the coupling weight values ​​of each time segment, the mutual information intensity and rhythmic synergy index within each segment are calculated by the segmented entropy weight matching algorithm, and weighted fusion is performed across all segments to generate a set of coupling indicators corresponding to the phase difference between the input start response, which is used to characterize the overall rhythmic coupling features. Based on the coupling weight values ​​of each time segment, a segmented entropy-weighted matching algorithm can be applied to further identify the synergistic relationship between the surface hydration heat release process and the internal volume contraction process in terms of temporal rhythm. Specifically, the mutual information intensity is first calculated for the two sets of response data within each time segment. This value characterizes the correlation between the two response signals and reflects the information transmission efficiency. Then, combined with the previously calculated coupling weight values, different matching priorities are assigned to different time segments using the entropy-weighted allocation principle, and a rhythm synergy index is calculated to reflect the contribution of each time segment to rhythm synchronization. Finally, across all time segments, the mutual information intensity and the rhythm synergy index are weighted and fused to generate a set of coupling indices corresponding to the initiation response phase difference. This set of coupling indices contains multi-dimensional rhythm synergy features, which can refine the expression of the degree of cooperation between the overall thermo-mechanical response process across multiple time periods. The segmented entropy weight matching algorithm solves the problem of overall judgment failure when responses are asynchronous by identifying coupling relationships locally in the time series and integrating them with global weights. It improves the accuracy of identifying rhythmic characteristic changes in the complex process of concrete hydration heat and volume shrinkage. It is a structural optimization and high-order upgrade of the traditional whole-segment signal matching algorithm.

[0039] After obtaining the set of coupling indicators, the weighted average synergy value among the indicators is calculated and standardized to obtain the synchronization parameter. This synchronization parameter is used to quantify the temporal synchronization between the surface hydration heat release curve and the internal volume contraction curve, providing a quantitative basis for subsequent rhythm matching judgment.

[0040] After obtaining the set of coupling indices, a weighted average calculation is needed for each index. First, each index is assigned a weight determined by the entropy weight method. These weights are based on the rhythmic synergy and information intensity reflected by the preceding segmented entropy weight matching algorithm; a higher weight indicates a greater contribution of the index to describing thermo-mechanical synchronicity. Then, each index is multiplied by its corresponding weight and summed to obtain a synergy value reflecting the overall coupling level. To ensure the comparability of this synergy value under different concrete mix proportions, curing conditions, or construction environments, it needs to be standardized, mapping the value to a fixed interval so that the final result is unaffected by external scale differences. The resulting synchronicity parameter is a uniformly dimensional numerical quantification index used to measure the consistency of the response between the surface hydration heat release curve and the internal volume shrinkage curve over time. This parameter provides a continuous, calculable, and traceable criterion, not only laying the foundation for subsequent rhythm matching and mismatch identification but also serving as an input variable in dynamic control algorithms, enabling quantitative modeling and fine-grained control of the coordinated behavior of hydration heat and shrinkage response in high-performance, large-volume concrete.

[0041] S4. Based on the judgment results, call the threshold stratification model to classify the concrete state into rhythm matching, moderate mismatch or severe mismatch, and obtain the state level results corresponding to the synchronicity parameters. In this embodiment, S4 specifically refers to: A threshold hierarchical model for classifying concrete condition levels is constructed. Multidimensional cluster analysis is performed on historical data of synchronicity parameters obtained under different construction conditions. Based on the distribution characteristics and cluster boundaries of synchronicity parameters in numerical space, the threshold intervals of synchronicity parameters corresponding to three conditions—rhythm matching, moderate mismatch, and severe mismatch—are defined to form a grade hierarchical mapping table. To construct a threshold-based hierarchical model for classifying concrete condition levels, a large amount of synchronicity parameter sample data needs to be collected from multiple actual construction scenarios, and the corresponding concrete temperature control and shrinkage performance needs to be recorded. After data collection, density-based multidimensional clustering algorithms such as DBSCAN or distance-based K-means variants can be used to perform multidimensional clustering of the synchronicity parameters, extracting their natural clustering distribution characteristics in the numerical space. The boundary regions between cluster centers are used as grade interval separators. By analyzing the crack distribution, shrinkage runaway probability, and thermo-mechanical response offset of each group of concrete at the cluster boundaries, the entire synchronicity parameter space is divided into rhythm matching zones, moderate mismatch zones, and severe mismatch zones. Finally, a hierarchical mapping table with a one-to-one correspondence between synchronicity parameters and grade labels is constructed as the basis for subsequent judgment of concrete condition levels, realizing the objectification of condition determination and data-driven logical modeling.

[0042] The threshold hierarchical model is a judgment tool based on the fusion of statistical distribution boundaries and machine learning classification mechanisms. Its core is to map a continuous numerical parameter space into discrete state level intervals, thereby achieving multi-state hierarchical identification. Its construction process relies on multidimensional clustering analysis technology, which models and identifies the numerical density, gradient changes, and spatial similarity of historical synchronous parameter samples to recognize the natural grouping structure of samples in multidimensional space. Clustering analysis not only removes noisy data but also identifies typical boundaries of concrete rhythm mismatch under different response modes, ensuring the robustness and engineering adaptability of threshold settings. The model's output is a clear boundary interval, rather than a fuzzy judgment rule, making it suitable for rapid hierarchical judgment and real-time state feedback in dynamic control scenarios.

[0043] Based on the judgment result, the current synchronization parameter value is obtained, and the synchronization parameter is input into the threshold hierarchical model. The synchronization parameter is compared with the three threshold intervals of rhythm matching, moderate mismatch and severe mismatch in the hierarchical mapping table to identify the interval to which the synchronization parameter belongs in the threshold structure. After calculating the synchronicity parameter, the current synchronicity parameter value needs to be obtained and loaded into the constructed threshold hierarchical model. This model includes predefined boundaries for three levels of synchronicity parameter intervals: rhythm matching, moderate mismatch, and severe mismatch. A linear interval matching function is implemented programmatically to compare the input synchronicity parameter with multiple intervals in the hierarchical mapping table step by step until the specific interval containing the parameter is found, thereby identifying the current state level between the release of surface hydration heat and internal volume shrinkage of the concrete. For example, in an engineering case, if the synchronicity parameter is 0.82, the rhythm matching interval is 0.90~1.00, the moderate mismatch interval is 0.70~0.89, and the severe mismatch interval is 0.00~0.69, the system will determine that the current state belongs to the moderate mismatch level and pass this result to the subsequent control module to guide the accuracy and response of the insulation and shrinkage adjustment strategies.

[0044] Synchronicity parameters, as an indicator of the temporal consistency between surface hydration heat release and internal volume contraction, must be compared against a tiered range constructed through multidimensional clustering analysis. The tiered mapping table, by extracting cluster boundaries from historical response data, ensures that the upper and lower limits of each tiered range possess statistical significance and engineering discriminative meaning. In practice, this comparison process is typically constructed using a two-layer conditional judgment logic: the first layer checks whether the synchronicity parameter is below the upper threshold, and the second layer determines whether it is above the lower threshold, thus achieving stable three-zone assignment calculation. This mechanism not only avoids the subjectivity of parameter determination but also ensures the traceability and continuity of tiered judgment results, forming the foundation for achieving state perception and dynamic response.

[0045] Based on the synchronicity parameter's assignment within the rhythm level range, the current state level of the concrete is output, and this state level result is used as a classification basis to transmit to the subsequent dynamic control stage, thereby achieving accurate state identification and closed-loop matching of rhythm response.

[0046] After determining the synchronicity parameters within the rhythm level range, the current state level of the concrete can be directly output, such as rhythm matching, moderate mismatch, or severe mismatch. This state level result is not merely a static analysis conclusion but also serves as an input condition in the control logic, immediately passed to the subsequent control execution module. Specifically, a state-triggered mechanism can be employed. Through a preset state-strategy mapping logic, different insulation adjustment plans, humidity compensation rates, or shrinkage agent release scheduling strategies are invoked for different state levels. For example, when a severe mismatch is identified, the system will automatically trigger an earlier, more precise insulation time interval, accelerate the humidity recompensation process, and adjust the shrinkage control agent release window, thereby achieving a state-driven dynamic response in the control decision chain. This implementation ensures a synchronous closed loop for concrete structural performance control in both time and state dimensions.

[0047] The state level is not a single label, but rather a result of judging the response level of synchronization parameters in a multi-dimensional coupled structure, possessing quantifiable, hierarchical, and triggerable attributes. In implementation, a digital coding system containing three state levels needs to be constructed; for example, level 0 represents rhythm matching, level 1 represents moderate mismatch, and level 2 represents severe mismatch, enabling it to be dynamically read and invoked. This level is not only used for state display but also directly drives the judgment branches and parameter scheduling modules in subsequent closed-loop control, ensuring the automation, parameter linkage, and precise execution of the response link. This level-driven control method breaks through the limitations of manual judgment and static countermeasures in traditional concrete construction, improving the intelligent level of hydration heat and shrinkage control in large-volume concrete.

[0048] S5. Based on the state level results, execute a recursive adjustment algorithm to adjust the surface insulation sequence, internal humidity compensation rate, and shrinkage compensator release time, so as to achieve dynamic control of the release of hydration heat of concrete surface and the rhythm of internal shrinkage control.

[0049] In this embodiment, S5 specifically refers to: Based on the state level results corresponding to the synchronization parameters, the initial adjustment template matching the level is retrieved from the preset dynamic control command database. The current temperature field and humidity field data are used as environmental boundary conditions to input the recursive adjustment algorithm to initialize the reference adjustment range of the surface insulation sequence, internal humidity compensation rate and shrinkage compensator release time point. When initializing control based on the state level results corresponding to the synchronization parameters, level matching can be performed through a pre-set dynamic control instruction database. This database pre-constructs multiple control templates, each corresponding to a specific state level, including categories such as rhythm matching, moderate rhythm mismatch, and severe rhythm mismatch. Each template defines the initial insulation timing adjustment range, the recommended humidity compensation rate, and the shrinkage compensator release time window. In practical applications, the corresponding template is first indexed by the level of the synchronization parameter. Then, combined with the environmental boundary conditions such as the temperature distribution and humidity changes of the concrete surface and interior collected on-site, these time-varying data are fed into the recursive control algorithm as input. This algorithm uses the level template as the basic control framework, sets the adjustment starting point using boundary condition parameters, and initializes the insulation timing range, humidity compensation rate range, and shrinkage compensator release time interval that satisfy the current construction state, providing a basic solution space for subsequent dynamic control recursive optimization. For example, under the severe mismatch level, the initial control template can preset a larger insulation advance period, a higher humidity compensation rate upper limit, and an earlier shrinkage compensator release node.

[0050] "The state level result corresponding to the synchronicity parameter" refers to the rhythm synchronicity evaluation result obtained through coupling judgment in the previous process, used to characterize the category and classification of the current internal and surface rhythm coupling state of the concrete. "Dynamic control instruction database" is a structured control knowledge base covering parameter adjustment strategies under different state levels, with on-demand calling capability. "Initial control template" is a parameter set, including suggested ranges for the start and end times of insulation control, suggested slopes for the internal humidity control curve, and suggested timing segments for compensator release, used to provide initial input for the control algorithm. "Temperature and humidity field data" are derived from a multi-point sampling data matrix deployed at different depths of the concrete, used to reflect the actual state of the concrete's thermal and moisture behavior. "Environmental boundary conditions" represent the external temperature and humidity influencing factors considered when adjusting control parameters. "Recursive control algorithm" is an intelligent algorithm with a backtracking optimization mechanism, which can dynamically adjust the timing of insulation, water replenishment, and compensator release based on the initial template and boundary conditions, improving the control strategy's responsiveness to complex changing conditions. The entire process achieves a closed-loop transition from state identification to initial control target setting.

[0051] Based on the initial adjustment range, the recursive adjustment algorithm adjusts the insulation timing offset, humidity compensation rate curve gradient, and delay window of shrinkage compensator release time according to the degree of rhythm mismatch. It also introduces a multivariate feedback backtracking mechanism to evaluate the sensitivity of the adjustment strategy to the synchronicity parameter, forming a multi-round iterative optimization path. After the initial adjustment range is set, the recursive adjustment algorithm begins iterative optimization of multi-dimensional parameters based on the degree of rhythm mismatch. Specifically, the intensity level of rhythm mismatch is first determined based on the degree of deviation between the synchronicity parameter and the level boundary, and this is converted into a factor weight for the adjustment amplitude. Subsequently, the algorithm adjusts the insulation timing offset according to this weight, i.e., changing the start or end time of insulation, so that the heat release process aligns with the internal control rhythm. Simultaneously, the gradient of the humidity compensation rate curve is adjusted to control the rate of increase in internal humidity, and the release time of the shrinkage compensator is optimized by setting a delay window to align its release node with the target shrinkage control rhythm. After each round of parameter updates, the algorithm recalculates the synchronicity parameter response value under the current adjustment and compares it with the result of the previous round, introducing a multivariate feedback backtracking mechanism to evaluate the effectiveness of the adjustment path in improving synchronicity. The entire process converges continuously through multiple rounds of recursive optimization, ultimately obtaining the optimal combination strategy of surface insulation, internal water replenishment, and compensator delivery. For example, when there is a significant shift in synchronization, the algorithm can delay the end of the insulation period and simultaneously increase the slope of the humidity compensation rate to encourage synchronization to return to the target range.

[0052] "Initialized adjustment range" refers to the parameter adjustment range established based on the initial template and boundary conditions, providing operable upper and lower limits for subsequent regulation. "Recursive adjustment algorithm" is an iterative control logic structure that gradually optimizes control parameters by repeatedly calling the calculation path, possessing path memory and adaptive capabilities. "Rhythm mismatch degree" measures the numerical deviation between the synchronization parameter and the target rhythm, directly determining the amplitude and direction of the adjustment action. "Insulation timing offset" represents the dynamic correction of the start and end times of the insulation operation, used to match the actual process of concrete hydration heat release. "Humidity compensation rate curve gradient" refers to the slope of the internal moisture replenishment rate over time, used to adjust the internal moisture distribution trend. "Delay window for shrinkage compensator release time point" defines the allowable delay range for the release action, ensuring its chemical action is synchronized with the shrinkage stress release process. "Multivariable feedback backtracking mechanism" is a method that updates the adjustment weights in reverse based on historical adjustment paths and response data, making the adjustment process more sensitive and accurate to changes in synchronization parameters, improving the adaptability and precision of rhythm regulation.

[0053] The synchronicity parameter change trend obtained after each round of optimization is compared with the rhythm matching interval. When the synchronicity parameter converges to the rhythm matching interval or meets the minimum change rate condition, the recursive adjustment algorithm is terminated, and the final heat preservation timing adjustment command, humidity compensation rate value and shrinkage compensator release time point are output to complete the dynamic regulation of the release of hydration heat of concrete surface and the internal shrinkage control rhythm.

[0054] After each round of recursive adjustment, the system recalculates the synchronicity parameter between the release of surface hydration heat and internal volume shrinkage of the concrete based on the updated parameters, and records the trend of this parameter over time. The algorithm determines whether to terminate the adjustment by setting two stopping conditions: first, whether the synchronicity parameter has entered the rhythm matching interval; second, whether the rate of change of the synchronicity parameter between two consecutive optimization rounds is lower than the minimum rate of change threshold. When either condition is met, the system determines that the synchronicity control has reached a stable state or that further adjustment will not yield significant benefits, immediately terminates the recursive process, and outputs the final control instruction, including the determined insulation timing adjustment result, the specific humidity compensation rate, and the release time of the shrinkage compensator, thus completing the deployment of the dynamic control strategy. For example, when the synchronicity parameter is stable near the center value of the rhythm matching interval for three consecutive rounds, and the rate of change tends to zero, the system considers that the concrete control state is highly consistent with the target rhythm and promptly outputs the control execution instruction to avoid over-adjustment.

[0055] "Synchronization parameter change trend" refers to the numerical fluctuation trajectory of the synchronicity quantification index in the time dimension during multiple iterations, used to evaluate the effectiveness of the adjustment behavior. "Rhythm matching interval" is the ideal synchronicity parameter range defined in the synergistic entropy mapping model, representing the target value range for coupling the release of surface hydration heat and the internal shrinkage control rhythm of concrete. "Minimum rate of change condition" is one of the termination criteria set by the control system to determine the convergence state of the adjustment; it is usually set to a low rate of change threshold, indicating that the system is considered stable when multiple rounds of optimization have failed to effectively improve synchronicity performance. "Termination of recursive adjustment algorithm" means that the system enters the output stage, stops parameter iteration, and locks the current optimal control strategy. "Insulation timing adjustment instruction" specifies the adjustment scheme for the time period of insulation material laying and removal; "humidity compensation rate value" is used to control the operating intensity and frequency of the internal humidification system; "shrinkage compensator release time point" is used to set the trigger time of the admixture. These three factors together determine the degree of coupling between the thermal-humidity-chemical state and rhythm of the concrete, thereby achieving dynamic closed-loop control throughout the entire process.

[0056] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions according to the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means (e.g., infrared, wireless, microwave, etc.). A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0057] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0058] 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 in 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. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0059] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0060] 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; that is, 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 according to actual needs.

[0061] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0062] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations 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. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for controlling the heat of hydration and shrinkage of high-performance mass concrete, characterized in that, Specifically, the following steps are included: S1. Collect data on the surface temperature change rate and surface moisture response change after concrete pouring, construct the surface water film dissipation rate curve, and determine whether the instantaneous slope of the surface water film dissipation rate curve exceeds the preset threshold during the ventilation treatment stage by analyzing whether local dehydration caused by surface ventilation treatment has occurred. S2. In the case of local dehydration caused by surface ventilation treatment, collect the hydration heat release rate curve of the local dehydration area and the strain response curve of the internal area, establish a heat-strain time series comparison matrix, calculate the start-up response phase difference, and determine the asynchronous phenomenon of hydration start-up behavior under the local dehydration caused by surface ventilation treatment. S3. Based on the determined asynchronous phenomenon of hydration initiation behavior, the collaborative entropy mapping model of the relationship between hydration heat release and volume shrinkage rhythm is constructed by inputting the phase difference of the initiation response. The synchronicity parameter between the surface hydration heat release curve and the internal volume shrinkage curve is generated to determine whether the surface hydration heat release of concrete matches the internal shrinkage control rhythm. S4. Based on the judgment results, call the threshold stratification model to classify the concrete state into rhythm matching, moderate mismatch or severe mismatch, and obtain the state level results corresponding to the synchronicity parameters. S5. Based on the state level results, execute a recursive adjustment algorithm to adjust the surface insulation sequence, internal humidity compensation rate, and shrinkage compensator release time, so as to achieve dynamic control of the release of hydration heat of concrete surface and the rhythm of internal shrinkage control.

2. The method for controlling high-performance mass concrete based on hydration heat and shrinkage control according to claim 1, characterized in that, S1 specifically refers to: After the concrete pouring is completed, temperature and humidity sensors are installed in the monitoring area. Surface temperature and humidity data are collected at fixed time intervals. The derivatives of each set of time series data are calculated to obtain surface temperature change rate data and surface humidity response change data. The surface temperature change rate data and the surface moisture response change data were normalized respectively, and the weighted superposition algorithm was used to calculate the joint change value of the temperature change rate and the moisture response change rate. The surface water film dissipation rate curve was constructed with time as the horizontal axis and the joint change value as the vertical axis. Extract the time interval corresponding to the ventilation treatment stage from the surface water film dissipation rate curve, calculate the instantaneous slope change value of the curve in the time interval, and compare the instantaneous slope change value with a preset threshold. When the slope change value of the curve is continuously higher than the preset threshold, it is determined that local dehydration caused by surface ventilation treatment has occurred.

3. The method for controlling high-performance mass concrete based on hydration heat and shrinkage control according to claim 1, characterized in that, S2 specifically refers to: In the case of localized dehydration caused by surface ventilation, temperature sensors installed in the localized dehydration area collect internal concrete temperature data at fixed time intervals, and obtain the hydration heat release rate curve of the localized dehydration area by dividing the temperature difference between adjacent time points by the time interval; strain sensors are installed in the internal area unaffected by ventilation, and the data sequence of strain changes over time is recorded at the same sampling frequency, and the strain response curve of the internal area is generated by calculating the time derivative. Based on the hydration heat release rate curve of the local dehydration region and the strain response curve of the internal region, data are paired according to the same time node to establish a heat-strain time series comparison matrix with time as the horizontal axis and heat release rate and strain response as the vertical data pairs. In the thermal-strain time series comparison matrix, the starting point of the initial rising segment of each of the two curves is identified. The phase difference of the start-up response is obtained by calculating the time difference between the two starting points. The phase difference of the start-up response is compared with the preset synchronous response judgment threshold. When the phase difference of the start-up response exceeds the preset synchronous response judgment threshold, it is determined to be an asynchronous phenomenon of hydration start-up behavior under local dehydration caused by surface ventilation treatment.

4. The method for controlling high-performance mass concrete based on hydration heat and shrinkage control according to claim 1, characterized in that, S3 specifically includes the following steps: S301. Based on the determined asynchronous phenomenon of hydration initiation behavior, a collaborative entropy mapping model is constructed by inputting the phase difference of the initiation response to establish the relationship between the hydration heat release and the volume contraction rhythm. The collaborative entropy mapping model aggregates multiple sets of historical hydration heat release curves and volume contraction curve samples, uses an improved mutual information evaluation mechanism to extract the coupling features of the historical hydration heat release curves and volume contraction curves in terms of rhythm consistency, and establishes a mapping channel between the initiation response phase difference and the coupling features. S302. In the completed collaborative entropy mapping model, based on the input start-up response phase difference, the set of coupling indicators corresponding to the start-up response phase difference is extracted by the segmented entropy weight matching algorithm, and the synchronization parameter is calculated. This synchronization parameter is used to quantify the temporal synchronization degree between the surface hydration heat release curve and the internal volume contraction curve. S303. Compare the synchronicity parameter with the rhythm tolerance range in the constructed synchronicity assessment benchmark model. If the synchronicity parameter falls within the rhythm tolerance range, it is determined that the release of hydration heat on the concrete surface matches the rhythm of internal shrinkage control. If it exceeds the range, it is determined that the rhythm does not match, which is used for subsequent classification and control decisions.

5. The method for controlling high-performance mass concrete based on hydration heat and shrinkage control according to claim 4, characterized in that, S302 specifically refers to: In the completed collaborative entropy mapping model, the phase difference of the initiation response is discretized into time segment intervals, and the local entropy weight is calculated for the surface hydration heat release curve and the internal volume contraction curve in each interval to obtain the coupling weight value of each time segment. Based on the coupling weight values ​​of each time segment, the mutual information intensity and rhythmic synergy index within each segment are calculated by the segmented entropy weight matching algorithm, and weighted fusion is performed across all segments to generate a set of coupling indicators corresponding to the phase difference between the input start response, which is used to characterize the overall rhythmic coupling features. After obtaining the set of coupling indicators, the weighted average synergy value among the indicators is calculated and standardized to obtain the synchronization parameter. This synchronization parameter is used to quantify the temporal synchronization between the surface hydration heat release curve and the internal volume contraction curve.

6. The method for controlling high-performance mass concrete based on hydration heat and shrinkage control according to claim 1, characterized in that, S4 specifically refers to: A threshold hierarchical model for classifying concrete condition levels is constructed. Multidimensional cluster analysis is performed on historical data of synchronicity parameters obtained under different construction conditions. Based on the distribution characteristics and cluster boundaries of synchronicity parameters in numerical space, the threshold intervals of synchronicity parameters corresponding to three conditions—rhythm matching, moderate mismatch, and severe mismatch—are defined to form a grade hierarchical mapping table. Based on the judgment result, the current synchronization parameter value is obtained, and the synchronization parameter is input into the threshold hierarchical model. The synchronization parameter is compared with the three threshold intervals of rhythm matching, moderate mismatch and severe mismatch in the hierarchical mapping table to identify the interval to which the synchronization parameter belongs in the threshold structure. Based on the assignment of the synchronicity parameter in the rhythm level range, the current state level of the concrete is output, and this state level result is used as the classification basis to transmit to the subsequent dynamic control stage.

7. The method for controlling high-performance mass concrete based on hydration heat and shrinkage control according to claim 1, characterized in that, S5 specifically refers to: Based on the state level results corresponding to the synchronization parameters, the initial adjustment template matching the level is retrieved from the preset dynamic control command database. The current temperature field and humidity field data are used as environmental boundary conditions to input the recursive adjustment algorithm to initialize the reference adjustment range of the surface insulation sequence, internal humidity compensation rate and shrinkage compensator release time point. Based on the initial adjustment range, the recursive adjustment algorithm adjusts the insulation timing offset, humidity compensation rate curve gradient, and delay window of shrinkage compensator release time according to the degree of rhythm mismatch. It also introduces a multivariate feedback backtracking mechanism to evaluate the sensitivity of the adjustment strategy to the synchronicity parameter, forming a multi-round iterative optimization path. The synchronicity parameter change trend obtained after each round of optimization is compared with the rhythm matching interval. When the synchronicity parameter converges to the rhythm matching interval or meets the minimum change rate condition, the recursive adjustment algorithm is terminated, and the final heat preservation timing adjustment command, humidity compensation rate value and shrinkage compensator release time point are output to complete the dynamic regulation of the release of hydration heat of concrete surface and the internal shrinkage control rhythm.

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