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

Abstract

The application discloses a high-performance mass concrete regulation and control method based on hydration heat and shrinkage control, relates to the technical field of concrete regulation and control, and comprises the following steps: based on the determined hydration starting behavior asynchronous phenomenon, inputting a starting response phase difference into a constructed synergy entropy mapping model of hydration heat release and volume shrinkage rhythm relationship, generating a synchronism parameter between a surface hydration heat release curve and an internal volume shrinkage curve, and determining whether the concrete surface hydration heat release matches the internal shrinkage control rhythm; according to the determination result, calling a threshold layering model to divide the concrete state into rhythm matching, moderate mismatch or serious mismatch, and obtaining a state grade result corresponding to the synchronism parameter. The application solves the problem that the concrete hydration starting behavior asynchronism caused by surface ventilation treatment is difficult to identify and control, and realizes accurate matching and dynamic regulation and control of the surface hydration heat and internal shrinkage rhythm.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of concrete regulation, and particularly relates to a high-performance mass concrete regulation method based on hydration heat and shrinkage control. BACKGROUND

[0002] The high-performance mass concrete regulation based on hydration heat and shrinkage control is a systematic method for realizing early strength, crack resistance and volume stability of concrete under high volume conditions through optimization of material system and construction process. The core of this regulation method is to surround the thermal-mechanical-volume change characteristics in the hydration heat release process of concrete, and to combine the shrinkage control mechanism to optimize the whole process from material design, internal and external curing to prestress application. The existing such technology mainly reduces the hydration heat peak value and improves the early strength by using "low-heat cementitious material system + early strength functional component". C-S-H crystal nucleus seeds and non-chloride salt early strength agents are introduced within 8-24 hours of the early stage of concrete to ensure that it has sufficient tensile capacity when the temperature stress peak appears. At the same time, a double-time expansion compensation system composed of "microencapsulated CSA + medium slow reaction MgO" is used. The former releases expansion stress within 2-6 hours to offset plastic shrinkage, and the latter continuously releases expansion effect within 3-14 days to control the temperature and dry shrinkage effect in the middle and later stages. On this basis, SAP or saturated lightweight aggregate is used to realize internal slow-release water supply, and SRA shrinkage reducing agent is used to reduce capillary stress, further inhibiting autogenous shrinkage. In the construction link, the traditional cooling water pipe cooling method is abandoned, and full-range external insulation control is realized through double-layer insulation felt, plastic film, insulation formwork and other measures, so that the cooling rate is stabilized at 1.0-1.5℃ / h, and the internal and external temperature difference is controlled within 25℃, avoiding cracks caused by sudden cooling. Finally, the early strength system ensures that the concrete reaches 15-20MPa compressive strength and 2.0-2.5MPa tensile strength within 24 hours to meet the prestressed tensioning requirements, ensuring that the concrete does not crack during prestress application, and improving the overall structure and durability. The above regulation technology system covers multiple coordinated links such as material optimization, heat control, shrinkage compensation, internal and external curing, and prestressed construction, realizing the systematic regulation of the whole age performance of mass concrete from the early stage to the middle and later stages.

[0003] The prior art has the following disadvantages:

[0004] In the process of realizing high-performance mass concrete regulation and control based on hydration heat and shrinkage control, if short-term ventilation treatment is performed on the concrete surface before thermal insulation covering in order to reduce the overall temperature control difficulty in the construction link, in the concrete system using high-fineness cementitious materials, the hydration reaction of the surface layer of the concrete is easily started in advance due to local dehydration, and hydration heat is released. Such early reaction makes the hydration heat release rhythm of the surface layer different from that of the internal concrete, destroys the original coordination between hydration heat release and volume shrinkage control, and easily causes hydration peak misplacement and local shrinkage out of control. However, the existing high-performance mass concrete regulation and control technology based on hydration heat and shrinkage control cannot determine whether the hydration heat release of the surface layer is still matched with the internal shrinkage control rhythm according to the hydration starting behavior asynchronous phenomenon caused by the local dehydration due to the surface ventilation treatment, so as to adjust the thermal insulation measures and shrinkage compensation strategy in time, leading to the aggravation of the surface temperature difference gradient, the increase of the early crack risk, and the formation of potential weak interface in the structure service stage, affecting the integrity and durability of the mass concrete structure.

[0005] The above information disclosed in the background section is only intended to strengthen the understanding of the background of the present disclosure, and therefore it can include information that does not constitute the prior art known to those of ordinary skill in the art. SUMMARY

[0006] The purpose of the present application is to provide a high-performance mass concrete regulation and control method based on hydration heat and shrinkage control to solve the problems in the background.

[0007] In order to achieve the above-mentioned purpose, the present application provides the following technical scheme: a high-performance mass concrete regulation and control method based on hydration heat and shrinkage control, specifically comprising the following steps:

[0008] S1, collecting the surface layer temperature change rate and surface layer moisture content response change data after the concrete is poured, constructing a surface water film dissipation rate curve, and determining whether the local dehydration condition caused by the surface ventilation treatment occurs by analyzing whether the instantaneous slope of the surface water film dissipation rate curve in the ventilation treatment stage exceeds a preset threshold value;

[0009] S2, collecting the hydration heat release rate curve of the local dehydration area and the strain response curve of the internal area under the condition of the local dehydration caused by the surface ventilation treatment, establishing a thermal-strain time sequence comparison matrix, calculating the starting response phase difference, and determining the hydration starting behavior asynchronous phenomenon under the condition of the local dehydration caused by the surface ventilation treatment;

[0010] S3, based on the determined hydration start-up behavior asynchronous phenomenon, input the start response phase difference into the constructed synergy entropy mapping model of hydration heat release and volume shrinkage rhythm relationship, generate the synchronization parameter between the surface hydration heat release curve and the internal volume shrinkage curve, to judge whether the concrete surface hydration heat release matches the internal shrinkage control rhythm or not;

[0011] S4, according to the judgment result, call the threshold layering model to divide the concrete state into rhythm matching, moderate mismatch or serious mismatch, and obtain the state level result corresponding to the synchronization parameter;

[0012] S5, based on the state level result, execute the recursive adjustment algorithm to adjust the surface temperature maintaining time sequence, internal humidity compensation rate and shrinkage compensation agent release time point, realize the dynamic regulation and control of the concrete surface hydration heat release and internal shrinkage control rhythm.

[0013] Preferably, S1 is specifically:

[0014] After the concrete pouring is completed, temperature sensors and humidity sensors are arranged in the monitoring area, surface temperature data and surface moisture content data are collected at fixed time intervals, and derivative calculation is performed on each group of time series data to obtain surface temperature change rate data and surface moisture content response change data;

[0015] The surface temperature change rate data and the surface moisture content response change data are normalized respectively, and the joint change value of the temperature change rate and the moisture content response change rate is calculated by using the weighted superposition algorithm, to construct the surface water film dissipation rate curve with time as the horizontal axis and the joint change value as the vertical axis;

[0016] The time interval corresponding to the ventilation treatment stage in the surface water film dissipation rate curve is extracted, the instantaneous slope change value of the curve in the time interval is calculated, and the instantaneous slope change value is compared with the preset threshold value, when the curve slope change value is continuously higher than the preset threshold value, it is determined that the local dehydration caused by surface ventilation treatment occurs.

[0017] Preferably, S2 is specifically:

[0018] In the case of local dehydration caused by surface ventilation treatment, the temperature sensing device arranged in the local dehydration area collects internal temperature data of the concrete at fixed time intervals, and the hydration heat release rate curve of the local dehydration area is obtained by dividing the temperature difference between adjacent time points by the time interval; strain sensing devices are arranged in the internal area not affected by ventilation to record the data sequence of strain change with time at the same sampling frequency, and the strain response curve of the internal area is generated by time derivative calculation;

[0019] Based on the hydration heat release rate curve of the local dehydrated area and the strain response curve of the internal area, data pairing is performed at the same time node to establish a heat-strain time sequence comparison matrix with time as the horizontal axis and heat release rate and strain response as the vertical data pair;

[0020] In the heat-strain time sequence comparison matrix, the starting points of the initial rising sections of the two curves are identified, the time difference between the two starting points is calculated to obtain the starting response phase difference, and the starting response phase difference is compared with the preset synchronous response judgment threshold. When the starting response phase difference exceeds the preset synchronous response judgment threshold, it is determined that the hydration starting behavior asynchrony phenomenon under the local dehydrated condition caused by surface ventilation treatment.

[0021] Preferably, S3 specifically comprises the following steps:

[0022] S301, based on the determined hydration starting behavior asynchrony, input the starting response phase difference into the constructed collaborative entropy mapping model of the relationship between hydration heat release and volume contraction rhythm, the collaborative entropy mapping model aggregates multiple groups of historical hydration heat release curve and volume contraction curve samples, extracts the coupling features of the historical hydration heat release curve and volume contraction curve in rhythm consistency using an improved mutual information evaluation mechanism, and establishes a mapping channel between the starting response phase difference and the coupling features;

[0023] S302, in the constructed collaborative entropy mapping model, according to the input starting response phase difference, the coupling index set corresponding to the starting response phase difference is extracted by a segmented entropy weight matching algorithm, and a synchronization parameter is calculated. The synchronization parameter is used to quantify the time sequence synchronization degree between the surface hydration heat release curve and the internal volume contraction curve;

[0024] S303, compare the synchronization parameter with the rhythm tolerance interval in the constructed synchronization evaluation benchmark model. If the synchronization parameter falls within the rhythm tolerance interval, it is determined that the concrete surface hydration heat release and internal contraction control rhythm are matched. If it exceeds the interval, it is determined that the rhythm is not matched, which is used for subsequent classification processing and control decision.

[0025] Preferably, S302 specifically comprises:

[0026] In the constructed collaborative entropy mapping model, the starting response phase difference is discretized into time segment intervals, and the local entropy weight of the surface hydration heat release curve and the internal volume contraction curve in each interval is calculated to obtain the coupling weight value of each time segment;

[0027] Based on the coupling weight value of each time segment, the mutual information intensity and rhythm coordination index in each segment are calculated by a segmented entropy weight matching algorithm, and weighted fusion is performed in the range of all segments to generate a coupling index set corresponding to the input start response phase difference, which is used to represent the overall rhythm coupling characteristics;

[0028] After obtaining the coupling index set, the weighted average coordination value between each index is calculated and standardized to obtain a synchronization parameter, which is used to quantify the timing synchronization degree between the surface hydration heat release curve and the internal volume shrinkage curve.

[0029] Preferably, S4 specifically is:

[0030] A threshold layering model for dividing the state level of concrete is constructed, multi-dimensional cluster analysis is performed on the historical data of synchronization parameters obtained under different construction conditions, based on the distribution characteristics and cluster boundaries of the synchronization parameters in the numerical space, the threshold value intervals of the synchronization parameters corresponding to the three states of rhythm matching, moderate mismatch and severe mismatch are demarcated, and a level layering mapping table is formed;

[0031] According to the judgment result, the current synchronization parameter value is obtained, and the synchronization parameter is input into the threshold layering model, and the synchronization parameter is compared with the three threshold value intervals of rhythm matching, moderate mismatch and severe mismatch in the level layering mapping table, to identify the belonging interval of the synchronization parameter in the threshold structure;

[0032] According to the belonging result of the synchronization parameter in the rhythm level interval, the current state level result of the concrete is output, and the state level result is transmitted to the subsequent dynamic regulation link as the classification basis.

[0033] Preferably, S5 specifically is:

[0034] Based on the state level result corresponding to the synchronization parameter, an initial adjustment template of the matching level is searched in the preset dynamic regulation instruction database, the current temperature field and humidity field data are input into the recursive adjustment algorithm as the environmental boundary conditions to initialize the reference adjustment interval of the surface heat preservation time sequence, the internal humidity compensation rate and the shrinkage compensation agent release time point;

[0035] Based on the initialization of the adjustment interval, the recursive adjustment algorithm adjusts the heat preservation time sequence offset, the humidity compensation rate curve gradient and the delay window of the shrinkage compensation agent release time point according to the rhythm mismatch degree, and introduces a multivariate feedback backtracking mechanism to evaluate the response sensitivity of the adjustment strategy to the synchronization parameter, forming a multi-round iteration optimization path;

[0036] The trend of synchronization parameter change obtained after each round of optimization is compared with the rhythm matching interval, when the synchronization parameter converges to the rhythm matching interval or meets the minimum change rate condition, the recursive adjustment algorithm is terminated, the final heat preservation timing adjustment instruction, humidity compensation rate value and shrinkage compensation agent release time point are output, and the dynamic regulation and control of the hydration heat release and internal shrinkage control rhythm of the concrete surface layer is completed.

[0037] In the above technical solution, the technical effects and advantages provided by the present application are as follows:

[0038] 1. The present application can accurately identify the early hydration phenomenon induced by local dehydration of the surface layer by constructing the surface layer water film dissipation rate curve and combining the slope change characteristics of the ventilation stage, further constructing the heat-strain timing comparison matrix by the hydration heat release curve and the strain response curve, extracting the starting response phase difference and determining the hydration starting behavior asynchronous phenomenon, and effectively realizing the dynamic capture of the non-synchronous starting state of the surface layer hydration reaction. Based on the phase difference, a cooperative entropy mapping model is constructed, which can convert the coupling relationship between different hydration heat and volume shrinkage rhythm into a quantifiable synchronization parameter, providing strong mathematical support for judging the rhythm matching degree. The whole identification link combines multi-layer data fusion and response feature extraction, and has the technical advantages of accurate identification, fast response and strong adaptability.

[0039] 2. The present application introduces a synchronization parameter grade division model and a recursive adjustment algorithm to construct a dynamic regulation and control closed loop mechanism, which can dynamically adjust the surface layer heat preservation timing, internal humidity compensation rate and shrinkage compensation agent release time point according to the grade difference of rhythm mismatch, and ensure that the concrete realizes the accurate cooperation of surface layer hydration heat release and internal volume shrinkage rhythm under different curing and construction conditions. By constructing the rhythm tolerance interval and threshold hierarchical mapping table, the synchronization parameter has operability and evaluation standard, and the control strategy has high controllability and judgment basis. Combined with the multivariate feedback backtracking mechanism and the minimum change rate judgment strategy, the iterative optimization of the control response path is realized, and finally the automatic convergence of the control target is completed. This technology can not only effectively reduce the crack risk caused by surface heat release disorder, but also can improve the overall stability and service durability of mass concrete structure, and is especially suitable for high-performance concrete engineering application scenarios in complex construction environment. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0041] Figure 1A flowchart of the method for controlling high-performance mass concrete based on hydration heat and shrinkage control according to the present application is shown. DETAILED DESCRIPTION

[0042] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations may, however, be implemented in many different forms and should not be construed as limited to the examples set forth herein; rather, these example implementations are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example implementations to those skilled in the art.

[0043] The present application provides a method for controlling high-performance mass concrete based on hydration heat and shrinkage control, as shown in Figure 1 The method comprises the following steps:

[0044] S1, collecting the surface temperature change rate and surface moisture response change data after concrete pouring, constructing a surface water film dissipation rate curve, and determining whether a local dehydration situation caused by surface ventilation treatment occurs by analyzing whether the instantaneous slope of the surface water film dissipation rate curve in the ventilation treatment stage exceeds a preset threshold value;

[0045] In this embodiment, S1 specifically comprises:

[0046] After the concrete pouring is completed, temperature sensors and humidity sensors are arranged in the monitoring area, surface temperature data and surface moisture data are collected at fixed time intervals, and derivative calculation is performed on each group of time series data to obtain surface temperature change rate data and surface moisture response change data.

[0047] After the concrete pouring is completed, in order to obtain the temperature change rate and moisture response change data of the surface in real time, high-precision digital temperature sensors and humidity sensors can be arranged at preset positions on the surface of the concrete. The temperature sensors can be selected from thermistors or infrared non-contact types, and the humidity sensors can be selected from capacitive surface moisture probes, all of which need to have a data remote transmission function. The collection frequency is set to record data once every minute, and the continuously recorded temperature data and moisture data are arranged in time series. In order to obtain the surface temperature change rate and moisture response change, first derivative processing is required for each group of continuously collected data, that is, the temperature change rate of each time period is obtained by dividing the temperature difference between two adjacent time points by the time difference, and the moisture response change data can be obtained in the same way. For example, the temperature at a certain time point is 26.3°C, the temperature at the next time point is 26.6°C, and the interval time is 1 minute, so the temperature change rate is 0.3°C / min. In this way, a complete temperature change rate sequence and moisture response change sequence are formed, which lays a foundation for subsequent analysis of the heat and moisture coupling behavior. This is done to timely capture the influence of the rapid change of the temperature and humidity environment in the surface ventilation treatment process on the initial hydration behavior of the concrete, so as to realize early identification of abnormal areas.

[0048] The purpose of laying temperature sensors and humidity sensors is to monitor the concrete surface environment in real time at high frequency, and to accurately perceive the change trend of temperature and humidity in the early curing process. Collecting "surface temperature data" and "surface moisture content data" is to obtain dynamic information of the thermal and humid state of the concrete surface under specific working conditions, especially in the key stage of heat release fluctuation and water film dissipation after ventilation disturbance. The setting of "fixed time interval" ensures the continuity and uniformity of the data, making the change trend have time dimension traceability. The action of "respectively performing derivative calculation on each group of time series data" is essentially a data differentiation process, which extracts the change rate of the original variable, thereby reflecting the response speed of the concrete surface layer. "Surface temperature change rate data" reveals the speed characteristics of temperature rise or fall, and "surface moisture content response change data" describes the loss degree of water film or capillary water, both of which constitute the response basic signals of the early water and heat behavior of the concrete surface layer, and are indispensable original parameter sources for constructing the surface water film dissipation rate curve. These technical features together realize the real-time perception and quantitative expression of the early hydration environment fluctuation.

[0049] The surface temperature change rate data and the surface moisture content response change data are normalized respectively, and the weighted superposition algorithm is used to calculate the joint change value of the temperature change rate and the moisture content response change rate, and the surface water film dissipation rate curve is constructed with time as the horizontal axis and the joint change value as the vertical axis;

[0050] In order to use the surface temperature change rate data and the surface moisture content response change data for joint analysis, the two different physical quantities need to be normalized first. Normalization can be done by subtracting the minimum value of each data sequence from the sequence and dividing by the range of the sequence, so that the value range is limited between zero and one. This can eliminate the differences in temperature and humidity dimensions and orders of magnitude, and avoid the dominance of one variable in subsequent calculations. After normalization, the weighted superposition algorithm is used to fuse the two normalized sequences into a single sequence. The weighting coefficients can be based on the experimental weight setting of the influence of temperature change rate and moisture content response change rate on surface dehydration, for example, the temperature weight is 0.6 and the moisture weight is 0.4. The joint change value obtained after weighted superposition constitutes a new data sequence, with time as the horizontal axis and the joint change value as the vertical axis, the sequence is visualized in time order, and the surface water film dissipation rate curve is obtained. For example, when the temperature change normalized value is 0.75 and the moisture content response normalized value is 0.65, after multiplying by the weight and adding, the joint change value is 0.71, corresponding to a specific time point. This curve is used to reflect the water film loss trend of the concrete surface, and is an important parameter carrier for identifying the ventilation-induced dehydration state.

[0051] The normalization process refers to the conversion of two dimensionally different variable data to the same scale range, avoiding the difference in physical dimensions from causing bias in subsequent fusion. Through normalization, the temperature change rate and the humidity response change rate can participate in joint analysis under the same dimension. The weighted superposition algorithm refers to the calculation method of assigning weight values to multiple input variables and performing linear combination, which has outstanding advantages in data fusion analysis. Setting different weights can reflect the relative importance of the two variables to the target phenomenon, and giving more weight to high impact factors can enhance the sensitivity of the model. The joint change value is a single quantitative parameter obtained by this algorithm to describe the comprehensive response of multiple factors, which not only integrates the driving effect of temperature change, but also reflects the dehydration trend of humidity response. The surface water film dissipation rate curve drawn with time as the horizontal axis and the joint change value as the vertical axis can reflect the fluctuation interval in the water film change process in the form of a continuous curve, providing mathematical basis and visual support for subsequent judgment of whether there is local dehydration. This group of technical features constitutes the key analysis basis for quantitatively describing the early surface dehydration trend under the influence of ventilation.

[0052] Extract the time interval corresponding to the ventilation processing stage in the surface water film dissipation rate curve, calculate the instantaneous slope change value of the curve in this time interval, and compare the instantaneous slope change value with the preset threshold value. When the slope change value of the curve is continuously higher than the preset threshold value, it is determined that there is local dehydration caused by surface ventilation processing.

[0053] In order to identify whether the ventilation processing causes local dehydration of the concrete surface, it is necessary to locate the time interval from the start to the end of the ventilation processing on the surface water film dissipation rate curve. The start and end time of ventilation can be recorded simultaneously during construction, and marked on the water film dissipation rate curve to form a clear time window. In this time interval, the joint change value of each group of adjacent time points is subjected to difference operation to obtain the instantaneous slope change value sequence in this interval. The slope reflects the rate of change of water film dissipation degree, and the higher the value, the faster the water evaporation speed. Then, each instantaneous slope value is compared with the preset threshold value. When the slope values of consecutive multiple time points exceed the preset threshold value and remain for a certain duration, for example, for several minutes, it can be determined that the concrete surface has undergone strong dehydration in this time period, thereby determining the existence of local dehydration. For example, if the slope value is higher than 0.12 for five consecutive minutes, and much higher than the background value before ventilation, it can be identified as dehydration behavior induced by water film rupture. This judgment logic makes the ventilation influence have quantifiable and traceable feature expression at the data level.

[0054] The time interval of the ventilation treatment stage is an actual time window determined based on field ventilation operation control behavior, and is a core boundary condition of the judgment step. The instantaneous slope change value is a derivative data obtained by first-order difference in the surface water film dissipation rate curve, and is used to reflect the change speed of the combined change value at each moment, and reveal the dynamic characteristics of the water film dissipation speed. Through the continuous rise of the slope, whether the water loss is concentrated and burst can be revealed, and the normal evaporation and the abnormal dehydration process are distinguished. The preset threshold is a numerical limit set by an experience or training model according to a large number of engineering sample statistics, and is used as a standard for judging the critical state. The selection needs to consider the concrete type, environmental temperature, wind speed and other parameters, and can be adjusted according to the actual situation in different projects. The comparison between the instantaneous slope and the preset threshold realizes the transformation from qualitative observation to quantitative judgment, so that the water film rupture state can be accurately identified. The coordinated cooperation of these technical features provides the basis for data expression and logical judgment for the concrete surface ventilation induced dehydration state, and is a prerequisite for the subsequent analysis of the thermal and moisture synchronicity.

[0055] S2, in the presence of local dehydration caused by surface ventilation treatment, collecting the hydration heat release rate curve of the local dehydration area and the strain response curve of the internal area, establishing a thermal-strain time sequence comparison matrix, calculating the start response phase difference, and determining the hydration start behavior asynchronous phenomenon in the presence of local dehydration caused by surface ventilation treatment;

[0056] In this embodiment, S2 is specifically:

[0057] In the presence of local dehydration caused by surface ventilation treatment, the temperature sensing device arranged in the local dehydration area collects internal temperature data of the concrete at fixed time intervals, and obtains the hydration heat release rate curve of the local dehydration area by dividing the temperature difference between adjacent time points by the time interval. The strain sensing device is arranged in the internal area not affected by ventilation to record the data sequence of strain change with time at the same sampling frequency, and the strain response curve of the internal area is generated by time derivative calculation;

[0058] After the concrete is finished, in order to capture the hydration heat response characteristics in the case of local dehydration caused by surface ventilation treatment, high-sensitivity temperature collection devices such as distributed optical fiber temperature sensors or high-resolution thermocouples can be selected and pre-buried in the surface layer area expected to be affected by ventilation. The data acquisition system records a continuous temperature value sequence at fixed time intervals (such as once every minute). In order 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 heat change trend per unit time. At the same time, strain sensors such as fiber Bragg gratings or resistance strain gauges are arranged in the internal core area of the concrete to ensure that they are not affected by ventilation. The collected raw strain data are recorded at the same time frequency as the temperature data, and the strain response change rate sequence is generated by dividing the strain change between adjacent time points by the interval time, indicating the volume change trend of the internal structure. For example, when the heat released in advance due to ventilation-induced dehydration in the surface layer, the surface temperature change rate curve will appear a rapid rising trend in advance, while the strain response change curve of the internal area may be delayed to start, and the time difference between the two can be used for subsequent judgment of whether the starting response is synchronized.

[0059] The temperature change rate curve reflects the rhythm and intensity of the heat released by the hydration reaction in the local dehydration area, and the strain response change curve reflects the actual feedback of the internal volume shrinkage control of the structure. The two constitute the key physical indicators of heat-force linkage. The independent arrangement of temperature sensing devices and strain sensing devices ensures the separation in the spatial dimension, and thus the independent behavior characteristics of the surface layer and the core area can be analyzed respectively. Fixed time interval collection ensures the time comparability of the data, which is the basis for building the time sequence comparison matrix. The change rate curve obtained by derivative calculation avoids the interference of direct temperature or strain values, and more accurately reflects the dynamic response trend. This technical link realizes the transformation from basic data collection to dynamic response extraction, which is the prerequisite for identifying whether the hydration heat release rhythm is abnormal, and provides an accurate data basis for subsequent phase difference and synchronization analysis.

[0060] Based on the hydration heat release rate curve of the local dehydration area and the strain response curve of the internal area, the data pairs are paired according to the same time nodes to establish a heat-strain time sequence comparison matrix with time as the horizontal axis and heat release rate and strain response as the vertical data pairs;

[0061] In order to establish the time correspondence between the heat release rate and the strain response, the hydration heat release rate curve from the local dehydration area and the strain response curve from the internal area 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. Subsequently, according to each time point, the heat release rate value and the strain response value corresponding to the time point are combined to form an ordered data pair. This series of data pairs are arranged in chronological order to construct a heat-strain time sequence comparison matrix with time as the horizontal axis and heat release rate and strain response as the vertical two-dimensional data structure. For example, when the heat release rate in the surface area is a certain value after 10 minutes, and the strain response value in the internal area at this time is another value, they form the data pair of the tenth minute. Through continuous pairing, a time sequence matrix containing multiple time nodes is obtained, which can observe the coupling of the two physical processes in the time domain and provide basic data for phase difference analysis.

[0062] The construction process of this heat-strain time sequence comparison matrix fully embodies the dynamic coupling characteristics between thermal response and force response, and is a key link for judging whether the hydration heat release rhythm matches the volume shrinkage control rhythm. Time is the unified reference axis, ensuring the horizontal consistency in subsequent analysis; while the two dimensions on the vertical axis, heat release rate and strain response value, are respectively from different sensing channels in the surface and internal areas, and respectively represent independent physical processes of thermal excitation behavior and volume feedback behavior. Data pairing emphasizes synchronous acquisition at the same time node, avoiding misjudgment caused by time misalignment. Through the matrix structure, the change rhythm of the two physical curves is integrated, providing a formal and calculable carrier for subsequent phase difference extraction and synchronization judgment, thereby supporting the subsequent judgment step of identifying hydration initiation behavior asynchrony based on rhythm deviation.

[0063] In the heat-strain time sequence comparison matrix, the starting points of the initial rising sections of the two curves are identified, the time difference between the two starting points is calculated to obtain the initiation response phase difference, and the initiation response phase difference is compared with the preset synchronization response judgment threshold. When the initiation response phase difference exceeds the preset synchronization response judgment threshold, it is determined that the hydration initiation behavior asynchrony phenomenon under the condition of local dehydration caused by surface ventilation treatment.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] In this embodiment, S3 specifically includes the following steps:

[0068] 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.

[0069] After determining the hydration initiation behavior asynchronous phenomenon in the concrete, in order to accurately describe the rhythm relationship between the surface hydration heat release process and the internal volume shrinkage process, it is necessary to build a synergy entropy mapping model of the hydration heat release and volume shrinkage rhythm relationship. The establishment of the model can be realized by collecting the heat-force monitoring data of multiple historical pouring projects, including the hydration heat release curve of the surface layer and the volume shrinkage curve of the internal region. First, the time sequence samples are standardized to make the data of different scales and different material systems comparable in the same dimension. Then, the time correlation and information coupling strength between each set of hydration heat release curve and volume shrinkage curve are calculated by using the improved mutual information evaluation mechanism, so as to extract the coupling characteristics of the two physical processes in rhythm consistency. Then, the actual detected initiation response phase difference is input into the model, and the corresponding coupling feature position of the phase difference in the model space is determined by using the mapping function. For example, when the input phase difference is small, the coupling characteristic value output by the model tends to be high consistency, indicating that the heat and shrinkage process is highly synchronized; when the phase difference is large, the output coupling characteristic value decreases, reflecting the weakening of the synchronization. This method makes the complex heat-force rhythm relationship be digitized, providing a quantitative basis for subsequent synchronization judgment.

[0070] The synergy entropy mapping model of the hydration heat release and volume shrinkage rhythm relationship is a time series correlation modeling structure based on statistical entropy theory, and its core is to quantify the information coupling strength between two nonlinear processes. The historical hydration heat release curve and volume shrinkage curve samples represent the typical distribution of concrete heat-force behavior under different working conditions, which are used to train the model to identify the regular characteristics of rhythm matching and deviation. The improved mutual information evaluation mechanism introduces time delay window and weight distribution coefficient into the traditional mutual information calculation, not only considers the synchronization of data points, but also strengthens the response ability to rhythm change trend, so as to more accurately capture the dynamic consistency of heat release and shrinkage change. The coupling characteristics of rhythm consistency are the core results of the model output, which are used to represent the synchronization level of the heat-force system in the time evolution process, and the numerical value reflects the coordination degree of heat response and shrinkage response in rhythm. In this way, the complex multi-time series heat-force response relationship is transformed into a calculable entropy space mapping structure, so that the hydration behavior characteristics of concrete under different environments and material systems can be uniformly represented and analyzed.

[0071] S302、In the constructed synergy entropy mapping model, according to the input initiation response phase difference, the coupling index set corresponding to the initiation response phase difference is extracted by using the segmented entropy weight matching algorithm, and a synchronization parameter is calculated, which is used to quantify the time sequence synchronization degree between the surface hydration heat release curve and the internal volume shrinkage curve;

[0072] S303, compare the synchronism parameter with the rhythm tolerance interval in the constructed synchronism evaluation reference model, if the synchronism parameter falls into the rhythm tolerance interval, it is judged that the hydration heat release of the concrete surface layer and the internal shrinkage control rhythm match, if it exceeds the interval, it is judged that the rhythm does not match, which is used for subsequent classification processing and control decision.

[0073] After obtaining the synchronism parameter, in order to realize the automatic judgment of rhythm matching, the synchronism parameter needs to be compared with the rhythm tolerance interval in the synchronism evaluation reference model. The synchronism evaluation reference model can be constructed by historical experimental data and simulation analysis. The model records the reference parameter distribution interval under the condition that the hydration heat release and the volume shrinkage rhythm are synchronized under different material systems, construction conditions and temperature environment. In specific implementation, the system inputs the currently calculated synchronism parameter into the model, and matches it with the rhythm tolerance interval under the corresponding working condition in the model. When the synchronism parameter value falls within the tolerance interval, 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 in good synchronism. If the synchronism parameter exceeds the upper limit or is lower than the lower limit, it indicates that there is a lag or advance phenomenon between the thermal response and the volume response, that is, the rhythm does not match. For example, in winter low temperature construction, if the synchronism parameter is lower than the lower limit of the tolerance interval, it means that the surface layer hydration is delayed, which may lead to shrinkage before hydration heat release, thereby indicating an increased risk of cracks. This result will be transmitted to the control decision module to trigger the heat preservation strengthening or shrinkage compensation strategy.

[0074] The synchronism evaluation reference model is a judgment model based on multi-dimensional feature samples. Its structure integrates statistical experience and machine learning recognition mechanism. By clustering and regression analysis on a large number of concrete thermal-mechanical coupling experimental data, the feature distribution law of the synchronism parameter under different synchronization states is extracted. Each group of sample data in the model includes the mixing ratio parameters of concrete, environmental conditions, temperature control history and response rhythm results, thereby forming a synchronism standard system that can be used in different application scenarios. The rhythm tolerance interval is a key result of the model, which is a numerical boundary reflecting the normal fluctuation range of the synchronism parameter. It is usually centered on the mean value of the synchronism parameter, and the upper and lower limits are set in combination with the standard deviation and the actual tolerance. This interval reflects the tolerance degree of the system in judging the coordination of thermal-mechanical response rhythm. Too narrow may lead to misjudgment, and too wide may reduce sensitivity. By comparing the synchronism parameter with the rhythm tolerance interval, the model converts the continuous response signal into a discrete rhythm matching judgment result, realizes the logical closed loop from physical data to control decision, and makes the hydration heat and shrinkage control maintain dynamic coordination throughout the whole age, ensuring the overall stability and structural safety of mass concrete.

[0075] In this embodiment, S302 specifically comprises:

[0076] In the constructed collaborative entropy mapping model, the start-up response phase difference is discretized into time segmented intervals, and the local entropy weight calculation is performed on the surface hydration heat release curve and the internal volume shrinkage curve in each interval to obtain the coupling weight value of each time segment, so as to reflect the relative information contribution degree of thermal-mechanical response in different time periods;

[0077] In the constructed collaborative entropy mapping model, in order to improve the resolution ability of the rhythm change of thermal-mechanical process, first, the start-up response phase difference is discretized according to the time dimension, that is, the whole response time axis is divided into several continuous and non-overlapping time segmented intervals. In each time segmented interval, the sub-sequence of the surface hydration heat release curve and the internal volume shrinkage curve in the time period is extracted, and the local entropy weight calculation is performed based on the information entropy theory. The local entropy weight calculation is to evaluate the information content in the time period by measuring the uncertainty of the data fluctuation in each curve, and then the coupling weight value is calculated by combining the joint entropy or mutual information of the two curves in the interval, which is used to reflect the degree of synchronization of thermal-mechanical response in the time period. In this way, each time segment not only retains the time information, but also forms a local coupling degree distribution map in space. For example, in the early hydration stage, it may show high coupling and low synchronization in the middle and late stages, and the local entropy weight calculation result can intuitively reflect this trend. This process effectively avoids the loss of details caused by the average processing of the whole data, makes the rhythm relationship analysis more resolution, and provides a high-quality data basis for subsequent rhythm synchronization parameter generation and judgment. This method has the advantages of high dynamic response sensitivity and strong local discrimination ability, and is an important expansion of the traditional overall coupling evaluation method.

[0078] Based on the coupling weight value of each time segment, the mutual information intensity and rhythm synchronization index in each segment are calculated by the segmented entropy weight matching algorithm, and the weighted fusion is performed in the range of all segments to generate the coupling index set corresponding to the input start-up response phase difference, which is used to represent the overall rhythm coupling characteristics;

[0079] Based on the coupling weight values of each time segment, the segmented entropy weight matching algorithm can further identify the coordination relationship between the surface hydration heat release process and the internal volume shrinkage process in time rhythm. In specific implementation, first, the mutual information intensity of each time segment is calculated, which is used to describe the correlation between the two response signals and reflects the information transmission efficiency; then, combined with the coupling weight values calculated in the previous stage, the entropy weight distribution principle is used to give different time segments different matching priorities, calculate the rhythm coordination index, and reflect the contribution of each time segment in rhythm synchronization. Finally, in the range of all time segments, the mutual information intensity and the rhythm coordination index are weighted and fused to generate the coupling index set corresponding to the start response phase difference. The coupling index set contains multiple rhythm coordination characteristics in multiple dimensions, which can refine the expression of the mutual coordination degree of the overall thermal-mechanical response process in multiple time periods. The segmented entropy weight matching algorithm solves the problem of invalid overall judgment when the responses are not synchronized by locally identifying the coupling relationship in the time sequence and integrating the global weight, which improves the recognition accuracy of rhythm characteristic changes in the complex concrete hydration heat and volume shrinkage process, and is a structural optimization and high-order upgrade of the traditional whole signal matching algorithm.

[0080] After obtaining the coupling index set, the weighted average coordination value between the indexes is calculated and standardized to obtain the synchronization parameter, which is used to quantify the time sequence synchronization degree between the surface hydration heat release curve and the internal volume shrinkage curve, and provides a quantitative basis for subsequent rhythm matching judgment.

[0081] After obtaining the coupling index set, the weighted average calculation of each index in the set is needed. First, each index is assigned a weight value determined by the entropy weight method. These weight values are determined by the rhythm coordination and information intensity reflected by the previous segmented entropy weight matching algorithm. The higher the weight, the greater the contribution of the index in describing the thermal-mechanical synchronization. Then, each index is multiplied by its corresponding weight and summed to obtain a coordination value reflecting the overall coupling level. In order to ensure that the coordination value has comparability under different concrete proportions, curing conditions or construction environments, it needs to be standardized to map the value to a fixed interval, so that the final result is not affected by external scale differences. The final synchronization parameter is a unified dimension numerical index, which is used to measure the response consistency degree between the surface hydration heat release curve and the internal volume shrinkage curve in time process. This parameter provides a continuous, calculable and traceable judgment basis, which not only provides a basis for subsequent rhythm matching and mismatch identification, but also can be used as an input variable in dynamic control algorithm to realize quantitative modeling and fine control of hydration heat and shrinkage response coordination behavior of high-performance mass concrete.

[0082] 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.

[0083] In this embodiment, S4 specifically refers to:

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] After the calculation of the synchronicity parameter is completed, the value of the synchronicity parameter corresponding to the current time is obtained, and the value is loaded as input into the threshold hierarchical model constructed. The model includes the pre-defined rhythm matching, moderate mismatch and severe mismatch three levels of synchronicity parameter interval boundaries. By programming, a linear interval matching function is realized, and the input synchronicity parameter is compared with the multiple intervals in the level hierarchical mapping table one by one, until the specific interval where the parameter belongs is found, so as to identify the state level belonging of the current concrete surface hydration heat release and internal volume shrinkage. Taking an engineering case as an example, if the synchronicity parameter is 0.82, and the rhythm matching zone is 0.90~1.00, the moderate mismatch zone is 0.70~0.89, and the severe mismatch zone is 0.00~0.69, the system will judge that the current state belongs to the moderate mismatch level, and the result will be passed to the subsequent regulation module to guide the accuracy and response mode of the heat preservation and shrinkage adjustment strategy.

[0089] As an index for quantifying the timing consistency of surface hydration heat release and internal volume shrinkage, the synchronicity parameter must be compared with the hierarchical interval constructed by multi-dimensional cluster analysis. The hierarchical mapping table extracts the cluster boundary of historical response data to ensure that the upper and lower limits of each level interval have statistical significance and engineering discriminant meaning. In specific implementation, the comparison process is usually constructed with double-layer conditional judgment logic: the first layer matches whether the synchronicity parameter is lower than the upper threshold, and the second layer judges whether it is higher than the lower threshold, so as to realize stable three-zone attribution calculation. This mechanism not only avoids the subjectivity of parameter judgment, but also makes the hierarchical judgment result have traceability and regulation continuity, which is the basis for realizing state perception and dynamic response.

[0090] According to the attribution result of the synchronicity parameter in the rhythm level interval, the current state level result of the concrete is output, and the state level result is passed to the subsequent dynamic regulation link as a classification basis to realize state accurate identification and rhythm response closed-loop matching.

[0091] When the attribution of the synchronism parameters in the rhythm level interval is determined, the current state level result of the concrete can be directly output, such as rhythm matching, moderate mismatch or serious mismatch. The state level result is not only a static analysis conclusion, but also an input condition in the control logic, which is immediately passed to the subsequent regulation and execution module. The specific implementation can adopt a state triggering mechanism, and through the preset state-strategy mapping logic, different heat preservation adjustment plans, humidity compensation rates or shrinkage agent release scheduling strategies are called corresponding to different state levels. For example, when the state level is identified as serious mismatch, the advance encryption heat preservation time interval is triggered, the humidity compensation process is accelerated, and the release window of the shrinkage control agent is adjusted, so as to realize the state-driven dynamic response in the control decision chain. This implementation ensures the synchronous closed loop of the concrete structure performance regulation in the time and state dimensions.

[0092] The state level is not a single label, but the result of the response level of the synchronism parameters in the multi-dimensional coupled structure after identification, which has the properties of quantification, stratification and triggering. In implementation, a digital coding system including three state levels is constructed, for example, level 0 represents rhythm matching, level 1 represents moderate mismatch, and level 2 represents serious mismatch, so that it has a structure that can be dynamically read and called. The level is not only used for state display, but also directly drives the judgment branch and parameter scheduling module in the subsequent closed loop control, ensuring the automation of the response link, parameter linkage and accurate execution. This level-driven regulation method breaks through the limitations of manual judgment and static countermeasures in traditional concrete construction, and improves the intelligent level of hydration heat and shrinkage control of mass concrete.

[0093] S5, based on the state level result, a recursive adjustment algorithm is executed to adjust the surface heat preservation time sequence, internal humidity compensation rate and shrinkage compensation agent release time point, to realize dynamic regulation and control of the hydration heat release of the concrete surface and the internal shrinkage control rhythm.

[0094] In this embodiment, S5 is specifically:

[0095] Based on the state level result corresponding to the synchronism parameters, the initial adjustment template of the matching level is searched in the preset dynamic regulation instruction database, the current temperature field and humidity field data are input as the environmental boundary conditions into the recursive adjustment algorithm, and the baseline adjustment interval of the surface heat preservation time sequence, the internal humidity compensation rate and the shrinkage compensation agent release time point is initialized;

[0096] In the process of regulating and initializing the state level result corresponding to the synchronization parameter, a preset dynamic regulation instruction database can be used for level matching operation. The database pre-constructs multiple sets of regulation templates, each set of template corresponding to a specific state level, including rhythm matching, rhythm moderate mismatch and rhythm serious mismatch. In each template, the initial heat preservation time adjustment range, humidity compensation rate suggestion value and shrinkage compensation agent release time window are defined. In actual application, first, the synchronization parameter is indexed to the corresponding template according to the level, and then the environment boundary conditions such as the current field collected concrete surface and internal temperature distribution, humidity change and other environmental boundary conditions are input into the recursive regulation algorithm as input. The algorithm uses the level template as the basis for adjusting the framework, uses the boundary condition parameters to set the adjustment starting point, and initializes the heat preservation time range, humidity compensation rate range and shrinkage compensation agent release time interval that meet the current construction state, to provide a basic solution space for subsequent dynamic regulation recursive optimization. For example, in the serious mismatch level, the initial regulation template can preset a larger heat preservation advance period, a higher humidity compensation rate upper limit and an earlier start of shrinkage compensation agent release node.

[0097] The "state level result corresponding to the synchronization parameter" refers to the rhythm synchronization evaluation result obtained by coupling determination in the previous process, which is used to represent the category classification of the current internal and surface rhythm coupling state of the concrete. The "dynamic regulation instruction database" is a structured regulation knowledge base, which covers parameter adjustment strategies under different state levels and has on-demand calling capability. The "initial regulation template" is a parameter set, including the heat preservation control start and end time suggestion range, the internal humidity adjustment curve suggestion slope and the compensation agent release suggestion time sequence section, which is used to provide the initial input of the regulation algorithm. The "temperature field and humidity field data" are derived from the multi-point sampling data matrix arranged at different depths of the concrete, which is used to reflect the actual state of the concrete thermal and humidity behavior. The "environmental boundary conditions" represent the peripheral temperature and humidity influencing factors considered in the regulation parameter adjustment. The "recursive regulation algorithm" is an intelligent algorithm with a backtracking optimization mechanism, which can dynamically adjust the heat preservation, water compensation and compensation agent release timing based on the initial template and boundary conditions, to improve the response ability of the control strategy to complex changing conditions. The whole process realizes the closed-loop transition from state recognition to preliminary regulation target setting.

[0098] On the basis of initializing the regulation interval, the recursive regulation algorithm adjusts the heat preservation time sequence offset, humidity compensation rate curve gradient and shrinkage compensation agent release time point delay window according to the rhythm mismatch degree, and introduces a multivariable feedback backtracking mechanism to evaluate the response sensitivity of the adjustment strategy to the synchronization parameter, forming a multi-round iteration optimization path.

[0099] After the initialization adjustment interval setting is completed, the recursive adjustment algorithm starts to iteratively optimize the multi-dimensional parameters according to the rhythm mismatch degree. In the specific implementation process, first, the strength level of the rhythm mismatch is determined according to the offset degree of the synchronicity parameter and the level boundary, which is converted into a factor weight of the adjustment amplitude. Subsequently, the algorithm adjusts the heat preservation time offset according to the weight, that is, changes the heat preservation start or end time, so that the heat release process is close to the internal control rhythm. At the same time, the gradient of the humidity compensation rate curve is adjusted to control the internal moisture level and optimize the release time of the shrinkage compensation agent, and through the delay window setting, the release node is aligned with the target shrinkage control rhythm. After each round of parameter update, the algorithm will recalculate the synchronicity parameter response value under the current adjustment, and compare it with the result of the last round, and introduce a multivariate feedback backtracking mechanism to evaluate the improvement efficiency of the adjustment path on the synchronicity. The whole process converges continuously in a multi-round recursive optimization mode, and finally obtains the optimal surface heat preservation, internal water compensation and compensation agent release combination strategy. For example, when the synchronicity deviation is obvious, the algorithm can delay the heat preservation end time and simultaneously increase the humidity compensation rate slope to promote the synchronicity to return to the target interval.

[0100] The "initialization adjustment interval" refers to the parameter adjustment range established on the basis of the initial template according to the boundary conditions, which provides the upper and lower limits of the variable for subsequent control. The "recursive adjustment algorithm" is an iterative control logic structure that gradually optimizes the control parameters through repeated calculation paths, with path memory and adaptive ability. The "rhythm mismatch degree" is used to measure the numerical deviation between the synchronicity parameter and the target rhythm, which directly determines the amplitude and direction of the adjustment action. The "heat preservation time offset" represents the dynamic correction of the start and end time of the heat preservation operation, which is used to match the actual progress of the concrete hydration heat release. The "humidity compensation rate curve gradient" refers to the growth slope of the internal moisture supplement rate in the time dimension, which is used to adjust the internal humidity distribution trend. The "delay window of the shrinkage compensation agent release time point" defines the allowable delay range of the release action, ensuring that its chemical action is synchronized with the shrinkage stress release process. The "multivariate feedback backtracking mechanism" is a method of updating the adjustment weight in the reverse direction based on the historical adjustment path and response data, making the adjustment process more sensitive and accurate to the changes in the synchronicity parameter, and improving the adaptability and accuracy of the rhythm control.

[0101] The trend of the synchronicity parameter 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 time adjustment instruction, humidity compensation rate value and shrinkage compensation agent release time point are output, completing the dynamic control of the concrete surface hydration heat release and internal shrinkage control rhythm.

[0102] After each round of recursive adjustment, the system recalculates the synchronization parameter between the concrete surface hydration heat release and the internal volume shrinkage according to the updated parameters, and records the trend of the parameter on the time sequence. The algorithm determines whether to terminate the adjustment by setting two stop conditions: one is whether the synchronization parameter has entered the rhythm matching interval; the second is whether the change rate of the synchronization parameter between two consecutive rounds of optimization is lower than the minimum change rate threshold. When either condition is met, the system determines that the synchronization regulation has reached a stable state or further adjustment has no obvious benefit, and immediately terminates the recursive process and outputs the final regulation instruction, including the determined curing time adjustment result, the specific humidity compensation rate and the release time point of the shrinkage compensation agent, thereby completing the deployment of the dynamic regulation strategy. For example, when the synchronization parameter has been stable near the center value of the rhythm matching interval for three consecutive rounds, and the change rate tends to zero, the system will consider that the concrete regulation state has highly matched the target rhythm, and timely output the control execution instruction to avoid over-regulation.

[0103] The "synchronization parameter change trend" refers to the numerical fluctuation trajectory of the synchronization quantitative index in the time dimension in multiple iterations, which is used to evaluate the effect of the adjustment behavior. The "rhythm matching interval" is the ideal synchronization parameter range defined in the coordination entropy mapping model, representing the target value range of the coupling of the concrete surface hydration heat release and the internal shrinkage control rhythm. The "minimum change rate condition" is one of the termination criteria set by the regulation system to judge the convergence state of the adjustment, which is usually set as a lower change rate threshold, indicating that when multiple rounds of optimization fail to effectively improve the synchronization performance, it is considered to have stabilized. The "termination of the recursive adjustment algorithm" means that the system enters the output stage, stops parameter iteration, and locks the current optimal control strategy. The "curing time adjustment instruction" specifies the time adjustment scheme of the curing material laying and removal, the "humidity compensation rate value" is used to control the running intensity and frequency of the internal humidification system, and the "shrinkage compensation agent release time point" is used to set the trigger time of the additive, which together determines the coupling degree between the thermal-hygro-chemical state of the concrete and the rhythm, thereby realizing the whole-process dynamic closed-loop regulation.

[0104] The above-described embodiments can be implemented in part or in whole through software, hardware, firmware or any combination thereof. When implemented in software, the above-described embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When loaded and executed by a computer, the computer instructions or computer programs cause the computer to perform all or part of the processes or functions described in the embodiments of the present application. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable apparatus. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through a wired or wireless (for example, infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0105] It should be understood that the size of the sequence number of each process described above in various embodiments of the present application does not mean the order of execution, and the execution order of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0106] Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized 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 the present application.

[0107] In several embodiments provided in the present application, it should be understood that the disclosed system and method can be implemented in other ways. For example, the above-described embodiments are only illustrative, for example, the division of units is only a logical function division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed each other can be indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0108] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may also be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0109] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0110] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection 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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