Method for testing expansion and shrinkage performance of mirror-finished fair-faced concrete

Abstract

The present application relates to the technical field of concrete testing, in particular to a mirror-finished fair-faced concrete expansion and shrinkage performance testing method, comprising: collecting an expansion and shrinkage sequence and an apparent image sequence; extracting constraint conditions and shrinkage timing characteristics of each state from the expansion and shrinkage sequence, and establishing an expansion and shrinkage characteristic table; using a three-dimensional temperature sequence containing internal temperature, surface temperature and ambient temperature of the sample, calculating the coupling relationship between temperature and expansion and shrinkage in any time period, and determining the characteristic mutation point of expansion and shrinkage; based on the characteristic mutation point of expansion and shrinkage, taking the defect type corresponding to the apparent image sequence as a synergistic factor, solving the characteristic mutation mode corresponding to the concrete sample; according to the characteristic mutation mode corresponding to the concrete sample, triggering a multi-parameter synergistic alarm when the characteristic mutation mode repeatedly appears, and determining the target parameter of the concrete sample. The processing efficiency and accuracy of concrete testing are improved.

Description

Technical Field

[0001] This invention relates to the field of concrete testing technology, specifically a method for testing the expansion and shrinkage properties of mirror-finished fair-faced concrete. Background Technology

[0002] The expansion and contraction deformation of concrete is the core cause of early cracking. In particular, to achieve a high mirror effect, mirror-finish concrete usually adopts a mix proportion system with a low water-cement ratio and a high amount of cementitious materials. Its early-age hydration rate is fast, the heat of hydration is concentrated, and the superposition effect of autogenous shrinkage and temperature shrinkage is significant. The time-varying characteristics of expansion and contraction behavior are stronger, and the risk of cracking is much higher than that of ordinary concrete.

[0003] For example, Chinese Patent Publication No. CN120703219A discloses a method and system for evaluating the crack resistance performance of concrete based on its shrinkage rate. This application first collects real-time sequence data of the shrinkage rate of concrete samples during the curing process and acoustic emission signal streams. High-frequency elastic wave energy attenuation characteristic values ​​are separated from the acoustic emission signal streams and spatiotemporally coupled with the shrinkage rate jump points in the real-time shrinkage rate sequence data to generate a crack initiation tendency index. Then, based on the environmental temperature hysteresis effect, the temperature drift error of the crack initiation tendency index is calibrated to obtain a calibrated crack initiation tendency index. Finally, based on the gradation deviation between the pre-designed gradation parameters and the fluctuation range of the calibrated crack initiation tendency index, the crack resistance performance level is output.

[0004] For example, Chinese Patent Publication No. CN119125519A discloses a performance testing method and system for ultra-high performance concrete. This method first acquires displacement data of concrete samples from the test group within a preset time period, and penetration resistance data of concrete samples from the control group within the same preset time period. It also acquires the first initial setting time period and the initial setting time point of the control group. The displacement data in each time segment is corrected to obtain optimized displacement data for the preset time period. Based on the difference between the penetration resistance data on both sides of the initial setting time point and the optimized displacement data on both sides of the target time segment, the initial setting time of the test group is selected. Finally, based on the difference in optimized displacement data between the end time of the preset time period and the initial setting time, the early-age autogenous shrinkage deformation coefficient of the concrete sample is obtained.

[0005] Existing technologies record the fluctuation range of concrete shrinkage under the scenario by using signal energy and environmental hysteresis response; or infer the corresponding moment of initial setting of concrete by the change of displacement data. However, existing technologies tend to focus on data verification during the initial setting process, and easily ignore the expansion change characteristics during the hydration curing process after final setting. This leads to a single dimension of concrete condition assessment, limited data processing, and inaccurate final output indicators. Summary of the Invention

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a method for testing the expansion and shrinkage performance of mirror-finished concrete, including: S1, collecting the expansion and shrinkage sequence and appearance image sequence of concrete samples prepared based on preset mix proportion parameters during the hydration curing process.

[0007] S2, extract the constraints and contraction time sequence features of each state from the expansion and contraction sequence, and establish an expansion and contraction feature table.

[0008] S3 uses a three-dimensional temperature sequence that includes the internal temperature, surface temperature, and ambient temperature of the sample to calculate the coupling relationship between temperature and expansion / contraction over any time period and to determine the characteristic abrupt change points of expansion / contraction.

[0009] S4, based on the characteristic mutation points of expansion and contraction, uses the defect type corresponding to the apparent image sequence as a co-factor to solve the characteristic mutation mode corresponding to the concrete sample.

[0010] S5, based on the characteristic mutation pattern corresponding to the concrete sample, triggers a multi-parameter collaborative alarm when the characteristic mutation pattern recurs, and determines the target parameters of the concrete sample.

[0011] The beneficial effects of this invention are as follows: First, this invention simultaneously acquires the expansion and contraction sequence, apparent image sequence, and three-dimensional temperature sequence including internal / surface / ambient temperature during the hydration and curing of mirror-finished fair-faced concrete. Using the expansion and contraction change of the concrete sample relative to its initial length as the core indicator, the rate of expansion and contraction is determined, and a time-axis mapping relationship between the apparent image sequence and the expansion and contraction sequence is established. This clarifies the temporal relationship of concrete changes during hydration and curing and identifies the influencing factors affecting the expansion and contraction behavior of concrete, providing a data foundation for subsequent data testing.

[0012] II. This invention constructs a state transition frequency table by dividing the state label pairs corresponding to forward expansion and reverse contraction based on the amount and rate of expansion and contraction. Combining the state transition probability, the duration and intensity of each state, dynamic constraints are set differently for two types of scenarios: those where the preceding state is consistent with the current state and those where it is inconsistent. Using the time point when the sequence tends to stabilize as a benchmark, the sample contraction convergence value is determined as the contraction temporal feature. By integrating the constraints, contraction temporal features, and the expansion and contraction changes in the corresponding time periods, a standardized expansion and contraction feature table is constructed. This clearly represents the dynamic behavior of the entire expansion-contraction cycle, ensuring that the output features cover all state changes of both forward expansion and reverse contraction, guaranteeing the constraint dimensions under different state scenarios, and thus improving the generalization and scenario matching of data processing.

[0013] Third, this invention extracts instantaneous temperature rise / fall rates, internal and external temperature differences, and surface environment temperature differences from three-dimensional temperature sequences to ensure complete temporal alignment between temperature features and expansion / contraction feature sub-items. It calculates the Pearson correlation coefficient between temperature features and expansion / contraction feature sub-items within the same time period, using the correlation strength value to determine the coupling relationship between temperature and expansion / contraction, generating a standardized coupling vector. Finally, it employs a dual-class system of suspected mutation points to determine the output feature mutation points. By simultaneously considering anomalous changes and relative correlation changes in expansion and contraction, the data analysis process becomes more focused on identifying mutations in hydration behavior, improving the accuracy and interpretability of mutation point identification.

[0014] IV. This invention constructs a standard effect value based on the tensile stress of a single expansion and contraction process, using the maximum critical value of tensile stress in a preset mix ratio as a threshold to screen effective mutation points related to defect evolution. These effective mutation points are sorted by age and synchronized with the temporal sequence of appearance images. By comparing the appearance images before and after the mutation point, it is determined whether the mutation point corresponds to a change in the defect's state. The mapping relationship between effective mutation points and changes in defect state is defined as a characteristic mutation pattern. This clarifies the correlation between effective mutations and appearance defects, further aligning with the appearance inspection requirements of mirror-finished fair-faced concrete and improving the processing effectiveness of the testing method. Finally, for recurring characteristic mutation patterns, multiple sets of early warning data are divided, ultimately outputting target parameters. This provides data support for adjusting on-site curing processes and further improves the efficiency of concrete production. Attached Figure Description

[0015] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0016] Figure 1 This is a flowchart illustrating the testing method for the expansion and shrinkage properties of mirror-finished fair-faced concrete.

[0017] Figure 2 This is a flowchart illustrating step S2 of the test method for the expansion and shrinkage performance of mirror-finished concrete.

[0018] Figure 3 This is a flowchart illustrating step S3 of the test method for the expansion and shrinkage performance of mirror-finished concrete.

[0019] Figure 4 This is a flowchart illustrating step S4 of the test method for the expansion and shrinkage performance of mirror-finished fair-faced concrete. Detailed Implementation

[0020] The embodiments of the present invention are described in detail below. The embodiments described below are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. Where specific techniques or conditions are not specified in the embodiments, they shall be performed in accordance with the techniques or conditions described in the literature in the art or in accordance with the product manual.

[0021] See Figure 1 A test method for the expansion and shrinkage performance of mirror-finished concrete includes: S1, collecting expansion and shrinkage sequences and appearance image sequences of concrete samples prepared based on preset mix proportions during the hydration and curing process.

[0022] S2, extract the constraints and contraction time sequence features of each state from the expansion and contraction sequence, and establish an expansion and contraction feature table.

[0023] S3 uses a three-dimensional temperature sequence that includes the internal temperature, surface temperature, and ambient temperature of the sample to calculate the coupling relationship between temperature and expansion / contraction over any time period and to determine the characteristic abrupt change points of expansion / contraction.

[0024] S4, based on the characteristic mutation points of expansion and contraction, uses the defect type corresponding to the apparent image sequence as a co-factor to solve the characteristic mutation mode corresponding to the concrete sample.

[0025] S5, based on the characteristic mutation pattern corresponding to the concrete sample, triggers a multi-parameter collaborative alarm when the characteristic mutation pattern recurs, and determines the target parameters of the concrete sample.

[0026] In the scenario described in this invention, the expansion and shrinkage scenario is a hydration curing scenario. This occurs after the initial setting and finishing of the concrete pouring, with the exposed concrete surface covered and cured for 14 days. Samples after the covering is removed are continuously tested. This scenario characterizes the changes in the expansion and shrinkage properties of concrete by recording the positive volume expansion and negative volume shrinkage throughout the entire hydration curing cycle. The preset mix proportions consist of cement, calcium carbonate, fly ash, mineral powder, sand, crushed stone, water-reducing agent, additives, water, calcium carbonate content, fly ash content, and mineral powder content. The system records the expansion and shrinkage patterns under different mix proportions, recording the expansion and shrinkage conditions of the concrete samples to obtain the optimal test results for the current scenario. The mix proportions for the current scenario are shown in Table 1.

[0027] Table 1. Schematic diagram of doping ratio

[0028]

[0029] Table 1 illustrates the current concrete sample mix proportions. Cement, calcium carbonate, fly ash, mineral powder, sand, crushed stone, and water-reducing agent are measured in kilograms per cubic meter. "Minor ingredients" specifically refers to the water-reducing agent's dosage as 0.28% of the total mass of the cementitious materials (cement + mineral admixtures). The calcium carbonate and fly ash dosages represent their percentage values ​​relative to the total mass of the cementitious materials. After setting the preset mix proportions, concrete samples are poured into specific molds, such as rectangular or cylindrical molds. Once the initial setting and shaping of the concrete sample is achieved, its appearance and expansion / shrinkage are recorded in real time.

[0030] Furthermore, in actual data acquisition, the length of the concrete sample at the time of demolding is measured using a length comparator or electronic displacement sensor as the initial length, and the volume corresponding to positive expansion and negative contraction is calculated based on the changes in the length value at multiple times.

[0031] As for the apparent image sequence, it uses a camera to check whether there are defects such as honeycomb, holes, cracks, and exposed reinforcement on the surface of concrete samples. The corresponding defects are mapped with preset mix proportion parameters and expansion changes to determine the impact of expansion and shrinkage on concrete performance.

[0032] Specifically, to determine the performance differences in expansion and contraction at different time periods, data was collected according to different stages of hydration curing. The hydration curing process was divided into early hydration stage, mid-stage hydration stage, and late hydration stage, representing the concrete's final set → 7 days, 7 days → 14 days, and 14 days → 28 days, respectively. The time interval for collecting data at each stage was set according to the data pairs of expansion and contraction, temperature, and appearance images, and three sets of test methods were set, including: early hydration stage (2h / time, 1h / time, 4h / time), mid-stage hydration stage (4h / time, 4h / time, 8h / time), and late hydration stage (24h / time, 12h / time, 24h / time), thereby gradually obtaining the expansion and contraction sequence, three-dimensional temperature sequence, and appearance image sequence.

[0033] In one implementation of step S1: S11, the amount of expansion and contraction of the concrete sample at any time relative to the initial length is selected as the index value of the expansion and contraction sequence, and the rate of change of the expansion and contraction sequence is determined.

[0034] S12. Based on the index values ​​of the expansion and contraction sequence and the fixed time period of concrete sample analysis, obtain the defect type of the appearance image in the corresponding time period and establish the mapping relationship between the appearance image sequence and the expansion and contraction sequence.

[0035] Furthermore, the fixed time period for concrete sample analysis corresponds to the time interval for data collection. Each data collection is based on the amount of expansion and contraction changes that occur during a single collection and the cumulative amount of expansion and contraction changes, thus determining the process of expansion and contraction.

[0036] In one embodiment of the present invention, the implementation of step S2, establishing the expansion and contraction feature table, further includes: using the cumulative expansion and contraction change of the expansion and contraction sequence in each time period as the statistical basis, classifying those with a cumulative expansion and contraction change greater than 0 as positive expansion, and classifying those with a cumulative expansion and contraction change less than 0 as negative contraction; and refining the trend features of expansion and contraction according to the rate of change of adjacent time periods, dividing positive expansion into expansion acceleration and expansion deceleration state labels, and negative contraction into contraction acceleration and contraction deceleration state labels.

[0037] Secondly, using a sliding step of 1 unit time period, starting from the second time period, the label pairs of adjacent time periods are extracted sequentially to form a set of state label pairs for the entire sequence.

[0038] Then, using the previous state as rows and the current state as columns, we count the frequency of occurrence, cumulative duration, and cumulative change intensity (the sum of the expansion and contraction changes in all time periods under this state) of each state label pair to construct a state transition frequency table.

[0039] Finally, based on the state transition frequency table, and taking the transition probability and the approach quantity as the basis, the feature table for expansion and contraction is configured.

[0040] like Figure 2 As shown, one implementation of step S2 includes: S21, in response to the expansion and contraction changes of the expansion and contraction sequence, comparing the change rates of adjacent time periods according to the change patterns of forward expansion and reverse contraction, and obtaining the state label pairs corresponding to the change rates.

[0041] S22. Based on the state label pairs of adjacent time periods, construct a state transition frequency table according to the frequency of occurrence of contraction and expansion states.

[0042] S23. Calculate the transition probability for each time period by using the frequency of occurrence of each state in the state transition frequency table. Set constraints by combining the transition probability with the duration and intensity of change of each state.

[0043] S24. Using the time point when the expansion and contraction sequence tends to stabilize as the dividing criterion, the sample convergence value is determined and regarded as the contraction time series feature.

[0044] S25, based on the constraints and the contraction time sequence characteristics, the expansion and contraction changes in the corresponding time period are sequentially combined into an expansion and contraction characteristic table.

[0045] Furthermore, the above-mentioned transition probability is expressed as the conditional probability of the current state occurring when the preceding state occurs. Based on this, considering the scenario changes of the expansion constraint, it is also necessary to determine the duration and intensity of change of a specific state in order to determine the expansion changes during tests with different concrete mix proportions.

[0046] Specifically, the implementation of setting constraints in step S23 includes: determining whether the previous state label is consistent with the current state label based on the composition of each state in the state transition frequency table.

[0047] If the preceding state label is consistent with the current state label, the constraint conditions are set based on the duration and intensity of change at the corresponding moment. That is, in the scenario of continuous contraction or continuous expansion, the time and total amount of expansion and contraction change belonging to any of the state labels of expansion acceleration, expansion deceleration, contraction acceleration, and contraction deceleration are used as constraints. The corresponding values ​​are checked from the database to see if they comply with the concrete production specifications. If they do, only the values ​​are recorded as constraints for this scenario. Otherwise, the concrete production specifications need to be introduced as constraints here.

[0048] Furthermore, when defining the state labels for expansion acceleration and expansion deceleration, as well as contraction acceleration and contraction deceleration, the average rate of change within their age period is used as the distinguishing factor to define the acceleration and deceleration state labels.

[0049] If the previous state label is inconsistent with the current state label, the transition probability of the corresponding state label pair is calculated, and constraints are set based on the values ​​of the transition probability and the rate of change. When setting constraints here, it means that at least the process of expansion acceleration → expansion deceleration has occurred in the continuous time period, and constraints need to be set on the transition probability and the rate of change.

[0050] Furthermore, the aforementioned sample approximation value refers to the cumulative expansion and contraction change that gradually decreases to near zero during the hydration and hardening process of concrete, or the cumulative expansion and contraction change that the specimen reaches a stable level.

[0051] When calculating the sample convergent value, the starting point is the moment when the maximum expansion is reached. The rate of change during contraction is observed. When using μm / m·d as the unit, the calculation starts from the age when the daily contraction is less than 1 μm for three consecutive times. The arithmetic mean of the cumulative expansion and contraction changes corresponding to each data point is used as the convergent value here.

[0052] Furthermore, the output shrinkage time series characteristics refer to the time series characteristic parameters that characterize the changes in the expansion and shrinkage behavior of concrete with age, including sample approach values, expansion and shrinkage change rates, duration, etc.

[0053] If the current contraction follows a standard curve downward trend, then the stable value of the current scenario region can be defined according to the curve.

[0054] For example ;in, express The cumulative contraction corresponding to the age period, which is the cumulative change in expansion and contraction at the corresponding number of days; Represents the fitted parameters, representing The value that approaches infinity is the parameter being solved for. This indicates the age at which contraction begins, representing the number of days it takes to reach maximum expansion. The contraction time constant represents the rate of contraction development and is calculated using the least squares fitting method. This indicates the test age, representing the number of days corresponding to the current calculation.

[0055] The fitting parameters obtained through the above process represent the cumulative expansion and contraction changes as the curve descends, ultimately leading to an understanding of the current expansion and contraction process of the concrete sample.

[0056] In one embodiment of the present invention, the temperature value of the concrete sample will be further introduced, and the temperature value will be correlated with the amount of expansion and contraction to characterize the data points where abrupt changes occur in expansion and contraction.

[0057] like Figure 3 As shown, the implementation of step S3 includes: S31, extracting temperature features during hydration curing based on the collected three-dimensional temperature sequence. The temperature features include instantaneous temperature rise rate, instantaneous temperature drop rate, temperature difference between inside and outside the sample, and temperature difference between the surface environment. Among them, the temperature difference between inside and outside the sample represents the difference between the internal temperature and the surface temperature of the sample.

[0058] S32, obtain the feature sub-items of the expansion and contraction feature table during the expansion and contraction process, and determine the coupling relationship between temperature and expansion and contraction based on the correlation strength value between the feature sub-items and the temperature feature in the same time period.

[0059] S33, based on the coupling relationship between temperature and expansion and contraction, combines the data of each time period into a corresponding vector form, and outputs them as coupled vectors in chronological order.

[0060] S34. For each output coupling vector, retrieve the normal parameters of the sample under the corresponding time period, and extract feature mutation points based on the difference between the coupling vector and the normal parameters of the sample.

[0061] The aforementioned feature sub-items represent feature values ​​segmented through the two processes of expansion and contraction. Specifically, they are the state labels, expansion / contraction changes, transition probabilities, constraints, and sample convergent values ​​output in step S2, and are further segmented into multiple specific feature sub-items according to the corresponding parts of contraction and expansion. The coupling vector refers to a multi-dimensional vector formed by combining temperature feature parameters and expansion / contraction feature parameters in a fixed order within the same time period, used to characterize the coupling relationship between temperature and expansion / contraction.

[0062] Furthermore, in the correlation processing of temperature and expansion / contraction, the implementation of the feature sub-items and temperature features includes: for the temperature feature and the corresponding feature sub-item within any time period, calculating the Pearson correlation coefficient between the temperature feature and the feature sub-item, and establishing the correlation relationship based on the value of the Pearson correlation coefficient.

[0063] Specifically, the temperature features, including the rate of temperature rise, rate of temperature fall, and the internal and external temperature difference and expansion / contraction sub-items, are calculated. When the sub-items are represented as statistical values ​​over a specific time period, the temperature features are also transformed into statistical values ​​for the corresponding time period, ensuring that the temperature features are always aligned with the time dimension of the sub-items. After normalizing these data, the Pearson correlation coefficient is calculated using the vector corresponding to the temperature and the vector corresponding to the sub-items. A Pearson correlation coefficient greater than 0 is considered a positive correlation, while a Pearson correlation coefficient less than 0 is considered a negative correlation, thus obtaining the coupling vector of the correlation mapping.

[0064] Furthermore, the above-mentioned normal parameters represent the values ​​of the concrete samples under normal curing conditions. The values ​​are set based on the median value of the qualified data of the same concrete sample in historical data, and do not exceed the constraint range set in step S2. Values ​​that exceed the normal range are regarded as characteristic mutation points here, and their corresponding time points are marked.

[0065] Specifically, the implementation method for extracting feature mutation points in step S34 includes: according to the time period corresponding to the coupling vector, the coupling vector is checked using the normal parameters of the sample, and the first type of suspected mutation points are marked; wherein, the first type of suspected mutation points indicate that some features at any time point exceed the normal value range and belong to the data features of suspected mutation.

[0066] Specifically, when any feature parameter in the coupling vector exceeds the range of ±2 standard deviations of the normal parameters of the sample, it is marked as a suspected mutation point of the first type.

[0067] Based on the correlation strength value corresponding to the coupling vector, second-type suspected mutation points with abrupt changes in correlation strength at adjacent time periods are extracted.

[0068] Among them, the second type of suspected mutation points represent the mutations in the coupling mapping of their corresponding features at adjacent time points. These mutations can characterize the non-strong correlation between temperature and expansion / contraction features, and often represent large value changes and fluctuations. These data points need to be marked as suspected mutation points. As for data with strong correlations, their overall changes will be relatively stable, showing a relatively monotonous or stable form. The feature mutation points can be screened directly by restricting the values ​​under constraints or by using normal parameters of the samples.

[0069] Specifically, when extracting the second type of suspected mutation points, the conditions are that the correlation strength changes abruptly or the correlation direction reverses in adjacent time periods. Data with a change of ≥0.5 within adjacent windows, as well as data that change from positive to negative correlation, are considered as suspected mutation points in this output.

[0070] The first and second types of suspected mutation points are merged according to their timestamps, and duplicate nodes are removed to obtain the characteristic mutation points for output.

[0071] Based on the identification of suspected mutation points through correlation strength screening, the correlation transition between normal parameters of the sample and the coupling vector is introduced, so that when specific correlation changes occur in the expansion and contraction scenario, they can be detected and guided in a timely manner, thereby reducing defects in the concrete production process.

[0072] In one embodiment of the present invention, the defect type corresponding to the appearance image is used as the content of the collaborative analysis to determine the expansion and contraction changes when a defect occurs, and then to determine the influence relationship of defect formation.

[0073] like Figure 4 As shown, the implementation of step S4 includes: S41, for the expansion and contraction effect of the characteristic mutation point at the corresponding time, let the tensile stress statistically recorded at each time be used as the standard effect value of a single expansion and contraction, and select the effective mutation points related to the defect evolution based on the standard effect value.

[0074] S42, for the selected effective mutation points, sort them according to age, and synchronize the time order of the effective mutation points and the appearance image sequence.

[0075] Specifically, the entire hydration maintenance cycle is divided into multiple consecutive time periods; each time period is demarcated by the effective mutation point corresponding to the time boundary, so as to determine the process of defect evolution on the corresponding image under shrinkage and expansion mutation.

[0076] S43. Based on the appearance images of the two time periods before and after the effective mutation point, determine whether each effective mutation point corresponds to a change in the state of the defect. If they correspond, the mapping relationship between the effective mutation point and the defect is regarded as the output feature mutation pattern.

[0077] If not, the concrete curing process is relatively stable in the current scenario, with no obvious changes in crack patterns, and the overall appearance of the concrete is minimally affected by expansion and contraction.

[0078] Furthermore, changes in the defect state include, but are not limited to, the following conditions: 1. The appearance of new microcracks, or a significant increase in the number / area of ​​existing microcracks (growth rate greater than 30%); 2. The appearance of new cracks, or a significant expansion in the length / width / area of ​​existing cracks (expansion rate ≥ 50%); 3. All cracks stop expanding, the defect area does not increase, or cracks close. These three situations represent states such as dense crack distribution, rapid crack growth, and crack cessation, which can be used as a correspondence between expansion / contraction-temperature changes and defect development trends. The evolution of the testing process is completed by semantically describing the temperature changes, expansion / contraction changes, and defect development over a continuous time period.

[0079] In one embodiment of the present invention, the tensile stress is a stress value under temperature change, used to determine whether the critical stress for cracking occurs at a corresponding time, and thus determine whether the abrupt expansion and contraction can correspond to the initiation / promotion of defects.

[0080] Specifically, the effective mutation point in step S41 is achieved by extracting the maximum critical value of the acceptable range of tensile stress according to the preset ratio parameters.

[0081] The tensile stress values ​​are detected in segments along the time axis corresponding to the characteristic abrupt change points. When the tensile stress exceeds the maximum critical value, it is marked as a valid abrupt change point.

[0082] Furthermore, when quickly verifying the numerical range of tensile stress, tensile stress is divided into self-constrained tensile stress and externally constrained tensile stress according to temperature changes.

[0083] Self-constrained tensile stress can be used to view the elastic modulus and relaxation coefficient of specimens with the same mix ratio and age. The relaxation coefficient, elastic modulus and the internal and external temperature difference at the corresponding age are multiplied, and the tensile stress is quickly screened based on the ratio of the product to the maximum critical value.

[0084] The external constraint tensile stress is the product of the elastic modulus, the overall temperature difference, the relaxation coefficient, and the constraint coefficient at the corresponding age. The product is used as the basis for verification. The overall temperature difference includes four temperatures: internal temperature, surface temperature, shrinkage equivalent temperature, and final stability temperature.

[0085] Furthermore, the calculation method for self-constrained tensile stress is as follows.

[0086] ;in, Indicates age as Self-restrained tensile stress, This represents the calculation segment index, which is a set of time periods divided by the positions of valid mutation points on the time axis. Its value ranges from 1 to n; n represents the total number of calculation segments. This represents the coefficient of linear expansion of concrete. This represents the increment of the temperature difference between the inside and outside of the concrete in the i-th calculation section at age t. This represents the elastic modulus of concrete at age t within the i-th calculation segment; Indicates age as The constraint stress generated in the i-th calculation section is extended to the relaxation coefficient at time t. The self-constrained tensile stress calculated here reflects the stress situation during hydration reaction expansion, taking into account the expansion and contraction of concrete caused by its own temperature changes.

[0087] The calculation method for external constraint tensile stress is as follows.

[0088] ;in, Indicates age as The external constraint tensile stress is specifically manifested as the stress situation under contraction scenarios; This represents the increment of the overall temperature drop difference of the concrete in the i-th calculation section at age t; This represents the Poisson's ratio of the concrete, taken as 0.15. This represents the constraint coefficient of the external constraint at age t within the i-th calculation segment. This value can be calculated using the length, thickness, and horizontal deformation stiffness of the concrete casting, which will not be explained in detail here.

[0089] The overall cooling difference ;in, This represents the maximum internal temperature of the sample at age t, i.e., the highest internal temperature of the concrete. , This indicates the temperature of the upper and lower surfaces of the concrete at age t. This represents the equivalent temperature of concrete shrinkage at age t. This represents the final stable temperature of the concrete, which can be the daily average temperature at age t or the current annual average temperature.

[0090] After obtaining the external and self-constrained tensile stresses of the concrete, the time of the characteristic abrupt change point is checked according to the form of the tensile stress. It is determined whether the value exceeds the maximum critical value in the corresponding interval, and the part exceeding the maximum critical value is regarded as the part that is currently the main solution.

[0091] In one embodiment of the present invention, step S5 is used to statistically analyze the parameter indices that recur under the abrupt change in characteristics, and to determine the warning time and related information in the concrete test scenario according to the strongly correlated parts of concrete, thereby locking in the parameters that need to be adjusted.

[0092] The implementation of step S5 includes: S51, randomly dividing the dataset of the feature mutation pattern for the time points when the feature mutation pattern recurs, and obtaining multiple sets of early warning data.

[0093] S52, based on the time point when the defect first appears in each set of early warning data as the initial point of the anomaly, statistically analyze the duration and time interval distribution of the corresponding defect type; determine whether the abnormal state of expansion and contraction is an instantaneous fluctuation or a continuous anomaly, and regard the relevant parameters as the target parameters of the output.

[0094] Specifically, the output parameters will be concentrated on the corresponding time period, and the corresponding temperature value, elastic modulus, expansion and contraction changes, and image statistical data information will be used as the current output data part to determine the composition adjustment and process adjustment for the concrete sample in subsequent projects.

[0095] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention, which are still covered within the protection scope of the present invention.

Claims

1. A method for testing the expansion and shrinkage properties of mirror-finish fair-faced concrete, characterized in that, include: S1, collect the expansion and shrinkage sequence and appearance image sequence of concrete samples prepared based on preset mix parameters during the hydration curing process; S2, extract the constraints and contraction time sequence features of each state from the expansion and contraction sequence, and establish an expansion and contraction feature table; S3 uses a three-dimensional temperature sequence that includes the internal temperature, surface temperature and ambient temperature of the sample to calculate the coupling relationship between temperature and expansion and contraction within any time period and determine the characteristic abrupt change points of expansion and contraction. S4. Based on the characteristic mutation points of expansion and contraction, the defect type corresponding to the apparent image sequence is used as a coordinating factor to solve the characteristic mutation mode corresponding to the concrete sample. S5, based on the characteristic mutation pattern corresponding to the concrete sample, when the characteristic mutation pattern recurs, trigger a multi-parameter collaborative alarm to determine the target parameters of the concrete sample. The implementation methods of the expansion and contraction feature table in step S2 include: S21, In response to the expansion and contraction changes of the expansion and contraction sequence, according to the change patterns of forward expansion and reverse contraction, compare the change rates of adjacent time periods and obtain the state label pairs corresponding to the change rates. S22, Based on the state label pairs of adjacent time periods, construct a state transition frequency table according to the frequency of occurrence of contraction and expansion states; S23. Calculate the transition probability of each time period by using the frequency of occurrence of each state in the state transition frequency table, and set constraints by combining the transition probability with the duration and intensity of change of each state. S24. Using the time point when the expansion and contraction sequence tends to stabilize as the dividing criterion, the sample convergence value is determined and its sample convergence value is regarded as the contraction time series feature. S25, based on the constraints and the contraction time sequence characteristics, the expansion and contraction changes in the corresponding time period are sequentially combined into an expansion and contraction characteristic table; The sample approach value refers to the cumulative expansion and contraction change that gradually decreases to near 0 during the hydration and hardening process of concrete, or the cumulative expansion and contraction change that the specimen reaches a stable value. The methods for setting constraints in step S23 include: Based on the composition of each state in the state transition frequency table, determine whether the label of the preceding state is consistent with the label of the current state. If the previous state label is consistent with the current state label, set the constraints based on the duration and intensity of change at the corresponding moment; If the previous state label is inconsistent with the current state label, the transition probability of the corresponding state label pair is calculated, and constraints are set based on the values ​​of the transition probability and the rate of change.

2. The method for testing the expansion and shrinkage properties of mirror-finish fair-faced concrete according to claim 1, characterized in that, The implementation methods of the dilation / contraction sequence and the apparent image sequence in step S1 include: S11, Select the amount of expansion and contraction of the concrete sample at any time relative to the initial length as the index value of the expansion and contraction sequence, and determine the rate of change of the expansion and contraction sequence. S12. Based on the index values ​​of the expansion and contraction sequence and the fixed time period of concrete sample analysis, obtain the defect type of the appearance image in the corresponding time period and establish the mapping relationship between the appearance image sequence and the expansion and contraction sequence.

3. The method for testing the expansion and shrinkage properties of mirror-finish fair-faced concrete according to claim 1, characterized in that, The implementation methods of the characteristic mutation points in step S3 include: S31. Based on the collected three-dimensional temperature sequence, extract the temperature characteristics during hydration curing. The temperature characteristics include instantaneous temperature rise rate, instantaneous temperature drop rate, temperature difference between inside and outside the sample, and temperature difference between the surface environment. S32, obtain the feature sub-items of the expansion and contraction feature table during the expansion and contraction process, and determine the coupling relationship between temperature and expansion and contraction based on the correlation strength value between the feature sub-items and the temperature feature in the same time period; S33, based on the coupling relationship between temperature and expansion and contraction, combines the data of each time period into a corresponding vector form, and outputs them as a coupling vector in chronological order; S34. For each output coupling vector, retrieve the normal parameters of the sample under the corresponding time period, and extract feature mutation points based on the difference between the coupling vector and the normal parameters of the sample.

4. The method for testing the expansion and shrinkage properties of mirror-finish fair-faced concrete according to claim 3, characterized in that, The coupling relationship between temperature and expansion / contraction can be achieved through the following methods: For any temperature feature and its corresponding sub-feature within a given time period, calculate the Pearson correlation coefficient between the temperature feature and the sub-feature, and establish a correlation based on the value of the Pearson correlation coefficient.

5. The method for testing the expansion and shrinkage properties of mirror-finish fair-faced concrete according to claim 3, characterized in that, The methods for extracting characteristic mutation points in step S34 include: Based on the time period corresponding to the coupling vector, the coupling vector is checked using the normal parameters of the sample, and the first type of suspected mutation point is marked. Based on the correlation strength value corresponding to the coupling vector, second-type suspected mutation points of abrupt changes in correlation strength at adjacent time periods are extracted; The first and second types of suspected mutation points are merged according to their timestamps, and duplicate nodes are removed to obtain the characteristic mutation points for output.

6. The method for testing the expansion and shrinkage properties of mirror-finish fair-faced concrete according to claim 1, characterized in that, The implementation methods of the characteristic mutation mode in step S4 include: S41, for the expansion and contraction effect of the characteristic mutation point at the corresponding time, let the tensile stress statistically measured at each time be the standard effect value of a single expansion and contraction, and screen out the effective mutation points related to the defect evolution based on the standard effect value. S42, For the selected effective mutation points, sort them according to age and synchronize the time order of the effective mutation points and the appearance image sequence. S43. Based on the appearance images of the two time periods before and after the effective mutation point, determine whether each effective mutation point corresponds to a change in the state of the defect. If they correspond, the mapping relationship between the effective mutation point and the defect is regarded as the output feature mutation pattern.

7. The method for testing the expansion and shrinkage properties of mirror-finish fair-faced concrete according to claim 6, characterized in that, The effective mutation points in step S41 are implemented in the following ways: Based on the preset proportioning parameters, extract the maximum critical value of the acceptable range of tensile stress; The tensile stress values ​​are detected in segments along the time axis corresponding to the characteristic abrupt change points. When the tensile stress exceeds the maximum critical value, it is marked as a valid abrupt change point.

8. The method for testing the expansion and shrinkage properties of mirror-finish fair-faced concrete according to claim 1, characterized in that, The implementation methods for the target parameters in step S5 include: S51, for the time points when the characteristic mutation pattern recurs, the data set of the characteristic mutation pattern is randomly divided to obtain multiple sets of early warning data; S52, based on the time point when the defect first appears in each set of early warning data as the initial point of the anomaly, statistically analyze the duration and time interval distribution of the corresponding defect type; determine whether the abnormal state of expansion and contraction is an instantaneous fluctuation or a continuous anomaly, and regard the relevant parameters as the target parameters of the output.

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