Method and device for predicting early age strength of concrete based on maturity and resistivity

Abstract

The application discloses a concrete early age strength prediction method and device based on maturity and resistivity, and relates to the technical field of engineering construction. The method comprises the following steps: acquiring in-situ temperature and in-situ resistivity of to-be-detected concrete; calculating an early age strength baseline estimation value of the to-be-detected concrete by applying a maturity method according to the in-situ temperature of the to-be-detected concrete; calculating an early age strength correction value of the to-be-detected concrete according to the in-situ resistivity of the to-be-detected concrete; and calculating a final early age strength prediction value of the to-be-detected concrete according to the early age strength baseline estimation value and the early age strength correction value. The application combines the'maturity' representing external thermal driving force with the'resistivity change rate' representing the internal microstructure evolution state, and realizes real-time and high-precision estimation of the early age strength of concrete under field conditions.

Description

Technical Field

[0001] This application relates to the field of engineering construction technology, and in particular to a method and apparatus for predicting the early-age strength of concrete based on maturity and resistivity. Background Technology

[0002] In concrete construction, accurately grasping the real-time strength development of the concrete inside the structure is crucial for determining the timing of key processes such as formwork removal, prestressing, and loading. Premature actions may lead to structural safety hazards, while excessive waiting results in project delays and increased costs. Therefore, the prediction and monitoring technology of early-age concrete strength has always been one of the core requirements of engineering practice.

[0003] Currently, the most commonly used method for predicting early-age strength in engineering is the "maturity method." The core idea of ​​this method is that the strength development of concrete is a function of its curing temperature history. By monitoring the temperature changes inside the concrete over time, this is accumulated and converted into an equivalent curing time (equivalent age) at a certain standard reference temperature (usually 20℃). Then, using a pre-calibrated "strength-equivalent age" relationship curve, the current strength of the concrete in the field is estimated. This method only requires a temperature sensor, is relatively simple to implement, and is one of the most widely used prediction techniques.

[0004] Although the maturity method is widely used, it cannot take into account the influence of moisture conditions and ignores the real-time response of the internal state of the material, resulting in limited accuracy in predicting the early-age strength of concrete under complex field conditions. Summary of the Invention

[0005] The purpose of this application is to provide a method and device for predicting the early-age strength of concrete based on maturity and resistivity. This method combines "maturity," which characterizes the external thermal driving force, with "resistivity change rate," which characterizes the evolution state of the internal microstructure, to achieve real-time and high-precision estimation of the early-age strength of concrete under field conditions.

[0006] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for predicting the early-age strength of concrete based on maturity and resistivity, including: Obtain the in-situ temperature and in-situ resistivity of the concrete to be tested; Based on the in-situ temperature of the concrete to be tested, the baseline strength of the early-age concrete to be tested is estimated using the maturity method. The strength correction value of the concrete at early age is calculated based on the in-situ resistivity of the concrete to be tested. The final predicted strength of the concrete under test at early age is calculated based on the baseline strength estimate and strength correction value at early age. The calculation of the early-age strength correction value of the concrete under test based on its in-situ resistivity includes: The rate of change of resistivity of the concrete to be tested is calculated based on the in-situ resistivity of the concrete to be tested. The strength correction value of the concrete under test at early age is calculated based on the resistivity change rate. The expression for the intensity correction value is as follows: in, Indicates the strength correction value; Indicates the rate of change of resistivity; , This represents the correction factor.

[0007] Secondly, this application provides a device for predicting the early-age strength of concrete based on maturity and resistivity, comprising: The data acquisition module is used to acquire the in-situ temperature and in-situ resistivity of the concrete to be tested. The strength baseline estimation module is used to calculate the strength baseline estimate of the concrete at an early age based on the in-situ temperature of the concrete to be tested and the maturity method. The strength correction module is used to calculate the strength correction value of the concrete at early age based on the in-situ resistivity of the concrete to be tested. The calculation of the early-age strength correction value of the concrete under test based on its in-situ resistivity includes: The rate of change of resistivity of the concrete to be tested is calculated based on the in-situ resistivity of the concrete to be tested. The strength correction value of the concrete under test at early age is calculated based on the resistivity change rate. The expression for the intensity correction value is as follows: in, Indicates the strength correction value; Indicates the rate of change of resistivity; , Indicates the correction factor; The target strength calculation module is used to calculate the final predicted strength of the concrete under test at early age based on the early-age strength baseline estimate and strength correction value.

[0008] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to implement the above-described method for predicting the early-age strength of concrete based on maturity and resistivity.

[0009] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for predicting the early-age strength of concrete based on maturity and resistivity.

[0010] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for predicting the early-age strength of concrete based on maturity and resistivity.

[0011] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a method and apparatus for predicting the early-age strength of concrete based on maturity and resistivity. The method includes: acquiring the in-situ temperature and in-situ resistivity of the concrete to be tested; calculating the baseline estimate of the early-age strength of the concrete to be tested using a maturity method based on the in-situ temperature; calculating the corrected strength value of the early-age concrete to be tested based on the in-situ resistivity; and calculating the final predicted strength value of the early-age concrete to be tested based on the baseline estimate and the corrected strength value. The inventors of this application discovered a strong correlation between the residual strength calculated by the maturity model (also known as the baseline maturity model) and the rate of change of resistivity. Based on this correlation, resistivity is introduced as a correction factor for the internal state of concrete to quantify the deviation of the strength calculated by the maturity method under varying field conditions. Therefore, this application combines "maturity," which characterizes the external thermal driving force, with "rate of change of resistivity," which characterizes the evolution state of the internal microstructure, to achieve real-time, high-precision estimation of the early-age strength of concrete under field conditions. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is an application environment diagram of a method for predicting the early-age strength of concrete based on maturity and resistivity in one embodiment of this application. Figure 2 A flowchart illustrating a method for predicting the early-age strength of concrete based on maturity and resistivity, provided as an embodiment of this application. Figure 3 A schematic diagram showing the comparison between the predicted strength and the measured strength of a field-cured specimen provided in an embodiment of this application; Figure 4A schematic diagram of the functional modules of a concrete early-age strength prediction device based on maturity and resistivity provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0014] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0015] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0016] The method for predicting the early-age strength of concrete based on maturity and resistivity provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 101 via a network. A data storage system can store the data that server 101 needs to process. The data storage system can be set up independently, integrated into server 101, or placed in the cloud or on another server. Terminal 102 can send the in-situ temperature and resistivity of the concrete to be tested to server 101. After receiving the in-situ temperature and resistivity, server 101 calculates the baseline strength estimate of the early-age concrete using a maturity method based on the in-situ temperature; calculates the corrected strength value of the early-age concrete based on the in-situ resistivity; and calculates the final predicted strength value of the early-age concrete based on the baseline strength estimate and the corrected strength value. Server 101 can then feed back the final predicted strength value of the early-age concrete to terminal 102. Furthermore, in some embodiments, the method for predicting the early-age strength of concrete based on maturity and resistivity can also be implemented separately by the server 101 or the terminal 102. For example, the terminal 102 can directly predict the early-age strength of concrete based on maturity and resistivity using the in-situ temperature and in-situ resistivity of the concrete to be tested. Alternatively, the server 101 can obtain the in-situ temperature and in-situ resistivity of the concrete to be tested from the data storage system and perform the prediction of the early-age strength of concrete based on maturity and resistivity.

[0017] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices and portable wearable devices. The server 101 can be implemented as a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0018] In one exemplary embodiment, such as Figure 2 As shown, a method for predicting the early-age strength of concrete based on maturity and resistivity is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 101 as an example, the explanation includes the following steps 201 to 204.

[0019] Step 201: Obtain the in-situ temperature and in-situ resistivity of the concrete to be tested.

[0020] Temperature sensors and resistivity electrodes are pre-embedded inside the concrete of the engineering structure to be tested (such as beams, columns, and walls). Starting from the completion of pouring, in-situ temperature data inside the concrete is continuously and in real-time collected under field conditions. and in-situ resistivity data .

[0021] Step 202: Based on the in-situ temperature of the concrete to be tested, calculate the baseline strength estimate of the early-age concrete using the maturity method.

[0022] Step 203: Calculate the early-age strength correction value of the concrete to be tested based on the in-situ resistivity of the concrete to be tested.

[0023] Step 204: Calculate the final predicted strength value of the concrete under test at early age based on the early age strength baseline estimate and strength correction value.

[0024] Classical maturity theory implicitly assumes an ideal condition: that concrete retains sufficient moisture throughout the curing process for continuous hydration. However, in actual engineering sites, concrete is exposed to the natural environment and inevitably suffers moisture loss due to wind, sunlight, low humidity, and other factors. Insufficient moisture severely inhibits or even halts cement hydration. Traditional maturity models, based solely on temperature history, cannot perceive or quantify this lag or termination of strength development caused by water loss, thus systematically overestimating the strength of concrete on-site and introducing risks into engineering decisions. Furthermore, existing maturity methods are essentially indirect empirical models based on "external drivers" (temperature). They assume that under the same temperature history, the microstructure development of different concretes is identical, but this is not the case. Microstructural indicators such as resistivity directly and in real-time reflect internal states such as pore structure refinement and changes in pore solution ion concentration. Existing maturity methods lack correlation with these internal state responses. Therefore, when external temperature conditions are similar but internal reactions differ due to variations in moisture and materials, these maturity methods cannot distinguish between them and cannot accurately predict the early-age strength of concrete. In response, the inventors of this application, when researching the technical solution, firstly calculated the "strength residual": under standard curing conditions, a calibrated maturity model was used to predict the strength of concrete specimens at each test age, and then the difference between the estimated baseline strength value and the measured strength value of the concrete specimen was calculated, i.e., the strength residual. Under ideal conditions, this strength residual itself is very small, but it contains subtle fluctuation information not captured by the model. Secondly, a correlation analysis was conducted: it was first assumed that the resistivity change rate could reflect some information about the concrete hydration process, and based on this, the resistivity data continuously monitored by the "standard curing" specimens were analyzed to calculate the resistivity change rate V (i.e., the instantaneous resistivity change rate). The V value directly reflects the evolution rate of the concrete microstructure (pore structure refinement, changes in pore solution ion concentration). Subsequently, through analysis of the monitored resistivity data, it was found that under standard curing conditions, there is a consistent nonlinear correlation between the calculated strength residual R and the resistivity change rate V. The inventors of this application discovered that, under stable curing conditions, minute changes in resistivity evolution kinetics correspond to minute deviations in maturity model predictions. Therefore, based on the finding of a strong correlation between strength residuals and resistivity change rate V based on maturity models, resistivity is introduced as a correction factor for the internal state of concrete to quantify the strength deviation calculated by maturity methods under varying field conditions. Traditional maturity methods, under dry conditions, especially in the early and middle stages, systematically overestimate concrete strength (primarily early strength, with the largest error at 3 days) because they cannot detect hydration stagnation. Resistivity is extremely sensitive to moisture; the initial stage of water loss may cause a temporary increase in pore fluid ion concentration, resulting in abnormal resistivity changes. A strength correction function based on resistivity can provide a negative correction accordingly.Microstructural analysis of concrete specimens (e.g., XRD and TG spectra) confirmed that the hydration product content of field-cured specimens was indeed lower than that of standard-cured specimens, consistent with the trend reflected by resistivity. This application encodes the physical mechanism of "moisture loss leading to insufficient microstructural development, resulting in lower-than-expected strength" using the resistivity change rate V, and achieves quantitative compensation through a strength correction term. This application combines "maturity," characterizing external thermal driving force, with "resistivity change rate," characterizing the evolution state of internal microstructure, to achieve real-time, high-precision estimation of the early-age strength of concrete under field conditions.

[0025] In another exemplary embodiment of this application, before calculating the baseline strength estimate of the early-age concrete under test using the maturity method and before calculating the corrected strength value of the early-age concrete under test using the expression for the strength correction value, parameter calibration is required based on experimental data from concrete specimens with the same mix proportion as the concrete under test. This allows for the acquisition of intrinsic parameters of the specific concrete mix proportion under controlled laboratory conditions. First, concrete specimens are poured according to the required mix proportion for the project. All specimens are then cured in a standard curing room (temperature 20±2℃, relative humidity >95%). Second, during the standard curing period, the concrete specimens are simultaneously monitored and tested to obtain the following data: (1) Maturity data: Record the temperature history T(t) inside the concrete specimen, where t is the concrete age.

[0026] (2) Resistivity data: The volume resistivity of concrete specimens was continuously measured historically using pre-embedded electrodes. ρ (t).

[0027] (3) Measured strength data: Destructive compressive strength tests were conducted on concrete specimens at specific ages (e.g., 1, 3, 7, 28 days) to obtain the measured strength values. .

[0028] Based on the above data, parameter calibration was performed, including maturity model calibration and intensity correction function calibration based on resistivity.

[0029] (1) Maturity model calibration: Using the measured temperature history T(t), the equivalent age is calculated using the Arrhenius equation (Equation (1)). The relationship between strength and equivalent age was fitted using a hyperbolic function model (Equation (2)) to determine the key parameters of the concrete specimens with this mix proportion: ultimate strength. Rate constant k, reference age In the fitting calculation, the measured intensity data is used. Substitute the maturity model into formula (2) as the baseline estimate of the intensity. .

[0030] (1) in, For activation energy, Here is the molar gas constant at normal temperatures. 4000 can be taken as an example, or it can be measured experimentally. Then perform the calculation; For reference temperature, 20℃ is used; For time intervals; This represents the average temperature over the time interval.

[0031] (2) in, This is an estimate of the baseline intensity for early age. Equivalent age; The reference age is specifically the age at initial setting or the age at which the strength is 0, and the unit is hours. It is the rate constant; For time intervals; This represents the average temperature over the time interval.

[0032] (2) Intensity correction function calibration based on resistivity Before calculating the early-age strength correction value of the concrete to be tested using the expression for strength correction, it is necessary to pre-fit the correlation coefficient in the strength correction value expression using resistivity data from concrete specimens with the same mix proportion as the concrete to be tested. Therefore, the method for predicting the early-age strength of concrete based on maturity and resistivity further includes: (a1) Obtain the measured strength values ​​of concrete specimens at early age and the baseline strength estimates derived from the maturity method. and resistivity.

[0033] (a2) Calculate the strength residual value R based on the measured strength value and the estimated strength baseline value of the early-age concrete specimen. = - .

[0034] (a3) Based on the strength residual value of the concrete specimen and the corresponding resistivity change rate (V=d) ρ / dt) is the correction coefficient in the expression for the strength correction value (Equation (3)). and The fitting process is performed to obtain the fitting correction coefficient, which is then substituted into the expression for the strength correction value to calculate the early-age strength correction value of the concrete to be tested.

[0035] The expression for the intensity correction value is as follows: (3) in, Indicates the strength correction value; Indicates the rate of change of resistivity; , This represents the correction factor.

[0036] In another exemplary embodiment of this application, in step 202, the baseline strength estimate of the early-age concrete is calculated using a maturity method based on the in-situ temperature of the concrete to be tested. Specifically: (1) The in-situ temperature data under the field conditions Input the formula (1) to calculate the equivalent age of the concrete to be tested under the current mix proportions under the field conditions. .

[0037] (2) Determine the equivalent age of the concrete to be tested. Substituting into the calibrated maturity model (Equation (2)), the baseline estimate of intensity based solely on temperature history is calculated. .

[0038] In another exemplary embodiment of this application, step 203, calculating the early-age strength correction value of the concrete to be tested based on the in-situ resistivity of the concrete to be tested, specifically includes: (a1) Based on the in-situ resistivity of the concrete to be tested Calculate the rate of change of resistivity of the concrete to be tested. / dt.

[0039] (a2) Calculate the strength correction value of the early-age concrete under test based on the resistivity change rate. .

[0040] For concrete with different mix proportions, the resistivity development trajectory is different, and the correction coefficients (α, β) of the calibrated strength correction function are also different. A set of parameters can be calibrated separately for each main mix proportion of concrete. In application, simply call the parameters for the corresponding mix proportion to accurately predict the early-age strength performance of the material under field conditions.

[0041] In another exemplary embodiment of this application, step 204, calculating the final predicted strength value of the concrete at early age based on the early-age strength baseline estimate and the strength correction value, specifically includes: The baseline strength estimate and the strength correction value at early age are added together to obtain the final predicted strength value of the concrete at early age. .

[0042] (4) The differences between this application and existing technologies (traditional maturity methods) are as follows: (1) Difference in monitoring parameters: Existing technologies only monitor a single parameter, temperature. This application monitors two physical quantities simultaneously: temperature and resistivity. Temperature reflects the “driving speed” of the hydration reaction on the external environment, while resistivity directly reflects the “internal state result” of the formation of hydration products and the evolution of pore structure.

[0043] (2) Differences in the input for intensity calculation: The input for existing maturity models is only the equivalent age. The output is a single intensity value. However, the model in this application has a two-stage integrated structure. In the first stage, the maturity model is similar to existing maturity models, with the input being... Output The key difference lies in the addition of a second stage in this application: a strength correction section, whose input is the resistivity change rate V, and whose output is the strength correction amount. This correction is specifically designed to quantify the extent to which actual strength development deviates from the ideal temperature-driven path due to factors such as moisture loss and material variations.

[0044] (3) Difference in parameter calibration logic: Existing technologies calibrate a single relationship of "strength-equivalent age". This application first calibrates the relationship of "strength-equivalent age" using standard maintenance data, and at the same time calibrates the relationship of "maturity model prediction residual-resistivity change rate", which is equivalent to adding the ability to perceive and compensate for adverse field conditions to the maturity model.

[0045] Based on the aforementioned differences in technical means, this application has the following beneficial effects compared with the prior art: (1) Addressing the impact of moisture: When concrete experiences moisture loss under field conditions, hydration slows down, and the rate of resistivity increase deviates from the trend under standard curing conditions. A strength correction function based on resistivity is needed. This anomalous signal can be captured. Since water loss typically lags intensity development, the intensity residual R in the maturity model is negative, and the strongly correlated correction term... It is also a negative value, thus automatically estimating from the intensity baseline. By deducting the strength loss caused by water loss, the method avoids the risk of overestimation in traditional methods and significantly improves the prediction accuracy of early-age strength of concrete.

[0046] (2) Response to internal state: The resistivity change rate V is a direct representation of the microstructure evolution rate. Regardless of whether it is due to differences in material activity or changes in curing conditions, as long as the development speed of the internal structure of concrete deviates from the expectation based on the standard temperature history, these changes will be reflected by the resistivity change rate V value, and the prediction results will be adjusted in real time through the strength correction term. This upgrades the early-age strength prediction from a simple "external driving" model to a dual-perception model of "external driving + internal response", which significantly improves the prediction accuracy of early-age strength of concrete.

[0047] (3) It combines theoretical basis and engineering applicability: all parameters of the strength prediction model in this application ( , , , , All parameters can be obtained through standard laboratory testing and calibration, eliminating the need for complex on-site calibration. Once the model parameters are obtained, only real-time reading of temperature and resistivity data is required on-site, and the embedded system can automatically calculate and output predicted strength values. This provides real-time and reliable data support for construction decisions (such as formwork removal, tensioning, and loading), achieving a leap from "experience-based judgment" to "data-driven decision-making," and significantly improving the safety, efficiency, and quality control of large and complex projects.

[0048] To verify the strength residual R= of the baseline maturity model - The conclusion that there is a strong correlation with the rate of change of resistivity V is supported by the following experimental procedure: (1) Preparation of experimental materials and specimens Three concrete mix proportions with different cementitious material systems were adopted, labeled M1, M2, and M3, respectively, and their detailed mix proportions are shown in Table 1. The raw materials used include P·O42.5 ordinary Portland cement, Grade II fly ash, S95 grade slag powder, limestone powder, silica fume, two types of manufactured sand, two types of crushed stone with different particle sizes, and polycarboxylate-based high-performance water-reducing agent.

[0049] Table 1. Experimental mix proportions

[0050] For each mix proportion, two types of specimens were prepared: 150mm cubic specimens were used for destructive compressive strength testing to obtain measured strength data for the specimens under that mix proportion; larger 800×200mm cuboid specimens were used for embedded monitoring to obtain temperature and resistivity data for the specimens under that mix proportion. All specimens were cast, vibrated, and cured under a film for 24 hours before demolding, followed by curing under different conditions: 1) Standard curing: After preparing cubic and cuboid specimens of the above dimensions, they were placed in a standard curing room at a temperature of 20±2℃ and a relative humidity >95% to obtain cubic and cuboid specimens under standard curing conditions. 2) Field curing: After preparing cubic and cuboid specimens of the above dimensions, they were placed in the construction site environment for curing under the same conditions to obtain cubic and cuboid specimens under field curing conditions. This construction site environment has uncontrolled daily temperature fluctuations, varying humidity, and wind-drying effects, which can accurately reflect moisture loss and temperature history in actual engineering projects.

[0051] (2) Testing and monitoring Real-time embedded monitoring: For cuboid specimens under standard curing conditions and on-site curing conditions, the internal temperature of the specimen is monitored using thermocouples embedded in the specimen, and the resistivity of the specimen is measured using embedded electrodes. From the moment the specimen is cast, both types of monitoring data are automatically recorded at programmable intervals (as an example, the monitoring interval is set to 0.15 days).

[0052] Destructive mechanical testing: In accordance with standard GB-T50081-2019 "Standard for Test Methods of Physical and Mechanical Properties of Concrete", compressive strength tests were conducted on cubic specimens under standard curing conditions and field curing conditions at 1, 3, 7, and 28 days of age. For each age, the average strength of three cubic specimens was used as the measured compressive strength. Table 2 shows the resistivity change rate and measured strength (measured compressive strength) at equivalent ages (derived from temperature data) of 1, 3, 7, and 28 days.

[0053] Table 2 Experimental Data

[0054] (3) Data processing and model building framework Equivalent age calculation: The temperature history is converted into the equivalent age at a standard reference temperature (20°C) using a traditional equivalent age maturity model based on the Arrhenius equation. .

[0055] Calculation of resistivity change rate: The resistivity history is smoothed and its instantaneous change rate V=dρ / dt is numerically calculated at the corresponding moment of the strength test.

[0056] The model parameters were calibrated using data obtained from standard-cured specimens. Specifically, the baseline maturity model was calibrated by fitting the "strength-equivalent age" relationship of the standard-cured specimens with a hyperbolic function (i.e., formula (2)) to determine the ultimate strength. Parameters such as the rate constant k.

[0057] Intensity correction function calibration based on resistivity: Calculating the maturity model prediction values ​​of standard cured specimens at each stage. Compared with the measured strength The strength residual R was calculated. The Bradley function (Equation (3)) was used to optimally fit the relationship between the strength residual R and the resistivity change rate V, and the correction coefficients α and β were calibrated. Table 3 shows the parameter calibration results of the strength correction function. Table 4 shows the strength correction fitting value and the strength difference after strength correction based on the strength correction function.

[0058] Table 3. Parameter calibration results of the intensity correction function

[0059] Table 4. Difference between the fitted value of the strength correction based on the strength correction function and the strength after strength correction.

[0060] This application analyzes the above experimental data, and through optimal fitting of the relationship between the strength residual R and the resistivity change rate V, calibrates the correction coefficients α and β, proving that the strength residual R of the baseline maturity model is optimal. = - There is a strong correlation with the rate of change of resistivity V. And this relationship can be fitted using an intensity correction function (e.g., the Bradley function, equation (3)). In essence, this application fits the relationship between the systematic error of the original maturity model and the rate of change of resistivity, and then the intensity correction value of the maturity model can be obtained.

[0061] The maturity model parameters and correction coefficients are calibrated and adjusted through the laboratory calibration phase. and After calibration, it can be directly applied to concrete under field conditions for real-time strength estimation. No further destructive testing is required under subsequent field conditions.

[0062] This application validates its prediction method using completely independent datasets of field-cured specimens from different mix proportions (M1, M2, M3). Root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²) are used. 2 The intensity prediction results of the method in this application are compared with the intensity prediction results of the traditional maturity model (formula (2)), as shown in Table 5.

[0063] Table 5. Comparison of estimation accuracy of field-cured specimens using traditional baseline maturity models and the prediction method of this application.

[0064] Table 5 shows that the root mean square error (RMSE) of the prediction method in this application is reduced by 67%, 53%, and 76% respectively compared with the traditional method, as are the mean absolute error (MAE) and coefficient of determination (R²). 2 The accuracy has also been significantly improved, demonstrating that the prediction method of this application can more realistically reflect the impact of complex on-site conditions on intensity development, and exhibits significantly higher accuracy. Figure 3 As shown, the comparison results between the predicted strength and the measured strength of the field-cured specimens are presented. Figure 3 In comparison with traditional maturity methods, the prediction method of this application estimates intensity values ​​that cluster more closely around the consensus line (y=x).

[0065] In this application, not only was the strength residual R= based on the maturity model discovered, but also... - There is a strong correlation between resistivity and the rate of change of resistivity V. Resistivity is introduced as a correction factor for the internal state of concrete to correct the strength deviation calculated by the maturity method. Moreover, the effectiveness of this finding is verified by experimental data.

[0066] This application also provides an application scenario in which the above-mentioned method for predicting the early-age strength of concrete based on maturity and resistivity is applied. Specifically, the method for predicting the early-age strength of concrete based on maturity and resistivity provided in this embodiment can be applied to the scenario of predicting the early-age strength of concrete bridges. This scenario includes a data acquisition stage, a data processing stage, and a concrete bridge pouring evaluation stage. The data acquisition stage is used to acquire the in-situ temperature and in-situ resistivity of the concrete to be tested. The data processing stage is used to calculate the baseline estimate of the early-age strength of the concrete to be tested based on the in-situ temperature of the concrete to be tested, using the maturity method; to calculate the correction value of the early-age strength of the concrete to be tested based on the in-situ resistivity of the concrete to be tested; and to calculate the final predicted value of the early-age strength of the concrete to be tested based on the baseline estimate and the correction value. The concrete bridge pouring evaluation stage uses the final predicted value of the early-age strength of the concrete to be tested to evaluate the performance of the poured bridge. The method for predicting the early-age strength of concrete based on maturity and resistivity provided in this embodiment belongs to the data processing stage.

[0067] Based on the same inventive concept, this application also provides a device for predicting the early-age strength of concrete based on maturity and resistivity, used to implement the aforementioned method for predicting the early-age strength of concrete based on maturity and resistivity. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the device for predicting the early-age strength of concrete based on maturity and resistivity provided below can be found in the limitations of the method for predicting the early-age strength of concrete based on maturity and resistivity described above, and will not be repeated here.

[0068] In one exemplary embodiment, such as Figure 4 As shown, a device for predicting the early-age strength of concrete based on maturity and resistivity is provided, comprising: The data acquisition module T1 is used to acquire the in-situ temperature and in-situ resistivity of the concrete to be tested.

[0069] The strength baseline estimation module T2 is used to calculate the strength baseline estimate of the concrete at its early age based on the in-situ temperature of the concrete and the maturity method.

[0070] The strength correction module T3 is used to calculate the strength correction value of the concrete at early age based on the in-situ resistivity of the concrete to be tested.

[0071] The calculation of the early-age strength correction value of the concrete under test based on its in-situ resistivity includes: The rate of change of resistivity of the concrete to be tested is calculated based on the in-situ resistivity of the concrete to be tested. The strength correction value of the concrete under test at early age is calculated based on the resistivity change rate. The expression for the intensity correction value is as follows: in, Indicates the strength correction value; Indicates the rate of change of resistivity; , This represents the correction factor.

[0072] The target strength calculation module T4 is used to calculate the final predicted strength of the concrete under test at early age based on the early-age strength baseline estimate and strength correction value.

[0073] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 5 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores early-age strength prediction data for concrete based on maturity and resistivity. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for predicting the early-age strength of concrete based on maturity and resistivity.

[0074] Those skilled in the art will understand that Figure 5The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0075] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0076] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0077] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Moreover, the collection, use and processing of the relevant data are carried out in compliance with the relevant data protection laws and policies of the country where the location is located, and with the authorization granted by the owner of the corresponding device.

[0078] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0079] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0080] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0081] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for predicting the early-age strength of concrete based on maturity and resistivity, characterized in that, include: Obtain the in-situ temperature and in-situ resistivity of the concrete to be tested; Based on the in-situ temperature of the concrete to be tested, the baseline strength of the early-age concrete to be tested is estimated using the maturity method. The strength correction value of the concrete at early age is calculated based on the in-situ resistivity of the concrete to be tested. The final predicted strength of the concrete under test at early age is calculated based on the baseline strength estimate and strength correction value at early age. The calculation of the early-age strength correction value of the concrete under test based on its in-situ resistivity includes: The rate of change of resistivity of the concrete to be tested is calculated based on the in-situ resistivity of the concrete to be tested. The strength correction value of the concrete under test at early age is calculated based on the resistivity change rate. The expression for the intensity correction value is as follows: in, Indicates the strength correction value; Indicates the rate of change of resistivity; , This represents the correction factor.

2. The method for predicting the early-age strength of concrete based on maturity and resistivity according to claim 1, characterized in that, The method for predicting the early-age strength of concrete based on maturity and resistivity also includes: Obtain the measured strength values ​​of concrete specimens at early age, the baseline strength estimate based on the maturity method, and the resistivity; The strength residual value is calculated based on the measured strength value and the estimated strength baseline value of the concrete specimen; The correction coefficients in the strength correction value expression are fitted based on the strength residual value and the corresponding resistivity change rate of the concrete specimen to obtain the fitted correction coefficients, which are then substituted into the strength correction value expression to calculate the early-age strength correction value of the concrete to be tested.

3. The method for predicting the early-age strength of concrete based on maturity and resistivity according to claim 1, characterized in that, The final predicted strength value of the concrete under test at early age is calculated based on the early-age strength baseline estimate and strength correction value, specifically including: The baseline strength estimate and the strength correction value at early age are added together to obtain the final predicted strength value of the concrete at early age.

4. The method for predicting the early-age strength of concrete based on maturity and resistivity according to claim 1, characterized in that, The formula for calculating the baseline strength estimate is as follows: in, In the formula, This is the baseline estimate of the intensity. Equivalent age; It is the ultimate strength; It is the rate constant; The baseline age; Activation energy; This is basic knowledge about molar gases; For reference temperature; For time intervals; The average temperature over the time interval; This refers to the age of the concrete.

5. A device for predicting the early-age strength of concrete based on maturity and resistivity, characterized in that, include: The data acquisition module is used to acquire the in-situ temperature and in-situ resistivity of the concrete to be tested. The strength baseline estimation module is used to calculate the strength baseline estimate of the concrete at an early age based on the in-situ temperature of the concrete to be tested and the maturity method. The strength correction module is used to calculate the strength correction value of the concrete at early age based on the in-situ resistivity of the concrete to be tested. The calculation of the early-age strength correction value of the concrete under test based on its in-situ resistivity includes: The rate of change of resistivity of the concrete to be tested is calculated based on the in-situ resistivity of the concrete to be tested. The strength correction value of the concrete under test at early age is calculated based on the resistivity change rate. The expression for the intensity correction value is as follows: in, Indicates the strength correction value; Indicates the rate of change of resistivity; , Indicates the correction factor; The target strength calculation module is used to calculate the final predicted strength of the concrete under test at early age based on the early-age strength baseline estimate and strength correction value.

6. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes the computer program to implement the method for predicting the early-age strength of concrete based on maturity and resistivity as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for predicting the early-age strength of concrete based on maturity and resistivity as described in any one of claims 1-4.

8. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for predicting the early-age strength of concrete based on maturity and resistivity as described in any one of claims 1-4.

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