A method for predicting the strength of steam-cured concrete and a method for optimizing steam-curing parameters

Abstract

The present invention relates to a method for predicting the strength of steam-cured concrete and a method for optimizing steam-curing parameters. A model is established using test data obtained in a laboratory curing environment, and curing parameter optimization is performed based on response surface analysis. The prediction model obtained by the present invention is continuous and can continuously analyze each level of the experiment, rather than analyzing each independent experimental point. The method fully considers the influence of various process parameters on strength in actual production, as well as the mutual influence between them. Therefore, when optimizing, specific process parameters are also targeted, providing practical guidance for concrete curing plans in actual production. During the entire curing period of concrete, by rationally arranging curing parameters, the concrete strength meets the requirements of the project, strength loss due to curing is reduced, energy consumption is minimized, and the method is easy to use, effective, and has good social and economic benefits.

Description

Technical Field

[0001] The present invention relates to the technical field of concrete curing, in particular to a method for predicting the strength of steam-cured concrete and a method for optimizing steam-curing parameters. Background Art

[0002] Concrete components, a crucial building element in current production, are subject to a variety of molding and curing methods. However, steam curing is currently the most common method for precast concrete components, ensuring they reach demolding strength quickly, enabling rapid mold or formwork replacement, improving labor productivity, and increasing unit output per unit time. Consequently, the design of a curing plan for concrete is crucial. In practical projects, it is crucial to balance construction schedules with minimizing curing costs while meeting design requirements. Therefore, developing a sound concrete curing plan is crucial for prefabricated buildings.

[0003] At present, the design of curing plan mainly starts from process parameters, and the approximate range is determined according to the actual strength requirements. This general parameter control often causes the following problems of concrete in actual projects:

[0004] (1) Insufficient constant temperature curing time will result in the concrete failing to meet the required strength standards;

[0005] (2) If the constant temperature maintenance time is too long, a large amount of energy will be consumed, resulting in waste of resources and increased project costs;

[0006] (3) If the constant temperature is too low, the curing time will be prolonged, while if it is too high, the concrete will lose its strength in the later stage.

[0007] (4) Too fast a heating and cooling rate will cause the concrete to lose strength or crack;

[0008] (5) Too slow a heating and cooling speed will make the process take too long and slow down the production speed;

[0009] For example, the Chinese invention patent, entitled "A System for Predicting Concrete Strength at Construction Sites," with authorization publication number CN111505252A, aims to rapidly predict concrete strength in a construction site environment. The concrete strength prediction system includes a temperature and humidity sensor, a data center, and a user terminal. The temperature and humidity sensor monitors the temperature and humidity inside the concrete; the data center stores temperature and humidity data and concrete strength predictions; and the user terminal is used to view and analyze the data.

[0010] The shortcomings of the prior art, including the aforementioned application, lie in the need to optimize the concrete curing schedule, particularly adjusting the curing parameters, in order to improve the quality of steam-cured concrete, reduce curing energy consumption, and enhance the accuracy of concrete strength prediction. Furthermore, concrete strength prediction requires considering the concrete's maturity or internal temperature and humidity variations, which cannot guarantee the accuracy of concrete strength predictions under varying temperature and humidity conditions. Summary of the Invention

[0011] In view of the above situation, in order to overcome the defects of the existing technology, the purpose of the present invention is to provide a method for predicting the strength of steam-cured concrete and a method for optimizing steam-curing parameters. During the entire curing period of concrete, by reasonably arranging the curing parameters, the concrete strength can meet the requirements of the project, the strength loss caused by curing can be reduced, and the energy consumption can be minimized.

[0012] The technical solution provided by the present invention is:

[0013] A method for predicting the strength of steam-cured concrete comprises the following steps:

[0014] Step 1: Determine the curing parameters for predicting the strength of steam-cured concrete: constant temperature time, constant temperature, heating rate, and cooling rate;

[0015] Single factor tests were conducted on the four parameters in sequence, that is, three of the curing parameters were fixed in sequence, and the value of the remaining curing parameter was changed to cure the steam-cured concrete, and the compressive strength growth curve of the steam-cured concrete under the single factor change value was obtained;

[0016] Step 2: Obtain the optimal value of each curing parameter based on the changing trend of the compressive strength growth curve of steam-cured concrete;

[0017] Through 4 groups of single factor experiments, the optimal constant temperature time t1, the optimal constant temperature temperature T1, and the optimal heating rate V a and the optimal cooling rate V j ;

[0018] Step 3: Based on the optimal values ​​of each curing parameter obtained from the single-factor experiment, a four-factor, three-level response surface central composite experiment was established to obtain the compressive strength of steam-cured concrete under 29 groups of different constant temperature time, constant temperature, heating rate, and cooling rate;

[0019] The four factors include: constant temperature, constant temperature time, heating rate, and cooling rate, which are the four external factors that can affect the strength of steam-cured concrete;

[0020] The three levels include: high, medium, and low, that is, with the optimal value as the center, the high level and low level are taken on its left and right respectively. The high and low levels are the optimal values ​​obtained according to the single factor experiment, and the values ​​on the left and right are the low level and high level;

[0021] The constant temperature time was taken as the optimal value ± 6 h for low and high levels;

[0022] The constant temperature was taken as the optimum value ±10°C as low and high levels;

[0023] The heating rate takes the optimal value ±5℃ / h as the low and high levels;

[0024] The cooling rate takes the optimal value ±5°C / h as the low and high levels;

[0025] Step 4: Perform multivariate quadratic response surface regression analysis to obtain the prediction model equation for steam-cured concrete strength;

[0026] The formula used for multivariate quadratic response surface regression analysis is:

[0027] f(t,T,V a , V j )=a+bt+cT+dV a +eV j +ftT+gtV a +htV j +iTV a +jTV j +kV a V j +lt 2 +mT 2 +nV a 2 +oV j 2

[0028] Where: a is a constant term; b, c, d…o are independent variable coefficients; t is the constant temperature time; T is the constant temperature; V a is the heating rate; V j is the cooling rate;

[0029] Substitute the 29 groups of compressive strengths of steam-cured concrete under different constant temperature times, constant temperatures, heating rates, and cooling rates obtained in step 3 into the above formula, and obtain the constant term and the coefficients of each independent variable in the formula to obtain the final steam-cured concrete strength prediction model equation. The steam-cured concrete strength prediction model equation can be used to predict the compressive strength of concrete under different curing parameters.

[0030] Preferably, in step 1, the standard for obtaining the optimal values ​​of the remaining curing parameters according to the changing trend of the compressive strength growth curve of the steam-cured concrete is to simultaneously meet the following two conditions:

[0031] (1) The compressive strength of concrete is greater than or equal to the design strength value of the selected grade of concrete;

[0032] (2) The compressive strength of concrete has a slow growth trend or no growth. The slow growth trend is defined as continuous growth for six consecutive hours with the growth rate within 5%.

[0033] During the single factor test, when three of the curing parameters are fixed, the following conditions should be met:

[0034] Constant temperature time ≤ 45h, constant temperature ≤ 75℃, heating rate ≤ 10℃ / h, cooling rate ≤ 10℃ / h;

[0035] When changing the value of the remaining maintenance parameter, select from low to high. The initial value, maximum value and change amount are as follows:

[0036] The initial value of constant temperature time is 12h, the maximum value is 54h, and the variation is 6h;

[0037] The initial value of the constant temperature is 30°C, the maximum value is 90°C, and the variation is 10°C;

[0038] The initial value of the heating rate is 5℃ / h, the maximum value is 30℃ / h, and the change is 5℃ / h;

[0039] The initial value of the cooling rate is 5℃ / h, the maximum value is 30℃ / h, and the change is 5℃ / h.

[0040] A method for optimizing concrete steam curing parameters comprises the following steps:

[0041] Step 1: Determine the curing parameters for predicting the strength of steam-cured concrete: constant temperature time, constant temperature, heating rate, and cooling rate;

[0042] Single factor tests were conducted on the four parameters in sequence, that is, three of the curing parameters were fixed in sequence, and the value of the remaining curing parameter was changed to cure the steam-cured concrete, and the compressive strength growth curve of the steam-cured concrete under the single factor change value was obtained.

[0043] Step 2: Obtain the optimal values ​​of various curing parameters according to the changing trend of the compressive strength growth curve of steam-cured concrete; finally obtain the optimal constant temperature time t1, optimal constant temperature T1, and optimal heating rate V through 4 groups of single factor experiments. a and the optimal cooling rate V j ;

[0044] Step 3: Based on the optimal values ​​of each curing parameter obtained from the single-factor experiment, a four-factor three-level response surface central composite experiment was established. Open the Design-Expert software, select Create New Experimental Design (newdesign), use the established four-factor three-level response surface central composite experiment as the input value, and enter "compressive strength" as the response value to generate the experimental design table.

[0045] The factor values ​​are converted into coded values, that is, into three levels, with the high point coded as 1, the midpoint coded as 0, and the low point coded as -1;

[0046] Step 4: Data Supplementation

[0047] According to the experimental design table generated in step 3, the corresponding test was completed. That is, the compressive strength of the steam-cured concrete was obtained under different constant temperature times, constant temperature, heating rates, and cooling rates. The compressive strength data was entered into the experimental design table to obtain 29 groups of compressive strengths of the steam-cured concrete under different constant temperature times, constant temperature, heating rates, and cooling rates.

[0048] Step 5: Perform multivariate quadratic response surface regression analysis to obtain the prediction model equation for steam-cured concrete strength;

[0049] The multivariate quadratic response surface regression analysis uses the Design-Expert software's own quaternary quadratic fitting model. Random points can be used to predict the corresponding response. The selected formula is:

[0050] f(t,T,V a , V j )=a+bt+cT+dV a +eV j +ftT+gtV a +htV j +iTV a +jTV j +kV a V j +lt 2 +mT 2 +nV a 2 +oV j 2

[0051] Where: a is a constant term; b, c, d…o are independent variable coefficients; t is the constant temperature time; T is the constant temperature; V a is the heating rate; V j is the cooling rate;

[0052] Substitute the experimental design table obtained in step 4 into the above formula to solve it, and obtain the constant term and the coefficients of each independent variable in the formula to obtain the regression model equation of concrete steam curing strength under different curing parameters. The regression model equation can be used to predict the compressive strength of concrete under different curing parameters.

[0053] Step 6: Based on the Design-Expert software, determine the range of each factor according to the constraints:

[0054] The restrictions are:

[0055] ①The lowest constant temperature ensures the least energy consumption during maintenance;

[0056] ② The constant temperature time is the shortest, ensuring the minimum energy consumption during maintenance and reducing the time spent in this production link, thus speeding up the production progress;

[0057] ③ The heating rate should be slow to ensure the volume stability of the concrete in the early stage and the growth of strength in the later stage;

[0058] ④ While the cooling rate complies with industry standards, select the maximum value to reduce the time consumed in the process;

[0059] The input factor ranges are set as follows:

[0060] a. Constant temperature time (t): the minimum option within the value range;

[0061] b. Constant temperature (T): the minimum option within the value range;

[0062] c. Heating rate (V a ): Any option within the value range;

[0063] d. Cooling rate (V j ): the maximum option within the value range;

[0064] Step 7: Click the Solutions option. The Design-Expert software will automatically solve the regression model equation according to the limited conditions to obtain the optimal curing parameter value and complete the optimization of concrete steam curing parameters.

[0065] Compared to existing technologies, the present invention provides a practical and feasible method based on experimental data. For situations where on-site testing is not permitted, the present invention utilizes experimental data obtained in a laboratory curing environment to establish a model and optimize curing parameters based on response surface analysis. This method is a simple and effective means of addressing this situation. The prediction model obtained by the present invention is continuous, enabling continuous analysis of each experimental level, rather than analyzing each individual experimental point. Traditional prediction models fail to fully reflect the sensitive interactions between multiple factors in steam-cured concrete. For example, neural network predictions of steam-cured concrete strength fail to reflect the impact of curing process parameters. These predictions are based solely on data and cannot be integrated with actual production parameters. They can only provide mechanical strength predictions, but fail to fully reflect the interactions between strength and various process parameters. Most models focus solely on the accuracy of concrete strength prediction models, typically using mix ratios or data-based neural network algorithms. These models fail to understand and explain the relationship between curing process parameters and strength based on the actual concrete production process, and thus offer limited guidance for the design of curing plans for steam-cured concrete. The present invention fully considers the impact of various process parameters on strength in actual production, as well as their mutual influences. Therefore, optimization is targeted at specific process parameters, providing practical guidance for concrete curing plans in actual production. By rationally arranging curing parameters throughout the entire curing period, concrete strength meets project requirements, strength loss due to curing is reduced, energy consumption is minimized, and the process is easy to use, effective, and has positive social and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1-4 This is a curve diagram of the compressive strength growth of steam-cured concrete under various single factor change values ​​obtained from the single factor test in the embodiment of the present invention.

[0067] Figure 5 This is a comparison chart of the predicted value and the true value of the embodiment of the present invention. DETAILED DESCRIPTION

[0068] The specific embodiments of the present invention are further described in detail below with reference to the examples.

[0069] The embodiment of the present invention takes C50 concrete as an example. The present invention is also applicable to concrete of other strength grades, and the relevant implementation steps are also applicable.

[0070] Step 1: Determine the curing parameters for predicting the strength of steam-cured concrete: constant temperature time, constant temperature, heating rate, and cooling rate;

[0071] Single factor tests were conducted on the four parameters in sequence, that is, three of the curing parameters were fixed in sequence, and the value of the remaining curing parameter was changed to cure the steam-cured concrete, and the compressive strength growth curve of the steam-cured concrete under the single factor change value was obtained.

[0072] Step 2: Obtain the optimal values ​​of various curing parameters according to the changing trend of the compressive strength growth curve of steam-cured concrete; finally obtain the optimal constant temperature time t1, optimal constant temperature T1, and optimal heating rate V through 4 groups of single factor experiments. a and the optimal cooling rate V j ;

[0073] Specifically:

[0074] S1: Fixed constant temperature, heating rate, and cooling rate at 50℃, 10℃ / h, and 10℃ / h respectively, divided the constant temperature time into 12h, 18h, 24h, 30h, 36h, 42h, 48h, and 54h, and carried out single parameter test to determine the steam curing concrete strength growth curve (such as Figure 1 As shown), the maximum strength under this condition (compressive strength ≥ 50MPa) is obtained, and the strength increases slowly after the constant temperature time exceeds 30h, so the optimal constant temperature time t1 is selected as 30h;

[0075] S2: The constant temperature time, heating rate, and cooling rate are fixed at 24h, 10℃ / h, and 10℃ / h respectively. The constant temperature is divided into 30℃, 40℃, 50℃, 60℃, 70℃, 80℃, and 90℃ in sequence. Single parameter tests are carried out to determine the strength growth curve of steam-cured concrete. The optimal constant temperature T1 corresponding to the maximum strength under this condition is 50 degrees Celsius. The selection criteria for the optimal constant temperature are: ① compressive strength ≥ 50MPa; ② no increasing trend in compressive strength, such as Figure 2 shown.

[0076] S3: Fixed constant temperature time, constant temperature, and cooling rate to 24h, 50℃, and 10℃ / h, respectively. The heating rate was divided into 5℃ / h, 10℃ / h, 15℃ / h, 20℃ / h, 25℃ / h, and 30℃ / h. Single parameter tests were conducted to determine the strength growth curve of steam-cured concrete and obtain the optimal heating rate V corresponding to the maximum strength under these conditions. a The best heating rate selection criteria are: ① compressive strength ≥ 50MPa; ② there is no growth trend in strength after the heating rate is greater than 10℃ / h, such as Figure 3 As shown;

[0077] S4: Fixed constant temperature time, constant temperature, and heating rate to 24h, 50℃, and 10℃ / h, respectively. The cooling rate was divided into 5℃ / h, 10℃ / h, 15℃ / h, 20℃ / h, 25℃, and 30℃ / h. Single parameter test was conducted to determine the strength growth curve of steam-cured concrete and obtain the optimal cooling rate V corresponding to the maximum strength under this condition. j The best cooling rate selection criteria are: ① compressive strength ≥ 50MPa; ② there is no growth trend in strength after the cooling rate is greater than 10℃ / h, such as Figure 4 As shown;

[0078] Step 3: Based on the optimal values ​​of each curing parameter obtained from the single-factor experiment, a four-factor three-level response surface central composite experiment was established. Open the Design-Expert software, select Create a new experimental design (newdesign), and use the established four-factor three-level response surface central composite experiment as the input value, as shown in Table 1:

[0079]

[0080] Table 2 - Experimental Design Table

[0081]

[0082]

[0083] Step 4: According to the experimental design table generated in step 3, complete the corresponding test and supplement the test results, that is, obtain the compressive strength of steam-cured concrete under different constant temperature time, constant temperature, heating rate, and cooling rate. Fill the compressive strength data into the experimental design table to obtain 29 groups of compressive strength of steam-cured concrete under different constant temperature time, constant temperature, heating rate, and cooling rate; and convert the factor values ​​into coding values, that is, convert them into three levels, with the high point coded as 1, the midpoint coded as 0, and the low point coded as -1; as shown in Table 3:

[0084] Table 3 - Experimental design and test results

[0085]

[0086]

[0087] Step 5: Perform multivariate quadratic response surface regression analysis to obtain the prediction model equation for steam-cured concrete strength;

[0088] The multivariate quadratic response surface regression analysis uses the Design-Expert software's own quaternary quadratic fitting model. Random points can be used to predict the corresponding response. The selected formula is:

[0089] f(t,T,V a , V j )=a+bt+cT+dV a +eV j +ftT+gtV a +htV j +iTV a +jTV j +kV a V j +lt 2 +mT 2 +nV a 2 +oV j 2

[0090] Where: a is a constant term; b, c, d…o are independent variable coefficients; t is the constant temperature time; T is the constant temperature; V a is the heating rate; V j is the cooling rate;

[0091] Substitute the experimental design table obtained in step 4 into the above formula to solve it. The software will calculate the constant term and the coefficients of each independent variable in the formula, and obtain the regression model equation of concrete steam curing strength with different curing parameters:

[0092] Compressive strength = 56.50 + 4.17t + 2.63T - 0.37V a +0.067V j -0.25Tt+0.15tV a -0.1tV j -0.4TV a -0.05TV j +0.25V a V j -3.29t 2 -1.72T 2 -0.57V a 2 -0.19V j 2

[0093] The compressive strength of concrete under different curing parameters can be predicted through the regression model equation;

[0094] Model correlation and accuracy analysis;

[0095] The variance analysis was calculated using the following formula:

[0096]

[0097] Where: y i is the test value of compressive strength, is the average value of the predicted intensity, The average value of the test strength is finally obtained as R 2 =0.9745, and when R 2 When >0.8, it indicates that the experimental value and the predicted value in the model have a good correlation; the comparison chart of the predicted value and the true value is as follows Figure 5 shown.

[0098] Step 6: Based on the Design-Expert software, determine the range of each factor according to the constraints:

[0099] The restrictions are:

[0100] ①The lowest constant temperature ensures the least energy consumption during maintenance;

[0101] ② The constant temperature time is the shortest, ensuring the minimum energy consumption during maintenance and reducing the time spent in this production link, thus speeding up the production progress;

[0102] ③ The heating rate should be slow to ensure the volume stability of the concrete in the early stage and the growth of strength in the later stage;

[0103] ④ While the cooling rate complies with industry standards, select the maximum value to reduce the time consumed in the process;

[0104] The input factor ranges are set as follows:

[0105] a. Constant temperature time (t): minimum option, value range 24h-36h;

[0106] b. Constant temperature (T): minimum option, value range 40℃-60℃;

[0107] c. Heating rate (V a ): Within the range, the value range is 5℃ / h-15℃ / h;

[0108] d. Cooling rate (V j ): Maximum option, value range 5℃ / h-15℃ / h;

[0109] Step 7: Click the Solutions option, and the Design-Expert software will automatically solve the regression model equation according to the limited conditions to obtain the optimal curing parameter values: the corresponding constant temperature time t is 28.6h, the constant temperature T is 43.3℃, ​​the heating rate is 11.2℃ / h, and the cooling rate is 15℃ / h. Combined with actual production, the values ​​are taken as 28h, 43℃, 10℃ / h, and 15℃ / h in sequence to complete the optimization of concrete steam curing parameters.

Claims

1. A method for optimizing concrete steam curing parameters, characterized in that: The following steps are involved: Step 1: Determine the curing parameters for predicting the strength of steam-cured concrete: constant temperature time, constant temperature, heating rate, and cooling rate; Single factor tests were conducted on the four parameters in sequence, that is, three of the curing parameters were fixed in sequence, and the value of the remaining curing parameter was changed to cure the steam-cured concrete, and the compressive strength growth curve of the steam-cured concrete under the single factor change value was obtained; Step 2: Obtain the optimal values ​​of various curing parameters according to the changing trend of the compressive strength growth curve of steam-cured concrete; finally obtain the optimal constant temperature time t1, optimal constant temperature T1, and optimal heating rate V through 4 groups of single factor experiments. a and the optimal cooling rate V j ; Step 3: Using the optimal values ​​of each curing parameter obtained from the single-factor experiment as the center, establish a four-factor, three-level response surface central composite experiment. Open the Design-Expert software, select Create New Experimental Design, use the established four-factor, three-level response surface central composite experiment as the input value, and enter "compressive strength" as the response value to generate the experimental design table. Step 4: Data Supplementation According to the experimental design table generated in step 3, the corresponding test was completed. That is, the compressive strength of the steam-cured concrete was obtained under different constant temperature times, constant temperature, heating rates, and cooling rates. The compressive strength data was entered into the experimental design table to obtain 29 groups of compressive strengths of the steam-cured concrete under different constant temperature times, constant temperature, heating rates, and cooling rates. Step 5: Perform multivariate quadratic response surface regression analysis to obtain the prediction model equation for steam-cured concrete strength; The multivariate quadratic response surface regression analysis uses the Design-Expert software's own quaternary quadratic fitting model. Random points can be used to predict the corresponding response. The selected formula is: f(t, T, V a ,V j )=a+bt+cT+dV a +eV j +ftT+gtV a +htV j +iTV a +jTV j +kV a V j +lt 2 +mT 2 +nV a 2 +oV j 2 Where: a is a constant term; b, d, e, f…o are independent variable coefficients; t is the constant temperature time; T is the constant temperature; V a is the heating rate; V j is the cooling rate; Substitute the experimental design table obtained in step 4 into the above formula to solve it, and obtain the constant term and the coefficients of each independent variable in the formula to obtain the regression model equation of concrete steam curing strength under different curing parameters. The regression model equation can be used to predict the compressive strength of concrete under different curing parameters. Step 6: Based on the Design-Expert software, determine the range of each factor according to the constraints: The restrictions are: ①The lowest constant temperature ensures the least energy consumption during maintenance; ② The constant temperature time is the shortest, ensuring the minimum energy consumption during maintenance and reducing the time spent in this production link, thus speeding up the production progress; ③ The heating rate should be slow to ensure the volume stability of the concrete in the early stage and the growth of strength in the later stage; ④ While the cooling rate complies with industry standards, select the maximum value to reduce the time consumed in the process; The input factor ranges are set as follows: a. Constant temperature time (t): the minimum option within the value range; b. Constant temperature (T): the minimum option within the value range; c. Heating rate (V a ): Any option within the value range; d. Cooling rate (V j ): the maximum option within the value range; Step 7: Click the Solutions option. The Design-Expert software will automatically solve the regression model equation according to the limited conditions to obtain the optimal curing parameter value and complete the optimization of concrete steam curing parameters.

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