Concrete slump online monitoring method based on data analysis

Abstract

The invention relates to the technical field of concrete slump detection, in particular to a concrete slump online monitoring method based on data analysis. The method comprises the following steps: S1, collecting temperature, humidity, rotating speed, torque, stirring time, water cement ratio, additive dosage and reaction rate data of concrete in a stirrer in real time; s2, calculating concrete slump by using a dynamic physical property slump estimation algorithm; s3, calculating the concrete slump by using a chemical reaction dynamic slump prediction algorithm; s4, comparing a preset threshold value with a difference value between the two slumps; and S5, measuring the shear stress and shear rate curve of the concrete, and predicting the slump by using the rheological model. According to the concrete slump online monitoring method based on data analysis, accurate monitoring and real-time control of the concrete slump are realized through mutual verification of a dynamic physical property slump estimation algorithm and a chemical reaction dynamic slump prediction algorithm.

Description

Technical Field

[0001] The invention relates to the technical field of concrete slump detection, and in particular to an online monitoring method for concrete slump based on data analysis. Background Art

[0002] An online monitoring method for concrete slump based on data analysis aims to monitor the slump of concrete online in real time by analyzing the physical and chemical properties of concrete. The slump of concrete under the dynamic physical property slump estimation algorithm and the chemical reaction dynamic slump prediction algorithm are monitored respectively and compared with the preset threshold value to achieve real-time and accurate monitoring of concrete slump.

[0003] The existing online monitoring methods for concrete slump are usually difficult to comprehensively consider the physical and chemical properties of concrete in real time. In addition, during the transportation or use of concrete, factors such as the temperature and humidity of the concrete will affect the slump of the concrete in real time, which will lead to the problem that the slump of the concrete cannot be monitored in time and does not meet the standards. In summary, an online monitoring method for concrete slump based on data analysis is provided. Summary of the invention

[0004] The purpose of the present invention is to provide an online monitoring method for concrete slump based on data analysis, so as to solve the problem raised in the above background technology that during the transportation or use of concrete, factors such as temperature and humidity of concrete will affect the slump of concrete in real time, resulting in the inability to monitor the slump of concrete in time and the failure to meet the standards.

[0005] To achieve the above object, the present invention aims to provide a method for online monitoring of concrete slump based on data analysis, comprising the following steps:

[0006] S1. Use sensors to collect data on the temperature, humidity, mixing speed, mixing torque and mixing time of concrete in the mixer in real time, and collect data on the water-cement ratio, admixture dosage and concrete reaction rate;

[0007] S2, calculating the concrete slump S1(t) according to the temperature, humidity, mixing speed, mixing torque and mixing time data of the mixer and using the dynamic physical property slump estimation algorithm;

[0008] S3, calculate the concrete slump S2(t) by using the water-cement ratio, admixture dosage and concrete reaction rate data and according to the concrete reaction process and the chemical reaction dynamic slump prediction algorithm;

[0009] S4, comparing the preset threshold with the difference between the slump S1(t) and the slump S2(t), if the difference is less than or equal to the preset threshold, taking the average value as the final slump value, if the difference is greater than the preset threshold, continuing to use S5 to further detect the slump;

[0010] S5. By measuring the shear stress and shear rate curve of concrete, the slump S3(t) is monitored using a rheological model.

[0011] As a further improvement of the technical solution, in S1, a sensor is used to collect the temperature, humidity, stirring speed, stirring torque and stirring time data of the concrete in the mixer in real time, and the specific method is as follows:

[0012] S1.1.1. Use the temperature sensor and humidity sensor installed inside the concrete mixer to collect real-time temperature data and humidity data of the concrete;

[0013] S1.1.2. Use the Hall effect sensor installed on the mixer shaft to record the mixing speed of the mixer in real time;

[0014] S1.1.3. Use a torque sensor installed on the mixer drive device to record the mixing torque of the mixer in real time;

[0015] S1.1.4. Automatically record time data through the mixer control system;

[0016] In S1, data on water-cement ratio, admixture dosage and concrete reaction rate are collected, and the specific method is as follows:

[0017] S1.2.1. From the concrete batching system, record and collect the water-cement ratio of cement and water and the amount of admixtures in real time;

[0018] S1.2.2. Determine the reaction rate of concrete in the laboratory.

[0019] As a further improvement of the technical solution, in S2, the dynamic physical property slump estimation algorithm is based on the physical data of concrete during the mixing process, and is combined with an exponential decay function, a nonlinear function, a time delay integral function and a time decay model to calculate the concrete slump S1(t);

[0020] The concrete slump S1(t) is calculated using the dynamic physical property slump estimation algorithm. The specific steps are as follows:

[0021] S2.1, obtaining the temperature T(t), humidity H(t), mixing speed R(t), mixing torque M(t), and mixing time t of the concrete at time t;

[0022] S2.2. Use hyperbolic function to calculate the effect of temperature T(t) on slump f T (t);

[0023] S2.3. Calculate the effect of humidity H(t) on slump using exponential decay function H (t);

[0024] S2.4. Calculate the effect of mixing speed R(t) on slump using nonlinear function R (t);

[0025] S2.5. Combined with the time decay model, calculate the effect of mixing torque M(t) on slump f M (t);

[0026] S2.6. Use the time delay integral function to calculate the effect of mixing time t on slump f t (t);

[0027] S2.7, the previously calculated f T (t), f H (t), f R (t), f M (t) and f t (t) is integrated and nonlinearly combined to obtain the slump S1(t);

[0028] S2.8. Output the calculated slump S1(t) to the monitoring system.

[0029] As a further improvement of the technical solution, in S2.2, a hyperbolic function is used to calculate the effect of temperature T(t) on slump, and the specific method is as follows:

[0030]

[0031] Among them, f T (t) is the effect function of temperature T(t) on slump; T(t) is the real-time temperature; T0 is the ideal temperature; a T is the temperature sensitivity coefficient, which is used to control the sensitivity of the effect of temperature change on slump; e is the base of the natural logarithm;

[0032] In S2.3, an exponential decay function is used to calculate the effect of humidity H(t) on slump, and the specific method is as follows:

[0033]

[0034] Among them, f H (t) is the influence function of humidity H(t) on slump; H(t) is the real-time concrete humidity; H0 is the ideal humidity; b His the moisture sensitivity coefficient, which is used to control the sensitivity of the effect of humidity changes on slump;

[0035] In S2.4, a nonlinear function is used to calculate the effect of the stirring speed R(t) on the slump. The method is as follows:

[0036]

[0037] Among them, f R (t) is the effect function of stirring speed R(t) on slump; R(t) is the stirring speed; k R It is the saturation coefficient of the mixing speed, which is used to control the nonlinear degree of the effect of the mixing speed on the slump.

[0038] As a further improvement of the technical solution, in S2.5, the influence of the stirring torque M(t) on the slump is calculated in combination with the time decay model, and the specific method is as follows:

[0039]

[0040] Among them, f M (t) is the influence function of the stirring torque M(t) on the slump at time t; M(t) is the stirring torque; c M is the time attenuation coefficient;

[0041] In S2.6, the time delay integral function is used to calculate the effect of the mixing time t on the slump. The specific method is as follows:

[0042]

[0043] Among them, f t (t) is the time delay function, which is used to express the effect of mixing time t on slump; τ is the integral variable; t0 is the ideal mixing time, which is a constant; d t It is the time sensitivity coefficient, which is used to control the influence of mixing time delay effect on slump.

[0044] As a further improvement of the technical solution, in S2.7, the previously calculated f T (t), f H (t), f R (t), f M (t) and f t (t) is integrated and nonlinearly combined to obtain the slump S1(t). The specific method is as follows:

[0045]

[0046] Among them, C1 is the normalization coefficient; n1 is the nonlinear impact factor.

[0047] As a further improvement of the technical solution, in S3, the chemical reaction dynamic slump prediction algorithm is based on the chemical reaction kinetic characteristics in concrete, through the water-cement ratio, admixture dosage and concrete reaction rate data, combined with the nonlinear reaction model and empirical formula, to calculate the concrete slump S2(t) in real time. The specific steps are as follows:

[0048] S3.1. Obtain water-cement ratio W / C and admixture dosage A d and concrete reaction rate data α(t);

[0049] S3.2. Use the combined model of logarithmic and power functions to calculate the effect of the water-cement W / C ratio on slump f W / C ;

[0050] S3.3. Use piecewise function model to describe different admixture dosages A d Effect of admixtures on slump

[0051] S3.4. Using nonlinear differential equations to describe the dynamic changes of concrete reaction rate α(t)

[0052] S3.5. The calculated f W / C , Integrate and nonlinearly combine with α(t) to obtain the slump S2(t);

[0053] S3.6. Output the calculated slump S2(t) to the monitoring system.

[0054] As a further improvement of the technical solution, in S3.2, a logarithmic and power function combination model is used to calculate the effect of the water-cement W / C ratio on the slump, and the specific method is as follows:

[0055]

[0056] Among them, f W / C is the influence function of water-cement W / C ratio on slump; α is the empirical coefficient;

[0057] In S3.3, a piecewise function model is used to describe the different admixture dosages A d The effect of admixtures on slump is as follows:

[0058]

[0059] in, For different admixture dosage A d The influence function of the admixture on the slump; β1 and β2 are constants; γ1 and γ2 are nonlinear coefficients; is the critical admixture dosage;

[0060] In S3.4, a nonlinear differential equation is used to describe the dynamic change of the concrete reaction rate α(t), and the specific method is as follows:

[0061]

[0062] Among them, k is the rate constant; b is the reaction inhibition factor, which describes the gradual progress and inhibition effect of the reaction;

[0063] In S3.5, the calculated f W / C , The slump S2(t) is obtained by integrating and nonlinearly combining α(t). The specific method is as follows:

[0064]

[0065] Among them, C2 is the normalization coefficient, which is used to adjust the calculated slump value; n2 is the nonlinear influencing factor, which is used to adjust the nonlinear relationship in the model.

[0066] As a further improvement of the technical solution, in S4, the preset threshold is compared with the difference between the slump S1(t) and the slump S2(t). If the difference is less than or equal to the preset threshold, the average value is taken as the final slump value. If the difference is greater than the preset threshold, S5 is continued to be used to further detect the slump. The specific steps involved are as follows:

[0067] S4.1. Calculate the absolute difference ΔS(t) between the slump S1(t) and the slump S2(t):

[0068] ΔS(t)=|S1(t)-S2(t)|;

[0069] S4.2, set a difference threshold ∈, representing the maximum allowable difference between the two slump calculation results, and compare the size between ΔS(t) and ∈;

[0070] S4.3. If ΔS(t)≤∈, the final slump value is S final (t) and output to the monitoring system;

[0071]

[0072] S4.4. If ΔS(t)>∈, proceed to S5 to analyze the slump using rheology based on the shear stress-shear rate curve.

[0073] As a further improvement of the technical solution, in S5, by measuring the shear stress and shear rate curve of concrete, the slump S3(t) is predicted using a rheological model, and the specific steps are as follows:

[0074] S5.1. Obtain the shear stress and shear rate data of concrete by applying different shear rates using a rheometer. And measure the corresponding shear stress τ to obtain data, measure the shear stress at different shear rates, and record each shear rate and the corresponding shear stress τ, forming a set of shear stress-shear rate data;

[0075] S5.2, at shear rate As the abscissa, the shear stress τ is the ordinate, and the shear stress-shear rate curve is drawn;

[0076] S5.3. According to the shape of the curve, select a suitable rheological model, and use the rheological model to fit the shear stress-shear rate curve to extract the yield stress τ0;

[0077] S5.3. Calculate the slump S3(t) based on the yield stress τ0 according to the empirical formula:

[0078]

[0079] Among them, C3 is the normalization coefficient and n3 is the nonlinear impact factor.

[0080] Compared with the prior art, the present invention has the following beneficial effects:

[0081] 1. In this method for online monitoring of concrete slump based on data analysis, the slump of concrete is calculated by using a slump estimation algorithm based on dynamic physical characteristics, so as to realize real-time monitoring of the physical changes of concrete during the construction process, and ensure that the working performance of concrete is always within the design requirements. Through accurate analysis of key physical parameters such as temperature, humidity, mixing speed, torque, etc., the construction parameters can be dynamically adjusted to avoid fluctuations in concrete performance due to changes in the external environment or equipment status, thereby ensuring the consistency and reliability of construction quality.

[0082] 2. In this method of online monitoring of concrete slump based on data analysis, by using the chemical reaction dynamic slump prediction algorithm to calculate the concrete slump and comparing the difference between the two with the threshold, the changes in the chemical reaction process of the concrete can be effectively detected, thereby identifying potential quality problems or abnormal reactions. Through in-depth analysis of the water-cement ratio, admixture dosage, cement type and reaction rate chemical parameters, the accuracy of the concrete slump can be further monitored. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 The figure is a flow chart of the overall method of the present invention. DETAILED DESCRIPTION

[0084] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0085] Example:

[0086] See also Figure 1 As shown, this embodiment provides a method for online monitoring of concrete slump based on data analysis, comprising the following steps:

[0087] S1. Use sensors to collect data on the temperature, humidity, mixing speed, mixing torque and mixing time of concrete in the mixer in real time, and collect data on the water-cement ratio, admixture dosage and concrete reaction rate;

[0088] Use sensors to collect real-time data on the temperature, humidity, mixing speed, mixing torque and mixing time of concrete in the mixer. The specific method is as follows:

[0089] S1.1.1. Use the temperature sensor and humidity sensor installed inside the concrete mixer to collect real-time temperature data and humidity data of the concrete;

[0090] S1.1.2. Use the Hall effect sensor installed on the mixer shaft to record the mixing speed of the mixer in real time;

[0091] S1.1.3. Use a torque sensor installed on the mixer drive device to record the mixing torque of the mixer in real time;

[0092] S1.1.4. Automatically record time data through the mixer control system;

[0093] Collect data on water-cement ratio, admixture dosage and concrete reaction rate. The specific method is as follows:

[0094] S1.2.1. From the concrete batching system, record and collect the water-cement ratio of cement and water and the amount of admixtures in real time;

[0095] S1.2.2. Determine the reaction rate of concrete in the laboratory.

[0096] S2, calculating the concrete slump S1(t) according to the temperature, humidity, mixing speed, mixing torque and mixing time data of the mixer and using the dynamic physical property slump estimation algorithm;

[0097] The dynamic physical property slump estimation algorithm is based on the physical data of concrete during the mixing process and combines the exponential decay function, nonlinear function, time delay integral function and time decay model to calculate the concrete slump S1(t);

[0098] The relationship between physical quantities such as temperature, humidity, rotation speed, torque and slump is not linear, so nonlinear functions are used to describe these relationships; the influence of physical quantities on concrete slump is dynamically accumulated and calculated by integrating the time variable; considering that some influences gradually weaken or strengthen over time, time decay functions are used to describe these dynamic changes;

[0099] The concrete slump S1(t) is calculated using the dynamic physical property slump estimation algorithm. The specific steps are as follows:

[0100] S2.1, obtaining the temperature T(t), humidity H(t), mixing speed R(t), mixing torque M(t), and mixing time t of the concrete at time t;

[0101] S2.2. Use hyperbolic function to calculate the effect of temperature T(t) on slump f T (t);

[0102] S2.3. Calculate the effect of humidity H(t) on slump using exponential decay function H (t);

[0103] S2.4. Calculate the effect of mixing speed R(t) on slump using nonlinear function R (t);

[0104] S2.5. Combined with the time decay model, calculate the effect of mixing torque M(t) on slump f M (t);

[0105] S2.6. Use the time delay integral function to calculate the effect of mixing time t on slump f t (t);

[0106] S2.7, the previously calculated f T (t), f H (t), f R (t), f M (t) and f t (t) is integrated and nonlinearly combined to obtain the slump S1(t);

[0107] S2.8. Output the calculated slump S1(t) to the monitoring system.

[0108] The effect of temperature T(t) on slump is calculated using a hyperbolic function. The specific method is as follows:

[0109]

[0110] Among them, f T (t) is the effect function of temperature T(t) on slump; T(t) is the real-time temperature; T0 is the ideal temperature; a T is the temperature sensitivity coefficient, which is used to control the sensitivity of the effect of temperature change on slump; e is the base of the natural logarithm;

[0111] f T a (t) is a dimensionless value used to indicate the influence of temperature T(t) on slump at time t. The value is usually between 0 and 1. The closer it is to 1, the greater the positive influence of temperature on slump. T The larger the value, the more sensitive the temperature is to the slump, that is, the change in temperature will have a more significant impact on the slump; if a T >0, the higher the temperature f T (t) is larger; on the contrary, if a T <0, the lower the temperature, f T The larger (t) is, the T The method for determining a is experimental determination: slump tests are performed under different temperature conditions and the corresponding slump values ​​are recorded; using these data, a is determined by curve fitting method. T The best value of

[0112] The exponential decay function is used to calculate the effect of humidity H(t) on slump. The specific method is as follows:

[0113]

[0114] Among them, f H (t) is the influence function of humidity H(t) on slump; H(t) is the real-time concrete humidity; H0 is the ideal humidity; b H is the moisture sensitivity coefficient, which is used to control the sensitivity of the effect of humidity changes on slump;

[0115] f H b(t) is a dimensionless value used to indicate the influence of humidity H(t) on the slump of concrete at time t, ranging between 0 and 1. The smaller the value, the smaller the influence of humidity on the slump; the larger the value, the greater the influence; b H A positive value indicates that when the humidity H(t) is higher than H0, the effect of slump will be weakened; if the humidity is lower than H0, the effect will be enhanced. H The method for determining b is experimental determination: slump tests are performed under different humidity conditions and the corresponding slump values ​​are recorded; using these data, b is determined by curve fitting method. HThe best value of

[0116] The effect of mixing speed R(t) on slump is calculated using a nonlinear function. The method is as follows:

[0117]

[0118] Among them, f R (t) is the effect function of stirring speed R(t) on slump; R(t) is the stirring speed; k R It is the saturation coefficient of the mixing speed, which is used to control the nonlinear degree of the effect of the mixing speed on the slump.

[0119] f R (t) is a dimensionless value used to indicate the influence of the mixing speed R(t) on the slump of concrete at time t. R The value of (t) ranges between 0 and 1. The closer the value is to 1, the greater the positive effect of the mixing speed on the slump. R The larger the value, the more it means that after the mixing speed reaches a certain level, its influence on the slump tends to be saturated, that is, the influence gradually decreases. R The method for determining k is experimental determination: slump tests are performed under different mixing speed conditions and the corresponding slump values ​​are recorded; using these data, k is determined by curve fitting method. R The best value of

[0120] Combined with the time decay model, the influence of the mixing torque M(t) on the slump is calculated. The specific method is as follows:

[0121]

[0122] Among them, f M (t) is the influence function of the stirring torque M(t) on the slump; τ is the integral variable; M(τ) is the stirring torque at time τ; t is the current time; c M is the time decay coefficient, which is used to control the intensity of the time decay effect;

[0123] Among them, f M (t) is the influence function of the stirring torque M(t) on the slump at time t; M(t) is the stirring torque; c M is the time attenuation coefficient;

[0124] Use the time delay integral function to calculate the effect of mixing time t on slump. The specific method is as follows:

[0125]

[0126] Among them, f t(t) is the time delay function, which is used to express the effect of mixing time t on slump; τ is the integral variable; t0 is the ideal mixing time, which is a constant; d t It is the time sensitivity coefficient, which is used to control the influence of mixing time delay effect on slump.

[0127] d t It can also be interpreted as the rate of time decay, which determines how the delayed effect of mixing time affects the overall slump;

[0128] The previously calculated f T (t), f H (t), f R (t), f M (t) and f t (t) is integrated and nonlinearly combined to obtain the slump S1(t). The specific method is as follows:

[0129]

[0130] Among them, C1 is the normalization coefficient; n1 is the nonlinear impact factor.

[0131] C1 is a coefficient used to normalize the slump result to ensure that the calculated slump S1(t) conforms to the actual physical range and unit. It is used to adjust the proportion of the final calculation result so that the result is within a reasonable range. The determination method of C1 is as follows:

[0132] Experimental calibration: The actual slump under different mixing conditions is measured experimentally and compared with the calculated results, and C1 is adjusted to make the ratio of the calculated value to the experimental value consistent;

[0133] Model calibration: Use a known standard data set to determine the optimal value of C1 through the least squares method so that the output of the model matches the known data;

[0134] n1 is an exponential factor that controls the nonlinearity of the calculation results. It adjusts the impact of input variables, such as mixing speed, temperature, and humidity, on the final slump, so that the interaction between different influencing factors can be nonlinearly integrated. The determination method of n1 is as follows:

[0135] Experimental fitting: Determine the value of n1 by fitting experimental data;

[0136] Expert experience: When there is not enough experimental data, an empirical value can be selected based on engineering experience or domain knowledge to adjust the strength of nonlinear effects;

[0137] S3, calculate the concrete slump S2(t) by using the water-cement ratio, admixture dosage and concrete reaction rate data and according to the concrete reaction process and the chemical reaction dynamic slump prediction algorithm;

[0138] The chemical reaction dynamic slump prediction algorithm is based on the chemical reaction kinetics characteristics of concrete. It calculates the concrete slump S2(t) in real time through the water-cement ratio, admixture dosage and concrete reaction rate data, combined with the nonlinear reaction model and empirical formula. The specific steps are as follows:

[0139] S3.1. Obtain water-cement ratio W / C and admixture dosage A d and concrete reaction rate data α(t);

[0140] S3.2. Use the combined model of logarithmic and power functions to calculate the effect of the water-cement W / C ratio on slump f W / C ;

[0141] S3.3. Use piecewise function model to describe different admixture dosages A d Effect of admixtures on slump

[0142] S3.4. Using nonlinear differential equations to describe the dynamic changes of concrete reaction rate α(t)

[0143] S3.5. The calculated f W / C , Integrate and nonlinearly combine with α(t) to obtain the slump S2(t);

[0144] S3.6. Output the calculated slump S2(t) to the monitoring system.

[0145] The influence of water-cement W / C ratio on slump is calculated using a combined model of logarithmic and power functions. The specific method is as follows:

[0146]

[0147] Among them, f W / C is the influence function of water-cement W / C ratio on slump; α is the empirical coefficient;

[0148] The relationship between water-cement ratio and slump is usually not linear. At low water-cement ratios, a small change may have a significant effect on slump; at high water-cement ratios, the effect of the change may gradually decrease. The combination of logarithmic and power functions can better capture this nonlinear relationship and make the model more realistic. The power function can describe the sharp change of water-cement ratio on slump within a certain range, while the logarithmic function can smooth the characteristics of the gentle effect at higher water-cement ratios.

[0149] Use piecewise function model to describe different admixture dosage A d The effect of admixtures on slump is as follows:

[0150]

[0151] in, For different admixture dosage A d The influence function of the admixture on the slump; β1 and β2 are constants; γ1 and γ2 are nonlinear coefficients; is the critical admixture dosage;

[0152] The effect of admixtures on slump at low and high doses often presents different nonlinear characteristics; the piecewise function model allows different function forms to be used in different dosage ranges to accurately describe the different effects of admixture dosage on slump; the effect of admixtures may be more significant at low doses, but after reaching a certain critical dose, continuing to increase the dosage may lead to a weakened effect or even a negative impact; the piecewise function can model the behaviors of these different stages separately, thereby more accurately predicting slump;

[0153] The nonlinear differential equation is used to describe the dynamic change of concrete reaction rate α(t). The specific method is as follows:

[0154]

[0155] Among them, k is the rate constant; b is the reaction inhibition factor, which describes the gradual progress and inhibition effect of the reaction;

[0156] The calculated f W / C , The slump S2(t) is obtained by integrating and nonlinearly combining α(t). The specific method is as follows:

[0157]

[0158] Among them, C2 is the normalization coefficient, which is used to adjust the calculated slump value; n2 is the nonlinear influence factor, which is used to adjust the nonlinear relationship in the model;

[0159] The normalization coefficient C2 is obtained by measuring the slump value of concrete through actual experiments, and then adjusting the normalization coefficient C2 according to the model prediction value, so that the model output matches the experimental data as much as possible; the nonlinear influence factor n2 is determined by determining the strength of the nonlinear relationship through experimental data;

[0160] S4, comparing the preset threshold with the difference between the slump S1(t) and the slump S2(t), if the difference is less than or equal to the preset threshold, taking the average value as the final slump value, if the difference is greater than the preset threshold, continuing to use S5 to further detect the slump;

[0161] Compare the preset threshold with the difference between the slump S1(t) and the slump S2(t). If the difference is less than or equal to the preset threshold, take the average value as the final slump value. If the difference is greater than the preset threshold, continue to use S5 to further detect the slump. The specific steps involved are as follows:

[0162] S4.1. Calculate the absolute difference ΔS(t) between the slump S1(t) and the slump S2(t):

[0163] ΔS(t)=|S1(t)-S2(t)|;

[0164] S4.2, set a difference threshold ∈, representing the maximum allowable difference between the two slump calculation results, and compare the size between ΔS(t) and ∈;

[0165] S4.3. If ΔS(t)≤∈, the final slump value is S final (t) and output to the monitoring system;

[0166]

[0167] S4.4. If ΔS(t)>∈, proceed to S5 to analyze the slump using rheology based on the shear stress-shear rate curve.

[0168] S5. By measuring the shear stress and shear rate curve of concrete, the rheological model is used to monitor the slump S3(t). The specific steps are as follows:

[0169] S5.1. Obtain the shear stress and shear rate data of concrete by applying different shear rates using a rheometer. And measure the corresponding shear stress τ to obtain data, measure the shear stress at different shear rates, and record each shear rate and the corresponding shear stress τ, forming a set of shear stress-shear rate data;

[0170] S5.2, at shear rate As the abscissa, the shear stress τ is the ordinate, and the shear stress-shear rate curve is drawn;

[0171] S5.3. According to the shape of the curve, select a suitable rheological model, and use the rheological model to fit the shear stress-shear rate curve to extract the yield stress τ0;

[0172] S5.3. Calculate the slump S3(t) based on the yield stress τ0 according to the empirical formula:

[0173]

[0174] Among them, C3 is the normalization coefficient, n3 is the nonlinear impact factor;

[0175] The normalization coefficient C3 is optimized using experimental data for S3(t) to determine the values ​​of C3 and n3;

[0176] The rheological model can describe the complex nonlinear relationship between shear stress and shear rate, which is particularly important for accurately predicting the slump of concrete, especially when the material exhibits complex rheological behaviors such as yield stress, shear thinning or shear thickening. The shear stress and shear rate can be measured in real time, which enables the rheological model to monitor the slump change of concrete in real time and provide dynamic working performance feedback.

[0177] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A method for online monitoring of concrete slump based on data analysis, characterized in that: The following steps are involved: S1. Use sensors to collect data on the temperature, humidity, mixing speed, mixing torque and mixing time of concrete in the mixer in real time, and collect data on the water-cement ratio, admixture dosage and concrete reaction rate; S2, calculating the concrete slump S1(t) according to the temperature, humidity, mixing speed, mixing torque and mixing time data of the mixer and using the dynamic physical property slump estimation algorithm; S3, calculate the concrete slump S2(t) by using the water-cement ratio, admixture dosage and concrete reaction rate data and according to the concrete reaction process and the chemical reaction dynamic slump prediction algorithm; S4, comparing the preset threshold with the difference between the slump S1(t) and the slump S2(t), if the difference is less than or equal to the preset threshold, taking the average value as the final slump value, if the difference is greater than the preset threshold, continuing to use S5 to further detect the slump; S5. By measuring the shear stress and shear rate curve of concrete, the slump S3(t) is monitored using a rheological model.

2. The method for online monitoring of concrete slump based on data analysis according to claim 1, characterized in that: In S1, sensors are used to collect data on the temperature, humidity, stirring speed, stirring torque and stirring time of concrete in the mixer in real time. The specific method is as follows: S1.1.

1. Use the temperature sensor and humidity sensor installed inside the concrete mixer to collect real-time temperature data and humidity data of the concrete; S1.1.

2. Use the Hall effect sensor installed on the mixer shaft to record the mixing speed of the mixer in real time; S1.1.

3. Use a torque sensor installed on the mixer drive device to record the mixing torque of the mixer in real time; S1.1.

4. Automatically record time data through the mixer control system; In S1, data on water-cement ratio, admixture dosage and concrete reaction rate are collected, and the specific method is as follows: S1.2.

1. From the concrete batching system, record and collect the water-cement ratio of cement and water and the amount of admixtures in real time; S1.2.

2. Determine the reaction rate of concrete in the laboratory.

3. The method for online monitoring of concrete slump based on data analysis according to claim 1, characterized in that: In S2, the dynamic physical property slump estimation algorithm is based on the physical data of concrete during the mixing process, and is combined with an exponential decay function, a nonlinear function, a time delay integral function and a time decay model to calculate the concrete slump S1(t); The concrete slump S1(t) is calculated using the dynamic physical property slump estimation algorithm. The specific steps are as follows: S2.1, obtaining the temperature T(t), humidity H(t), mixing speed R(t), mixing torque M(t), and mixing time t of the concrete at time t; S2.

2. Use hyperbolic function to calculate the effect of temperature T(t) on slump f T (t); S2.

3. Calculate the effect of humidity H(t) on slump using exponential decay function H (t); S2.

4. Calculate the effect of mixing speed R(t) on slump using nonlinear function R (t); S2.

5. Combined with the time decay model, calculate the effect of mixing torque M(t) on slump f M (t); S2.

6. Use the time delay integral function to calculate the effect of mixing time t on slump f t (t); S2.7, the previously calculated f T (t), f H (t), f R (t), f M (t) and f t (t) is integrated and nonlinearly combined to obtain the slump S1(t); S2.

8. Output the calculated slump S1(t) to the monitoring system.

4. The method for online monitoring of concrete slump based on data analysis according to claim 3, characterized in that: In S2.2, a hyperbolic function is used to calculate the effect of temperature T(t) on slump, and the specific method is as follows: Among them, f T (t) is the effect function of temperature T(t) on slump; T(t) is the real-time temperature; T0 is the ideal temperature; a T is the temperature sensitivity coefficient, which is used to control the sensitivity of the effect of temperature change on slump; e is the base of the natural logarithm; In S2.3, an exponential decay function is used to calculate the effect of humidity H(t) on slump, and the specific method is as follows: Among them, f H (t) is the influence function of humidity H(t) on slump; H(t) is the real-time concrete humidity; H0 is the ideal humidity; b H is the moisture sensitivity coefficient, which is used to control the sensitivity of the effect of humidity changes on slump; In S2.4, a nonlinear function is used to calculate the effect of the stirring speed R(t) on the slump. The method is as follows: Among them, f R (t) is the effect function of stirring speed R(t) on slump; R(t) is the stirring speed; k R It is the saturation coefficient of the mixing speed, which is used to control the nonlinear degree of the effect of the mixing speed on the slump.

5. The method for online monitoring of concrete slump based on data analysis according to claim 3, characterized in that: In S2.5, the influence of the stirring torque M(t) on the slump is calculated in combination with the time decay model. The specific method is as follows: Among them, f M (t) is the influence function of the stirring torque M(t) on the slump at time t; M(t) is the stirring torque; c M is the time attenuation coefficient; In S2.6, the time delay integral function is used to calculate the effect of the mixing time t on the slump. The specific method is as follows: Among them, f t (t) is the time delay function, which is used to express the effect of mixing time t on slump; τ is the integral variable; t0 is the ideal mixing time, which is a constant; d t It is the time sensitivity coefficient, which is used to control the influence of the mixing time delay effect on the slump.

6. The method for online monitoring of concrete slump based on data analysis according to claim 3, characterized in that: In S2.7, the previously calculated f T (t), f H (t), f R (t), f M (t) and f t (t) is integrated and nonlinearly combined to obtain the slump S1(t). The specific method is as follows: Among them, C1 is the normalization coefficient; n1 is the nonlinear impact factor.

7. The method for online monitoring of concrete slump based on data analysis according to claim 1, characterized in that: In S3, the chemical reaction dynamic slump prediction algorithm is based on the chemical reaction dynamics characteristics in concrete, through the water-cement ratio, admixture dosage and concrete reaction rate data, combined with the nonlinear reaction model and empirical formula, to calculate the concrete slump S2(t) in real time. The specific steps are as follows: S3.

1. Obtain water-cement ratio W / C and admixture dosage A d and concrete reaction rate data α(t); S3.

2. Use the combined model of logarithmic and power functions to calculate the effect of the water-cement W / C ratio on slump f W / C ; S3.

3. Use piecewise function model to describe different admixture dosages A d Effect of admixtures on slump S3.

4. Using nonlinear differential equations to describe the dynamic changes of concrete reaction rate α(t) S3.

5. The calculated f W / C , Integrate and nonlinearly combine with α(t) to obtain the slump S2(t); S3.

6. Output the calculated slump S2(t) to the monitoring system.

8. The method for online monitoring of concrete slump based on data analysis according to claim 7, characterized in that: In S3.2, a combined model of logarithmic and power functions is used to calculate the effect of the water-cement W / C ratio on the slump. The specific method is as follows: Among them, f W / C is the influence function of water-cement W / C ratio on slump; α is the empirical coefficient; In S3.3, a piecewise function model is used to describe the different admixture dosages A d The effect of admixtures on slump is as follows: in, For different admixture dosage A d The influence function of the admixture on the slump; β1 and β2 are constants; γ1 and γ2 are nonlinear coefficients; is the critical admixture dosage; In S3.4, a nonlinear differential equation is used to describe the dynamic change of the concrete reaction rate α(t), and the specific method is as follows: Among them, k is the rate constant; b is the reaction inhibition factor, which describes the step-by-step progress and inhibition effect of the reaction; In S3.5, the calculated f W / C , The slump S2(t) is obtained by integrating and nonlinearly combining α(t). The specific method is as follows: Among them, C2 is the normalization coefficient, which is used to adjust the calculated slump value; n2 is the nonlinear influencing factor, which is used to adjust the nonlinear relationship in the model.

9. The method for online monitoring of concrete slump based on data analysis according to claim 1, characterized in that: In S4, the preset threshold is compared with the difference between the slump S1(t) and the slump S2(t). If the difference is less than or equal to the preset threshold, the average value is taken as the final slump value. If the difference is greater than the preset threshold, the slump is further detected using S5. The specific steps involved are as follows: S4.

1. Calculate the absolute difference ΔS(t) between the slump S1(t) and the slump S2(t): ΔS(t)=|S1(t)-S2(t)|; S4.2, set a difference threshold ∈, representing the maximum allowable difference between the two slump calculation results, and compare the size between ΔS(t) and ∈; S4.

3. If ΔS(t)≤∈, the final slump value is S final (t) and output to the monitoring system; S4.

4. If ΔS(T)>∈, proceed to S5 to analyze the slump using rheology based on the shear stress-shear rate curve.

10. The method for online monitoring of concrete slump based on data analysis according to claim 1, characterized in that: In S5, the slump S3(t) is predicted by measuring the shear stress and shear rate curve of the concrete using a rheological model, and the specific steps are as follows: S5.

1. Obtain the shear stress and shear rate data of concrete by applying different shear rates using a rheometer. And measure the corresponding shear stress τ to obtain data, measure the shear stress at different shear rates, and record each shear rate and the corresponding shear stress τ, forming a set of shear stress-shear rate data; S5.2, at shear rate As the abscissa, the shear stress τ is the ordinate, and the shear stress-shear rate curve is drawn; S5.

3. According to the shape of the curve, select a suitable rheological model, and use the rheological model to fit the shear stress-shear rate curve to extract the yield stress τ0; S5.

3. Calculate the slump S3(t) based on the yield stress τ0 according to the empirical formula: Among them, C3 is the normalization coefficient and n3 is the nonlinear impact factor.

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