UHPC mix proportion design method based on early hydration dynamic adjustment

Abstract

The invention relates to the technical field of concrete configuration, in particular to a UHPC mix proportion design method based on early hydration dynamic adjustment, comprising: acquiring preparation information and ambient temperature of current concrete, the preparation information comprising stirring time, stirring mode, planned addition amount of cementing material and basic data; determining a first hydration influence rate of the cementing material based on the stirring time, the stirring mode and the basic data; determining a second hydration influence rate of the cementing material based on the influence degree of the environment temperature on the cementing material; based on the first hydration influence rate and the second hydration influence rate, the adjustment amount and the actual addition amount of the cementing material are calculated; inputting the actual addition amount, the required concrete performance parameters and the required working performance parameters into a pre-constructed database to obtain addition amounts of other components; by quantifying the influence of dynamic factors, the use amount of the cementing material is determined, other components are matched, and the design reliability of the mix proportion is improved.

Description

Technical Field

[0001] This invention relates to the technical field of concrete mix design, and in particular to a UHPC mix design method based on early hydration dynamic adjustment. Background Technology

[0002] Ultra-high performance concrete (UHPC), as an advanced building material with ultra-high strength, excellent durability and good workability, is increasingly widely used in bridge engineering, high-rise buildings, special structures and other fields.

[0003] Mix design is a core element determining the performance of ultra-high performance concrete, and cementitious materials, as key components in concrete strength and structural formation, directly affect the final performance of the concrete through their dosage and degree of hydration. Traditional mix design methods typically calculate cementitious material dosage based on empirical formulas or fixed parameters. While these methods can meet basic performance requirements, they have the following shortcomings: The early hydration reaction of cementitious materials is dynamically affected by a variety of factors, such as stirring time, stirring method and ambient temperature. Traditional methods are difficult to quantify the combined effect of these factors on the degree of hydration, which leads to deviations in the calculation of cementitious material dosage. Existing designs often use fixed planned amounts of cementitious materials without dynamically adjusting the hydration impact rate based on real-time parameters and environmental conditions during construction. This can easily lead to discrepancies between the actual amount of cementitious materials used and the theoretical value, thereby affecting the stability of the workability and mechanical properties of concrete.

[0004] How to comprehensively consider the effects of mixing time, mixing method and ambient temperature on the early hydration of cementitious materials, establish a dynamic adjustment mechanism to accurately determine the amount of cementitious materials, and achieve synergistic matching with other components has become a key issue in improving the accuracy and reliability of ultra-high performance concrete mix design.

[0005] The information disclosed in this background section is intended only to enhance the understanding of the overall background of the present invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a UHPC mix design method based on early hydration dynamic adjustment. By quantifying the influence of dynamic factors, the amount of cementitious materials is determined and other components are matched, thereby improving the reliability of the mix design.

[0007] The first objective of this invention is to provide a UHPC mix design method based on early hydration dynamic adjustment, comprising: Obtain the current concrete mix design information and ambient temperature. The mix design information includes mixing time, mixing method, planned amount of cementitious materials to be added, and basic data. Based on stirring time, stirring method, and basic data, the first hydration influence rate of the cementitious material was determined. Based on the degree of influence of ambient temperature on cementitious materials, the second hydration influence rate of cementitious materials is determined. Based on the first hydration influence rate and the second hydration influence rate, calculate the adjustment amount and actual addition amount of cementitious material; The actual amount of additives, the required concrete performance parameters, and the required workability parameters are input into a pre-built database to obtain the amount of other components to be added.

[0008] As a preferred embodiment of the present invention, the basic data of the cementitious material includes the specific surface area, particle size distribution and type of the cementitious material.

[0009] Preferably, the method for calculating particle size distribution includes: The cementitious material particles are divided into preset particle size ranges, and the percentage of particle mass in each range to the total mass is measured to obtain multiple sets of range percentage data. Based on the above proportion data, the core characteristic parameters are calculated: median particle size, uniformity coefficient, and proportion of fine particles. Based on the influence weights of core characteristic parameters on early hydration reactions, the core characteristic parameters are standardized and then added together according to their weights to obtain particle size distribution, thereby realizing the transformation of multi-range particle size distribution data into a single quantitative index.

[0010] Preferably, the method for determining the weights of the core feature parameters includes: Multiple groups of cementitious materials with different gradations were selected, and the core characteristic parameters and 28-day hydration degree of each group were measured. The parameter values ​​were normalized to the [0,1] interval to eliminate dimensional differences. Using the hydration degree as a reference sequence, the grey relational degree between the three core characteristic parameters and the hydration degree was calculated. The grey relational degree was normalized to obtain the weight, i.e., w. i =r i / (r1+r2+r3), where w i Let r1, r2, and r3 be the weights of the i-th core feature parameter, where r1, r2, and r3 are the normalized values ​​of the three core feature parameters, and i is the i-th core feature parameter.

[0011] As a preferred embodiment of the present invention, the method for determining the first hydration influence rate includes: Characteristic coefficients are assigned based on the type of cementitious material, and the product of specific surface area, particle size distribution and characteristic coefficients is used as the material property factor. Set the influence coefficients for stirring time and stirring method respectively; The process parameter factor is obtained by weighted summing the influence coefficients of stirring time and stirring method. The first hydration influence rate is obtained by calculating the material property factors and process parameter factors using a preset model.

[0012] As a preferred embodiment of the present invention, when weighting the influence coefficient of stirring time and the influence coefficient of stirring method, the weighting method is determined as follows: With the basic data of the cementitious materials fixed, single-variable experiments were carried out. One set of experiments only changed the stirring time and measured the change in hydration degree under different stirring times; the other set of experiments only changed the stirring method and measured the change in hydration degree under different water temperatures. Calculate the hydration degree fluctuation coefficients of both, and determine the weights based on the ratio of the fluctuation coefficients.

[0013] Preferably, the preset model is a multivariate function model, whose inputs are material property factor F1 and process parameter factor F2, and whose output is the first hydration influence rate H1. The model expression is: H1=a×F1+b×F2+c×F1×F2+d, where a, b, c and d are model parameters. The method for determining model parameters is as follows: Multiple sets of combined working conditions covering different types of cementitious materials, specific surface areas, particle sizes, and different mixing times and methods were selected, and the degree of hydration of the cementitious materials was measured under each set of working conditions. Substituting F1, F2 and measured hydration degree of multiple working conditions into the above function, the specific values ​​of a, b, c and d are obtained by fitting the solution using the least squares method.

[0014] As a preferred embodiment of the present invention, the method for obtaining the influence coefficient is as follows: The degree of hydration of the corresponding cementitious materials was determined with stirring time and stirring method as variables. Using the stirring time or stirring method corresponding to the maximum hydration degree in each group of experiments as the benchmark value, the influence coefficient corresponding to the benchmark value is set to 1, and the influence coefficients corresponding to other parameter values ​​are the ratios of the corresponding hydration degree to the maximum hydration degree.

[0015] As a preferred embodiment of the present invention, the method for determining the second hydration influence rate includes: Under the condition that all other factors remain unchanged, the degree of hydration of cementitious materials corresponding to different ambient temperatures was measured; The ambient temperature at which the degree of hydration is highest is taken as the reference temperature, and the second hydration influence rate corresponding to the reference temperature is set to 1. The second hydration influence rate for each ambient temperature is obtained by calculating the ratio of the degree of hydration corresponding to other ambient temperatures to the highest degree of hydration.

[0016] As a preferred embodiment of the present invention, the method for calculating the adjustment amount and the actual added amount includes: A coupled correction formula is constructed, which includes the first hydration influence rate, the second hydration influence rate, and their interaction term. The interaction term is the product of the first and second hydration influence rates. The coupled correction formula is as follows: Adjustment amount = Planned addition amount × (k1 × First hydration impact rate + k2 × Second hydration impact rate + k3 × First hydration impact rate × Second hydration impact rate); Where k1 is the weighting coefficient of the first hydration influence rate, k2 is the weighting coefficient of the second hydration influence rate, k3 is the interaction coefficient between the two, and k1+k2+k3=1. Actual addition amount = planned addition amount + adjustment amount.

[0017] As a preferred embodiment of the present invention, the method for determining k1, k2, and k3 includes: Cementitious materials are classified according to their activity and sensitivity. For each type of cementitious material, the following steps are performed: Multiple combinations of working conditions with different first and second hydration influence rates were selected. Concrete was prepared according to the planned addition amount under each working condition, and its 28-day compressive strength and initial spread were measured. With the constraints of achieving the required 28-day compressive strength of concrete and meeting the preset range of initial spread, an optimization model is constructed with the objective of minimizing the actual amount of cementitious material added. Substitute the first hydration influence rate, the second hydration influence rate, and the actual addition amount obtained by the optimization model under each working condition into the coupling correction formula, and use the least squares method to fit and solve k1, k2, and k3 to obtain the values ​​of k1, k2, and k3 that satisfy the constraints.

[0018] As a preferred embodiment of the present invention, the database construction method includes: Collect test data of concrete with different mix proportions, including the amount of cementitious materials added, the amount of other components added, concrete performance parameters and workability parameters; A random forest algorithm was used to train concrete test data to establish a mapping relationship model between the actual amount of cementitious materials added and the amount of other components added.

[0019] As a preferred embodiment of the present invention, the cementing material is at least one of cement, slag powder, silica fume, fly ash, limestone powder, and metakaolin.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: The UHPC mix design method based on early hydration dynamic adjustment of the present invention quantifies the early hydration degree of cementitious materials affected by multiple dynamic factors by determining the first hydration influence rate affected by stirring time, stirring method and basic data and the second hydration influence rate affected by ambient temperature. Based on the first hydration influence rate and the second hydration influence rate, the adjustment amount and actual addition amount of cementitious materials are calculated, and a dynamic adjustment mechanism is established to effectively avoid the deviation between the actual amount and the theoretical value. In addition, the addition amount of other components is obtained by combining with a pre-constructed database, realizing the synergistic matching of cementitious materials with other components, thereby improving the reliability of ultra-high performance concrete mix design. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the process for designing UHPC mix proportions based on early dynamic adjustment of hydration, as described in this invention. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Example: Reference Figure 1 This embodiment provides a UHPC mix design method based on early hydration dynamic adjustment, including: S1 obtains the current concrete mix design information and ambient temperature. The mix design information includes mixing time, mixing method, planned amount of cementitious materials to be added, and basic data. The ambient temperature refers to the ambient temperature during the concrete mixing stage. S2 determines the first hydration influence rate of cementitious materials based on stirring time, stirring method and basic data; S3 determines the second hydration influence rate of cementitious materials based on the degree of influence of ambient temperature on cementitious materials; S4 calculates the adjustment amount and actual addition amount of cementitious material based on the first hydration influence rate and the second hydration influence rate; S5 inputs the actual amount of additives, the required concrete performance parameters, and the required workability parameters into a pre-built database to obtain the amount of other components to be added.

[0026] The UHPC mix design method based on early hydration dynamic adjustment of the present invention quantifies the influence of multiple dynamic factors on the hydration degree of cementitious materials by determining the first hydration influence rate affected by mixing time, mixing method and basic data and the second hydration influence rate affected by ambient temperature. Based on the first and second hydration influence rates, the adjustment amount and actual addition amount of cementitious materials are calculated to establish a dynamic adjustment mechanism, which effectively avoids the deviation between the actual amount and the theoretical value. In addition, the addition amount of other components is obtained by combining with a pre-constructed database, realizing the synergistic matching of cementitious materials with other components, thereby improving the reliability of ultra-high performance concrete mix design.

[0027] In some embodiments of the present invention, the basic data of the cementitious material includes the specific surface area, particle size distribution and type of the cementitious material.

[0028] More specifically, the specific surface area of ​​cementitious materials is directly related to the contact area of ​​hydration reaction, particle size distribution affects the packing density of hydration products, and the type determines the inherent differences in hydration activity. The three factors work together as inputs for calculating the first hydration influence rate, which can comprehensively quantify the basic influence of the material's essential properties on the hydration reaction from different dimensions. This avoids the hydration influence assessment bias caused by the general description of material properties in traditional methods, and shifts the calculation of the first hydration influence rate from relying on experience to precise quantification based on specific parameters. This lays a reliable material property benchmark for subsequent calculation of the comprehensive hydration influence in combination with process parameters. Since the same cementitious material can have multiple gradation ranges, the methods for calculating particle size distribution include: The cementitious material particles are divided into preset particle size ranges, and the percentage of particle mass in each range to the total mass is measured by equipment such as a laser particle size analyzer to obtain multiple sets of range percentage data. Based on the above percentage data, calculate three core feature parameters: Median particle size (D50): The particle size through which 50% of the particle mass passes, reflecting the overall fineness of the particles; Uniformity coefficient: expressed as the ratio of D60 (the particle size through which 60% of the particles pass) to D10 (the particle size through which 10% of the particles pass), reflecting the width of the particle size distribution; Fine particle percentage: The percentage of particles below a certain fine particle size threshold, highlighting the content of ultrafine particles that have a significant impact on early hydration; Determine the weights of the core characteristic parameters: Select multiple groups of cementitious materials with different gradations, and measure the core characteristic parameters and 28-day hydration degree of each group; normalize the parameter values ​​to the [0,1] interval to eliminate dimensional differences; using the hydration degree as a reference sequence, calculate the grey relational degree between the three core characteristic parameters and the hydration degree; normalize the grey relational degree to obtain the weights, i.e.: w i =r i / (r1+r2+r3), where w i Let r1, r2, and r3 be the weights of the i-th core feature parameter, where r1, r2, and r3 are the normalized values ​​of the three core feature parameters, and i is the i-th core feature parameter. Based on the weights of the aforementioned core characteristic parameters to the early hydration reaction, the three core characteristic parameters are standardized and then added together according to their weights to obtain the particle size distribution, thus realizing the transformation of multi-range particle size distribution data into a single quantitative index.

[0029] In some embodiments of the present invention, the method for determining the first hydration influence rate includes: Characteristic coefficients are assigned based on the type of cementitious material, and the product of specific surface area, particle size distribution and characteristic coefficients is used as the material property factor. More specifically, characteristic coefficients are assigned based on the differences in hydration activity of different cementitious materials; the product of these three factors is used as a material characteristic factor, which enables the synergistic quantification of the three-dimensional properties of material type, reaction surface and particle combination. This not only preserves the independent influence of various parameters on the early hydration reaction, but also forms a unified characteristic index through product operation, avoiding the one-sidedness of single parameter description, and transforming the influence of material characteristics on hydration from a dispersed qualitative description to a concentrated quantitative characterization. Set the influence coefficients for stirring time and stirring method respectively; The process parameter factor is obtained by weighted summing of the influence coefficients of stirring time and stirring method; more specifically, the process parameter factor is obtained by setting weights according to the degree of influence of stirring time and stirring method on the early hydration reaction, and adding the influence coefficients of the two according to the weights. More specifically, the weights are determined by controlled variable experiments: with the basic data of the cementitious materials fixed, single-variable experiments are conducted. One set of experiments changes only the stirring time and measures the change in hydration degree under different stirring times; the other set of experiments changes only the stirring method and measures the change in hydration degree under different stirring methods. The hydration degree fluctuation coefficient of the two sets of experiments is calculated, which is the difference between the maximum and minimum hydration degree. The weights are determined by the ratio of the fluctuation coefficients. The first hydration influence rate is obtained by calculating the material property factors and process parameter factors through a preset model; More specifically, the preset model is a multivariate function model obtained by fitting experimental data. Its inputs are material property factors (F1) and process parameter factors (F2), and its output is the first hydration influence rate (H1). The model expression is: H1=a×F1+b×F2+c×F1×F2+d, where a, b, c and d are model parameters. The above model parameters were determined in the following way: at least 30 sets of combined working conditions covering different types of cementitious materials, specific surface area, particle size distribution and different stirring times and stirring methods were selected, and the degree of hydration of the cementitious materials was measured under each set of working conditions; Substitute multiple sets of F1, F2 and the measured degree of hydration into the above function, and use the least squares method to fit and solve for the specific values ​​of a, b, c and d, so that the error between the model calculation value and the measured degree of hydration is within 5%.

[0030] The above-mentioned method for determining the first hydration influence rate quantifies the three-dimensional properties of materials by multiplying the characteristic coefficients, specific surface area, and particle size distribution. It combines the weighted summation of the influence coefficients of stirring time and stirring method with multivariate function model coupling calculation. This achieves both accurate characterization of the inherent properties of materials and process parameters, and reflects the synergistic effect of the two through model integration. It avoids the one-sidedness of single-factor analysis and the ambiguity of empirical estimation, upgrading the calculation of the first hydration influence rate from a qualitative description to a quantitative characterization based on experimental data.

[0031] In some embodiments of the present invention, the method for obtaining the influence coefficient is as follows: The degree of hydration of the corresponding cementitious materials was determined by using stirring time and stirring method as variables respectively; more specifically, stirring time and stirring method were treated as independent variables, and two sets of parallel experiments were carried out, with only one variable changed in each set of experiments, while the other process parameters remained the same. Using the stirring time or stirring method corresponding to the maximum degree of hydration in each group of experiments as the benchmark value, the influence coefficient corresponding to the benchmark value is set to 1, and the influence coefficients corresponding to other parameter values ​​are the ratios of the corresponding degree of hydration to the maximum degree of hydration. Hydration degree is determined by using stirring time as a variable: Under fixed conditions, multiple sets of different stirring time gradients are set. After each set of stirring is completed within the set time, the hydration degree of the cementitious material is determined by standard methods such as thermogravimetric analysis, and the hydration degree data corresponding to each set of time is recorded. Hydration degree was determined by using stirring method as a variable: Under fixed conditions, multiple different stirring methods were set up. After mixing each group according to the set stirring method, the hydration degree was measured and the hydration degree data corresponding to each stirring method was recorded. From the hydration degree data of the stirring time variable group, find the stirring time corresponding to the maximum hydration degree as the benchmark value, and set the influence coefficient of the benchmark value to 1. The influence coefficients of other stirring times are calculated as "hydration degree corresponding to the time ÷ maximum hydration degree"; similarly, the influence coefficients of the stirring method are calculated.

[0032] By using the above steps and designing experiments with stirring time and stirring method as variables, we can accurately capture the influence of a single process parameter on hydration. Furthermore, by normalizing the baseline value, we can form a unified quantitative standard for the influence coefficient, thereby achieving the measurability and comparability of the influence of process parameters and effectively improving the reliability of the calculation of the first hydration influence rate.

[0033] In some embodiments of the present invention, the method for determining the second hydration influence rate includes: Under the condition that all other factors remain unchanged, the degree of hydration of cementitious materials corresponding to different ambient temperatures is measured; more specifically, only the single variable of ambient temperature is changed, so that the influence of other factors on the degree of hydration is eliminated, and only the independent effect of ambient temperature is focused. Using the ambient temperature at which the hydration degree is highest as the reference temperature, the second hydration influence rate corresponding to the reference temperature is set to 1. More specifically, an ambient temperature range covering the actual construction conditions is set. Under each temperature condition, concrete mixing is completed according to fixed mix information. The hydration degree of cementitious materials at the corresponding ambient temperature is measured using a hydration degree measurement method, and the data is recorded. From the multiple sets of data obtained, the ambient temperature corresponding to the highest hydration degree is selected as the reference temperature, and the second hydration influence rate of this reference temperature is set to 1. The second hydration influence rate for each ambient temperature is obtained by calculating the ratio of the degree of hydration at other ambient temperatures to the highest degree of hydration. More specifically, for other ambient temperatures, the second hydration influence rate for each temperature is obtained by calculating the formula "degree of hydration at this temperature ÷ highest degree of hydration".

[0034] In the above steps, by focusing on the influence of ambient temperature using the controlled variable method and combining it with the normalization process of the reference temperature, the second hydration influence rate can not only accurately reflect the independent effect of ambient temperature on hydration, but also form a unified dimension with the first hydration influence rate. It is in synergy with the determination logic of the first hydration influence rate, so as to achieve a comprehensive quantification of the influence of process parameters and environmental factors on hydration and overcome the defects of traditional methods in the fuzzy treatment of environmental factors.

[0035] In some embodiments of the present invention, the method for calculating the adjustment amount and the actual added amount includes: A coupled correction formula is constructed, which includes the first hydration influence rate, the second hydration influence rate, and their interaction term. The formula is as follows: Adjustment amount = Planned addition amount × (k1 × First hydration impact rate + k2 × Second hydration impact rate + k3 × First hydration impact rate × Second hydration impact rate); Where k1 is the weighting coefficient of the first hydration influence rate, k2 is the weighting coefficient of the second hydration influence rate, k3 is the interaction coefficient between the two, and k1+k2+k3=1. Actual addition amount = planned addition amount + adjustment amount.

[0036] More specifically, the interaction term refers to the product of the first hydration influence rate and the second hydration influence rate, which is used to capture the synergistic effect of the two influencing factors in the actual hydration reaction. For example, changes in ambient temperature may amplify or weaken the effect of stirring time and material properties on hydration. This interaction is not a simple superposition. By incorporating the interaction term into the correction formula, the adjustment of the amount of cementitious material is made to better fit the actual reaction law, avoiding calculation deviations caused by ignoring the correlation between factors.

[0037] In some embodiments of the present invention, the method for determining k1, k2, and k3 includes: Cementitious materials are classified according to their activity and sensitivity. The following steps are performed for each type of cementitious material. Multiple combinations of working conditions with different first and second hydration influence rates were selected. For each working condition, concrete was prepared according to the planned addition amount, and its 28-day compressive strength and initial spread were measured. More specifically, multiple combinations of working conditions covering low, medium, and high influence levels were selected from the first and second hydration influence rates. For each working condition, concrete was prepared strictly according to the planned addition amount, and the 28-day compressive strength and initial spread were measured using standard test methods. The influence rate values ​​and corresponding performance data for each working condition were recorded. The 28-day compressive strength was obtained by molding standard test blocks and curing them according to standard conditions using a compression testing machine. The initial spread was obtained by measuring the free spread diameter of the fresh concrete using a spread tester. With the constraints of achieving the required 28-day compressive strength of concrete and meeting the preset range of initial spread, an optimization model is constructed with the objective of minimizing the actual amount of cementitious material added. More specifically, the optimization model construction method is as follows: Using the actual amount of cementitious material added as the optimization variable, and taking the concrete 28-day compressive strength reaching the design strength grade requirement and the initial spread within the project's preset qualified range as constraints, the objective function is to minimize the actual amount of cementitious material added. In the specific construction, the quantitative standards of the constraints are first clarified, namely, the 28-day compressive strength shall not be lower than the design value and the initial expansion shall fall within the preset qualified range. Then, the actual amount of cementitious material added is used as the variable parameter to be optimized. The objective function is defined by mathematical expression to minimize the value of the variable parameter. At the same time, the constraints are transformed into corresponding inequality expressions, which together with the objective function constitute a complete optimization model. For each set of working conditions, the amount of cementitious material is gradually adjusted, and the amount is increased or decreased based on the planned amount. The concrete is then remixed and its performance is verified until the actual amount of cementitious material that meets the constraints and has the lowest amount is found. This amount achieves the optimal utilization of materials while meeting the performance standards. Substitute the first hydration influence rate, the second hydration influence rate, and the actual addition amount obtained by the optimization model under each working condition into the coupling correction formula, and use the least squares method to fit and solve k1, k2, and k3 to obtain the values ​​of k1, k2, and k3 that satisfy the constraints. More specifically, the first and second hydration impact rates for each working condition, along with the actual and planned addition amounts obtained from the optimization model, are substituted into the above-mentioned coupled correction formula. The adjustment amount is the actual addition amount minus the planned addition amount, forming a set of equations containing k1, k2, and k3. The least squares method is used to fit and calculate multiple sets of equations to find the values ​​of k1, k2, and k3 that minimize the deviation between the calculated value and the actual adjustment amount, and the sum of the three is 1. Once k1, k2, and k3 are determined, a cementitious material combination type is added to the database, and the corresponding k value is automatically matched according to the actual cementitious material combination selected.

[0038] In some embodiments of the present invention, the method for constructing a database includes: Collect test data of concrete with different mix proportions, including the amount of cementitious materials added, the amount of other components added, concrete performance parameters and workability parameters; More specifically, we collected multiple sets of concrete mix design test data covering different engineering scenarios. The amount of cementitious materials added included the amounts of cement, slag powder, and silica fume; the amount of other components added included the amounts of coarse aggregate, fine aggregate, water-reducing agent, and water; the concrete performance parameters included mechanical properties such as compressive strength and modulus of elasticity; and the workability parameters included performance indicators such as spreadability and flowability. The data needed to cover different material combinations, dosage gradients, and performance requirements to improve the diversity and representativeness of the samples. Random forest algorithm was used to train concrete test data to establish a mapping relationship model between the actual amount of cementitious material added and the amount of other components added; More specifically, the collected experimental data are preprocessed to remove outliers and standardize them; the actual amount of cementitious materials added, concrete performance parameters, and workability parameters are used as input features, and the amount of other components added is used as output labels to divide the data into training and testing sets; the random forest algorithm is used for model training, and the nonlinear relationship between input and output is fitted through ensemble learning of multiple decision trees; during the training process, the model is optimized by adjusting parameters such as the number and depth of trees until the model's prediction accuracy on the test set meets the preset requirements, and finally a stable mapping relationship model is formed, that is, a database that can be directly accessed is constructed.

[0039] The advantages of the above construction method are as follows: by collecting experimental data covering multiple scenarios and combinations, it ensures that the sample covers a sufficiently rich variety of variable relationships; after preprocessing to improve data quality, the ensemble learning capability of the random forest algorithm is used to accurately capture the nonlinear correlation between input and output; and the model stability is further enhanced through parameter optimization. The synergistic logic of comprehensive data support and intelligent algorithm fitting inherits the practical reliability of experimental data and leverages the algorithm's ability to analyze complex relationships, enabling the database to quickly output the dosage of other components that match the actual addition amount and performance requirements of the cementitious material. The overall accuracy and efficiency of the ratio achieved are far superior to the effects of traditional experience summaries or single models.

[0040] In some embodiments of the present invention, the cementing material is at least one of cement, slag powder, silica fume, fly ash, limestone powder and metakaolin. The above-mentioned cementing material can be used in combination with other cementing materials according to the specific engineering requirements for the workability, mechanical properties and durability of concrete, so as to meet the design goals of ultra-high performance concrete.

[0041] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A UHPC mix design method based on early hydration dynamic adjustment, characterized in that, include: Obtain the current concrete mix design information and ambient temperature. The mix design information includes mixing time, mixing method, planned amount of cementitious materials to be added, and basic data. Based on the stirring time, the stirring method, and the basic data, the first hydration influence rate of the cementitious material is determined. Based on the degree of influence of the ambient temperature on the cementitious material, the second hydration influence rate of the cementitious material is determined. Based on the first hydration influence rate and the second hydration influence rate, calculate the adjustment amount and actual addition amount of the cementitious material; The actual amount of additives, the required concrete performance parameters, and the required workability parameters are input into a pre-built database to obtain the amount of other components to be added.

2. The UHPC mix design method based on early hydration dynamic adjustment as described in claim 1, characterized in that, The basic data of the cementitious material includes its specific surface area, particle size distribution, and type.

3. The UHPC mix design method based on early hydration dynamic adjustment as described in claim 2, characterized in that, The method for determining the first hydration influence rate includes: Characteristic coefficients are assigned according to the type of cementitious material, and the product of specific surface area, particle size distribution and the characteristic coefficients is used as the material property factor. Set the influence coefficients for stirring time and stirring method respectively; The process parameter factor is obtained by weighted summing the influence coefficients of stirring time and stirring method. The first hydration influence rate is obtained by calculating the material property factors and process parameter factors using a preset model.

4. The UHPC mix design method based on early hydration dynamic adjustment as described in claim 3, characterized in that, The method for obtaining the influence coefficient is as follows: The degree of hydration of the corresponding cementitious materials was determined with stirring time and stirring method as variables. Using the stirring time or stirring method corresponding to the maximum hydration degree in each group of experiments as the benchmark value, the influence coefficient corresponding to the benchmark value is set to 1, and the influence coefficients corresponding to other parameter values ​​are the ratios of the corresponding hydration degree to the maximum hydration degree.

5. The UHPC mix design method based on early hydration dynamic adjustment as described in claim 3, characterized in that, When weighting and summing the influence coefficients of stirring time and stirring method, the weights are determined as follows: With the basic data of the cementitious materials fixed, single-variable experiments were carried out. One set of experiments only changed the stirring time and measured the change in hydration degree under different stirring times; the other set of experiments only changed the stirring method and measured the change in hydration degree under different water temperatures. Calculate the hydration degree fluctuation coefficients of both, and determine the weights based on the ratio of the fluctuation coefficients.

6. The UHPC mix design method based on early hydration dynamic adjustment as described in claim 1, characterized in that, The method for determining the second hydration influence rate includes: Under the condition that all other factors remain unchanged, the degree of hydration of cementitious materials corresponding to different ambient temperatures was measured; Using the ambient temperature at which the degree of hydration is highest as the reference temperature, the second hydration influence rate corresponding to the reference temperature is set to 1; The second hydration influence rate for each ambient temperature is obtained by calculating the ratio of the degree of hydration corresponding to other ambient temperatures to the highest degree of hydration.

7. The UHPC mix design method based on early hydration dynamic adjustment as described in claim 1, characterized in that, The calculation methods for the adjustment amount and the actual added amount include: Cementitious materials are classified according to their activity and sensitivity. For each type of cementitious material, the following steps are performed: A coupled correction formula is constructed, which includes the first hydration influence rate, the second hydration influence rate, and their interaction term. The interaction term is the product of the first and second hydration influence rates. The coupled correction formula is as follows: Adjustment amount = Planned addition amount × (k1 × First hydration impact rate + k2 × Second hydration impact rate + k3 × First hydration impact rate × Second hydration impact rate); Where k1 is the weighting coefficient of the first hydration influence rate, k2 is the weighting coefficient of the second hydration influence rate, k3 is the interaction coefficient between the two, and k1+k2+k3=1. Actual addition amount = planned addition amount + adjustment amount.

8. The UHPC mix design method based on early hydration dynamic adjustment as described in claim 7, characterized in that, The method for determining k1, k2, and k3 includes: Multiple combinations of working conditions with different first and second hydration influence rates were selected. Concrete was prepared according to the planned addition amount under each working condition, and its 28-day compressive strength and initial spread were measured. With the constraints of achieving the required 28-day compressive strength of concrete and meeting the preset range of initial spread, an optimization model is constructed with the objective of minimizing the actual amount of cementitious material added. Substitute the first hydration influence rate, the second hydration influence rate, and the actual addition amount obtained by the optimization model under each working condition into the coupling correction formula, and use the least squares method to fit and solve k1, k2, and k3 to obtain the values ​​of k1, k2, and k3 that satisfy the constraints.

9. The UHPC mix design method based on early hydration dynamic adjustment as described in claim 1, characterized in that, The method for constructing the database includes: Collect test data of concrete with different mix proportions, including the amount of cementitious materials added, the amount of other components added, concrete performance parameters and workability parameters; The random forest algorithm was used to train the concrete test data to establish a mapping relationship model between the actual amount of cementitious material added and the amount of other components added.

10. The UHPC mix design method based on early hydration dynamic adjustment as described in claim 1, characterized in that, The cementing material is at least one of cement, slag powder, silica fume, fly ash, limestone powder, and metakaolin.

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