Plastic concrete performance index evaluation method and proportion optimization method

Abstract

The invention discloses a plastic concrete performance index evaluation method and a proportion optimization method, the plastic concrete performance index and the following factors influencing the index are determined: concrete component characteristics, component proportion, process parameters and environmental conditions, and the environmental conditions comprise environmental temperature, maintenance mode and environmental erosion condition; constructing a plastic concrete performance evaluation polynomial model reflecting the relationship between the performance indexes and the multiple influence factors; determining the value range of the influence factors, planning a test scheme, preparing a concrete sample block by adopting the admixture with the gradient grading structure, and measuring the plastic concrete performance index of the concrete sample block; fitting coefficients and constants in the plastic concrete performance evaluation polynomial model based on the measured values; based on the fitting degree, selecting an optimal group of coefficients and constants; and evaluating the performance indexes of the plastic concrete by adopting the fitted model. According to the method, the strength and the elastic modulus of the plastic concrete can be predicted, and the performance target can be collaboratively optimized.

Description

Technical Field

[0001] This invention relates to the field of intelligent design and performance prediction technology for civil engineering materials, and in particular to a method for evaluating the performance indicators of plastic concrete and a method for optimizing its mix proportions. Background Technology

[0002] Currently, plastic concrete is a core material in water conservancy projects, including anti-seepage walls, diaphragm walls, and soft soil foundation reinforcement. The synergistic optimization of its strength, elastic modulus, and impermeability directly determines the long-term stability and safety of the engineering structure. In traditional design methods, material proportioning mainly relies on empirical formulas and an iterative "trial mix-correction" model, adjusting the water-cement ratio, aggregate gradation, and admixture types to meet predetermined performance targets. However, with increasingly complex geological conditions (such as high-permeability strata and seismically active zones) placing higher demands on material performance, existing technologies have revealed many significant limitations and cannot meet the needs of modern engineering.

[0003] First, the shortcomings of experience-driven design are one of the core problems of traditional methods. Traditional mix design, based on linear empirical formulas established from limited experimental data, struggles to accurately characterize the complex nonlinear coupling effects between multi-component materials such as bentonite, fly ash, and admixtures. Existing standards lack quantitative models governing the relationship between strength, elastic modulus, and permeability coefficient, often making it difficult to balance multiple performance requirements during the design process, thus affecting the overall design effectiveness.

[0004] Secondly, inaccurate predictions of material properties also limit the accuracy of design. Traditional strength prediction models typically estimate material properties based on a single factor (such as the water-cement ratio). However, with the addition of new material systems such as graded aggregates and modified bentonite, these single-factor-based models can no longer adapt to complex material behaviors, resulting in significant deviations in performance predictions and affecting the actual engineering results.

[0005] In addition, insufficient geological adaptability is also one of the bottlenecks of existing technologies. Traditional design methods have failed to establish an effective mapping relationship between material properties and geological parameters. Therefore, the same mix design may exhibit significant strength dispersion under different geological conditions, resulting in large differences in adaptability between different projects and reducing the universality and stability of the design.

[0006] Furthermore, the lack of intelligent technologies is a significant weakness in traditional design processes. In existing design workflows, isolated material databases, a disconnect between numerical simulation and experimental verification, and a high reliance on manual intervention for parameter optimization result in cumbersome and inefficient design processes. These issues make it difficult for designers to complete design tasks quickly and accurately.

[0007] With the rapid development of advanced technologies such as machine learning, multi-objective optimization algorithms, and materials informatics, building a data-driven intelligent design system has become a key path to overcome the aforementioned bottlenecks. By integrating large-scale experimental data, high-precision constitutive models, and real-time monitoring feedback, accurate prediction of material properties and collaborative optimization of multiple objectives can be achieved, thereby significantly improving design efficiency and accuracy. Summary of the Invention

[0008] This invention provides a method for evaluating the performance indicators of plastic concrete and a method for optimizing the mix proportion in order to solve the technical problems existing in the prior art.

[0009] The technical solution adopted by this invention to solve the technical problems existing in the prior art is as follows:

[0010] A method for evaluating the performance indicators of plastic concrete includes: determining the performance indicators of plastic concrete and the following factors affecting these indicators: concrete component characteristics, component ratio, curing age, process parameters, and environmental conditions, including ambient temperature, curing method, and environmental erosion conditions; constructing a polynomial model for evaluating plastic concrete performance reflecting the relationship between the performance indicators and multiple influencing factors; determining the value range of the influencing factors, planning the experimental scheme, using admixtures with a gradient gradation structure to prepare concrete samples, and measuring the plastic concrete performance indicators of the concrete samples; fitting the coefficients and constants in the polynomial model for evaluating plastic concrete performance based on the measured values; and calculating the goodness of fit R0. 2 The optimal set of coefficients and constants is selected.

[0011] A polynomial model for evaluating the performance of plastic concrete, fitted with completion coefficients and constants, is used to evaluate the performance indicators of plastic concrete.

[0012] Furthermore, the performance indicators of plastic concrete are determined, including its compressive strength and modulus of elasticity; the component characteristics and component ratio range of plastic concrete are set; multiple combinations of age, process parameters, and environmental conditions are conducted; and more than 200 sets of admixtures with different proportions are prepared under different combinations of age, process parameters, and environmental conditions; multi-objective collaborative experiments are conducted in conjunction with process parameters and environmental conditions; and the coefficients and constants in the polynomial model for evaluating the performance of plastic concrete are fitted using linear regression equations and / or genetic algorithms.

[0013] Furthermore, the following performance indicators for plastic concrete were set: triaxial compressive strength, uniaxial compressive strength, elastic modulus, and confining pressure. The uniaxial compressive strength, elastic modulus, and confining pressure of all samples were measured. The triaxial compressive strength and elastic modulus of a portion of the samples were measured. The mathematical model was a specific form model obtained by fitting actual engineering test data.

[0014] The following mathematical model for the triaxial compressive strength of plastic concrete is established:

[0015] σ tr =k5σ 3 us +k4σ 2 us +k3σ us p+k2σ us +k1p+k0;

[0016] Set up the following triaxial elastic modulus model for plastic concrete:

[0017] E tr =a4E 2 us -a3E us p+a2E us +a1p-a0;

[0018] In the formula:

[0019] σ tr It is the triaxial compressive strength;

[0020] σ us Uniaxial compressive strength;

[0021] E tr It is the triaxial elastic modulus;

[0022] E us It is the uniaxial elastic modulus;

[0023] p is the confining pressure;

[0024] k1~k5 are the polynomial coefficients of the mathematical model of triaxial compressive strength of plastic concrete; k0 is the polynomial constant of the mathematical model of triaxial compressive strength of plastic concrete.

[0025] a1~a4 are the polynomial coefficients of the triaxial elastic modulus model of plastic concrete; a0 is the polynomial constant of the triaxial elastic modulus model of plastic concrete.

[0026] Based on measured values, k0–k5 in the mathematical model of triaxial compressive strength of plastic concrete and a0–a4 in the model of triaxial elastic modulus of plastic concrete are fitted using linear regression equations and / or genetic algorithms; based on the goodness of fit R... 2 The optimal set of coefficients and constants is selected.

[0027] Using a mathematical model of triaxial compressive strength and a model of triaxial elastic modulus of plastic concrete fitted with completion coefficients and constants, the triaxial compressive strength of the remaining samples whose triaxial compressive strength was not measured was evaluated; the triaxial elastic modulus of the remaining samples whose triaxial elastic modulus was not measured was also evaluated.

[0028] Furthermore, set the fit R... 2 ≥0.95; the following mathematical model for the triaxial compressive strength of plastic concrete is obtained:

[0029] σ tr =-0.0489σ 3 us +0.7687σ 2 us +0.0005741σ us p-2.718σ us +0.0002243p+5.482;

[0030] Set the fit R 2 With a modulus ≥0.96, the following triaxial elastic modulus model for plastic concrete is obtained:

[0031] E tr =-9.979e -5 E 2 us -0.0002117E us p+1.172E us +0.07692p-37.45.

[0032] Furthermore, the concrete components are determined to include cement, fine aggregate, coarse aggregate, bentonite, admixtures, and water; the admixtures include plasticizers, water-reducing agents, waterproofing agents, and bentonite modifiers.

[0033] The ratio of fine aggregate to coarse aggregate ranges from 1:1.5 to 1:2.5; the water-cement ratio ranges from 1.3 to 3.1; the ratio of cement to fine aggregate ranges from 1:2 to 1:4; the ratio of cement to bentonite ranges from 1:0.1 to 1:0.6; and the ratio of cement to admixtures ranges from 1:0.01 to 1:0.05.

[0034] Furthermore, the particle size range of fine aggregate is 0.15 mm to 5 mm, and the particle size range of coarse aggregate is 5 mm to 20 mm.

[0035] The present invention also provides a method for optimizing the mix proportion of plastic concrete using the above-mentioned plastic concrete performance index evaluation method. The method determines the performance requirements of concrete based on the service environment of the concrete structure; sets optimization objectives and sets constraints such as concrete component characteristics and component proportion range, process parameters, and environmental conditions; and optimizes the mix proportion of plastic concrete based on a polynomial model for plastic concrete performance evaluation.

[0036] Furthermore, a data table of concrete component characteristics, component ratios, process parameters, environmental conditions, and performance indicators of plastic concrete was established and stored in a database. Based on the database, a polynomial model for evaluating the performance of plastic concrete was further trained to optimize the mix proportions of plastic concrete.

[0037] Furthermore, based on the database, a genetic algorithm is used to find the optimal set of concrete component characteristics and mix proportions, providing a recommended mix proportion that meets the design objectives.

[0038] Furthermore, a data table including water-cement ratio, admixtures, bentonite content, and performance indicators of plastic concrete was established and stored in a database. A three-dimensional response surface of water-cement ratio, admixture, and bentonite content as a function of performance indicators of plastic concrete was constructed using the response surface methodology. The optimal mix design was determined by combining simulation and experimental verification.

[0039] The advantages and positive effects of this invention are:

[0040] Precise prediction and optimization of plastic concrete performance: This invention breaks through the limitations of traditional empirical formulas by setting up a polynomial model for evaluating plastic concrete performance that reflects the relationship between plastic concrete performance indicators and multiple influencing factors. It comprehensively considers factors such as confining pressure and elastic modulus, establishes a multivariate nonlinear coupling relationship, significantly reduces prediction error, and can accurately predict the strength and elastic modulus of plastic concrete, and achieve synergistic optimization of these performance targets.

[0041] Intelligent Design and Optimization Process: Based on physical test data and a polynomial model for evaluating the performance of plastic concrete, this invention achieves a high degree of integration between numerical simulation, experimental verification, and intelligent inversion. This eliminates information silos in the traditional design process, reduces manual intervention, and improves design efficiency and accuracy. Through real-time monitoring and feedback, design parameters can be dynamically adjusted to ensure continuous optimization of the design scheme, driving the evolution of design towards data-driven and intelligent inversion.

[0042] Multi-objective synergistic optimization: This invention employs a multi-objective optimization method to successfully achieve synergistic optimization of the strength and elastic modulus of plastic concrete. This method is used to guide material selection, mix proportion adjustment, and engineering application implementation, solving the problem of mutual performance constraints in traditional methods. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the workflow of a plastic concrete mix proportion optimization method according to the present invention. Detailed Implementation

[0044] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0045] Please see Figure 1 A method for evaluating the performance indicators of plastic concrete is proposed, which determines the performance indicators of plastic concrete and the following factors affecting these indicators: concrete component characteristics, component ratio, curing age, process parameters, and environmental conditions, including ambient temperature, curing method, and environmental erosion conditions; constructs a polynomial model for evaluating the performance indicators of plastic concrete reflecting the relationship between these performance indicators and multiple influencing factors; determines the value range of the influencing factors, plans the experimental scheme, uses admixtures with a gradient gradation structure to prepare concrete samples, and measures the plastic concrete performance indicators of the concrete samples; based on the measured values, fits the coefficients and constants in the polynomial model for evaluating the performance of plastic concrete; and based on the goodness of fit R... 2 The optimal set of coefficients and constants is selected.

[0046] A polynomial model for evaluating the performance of plastic concrete, fitted with completion coefficients and constants, is used to evaluate the performance indicators of plastic concrete.

[0047] The coefficients and constants of the polynomial model for evaluating the performance of plastic concrete can be fitted using existing polynomial model fitting methods.

[0048] The order of the polynomial model for evaluating the performance of plastic concrete can be greater than or equal to 2, or less than or equal to 4.

[0049] Concrete component characteristics refer to the type and specifications of each component in the concrete. For example, the characteristics of cement refer to the type of cement, such as P.S52.5; the characteristics of fine aggregate refer to its specifications, such as a particle size of 0.3mm; the characteristics of coarse aggregate refer to its specifications, such as a particle size of 10mm, and so on.

[0050] Preferably, the determined performance indicators of plastic concrete may include the compressive strength and elastic modulus of plastic concrete; the component characteristics and component ratio range of plastic concrete are set, and multiple combinations of age, process parameters and environmental conditions are carried out. Under different combinations of age, process parameters and environmental conditions, more than 200 sets of admixtures with different proportions are prepared; in combination with process parameters and environmental conditions, multi-objective collaborative experiments are carried out, and the coefficients and constants in the polynomial model for evaluating the performance of plastic concrete are fitted using linear regression equations and / or genetic algorithms.

[0051] Preferably, the following performance indicators for plastic concrete can be set: triaxial compressive strength, uniaxial compressive strength, elastic modulus, and confining pressure; the uniaxial compressive strength, elastic modulus, and confining pressure of all samples are measured; the triaxial compressive strength and elastic modulus of a portion of the samples extracted from all samples are measured.

[0052] The following mathematical model for the triaxial compressive strength of plastic concrete can be set up:

[0053] σ tr =k5σ 3 us +k4σ 2 us +k3σ us p+k2σ us +k1p+k0;

[0054] The following triaxial elastic modulus model for plastic concrete can be set:

[0055] E tr =a4E 2 us -a3E us p+a2E us +a1p-a0;

[0056] In the formula:

[0057] σ tr It is the triaxial compressive strength;

[0058] σ us Uniaxial compressive strength;

[0059] E tr It is the triaxial elastic modulus;

[0060] E us It is the uniaxial elastic modulus;

[0061] p is the confining pressure;

[0062] k1~k5 are the polynomial coefficients of the mathematical model of triaxial compressive strength of plastic concrete;

[0063] k0 is a polynomial constant in the mathematical model of the triaxial compressive strength of plastic concrete;

[0064] a1 to a4 are the polynomial coefficients of the triaxial elastic modulus model of plastic concrete;

[0065] a0 is a polynomial constant of the triaxial elastic modulus model of plastic concrete.

[0066] Based on measured values, linear regression equations can be used to fit k0–k5 in the mathematical model of triaxial compressive strength of plastic concrete and a0–a4 in the model of triaxial elastic modulus of plastic concrete; based on the goodness of fit R... 2 The optimal set of coefficients and constants is selected.

[0067] The triaxial compressive strength and triaxial elastic modulus of plastic concrete, which are fitted with completion coefficients and constants, can be used to evaluate the triaxial compressive strength of other samples whose triaxial compressive strength has not been measured, and to evaluate the triaxial elastic modulus of other samples whose triaxial elastic modulus has not been measured.

[0068] Preferably, the fitting degree R can be set. 2 ≥0.95; the following mathematical model for the triaxial compressive strength of plastic concrete is obtained:

[0069] σ tr =-0.0489σ 3 us +0.7687σ 2 us +0.0005741σ us p-2.718σ us +0.0002243p+5.482;

[0070] Set the fit R 2 With a modulus ≥0.96, the following triaxial elastic modulus model for plastic concrete is obtained:

[0071] E tr =-9.979e -5 E 2 us -0.0002117E us p+1.172E us +0.07692p-37.45.

[0072] Preferably, the concrete components may include cement, fine aggregate, coarse aggregate, bentonite, admixtures, and water; the admixtures may include plasticizers, water-reducing agents, waterproofing agents, and bentonite modifiers.

[0073] Among them, the ratio of fine aggregate to coarse aggregate can be 1:1.5 to 1:2.5; the water-cement ratio can be 1.3 to 3.1; the ratio of cement to fine aggregate can be 1:2 to 1:4; the ratio of cement to bentonite can be 1:0.1 to 1:0.6; and the ratio of cement to admixture can be 1:0.01 to 1:0.05.

[0074] Preferably, the particle size range of fine aggregate can be 0.15mm to 5mm, and the particle size range of coarse aggregate can be 5mm to 20mm.

[0075] The present invention also provides a method for optimizing the mix proportion of plastic concrete using the above-mentioned plastic concrete performance index evaluation method. The method determines the performance requirements of concrete based on the service environment of the concrete structure; sets optimization objectives and sets constraints such as concrete component characteristics and component proportion range, process parameters, and environmental conditions; and optimizes the mix proportion of plastic concrete based on a polynomial model for plastic concrete performance evaluation.

[0076] Preferably, a data table of concrete component characteristics and component ratios, process parameters, environmental conditions and performance indicators of plastic concrete can be established and stored in a database; based on the database, a polynomial model for evaluating the performance of plastic concrete can be further trained to optimize the mix proportions of plastic concrete.

[0077] For example, the table below:

[0078] Data table of concrete component characteristics, component ratio, process parameters, environmental conditions and performance indicators of plastic concrete

[0079]

[0080] "-" indicates that the material was not added to concrete admixtures.

[0081] "Standard" refers to the model number of products commonly sold on the market.

[0082] Preferably, based on a database, a genetic algorithm is used to find the optimal set of concrete component characteristics and mix proportions. This is used to provide recommended mix proportions that meet design objectives.

[0083] Preferably, a data table including water-cement ratio, admixtures, bentonite content and performance indicators of plastic concrete is established and stored in a database; a three-dimensional response surface of water-cement ratio, admixture, bentonite content ratio and performance indicators of plastic concrete is constructed by response surface methodology, and the optimal mix design is determined by combining simulation and experimental verification.

[0084] The workflow and working principle of the present invention will be further described below with reference to a preferred embodiment:

[0085] A method for evaluating the performance indicators of plastic concrete is proposed. By adjusting the water-cement ratio (1.3-3.1) and the ratio of fine aggregate (0.15mm-5mm) to coarse aggregate (5mm-20mm) in plastic concrete to 1:1.5-1:2.5, a gradient gradation structure admixture is formed. This method calculates the relationship between mix proportion and performance, which comprehensively improves compressive strength, reduces elastic modulus, and optimizes impermeability. It is used to accurately evaluate the strength of plastic concrete and realize parametric design within the range of compressive strength 1MPa-5MPa and elastic modulus 800kPa-2000kPa.

[0086] The concrete samples were cast using a 100mm×100mm×100mm cube mold. After mixing, they were left to stand at room temperature (20℃±2℃) for 24 hours, then demolded and placed in a standard curing room for 28 days of curing. All performance tests were conducted on a universal testing machine with a load rate of 0.5MPa / s. Linear regression analysis was used for model fitting, and a genetic algorithm was employed with a crossover probability of 0.7, a mutation probability of 0.05, and a maximum number of iterations of 1000.

[0087] This invention sets the following performance indicators for plastic concrete: triaxial compressive strength, uniaxial compressive strength, uniaxial modulus of elasticity, confining pressure, triaxial modulus of elasticity, and uniaxial modulus of elasticity. The uniaxial compressive strength, uniaxial modulus of elasticity, confining pressure, and uniaxial modulus of elasticity of all samples are measured. The triaxial compressive strength and triaxial modulus of elasticity of a portion of the samples extracted from all the samples are also measured.

[0088] The following mathematical model for the triaxial compressive strength of plastic concrete is established:

[0089] σ tr =k5σ 3 us +k4σ 2 us +k3σ us p+k2σ us +k1p+k0;

[0090] Set up the following triaxial elastic modulus model for plastic concrete:

[0091] E tr =a4E 2 us -a3E us p+a2E us +a1p-a0;

[0092] In the formula:

[0093] σ tr It is the triaxial compressive strength;

[0094] σ us Uniaxial compressive strength;

[0095] E tr It is the triaxial elastic modulus;

[0096] E us It is the uniaxial elastic modulus;

[0097] p is the confining pressure;

[0098] k1~k5 are the polynomial coefficients of the mathematical model of triaxial compressive strength of plastic concrete; k0 is the polynomial constant of the mathematical model of triaxial compressive strength of plastic concrete.

[0099] a1~a4 are the polynomial coefficients of the triaxial elastic modulus model of plastic concrete; a0 is the polynomial constant of the triaxial elastic modulus model of plastic concrete.

[0100] Based on measured values, linear regression equations were used to fit k0–k5 in the mathematical model of triaxial compressive strength of plastic concrete and a0–a4 in the model of triaxial elastic modulus of plastic concrete; based on the goodness of fit R... 2 The optimal set of coefficients and constants is selected.

[0101] Using a mathematical model of triaxial compressive strength and a model of triaxial elastic modulus of plastic concrete fitted with completion coefficients and constants, the triaxial compressive strength of the remaining samples whose triaxial compressive strength was not measured was evaluated; the triaxial elastic modulus of the remaining samples whose triaxial elastic modulus was not measured was also evaluated.

[0102] Set the fit R 2 ≥0.95; the following mathematical model for the triaxial compressive strength of plastic concrete is obtained:

[0103] σ tr =-0.0489σ 2 us +0.7687σ 2 us +0.0005741σ us p-2.718σ us +0.0002243p+5.482;

[0104] The goodness of fit of the mathematical model for the triaxial compressive strength of plastic concrete is: R 2 =0.9574.

[0105] Set the fit R 2 With a modulus ≥0.96, the following triaxial elastic modulus model for plastic concrete is obtained:

[0106] E tr =-9.979e -5 E 2 us -0.0002117E us p+1.172E us +0.07692p-37.45.

[0107] The goodness of fit of the triaxial elastic modulus model for this plastic concrete is: R 2 =0.9659.

[0108] The concrete components include cement, fine aggregate, coarse aggregate, bentonite, admixtures, and water; admixtures include plasticizers, water-reducing agents, waterproofing agents, and bentonite conditioners.

[0109] The ratio of fine aggregate to coarse aggregate ranges from 1:1.5 to 1:2.5; the water-cement ratio ranges from 1.3 to 3.1; the ratio of cement to fine aggregate ranges from 1:2 to 1:4; the ratio of cement to bentonite ranges from 1:0.1 to 1:0.3; and the ratio of cement to admixtures ranges from 1:0.01 to 1:0.05.

[0110] Based on the actual performance targets of plastic concrete, experimental data such as compressive strength, elastic modulus and permeability coefficient of more than 200 mix design schemes are integrated. Combined with material composition, process parameters and environmental conditions, multi-objective collaborative design is carried out. Genetic algorithm is used to solve the strength-modulus problem to ensure the comprehensive balance of plastic concrete in terms of strength, elastic modulus and impermeability.

[0111] By precisely adjusting the proportions of each component in plastic concrete, including water-cement ratio, bentonite content, admixtures, and additives, the predetermined performance targets are achieved. Through a combination of simulation calculations and experimental verification, the three-dimensional response relationship surface of water-cement ratio, additives, and bentonite content is determined using the response surface methodology to optimize the mix design.

[0112] Bentonite modifiers are used to improve the impermeability of plastic concrete and further enhance the crack resistance and durability of concrete by improving the dispersibility of bentonite and its compatibility with other materials.

[0113] Special aggregate combinations optimize the ratio of fine to coarse aggregates, increasing the density of concrete, improving its mechanical properties and permeability, thereby enhancing the compressive strength and impermeability of concrete.

[0114] By establishing a database of concrete component characteristics, component ratios, process parameters, environmental conditions, and performance indicators of plastic concrete, and constructing a surface model of the response relationship between factors such as water-cement ratio, admixtures, and bentonite content to the performance indicators of plastic concrete, intelligent design and optimization are carried out according to the actual needs of different projects. After inputting the target strength (1MPa~5MPa) and elastic modulus (800kPa~2000kPa), 3-5 sets of optimal mix proportion schemes are automatically generated.

[0115] Through the above technical solutions, by adopting special aggregate combinations and bentonite modifiers, the mechanical properties and permeability of plastic concrete are significantly improved. This invention realizes the unmanned, intelligent and refined operation of the entire grouting process, which significantly improves construction efficiency and quality, reduces construction costs, and adapts to the diverse engineering needs under complex geological conditions.

[0116] Specific application examples of this invention are as follows:

[0117] The foundation of the dam's anti-seepage wall is a layer of sand and gravel (permeability coefficient K = 1.2 × 10⁻²). cm In this context, the plastic concrete used in the cutoff wall must meet the following design requirements: compressive strength not less than 2.5 MPa, and elastic modulus controlled below 1500 kPa, to ensure that the concrete has good compressive strength and suitable elastic properties, thereby ensuring the reliability and durability of the cutoff wall.

[0118] The material components include cement, fine aggregate, coarse aggregate, bentonite, admixtures, and water; the admixtures include bentonite conditioner.

[0119] The following parameters are used: fine aggregate (0.15-5mm in diameter) and coarse aggregate (5-20mm in diameter); the ratio of fine aggregate to coarse aggregate is 1:2.0; the water-cement ratio is 1.3-3.1; the ratio of cement to fine aggregate is 1:2-1:4; the ratio of cement to bentonite is 1:0.1-1:0.3; and the ratio of cement to bentonite conditioner is 1:0.01-1:0.05.

[0120] The target parameters are set as follows: compressive strength ≥ 2.5 MPa, elastic modulus ≤ 1500 kPa.

[0121] The process parameters are those specified by conventional process parameters or construction specifications, and the environmental conditions are at room temperature.

[0122] Intelligent mix design generation: This invention inputs different mix design combinations into a polynomial model for evaluating the performance of plastic concrete, and uses a genetic algorithm to generate an optimized mix design solution set, ensuring that the design requirements for strength and elastic modulus are met while adjusting the material composition.

[0123] Material preparation: According to design requirements, a fine aggregate (0.15-5mm particle size) to coarse aggregate (5-20mm particle size) ratio of 1:2.0 was used, and bentonite modifier was added to the concrete to improve its impermeability and mechanical properties. Performance verification: 28-day strength 2.8MPa (predicted value 2.76MPa, error +1.4%).

[0124] Performance verification: After 28 days of curing, the measured compressive strength of the plastic concrete was 2.8 MPa, with an error of +1.4% compared to the predicted value of 2.76 MPa; the elastic modulus was 1380 kPa, with an error of -3.3% compared to the predicted value of 1425 kPa. These results demonstrate that the design method of the present invention has high prediction accuracy and reliability in practical applications.

[0125] The methods for solving the coefficients and constants in the polynomial model for evaluating the performance of plastic concrete, such as the linear regression equation and the genetic algorithm, can be based on existing algorithms.

[0126] The cement, fine aggregate, coarse aggregate, bentonite, admixtures and water mentioned above; the admixtures include bentonite modifiers and other components are all products of the prior art, and their performance parameters can be the performance parameters of products of the prior art.

[0127] The above embodiments are only used to illustrate the technical ideas and features of the present invention. Their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The patent scope of the present invention should not be limited by these embodiments. That is, all equivalent changes or modifications made in accordance with the spirit disclosed in the present invention still fall within the patent scope of the present invention.

Claims

1. A method for evaluating the performance indicators of plastic concrete, characterized in that, The following factors were identified to determine the performance indicators of plastic concrete and their influencing factors: concrete component characteristics, component ratio, curing age, process parameters, and environmental conditions, including ambient temperature, curing method, and environmental erosion conditions. A polynomial model for evaluating the performance of plastic concrete was constructed to reflect the relationship between the performance indicators and these influencing factors. The range of values ​​for the influencing factors was determined, and an experimental plan was designed. Concrete samples were prepared using admixtures with a gradient gradation structure, and the performance indicators of the concrete samples were measured. Based on the measured values, the coefficients and constants in the polynomial model for evaluating the performance of plastic concrete were fitted. The goodness of fit R0 was then used to determine the optimal performance indicators. 2 The optimal set of coefficients and constants is selected. A polynomial model for evaluating the performance of plastic concrete, fitted with completion coefficients and constants, is used to evaluate the performance indicators of plastic concrete.

2. The method for evaluating the performance indicators of plastic concrete according to claim 1, characterized in that, The performance indicators of plastic concrete are determined, including its compressive strength and modulus of elasticity. The component characteristics and component ratio range of plastic concrete are set, and multiple combinations of age, process parameters and environmental conditions are carried out. Under different combinations of age, process parameters and environmental conditions, more than 200 sets of admixtures with different proportions are prepared. Multi-objective collaborative experiments are carried out in combination with process parameters and environmental conditions, and the coefficients and constants in the polynomial model for evaluating the performance of plastic concrete are fitted using linear regression equations and / or genetic algorithms.

3. The method for evaluating the performance indicators of plastic concrete according to claim 2, characterized in that, The following performance indicators for plastic concrete were set: triaxial compressive strength, uniaxial compressive strength, elastic modulus, and confining pressure. The uniaxial compressive strength, elastic modulus, and confining pressure of all samples were measured. The triaxial compressive strength and elastic modulus of a subset of samples were measured. The mathematical model was a specific form model obtained by fitting actual engineering test data. The following mathematical model for the triaxial compressive strength of plastic concrete is established: s tr =k5σ 3 us +k4σ 2 us +k3σ us p+k2σ us +k1p+k0; Set up the following triaxial elastic modulus model for plastic concrete: It is tr =a4E 2 us -a3E us p+a2E us +a1p-a0; In the formula: σ tr It is the triaxial compressive strength; σ us Uniaxial compressive strength; E tr It is the triaxial elastic modulus; E us It is the uniaxial elastic modulus; p is the confining pressure; k1~k5 are the polynomial coefficients of the mathematical model of triaxial compressive strength of plastic concrete; k0 is the polynomial constant of the mathematical model of triaxial compressive strength of plastic concrete. a1~a4 are the polynomial coefficients of the triaxial elastic modulus model of plastic concrete; a0 is the polynomial constant of the triaxial elastic modulus model of plastic concrete. Based on measured values, k0–k5 in the mathematical model of triaxial compressive strength of plastic concrete and a0–a4 in the model of triaxial elastic modulus of plastic concrete are fitted using linear regression equations and / or genetic algorithms; based on the goodness of fit R... 2 The optimal set of coefficients and constants is selected. Using a mathematical model of triaxial compressive strength and a model of triaxial elastic modulus of plastic concrete fitted with completion coefficients and constants, the triaxial compressive strength of the remaining samples whose triaxial compressive strength was not measured was evaluated; the triaxial elastic modulus of the remaining samples whose triaxial elastic modulus was not measured was also evaluated.

4. The method for evaluating the performance indicators of plastic concrete according to claim 3, characterized in that, Set the fit R 2 ≥0.95; the following mathematical model for the triaxial compressive strength of plastic concrete is obtained: s tr =-0.0489σ 3 us +0.7687σ 2 us +0.0005741σ us p-2.718p is +0.0002243p+5.482; Set the fit R 2 With a modulus ≥0.96, the following triaxial elastic modulus model for plastic concrete is obtained: E tr =-9.979e -5 E 2 us -0.0002117E us p+1.172E us +0.07692p-37.45。 5. The method for evaluating the performance indicators of plastic concrete according to claim 2, characterized in that, The concrete components include cement, fine aggregate, coarse aggregate, bentonite, admixtures, and water; admixtures include plasticizers, water-reducing agents, waterproofing agents, and bentonite conditioners. The ratio of fine aggregate to coarse aggregate ranges from 1:1.5 to 1:2.5; the water-cement ratio ranges from 1.3 to 3.1; the ratio of cement to fine aggregate ranges from 1:2 to 1:4; the ratio of cement to bentonite ranges from 1:0.1 to 1:0.6; and the ratio of cement to admixtures ranges from 1:0.01 to 1:0.

05.

6. The method for evaluating the performance indicators of plastic concrete according to claim 5, characterized in that, The particle size range of fine aggregate is 0.15mm to 5mm, and the particle size range of coarse aggregate is 5mm to 20mm.

7. A method for optimizing the mix proportion of plastic concrete using the performance index evaluation method for plastic concrete according to claims 1 to 6, characterized in that, Determine the performance requirements of concrete based on the service environment of the concrete structure; set optimization objectives, and set constraints on concrete component characteristics and component ratio range, process parameters, and environmental conditions; Based on a polynomial model for evaluating the performance of plastic concrete, the mix proportions of plastic concrete are optimized.

8. The method for optimizing the mix proportion of plastic concrete according to claim 7, characterized in that, Establish a data table of concrete component characteristics and component ratios, process parameters, environmental conditions and performance indicators of plastic concrete, and store it in a database; based on the database, further train a polynomial model for evaluating the performance of plastic concrete to optimize the mix proportions of plastic concrete.

9. The method for optimizing the mix proportion of plastic concrete according to claim 8, characterized in that, Based on the database, a genetic algorithm is used to find the optimal set of concrete component characteristics and mix proportions, providing recommended mix proportions that meet design objectives.

10. The method for optimizing the mix proportion of plastic concrete according to claim 8, characterized in that, A data table including water-cement ratio, admixtures, bentonite content, and performance indicators of plastic concrete was established and stored in a database. A three-dimensional response surface of water-cement ratio, admixture, and bentonite content as a function of performance indicators of plastic concrete was constructed using the response surface methodology. The optimal mix design was determined by combining simulation and experimental verification.

Images