Geopolymer underwater non-dispersible concrete mix proportion optimization method and system

Abstract

The invention provides a geopolymer underwater non-dispersible concrete mix proportion optimization method and a geopolymer underwater non-dispersible concrete mix proportion optimization system, and relates to the technical field of concrete mix proportion optimization. A multi-dimensional nonlinear prediction model covering fluidity, dispersion resistance and mechanical properties is constructed based on experimental data, and a coupling relation and a mixing amount optimization rule between material properties are effectively revealed; by combining the particle swarm optimization algorithm, the intelligent optimization design of the concrete mix proportion under the constraint of multiple performance indexes is realized, the construction flowability and durability requirements are ensured, and the compressive strength and the structural stability are improved, so that the scientificity and the accuracy of the design are improved, the test cost is reduced, and the design period is shortened.

Description

Technical Field

[0001] This invention relates to the field of concrete mix design optimization technology, specifically to a method and system for optimizing the mix design of geopolymer underwater non-dispersible concrete. Background Technology

[0002] With the continuous development of modern infrastructure construction, underwater concrete engineering is widely used in bridges, wharves, breakwaters, and offshore platforms. However, traditional underwater concrete is prone to problems such as material dispersion, loss, and insufficient strength during the pouring process, seriously affecting project quality and structural durability. To solve these problems, geopolymer concrete, as a new type of environmentally friendly material, has gradually attracted attention due to its good corrosion resistance and high strength. However, because the flowability and stability of geopolymer concrete are affected by various factors, especially in underwater environments, there are complex nonlinear relationships between the slump, anti-dispersion properties, and mechanical properties of concrete. Furthermore, the influence of different types and proportions of flocculants on material properties has not been systematically clarified, leading to a reliance on experience in mix design and a lack of scientific optimization methods, making it difficult to meet the design requirements of high-performance underwater concrete.

[0003] In the prior art, CN114656204A discloses a design method for the mix proportion of eco-friendly ultra-high performance concrete containing multiple materials. The method includes the following steps: S1, establishing a numerical model using a quadratic saturated D-optimization design and performing optimization to determine the basic mix proportion; S2, preparing and curing UHPC specimens, conducting experiments, and obtaining experimental data on UHPC performance indicators; S3, performing multiple linear regression analysis to determine the numerical model and assess its accuracy; and S4, establishing a multivariate optimization combination model to design the eco-friendly UHPC mix proportion. While this method can optimize the concrete mix proportion, it lacks models for anti-dispersion and flowability in underwater environments, fails to consider the mechanical property growth patterns during curing, and lacks intelligent algorithms to assist in multi-objective optimization. This results in insufficient precision and adaptability for controlling the performance of underwater concrete in practical applications.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for optimizing the mix proportion of geopolymer underwater non-dispersible concrete, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The method for optimizing the mix proportion of geopolymer-based underwater non-dispersible concrete includes the following steps: S1: Concrete was prepared based on metakaolin, aggregate, alkaline activator and flocculant as raw materials, and different concrete samples were constructed by changing the mix ratio of different flocculants in the concrete. S2: Slump test was conducted on concrete samples to quantify the flowability of concrete based on slump spread, and a flowability model of concrete was constructed to reflect the influence of different flocculant mix proportions on the flowability of concrete. Finally, all concrete samples were poured into molds of the same size to construct concrete specimens. S3: The concrete specimens were immersed in water and their mass loss was tested periodically to quantify the concrete's resistance to dispersion, and a concrete resistance to dispersion model was constructed to reflect the effect of different flocculant mix proportions on the concrete's resistance to dispersion. S4: Compressive strength tests were conducted on the soaked concrete specimens. A mechanical performance prediction model was constructed based on the multivariate growth curve to reflect the nonlinear relationship between concrete compressive strength, curing age, and different flocculant mix proportions. S5: Using different flocculant mix proportions as decision variables, and concrete slump spread, mass loss rate, and compressive strength as optimization objectives, the optimization algorithm model is used to solve the preset objective function within the given constraints to obtain the optimal mix proportions of different flocculants. S6: Concrete specimens are constructed based on the optimal mix proportions of different flocculants. When the slump spread, mass loss rate, and compressive strength of the concrete specimens meet the design requirements, the optimal mix proportions of different flocculants are considered effective.

[0007] Preferably, the flocculant includes, but is not limited to, gum arabic, xanthan gum, polyacrylamide, and UWB-II type flocculant, and the alkaline activator includes, but is not limited to, sodium hydroxide and sodium silicate; When constructing concrete samples, they were grouped according to different flocculant mix proportions, with each group including at least 3 concrete samples. At the same time, a set of concrete samples without flocculant and alkaline activator was prepared as a control. When constructing the flowability and anti-dispersion models of concrete, the average values ​​of slump expansion and mass loss rate of each group of concrete samples or corresponding concrete specimens are used as the inputs to the models.

[0008] Preferably, different flocculants are classified into groups according to their type. ,gather ,in Indicates the first Flocculants, An index indicating the types of flocculants. Indicates the number of types of flocculants; The first The mix proportion of the flocculant in concrete is specified as follows: The mix proportions of different flocculants in concrete are standardized as aggregates. ,gather Simultaneously satisfying: ; In the formula This indicates the maximum allowable mix proportion of all flocculants in concrete.

[0009] Preferably, the concrete fluidity model is constructed using a multivariable cubic polynomial, and cross terms are introduced to reflect the interaction between different flocculants. The functional expression of the fluidity model is: ; In the formula This represents the average slump flow of a set of concrete samples. , ~ Both and represent the fitting coefficients of the liquidity model. Indicates an index for another type of flocculant, and .

[0010] Preferably, the logic for constructing a dispersion-resistant model is as follows: First, the mass loss rate is obtained by dividing the mass loss obtained from periodic testing by the initial mass of the concrete specimen. Next, based on the obtained multiple sets of mass loss rates, a weighted negative exponential function form is used, combined with the theoretical minimum mass loss rate, to construct an anti-dispersion model. Similarly, an interaction term is introduced into the anti-dispersion model to reflect the interaction between different flocculants. The functional expression of the anti-dispersion model is as follows: ; In the formula This represents the average mass loss rate of a set of concrete specimens. , The coefficients represent the fit of the anti-dispersion model. This represents a reference value for the mass loss rate, determined by the mass loss rate of concrete specimens without added flocculants. This indicates the preset minimum mass loss rate.

[0011] Preferably, the construction logic of the mechanical performance prediction model is as follows: Different curing ages were set for the concrete specimens in each group, and the concrete specimens under different curing ages were tested to obtain the compressive strength data of the concrete specimens under different curing ages and different flocculant mix ratios. An exponential function is used to represent the relationship between the maximum compressive strength of each concrete specimen and different flocculant mix proportions. Based on the Logistic growth curve, a predictive model for mechanical properties of compressive strength, different flocculant mix proportions, and curing age is constructed.

[0012] Preferably, the function expression for the exponential function representing the relationship between the maximum compressive strength of each concrete specimen and different flocculant mix proportions is: ; In the formula This indicates the mixing ratio of different flocculants. The maximum compressive strength of the concrete specimen, This represents the theoretical maximum compressive strength. , Represents the fitting coefficient of the exponential function; The functional expression of the mechanical performance prediction model is: ; In the formula This indicates the mixing ratio of different flocculants. The concrete specimens were cured for a period of time of [missing information]. The compressive strength of the weather, This indicates the saturation age, with a value ranging from 26 to 30 days. This represents an empirical parameter.

[0013] Preferably, the optimization algorithm model adopts the particle swarm optimization algorithm, and the optimization logic is as follows: Initialize the particle swarm and randomly generate parameter sets for different flocculant ratios that satisfy the constraints; Calculate the objective function value for each particle and determine whether it meets the constraints of collapse expansion, mass loss rate, and compressive strength. Based on the objective function and constraints, the velocity and position of the particles are updated, and the optimal mixing ratio of different flocculants is iteratively searched using the particle swarm optimization algorithm. Once the iteration termination condition is met, the optimal mixing ratio of different flocculants is output, completing the optimization.

[0014] Preferably, the function expression of the objective function is: ; In the formula This represents the score of the objective function. ~ Both represent model weights, and , This represents the target value for collapse expansion. This indicates the maximum allowable quality loss rate. This indicates the target value for compressive strength.

[0015] A geopolymer underwater non-dispersible concrete mix design optimization system, wherein the optimization system is used to perform the above-mentioned optimization method, specifically including: The data acquisition module is used to acquire data on the slump expansion, mass loss rate, and compressive strength of concrete samples or concrete specimens. The model building module is used to build flowability models, anti-dispersion models, and mechanical property prediction models to analyze concrete samples or concrete specimens. The optimization algorithm module includes a pre-set particle swarm optimization algorithm, which is used to solve a pre-set objective function within a given constraint range to obtain the optimal mixing ratio of different flocculants.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention systematically studies the effects of different flocculant types and mix proportions on the key properties of geopolymer-free underwater concrete. Based on experimental data, a multidimensional nonlinear prediction model covering fluidity, anti-dispersion, and mechanical properties is constructed, effectively revealing the coupling relationship between material properties and the optimization law of dosage. Combined with particle swarm optimization algorithm, intelligent optimization design of concrete mix proportion under multiple performance index constraints is realized, which not only ensures the requirements of construction fluidity and durability, but also improves compressive strength and structural stability, thereby improving the scientificity and accuracy of the design and reducing experimental costs and design cycle. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the overall method flow of the present invention; Figure 2 This is a graph showing the error fluctuations in collapse spread, mass loss rate, and maximum compressive strength in this invention. Figure 3 This is a schematic diagram of the module structure of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0019] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0020] Example: Please see Figures 1-2 The present invention provides a technical solution: The method for optimizing the mix proportion of geopolymer-based underwater non-dispersible concrete includes the following steps: S1: Concrete was prepared using metakaolin, aggregates, alkaline activators, and flocculants as raw materials. Different concrete samples were constructed by varying the proportions of different flocculants in the concrete mix. Concrete samples were grouped according to different flocculant proportions, with each group including at least three concrete samples. A control group of concrete samples without flocculants and alkaline activators was also prepared. Flocculants included, but were not limited to, gum arabic, xanthan gum, polyacrylamide, and UWB-II type flocculant. Alkaline activators included, but were not limited to, sodium hydroxide and sodium silicate.

[0021] The aggregates here include coarse aggregates (pebbles, crushed stone) and fine aggregates (river sand). The mix proportions of both in concrete can be obtained from existing professional experience or engineering specifications, so they will not be analyzed. Compared with traditional concrete, the geopolymer concrete in this scheme requires activation with an alkaline activator to produce gel. Its mix proportion and concentration can also be obtained from existing professional experience or engineering specifications (the commonly used sodium hydroxide solution is 12 mol / L, and the sodium silicate solution concentration is 45%), so it will not be analyzed either. Only the performance effects of changing the mix proportions of different flocculants will be analyzed.

[0022] By explicitly using different flocculants (gum arabic, xanthan gum, polyacrylamide, UWB-II, etc.) as variables, grouping them into groups with at least 3 samples per group, and setting up a control group without flocculants, the scientific nature of the experimental design and the reliability of the data were ensured. Furthermore, the multiple sample retests ensured the statistical significance of the data, avoided the influence of random errors, and improved the accuracy and generalization ability of the model construction.

[0023] S2: Slump tests were conducted on the concrete samples to quantify the flowability of the concrete based on the slump spread. A flowability model was constructed to reflect the influence of different flocculant mix proportions on the flowability of the concrete. Finally, all concrete samples were poured into molds of the same size to construct concrete specimens. When constructing the flowability model, the average slump spread and mass loss rate of each group of concrete samples were used as the inputs to the model. It can be understood that the concrete samples and concrete specimens are actually the same product, differing only in shape. In this embodiment, the concrete specimens are cubic specimens with a side length of 100 mm.

[0024] The slump test can be set up according to engineering specifications. Specifically, the test should be carried out according to the following steps: (1) Take a sample of no less than 10L from the mixed underwater self-protecting concrete and put it into a container. (2) Rinse the slump cylinder and the measuring steel plate clean and keep them moist. Level the steel plate, place the slump cylinder on the steel plate, and press the pedal firmly with both feet. (3) Pour the underwater self-protecting concrete mixture from the container into the slump cone continuously. After filling the cone, use a trowel to smooth the mixture along the cone opening and remove the concrete around the outside of the cone. Do not vibrate or tamp the concrete during the entire process. (4) After lifting the slump cone vertically by about 300 mm within 2s to 3s, place it at the corner of the steel plate. When the concrete sample no longer flows and spreads, use a steel ruler or tape measure to measure the maximum expansion diameter of the sample and the expansion diameter perpendicular to it. (5) If the difference between the two measured expansion diameters is greater than 50 mm, resample and test; (6) Each collapse expansion test shall be completed within 2 to 3 minutes.

[0025] Furthermore, different flocculants are classified into groups according to their types. ,gather ,in Indicates the first Flocculants, An index indicating the types of flocculants. Indicates the number of types of flocculants; The first The mix proportion of the flocculant in concrete is specified as follows: The mix proportions of different flocculants in concrete are standardized as aggregates. ,gather Simultaneously satisfying: ; In the formula This indicates the maximum allowable mix proportion of all flocculants in concrete.

[0026] The flowability model of concrete is constructed using a multivariable cubic polynomial, and cross terms are introduced to reflect the interaction between different flocculants. The functional expression of the flowability model is as follows: ; In the formula This represents the average slump flow of a set of concrete samples. , ~ Both and represent the fitting coefficients of the liquidity model. Indicates an index for another type of flocculant, and .

[0027] As can be seen from the functional expression of the liquidity model, the head... The actual slump flow is the baseline value, which can be determined by the slump flow value of the concrete without flocculant in the control group. ~ Then, fitting can be performed using experimental data. The cubic polynomial representation in the middle is used to reflect the third... The model describes the nonlinear effect of flocculant dosage on flowability. The cross terms at the tail are actually a traversal of all pairwise combinations of flocculants, representing the contribution of interactions between different flocculants to flowability. Positive cross terms indicate synergistic effects (such as compatibility enhancement), while negative cross terms indicate antagonistic effects (such as competitive adsorption), making the model more closely resemble the complexity of actual mixing systems. Furthermore, the overall trend of the model aligns with the actual physical phenomenon that "the flowability of concrete decreases with the gradual increase of flocculant dosage."

[0028] Since the fluidity of concrete is affected by many factors, a simple linear model is difficult to accurately describe the complex relationships. Therefore, a cubic polynomial is used to describe it. The cubic polynomial allows the model to capture the increasing, saturating and possible decreasing trends of fluidity with the change of admixture, which meets the complex material behavior in engineering. At the same time, the introduction of second-order interaction terms can not only control the complexity of the model, capture key interactions, balance accuracy and generalization ability, but also avoid overfitting and model redundancy, thereby enhancing the accuracy and reliability of material performance regulation.

[0029] S3: The concrete specimens were immersed in water and their mass loss was periodically measured to quantify the concrete's resistance to dispersion. A concrete resistance to dispersion model was constructed to reflect the influence of different flocculant mix proportions on the concrete's resistance to dispersion. When constructing the concrete resistance to dispersion model, the average slump loss rate of each group of concrete specimens was used as the model input.

[0030] The logic for constructing a non-dispersion model is as follows: First, the mass loss rate is obtained by dividing the mass loss obtained from periodic testing by the initial mass of the concrete specimen. Next, based on the obtained multiple sets of mass loss rates, a weighted negative exponential function was used to construct an anti-dispersion model in combination with the theoretical minimum mass loss rate. Similarly, a cross term was introduced into the anti-dispersion model to reflect the interaction between different flocculants. This is because the negative exponential function conforms to the law that the anti-dispersion performance decreases rapidly with the increase of flocculant dosage and then tends to stabilize.

[0031] The functional expression of the anti-dispersion model is: ; In the formula This represents the average mass loss rate of a set of concrete specimens. , The coefficients represent the fit of the anti-dispersion model. This represents a reference value for the mass loss rate, determined by the mass loss rate of concrete specimens without added flocculants. This indicates the preset minimum mass loss rate.

[0032] The functional expression of the anti-dispersion model shows that the experimental group with a higher flocculant dosage exhibits better anti-dispersion performance. However, it is understandable that in actual engineering, even with a large amount of flocculant, concrete cannot completely avoid minor mass loss or material loss. This is due to unavoidable factors such as the material's pore structure, microcracks, and physicochemical reactions, leading to phenomena like trace ion erosion and electrolyte diffusion. Therefore, the preset minimum mass loss rate actually represents the theoretical limit of anti-dispersion performance, reflecting the lower limit of experimental data. In other words, it is observed in experiments that the mass loss rate will not infinitely approach zero, but will tend to stabilize around a certain small value. This stable value can be set as [value missing]. Add to the functional expression of the anti-dispersion model This can prevent the model from making non-physical predictions in the high doping range, making the model more consistent with actual physical laws.

[0033] In this step, by using a reasonably designed mass loss rate measurement and a weighted negative exponential anti-dispersion model that includes theoretical limit values, the influence of different flocculant ratios on the anti-dispersion performance of concrete can be well reflected. It can also be used in conjunction with flowability and mechanical property models to construct a multi-dimensional performance prediction system and achieve synergistic optimization of concrete performance.

[0034] S4: Compressive strength tests were conducted on the soaked concrete specimens. A mechanical performance prediction model was constructed based on multivariate growth curves to reflect the nonlinear relationship between concrete compressive strength, curing age, and different flocculant mix proportions.

[0035] The construction logic of the mechanical performance prediction model is as follows: Different curing ages (e.g., 3d, 7d, 28d) were set for the concrete specimens in each group, and the concrete specimens under different curing ages were tested to obtain the compressive strength data of the concrete specimens under different curing ages and different flocculant mix ratios. An exponential function is used to represent the relationship between the maximum compressive strength of each concrete specimen and different flocculant mix proportions. Based on the Logistic growth curve, a predictive model for mechanical properties of compressive strength, different flocculant mix proportions, and curing age is constructed.

[0036] By setting different curing ages, the system obtains the compressive strength data of concrete in the early, middle and late stages, comprehensively reflecting the evolution law of material performance. This avoids the one-sidedness of mechanical performance evaluation under a single age. Moreover, by using exponential relationship and Logistic growth curve to express the nonlinear characteristics of material strength growth, it can fully consider the actual law of rapid early growth and later saturation of concrete, avoiding the distortion of linear or simple polynomial models.

[0037] The following test steps can be used when conducting a test to measure compressive strength: (1) Take out the specimens for the corresponding curing age and wipe the specimens and the pressure platform of the testing machine clean; (2) Fix the specimen on the pressure plate of the testing machine and align it with the upper and lower pressure plates of the testing machine; (3) Start the testing machine, set the loading rate to 1 mm / min, and maintain continuous and uniform loading; (4) Pressurize until the specimen is completely destroyed, record the failure load, and obtain the maximum compressive strength.

[0038] The function expression for the exponential function used to represent the relationship between the maximum compressive strength of each concrete specimen and different flocculant mix proportions is as follows: ; ; In the formula This indicates the maximum compressive strength of the concrete specimen. This represents the theoretical maximum compressive strength. , This represents the fitting coefficient of the exponential function.

[0039] Professional research indicates that the compressive strength of concrete gradually increases with the increase of flocculant proportion. Once the flocculant reaches a certain level, the compressive strength plateaus. Taking the concrete specimen in this example as an example, the strength stabilizes at around 45MPa~46MPa. Therefore, an exponential function is used to describe the change in concrete strength, reflecting the rapid initial increase in concrete strength with increasing flocculant dosage, followed by a gradual approach to the maximum value due to limitations such as material structure.

[0040] The functional expression of the mechanical performance prediction model is: ; In the formula This indicates that the concrete specimens were cured for a certain period of time. The compressive strength of the weather, This indicates the saturation age, with a value ranging from 26 to 30 days. This represents an empirical parameter.

[0041] As can be seen from the functional expression of the mechanical performance prediction model, it conforms to the typical S-shaped growth law of concrete curing strength over time, that is, the hydration reaction is rapid in the early stage of curing, the strength increases rapidly, and then the growth slows down as the reaction approaches completion.

[0042] In this step, by comprehensively examining different ages and proportions, we can not only adapt to a wider range of actual construction needs and improve the applicability of the scheme, but also support the strength target constraints of the overall proportion optimization, ensuring that the design scheme is usable in engineering.

[0043] S5: Using different flocculant mix proportions as decision variables, and concrete slump spread, mass loss rate, and compressive strength as optimization objectives, the optimization algorithm model is used to solve the preset objective function within the given constraints to obtain the optimal mix proportions of different flocculants.

[0044] The optimization algorithm model adopts the particle swarm optimization (PSO) algorithm, and the optimization logic is as follows: Initialize the particle swarm and randomly generate parameter sets for different flocculant ratios that satisfy the constraints; Calculate the objective function value for each particle and determine whether it meets the constraints of collapse expansion, mass loss rate, and compressive strength. Based on the objective function and constraints, the velocity and position of the particles are updated, and the optimal mixing ratio of different flocculants is iteratively searched using the particle swarm optimization algorithm. Once the iteration termination condition is met, the optimal mixing ratio of different flocculants is output, completing the optimization.

[0045] In practical applications, a fixed curing period can be set in advance according to project needs, so that the optimal mixing ratio of different flocculants can be obtained by using optimization algorithms. Alternatively, a rough range of curing period can be set, and the curing period can also be used as an optimization target to obtain the optimal synergistic solution between curing period and optimal mixing ratio of different flocculants.

[0046] The PSO algorithm is suitable for multi-objective nonlinear optimization problems with continuous variables. It has the advantages of strong global search capability, simple implementation, and high computational efficiency. It can handle complex optimization of multi-dimensional combinations of flocculant ratios. It takes three core indicators as optimization objectives: slump spread (flowability), mass loss rate (anti-dispersion), and compressive strength (mechanical properties). It takes into account both construction performance and durability performance, and avoids the risk of other performance degradation caused by optimizing a single indicator.

[0047] The objective function is expressed as follows: ; In the formula This represents the score of the objective function. ~ Both represent model weights, and It can be adjusted according to the specific project priorities to achieve differentiated optimization for ease of construction, durability, or strength. This represents the target value for collapse expansion. This indicates the maximum allowable quality loss rate. These represent the target compressive strength value, and the three values ​​can be determined according to design requirements and engineering specifications.

[0048] As can be seen from the expression of the objective function, the first term reflects the relative deviation between the actual concrete flowability and the target flowability; the smaller the value, the better it matches expectations. The second term reflects the concrete's anti-dispersion performance; the smaller the proportion, the better the anti-dispersion performance. The third term represents the missing proportion of compressive strength relative to the target compressive strength value; the smaller the value, the closer the strength is to or exceeds the target. The overall objective function is constructed in a weighted sum form, and the objective is to minimize this function, thereby achieving a comprehensive optimization that minimizes the flowability from the target, minimizes the mass loss rate, and maximizes the strength.

[0049] Constraints on slump spread, mass loss rate, and compressive strength can be set by comparing them with target values ​​for slump spread, maximum allowable mass loss rate, and target compressive strength. For example, the deviation between slump spread and compressive strength and their target values ​​should not exceed a certain threshold, and the ratio of mass loss rate to maximum allowable mass loss rate should not exceed a certain threshold. Alternatively, the upper and lower limits of slump spread, mass loss rate, and compressive strength can be determined based on actual design requirements and engineering specifications.

[0050] In this step, by combining the previous models of fluidity, anti-dispersion, and mechanical properties, the design objectives and constraints are transformed into mathematical forms, enhancing the repeatability, standardization, and automation of the proportioning design, realizing intelligent proportioning design, achieving data-driven optimization design, and enhancing the systematicness and integrity of the scheme.

[0051] S6: Concrete specimens are constructed based on the optimal mix proportions of different flocculants. When the slump spread, mass loss rate, and compressive strength of the concrete specimens meet the design requirements, the optimal mix proportions of different flocculants are considered effective.

[0052] In this embodiment, three typical flocculants—gum arabic, xanthan gum, and polyacrylamide—were selected and labeled as follows: ~ The overall mix proportion ranged from 0% to 1.5%, with a step size of 0.3%. Other components and the proportion of the alkaline activator were kept constant to ensure that the aggregate and alkaline activator factors remained constant. Ten concrete specimens were constructed using a uniform 3-day curing period. The slump flow, mass loss rate, and maximum compressive strength were calculated using the various models in this scheme. The errors between these three values ​​and the actual measured values ​​of the concrete specimens were also calculated. Specific data are as follows: Table 1: Theoretical Experiment Data

[0053] As can be seen from the data in the table above, the absolute values ​​of the errors in slump spread and maximum compressive strength are both less than 5%, but the error in the measured mass loss rate is larger, fluctuating within ±10%. However, considering that the mass loss rate is small, the absolute error is limited, and it conforms to the characteristics of small fluctuations in actual measurements, it is still considered to be consistent with the measurement and sample preparation fluctuations commonly seen in mechanical property tests. Furthermore, the trends in the theoretical slump spread, mass loss rate, and maximum compressive strength are consistent with the measured values, proving that the model prediction has the reliability for engineering applications.

[0054] A geopolymer underwater non-dispersible concrete mix design optimization system. This system is used to execute the aforementioned optimization methods, specifically including: The data acquisition module is used to collect data on the slump spread, mass loss rate, and compressive strength of concrete samples or concrete specimens. The model building module is used to build flowability models, anti-dispersion models, and mechanical property prediction models to analyze concrete samples or concrete specimens. The optimization algorithm module includes a pre-set particle swarm optimization algorithm, which is used to solve a pre-set objective function within a given constraint range to obtain the optimal mix ratio of different flocculants.

[0055] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0056] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0057] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0058] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for optimizing the mix proportion of geopolymer-based underwater non-dispersible concrete, characterized in that, The specific steps include: S1: Concrete was prepared based on metakaolin, aggregate, alkaline activator and flocculant as raw materials, and different concrete samples were constructed by changing the mix ratio of different flocculants in the concrete. S2: Slump test was conducted on concrete samples to quantify the flowability of concrete based on slump spread, and a flowability model of concrete was constructed to reflect the influence of different flocculant mix proportions on the flowability of concrete. Finally, all concrete samples were poured into molds of the same size to construct concrete specimens. S3: The concrete specimens were immersed in water and their mass loss was tested periodically to quantify the concrete's resistance to dispersion, and a concrete resistance to dispersion model was constructed to reflect the effect of different flocculant mix proportions on the concrete's resistance to dispersion. S4: Compressive strength tests were conducted on the soaked concrete specimens. A mechanical performance prediction model was constructed based on the multivariate growth curve to reflect the nonlinear relationship between concrete compressive strength, curing age, and different flocculant mix proportions. S5: Using different flocculant mix proportions as decision variables, and concrete slump spread, mass loss rate, and compressive strength as optimization objectives, the optimization algorithm model is used to solve the preset objective function within the given constraints to obtain the optimal mix proportions of different flocculants. S6: Concrete specimens are constructed based on the optimal mix proportions of different flocculants. When the slump spread, mass loss rate, and compressive strength of the concrete specimens meet the design requirements, the optimal mix proportions of different flocculants are considered effective.

2. The method for optimizing the mix proportion of geopolymer underwater non-dispersible concrete according to claim 1, characterized in that: The flocculants include, but are not limited to, gum arabic, xanthan gum, polyacrylamide, and UWB-II type flocculants; the alkaline activators include, but are not limited to, sodium hydroxide and sodium silicate. When constructing concrete samples, they were grouped according to different flocculant mix proportions, with each group including at least 3 concrete samples. At the same time, a set of concrete samples without flocculant and alkaline activator was prepared as a control. When constructing the flowability and anti-dispersion models of concrete, the flocculant mix proportion of each group of concrete samples or corresponding concrete specimens is used as the input of the model, and the average values ​​of slump expansion and mass loss rate are used as the output of the model.

3. The method for optimizing the mix proportion of geopolymer underwater non-dispersible concrete according to claim 2, characterized in that: Different flocculants are classified into groups according to their types. ,gather ,in Indicates the first Flocculants, An index indicating the types of flocculants. Indicates the number of types of flocculants; The first The mix proportion of the flocculant in concrete is specified as follows: The mix proportions of each flocculant in the concrete sample were calibrated as aggregates. ,gather Simultaneously satisfying: ； In the formula This indicates the maximum allowable mix proportion of all flocculants in concrete.

4. The method for optimizing the mix proportion of geopolymer underwater non-dispersible concrete according to claim 3, characterized in that: The concrete fluidity model is constructed using a multivariable cubic polynomial, and cross terms are introduced to reflect the interaction between different flocculants. The functional expression of the fluidity model is as follows: ； In the formula This represents the average slump flow of the same group of concrete samples. , ~ Both and represent the fitting coefficients of the liquidity model. It also serves as an index for types of flocculants.

5. The method for optimizing the mix proportion of geopolymer underwater non-dispersible concrete according to claim 3, characterized in that: The logic for constructing a non-dispersion model is as follows: First, the mass loss rate is obtained by dividing the mass loss obtained from periodic testing by the initial mass of the concrete specimen. Next, based on the obtained multiple sets of mass loss rates, a weighted negative exponential function form is used, combined with the theoretical minimum mass loss rate, to construct an anti-dispersion model. Similarly, an interaction term is introduced into the anti-dispersion model to reflect the interaction between different flocculants. The functional expression of the anti-dispersion model is as follows: ； In the formula This represents the average mass loss rate of the same group of concrete specimens. , The coefficients represent the fit of the anti-dispersion model. This represents a reference value for the mass loss rate, determined by the mass loss rate of concrete specimens without added flocculants. This indicates the preset minimum mass loss rate.

6. The method for optimizing the mix proportion of geopolymer underwater non-dispersible concrete according to claim 5, characterized in that: The construction logic of the mechanical performance prediction model is as follows: Different curing ages were set for the concrete specimens in each group, and the concrete specimens under different curing ages were tested to obtain the compressive strength data of the concrete specimens under different curing ages and different flocculant mix ratios. An exponential function is used to represent the relationship between the maximum compressive strength of each concrete specimen and different flocculant mix proportions. Based on the Logistic growth curve, a predictive model for mechanical properties of compressive strength, different flocculant mix proportions, and curing age is constructed.

7. The method for optimizing the mix proportion of geopolymer underwater non-dispersible concrete according to claim 6, characterized in that: The function expression for the exponential function used to represent the relationship between the maximum compressive strength of each concrete specimen and different flocculant mix proportions is as follows: ； In the formula This indicates the maximum compressive strength of the concrete specimen. This represents the theoretical maximum compressive strength. , Represents the fitting coefficient of the exponential function; The functional expression of the mechanical performance prediction model is: ； In the formula This indicates that the concrete specimens were cured for a certain period of time. The compressive strength of the weather, This indicates the saturation age, with a value ranging from 26 to 30 days. This represents an empirical parameter.

8. The method for optimizing the mix proportion of geopolymer underwater non-dispersible concrete according to claim 7, characterized in that: The optimization algorithm model adopts the particle swarm optimization algorithm, and the optimization logic is as follows: Initialize the particle swarm and randomly generate parameter sets for different flocculant ratios that satisfy the constraints; Calculate the objective function value for each particle and determine whether it meets the constraints of collapse expansion, mass loss rate, and compressive strength. Based on the objective function and constraints, the velocity and position of the particles are updated, and the optimal mixing ratio of different flocculants is iteratively searched using the particle swarm optimization algorithm. Once the iteration termination condition is met, the optimal mixing ratio of different flocculants is output, completing the optimization.

9. The method for optimizing the mix proportion of geopolymer underwater non-dispersible concrete according to claim 8, characterized in that: The function expression of the objective function is: ； In the formula This represents the score of the objective function. ~ Both represent model weights, and , This represents the target value for collapse expansion. This indicates the maximum allowable quality loss rate. This indicates the target value for compressive strength.

10. A geopolymer underwater non-dispersible concrete mix design optimization system, characterized in that: The optimization system is used to execute the optimization method as described in any one of claims 1-9, specifically including: The data acquisition module is used to acquire data on the slump expansion, mass loss rate, and compressive strength of concrete samples or concrete specimens. The model building module is used to build flowability models, anti-dispersion models, and mechanical property prediction models to analyze concrete samples or concrete specimens. The optimization algorithm module includes a pre-set particle swarm optimization algorithm, which is used to solve a pre-set objective function within a given constraint range to obtain the optimal mixing ratio of different flocculants.

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