High-strength and high-pervious concrete mix proportion design method based on aggregate characteristics

Abstract

The invention relates to the technical field of concrete, in particular to a high-strength and high-permeability concrete mix proportion design method based on aggregate characteristics. The method comprises the following steps: firstly, providing a dynamic multivariate prediction model for comprehensively analyzing a relationship between aggregate characteristics and concrete performance, and realizing selection scheme optimization of the aggregate characteristics through regression analysis of a strength and water permeability objective function; secondly, the invention provides an inertia self-adaptive particle swarm optimization algorithm, and by dynamically adjusting inertia weights and learning factors of particles, efficient solution and optimization of a dynamic multivariate prediction model are realized, optimal aggregate characteristic parameters are accurately searched and determined, and optimal balance of concrete performance is ensured to be realized under various requirements; finally, a cost calculation function is provided for the mix proportion of the high-strength and high-permeability concrete, the cost required by the mix proportion scheme is evaluated through accurate accounting of all raw materials and other costs, and a scientific economic decision basis is provided for design of the mix proportion of the concrete.

Description

Technical Field

[0001] The present invention relates to the technical field of concrete, and particularly to a method for designing the mix proportion of high-strength and highly permeable concrete based on the characteristics of aggregates. Background Technique

[0002] High-strength and highly permeable concrete is an innovative building material designed to combine the two characteristics of high strength and high permeability. Different from traditional concrete, this material deliberately retains a large porosity during the mixing process, enabling rainwater or other liquids to penetrate quickly. In modern society, especially in areas with highly developed urbanization, the application of high-strength and highly permeable concrete has important environmental and functional significance. First of all, it can effectively alleviate the problem of urban waterlogging. With the increase of hardened ground in the city, rainwater is difficult to penetrate naturally, resulting in increased surface runoff and thus causing waterlogging. High-strength and highly permeable concrete allows rainwater to quickly infiltrate into the ground through its unique pore structure, reducing surface water accumulation and alleviating the load on the drainage system. Secondly, this material helps to replenish groundwater resources and relieve the problem of water resource shortage. At the same time, permeable ground can also reduce the heat island effect and improve the urban microclimate. Therefore, high-strength and highly permeable concrete not only plays a positive role in environmental protection, but also provides an important guarantee for the sustainable development of urban infrastructure.

[0003] At present, a large amount of bottom ash from municipal solid waste incineration is regarded as waste and landfilled or stacked, which not only occupies land resources but may also cause environmental pollution. However, as a potential alternative material, bottom ash from municipal solid waste incineration has good reutilization value and can partially replace traditional natural aggregates for the preparation of high-strength and highly permeable concrete. By resourcefully utilizing bottom ash from municipal solid waste incineration, not only can the consumption of natural resources be reduced and production costs be lowered, but also the environmental problems in the waste treatment process can be effectively solved, realizing the resource utilization and recycling of waste.

[0004] At the present stage, some projects have attempted to use bottom ash from municipal solid waste incineration as alternative aggregates, but there are still many problems in actual applications. The traditional mix proportion design method based on empirical formulas lacks sufficient experimental data and cannot effectively obtain the optimal aggregate characteristic selection scheme, resulting in large fluctuations in the performance of the prepared concrete and making it difficult to stably control the strength and permeability. In addition, there is a common phenomenon of neglecting material costs in the mix proportion design of high-strength and highly permeable concrete at the present stage, so that problems such as shortage of funds occur during the actual construction process, leading to delays or even termination of the project and causing waste of manpower and material resources.

[0005] Therefore, a method for designing the mix proportion of high-strength and highly permeable concrete based on the characteristics of aggregates is proposed. Summary of the Invention

[0006] The object of the present invention is to provide a mix proportion design method for high-strength and highly permeable concrete based on aggregate characteristics, which is used to realize a scientific and reasonable mix proportion design of high-strength and highly permeable concrete. To solve the problems existing in the prior art, the present invention first proposes a dynamic multi-variable prediction model for comprehensively analyzing the relationship between aggregate characteristics and concrete performance, and realizes the optimization of the selection scheme of aggregate characteristics through the regression analysis of the strength and water permeability objective functions; secondly, the present invention proposes an inertia self-adaptive particle swarm optimization algorithm, which realizes the efficient solution and optimization of the dynamic multi-variable prediction model by dynamically adjusting the inertia weight and learning factor of the particle, accurately searches and determines the optimal aggregate characteristic parameters, and ensures the optimal balance of concrete performance under various requirements; finally, the present invention proposes a cost calculation function for the mix proportion of high-strength and highly permeable concrete, and evaluates the required cost of the mix proportion scheme by accurately calculating the costs of various raw materials and other costs, providing a scientific economic decision-making basis for the concrete mix proportion design.

[0007] A mix proportion design method for high-strength and highly permeable concrete based on aggregate characteristics includes:

[0008] Collect municipal solid waste incineration bottom ash, pre-treat the municipal solid waste incineration bottom ash to obtain detoxified bottom ash;

[0009] Screen the detoxified bottom ash and classify it according to the particle size of the detoxified bottom ash;

[0010] Taking aggregate category, particle size, compact packing density, apparent density and crushing value as variables, conduct multiple groups of benchmark experiments, test the strength and water permeability coefficient of concrete under different mix proportion conditions, and obtain benchmark experiment data;

[0011] Based on the benchmark experiments, gradually increase the replacement rate of the detoxified bottom ash for the aggregate, conduct multiple groups of comparative experiments, test the strength and water permeability coefficient of concrete under different mix proportion conditions, and obtain comparative experiment data;

[0012] Based on the benchmark experiment data and the comparative experiment data, establish a dynamic multi-variable prediction model, and dynamically adjust the weights of the concrete strength and water permeability coefficient according to the requirements;

[0013] Use the inertia self-adaptive particle swarm optimization algorithm to calculate the dynamic multi-variable prediction model to obtain the optimal aggregate selection scheme;

[0014] According to the optimal aggregate selection scheme, calculate the concrete mix proportion;

[0015] Use the cost calculation function to calculate the construction cost per unit volume of concrete under the concrete mix proportion for cost evaluation.

[0016] Furthermore, the pre-treatment of the municipal solid waste incineration bottom ash includes:

[0017] Remove large impurities and metal residues by physical screening;

[0018] Wash with tap water to reduce heavy metal and salt content;

[0019] Use chemical stabilizers to fix heavy metal ions;

[0020] Obtain detoxified bottom ash through heat treatment, pickling, and alkali washing.

[0021] Further, classification according to the particle size of the detoxified bottom ash includes: fine-grained bottom ash of 2.4 - 4.75 mm, medium-grained bottom ash of 4.75 - 9.5 mm, and coarse-grained bottom ash of 9.5 - 13.2 mm.

[0022] Further, the aggregate categories include crushed stone and pebbles; the particle sizes include fine-grained aggregates of 2.4 - 4.75 mm, medium-grained aggregates of 4.75 - 9.5 mm, and coarse-grained aggregates of 9.5 - 13.2 mm; each group of the benchmark experiments includes three groups of benchmark parallel experiments.

[0023] Further, the substitution rate of the detoxified bottom ash for the aggregate is equal-mass substitution, the substitution ratio is 5% - 50%, and the single substitution gradient is 5%; each group of the comparative experiments includes three groups of comparative parallel experiments.

[0024] Further, the dynamic multi - prediction model includes a strength objective function, a water permeability objective function, and constraint conditions; the strength objective function contains strength regression coefficients corresponding to aggregate categories, particle sizes, tightly packed density, apparent density, crushing value, and bottom ash substitution rate, and the strength regression coefficients are obtained by linear regression of the benchmark experiment data and the comparative experiment data; the water permeability objective function contains water permeability regression coefficients corresponding to aggregate categories, particle sizes, tightly packed density, apparent density, crushing value, and bottom ash substitution rate, and the water permeability regression coefficients are obtained by linear regression of the benchmark experiment data and the comparative experiment data; the constraint conditions are range constraints for the corresponding values of aggregate categories, particle sizes, tightly packed density, apparent density, crushing value, and bottom ash substitution rate.

[0025] Further, the inertia - adaptive particle swarm optimization algorithm includes:

[0026] Set the number of particle swarms, the maximum number of iterations, the inertia weight, the learning factor, and the speed limit, and randomly generate initial positions and speeds for each of the particles;

[0027] Evaluate the current fitness of each of the particles, and calculate the performance of each of the particles in the optimization of concrete mix proportion through the objective function;

[0028] Update according to the current velocity, position, individual optimal position, and global optimal position of each particle to ensure that the particle moves towards the global optimal position;

[0029] Use a linearly decreasing method to dynamically adjust the inertia weight to enhance the early search ability and late convergence accuracy;

[0030] When the global optimal position does not change significantly in multiple iterations, fine-tune the global optimal position through a local perturbation search mechanism;

[0031] Check whether the maximum number of iterations is reached or the adjustment change of the global optimal position is less than the set threshold. If satisfied, stop; otherwise, continue the iteration;

[0032] Output the current global optimal position, which is the best selection scheme for aggregate characteristics.

[0033] Furthermore, determining the concrete mix ratio includes:

[0034] Calculate the aggregate dosage per unit volume according to the bulk density and correction coefficient of the aggregate;

[0035] Calculate the volume of the binder paste per unit volume according to the dense packing porosity and designed porosity of the aggregate;

[0036] Calculate the water consumption per unit volume of concrete and the cement consumption per unit volume of concrete according to the preset water-binder ratio;

[0037] Calculate the admixture dosage per unit volume according to the cement consumption per unit volume of concrete and the admixture content.

[0038] Furthermore, the raw materials of the binder paste include cement and water; when obtaining concrete with a compressive strength greater than 30 MPa, the binder paste needs to be admixed with a strengthening material, and the admixture amount of the strengthening material is calculated as a percentage of the cement consumption.

[0039] Furthermore, the cost calculation function formula is:

[0040]

[0041] where n is the number of categories of materials required for concrete, i is the category corresponding to the required material, P i is the price corresponding to material i, m i is the mass of material i required per unit volume of concrete, M is the total amount of municipal solid waste incineration bottom ash treated each time, M IBA is the mass of bottom ash required per unit volume of concrete, k is the adjustment coefficient, C 0 is the benchmark treatment price of municipal solid waste incineration bottom ash, S 0 is the subsidy price of municipal solid waste incineration bottom ash.

[0042] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0043] 1. Through the dynamic multi - prediction model, the present invention analyzes the effects of aggregate category, particle size, dense packing density, apparent density, crushing value, and bottom ash replacement rate on the strength and water permeability of concrete, establishes the objective functions of strength and water permeability, and performs linear regression in combination with experimental data to dynamically adjust the weights of various factors, so as to realize the optimal design of concrete performance. This model is based on a large amount of experimental data and takes into account various aggregate characteristics, and can flexibly adjust the proportions corresponding to strength and water permeability according to different engineering requirements, thus ensuring that the concrete has good comprehensive performance and application reliability.

[0044] 2. The present invention proposes an inertia - adaptive particle swarm optimization algorithm, which realizes the efficient solution and optimization of the dynamic multi - prediction model by dynamically adjusting the inertia weight and learning factor of the particles. This algorithm can automatically adjust the search range and convergence speed according to the iterative state of the particles during the search process, improve the global search ability, avoid falling into local optimal solutions, continuously update the speed and position of the particles during the iteration process, and quickly lock the best selection scheme of aggregate characteristics. Through the inertia - adaptive particle swarm optimization algorithm, it is ensured that the best selection scheme of aggregate characteristics can be accurately and quickly obtained, which is convenient for obtaining a reasonable concrete mix design method.

[0045] 3. The present invention accurately calculates each material (such as cement, aggregate, admixture, and reinforcement) in the concrete mix through a cost - calculation function, and at the same time takes into account the treatment cost and treatment subsidy of municipal solid waste incineration bottom ash to realize a comprehensive assessment of the construction cost. This function can quickly calculate the unit - volume concrete cost under the optimal mix design scheme according to the actual material consumption and market price. Through the cost - calculation function, it effectively helps project decision - makers evaluate the cost expenditures of different mix design schemes, and provides a scientific economic basis for the optimization of concrete mix design and the actual engineering implementation. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of a high - strength and high - water - permeability concrete mix design method based on aggregate characteristics provided by an embodiment of the present invention;

[0047] Figure 2 It is a flow chart of an inertia - adaptive particle swarm optimization algorithm provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0049] A method for designing the mix proportion of high-strength and highly permeable concrete based on the characteristics of aggregates, comprising:

[0050] Referring to Figure 1 in S100, collect the bottom ash from municipal solid waste incineration, pre-treat the bottom ash from municipal solid waste incineration to obtain detoxified bottom ash;

[0051] Further, the pre-treatment of the bottom ash from municipal solid waste incineration includes: removing large impurities and metal residues by physical screening; using tap water for washing to reduce the heavy metal and salt content; using chemical stabilizers to fix heavy metal ions; through heat treatment, pickling and alkali washing to obtain detoxified bottom ash.

[0052] Specifically, use a vibrating screen to classify the bottom ash according to the particle size, screen out the coarse particles larger than 13.2 mm and smaller than 2.4 mm, and then remove ferromagnetic substances through a magnetic separator; soak the screened bottom ash in clean water, stir for 30 minutes, let it stand and precipitate, then discard the supernatant, and repeat the water washing step 3-4 times until the washed water is clear; first soak the washed bottom ash in a phosphate solution to solidify heavy metals, with a reaction time of about 2 hours, continuously stir during this period, and then soak it in a sodium silicate solution to coat the heavy metal particles, with a reaction time of about 2 hours, continuously stir during this period; wash the bottom ash with water to remove the residual chemical reagents, soak the bottom ash in a 5% sodium hydroxide solution and react for 1 hour, continuously stir during this period, after the reaction is completed, rinse it with clean water multiple times until it is neutral, filter the bottom ash and put it into a muffle furnace, roast it at 500-700 °C for 1-2 hours, and cool it to room temperature to obtain detoxified bottom ash.

[0053] Through the pre-treatment steps, heavy metals, salts and organic pollutants in the bottom ash are systematically removed, and safe and usable detoxified bottom ash is obtained. The whole process combines physical, chemical and heat treatment methods to ensure that the treated bottom ash can be stably used in concrete, which not only improves the environmental protection performance of the material but also realizes the effective utilization of resources.

[0054] Referring to Figure 1 in S200, screen the detoxified bottom ash and classify it according to the particle size of the detoxified bottom ash;

[0055] Further, classification according to the particle size of the detoxified bottom ash includes: fine-grained bottom ash of 2.4 - 4.75 mm, medium-grained bottom ash of 4.75 - 9.5 mm, and coarse-grained bottom ash of 9.5 - 13.2 mm.

[0056] Screening the detoxified bottom ash and classifying it according to the particle size helps the scientific matching of the domestic waste incineration bottom ash and traditional coarse aggregates, ensures the coordinated matching of aggregates of various particle sizes, optimizes the grading structure of the concrete, and thus improves the overall performance and material utilization efficiency of the concrete.

[0057] Refer to Figure 1 S300 in, design multiple groups of benchmark experiments with the aggregate category, particle size, dense packing density, apparent density, and crushing value as variables, test the strength and water permeability coefficient of the concrete under different mix ratios, and obtain the benchmark experimental data;

[0058] Further, the aggregate category includes crushed stone and pebbles; the particle size includes fine-grained aggregates of 2.4 - 4.75 mm, medium-grained aggregates of 4.75 - 9.5 mm, and coarse-grained aggregates of 9.5 - 13.2 mm; each group of the benchmark experiments includes three groups of benchmark parallel experiments.

[0059] Specifically, the aggregate particle size includes fine-grained aggregates (2.4 - 4.75 mm), medium-grained aggregates (4.75 - 9.5 mm), and coarse-grained aggregates (9.5 - 13.2 mm); the aggregate crushing value < 15%; the dense packing density > 1350 kg / m 3 , the apparent density > 2500 kg / m 3 ; the 28-day compressive strength ≥ 20 MPa, and the data that do not meet the requirements are directly discarded; the water permeability coefficient ≥ 0.5 mm / s, and the data that do not meet the requirements are directly discarded; the result of the benchmark experiment takes the arithmetic mean of the results of the three groups of benchmark parallel experiments as the test result. If the difference between the maximum or minimum value and the intermediate value in the three groups of parallel experiments exceeds 15%, then the intermediate value is taken as the result of this group of experiments. If the differences between both the maximum and minimum values and the intermediate value exceed 15%, then the result of this group of experiments is regarded as invalid. In an implementable scheme, some of the benchmark experimental data and their corresponding results are shown in Table 1.

[0060] By designing multiple groups of benchmark experiments with the aggregate characteristics as variables and testing the 28-day compressive strength and water permeability coefficient of the concrete under different mix ratios, the benchmark experimental data are obtained, and the influence of each aggregate characteristic on the concrete performance is further clarified. Through multiple groups of parallel experiments on two types of aggregates, crushed stone and pebbles, in three particle size ranges of fine-grained, medium-grained, and coarse-grained, as well as the dense packing density and apparent density, the accuracy and reliability of the data are ensured, providing a scientific basis for subsequent optimization of the concrete mix ratio, improvement of strength and water permeability performance.

[0061] Refer to Figure 1In S400, based on the benchmark experiment, the replacement rate of the gradient-boosted detoxified bottom ash for the aggregate is designed with multiple groups of comparative experiments to test the strength and water permeability coefficient of the concrete under different mix ratios and obtain the comparative experiment data;

[0062] Further, the replacement rate of the detoxified bottom ash for the aggregate is equal-mass replacement, the replacement ratio is 5% - 50%, and the single replacement gradient is 5%; each group of the comparative experiments includes three groups of comparative parallel experiments.

[0063] Table 1. Partial data of the benchmark experiment and their corresponding results

[0064]

[0065] Specifically, the result of the comparative experiment takes the arithmetic mean of the results of the three groups of comparative parallel experiments as the test result. If the difference between the maximum or minimum value and the middle value in the three groups of comparative parallel experiments exceeds 15%, then the middle value is taken as the result of this group of experiments. If the differences between both the maximum and minimum values and the middle value exceed 15%, then the result of this group of experiments is regarded as invalid. In an implementable solution, the partial data of the comparative experiment and their corresponding results are shown in Table 2.

[0066] By increasing the replacement rate of the gradient-boosted detoxified bottom ash for the aggregate and conducting multiple groups of comparative experiments, the effects of different replacement rates on the strength and water permeability coefficient of the concrete can be systematically analyzed and evaluated. Setting the replacement rate to 5% - 50% and gradually increasing it at a gradient of 5% can accurately locate the optimal replacement ratio, making the experimental data more continuous and complete. At the same time, each group of comparative experiments includes three parallel experiments, and their average value is taken as the experimental result, effectively improving the reliability and repeatability of the data, reducing the experimental error, and providing a scientific basis and data support for optimizing the concrete mix ratio.

[0067] Refer to Figure 1 In S500, based on the benchmark experiment data and the comparative experiment data, a dynamic multi - prediction model is established to dynamically adjust the weights of the concrete strength and water permeability coefficient according to the requirements;

[0068] Further, the dynamic multi - prediction model includes a strength objective function, a water permeability objective function, and constraint conditions; the strength objective function includes strength regression coefficients corresponding to aggregate category, particle size, dense packing density, apparent density, crushing value, and bottom ash replacement rate, and the strength regression coefficients are obtained by linear regression of the reference experimental data and the comparative experimental data; the water permeability objective function includes water permeability regression coefficients corresponding to aggregate category, particle size, dense packing density, apparent density, crushing value, and bottom ash replacement rate, and the water permeability regression coefficients are obtained by linear regression of the reference experimental data and the comparative experimental data; the constraint conditions are range constraints on the corresponding values of aggregate category, particle size, dense packing density, apparent density, crushing value, and bottom ash replacement rate.

[0069] Table 2. Partial data of the comparative experiment and their corresponding results

[0070]

[0071] Specifically, the aggregate category is discretized into (1, 2), where 1 is crushed stone and 2 is pebble; the aggregate particle size is discretized into (1, 2, 3), where 1 is fine - grained aggregate (2.4 - 4.75 mm), 2 is medium - grained aggregate (4.75 - 9.5 mm), and 3 is coarse - grained aggregate (9.5 - 13.2 mm). Taking the aggregate category, particle size, dense packing density, apparent density, crushing value, and bottom ash replacement rate as independent variables and the compressive strength as the dependent variable, a linear strength objective function S = k 1 ·T + k 2 ·C + k 3 ·D + k 4 ·ρ 1 + k 5 ·ρ 2 + k 6 ·I + b is established, where T is the aggregate category, C is the crushing value, D is the particle size, ρ 1 is the dense packing density, ρ 2 is the apparent density, I is the bottom ash replacement rate, k 1 is the strength regression coefficient of the aggregate category, k 2 is the strength regression coefficient of the crushing value, k 3 is the strength regression coefficient of the particle size, k 4 is the strength regression coefficient of the dense packing density, k 5 is the strength regression coefficient of the apparent density bottom, k 6 is the ash replacement rate. Taking the aggregate category, particle size, dense packing density, apparent density, crushing value, and bottom ash replacement rate as independent variables and the water permeability coefficient as the dependent variable, a water permeability objective function P = m 1 ·T + m 2 ·C + m 3 ·D + m 4 ·ρ1 +m 5 ·ρ 2 +m 6 ·I + d, where m 1 、m 2 、m 3 、m 4 、m 5 and m 6 correspond to the permeability regression coefficients of aggregate category, crushing value, particle size, dense packing density, apparent density, and bottom ash replacement rate respectively. The constraint conditions refer to the industry standard CJJ / T 135 - 2009 "Technical Specification for Permeable Cement Concrete Pavement", including g 1 (D) = D - D max ≤0, g 2 (D) = D min - D ≤ 0, g 3 (S) = S min - S ≤ 0, g 4 (P) = P min - P ≤ 0,..., where D max is the maximum specified particle size, D min is the minimum specified particle size, S min is the minimum specified compressive strength, P min is the minimum specified permeability coefficient. Since there are many constraint conditions, they are not enumerated here. According to the strength objective function, permeability objective function, and constraint conditions, the dynamic multiple prediction model is obtained as:

[0072]

[0073] where α is the strength adjustment coefficient, β is the permeability adjustment coefficient, and the values of α and β can be dynamically adjusted according to the project's requirements for strength and permeability.

[0074] The dynamic multiple prediction model established through benchmark experimental data and comparative experimental data can flexibly adjust the weights of concrete strength and permeability coefficient, thus meeting different engineering requirements. This model describes the influence of aggregate category, particle size, dense packing density, apparent density, crushing value, and bottom ash replacement rate on concrete performance through strength and permeability objective functions, and obtains the regression coefficients through linear regression of experimental data to ensure the accuracy of the model. Combining reasonable constraint conditions, the model can not only optimize the concrete mix ratio but also dynamically adjust the concrete performance for different usage scenarios, providing a precise and scientific design basis and significantly improving the performance utilization efficiency and engineering adaptability of materials.

[0075] Refer to Figure 1 S600 in, and use the inertia - adaptive particle swarm optimization algorithm to calculate the dynamic multiple prediction model to obtain the optimal aggregate selection scheme;

[0076] Further, the inertia adaptive particle swarm optimization algorithm is as follows Figure 2 shown, including:

[0077] S601: Set the number of particle swarms, the maximum number of iterations, the inertia weight, the learning factor, and the speed limit, and randomly generate the initial position and speed for each particle;

[0078] S602: Evaluate the current fitness of each particle, and calculate the performance of each particle in the concrete mix proportion optimization through the objective function;

[0079] S603: Update according to the current speed, position, individual optimal position, and global optimal position of each particle to ensure that the particle moves towards the global optimal position;

[0080] S604: Dynamically adjust the inertia weight using the linear decreasing method to enhance the early search ability and the late convergence accuracy;

[0081] S605: When the global optimal position does not change significantly in multiple iterations, fine-tune the global optimal position through the local perturbation search mechanism;

[0082] S606: Check whether the maximum number of iterations is reached or the adjustment change of the global optimal position is less than the set threshold. If satisfied, stop the iteration; otherwise, continue the iteration;

[0083] S607: Output the current global optimal position, that is, the best selection scheme for aggregate characteristics.

[0084] Specifically, in step S601, set the number of particle swarms N, the maximum number of iterations T, the initial inertia weight w max and the final inertia weight w min , the learning factor c 1 , c 2 , set the speed range v min and v max , and randomly generate the initial position (corresponding to each variable in the concrete mix proportion) and speed for each particle; in step S602, for each particle, calculate the objective function value Update the individual optimal position p best and the global optimal position g best ; in step S603, update the particle speed To prevent the particle speed from being too fast, limit the particle speed Synchronously update the particle position In step S604, adjust the inertia weight using the linear decreasing method to enhance the early search ability and the late convergence accuracy; in step S605, if the global optimal position g best is not updated significantly, then the global optimal position is fine-tuned g′ best = g best + ∈·rand(-1, 1); in step S606, it is checked whether the convergence condition is satisfied. The convergence condition is to reach the maximum number of iterations or the change in the adjustment of the global optimal position is less than the set threshold. If it is satisfied, the process stops; otherwise, the iteration continues; in step S607, the current global optimal position is output, that is, the best selection scheme for aggregate characteristics.

[0085] By using the inertia adaptive particle swarm optimization algorithm to calculate the dynamic multi-variable prediction model, the best aggregate selection scheme can be obtained efficiently, so as to optimize the strength and water permeability of concrete. The algorithm randomly generates the initial position and velocity for each particle by setting the number of particle swarms, the maximum number of iterations, the inertia weight, the learning factor, and the velocity limit, and evaluates the fitness performance of each particle through the objective function. The velocity and position of the particle are dynamically updated according to the individual optimal and global optimal positions, and the inertia weight is adjusted by the linear decreasing method to enhance the early search ability and the late convergence accuracy. When the global optimal position does not change significantly, it is further fine-tuned through the local perturbation mechanism to ensure finding the optimal solution. The finally output global optimal position is the best selection scheme for aggregate characteristics.

[0086] Refer to Figure 1 S700 in

[0087] Further, determining the concrete mix ratio includes:

[0088] Calculating the aggregate dosage per unit volume according to the bulk density and correction coefficient of the aggregate;

[0089] Calculating the volume of the binder slurry per unit volume according to the porosity of the dense packing of the aggregate and the designed porosity;

[0090] Calculating the water consumption per unit volume of concrete and the cement consumption per unit volume of concrete according to the preset water-binder ratio;

[0091] Calculating the admixture dosage per unit volume according to the cement consumption per unit volume of concrete and the admixture content.

[0092] Specifically, the aggregate dosage per unit volume W G = α·ρ 1 , where α is the aggregate dosage correction coefficient, taking 0.98, and ρ 1 is the dense packing coefficient of the aggregate; the volume of the binder slurry per unit volume where R voidFor the designed porosity and the dense packing porosity where ρ 2 is the apparent density; the cement content per unit volume of concrete where R W / C is the water-binder ratio, ρ C is the cement density, ρ W is the density of water, and the water consumption per unit volume of concrete is W w = W c ·R w / c ; the admixture content per unit volume is M * = W c *a, where a is the admixture dosage rate.

[0093] By accurately calculating the mix proportion of concrete according to the optimal aggregate selection scheme, the material utilization efficiency and performance of concrete can be optimized. This process includes accurately calculating the aggregate content per unit volume based on the bulk density and correction factor of the aggregate, and then reasonably determining the amount of binder paste according to the dense packing porosity and designed porosity of the aggregate. The preset water-binder ratio ensures sufficient cement hydration, calculates the water consumption and cement content of concrete, and accurately adjusts the dosage of admixtures. This systematic calculation method can quickly obtain the amounts of various materials in concrete, reduce material waste and mix proportion errors, provide accurate concrete mix schemes for different engineering applications, and improve construction quality and efficiency.

[0094] Furthermore, the raw materials of the binder paste include cement and water; when obtaining concrete with a compressive strength greater than 30 MPa, the binder paste needs to be admixed with a strengthening material, and the dosage rate of the strengthening material is calculated as a percentage of the cement content.

[0095] By adding strengthening materials (such as silica fume, slag powder, nano materials, etc.) to the binder paste, the strength of concrete can be flexibly adjusted to meet the performance requirements of different projects. Adding the strengthening material as a percentage of the cement content facilitates controlling the dosage accuracy, ensuring the stability and predictability of the paste strength adjustment, thereby realizing the optimization of the mix proportion and improving the construction quality and concrete performance.

[0096] Refer to Figure 1 S800 in, and use the cost calculation function to calculate the cost required for the construction of per unit volume of concrete under this concrete mix proportion to evaluate the economic benefits;

[0097] Furthermore, the formula of the cost calculation function is:

[0098]

[0099] where n is the number of categories of materials required for concrete, i is the category corresponding to the required material, P i is the price corresponding to material i, m iThe mass of material i required for unit volume of concrete, M is the total amount of municipal solid waste incineration bottom ash treated in a single time, M IBA is the mass of bottom ash required for unit volume of concrete, k is the adjustment coefficient, C 0 is the benchmark treatment price of municipal solid waste incineration bottom ash, S 0 is the benchmark subsidy price of municipal solid waste incineration bottom ash.

[0100] Table 3. Materials required for high-strength and high-permeability concrete and the corresponding prices of materials

[0101]

[0102] Specifically, in a feasible implementation scheme, the materials required for concrete and the corresponding prices of materials are shown in Table 3, where only one of crushed stone and pebble can be selected in a single construction; the total amount of municipal solid waste incineration bottom ash treated in a single time is 10 tons, the benchmark treatment price of municipal solid waste incineration bottom ash is 163 yuan / ton, the subsidy price of municipal solid waste incineration bottom ash is 180 yuan / ton, and the adjustment coefficient is 0.0007. Using the cost calculation function to calculate the construction cost under the concrete mix ratio can accurately evaluate the use cost of materials and construction expenses, providing a scientific basis for decision-making. By accounting for the material costs of various materials such as aggregate, cement, admixture, and reinforcement one by one, combined with the treatment cost and treatment subsidy of municipal solid waste incineration bottom ash, the cost requirements of the concrete mix ratio scheme are comprehensively analyzed. This method not only helps to optimize the mix ratio design, reduce the overall cost, but also realizes the recycling of resources, improving the environmental friendliness and economic sustainability of the project.

[0103] Example 1

[0104] Design permeable concrete with a compressive strength greater than 40 MPa and a permeability coefficient k≥0.5 mm / s.

[0105] According to the design requirements, set the strength adjustment coefficient of the dynamic multi-prediction model to 0.8 and the permeability adjustment coefficient to 0.2; according to the inertia adaptive particle swarm optimization algorithm, select pebble as the aggregate category, with a crushing value ≤3%, select fine-grained aggregate (2.4 - 4.75 mm) for the particle size, and the dense packing density ρ 1 ≥1850 kg / m 3 , the apparent density ρ 2 ≥2880 kg / m 3 , and select 0% replacement rate of bottom ash for replacement. According to W G =α·ρ 1 , calculate that the aggregate dosage per unit volume is 1758 kg; according to calculate that the dense packing porosity is 35.8%, and from the design porosity R void =12% and It is obtained that the volume of the cementitious paste per unit volume is 25%; according to water-cement ratio R W / C = 0.28, cement density ρ C = 3000 kg / m 3 , it is calculated that the cement dosage per unit volume of concrete is 407 kg. According to W w = W c ·R w / c it is calculated that the water dosage per unit volume of concrete is 114 kg; according to M * = W c *a and admixture dosage a = 1.3%, it is calculated that the admixture dosage per unit volume is 5 kg; to enhance the strength of concrete, 15% silica fume is used as a strength enhancer, and it is calculated that the silica fume dosage per unit volume is 61 kg. According to the above material dosages, the corresponding prices in Table 3 and the cost calculation function, the cost per unit volume of concrete is calculated to be 547 yuan.

[0106] Example 2

[0107] Design permeable concrete with a compressive strength greater than 20 MPa and a permeability coefficient k ≥ 1.5 mm / s.

[0108] According to the design requirements, the strength adjustment coefficient of the dynamic multi - prediction model is set to 0.3, and the water permeability adjustment coefficient is set to 0.7; according to the inertia - adaptive particle swarm optimization algorithm, the aggregate category selected is crushed stone with a crushing value ≤ 15%, the particle size selected is coarse aggregate (9.5 - 13.2 mm), and the dense packing density ρ 1 ≥ 1630 kg / m 3 , the apparent density ρ 2 ≥ 2700 kg / m 3 , and the bottom ash replacement rate is selected to be 35% for replacement. According to the aggregate dosage per unit volume W G = α·ρ G , it is calculated that the aggregate dosage per unit volume is 1597 kg; according to it is calculated that the dense packing porosity is 39.6%. From the design porosity R void = 14% and it is obtained that the volume of the cementitious paste per unit volume is 27%; according to ρ C and water - cement ratio R W / C = 0.30, cement density ρ C = 3000 kg / m 3 , it is calculated that the cement dosage per unit volume of concrete is 426 kg. According to W w = W c ·R w / c it is calculated that the water dosage per unit volume of concrete is 128 kg; according to M * = Wc *The admixture dosage a = 0.8%, and the calculated dosage of admixture per unit volume is 3 kg. According to the above material dosages, the corresponding prices in Table 3, and the cost calculation function, the calculated cost of concrete per unit volume is 330 yuan.

[0109] Example 3

[0110] Design permeable concrete with a compressive strength greater than 30 MPa and a permeability coefficient k ≥ 1.2 mm / s.

[0111] According to the design requirements, set the strength adjustment coefficient of the dynamic multi - prediction model to 0.5 and the water permeability adjustment coefficient to 0.5; according to the inertia - adaptive particle swarm optimization algorithm, select pebble as the aggregate category, with a crushing value ≤ 10%, select medium - sized aggregate (4.75 - 9.5 mm) for the particle size, and the dense packing density ρ 1 ≥ 1750 kg / m 3 , the apparent density ρ 2 ≥ 2820 kg / m 3 , select 10% as the replacement rate of the bottom ash for replacement. According to the aggregate dosage per unit volume W G = α·ρ G , the calculated aggregate dosage per unit volume is 1715 kg; according to calculate the dense packing porosity to be 37.9%. From the designed porosity R void = 14% and obtain the volume of the cementitious material paste per unit volume to be 25%; according to and the water - binder ratio R W / C = 0.25, the cement density ρ C = 3000 kg / m 3 , calculate the cement dosage per unit volume of concrete to be 429 kg. According to W w = W c ·R w / c calculate the water dosage per unit volume of concrete to be 107 kg; according to M * = W c *The admixture dosage a = 0.9%, and the calculated dosage of admixture per unit volume is 4 kg. According to the above material dosages, the corresponding prices in Table 3, and the cost calculation function, the calculated cost of concrete per unit volume is 494 yuan.

[0112] Precisely analyze the strength and water permeability requirements of concrete through a dynamic multi - prediction model, enabling mix design to better meet engineering requirements. Based on the inertia - adaptive particle swarm optimization algorithm, systematically obtain the optimal aggregate selection scheme to ensure that the concrete performance reaches the best under various constraints. Subsequently, accurately calculate the dosage of each material and the overall construction cost through a cost - calculation function, and further optimize the overall cost control by combining the bottom - ash treatment cost and subsidies. Such a design process not only ensures high strength and good water permeability of concrete, but also significantly reduces raw material costs and construction expenses, avoids resource waste, and simultaneously realizes the efficient utilization of environmentally friendly materials.

[0113] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A high-strength and highly permeable concrete mix design method based on aggregate properties, characterized in that: include: Collecting bottom ash from incineration of domestic waste, pre-treating the bottom ash from incineration of domestic waste, and obtaining detoxified bottom ash; Screening the detoxified bottom ash and classifying the detoxified bottom ash according to its particle size; Taking aggregate type, particle size, compact packing density, apparent density and crushing value as variables, multiple groups of benchmark experiments were conducted to test the strength and water permeability coefficient of concrete under different mix ratios to obtain benchmark experimental data; Based on the benchmark experiment, the replacement rate of the detoxified bottom ash for aggregate is gradually increased, and multiple groups of comparative experiments are conducted to test the strength and water permeability coefficient of concrete under different mix ratios to obtain comparative experimental data; A dynamic multivariate prediction model is established based on the benchmark experimental data and the comparative experimental data, and the weights of concrete strength and water permeability coefficient are dynamically adjusted according to demand; Using an inertial adaptive particle swarm optimization algorithm to calculate the dynamic multivariate prediction model to obtain the best aggregate selection solution; Calculate the concrete mix ratio according to the optimal aggregate selection scheme; The cost calculation function is used to calculate the cost required for unit volume concrete construction under the concrete mix ratio to conduct cost evaluation.

2. A high-strength and highly permeable concrete mix design method based on aggregate properties according to claim 1, characterized in that: Pre-treating the domestic waste incineration bottom ash includes: Remove bulk impurities and metal residues through physical screening; Use tap water to wash to reduce heavy metal and salt content; Use chemical stabilizers to fix heavy metal ions; Detoxified bottom ash is obtained through heat treatment, acid washing and alkali washing.

3. The method for designing a mix ratio of high-strength and highly permeable concrete based on aggregate properties according to claim 1, characterized in that: The detoxified bottom ash is classified according to its particle size into: 2.4-4.75 mm fine-grained bottom ash, 4.75-9.5 mm medium-grained bottom ash and 9.5-13.2 mm coarse-grained bottom ash.

4. The method for designing a mix ratio of high-strength and highly permeable concrete based on aggregate properties according to claim 1, characterized in that: The aggregate categories include crushed stone and pebbles; the particle sizes include 2.4-4.75 mm fine aggregate, 4.75-9.5 mm medium aggregate and 9.5-13.2 mm coarse aggregate; each group of the benchmark experiments includes three groups of benchmark parallel experiments.

5. The method for designing a mix ratio of high-strength and highly permeable concrete based on aggregate properties according to claim 1, characterized in that: The replacement rate of the detoxified bottom ash for aggregate is equal mass replacement, the replacement ratio is 5% to 50%, and the single replacement gradient is 5%; each group of the comparative experiments includes three groups of comparative parallel experiments.

6. The method for designing a mix ratio of high-strength and highly permeable concrete based on aggregate properties according to claim 1, characterized in that: The dynamic multivariate prediction model includes a strength objective function, a permeability objective function and constraints; the strength objective function contains strength regression coefficients corresponding to aggregate type, particle size, close packing density, apparent density, crushing value and bottom ash replacement rate, and the strength regression coefficients are obtained by linear regression of the benchmark experimental data and the comparative experimental data; the permeability objective function contains permeability regression coefficients corresponding to aggregate type, particle size, close packing density, apparent density, crushing value and bottom ash replacement rate, and the permeability regression coefficients are obtained by linear regression of the benchmark experimental data and the comparative experimental data; the constraints are range constraints on the corresponding values ​​of aggregate type, particle size, close packing density, apparent density, crushing value and bottom ash replacement rate.

7. The method for designing a mix ratio of high-strength and highly permeable concrete based on aggregate properties according to claim 1, characterized in that: The inertial adaptive particle swarm optimization algorithm includes: The number of particle swarms, the maximum number of iterations, the inertia weight, the learning factor and the speed limit are set, and the initial position and speed are randomly generated for each particle; Evaluate the current fitness of each particle, and calculate the performance of each particle in concrete mix optimization through an objective function; Update according to the current speed, position, individual optimal position and global optimal position of each particle to ensure that the particle moves to the global optimal position; The inertia weight is dynamically adjusted using a linear decreasing method to enhance early search capability and late convergence accuracy; When the global optimal position does not change significantly in multiple iterations, fine-tuning the global optimal position through a local perturbation search mechanism; Check whether the maximum number of iterations is reached or the global optimal position adjustment change is less than a set threshold, if so, stop the iteration, otherwise continue the iteration; Output the current global optimal position, that is, the best selection scheme for aggregate properties.

8. The method for designing the mix ratio of high-strength and highly permeable concrete based on aggregate properties according to claim 1, characterized in that: Determining the concrete mix ratio includes: Calculate the amount of aggregate per unit volume based on the aggregate's bulk density and correction factor; Calculate the volume of cement paste per unit volume based on the aggregate close-packed porosity and the designed porosity; Calculate the water consumption per unit volume of concrete and the cement consumption per unit volume of concrete according to the preset water-binder ratio; The amount of admixture per unit volume is calculated based on the amount of cement and admixture added per unit volume of concrete.

9. A method for designing a mix ratio of high-strength and highly permeable concrete based on aggregate properties according to claim 8, characterized in that: The binder slurry is composed of raw materials including cement and water. When obtaining concrete with a compressive strength greater than 30 MPa, the binder slurry needs to be mixed with reinforcing material, and the amount of the reinforcing material added is calculated as a percentage of the cement dosage.

10. The method for designing a mix ratio of high-strength and highly permeable concrete based on aggregate properties according to claim 1, characterized in that: The cost calculation function formula is: Where n is the number of categories of materials required for concrete, i is the category corresponding to the required material, P i is the price corresponding to material i, m i is the mass of material i required for unit volume of concrete, M is the total amount of bottom ash treated in a single domestic waste incineration, M IBA is the mass of bottom ash required for unit volume of concrete, k is the adjustment coefficient, C0 is the benchmark treatment price of bottom ash from incineration of domestic waste, and S0 is the subsidy price of bottom ash from incineration of domestic waste.

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