Full-hole slag concrete collaborative deformation design method

Abstract

The invention relates to the technical field of building materials, and discloses a full-hole slag concrete collaborative deformation design method, which takes a collaborative deformation theory as a core, and comprises five steps of design parameter determination, material selection, machine learning output proportion, trial preparation and adjustment, and construction mix proportion determination. According to the method, a raw material-performance database is established, a neural network air shrinkage prediction model, a gradient lifting strength prediction model and a genetic algorithm are combined to carry out multi-objective optimization, and a recommendation ratio is output; after the workability, the strength and the dry shrinkage rate are ensured to reach the standard through trial preparation and adjustment, the construction mix proportion is determined by combining the water content of the aggregate. Project verification shows that the method can realize performance balance of C30-C60 grade concrete, and is suitable for resource utilization of hole slag aggregate.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building materials, in particular to a full-hole slag concrete collaborative deformation design method, which is based on collaborative deformation theory and data driving and is suitable for resource utilization of waste hole slag in tunnel engineering. BACKGROUND

[0002] Traditional concrete design is dominated by strength, ignoring the collaborative deformation of paste-aggregate-interface, resulting in high risk of dry shrinkage cracking. The stone powder of the hole slag mechanism aggregate contains various lithologies such as granite and limestone, and the dosage has a complex influence on shrinkage. There is a lack of quantitative control method for lithological differentiation. The traditional concrete mix design method relies on trial and error adjustment, and the mathematical model of material parameters-deformation performance has not been established, making it difficult to realize the synchronous optimization of mechanical properties and shrinkage deformation. SUMMARY

[0003] The purpose of the present application is to provide a full-hole slag concrete collaborative deformation design method, which integrates collaborative deformation theory, machine learning and multi-objective optimization, and realizes the balanced control of hole slag concrete strength and dry shrinkage deformation.

[0004] In order to achieve the above purpose, the technical scheme adopted is as follows:

[0005] The present application provides a full-hole slag concrete collaborative deformation design method, which comprises:

[0006] S1: determining the strength grade and workability requirement of the concrete; wherein the workability requirement includes the target value of slump;

[0007] S2: cement, hole slag aggregate, stone powder and water reducing agent as raw materials, wherein the cement is selected according to the strength grade to adapt to the grade of Portland cement, the hole slag aggregate includes machine-made sand and coarse aggregate, the machine-made sand is medium sand and the fineness modulus is 2.3-3.0, the maximum particle size of the coarse aggregate is 31.5mm, the stone powder is granite stone powder or limestone stone powder, and the water reducing agent is polycarboxylic acid with a water reducing rate of 20%-30%;

[0008] S3: based on the pre-set raw material-deformation performance-mechanical property database, performance prediction is carried out through the 56d deformation prediction model and the 28d mechanical prediction model, multi-objective optimization is carried out combined with genetic algorithm, and the recommended proportioning range is output; wherein the multi-objective optimization takes the maximum compressive strength and the minimum dry shrinkage rate as the target;

[0009] S4: trial mixing is carried out according to the recommended proportioning range, the slump, 7d compressive strength and 56d dry shrinkage rate are detected, if the slump does not meet the requirement, the water reducing agent dosage or water-binder ratio is adjusted; if the 7d compressive strength is less than 70% of the design value, the proportioning is re-adjusted; if the 56d dry shrinkage rate is not in the recommended range, the proportioning is re-adjusted;

[0010] S5: detecting the water content of the manufactured sand and the coarse aggregate, adjusting the amount of the manufactured sand, the amount of the coarse aggregate and the amount of water according to the water content, and obtaining a construction mixing proportion.

[0011] Preferably, in step S1, the strength grade of the concrete is C30-C60, and the target value of the slump is 100-220 mm.

[0012] Preferably, in step S2, the content of the stone powder satisfies: the content of the non-active stone powder is not more than 15%, and the content of the active stone powder is not more than 20%; the methylene blue value of the manufactured sand is ≤1.4, the crushing index is ≤20%, and the loose bulk density is ≥1400 kg / m 3 .

[0013] Preferably, in step S3, the 56d deformation prediction model is a prediction model based on a neural network, and the 28d mechanical prediction model is a prediction model based on a gradient boosting tree; in the recommended proportioning range, the 28d compressive strength range and the 56d drying shrinkage rate range of each strength grade are as follows:

[0014] C30: 28d compressive strength 30-40 MPa, 56d drying shrinkage rate 351-480×10 -6 ;

[0015] C40: 28d compressive strength 40-50 MPa, 56d drying shrinkage rate 304-455×10 -6 ;

[0016] C50: 28d compressive strength 50-60 MPa, 56d drying shrinkage rate 287-439×10 -6 ;

[0017] C60: 28d compressive strength 60-70 MPa, 56d drying shrinkage rate 250-368×10 -6 .

[0018] Preferably, in the recommended proportioning range output by the multi-objective optimization in step S3, the water-binder ratio, the cement content, the sand ratio, the coarse aggregate content and the water reducer content are fixed values, and the contents of fly ash, mineral powder and stone powder are adjustable ranges; the content range of the stone powder is determined according to the lithology, and is specifically as follows:

[0019] C30: granite stone powder 12.3-18.5%, limestone stone powder 9.5-14.8%;

[0020] C40: granite stone powder 8.3-14.4%, limestone stone powder 6.6-11.5%;

[0021] C50: granite stone powder 6.3-10%, limestone stone powder 5-8%;

[0022] C60: Granite stone powder 0-6.8%, limestone stone powder 0-5.4%.

[0023] Preferably, step S3 further comprises stone powder dosage correction, specifically comprising: using stone powder to replace fly ash and / or mineral powder in quality, determining the replacement amount based on a replacement coefficient, and the replacement coefficient is calculated by a formula:

[0024]

[0025] Wherein, μ i is the replacement coefficient, γ i is the correlation coefficient of stone powder and fly ash or mineral powder, σ S is the standard deviation of stone powder dosage, σ i is the standard deviation of fly ash or mineral powder dosage.

[0026] Preferably, in step S4, the adjustment method of slump includes:

[0027] When the slump is insufficient, increase the dosage of water reducing agent by 0.1% or fine-tune the water-binder ratio by ±0.01 each time; when the cohesiveness is poor, increase the sand rate by 1% each time;

[0028] When controlling the air content, if the air content does not meet 3%-5%, adjust the dosage of air-entraining agent by 0.005% each time.

[0029] Preferably, in step S4, cubic test blocks are prepared for compressive strength test, and prism test blocks are prepared for drying shrinkage test; the cubic test blocks are standard cured for 7d or 28d, and the prism test blocks are standard cured for 3d and then moved into a drying box with a temperature of 20±2℃ and a humidity of 60±5% for curing.

[0030] Preferably, in step S5, the adjustment formula of the amount of machine-made sand, the amount of coarse aggregate, and the amount of water is as follows:

[0031] Machine-made sand amount = theoretical machine-made sand amount × (1+α / 100)

[0032] Coarse aggregate amount = theoretical coarse aggregate amount × (1+β / 100)

[0033] Water amount = theoretical water amount - theoretical machine-made sand amount × α / 100 - theoretical coarse aggregate amount × β / 100

[0034] Wherein, α is the moisture content of machine-made sand (%), and β is the moisture content of coarse aggregate (%).

[0035] Preferably, the tunnel slag aggregate is obtained by processing the tunnel slag produced by tunnel excavation, and the coarse aggregate uses stone materials with particle sizes of 5-10mm, 10-20mm, and 20-31.5mm.

[0036] The beneficial effects of the present application are:

[0037] (1) The hole slag aggregate concrete design method is divided into five steps, which are determining design parameters (strength grade, workability), material selection (cement, machine-made aggregate, stone powder, admixture, etc.); using machine learning technology to output recommended proportion (based on neural network shrinkage model, gradient boosting strength prediction model and genetic algorithm multi-objective optimization); trial mixing adjustment (workability, strength, shrinkage rate test and optimization); finally determining the construction mixing proportion (considering the correction of aggregate moisture content).

[0038] (2) By fusing machine learning technology (including neural network-based shrinkage model and gradient boosting strength prediction model) and multi-objective genetic algorithm, a strength-shrinkage double-objective optimization model is established. The model can output the recommended proportion scheme of C30 to C60 grade concrete to ensure that its mechanical properties and deformation properties are reasonably controlled.

[0039] (3) Engineering application verification feasibility, taking a tunnel hole slag aggregate as an example, using C40 full hole slag concrete (water-cement ratio 0.48, sand ratio 45%, granite stone powder dosage 10%), the 28d strength is 45.1MPa, the 56d dry shrinkage rate is 301x10 -6 , the performance indicators meet the design requirements, and the engineering applicability of the collaborative design method is verified. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 is a general concrete design flowchart according to the prior art;

[0041] Figure 2 is a flowchart of a full hole slag concrete collaborative deformation design method provided by an embodiment of the present application;

[0042] Figure 3 is a flowchart of a genetic algorithm provided by an embodiment of the present application;

[0043] Figure 4 is a schematic diagram of concrete shrinkage performance analysis results corresponding to granite and limestone stone powder provided by an embodiment of the present application; wherein (a) is a comparative column chart of the dry shrinkage deformation of different numbered full hole slag concrete at each age; (b) is a broken line chart of the dry shrinkage deformation trend of full hole slag concrete with age; (c) is a comparative column chart of the compressive strength of different numbered full hole slag concrete at each age; (d) is a broken line chart of the compressive strength trend of full hole slag concrete with age. DETAILED DESCRIPTION

[0044] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0045] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples.

[0046] Example 1:

[0047] like Figure 1 The diagram shown is a flowchart of the ordinary concrete design process based on existing technology. The ordinary concrete design process includes five steps: determining design parameters, material selection, preliminary mix proportion calculation, trial mixing and adjustment, and determining the construction mix proportion. The existing ordinary concrete design process has the following shortcomings:

[0048] 1) Lack of accurate performance prediction. Without utilizing technologies such as machine learning, it is difficult to accurately predict the long-term deformation (drying shrinkage) and mechanical properties of concrete. Relying on experience-based trial mixing is inefficient, costly, and difficult to adapt to the complex properties of special aggregates such as full-cavity slag.

[0049] 2) Insufficient multi-objective optimization. The system failed to systematically optimize multiple properties such as strength and deformation. Simple adjustments alone were insufficient to achieve synergistic goals such as maximizing strength and minimizing deformation, resulting in poor synergistic control of the performance of the slag concrete throughout the tunnel.

[0050] 3) Limitations in adapting to special aggregates. For tunnel muck aggregates (including manufactured sand, stone powder, etc.), the impact of lithology and stone powder activity on performance is not considered, making it difficult to accurately design high-performance mix proportions suitable for all tunnel muck, thus limiting resource utilization.

[0051] 4) Lack of data-driven approach. Without a comprehensive raw material-performance database, it is difficult to accumulate and reuse data. When faced with new materials such as full-cavity slag, it is difficult to quickly iterate and optimize the mix ratio, resulting in a weak ability to adapt to new engineering needs.

[0052] To address the shortcomings of the existing technology, this invention provides a method for the coordinated deformation design of all-tunnel muck concrete. This design method, based on coordinated deformation theory and data-driven approaches, is applicable to the resource utilization of abandoned tunnel muck in tunnel engineering. The basic principles of the coordinated deformation theory are as follows:

[0053] Drying shrinkage is the phenomenon of volume reduction of concrete during water loss, mainly caused by hydration reaction and capillary pressure change of the paste part. The cooperative deformation design theory believes that through reasonable material proportioning, the deformation of the paste, aggregate and interface can be coordinated, thereby reducing the overall drying shrinkage. The applicant found that there is a correlation between raw materials and deformation performance, the deformation performance of concrete decreases with the increase of water-cement ratio and sand ratio, and increases with the increase of the amount of superplasticizer, and the three influencing factors have a nonlinear relationship with the deformation performance. The amount of stone powder is affected by multiple factors, and the drying shrinkage value of concrete mixed with granite stone powder and limestone increases and decreases with the increase of the amount of stone powder. Therefore, a data-driven full-hole slag concrete cooperative deformation design method is established.

[0054] As Figure 2 shown, a flow chart of a full-hole slag concrete cooperative deformation design method provided by an embodiment of the application, the hole slag concrete cooperative deformation design method comprises the following steps S1-S5.

[0055] S1: determining design parameters.

[0056] The strength grade and workability requirements of the concrete are determined, and the workability requirements include the target value of the slump; this step S1 provides the basis for the subsequent material selection and proportioning design, wherein the strength grade directly determines the type of cement and the mechanical property control standard in material selection, and the target value of the slump is the workability judgment benchmark for subsequent trial and adjustment.

[0057] S2: material selection.

[0058] Based on the strength grade determined in step S1, cement, hole slag aggregate, stone powder and superplasticizer are selected, wherein the cement is ordinary portland cement of 42.5 grade or 52.5 grade according to the strength grade, the hole slag aggregate includes machine-made sand and coarse aggregate, the machine-made sand is medium sand with a fineness modulus of 2.3-3.0, the maximum particle size of the coarse aggregate is 31.5mm, the stone powder is granite stone powder or limestone stone powder, and the superplasticizer is polycarboxylic acid with a water-reducing rate of 20%-30%; the materials selected in this step S2 are input variables for machine learning proportioning calculation in the subsequent step S3, and the performance parameters of the materials are included in the raw material-deformation performance-mechanical property database.

[0059] S3: machine learning output proportioning.

[0060] Based on the preset raw material-deformation performance-mechanical property database (containing performance data of the material selected in step S2), performance prediction is performed on the strength grade determined in step S1 by using a 56d deformation prediction model and a 28d mechanical property prediction model, and a genetic algorithm is used to maximize the compressive strength and minimize the drying shrinkage as multi-objective optimization, and a recommended proportioning range matching the strength grade is output; the proportioning range output in this step provides an initial proportioning basis for the proportioning in step S4, and the parameter boundaries of the proportioning range need to meet the performance requirements corresponding to the workability and strength in step S1;

[0061] S4 Proportioning and adjustment.

[0062] Proportioning is performed according to the recommended proportioning range output in step S3, and the slump (corresponding to the workability requirement in step S1), 7d compressive strength (which needs to be greater than or equal to 70% of the design value of the strength grade in step S1), and 56d drying shrinkage (which needs to be within the drying shrinkage range recommended in step S3) are detected; if the slump does not meet the requirement, the water reducing agent dosage or water-binder ratio is adjusted; if the 7d compressive strength or 56d drying shrinkage does not meet the requirement, the proportioning range is re-optimized in step S3;

[0063] S5 Determining the construction proportioning.

[0064] For the proportioning that passes the proportioning in step S4, the water contents of the manufactured sand and the coarse aggregate selected in step S2 are detected, and the amount of manufactured sand, the amount of coarse aggregate, and the amount of water are adjusted according to the water contents to obtain the construction proportioning.

[0065] In some embodiments, the adjustment formulas of the amount of manufactured sand, the amount of coarse aggregate, and the amount of water are as follows:

[0066] Manufactured sand amount = theoretical manufactured sand amount × (1 + α / 100);

[0067] Coarse aggregate amount = theoretical coarse aggregate amount × (1 + β / 100);

[0068] Water amount = theoretical water amount - theoretical manufactured sand amount × α / 100 - theoretical coarse aggregate amount × β / 100.

[0069] Wherein, α is the water content of the manufactured sand, β is the water content of the coarse aggregate, and the theoretical amount is determined based on the recommended proportioning in step S3 and the proportioning adjusted in step S4.

[0070] In some embodiments, in step S1, the strength grade is C30-C60, and the target value of the slump is 100-220mm; in step S2, the cement selection rule is that the strength grade C30-C50 corresponds to 42.5 grade ordinary portland cement, and the strength grade C60 corresponds to 52.5 grade ordinary portland cement.

[0071] In some embodiments, in step S2, the content of stone powder satisfies: the content of non-active stone powder is not more than 15%, and the content of active stone powder is not more than 20%; in step S3, when multi-objective optimization is performed, the content of stone powder needs to be corrected in combination with lithology, wherein the content of granite stone powder and limestone stone powder is determined according to the strength grade in step S1, and specifically: for C30, the content of granite stone powder is 12.3-18.5%, and the content of limestone stone powder is 9.5-14.8%; for C40, the content of granite stone powder is 8.3-14.4%, and the content of limestone stone powder is 6.6-11.5%; for C50, the content of granite stone powder is 6.3-10%, and the content of limestone stone powder is 5-8%; for C60, the content of granite stone powder is 0-6.8%, and the content of limestone stone powder is 0-5.4%.

[0072] In some embodiments, in step S3, the 56d deformation prediction model is a prediction model based on a neural network, and the 28d mechanical property prediction model is a prediction model based on a gradient boosting tree; in the mixing ratio range output by the multi-objective optimization, the water-binder ratio, the cement content, the sand ratio, the coarse aggregate content and the water reducing agent content are fixed values, and the contents of fly ash, mineral powder and stone powder are adjustable ranges, and the stone powder can replace part of the fly ash or mineral powder through a replacement coefficient (fly ash 0.32, mineral powder 0.35), and the replacement coefficient calculation formula is:

[0073]

[0074] wherein, μ i is the replacement coefficient, γ i is the correlation coefficient of stone powder and fly ash or mineral powder, σ S is the standard deviation of the content of stone powder, and σ i is the standard deviation of the content of fly ash or mineral powder.

[0075] In some embodiments, in step S4, a cube test block with a size of 150mm*150mm*150mm or 100mm*100mm*100mm is prepared for compressive strength test, and a prism test block with a size of 100mm*100mm*515mm is prepared for drying shrinkage test; the cube test block is standard cured for 7d or 28d, and the prism test block is standard cured for 3d and then moved into a drying box with a temperature of 20±2℃ and a humidity of 60±5% for curing, and the test data is used to verify whether the performance boundary of the prediction model in step S3 is met.

[0076] In some embodiments, the tunnel spoil aggregate is obtained by processing the tunnel spoil generated in tunnel excavation, in step S2, the coarse aggregate is stone with three particle sizes of 5-10mm, 10-20mm and 20-31.5mm, and the gradation is 442 gradation; in step S5, the amount of cementitious material remains unchanged, and only the amount of aggregate and the amount of water are adjusted to ensure that the water-binder ratio is consistent with the recommended mixing ratio in step S3.

[0077] Embodiment 2:

[0078] To further verify the engineering applicability of the full-hole slag concrete synergistic deformation design method provided in Embodiment 1, this embodiment takes the hole slag aggregate of a tunnel project as a case for experimental verification. Specifically, please refer to Figure 2 When the full-hole slag concrete synergistic deformation design method is applied to the hole slag aggregate of a tunnel project, the design process includes determining design parameters, material selection, machine learning output ratio, trial preparation and adjustment, and determining the construction mixing ratio. The mixing ratio design method takes the synergistic deformation theory as the core, and realizes the balance of shrinkage deformation and mechanical properties through the interaction of material components. This method is based on the double-scale design concept of microstructure regulation-macroscopic performance optimization, which includes the following steps 1 to step 6.

[0079] Step 1: Determine the design parameters.

[0080] That is, before using this design method, the basic design goal needs to be known, the required strength grade needs to be determined, and the workability requirement needs to be determined. According to the construction process, the appropriate fluidity is selected. The slump of the pump concrete is generally 100-200mm. The proportion of each data in this database with a slump of more than 180mm is 75.0%, and the proportion of more than 100mm is 97.3%. Therefore, the slump basically meets the requirements of most pump concretes.

[0081] Step 2: Material selection.

[0082] In this embodiment, the cement with a strength grade of C30-C50 is a 42.5 grade ordinary portland cement, and the fineness of the cement needs to be controlled at 300-350m 2 / kg. Excessive specific surface area will exacerbate early hydration shrinkage; the cement with a strength grade of C60 is a 52.5 grade ordinary portland cement to reduce hydration heat release and shrinkage deformation. Mechanism aggregate, mechanism sand uses medium sand with a fineness modulus of 2.3-3.0 to ensure dense particle packing; the maximum particle size of the coarse aggregate is 31.5, and the gradation is moderate to reduce interface stress concentration. In terms of stone powder content, the non-active stone powder content is not more than 15%, and the active stone powder content is not more than 20%. The water reducing agent is selected as a polycarboxylic acid type, the water reducing rate is 20%-30%, and it is suitable for concrete to reduce the water-binder ratio and reduce the shrinkage caused by free water evaporation.

[0083] Step 3: Multi-objective mixing ratio optimization.

[0084] Step 3 realizes the transformation from "empirical design" to "data-driven design" by establishing a raw material-deformation performance-mechanical property database, and using a series of data analysis methods to statistically analyze the range of shrinkage rate of each strength grade. The 56d shrinkage rate model based on neural network is used for the shrinkage performance, and the 28d compressive strength prediction model based on gradient boosting tree method is used for the mechanical performance. Finally, the recommended mix proportion is obtained through multi-objective optimization (aiming to maximize the compressive strength and minimize the shrinkage rate) by genetic algorithm. The process of the genetic algorithm used in this embodiment is shown in Figure 3 Table 1 is the range of shrinkage rate of each strength grade.

[0085] Table 1 is the range of shrinkage rate of each strength grade.

[0086] Strength class Compressive strength range (MPa) at 28d 56d range of dry shrinkage (10 -6 )]]> C30 30-40 351-480 C40 40-50 304-455 C50 50-60 287-439 C60 60-70 250-368

[0087] In the optimal solution set obtained by multi-objective optimization, some raw material parameters (such as water-binder ratio, cement content, sand ratio, coarse aggregate content, and water reducing agent content) show relatively concentrated fixed values when different target weights are balanced. This indicates that these parameters have a strong dominant effect on the objective function, and their optimal values have high certainty and convergence when meeting the performance requirements such as compressive strength and deformation control. Adjusting these parameters will significantly affect the performance prediction results, so the optimization algorithm tends to lock them at a relatively optimal level to ensure stable overall performance. In contrast, the value range of fly ash, slag and stone powder fluctuates within a certain interval, so they have a certain degree of replaceability and flexibility when meeting the performance constraints. These materials are usually used as supplementary cementitious materials or filling materials, and their influence on the mechanical and deformation properties of concrete is relatively mild, mainly reflected in the regulation of the mesostructure. Therefore, the optimization process allows these parameters to vary freely within the feasible region while considering the objective function value, thereby improving the adaptability of the mix design and the operability of the actual project.

[0088] In the multi-objective optimization of hole slag concrete mix proportion, both mechanical and volumetric deformation properties are considered. Although silica fume is a high-activity admixture, its importance in the mechanical property prediction model is not high, and it has little effect on strength within the sample range. Moreover, it is not included in the key variables of the deformation prediction model, and its contribution is limited after comprehensive optimization and trade-off. As an inert filler, stone powder has a small mechanical contribution but can improve aggregate gradation, fill pores, and increase paste density, making it well adapted to control dry shrinkage deformation and ensure construction performance. Moreover, it is widely available and cost-effective, consistent with the goal of local utilization of hole slag resources and sustainable development. Therefore, the optimal mix proportion scheme has no silica fume and retains a certain proportion of stone powder, which is the result of the synergistic optimization of material performance, economy, and structural performance, and reflects the effective integration and judgment ability of the multi-objective decision model for complex engineering variables.

[0089] The preliminary recommended proportioning range of each strength grade obtained by the embodiment is shown in Table 2.

[0090] Table 2 preliminary recommended proportioning range of each strength grade

[0091]

[0092] Step 4: stone powder content correction.

[0093] Stone powder has the potential to improve particle gradation and enhance the compactness of concrete as an inert filler. To simplify the admixture system and enhance the stability of the mixture composition, this study uses stone powder to replace the mass of fly ash and slag, aiming to reduce the active mineral content while optimizing the construction adaptability of the mixture. In this embodiment, the correlation coefficients of stone powder with fly ash and slag are 0.46 and 0.48, respectively. Based on the correlation analysis results and engineering practice experience, to achieve performance equivalent replacement, the following formula is used to determine the replacement coefficient of stone powder for fly ash and slag.

[0094]

[0095] In the formula, μ is the replacement coefficient, γ is the correlation coefficient, σ is the standard deviation of stone powder content, and σ is the standard deviation of fly ash or slag content. The calculation shows that the fly ash replacement coefficient is 0.32, and the slag replacement coefficient is 0.35. i i S i

[0096] Table 3 test results of dry shrinkage and compressive strength performance of whole-hole slag concrete

[0097]

[0098] Table 4 test data of dry shrinkage and compressive strength of concrete at different ages

[0099]

[0100]

[0101] According to the above formula, the replacement coefficient of stone powder for fly ash and slag is determined as follows: Figure 4 ​​​​The results of the analysis of the shrinkage performance of the concrete made of the indicated granite and limestone stone powder, as well as Table 3 and Table 4, show that the stone powder of different rock types has a significant impact on the shrinkage performance of the concrete. The shrinkage value of the granite stone powder (HG5, HG10, HG15) concrete is significantly higher than that of the benchmark group (DZ), and the dry shrinkage deformation gradually increases with the increase of the dosage; the shrinkage value of the limestone stone powder (SH5, SH10, SH15) concrete at each age is generally lower than that of DZ, although the dry shrinkage increases slightly with the increase of the dosage, but it is still significantly lower than that of the granite group. Therefore, when recommending the dosage of stone powder, it is not appropriate to adopt a unified fixed range, but the rock differences should be considered and targeted limitations should be made. Based on the experimental statistical results and the experience of material engineering practice, the results are shown in Table 5.

[0102] Table 5 Recommended partial allocation ratio range of each strength grade

[0103]

[0104] Step 5: Trial and adjustment.

[0105] According to the recommended mix proportion, a forced mixer is used, and the mixing time is ≥180s to ensure uniformity. For the workability index, the target value of the slump is generally 100-220mm (according to the actual needs), and the air content is generally 3%-5% for the related requirements. When the slump is insufficient, the dosage of water reducing agent can be increased (appropriately increase, each time can increase 0.1%) or the water-binder ratio can be adjusted (±0.01); when the cohesiveness is poor, the sand ratio can be increased (1% each time); when the air content does not meet the requirements, the dosage of air entraining agent can be adjusted (0.005% each time) to meet the actual requirements.

[0106] After the freshly mixed concrete meets the basic requirements, cube test blocks (150mm x 150mm x 150mm or 100mm x 100mm x 100mm, conversion factor 0.95) and prism test blocks (100mm x 100mm x 515mm) are prepared. The cube test blocks are standard cured for 7d or 28d, and the compressive strength is tested. The 7d strength should be greater than 70% of the design strength, and the 28d strength should be greater than 100% of the design strength. The prism test blocks are standard cured for 3d and then moved into a dry box, with a temperature of 20±2℃ and a humidity of 60±5%. The dry shrinkage rate is measured at 3d, 7d, 14d, 28d, and 56d, and the shrinkage rate should be within the range of the dry shrinkage rate of each strength grade in Table 5, and it should also meet the smaller principle.

[0107] Step 6: Determine the construction mix proportion.

[0108] Theoretical mix ratio is designed based on dry aggregate, and if the moisture content of aggregate changes, it will directly affect the actual water consumption, leading to the deviation of water-binder ratio from the design value. Therefore, adjusting the construction mix ratio can control the water-binder ratio, ensure the strength to meet the standard to ensure the safety of the structure, reduce the rework caused by the deviation of the mix ratio, and avoid the waste of materials. Test the moisture content of aggregate, set the moisture content of machine-made sand to be α%, and the moisture content of coarse aggregate to be β%, and calculate the adjusted value as follows:

[0109] Machine-made sand consumption: theoretical consumption x (1 + α);

[0110] Coarse aggregate consumption: theoretical consumption x (1 + β);

[0111] Water consumption: theoretical water consumption - sand moisture content - stone moisture content.

[0112] Assume that the theoretical mix ratio (consumption of materials per cubic meter of concrete) is cementitious material (C), water (W), machine-made sand (S), and coarse aggregate (G), and the unit is kg. The adjusted construction mix ratio is:

[0113] Machine-made sand consumption = S x (1 + α / 100);

[0114] Coarse aggregate consumption = G x (1 + β / 100);

[0115] Water consumption = W - S x α / 100 - G x β / 100;

[0116] Cementitious material consumption = C (unchanged).

[0117] Apply the machine-made aggregate produced by a tunnel project, the design requires that the lining concrete strength grade is C40, and there is no air content requirement. The tunnel excavation produces a hole slag aggregate, the rock type is granite, the methylene blue (MB) value of machine-made sand is 0.8, the machine-made sand stone powder content is 4.1%, there is no mud content, the crushing index is 17%, the loose bulk density is 1490 kg / m 3 , the fineness modulus is 2.7, the maximum particle size of coarse aggregate is 31.5, and there are three kinds of stone particles, which are 5-10 mm, 10-20 mm, and 20-31.5 mm, respectively. The gradation uses the commonly used 442 gradation (particle size from large to small) in engineering.

[0118] Prepare C40 concrete in the field laboratory according to the design method in Example 1, as shown in Table 6, the mix ratio is water-binder ratio 0.48, cement 381.1 kg / m 3 , sand rate 45%, granite stone powder dosage 10%, coarse aggregate 1076.4 kg / m 3 , water reducing agent 5.4 kg / m 3, the measured 28d strength is 45.1MPa, and the 56d drying shrinkage is 301x10 -6 The drying shrinkage is small and in the recommended range, and meets the actual requirements of engineering.

[0119] Table 6 C40 concrete mixing proportion and performance detection table based on the design method of example 1

[0120]

[0121] The above embodiments are only used to illustrate the present application, and are not intended to limit the present application. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application. Therefore, all equivalent technical solutions also belong to the scope of the present application, and the patent protection scope of the present application should be defined by the claims.

Claims

1. A full-hole slag concrete collaborative deformation design method, characterized in that, The method comprises: S1: determining the strength grade and workability requirement of the concrete; wherein the workability requirement comprises a slump target value; S2: cement, hole slag aggregate, stone powder and water reducing agent are used as raw materials, wherein the cement is selected according to the strength grade to adapt to the grade of Portland cement, the hole slag aggregate comprises machine-made sand and coarse aggregate, the machine-made sand is medium sand and the fineness modulus is 2.3-3.0, the maximum particle size of the coarse aggregate is 31.5mm, the stone powder is granite stone powder or limestone stone powder, and the water reducing agent is polycarboxylic acid and the water reducing rate is 20%-30%; S3: based on the preset raw material-deformation performance-mechanical property database, the performance is predicted through the 56d deformation prediction model and the 28d mechanical prediction model, the multi-objective optimization is carried out by combining the genetic algorithm, and the recommended proportioning range is output; wherein the multi-objective optimization takes the maximum compressive strength and the minimum dry shrinkage rate as the target; S4: the recommended proportioning range is used for trial proportioning, the slump, 7d compressive strength and 56d dry shrinkage rate are detected, if the slump does not meet the requirement, the water reducing agent dosage or water-binder ratio is adjusted; if the 7d compressive strength is less than 70% of the design value, the proportioning is re-adjusted; if the 56d dry shrinkage rate is not in the recommended range, the proportioning is re-adjusted; S5: the water content of the machine-made sand and the coarse aggregate is detected, the machine-made sand dosage, the coarse aggregate dosage and the water amount are adjusted according to the water content, and the construction proportioning is obtained.

2. The full bore concrete design method of claim 1, wherein, In step S1, the strength grade of the concrete is C30-C60, and the slump target value is 100-220mm.

3. The full bore slag concrete synergic deformation design method according to claim 1, characterized in that, In step S2, the content of the stone powder satisfies: the content of non-active stone powder is not more than 15%, and the content of active stone powder is not more than 20%; the methylene blue value of the machine-made sand is ≤1.4, the crushing index is ≤20%, and the loose bulk density is ≥1400kg / m 3 .

4. The full bore slag concrete synergic deformation design method according to claim 1, characterized in that, In step S3, the 56d deformation prediction model is a prediction model based on neural network, and the 28d mechanical prediction model is a prediction model based on gradient boosting tree; in the recommended proportioning range, the 28d compressive strength range and the 56d dry shrinkage rate range of each strength grade are as follows: C30: 28d compressive strength 30-40 MPa, 56d dry shrinkage 351-480 x 10 -6 ; C40: 28d compressive strength 40-50 MPa, 56d dry shrinkage 304-455 x 10 -6 ; C50: 28d compressive strength 50-60 MPa, 56d drying shrinkage 287-439 x 10 -6 ; C60: 28d compressive strength 60-70 MPa, 56d drying shrinkage 250-368 x 10 -6 .

5. The full bore slag concrete synergic deformation design method according to claim 1, characterized in that, In step S3, in the recommended proportioning range output by the multi-objective optimization, the water-binder ratio, the cement dosage, the sand ratio, the coarse aggregate dosage and the water reducing agent dosage are fixed values, and the dosages of fly ash, mineral powder and stone powder are adjustable ranges; the dosage range of the stone powder is determined according to the lithology, and specifically as follows: C30: granite stone powder 12.3-18.5%, limestone stone powder 9.5-14.8%; C40: granite stone powder 8.3-14.4%, limestone stone powder 6.6-11.5%; C50: granite stone powder 6.3-10%, limestone stone powder 5-8%; C60: granite stone powder 0-6.8%, limestone stone powder 0-5.4%.

6. The full bore slag concrete synergic deformation design method according to claim 1, characterized in that, Step S3 further comprises stone powder dosage correction, specifically comprising: using stone powder to replace fly ash and / or mineral powder in quality, determining the replacement amount based on a replacement coefficient, and the replacement coefficient is calculated by a formula: where μ i is the substitution coefficient, γ i is the correlation coefficient of stone powder and fly ash or slag, σ S is the standard deviation of the stone powder content, σ i is the standard deviation of the fly ash or slag content.

7. The full bore slag concrete synergic deformation design method according to claim 1, characterized in that, In step S4, the adjustment method of the slump comprises: When the slump is insufficient, the water reducing agent dosage is increased by 0.1% or the water-binder ratio is finely adjusted by ±0.01 each time; when the cohesiveness is poor, the sand ratio is increased by 1% each time; When the air content is controlled, if the air content does not meet 3%-5%, the air entraining agent dosage is adjusted by 0.005% each time.

8. The full bore slag concrete synergic deformation design method according to claim 1, characterized in that, In step S4, the cubic test block is prepared for compressive strength test, and the prism test block is prepared for shrinkage rate test; the cubic test block is cured for 7 days or 28 days, and the prism test block is cured for 3 days and then is moved into a drying box with a temperature of 20±2℃ and a humidity of 60±5% for curing.

9. The full bore slag concrete synergic deformation design method according to claim 1, characterized in that, In step S5, the adjustment formula of the machine-made sand amount, the coarse aggregate amount and the water amount is as follows: Machine-made sand amount = theoretical machine-made sand amount × (1+α / 100) Coarse aggregate amount = theoretical coarse aggregate amount × (1+β / 100) Water amount = theoretical water amount - theoretical machine-made sand amount × α / 100 - theoretical coarse aggregate amount × β / 100 Wherein, α is the water content of the machine-made sand, and β is the water content of the coarse aggregate.

10. The full bore slag concrete synergic deformation design method according to claim 1, characterized in that, The hole slag aggregate is obtained by processing the hole slag generated in tunnel excavation, and the coarse aggregate adopts stone with three particle sizes of 5-10mm, 10-20mm and 20-31.5mm.

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