Method for constructing shrinkage prediction model of fiber reinforced high-performance seawater-sea sand concrete

Abstract

The application belongs to the technical field of ocean engineering, and discloses a construction method of a fiber-reinforced high-performance seawater-sea sand concrete shrinkage prediction model, which comprises the following steps: S1, introducing a seawater concentration correction term into a limit shrinkage strain of an existing concrete shrinkage prediction model, and calculating a seawater concentration influence coefficient; S2, introducing an effective humidity correction term into a humidity function of the existing concrete shrinkage prediction model, and calculating a humidity perception coefficient; S3, constructing a fiber constraint function and introducing a fiber inhibition coefficient; S4, outputting the fiber-reinforced high-performance seawater-sea sand concrete shrinkage prediction model by using a time development function in combination with the seawater concentration correction term, the effective humidity correction term and the fiber inhibition coefficient. The shrinkage prediction model established by the application adopts a theoretical framework of multi-factor coupling, completely considers the interactive influence of seawater chemical effects, microphysical mechanisms and fiber mechanical constraints, and can accurately represent the shrinkage law of high-performance seawater-sea sand fiber concrete.

Description

Technical Field

[0001] This invention relates to the field of marine engineering construction materials technology, and in particular to a method for constructing a shrinkage prediction model for fiber-reinforced high-performance seawater sand concrete. Background Technology

[0002] With the continuous expansion of the scale of major marine engineering projects and the severe challenges of freshwater scarcity and natural river sand shortages faced by coastal and offshore areas, developing and utilizing marine resources such as seawater and sea sand to prepare concrete has become an inevitable choice for the sustainable development of marine engineering construction. However, due to the high Cl content in seawater... ﹣ SO4 2－ Mg 2＋ Soluble salts, such as those found in concrete, exert complex effects on its microstructure and long-term performance. These ions not only accelerate the early hydration process of cement, promoting the formation of hydration products such as Friedel's salt and ettringite, and refining the pore structure, but also crystallize and precipitate in the pore solution, forming an internal support system. This complex physicochemical process significantly alters the shrinkage characteristics of concrete, resulting in a fundamental difference in its drying shrinkage behavior compared to traditional freshwater concrete.

[0003] Currently, widely used concrete shrinkage prediction models both domestically and internationally, such as the ACI209 model, B3 model, CEB-FIP model, and GL2000 model, are all designed for ordinary freshwater concrete. Traditional models lack an understanding of the mechanisms by which seawater ions affect the concrete, failing to reflect the essence of changes in microscopic driving forces such as capillary tension and osmotic pressure caused by salinity. Secondly, the model parameter system does not consider the key variable of seawater concentration, making it unable to adapt to the actual situation of salinity variations in different sea areas. Furthermore, existing models cannot characterize the inhibitory effect of fiber reinforcement on shrinkage, making it difficult to guide the optimal design of fiber-reinforced concrete.

[0004] In summary, there is a need to design a method for constructing a shrinkage prediction model for fiber-reinforced high-performance seawater sand concrete to address the problems in the existing technology. Summary of the Invention

[0005] To address the problems in the prior art, this invention provides a method for constructing a shrinkage prediction model for fiber-reinforced high-performance seawater sand concrete, thus solving the problem that existing concrete shrinkage prediction models cannot accurately predict the shrinkage behavior of fiber-reinforced high-performance seawater sand concrete.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] The method for constructing a shrinkage prediction model for fiber-reinforced high-performance seawater sand concrete includes the following steps:

[0008] S1. Introduce a seawater concentration correction term into the ultimate shrinkage strain of the existing concrete shrinkage prediction model, and calculate the seawater concentration influence coefficient.

[0009] S2. Introduce an effective humidity correction term into the humidity function of the existing concrete shrinkage prediction model and calculate the humidity perception coefficient;

[0010] S3. Construct a fiber constraint function and introduce a fiber inhibition coefficient. Determine the fiber efficiency coefficient through fiber type and dosage experiments.

[0011] S4. Using the time evolution function, combined with the seawater concentration correction term, the effective humidity correction term, and the fiber inhibition coefficient, output a shrinkage prediction model for fiber-reinforced high-performance seawater sand concrete.

[0012] In some embodiments of the present invention, the calculation formula for the seawater concentration correction term in step S1 is: η c =A·c+1;

[0013] Where, η c Here, A is the seawater concentration correction term, and c is the seawater concentration influence coefficient.

[0014] In some embodiments of the present invention, the calculation formula for the effective humidity correction term in step S2 is: h eff =h·(B·c+1);

[0015] Among them, h eff For effective humidity correction, h is the ambient relative humidity, B is the humidity perception coefficient, and c is the seawater concentration.

[0016] In some embodiments of the present invention, the calculation steps for the seawater concentration influence coefficient A and the humidity perception coefficient B are as follows:

[0017] Assuming that seawater concentration has a positive correlation with the corrected limiting shrinkage strain and that seawater concentration affects effective humidity by altering the chemical potential of the pore solution, the following theoretical relationship is obtained:

[0018] ;

[0019] Where, ε shu This is the corrected limit shrinkage strain;

[0020] The actual shrinkage strain of concrete under different seawater concentrations and different ambient humidity was measured by experiments, and the corresponding experimental data were obtained.

[0021] Based on the experimental data, a multivariate nonlinear regression analysis was performed on the theoretical relationship to simultaneously obtain the seawater concentration influence coefficient A and the humidity perception coefficient B.

[0022] In some embodiments of the present invention, the step of determining the fiber efficiency coefficient in step S3 includes:

[0023] By constructing the fiber constraint function expression, the theoretical calculation formula for the fiber inhibition coefficient is obtained:

[0024] ;

[0025] Where, k fiber V is the fiber inhibition coefficient, α is the fiber efficiency coefficient, and V is the fiber inhibition coefficient. f The volumetric content of the fiber, l f / d f The aspect ratio of the fiber;

[0026] The volume fraction (V) of different fiber types and different volume fractions was measured using an experimental system. f and different aspect ratios l f / d f The actual shrinkage strain of the concrete was analyzed to obtain experimental data;

[0027] Based on the experimental data, the measured values ​​of the fiber inhibition coefficient under each working condition were calculated, and the fiber efficiency coefficients of different fiber types were obtained by fitting through inversion analysis.

[0028] In some embodiments of the present invention, the fiber type includes at least basalt fiber, polypropylene fiber and steel fiber.

[0029] In some embodiments of the present invention, the formula for the shrinkage prediction model of fiber-reinforced high-performance seawater sand concrete in step S4 is as follows:

[0030] ;

[0031] Where, ε cs (t) represents the shrinkage strain of fiber-reinforced high-performance seawater sand concrete at age t days. shu β(h) is the corrected limiting shrinkage strain, β(t) is the corrected humidity function, β(t) is the time function describing the shrinkage over time, and kfiber is the fiber inhibition coefficient.

[0032] In some embodiments of the present invention, a shrinkage prediction model for fiber-reinforced high-performance seawater sand concrete is obtained by the construction method described above, and is used to predict the long-term shrinkage deformation behavior of high-performance seawater sand concrete with added fibers in a marine environment.

[0033] In some embodiments of the present invention, an electronic device is provided, comprising:

[0034] A processor, and a memory and a transceiver communicatively connected to the processor;

[0035] The memory stores computer-executed instructions; the transceiver is used for sending and receiving data.

[0036] The processor executes the computer execution instructions stored in the memory to implement the above-described construction method.

[0037] In some embodiments of the present invention, a computer-readable storage medium is provided, characterized in that,

[0038] The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the above-described construction method.

[0039] The technical solution of the present invention has the following technical effects compared with the prior art:

[0040] The shrinkage prediction model established in this invention, by introducing seawater concentration correction, effective humidity mechanism and fiber inhibition coefficient, can accurately characterize the shrinkage law of high-performance seawater sand fiber concrete, and the prediction accuracy is significantly better than that of traditional models.

[0041] Meanwhile, by adopting a multi-factor coupling theoretical framework, the interaction between seawater chemical effects, microscopic physical mechanisms and fiber mechanical constraints is fully considered, forming a systematic prediction system and providing an innovative solution for predicting the shrinkage behavior of concrete in marine environments.

[0042] The fiber-reinforced high-performance seawater sand concrete shrinkage prediction model constructed in this invention has clear physical significance and strong engineering applicability. It can accurately predict concrete shrinkage deformation under different working conditions, providing reliable theoretical support for the durability design and long-term performance evaluation of marine engineering structures, and has important engineering application value. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 A flowchart illustrating the construction method of the fiber-reinforced high-performance seawater sand concrete shrinkage prediction model provided by this invention.

[0045] Figure 2 This is a comparison chart of the prediction results of the model in Example 1 with the measured values ​​and the calculation results of the traditional prediction model.

[0046] Figure 3 This is a comparison chart of the measured shrinkage results of concrete when the seawater concentration is 0%, and the calculation results of Example 2 and the traditional prediction model.

[0047] Figure 4 This is a comparison chart of the measured shrinkage results of concrete when the seawater concentration is 50%, and the calculation results of Example 2 and the traditional prediction model.

[0048] Figure 5 This is a comparison chart of the measured shrinkage results of concrete when the seawater concentration is 100%, and the calculation results of Example 2 and the traditional prediction model.

[0049] Figure 6 This is a comparison chart of the measured shrinkage results of concrete when the seawater concentration is 200%, and the calculation results of Example 2 and the traditional prediction model.

[0050] Figure 7 This is a comparison chart of the measured shrinkage results of concrete at an ambient humidity of 50% and with those calculated by Example 2 and the traditional prediction model when the seawater concentration is 100%.

[0051] Figure 8 This is a comparison chart of the measured shrinkage results of concrete at an ambient humidity of 65% with seawater concentration of 100%, and the calculation results of Example 2 and the traditional prediction model.

[0052] Figure 9 This is a comparison chart of the measured shrinkage results of concrete at an ambient humidity of 80% and with those calculated by Example 2 and the traditional prediction model when the seawater concentration is 100%.

[0053] Figure 10 This is a comparison chart of the measured shrinkage results of concrete with 0% polypropylene fiber content when the seawater concentration is 100%, and the calculation results of Example 2 and the traditional prediction model.

[0054] Figure 11 This is a comparison chart of the measured shrinkage results of concrete with 0.1% polypropylene fiber content when the seawater concentration is 100%, and the calculation results of Example 2 and the traditional prediction model.

[0055] Figure 12 This is a comparison chart of the measured shrinkage results of concrete with 0.2% polypropylene fiber content when the seawater concentration is 100%, and the calculation results of Example 2 and the traditional prediction model.

[0056] Figure 13 This is a comparison chart of the measured shrinkage results of concrete with 0.3% polypropylene fiber content when the seawater concentration is 100%, and the calculation results of Example 2 and the traditional prediction model. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0059] To address the technical challenge of existing concrete shrinkage prediction models failing to accurately predict the shrinkage behavior of fiber-reinforced high-performance seawater sand concrete, this invention establishes a multi-factor coupled shrinkage prediction model through in-depth research on the physicochemical effects of seawater ions in concrete and the micromechanical effects of fibers. This model, while inheriting the classical theoretical framework, significantly improves prediction accuracy and engineering applicability through a series of innovative modifications. The specific construction method of the shrinkage prediction model is as follows:

[0060] Example 1, Reference Figure 1 As shown, the method for constructing a shrinkage prediction model for fiber-reinforced high-performance seawater sand concrete includes the following steps:

[0061] S1. Introduce a seawater concentration correction term into the ultimate shrinkage strain of the existing concrete shrinkage prediction model, and calculate the seawater concentration influence coefficient.

[0062] Due to Cl in seawater - SO4 2- Plasma accelerates cement hydration, promotes the formation of products such as Friedel's salt and ettringite, refines the pore structure, and thus significantly increases the shrinkage potential of the material.

[0063] Therefore, a seawater concentration correction term η is introduced into the ultimate shrinkage strain of the existing concrete shrinkage prediction model. c Among them, the seawater concentration correction term η c The calculation formula is:

[0064] η c =A·c+1;

[0065] Where A is the seawater concentration influence coefficient, and c is the seawater concentration.

[0066] The seawater concentration influence coefficient A can be calculated by fitting the actual shrinkage value of concrete through experimental measurement. The specific calculation process is described below.

[0067] S2. Introduce an effective humidity correction term into the humidity function of the existing concrete shrinkage prediction model and calculate the humidity perception coefficient;

[0068] By introducing an effective humidity correction term, it is possible to effectively quantify the significant reduction in pore solution chemical potential caused by seawater ions, thereby establishing an additional osmotic pressure difference between the pore fluid and the ambient humidity, and thus enhancing the effect of the contraction driving force.

[0069] Specifically, the formula for calculating the effective humidity correction term is: h eff =h·(B·c+1);

[0070] Among them, h eff For effective humidity correction, h is the ambient relative humidity, B is the humidity perception coefficient, and c is the seawater concentration.

[0071] The calculation of the humidity perception coefficient B can be performed simultaneously with the calculation of the seawater concentration influence coefficient A, and the specific calculation steps are as follows:

[0072] I. Assuming the seawater concentration affects the corrected limiting contraction strain ε shu The effects are positively correlated, and it is assumed that seawater concentration affects the effective humidity h by changing the chemical potential of the pore solution. eff The following theoretical relationship is obtained:

[0073] ;

[0074] Second, the actual shrinkage strain of concrete under different seawater concentrations and different ambient humidity levels was measured experimentally to obtain corresponding test data. In this embodiment, the different seawater concentrations included 0%, 50%, 100%, 200%, and 300%; the different ambient humidity levels included 40%, 60%, and 80%.

[0075] Third, based on the experimental data, perform multivariate nonlinear regression analysis on the theoretical relationship in step one, and simultaneously fit to obtain the seawater concentration influence coefficient A and the humidity perception coefficient B.

[0076] Specifically, the seawater concentration influence coefficient A is set to 0.055, and the humidity perception coefficient B is set to -0.02.

[0077] In this embodiment, the existing concrete shrinkage prediction model adopts the GL2000 model; the corresponding corrected ultimate shrinkage strain and corrected humidity function are calculated based on the seawater concentration influence coefficient A and the humidity perception coefficient B.

[0078] The expression for the modified ultimate shrinkage strain is as follows:

[0079]

[0080] In the formula: K is the cement type coefficient; for ordinary Portland cement, K is 1.00; for slag cement and fly ash cement, K is 0.70~0.85; for early-strength cement, K is 1.10~1.15; c is the seawater concentration, expressed as a percentage; f cu denoted as 28-day cubic compressive strength of concrete, and A is the seawater concentration influence coefficient obtained by fitting experimental data.

[0081] The expression for the corrected humidity function is: ;

[0082] In the formula: h is the relative humidity of the environment, and B is the influence coefficient of seawater ions on humidity perception obtained by fitting experimental data.

[0083] S3. Construct a fiber constraint function and introduce a fiber inhibition coefficient. Determine the fiber efficiency coefficient through fiber type and dosage experiments.

[0084] Step S31: Based on the micromechanics of composite materials and the theory of fiber bridging, combined with interfacial bonding mechanics and stress transfer mechanism, construct the fiber constraint function expression to obtain the theoretical calculation formula for the fiber inhibition coefficient:

[0085] ;

[0086] Where, k fiber V is the fiber inhibition coefficient, α is the fiber efficiency coefficient, and V is the fiber inhibition coefficient. f The volumetric content of the fiber, l f / d f The aspect ratio of the fiber;

[0087] Step S32: Measure the volume fraction (V) of different fiber types and volumes using the experimental system. f and different aspect ratios l f / d f The actual shrinkage strain of the concrete was measured to obtain experimental data; the fiber types used in this step include basalt fiber, polypropylene fiber and steel fiber; in other embodiments, the fiber type can also be glass fiber, PVA fiber, etc.

[0088] Step S33: Based on the experimental data, calculate the measured values ​​of the fiber inhibition coefficient under each working condition. The fiber efficiency coefficients for different fiber types were obtained through inversion analysis and fitting.

[0089] Specifically, the fiber efficiency coefficient of different fiber types The value ranges are as follows: basalt fiber: Polypropylene fiber: Steel fiber: .

[0090] S4. Using the time evolution function, combined with the seawater concentration correction term, the effective humidity correction term, and the fiber inhibition coefficient, output a shrinkage prediction model for fiber-reinforced high-performance seawater sand concrete.

[0091] Step S41, Time Development Function of this Embodiment We choose to use the time evolution function of the classic GL2000 model, whose expression is:

[0092] =

[0093] Where t is the age of the concrete; t c This refers to the age at which the concrete finishes curing, i.e., begins to dry; V and S represent the volume and surface area of ​​the specimen, respectively.

[0094] Step S42: Combine the correction terms from steps S1 and S2 with the fiber inhibition coefficient from step S3, and combine this with the classical time evolution function from step 41 to obtain the shrinkage prediction model for fiber-reinforced high-performance seawater sand concrete. ;

[0095] In the formula, ε cs (t) represents the shrinkage strain of fiber-reinforced high-performance seawater sand concrete at age t days. shu β(h) is the corrected limiting shrinkage strain, β(t) is the corrected humidity function, and β(t) is the time function describing the shrinkage over time; k fiber This represents the fiber inhibition coefficient.

[0096] Specifically, to demonstrate the beneficial effects of the model, a design strength of 80 MPa was used. Concrete was poured using locally sourced natural seawater and sea sand, with 0.2% polypropylene fiber added by volume (aspect ratio...). Taking shrinkage prediction under an ambient humidity of 65% as an example, this embodiment will be specifically explained.

[0097] The concrete mix proportions are shown in Table 1. The cement used is PⅡ52.5R grade Portland cement, the fly ash is Grade I fly ash, the silica fume is microsilica fume with a SiO2 content of over 97%, the coarse aggregate is crushed granite aggregate, and the water-reducing agent is polycarboxylate high-performance water-reducing agent. Standard shrinkage test specimens with dimensions of 100mm × 100mm × 515mm were prepared, and the test environment temperature was 20±2℃.

[0098] Table 1 Concrete mix proportions for Example 1 (kg / m³)

[0099] Components natural sea water cement silica ash fly ash sea ​​sand coarse aggregate Water reducing agent content 128.33 401.04 80.21 52.64 651.68 1127.91 7.52

[0100] The model parameter values ​​and calculation process in this embodiment are as follows:

[0101] Step 1: Calculate the ultimate shrinkage strain considering the effect of seawater concentration. Where K = 1.15. =80.0MPa, c=1, A=0.055.

[0102]

[0103] Step 2, calculate the humidity function considering the effective humidity. Where h = 65%, c = 1, and B = -0.02.

[0104]

[0105]

[0106] Step 3, Calculation of fiber inhibition coefficient. Among them, =0.2%, =335, α=0.36 (polypropylene fiber).

[0107]

[0108] Step 4, Time function calculation (with t=365 days, t c (For example, 7 days, V / S=22mm)

[0109] .

[0110] Step 5: Calculate the final predicted shrinkage value at an age of t=365 days.

[0111] .

[0112] The comparison results between the experimental measured values ​​and the model predicted values ​​in this embodiment are shown in Table 2 and... Figure 2 .

[0113] Table 2 Comparison of model prediction accuracy in Example 1 (365-day shrinkage strain, ×10⁻) 6 )

[0114] Example 1 relative error Measured values ​​of the example <![CDATA[410.75×10 -6 ]]> Traditional GL2000 model <![CDATA[463.91×10 -6 ]]> +12.94% This invention model <![CDATA[402.4×10 -6 ]]> -2.03%

[0115] The results above show that the shrinkage prediction model proposed in this embodiment can well fit the long-term shrinkage curves of concrete with different seawater concentrations and different fiber content. For complex fiber-reinforced high-performance seawater sand concrete, the prediction error of the model of this invention is significantly lower than that of the traditional GL2000 model, and it can accurately predict its long-term shrinkage behavior, providing a reliable tool for the design and durability assessment of marine engineering structures.

[0116] Example 2: A shrinkage prediction model for fiber-reinforced high-performance seawater sand concrete, obtained using the construction method described in Example 1, is used to predict the long-term shrinkage deformation behavior of fiber-reinforced high-performance seawater sand concrete in a marine environment.

[0117] The beneficial effects of Embodiments 1 and 2 are as follows:

[0118] 1. The shrinkage prediction model established in this invention, by introducing seawater concentration correction, effective humidity mechanism and fiber inhibition coefficient, can accurately characterize the shrinkage law of high-performance seawater sand fiber concrete, and the prediction accuracy is significantly better than that of traditional models.

[0119] 2. By adopting a multi-factor coupling theoretical framework, the interaction between seawater chemical effects, microscopic physical mechanisms and fiber mechanical constraints is fully considered, forming a systematic prediction system and providing an innovative solution for predicting the shrinkage behavior of concrete in marine environments.

[0120] 3. The model has clear physical meaning and strong engineering applicability. It can accurately predict the shrinkage deformation of concrete under different working conditions, providing reliable theoretical support for the durability design and long-term performance evaluation of marine engineering structures, and has important engineering application value.

[0121] Experiment 1 was used to verify the predictive accuracy of the model in Example 2 under different seawater concentration conditions.

[0122] The concrete mix proportions are shown in Table 3, with a water-cement ratio of 0.24. Fresh water, a mixture of 50% fresh water and 50% natural seawater, natural seawater, and 200% artificial seawater were used for mixing. P.O52.5 ordinary Portland cement was used, with a silica content exceeding 97% in the silica fume. Grade I fly ash was used. The fineness modulus of the sea sand was 2.3–2.6, and the mud content was less than 1.0%. Granite crushed stone with a particle size of 5–20 mm was used as coarse aggregate. The polycarboxylate superplasticizer used had a water reduction rate of 28%. The specimen size was 100 mm × 100 mm × 515 mm. After standard curing, drying shrinkage tests were conducted in an environment with a temperature of 20 ± 2℃ and a relative humidity of 65 ± 5%. The 28-day cubic compressive strengths of the concrete were 111.4 MPa, 108.3 MPa, 109.8 MPa, and 106.2 MPa, respectively. Shrinkage values ​​were recorded after 7 days of standard curing following pouring.

[0123] Table 3 Concrete mix proportions for Experiment 1 (kg / m³)

[0124] serial number water Water types cement silica ash fly ash sea ​​sand coarse aggregate Water reducing agent 0%SWC 145.6 freshwater 455 91 60.6 739.3 1274 12.1 50%SWC 145.6 50% fresh water + 50% natural sea water 455 91 60.6 739.3 1274 12.1 100% SWC 145.6 natural sea water 455 91 60.6 739.3 1274 12.1 200% SWC 145.6 200% concentration artificial seawater 455 91 60.6 739.3 1274 12.1

[0125] In the model prediction of this experimental example, the parameters were set as follows: cement type coefficient K = 1.15, 28-day compressive strength... Fiber content was measured at pressures of 111.4 MPa, 108.3 MPa, 109.8 MPa, and 106.2 MPa, respectively. =0, the time function is taken as t=365 days. =7 days, V / S=20mm. Seawater concentration influence coefficient A=0.055, humidity influence coefficient B=-0.02, ambient humidity h=65%.

[0126] The comparison results of the 365-day shrinkage measured values ​​of concrete with different seawater concentrations, the model prediction curve of Example 2, the prediction curve of the traditional GL2000 model, the prediction curve of the traditional ACI209 model, the prediction curve of the traditional B3 model, the prediction curve of the traditional CABR model, and the prediction curve of the traditional MC2010 model are shown in [the original text]. Figures 3-6 The results show that the traditional ACI209, B3, CABR, and MC2010 models significantly underestimate the shrinkage value of high-strength concrete. The traditional GL2000 model, due to its failure to consider the chemical effects of seawater, also predicts a lower value and fails to reflect the influence of concentration gradients. In contrast, the model of this invention, by introducing a seawater concentration correction coefficient and an effective humidity function, achieves a prediction curve that closely matches the measured data at different seawater concentrations, accurately predicting the shrinkage value of concrete at various seawater concentrations.

[0127] Experiment 2 was used to verify the predictive accuracy of the model in Example 2 under different environmental humidity conditions.

[0128] The concrete mix proportions are shown in Table 4, with a water-cement ratio of 0.24. Natural seawater was used for mixing. P.O52.5 ordinary Portland cement was used, with a silica content exceeding 97% in the silica fume. Grade I fly ash was used, and the sea sand had a fineness modulus of 2.3–2.6 and a mud content of less than 1.0%. Granite crushed stone with a particle size of 5 mm–20 mm was selected as the coarse aggregate. The polycarboxylate superplasticizer used had a water reduction rate of 28%. The specimen dimensions were 100 mm × 100 mm × 515 mm. After standard curing, drying shrinkage tests were conducted in environments with temperatures of 20 ± 2℃ and relative humidity of 50 ± 5%, 65 ± 5%, and 50 ± 5% respectively. The 28-day cubic compressive strength of the concrete was 109.8 MPa. Shrinkage values ​​were recorded starting 7 days after pouring and standard curing.

[0129] Table 4 Concrete mix proportions for Experiment Example 2 (kg / m³)

[0130] serial number water Water types cement silica ash fly ash sea ​​sand coarse aggregate Water reducing agent 100% SWC 145.6 natural sea water 455 91 60.6 739.3 1274 12.1

[0131] In the model prediction of this experimental example, the parameters were set as follows: cement type coefficient K = 1.15, 28-day compressive strength... Take 109.8 MPa, fiber content =0, the time function is taken as t=365 days. =7 days, V / S=20mm. Seawater concentration influence coefficient A=0.055, humidity influence coefficient B=-0.02, and ambient humidity is taken as 0.50, 0.65, and 0.80 respectively.

[0132] The comparison results of the measured 365-day shrinkage values ​​of concrete under different relative humidities, the model prediction curve of Example 2, the prediction curve of the traditional GL2000 model, the prediction curve of the traditional ACI209 model, the prediction curve of the traditional B3 model, the prediction curve of the traditional CABR model, and the prediction curve of the traditional MC2010 model are shown in the figure. Figures 7-9 The results show that the traditional ACI209, B3, CABR, and MC2010 models still underestimate the shrinkage value of high-strength concrete. While the traditional GL2000 model can reflect the trend of increased shrinkage due to decreasing humidity, its predicted humidity sensitivity is lower than the measured value because it does not consider the additional osmotic pressure difference caused by seawater ions. The model in Example 2, by introducing effective humidity correction, accurately captures the shrinkage development behavior of seawater concrete under different humidity environments, and its prediction accuracy is significantly better than that of traditional models.

[0133] Experiment 3 was used to verify the predictive accuracy of the model in Example 2 under different fiber addition conditions (taking PP fiber as an example).

[0134] The concrete mix proportions are shown in Table 5, with a water-cement ratio of 0.24. Natural seawater was used for mixing. P.O52.5 ordinary Portland cement was used, with a silica content exceeding 97% in the silica fume. Grade I fly ash was used. The sea sand had a fineness modulus of 2.3–2.6 and a mud content of less than 1.0%. Granite crushed stone with a particle size of 5 mm–20 mm was selected as the coarse aggregate. The polycarboxylate superplasticizer used had a water reduction rate of 28%. Concrete specimens with fiber volume fractions of 0%, 0.1%, 0.2%, and 0.3% were prepared by varying the amount of PP fiber (12 mm in length, 30 μm in diameter). The specimens were 100 mm × 100 mm × 515 mm in size and, after standard curing, were placed in an environment with a temperature of 20 ± 2℃ and a relative humidity of 65 ± 5% for drying shrinkage tests. The 28-day cubic compressive strengths of the concrete were 109.8 MPa, 107.2 MPa, 111.3 MPa, and 105.1 MPa, respectively. Shrinkage values ​​were recorded after 7 days of standard curing following pouring.

[0135] Table 5 Concrete mix proportions for Example 3 (kg / m³)

[0136] serial number water Water types cement silica ash fly ash sea ​​sand coarse aggregate Water reducing agent Fiber content 0%PP 145.6 natural sea water 455 91 60.6 739.3 1274 12.1 0 0%PP 145.6 natural sea water 455 91 60.6 739.3 1274 12.1 0.1% 0%PP 145.6 natural sea water 455 91 60.6 739.3 1274 12.1 0.2% 0%PP 145.6 natural sea water 455 91 60.6 739.3 1274 12.1 0.3%

[0137] In this experiment, the model prediction parameters were set as follows: cement type coefficient K = 1.15, 28-day compressive strength... Fiber content was measured at pressures of 09.8 MPa, 107.2 MPa, 111.3 MPa, and 105.1 MPa, respectively. The values ​​are 0, 0.001, 0.002, and 0.003 respectively, and the time function is taken as t = 365 days. =7 days, V / S=20mm. Seawater concentration influence coefficient A=0.055, humidity influence coefficient B=-0.02, ambient humidity is taken as 0.65. Fiber inhibition coefficient. The fiber efficiency coefficient α is set to 0.036 for the polypropylene fiber used in this experiment, based on the fitting results of previous systematic tests.

[0138] The measured shrinkage values ​​of seawater concrete with different PP fiber contents are compared with the predicted curves of the model in Example 2, the traditional GL2000 model, the traditional ACI209 model, the traditional B3 model, the traditional CABR model, and the traditional MC2010 model. Figures 10-13 The results show that the traditional ACI209, B3, CABR, and MC2010 models still underestimate the shrinkage value of high-strength concrete, and the traditional models cannot consider the restraining effect of fibers; their predicted values ​​are only the shrinkage curves of plain concrete. The model in Example 2, by introducing a fiber inhibition coefficient derived from micromechanical theory, clearly quantifies the restraining effect of fibers on shrinkage strain. The predicted curves are highly consistent with the measured data for different fiber contents, fully demonstrating the applicability of the model in fiber-reinforced high-performance seawater sand concrete.

[0139] As can be seen from the above three experimental examples, the fiber-reinforced high-performance seawater sand concrete shrinkage prediction model proposed in this invention can simultaneously and accurately reflect the complex influence of three key factors—seawater concentration, environmental humidity, and fiber content—on the long-term shrinkage behavior of concrete. The prediction accuracy is significantly higher than that of traditional models, providing an effective technical tool for solving the problem of accurate prediction of concrete shrinkage deformation in marine engineering construction.

[0140] Example 3: An electronic device is provided, comprising:

[0141] A processor, and a memory and a transceiver communicatively connected to the processor;

[0142] The memory stores computer-executed instructions; the transceiver is used for sending and receiving data.

[0143] The processor executes the computer execution instructions stored in the memory to implement the construction method in Embodiment 1.

[0144] It should be understood that the electronic device can be used to perform the corresponding steps and / or processes in the above method embodiments. Optionally, the memory may include read-only memory and random access memory, and provide instructions and data to the processor. A portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information. The processor can be used to execute instructions stored in the memory, and when the processor executes the instructions, the processor 210 can perform the corresponding steps and / or processes in the above method embodiments.

[0145] It should be understood that, in the embodiments of this application, the processor may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0146] In implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software. The steps of the method disclosed in the embodiments of this application can be directly manifested as execution by a hardware processor, or as a combination of hardware and software modules within the processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor executes the instructions in the memory, combining them with its hardware to complete the steps of the above method. To avoid repetition, detailed descriptions are omitted here.

[0147] Example 4: In this example, a computer-readable storage medium is provided, which stores computer-executable instructions. When executed by a processor, the computer-executable instructions are used to implement the construction method in Example 1.

[0148] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0149] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0150] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0151] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0152] In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0153] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for constructing a shrinkage prediction model for fiber-reinforced high-performance seawater sand concrete, comprising the following steps: S1. A seawater concentration correction term is introduced into the ultimate shrinkage strain of the existing concrete shrinkage prediction model, and the seawater concentration influence coefficient is calculated; the calculation formula for the seawater concentration correction term is: η c =A·c+1; Where, η c Here, A is the seawater concentration correction term, and c is the seawater concentration influence coefficient. S2. An effective humidity correction term is introduced into the humidity function of the existing concrete shrinkage prediction model, and the humidity perception coefficient is calculated; the calculation formula for the effective humidity correction term is: h eff =h·(B·c+1); Among them, h eff For effective humidity correction, h is the ambient relative humidity, B is the humidity perception coefficient, and c is the seawater concentration; The calculation steps for the seawater concentration influence coefficient A and the humidity perception coefficient B are as follows: Assuming that seawater concentration has a positive correlation with the corrected limiting shrinkage strain and that seawater concentration affects effective humidity by altering the chemical potential of the pore solution, the following theoretical relationship is obtained: ； Where, ε shu The corrected ultimate shrinkage strain is used to measure the actual shrinkage strain of concrete under different seawater concentrations and different ambient humidity levels, and obtain the corresponding experimental data. Based on the experimental data, a multivariate nonlinear regression analysis was performed on the theoretical relationship to simultaneously obtain the seawater concentration influence coefficient A and the humidity perception coefficient B. S3. Construct a fiber constraint function and introduce a fiber inhibition coefficient. Determine the fiber efficiency coefficient through fiber type and dosage experiments. The theoretical formula for calculating the fiber inhibition coefficient is as follows: ； Where, k fiber V is the fiber inhibition coefficient, α is the fiber efficiency coefficient, and V is the fiber inhibition coefficient. f The volume fraction of the fiber, l f / d f The aspect ratio of the fiber; S4. Using the time evolution function, combined with the seawater concentration correction term, the effective humidity correction term, and the fiber inhibition coefficient, output a shrinkage prediction model for fiber-reinforced high-performance seawater sand concrete.

2. The construction method according to claim 1, characterized in that, The step of determining the fiber efficiency coefficient in step S3 includes: Construct the fiber constraint function expression to obtain the theoretical calculation formula for the fiber inhibition coefficient; The volume fraction (V) of different fiber types and different volume fractions was measured using an experimental system. f and different aspect ratios l f / d f The actual shrinkage strain of the concrete was analyzed to obtain experimental data; Based on the experimental data, the measured values ​​of the fiber inhibition coefficient under each working condition were calculated, and the fiber efficiency coefficients of different fiber types were obtained by fitting through inversion analysis.

3. The construction method according to claim 1, characterized in that, The fiber types include at least basalt fiber, polypropylene fiber, and steel fiber.

4. The construction method according to claim 1, characterized in that, The formula for the shrinkage prediction model of fiber-reinforced high-performance seawater sand concrete in step S4 is as follows: ； Where, ε cs (t) represents the shrinkage strain of fiber-reinforced high-performance seawater sand concrete at age t days. shu β(h) is the corrected limiting shrinkage strain, β(t) is the corrected humidity function, and β(t) is the time function describing the shrinkage over time; k fiber This represents the fiber inhibition coefficient.

5. A shrinkage prediction system for fiber-reinforced high-performance seawater sand concrete, characterized in that, The method described in any one of claims 1-4 is used to predict the long-term shrinkage deformation behavior of fiber-reinforced high-performance seawater sand concrete in a marine environment.

6. An electronic device, characterized in that, include: A processor, and a memory and a transceiver communicatively connected to the processor; The memory stores computer-executed instructions; the transceiver is used for sending and receiving data. The processor executes computer execution instructions stored in the memory to implement the construction method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the construction method as described in any one of claims 1-4.

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