Basalt fiber grid bridge deck pavement concrete carbonization prediction method

Abstract

The invention discloses a basalt fiber grid bridge deck pavement concrete carbonization prediction method, and belongs to the technical field of highway bridge construction and detection. Comprising the following steps: acquiring current bridge deck pavement concrete, performing an accelerated carbonization test on the acquired concrete, and at least acquiring stress of CO2 consumption A, a CO2 diffusion coefficient D, a stress influence coefficient k sigma and a gas permeability coefficient; building a prediction model based on the stress of the gas permeability coefficient; the method comprises the following steps: applying axial pressure stress and axial tensile stress to concrete paved on a current bridge deck, carrying out segmented fitting on the axial pressure stress and the axial tensile stress, testing the concrete through an Autoclam testing system to obtain a gas permeability coefficient, and establishing a function relationship between the axial pressure stress and the relative gas permeability coefficient as well as between the axial tensile stress and the relative gas permeability coefficient. And obtaining data through the function relationship to optimize the prediction model to obtain an optimization model. According to the method, the safety and durability of the bridge structure can be improved, and an important reference is provided for engineering maintenance and life prediction.

Description

Technical Field

[0001] The invention belongs to the technical field of highway and bridge construction and detection, and in particular relates to a method for predicting carbonization of basalt fiber grid bridge deck paving concrete. Background Art

[0002] Concrete carbonation refers to the penetration of carbon dioxide gas in the air into concrete, chemically reacting with the alkaline substances in it to produce carbonates and water, thereby reducing the alkalinity of the concrete. This process is also called neutralization. During the cement hydration process, a large amount of calcium hydroxide is produced, filling the voids in the concrete with a saturated calcium hydroxide solution. This alkaline medium has a good protective effect on steel bars because it causes insoluble Fe2O3 and Fe3O4 to form a passivation film on the surface of the steel bars. However, when the concrete carbonization exceeds the protective layer, the concrete loses its protective effect on the steel bars, causing the steel bars to begin to rust.

[0003] For plain concrete, carbonization does not directly lead to performance degradation, but can improve its durability. However, for reinforced concrete, carbonization will reduce the alkalinity of concrete and increase the number of hydrogen ions in the concrete pore solution, thereby weakening the protective effect of concrete on steel bars. Therefore, it is very important to obtain the carbonization parameters of concrete.

[0004] Existing technologies have been used to study the carbonation of concrete. However, most studies focus on the effect of a specific factor on early carbonation, without considering the effects of multiple factors. Therefore, further research is needed to fully understand the factors affecting concrete carbonation. Summary of the invention

[0005] Purpose of the invention: To provide a method for predicting carbonization of basalt fiber grid bridge deck paving concrete, which solves the above-mentioned problems existing in the prior art.

[0006] Technical solution: A method for predicting carbonization of basalt fiber grid bridge deck concrete, comprising the following steps:

[0007] Step S1, obtaining concrete for the current bridge deck pavement, performing an accelerated carbonation test on the obtained concrete, and obtaining at least the CO2 consumption A, CO2 diffusion coefficient D, stress influence coefficient kσ, and stress of the gas permeability coefficient;

[0008] Step S2, building a prediction model based on the stress of gas permeability coefficient;

[0009] Step S3, applying axial compressive stress and axial tensile stress to the concrete of the current bridge deck pavement, performing segmented fitting on the axial compressive stress and the axial tensile stress, testing the concrete through the Autoclam test system to obtain the gas permeability coefficient, establishing a functional relationship between the axial compressive stress and the axial tensile stress and the relative gas permeability coefficient, optimizing the prediction model through data obtained through the functional relationship, obtaining an optimized model, and predicting the carbonization depth value of the concrete of the current bridge deck pavement through the optimized model;

[0010] Step S4, obtain the CO2 consumption A, CO2 diffusion coefficient D and stress influence coefficient kσ in step S1, obtain the CO2 consumption a, CO2 diffusion coefficient d and stress influence coefficient σ of the optimization model, compare the data obtained from the carbonization test with the data obtained from the prediction model, and verify the accuracy of the optimization model.

[0011] Preferably, the prediction model building process in step S2 is as follows:

[0012] S21. Build a basic model based on the CO2 diffusion coefficient D, and the formula is as follows:

[0013]

[0014] Where: d represents the depth of the concrete surface, D represents the CO2 diffusion coefficient, m represents the CO2 concentration at the depth d of the concrete, t represents the carbonization time of the concrete, and b represents the consumption content of CO2 in the carbonization reaction of the concrete;

[0015] The relative humidity and wetting time of the concrete environment are obtained and a prediction model is built using formula (2). The formula is as follows:

[0016]

[0017] Where: σ is stress influence coefficient, Y is gas permeability coefficient, H is relative humidity, t is carbonization time, T is O W represents the wetting time, A represents the cement dosage, X ext represents the surface carbon dioxide concentration, Indicates the content of CO2 in unit mass of cement, N CaO Indicates the content of CaO in unit mass of cement. Indicates the amount of clinker in concrete.

[0018] Preferably, the process of obtaining the CO2 consumption A in step S1 is as follows:

[0019] Step S11, obtaining the CaCO3 content of the uncarbonized area in the silicate cement paste specimen, the calculation formula is as follows:

[0020] Ca(OH)2+CO2→CaCO3+H2O(3);

[0021] Step S12, under the condition of 20% CO2 carbonization, the CaCO3 content generated in the silicate cement paste specimen is calculated by formula (3) to calculate the amount of CH consumed. If the CaCO3 content obtained by the test is greater than the CaCO3 content generated in the calculation, the excess CaCO3 content in the test is generated by carbonization of CSH, unhydrated C2S and C3S. The consumed amounts of CSH, unhydrated C2S and C3S can be calculated by formulas (4) and (5);

[0022] 2CaO·SiO2+2CO2+nH2O→SiO2·nH2O+2CaCo3(4);

[0023] 3CaO·SiO2+3CO2+nH2O→SiO2·nH2O+3CaCo3(5);

[0024] Step S13: After calculating the consumption of CSH, unhydrated C2S and C3S by formula (3), (4) and (5), the CO2 consumption A can be obtained by formula (6), which is as follows:

[0025] A=0.565(CH)+0.37(CSH)+0.512(C2S)+0.579(C3S) (6);

[0026] By comparing the calculated CO2 consumption A with the CO2 consumption a obtained by the prediction model, the CO2 consumption a of the optimization model is verified and corrected.

[0027] Preferably, the CO2 diffusion coefficient D in step S1 is calculated by the concrete gas permeability coefficient, and its calculation formula is as follows:

[0028]

[0029] By comparing the calculated CO2 diffusion coefficient D with the CO2 diffusion coefficient d obtained by the prediction model, the CO2 diffusion coefficient d of the optimization model is verified and corrected.

[0030] Preferably, in step S1, the stress influence coefficient kσ takes the stress on the relative gas permeability coefficient as the influencing parameter, takes the concrete relative gas permeability coefficient as the evaluation index, and calculates the relative gas permeability coefficient by formula (8) based on the variation trend of the concrete gas permeability coefficient under the two stress states of axial tension and axial compression:

[0031]

[0032] Where: kb i Relative gas permeability coefficient, k srepresents the gas permeability coefficient of concrete under stress, k o It represents the gas permeability coefficient of concrete under no stress, i represents the stress ratio, where the stress ratio includes the axial tensile stress ratio and the axial compressive stress ratio. The stress influence coefficient kσ obtained by calculating the relative gas permeability coefficient is compared with the stress influence coefficient σ obtained by the prediction model, and the stress influence coefficient σ of the optimization model is verified and corrected.

[0033] Preferably, in step S3, the functional relationship between the axial compressive stress and the axial tensile stress and the relative gas permeability coefficient is established as follows:

[0034]

[0035] In the formula: c represents the stress ratio of concrete, where compressive stress is positive and tensile stress is negative.

[0036] Preferably, based on formulas (2), (6), (7), and (9), an optimization model under stress state based on relative gas permeability coefficient is obtained, and its formula is as follows:

[0037]

[0038] Beneficial effects: The present invention relates to a method for predicting the carbonization of basalt fiber grid bridge deck paving concrete. The CO2 consumption A, CO2 diffusion coefficient D, stress influence coefficient kσ and gas permeability coefficient of the concrete are obtained through accelerated carbonization test, and a prediction model is built based on these parameters. Subsequently, the concrete is tested by applying axial compressive stress and axial tensile stress to optimize the prediction model, and finally an accurate prediction of the carbonization depth value of the concrete is achieved. In addition, the accuracy of the optimization model is further verified by comparing the experimental data with the model prediction results.

[0039] Through CO2 consumption A, CO2 diffusion coefficient D, stress influence coefficient kσ and gas permeability coefficient, concrete is tested to optimize the prediction model, and finally the accurate prediction of concrete carbonation depth value is achieved, which not only helps to improve the safety and durability of bridge structures, but also provides an important reference for engineering maintenance and life prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Calculating a carbonization variation curve for the suburban prediction model of the present invention;

[0041] Figure 2 Calculating a carbonization change curve for the urban area prediction model of the present invention;

[0042] Figure 3 The carbonization variation curve is calculated for the industrial zone prediction model of the present invention. DETAILED DESCRIPTION

[0043] like Figures 1 to 3 The present invention provides a technical solution: a method for predicting carbonization of basalt fiber grid bridge deck paving concrete, comprising the following steps:

[0044] Step S1, obtaining concrete for the current bridge deck pavement, performing an accelerated carbonation test on the obtained concrete, and obtaining at least the CO2 consumption A, CO2 diffusion coefficient D, stress influence coefficient kσ, and stress of the gas permeability coefficient;

[0045] Among them, the process of obtaining CO2 consumption A is as follows:

[0046] Step S11, obtaining the CaCO3 content of the uncarbonized area in the silicate cement paste specimen, the calculation formula is as follows:

[0047] Ca(OH)2+CO2→CaCO3+H2O(3);

[0048] Step S12, under the condition of 20% CO2 carbonization, the CaCO3 content generated in the silicate cement paste specimen is calculated by formula (3) to calculate the amount of CH consumed. If the CaCO3 content obtained by the test is greater than the CaCO3 content generated in the calculation, the excess CaCO3 content in the test is generated by carbonization of CSH, unhydrated C2S and C3S. The consumed amounts of CSH, unhydrated C2S and C3S can be calculated by formulas (4) and (5);

[0049] 2CaO·SiO2+2CO2+nH2O→SiO2·nH2O+2CaCo3(4);

[0050] 3CaO·SiO2+3CO2+nH2O→SiO2·nH2O+3CaCo3(5);

[0051] Step S13: After calculating the consumption of CSH, unhydrated C2S and C3S by formula (3), (4) and (5), the CO2 consumption A can be obtained by formula (6), which is as follows:

[0052] A=0.565(CH)+0.37(CSH)+0.512(C2S)+0.579(C3S) (6);

[0053] The CO2 diffusion coefficient D is calculated by the concrete gas permeability coefficient, and its calculation formula is as follows

[0054]

[0055] The stress influence coefficient kσ takes the stress on the relative gas permeability coefficient as the influencing parameter and the relative gas permeability coefficient of concrete as the evaluation index. The relative gas permeability coefficient is calculated by formula (8) based on the variation trend of the concrete gas permeability coefficient under the two stress states of axial tension and axial compression:

[0056]

[0057] Where: kb i Relative gas permeability coefficient, k s represents the gas permeability coefficient of concrete under stress, k o represents the gas permeability coefficient of concrete under no stress, i represents the stress ratio, where the stress ratio includes the axial tensile stress ratio and the axial compressive stress ratio.

[0058] Step S2: Building a prediction model based on the stress of the gas permeability coefficient. The process of building the prediction model is as follows:

[0059] S21. Build a basic model based on the CO2 diffusion coefficient D, and the formula is as follows:

[0060]

[0061] Where: d represents the depth of the concrete surface, D represents the CO2 diffusion coefficient, m represents the CO2 concentration at the depth d of the concrete, t represents the carbonization time of the concrete, and b represents the consumption content of CO2 in the carbonization reaction of the concrete;

[0062] The relative humidity and wetting time of the concrete environment are obtained and a prediction model is built using formula (2). The formula is as follows:

[0063]

[0064] Where: σ is stress influence coefficient, Y is gas permeability coefficient, H is relative humidity, t is carbonization time, T is O W represents the wetting time, A represents the cement dosage, X ext Indicates the surface carbon dioxide concentration, Nco2 indicates the content of CO2 in unit mass of cement, N CaO Indicates the content of CaO in unit mass of cement. Indicates the amount of clinker in concrete.

[0065] Step S3, applying axial compressive stress and axial tensile stress to the concrete of the current bridge deck pavement, performing segmented fitting on the axial compressive stress and the axial tensile stress, testing the concrete through the Autoclam test system to obtain the gas permeability coefficient, and establishing a functional relationship between the axial compressive stress and the axial tensile stress and the relative gas permeability coefficient, wherein the functional relationship between the axial compressive stress and the axial tensile stress and the relative gas permeability coefficient is:

[0066]

[0067] Where: c represents the stress ratio of concrete, where compressive stress is positive and tensile stress is negative. The prediction model is optimized by obtaining data through functional relationship. Based on formulas (2), (6), (7), and (9), the optimization model under stress state based on relative gas permeability coefficient is obtained, and the formula is as follows:

[0068]

[0069] The optimization model is used to predict the concrete carbonation depth of the current bridge deck pavement.

[0070] Step S4, obtain the CO2 consumption A, CO2 diffusion coefficient D and stress influence coefficient kσ in step S1, obtain the CO2 consumption a, CO2 diffusion coefficient d and stress influence coefficient σ of the optimization model, verify and correct the CO2 consumption a of the optimization model by comparing the calculated CO2 consumption A with the CO2 consumption a obtained by the prediction model, verify and correct the CO2 diffusion coefficient d of the optimization model by comparing the calculated CO2 diffusion coefficient D with the CO2 diffusion coefficient d obtained by the prediction model, verify and correct the stress influence coefficient kσ of the optimization model by comparing the calculated relative gas permeability coefficient with the stress influence coefficient σ obtained by the prediction model, and verify and correct the stress influence coefficient σ of the optimization model.

[0071] Through the above technical solution, the present invention can achieve the following working process:

[0072] The C30 concrete structure of a certain project was selected as the research object. The designed concrete cover thicknesses were 20mm, 30mm, 40mm and 50mm, respectively, and they were subjected to axial compression and axial tension loads with stress ratios of 0.3 and 0.6, respectively. The use environment status is shown in Table 1, and the carbonization status of different cover thicknesses in different environments is shown in Table 2.

[0073] Table 1: Annual average CO2 gas concentration and relative humidity

[0074] Relative humidity CO2 gas concentration suburbs 54.2 0.034 Urban area 51.1 0.053 Industrial Area 58.5 0.067

[0075] Table 2: Effect of protective layer thickness on carbonization life of loaded concrete structures in different regions

[0076]

[0077] Figures 1 to 3In order to calculate the change curve of the calculated value of carbonization depth of concrete structure under different stresses with carbonization time through the prediction model, compared with the unstressed concrete, applying a stress ratio of 30% to the concrete will extend the service life of the concrete structure. When the stress ratio is increased to 60%, the service life of the concrete is significantly reduced.

[0078] Under high pressure stress and high tensile stress, the degree of concrete damage is aggravated, and the service life of concrete is reduced by about 15% to 42%. As the CO2 concentration increases, the service life of concrete gradually decreases, and the durability of concrete structures is significantly affected by the environment where they are located. Figures 1 to 3 From the perspective of the service life of concrete structures in busy urban areas, it is only about 41% of that of similar structures in suburban areas, but about 121% of that of concrete structures in industrial areas. In addition, the service life of concrete will be extended as the thickness of its protective layer increases. Specifically, when the thickness of the protective layer increases from 20 mm to 50 mm, the service life of the concrete structure can be increased to 6 times the original. Therefore, when designing the durability of carbonization-resistant and stress-resistant concrete structures, it is necessary not only to determine the appropriate thickness of the concrete protective layer according to the environment in which the structure is located, but also to pay special attention to those components that bear tensile forces and areas susceptible to carbonization erosion to ensure the overall durability and reliability of the structure.

[0079] The preferred embodiments of the present invention are described in detail above; however, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, various equivalent transformations can be made to the technical solutions of the present invention, and these equivalent transformations all belong to the protection scope of the present invention.

Claims

1. A method for predicting carbonization of basalt fiber grid bridge deck paving concrete, characterized in that: The following steps are involved: Step S1, obtaining concrete for the current bridge deck pavement, performing an accelerated carbonation test on the obtained concrete, and obtaining at least the CO2 consumption A, CO2 diffusion coefficient D, stress influence coefficient kσ, and stress of the gas permeability coefficient; Step S2, building a prediction model based on the stress of gas permeability coefficient; Step S3, applying axial compressive stress and axial tensile stress to the concrete of the current bridge deck pavement, performing segmented fitting on the axial compressive stress and the axial tensile stress, testing the concrete through the Autoclam test system to obtain the gas permeability coefficient, establishing a functional relationship between the axial compressive stress and the axial tensile stress and the relative gas permeability coefficient, optimizing the prediction model through data obtained through the functional relationship, obtaining an optimized model, and predicting the carbonization depth value of the concrete of the current bridge deck pavement through the optimized model; Step S4, obtain the CO2 consumption A, CO2 diffusion coefficient D and stress influence coefficient kσ in step S1, obtain the CO2 consumption a, CO2 diffusion coefficient d and stress influence coefficient σ of the optimization model, compare the data obtained from the carbonization test with the data obtained from the prediction model, and verify the accuracy of the optimization model.

2. A method for predicting carbonization of basalt fiber grid bridge deck concrete according to claim 1, characterized in that: The prediction model building process in step S2 is as follows: S21. Build a basic model based on the CO2 diffusion coefficient D, and the formula is as follows: Where: d represents the depth of the concrete surface, D represents the CO2 diffusion coefficient, m represents the CO2 concentration at the depth d of the concrete, t represents the carbonization time of the concrete, and b represents the consumption content of CO2 in the carbonization reaction of the concrete; The relative humidity and wetting time of the concrete environment are obtained and a prediction model is built using formula (2). The formula is as follows: Where: σ is stress influence coefficient, Y is gas permeability coefficient, H is relative humidity, t is carbonization time, T is O W represents the wetting time, A represents the cement dosage, X ext Indicates the surface carbon dioxide concentration, Nco2 indicates the content of CO2 in unit mass of cement, N CaO Indicates the content of CaO in unit mass of cement. Indicates the amount of clinker in concrete.

3. The method for predicting carbonization of basalt fiber grid bridge deck concrete according to claim 2, characterized in that: The process of obtaining the CO2 consumption A in step S1 is as follows: Step S11, obtaining the CaCO3 content of the uncarbonized area in the silicate cement paste specimen, the calculation formula is as follows: Ca(OH)2+CO2→CaCO3+H2O(3); Step S12, under the condition of 20% CO2 carbonization, the CaCO3 content generated in the silicate cement paste specimen is calculated by formula (3) to calculate the amount of CH consumed. If the CaCO3 content obtained by the test is greater than the CaCO3 content generated in the calculation, the excess CaCO3 content in the test is generated by carbonization of CSH, unhydrated C2S and C3S. The consumed amounts of CSH, unhydrated C2S and C3S can be calculated by formulas (4) and (5); 2CaO·SiO2+2CO2+nH2O→SiO2·nH2O+2CaCo3(4); 3CaO·SiO2+3CO2+nH2O→SiO2·nH2O+3CaCo3(5); Step S13: After calculating the consumption of CSH, unhydrated C2S and C3S by formula (3), (4) and (5), the CO2 consumption A can be obtained by formula (6), which is as follows: A=0.565(CH)+0.37(CSH)+0.512(C2S)+0.579(C3S) (6); By comparing the calculated CO2 consumption A with the CO2 consumption a obtained by the prediction model, the CO2 consumption a of the optimization model is verified and corrected.

4. A method for predicting carbonization of basalt fiber grid bridge deck paving concrete according to claim 3, characterized in that: In step S1, the CO2 diffusion coefficient D is calculated by the concrete gas permeability coefficient, and its calculation formula is as follows: By comparing the calculated CO2 diffusion coefficient D with the CO2 diffusion coefficient d obtained by the prediction model, the CO2 diffusion coefficient d of the optimization model is verified and corrected.

5. The method for predicting carbonization of basalt fiber grid bridge deck concrete according to claim 1, characterized in that: In step S1, the stress influence coefficient kσ takes the stress on the relative gas permeability coefficient as the influencing parameter and the concrete relative gas permeability coefficient as the evaluation index. The relative gas permeability coefficient is calculated by formula (8) based on the variation trend of the concrete gas permeability coefficient under the two stress states of axial tension and axial compression: Where: kb i Relative gas permeability coefficient, k s represents the gas permeability coefficient of concrete under stress, k o It represents the gas permeability coefficient of concrete under no stress, i represents the stress ratio, where the stress ratio includes the axial tensile stress ratio and the axial compressive stress ratio. The stress influence coefficient kσ obtained by calculating the relative gas permeability coefficient is compared with the stress influence coefficient σ obtained by the prediction model, and the stress influence coefficient σ of the optimization model is verified and corrected.

6. The method for predicting carbonization of basalt fiber grid bridge deck concrete according to claim 4, characterized in that: In step S3, the functional relationship between the axial compressive stress and the axial tensile stress and the relative gas permeability coefficient is established as follows: In the formula: c represents the stress ratio of concrete, where compressive stress is positive and tensile stress is negative.

7. A method for predicting carbonization of basalt fiber grid bridge deck paving concrete according to claim 6, characterized in that: Based on formulas (2), (6), (7), and (9), the optimization model under stress state based on relative gas permeability coefficient is obtained, and its formula is as follows:

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