Prediction method for sulfate freezing and thawing resistance and service life of recycled coarse aggregate self-compacting concrete

Abstract

The invention relates to a method for predicting sulfate freeze-thaw resistance and service life of recycled coarse aggregate self-compacting concrete, which comprises the following steps of: defining a durability comprehensive evaluation index of RCASCC under a sulfate freeze-thaw action based on an entropy weight method, and obtaining a change rule of the durability comprehensive evaluation index about an RCA substitution rate, a freeze-thaw environment and coupling action times. Based on four forms of a gray system theory GM (1, 1) model, the durability of the RCASCC subjected to sulfate freeze thawing is predicted, the model precision is subjected to grade evaluation according to a model precision evaluation table, and when the durability comprehensive evaluation index is reduced to 75%, the result is used as a RCASCC failure standard. Compared with the prior art, the method for predicting the sulfate freeze-thaw resistance and the service life is more comprehensive and accurate, so that the limitation of single index evaluation in the prior art is overcome, and the prediction accuracy and reliability are improved.

Description

Technical Field

[0001] The invention relates to the technical field of hydraulic concrete durability, and in particular to a method for predicting sulfate freeze-thaw resistance and service life of recycled coarse aggregate self-compacting concrete. Background Art

[0002] As a commonly used building material, concrete occupies a pivotal position in the construction industry. According to data from 2022, China's commercial concrete production reached 3.293 billion cubic meters. In the preparation of concrete, the indispensable aggregates are usually derived from nature, such as sand, crushed stone and gravel. With the development of the world and the surge in population, old buildings are constantly demolished to make room for the construction of new buildings. In the process of demolishing old buildings, a large amount of construction waste will inevitably be generated. Properly handling these construction wastes is an arduous task that requires a lot of management and financial resources. The public's attention to the resource utilization of construction waste is increasing. By recycling construction waste and converting it into raw materials for the production of concrete, it can not only reduce the pressure on the exploitation of natural resources, but also significantly reduce the amount of waste sent to landfills.

[0003] Recycled coarse aggregate concrete refers to a type of concrete prepared by using recycled coarse aggregate (RCA) to partially or completely replace natural coarse aggregate (NCA) after the concrete from the dismantling of old buildings or structures is crushed and graded. The use of recycled concrete helps to reduce the exploitation of natural resources and reduce the impact of construction waste on the environment. It is an environmentally friendly and sustainable building material. Self-compacting concrete (SCC) is a type of concrete with high fluidity, uniformity and stability. It can flow and fill all corners in the formwork by its own gravity without external vibration to achieve uniform compaction. SCC is considered to be one of the greatest advances in the field of modern building materials. Recycled coarse aggregate self-compacting concrete (RCASCC) is a new type of green concrete with many advantages of SCC, which is prepared by replacing NCA with RCA made from waste concrete. RCASCC can realize the recycling and reuse of waste concrete. The application of RCASCC in production and construction will have positive social and economic benefits in reducing pollution in the living environment and the development of the construction industry.

[0004] Hydraulic structures in Northeast, North and Northwest China face complex service conditions, mainly due to the region's severe and long winters, significant temperature and humidity changes in summer, and high sulfate ion concentrations in rivers, groundwater and soil. Hydraulic structures are threatened by the dual effects of long-term salt ion erosion and freeze-thaw cycles. Salt ion erosion and freeze-thaw cycles are considered to be the two main factors leading to the deterioration of hydraulic structure performance. This issue has attracted widespread attention in the engineering research community. For example, achieving sulfate freeze-thaw resistance and service life prediction of recycled coarse aggregate self-compacting concrete is the key to solving this problem. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a prediction method for the sulfate freeze-thaw resistance and service life of recycled coarse aggregate self-compacting concrete, aiming to solve the challenges of model construction caused by incomplete objective determination of evaluation indicators, unclear processing information or insufficient data, including insufficient accuracy, limited generalization ability, low precision, limited applicability and weak ability to handle multi-factor coordination.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] The present invention provides a method for predicting sulfate freeze-thaw resistance and service life of recycled coarse aggregate self-compacting concrete, comprising the following steps:

[0008] S1: Based on the quality of sulfate freeze-thaw resistance, dynamic elastic modulus, compressive strength and splitting tensile strength of recycled coarse aggregate self-compacting concrete, calculate the comprehensive evaluation index of durability, and analyze the change law of this index with the replacement rate of recycled coarse aggregate, freeze-thaw environment and the number of coupling actions;

[0009] S2: Using the comprehensive durability evaluation index obtained in S1, the GM (1,1) model in the grey system theory, including four forms of EGM, ODGM, EDGM, and DGM, was used to make a medium- and long-term prediction of the durability of RCASCC after sulfate freeze-thaw, and the predicted values ​​of each model were obtained;

[0010] S3: According to the model accuracy assessment standard, the accuracy level of the four forms of the GM (1,1) model is assessed, and the model with the required accuracy is selected to predict the durability change of recycled coarse aggregate self-compacting concrete after sulfate freeze-thaw action;

[0011] S4: The failure standard of recycled coarse aggregate self-compacting concrete is the reduction of the comprehensive durability evaluation index to the preset value. The service life of recycled coarse aggregate self-compacting concrete after being subjected to sulfate freeze-thaw in the outdoor environment of the target area is calculated.

[0012] in:

[0013] EGM (Exponential Grey Model): Exponential grey model.

[0014] ODGM (One-Variable Differential Grey Model): Single variable differential grey model.

[0015] EDGM (Economic Development Grey Model): Economic development grey model.

[0016] DGM (Differential Grey Model): Differential grey model.

[0018] Furthermore, the initial durability index matrix is ​​in the following form:

[0019]

[0020] Among them, A=[a ij ] uv is the initial durability index matrix, a ij is the value of the jth durability index of the ith evaluation object, and u and v represent the number of evaluation objects and durability indicators, respectively.

[0021] Furthermore, the construction process of the evaluation index matrix includes the following process:

[0022] Durability index standardization:

[0023]

[0024] Where W = [w ij ] u×v is the standardized durability index matrix, w ij is a ij The standard value of

[0025] Create a weight matrix:

[0026]

[0027] Where Q = [q ij ] u×v is the weight matrix, q ij is a ij The proportion of

[0028] Establish the information utility value matrix:

[0029]

[0030] Where P = [pj ] 1×v is the information utility value matrix, p j is the information utility value of the jth indicator,

[0031] Establish indicator weight matrix:

[0032]

[0033] Where K = [k j ] 1×j is the indicator weight matrix, k j is the weight of the jth indicator,

[0034] Establish an evaluation indicator matrix:

[0035]

[0036] Where D is the comprehensive evaluation index matrix of durability of RCASCC under sulfate freeze-thaw action, d i It is a comprehensive evaluation index of the durability of RCASCC under sulfate freeze-thaw action.

[0037] Furthermore, in S2, the process of mid- to long-term prediction of the durability of RCASCC after sulfate freeze-thaw includes:

[0038] The durability comprehensive evaluation index is taken as the original data, and the D value at zero sulfate freeze-thaw action is taken as the initial value, recorded as Z (0) ,

[0039]

[0040] By X (0) Perform an accumulation to obtain a new sequence Z (1) ,

[0041]

[0042] Calculate the predicted value of each model.

[0043]

[0044] Where: For EGM and ODGM, f(c,k) is e -ck and (1+c) -k , for EDGM and DGM, f(c,k) is

[0045] Furthermore, in the process of calculating the predicted values ​​of each model, the GM(1,1) model parameter calculation formula used includes:

[0046]

[0047] Note: is the background value c,d,β 1 ,β 2 is the model parameter

[0048] Furthermore, in the process of calculating the predicted values ​​of each model, the GM(1,1) model parameter calculation formula used also includes:

[0049]

[0050]

[0051] Note: is the background value c,d,β,β 2 is the model parameter

[0052] Furthermore, in the process of calculating the predicted values ​​of each model, the GM(1,1) model parameter calculation formula used also includes:

[0053]

[0054] Note: is the background value c,d,β 1 ,β 2 is the model parameter

[0055] Furthermore, in the process of calculating the predicted values ​​of each model, the GM(1,1) model parameter calculation formula used also includes:

[0056]

[0057] Note: is the background value c,d,β 1 ,β 2 is the model parameter

[0058] Furthermore, the process of evaluating the accuracy level of the four forms of the GM(1,1) model includes:

[0059] The model prediction value is taken as the prediction sequence, denoted as

[0060]

[0061] Define the residual sequence as ε (0) ,

[0062]

[0063] Define Z (0) The mean and variance of and The mean and variance of the residuals are defined as and

[0064]

[0065]

[0066] Define the mean square error ratio and the probability of small error as C and P, and evaluate the model accuracy with reference to the accuracy test level reference table, where:

[0067]

[0068] Furthermore, in S4, the durability comprehensive evaluation index is reduced to 75% as the failure standard of RCASCC, and the service life of RCASCC after being subjected to sulfate freeze-thaw in the outdoor environment of Northeast China, North China and Northwest China is calculated:

[0069] The following formula is used to calculate the service life of RCASCC after being subjected to sulfate freeze-thaw in outdoor environments in Northeast, North and Northwest China:

[0070]

[0071] Where: t is the working life of RCASCC after being subjected to sulfate freeze-thaw action, years, k is the indoor and outdoor freeze-thaw cycle conversion coefficient, k = 12, N max is the number of limiting coupling actions of RCASCC in indoor environment, M is the maximum number of natural sulfate freeze-thaw actions that can be tolerated in outdoor environment within 1 year, M=120 times / year in Northeast China, M=84 times / year in North China, and M=118 times / year in Northwest China.

[0072] Compared with the prior art, the present invention has the following technical advantages:

[0073] 1) The present invention provides a more comprehensive and accurate prediction method for sulfate freeze-thaw resistance and service life by comprehensively considering multiple durability indicators of recycled coarse aggregate self-compacting concrete, thereby overcoming the limitations of single indicator evaluation in the prior art and improving the accuracy and reliability of the prediction.

[0074] 2) The GM (1,1) model in the grey system theory is used for medium- and long-term prediction. The present invention can predict the durability change of RCASCC under the action of sulfate freeze-thaw based on limited data information, effectively reducing the test cycle and cost, while improving the efficiency and economy of the prediction.

[0075] 3) The present invention also provides a method based on model accuracy assessment to select the most appropriate prediction model, which ensures the accuracy and applicability of the prediction results, provides a scientific basis for the durability evaluation and life prediction of RCASCC in engineering practice, and helps to optimize material selection and structural design and extend the service life of buildings. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 This is the experimental operation flow chart.

[0077] Figure 2 The D value curve of RCASCC under different RCA substitution rates and different freeze-thaw environments ((a) different RCA substitution rates, (b) different freeze-thaw environments).

[0078] Figure 3 are the predicted model parameter values ​​((a) EGM, (b) ODGM, (c) EDGM, (d) DGM).

[0079] Figure 4 Calculation results for model accuracy assessment ((a) NC-H, (b) RC50-H, (c) RC100-H, (d) RC50-N, (e) RC50-M, (f) RC50-NM).

[0080] Figure 5 Comparison of model accuracy ((a) model accuracy calculation results, (b) model accuracy statistical analysis).

[0081] Figure 6 Lifespan prediction of RCASCC under different RCA replacement rates and freeze-thaw environments ((a) different RCA replacement rates, (b) different freeze-thaw environments).

[0082] Figure 7 To predict the working life of RCASCC (recycled coarse aggregate self-compacting concrete) in different regions of China. DETAILED DESCRIPTION

[0083] The present invention discloses a method for predicting the anti-sulfate freeze-thaw performance and service life of recycled coarse aggregate self-compacting concrete (RCASCC) based on the entropy weight method theory and the grey system theory, aiming to study the durability change of RCASCC under different recycled aggregate (RCA) replacement rates, freeze-thaw environments and freeze-thaw cycle times. The present invention first defines the durability comprehensive evaluation index of RCASCC under the action of sulfate freeze-thaw based on the entropy weight method, and obtains the variation law of the durability comprehensive evaluation index with respect to the RCA replacement rate, freeze-thaw environment and coupling action times. Then, the durability of RCASCC after being subjected to sulfate freeze-thaw is predicted based on the four forms of EGM, ODGM, EDGM and DGM of the GM (1,1) model of the grey system theory, and the model accuracy is graded with reference to the model accuracy evaluation table. By analogy with the compressive strength erosion coefficient, the durability comprehensive evaluation index is reduced to 75% as the failure standard of RCASCC, and the service life of RCASCC after being subjected to sulfate freeze-thaw in the outdoor environment of Northeast China, North China and Northwest China is calculated.

[0084] The use of mathematical models to predict the sulfate freeze-thaw resistance of concrete can significantly reduce the test cycle and reduce costs. Due to objective conditions, researchers are usually unable to obtain comprehensive information about the research object, which makes it difficult for researchers to accurately predict system changes. The rise of system science enables researchers to reveal the operating laws of the system by analyzing limited information. In the concrete sulfate freeze-thaw test, researchers only used a single indicator (compressive strength, quality, etc.) to evaluate durability. However, the changes in each indicator of concrete during the sulfate freeze-thaw process are different. Therefore, it is necessary to build a more comprehensive concrete durability evaluation index system. The entropy weight method is a method that uses the information entropy principle to objectively determine the weight of the evaluation index. The information validity is evaluated by calculating the entropy value. It is suitable for multi-index decision analysis and is objective. In the engineering field, evaluating the durability and expected service life of concrete is an important task. Gray theory is a new method created by Professor Deng Julong to analyze and predict uncertain systems with incomplete information and limited sample size. The core of gray theory prediction is to deal with incomplete, unclear or insufficient information, and to predict unknown or future trends by establishing mathematical models. Gray theory has been applied in many fields.

[0085] In the present invention, the sulfate freeze-thaw resistance of RCASCC is comprehensively evaluated through various tests and theoretical model analysis. The influence curves of mass, dynamic elastic modulus, compressive strength and splitting strength of RCASCC under different RCA replacement rates, freeze-thaw environments and freeze-thaw cycles are obtained through experiments. The durability evaluation index is defined based on the entropy weight method. The four forms of EGM, ODGM, EDGM and DGM of the grey system theory GM (1,1) model are used to predict the durability damage of RCASCC under sulfate freeze-thaw. By analogy with the compressive strength erosion coefficient, the comprehensive durability evaluation index is reduced to 75% as the failure standard of RCASCC, and the service life of RCASCC after being subjected to sulfate freeze-thaw in the outdoor environment of Northeast China, North China and Northwest China is calculated.

[0086] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. Component models, material names, connection structures, control methods, algorithms and other features not clearly described in this technical solution are all considered to be common technical features disclosed in the prior art.

[0087] Example 1

[0088] This embodiment provides a method for predicting the sulfate freeze-thaw resistance and service life of RCASCC based on the entropy weight method theory and the grey system theory, comprising the following steps:

[0089] Step 1: The quality, dynamic elastic modulus, compressive strength and splitting tensile strength of RCASCC sulfate freeze-thaw resistance are used as basic data to calculate the comprehensive durability evaluation index, and the change law of the comprehensive durability evaluation index with respect to RCA replacement rate, freeze-thaw environment and coupling times is obtained.

[0090] Step 2: The comprehensive durability evaluation index obtained in step 1 is used as the basic data for grey system theory analysis. The four forms of EGM, ODGM, EDGM and DGM of the grey system theory GM (1,1) model are used to make medium- and long-term predictions on the durability of RCASCC after sulfate freeze-thaw, and the predicted values ​​of each model are obtained.

[0091] Step 3: Refer to the model accuracy evaluation table to evaluate the accuracy of the four forms of the GM (1,1) model, namely, EGM, ODGM, EDGM and DGM. If the accuracy of the established model meets the requirements, it can be used to predict the durability change law of RCASCC after sulfate freeze-thaw.

[0092] Step 4: The durability comprehensive evaluation index is reduced to 75% as the failure standard of RCASCC, and the working life of RCASCC after being subjected to sulfate freeze-thaw in the outdoor environment of Northeast, North China and Northwest China is calculated.

[0093] Step 1: Calculate according to the following process:

[0094] (1) Initial durability index matrix.

[0095]

[0096] Among them, A=[a ij ] uv is the initial durability index matrix. ij is the value of the jth durability index of the ith evaluation object. u and v represent the number of evaluation objects and durability indexes, respectively.

[0097] (2) Standardization of durability indicators.

[0098]

[0099] Where W = [w ij ] u×v is the standardized durability index matrix. ij is a ij The standard value of .

[0100] (3) Establish a weight matrix.

[0101]

[0102] Where Q = [q ij ] u×v is the weight matrix. ij is a ij proportion.

[0103] (4) Establish information utility value matrix.

[0104]

[0105] Where P = [p j ] 1×v is the information utility value matrix. j is the information utility value of the j-th indicator.

[0106] (5) Establish an indicator weight matrix.

[0107]

[0108] Where K = [k j ] 1×j is the indicator weight matrix. k j is the weight of the jth indicator.

[0109] (6) Evaluation index matrix.

[0110]

[0111] Where D is the comprehensive evaluation index matrix of the durability of RCASCC under sulfate freeze-thaw. i It is a comprehensive evaluation index of the durability of RCASCC under sulfate freeze-thaw action.

[0112] Step 2 is calculated according to the following process:

[0113] (1) The durability comprehensive evaluation index is taken as the original data, and the D value at zero sulfate freeze-thaw action is taken as the initial value, denoted as Z (0) .

[0114]

[0115] (2) Through X (0) Perform an accumulation to obtain a new sequence Z (1) .

[0116]

[0117] (3) Use formula (10) and Table 1 to calculate the predicted values ​​of each model.

[0118]

[0119] Where: For EGM and ODGM, f(c,k) is e -ck and (1+c) -k For EDGM and DGM, f(c,k)

[0120] Table 1 GM(1,1) model parameter calculation formula.

[0121]

[0122] Note: is the background value. c,d,β 1 and β 2 are model parameters.

[0123] Step 3: Calculate according to the following process:

[0124] (1) Take the model prediction value as the prediction sequence, denoted as

[0125]

[0126] (2) Define the residual sequence as ε (0) .

[0127]

[0128] (3) Definition of Z (0) The mean and variance of and The mean and variance of the residuals are defined as and

[0129]

[0130] (4) Define the mean square error ratio and the probability of small error as C and P. Refer to Table 2 to evaluate the model accuracy.

[0131]

[0132] Table 2 Accuracy inspection level reference table.

[0133]

[0134] (5) To further compare the model accuracy of the four GM(1,1) models, the accuracy coefficient R is introduced here. 2 . Use formula (3) to calculate R 2 .

[0135]

[0136] Step 4: Calculate according to the following process:

[0137] Outdoor concrete in Northeast China, North China and Northwest China is often damaged by sulfate freeze-thaw. Previous research results show that the average annual freeze-thaw frequency in Northeast China, North China and Northwest China is 120 times / year, 84 times / year and 118 times / year respectively. Formula (4) is used to calculate the working life of RCASCC after being exposed to sulfate freeze-thaw in the outdoor environment of Northeast China, North China and Northwest China.

[0138]

[0139] Where: t is the working life of RCASCC after being subjected to sulfate freeze-thaw action, years. k is the indoor and outdoor freeze-thaw cycle conversion coefficient. In the present invention, k=12. max is the number of extreme coupling effects of RCASCC in indoor environment, times; M is the maximum number of natural sulfate freeze-thaw effects that can be tolerated in outdoor environment within 1 year, times / year. M=120 times / year is taken in Northeast China. M=84 times / year is taken in North China. M=118 times / year is taken in Northwest China.

[0140] The following is a further detailed description of the method for predicting the RCASCC sulfate freeze-thaw resistance and service life based on the entropy weight method theory and the grey system theory described in the present invention in combination with specific data.

[0141] Referring to previous studies, the RCA replacement rate was determined to be 0, 50% and 100%, and the mass fraction of sulfate solution was 0 and 5%. The experiment was divided into 2 groups. The first group changed the RCA replacement rate and was numbered NC-H, RC50-H and RC100-H, indicating that the freeze-thaw environment was water and the RCA replacement rate was 0, 50% and 100%, respectively. The second group changed the freeze-thaw environment and was numbered RC50-H, RC50-N, RC50-M and RC50-NM, indicating that the RCA replacement rate was 50% and the freeze-thaw environment was water, 5% mass fraction Na 2 SO 4 Solution, 5% mass fraction MgSO 4 Solution, 5% mass fraction Na 2 SO 4 With 5% mass fraction MgSO 4 Mixed solution. The mixing ratio of RCASCC is shown in Table 3. Table 4 shows the performance parameters of NCA and RCA. Table 5 shows the performance parameters of Na 2 SO 4 MgSO 4 Performance parameters of powders.

[0142] Table 3 Mix ratio of RCASCC.

[0143]

[0144] Table 4 Performance parameters of NCA and RCA.

[0145]

[0146] Table 5 Na 2 SO 4 MgSO 4 Performance parameters of powders.

[0147]

[0148] Before mixing concrete, keep the inner wall of the mixer cylinder moist and clean. Pour cement, sand, NCA and RCA into the mixer in turn and dry mix for 90 seconds, then pour the mixed solution of water and water reducer into the mixer and wet mix for 90 seconds. After mixing, test the collapse expansion of the concrete first. If the test result is 760-850mm, pour the remaining concrete into the plastic mold in two equal amounts. The dimensions of the concrete specimens are 100mm×100mm×100mm and 100mm×100mm×300mm. The specimens are demoulded after standing at room temperature for 24 hours, and then moved into the standard curing room for curing for 24 days.

[0149] After the specimens have been cured for 24 days, they are placed in water at 20±2℃ and soaked for 4 days. After soaking, the specimens are placed in a freeze-thaw machine. 2 SO 4 Powder and MgSO 4 Prepare salt solution with powder. Put the barrel into the freeze-thaw machine, start the machine and start the test. The test method is the rapid freeze-thaw method. When the number of coupling actions is 0, 25, 50, 75, 100 and 125 times, the mass, dynamic elastic modulus, compressive strength and splitting strength of the specimen are tested. The freeze-thaw machine used is HDK-3 concrete rapid freeze-thaw machine. The accuracy of the electronic scale for measuring the mass of the specimen is 1g and the range is 30kg. Use the DT-20 measuring instrument to measure the dynamic elastic modulus of the specimen. The compressive strength test uses the SHT4106 microcomputer-controlled electro-hydraulic servo universal testing machine at 0.5MPa·s -1 The splitting strength test was conducted using a CMT5305 microcomputer-controlled electro-hydraulic servo universal testing machine at a speed of 0.05 MPa·s -1 The test operation process is as follows: Figure 1 shown.

[0150] The present invention uses mass, dynamic elastic modulus, compressive strength and splitting strength as durability indicators of RCASCC. The larger the comprehensive durability evaluation index (D), the better the durability of concrete. Taking RC50-H as an example, the weight of each durability index is calculated. The data of RC50-H are shown in Table 6.

[0151] Table 6 Test data of RC50-H.

[0152]

[0153] (1) Constructing the initial durability index matrix

[0154]

[0155] (2) Standardize indicator samples

[0156]

[0157] (3) Calculate the weight matrix

[0158]

[0159] (4) Calculation of information entropy matrix of concrete durability index

[0160] E=[1.2925 1.2847 1.2915 1.2905]

[0161] (5) Calculate the weight matrix

[0162] W=[0.2523 0.2456 0.2515 0.2506]

[0163] The weights of mass, dynamic elastic modulus, compressive strength and splitting strength of NC-H, RC100-H, RC50-N, RC50-M and RC50-NM were calculated using the same calculation steps. The results are listed in Table 7. The durability comprehensive evaluation index of each group of RCASCC was calculated using formula (1), and the results are listed in Table 8.

[0164] Table 7 Durability index weight table.

[0165]

[0166]

[0167] Table 8 D value calculation results.

[0168]

[0169] Conduct comprehensive durability evaluation index analysis. Figure 2 It is a D value curve of RCASCC under different RCA substitution rates and different freeze-thaw environments.

[0170] The durability evaluation results of RCASCC based on grey system theory were obtained. The model parameters were calculated using the analytical formula in Table 1, and the results are listed in Figure 3 .from Figure 3 It can be seen that all model parameters meet the requirement of "-c < 0.3". This indicates that these four GM (1,1) models can be used to make medium- and long-term predictions of the durability of RCASCC after sulfate freeze-thaw. The predicted values ​​of each model were calculated using formula (10). The results are shown in Table 9.

[0171] Table 9 Model prediction values.

[0172]

[0173]

[0174] GM(1,1) model accuracy test results. Refer to Table 2 to evaluate the accuracy of the GM(1,1) model. Figure 4 From Table 2 and Figure 4 It can be seen that the C of all calculation models is less than 0.35, and P is greater than 0.95. Therefore, the model accuracy of the calculation model can be rated as level I. This shows that the accuracy of the established model meets the requirements and can be used to predict the durability change law of RCASCC after sulfate freeze-thaw. R is calculated using formula (19) 2 . R 2 The larger the value, the higher the model accuracy. Figure 5 (a). R of NC-H, RC50-H, RC100-H, RC50-N, RC50-M and RC50-NM 2 Sort in descending order. See the sorting results in Figure 5 (b) From Figure 5 (b) It can be seen that the accuracy of EGM ranks first among the four models; the accuracy of DGM and EDGM ranks second and third; and the accuracy of ODGM is the lowest. In summary, if the actual project has high requirements for model accuracy, it is recommended to use the EGM model to predict the durability of RCASCC under sulfate freeze-thaw.

[0175] When concrete is attacked by sulfate, it is often considered that the concrete is destroyed when the compressive strength erosion coefficient of the concrete is reduced to 75%. By analogy with the compressive strength erosion coefficient, it is determined that RCASCC fails when D is reduced to 75%. The life prediction graph of RCASCC under different RCA replacement rates and different freeze-thaw environments is drawn using the test values ​​and the predicted values ​​calculated by the EGM model, such as Figure 6 The present invention uses formula (4) to calculate the working life of RCASCC under the outdoor environment of Northeast China, North China and Northwest China after being subjected to the coupling effect of freeze-thaw and sulfate corrosion. The calculation results are shown in Figure 7 .

[0176] The above description of the embodiments is to facilitate the understanding and use of the invention by those skilled in the art. It is obvious that those skilled in the art can easily make various modifications to these embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the above embodiments, and improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the present invention should be within the scope of protection of the present invention.

Claims

1. A method for predicting sulfate freeze-thaw resistance and service life of recycled coarse aggregate self-compacting concrete, characterized in that: The following steps are involved: S1: Based on the quality of sulfate freeze-thaw resistance, dynamic elastic modulus, compressive strength and splitting tensile strength of recycled coarse aggregate self-compacting concrete, calculate the comprehensive evaluation index of durability, and analyze the change law of this index with the replacement rate of recycled coarse aggregate, freeze-thaw environment and the number of coupling actions; S2: Using the comprehensive durability evaluation index obtained in S1, the GM (1,1) model in the grey system theory, including four forms of EGM, ODGM, EDGM, and DGM, was used to make a medium- and long-term prediction of the durability of RCASCC after sulfate freeze-thaw, and the predicted values ​​of each model were obtained; S3: According to the model accuracy assessment standard, the accuracy level of the four forms of the GM (1,1) model is assessed, and the model with the required accuracy is selected to predict the durability change of recycled coarse aggregate self-compacting concrete after sulfate freeze-thaw action; S4: The failure standard of recycled coarse aggregate self-compacting concrete is the reduction of the comprehensive durability evaluation index to the preset value. The service life of recycled coarse aggregate self-compacting concrete after being subjected to sulfate freeze-thaw in the outdoor environment of the target area is calculated.

2. The method for predicting sulfate freeze-thaw resistance and service life of recycled coarse aggregate self-compacting concrete according to claim 1, characterized in that: The initial durability indicator matrix is ​​in the following form: Among them, A=[a ij ] uv is the initial durability index matrix, a ij is the first j The value of a durability index, u and v represent the number of evaluation objects and durability indicators respectively.

3. The method for predicting sulfate freeze-thaw resistance and service life of recycled coarse aggregate self-compacting concrete according to claim 2, characterized in that: The construction process of the evaluation index matrix includes the following steps: Durability index standardization: Where W = [w ij ] u×v is the standardized durability index matrix, w ij is a ij The standard value of Create a weight matrix: Where Q = [q ij ] u×v is the weight matrix, q ij is a ij The proportion of Establish the information utility value matrix: Where P = [p j ] 1×v is the information utility value matrix, p j It is j The information utility value of an indicator, Establish indicator weight matrix: Where K = [k j ] 1×j is the indicator weight matrix, k j is the weight of the jth indicator, Establish an evaluation indicator matrix: Where D is the comprehensive evaluation index matrix of durability of RCASCC under sulfate freeze-thaw action, d i It is a comprehensive evaluation index of the durability of RCASCC under sulfate freeze-thaw action.

4. The method for predicting sulfate freeze-thaw resistance and service life of recycled coarse aggregate self-compacting concrete according to claim 1, characterized in that: In S2, the process of predicting the durability of RCASCC in the medium and long term after sulfate freeze-thaw includes: The comprehensive durability evaluation index is taken as the original data, and the D value at zero sulfate freeze-thaw action is taken as the initial value, recorded as Z (0) , By X (0) Perform an accumulation to obtain a new sequence Z (1) , Calculate the predicted value of each model. Where: For EGM and ODGM, f(c,k) is e -ck and (1+c) -k , for EDGM and DGM, f(c,k) is 5. The method for predicting sulfate freeze-thaw resistance and service life of recycled coarse aggregate self-compacting concrete according to claim 4, characterized in that: In the process of calculating the predicted values ​​of each model, the GM(1,1) model parameter calculation formula used includes: is the background value c, d, β1, β2 are model parameters c=2(1-β1) / (1+β1).d=2β2 / (1+β1).

6. The method for predicting sulfate freeze-thaw resistance and service life of recycled coarse aggregate self-compacting concrete according to claim 5, characterized in that: In the process of calculating the predicted values ​​of each model, the GM(1,1) model parameter calculation formula used also includes: in: is the background value c, d, β1, β2 are model parameters c=2(1-β1) / (1+β1).d=2β2 / (1+β1).

7. The method for predicting sulfate freeze-thaw resistance and service life of recycled coarse aggregate self-compacting concrete according to claim 6, characterized in that: In the process of calculating the predicted values ​​of each model, the GM(1,1) model parameter calculation formula used also includes: in: is the background value c, d, β1, β2 are model parameters c=2(1-β1) / (1+β1).d=2β2 / (1+β1).

8. The method for predicting sulfate freeze-thaw resistance and service life of recycled coarse aggregate self-compacting concrete according to claim 5, characterized in that: In the process of calculating the predicted values ​​of each model, the GM(1,1) model parameter calculation formula used also includes: in: is the background value c, d, β1, β2 are model parameters c=2(1-β1) / (1+β1).d=2β2 / (1+β1).

9. The method for predicting sulfate freeze-thaw resistance and service life of recycled coarse aggregate self-compacting concrete according to claim 1, characterized in that: The process of evaluating the accuracy level of the four forms of the GM(1,1) model includes: The model prediction value is taken as the prediction sequence, denoted as Define the residual sequence as ε (0) , Define Z (0) The mean and variance of and The mean and variance of the residuals are defined as and Define the mean square error ratio and the probability of small error as C and P, and evaluate the model accuracy with reference to the accuracy test level reference table, where:

10. The method for predicting sulfate freeze-thaw resistance and service life of recycled coarse aggregate self-compacting concrete according to claim 1, characterized in that: In S4, the durability comprehensive evaluation index is reduced to 75% as the failure standard of RCASCC, and the service life of RCASCC after being subjected to sulfate freeze-thaw in the outdoor environment of Northeast, North and Northwest China is calculated: The following formula is used to calculate the service life of RCASCC after being subjected to sulfate freeze-thaw in outdoor environments in Northeast, North and Northwest China: Where: t is the working life of RCASCC after being subjected to sulfate freeze-thaw action, years, k is the indoor and outdoor freeze-thaw cycle conversion coefficient, k = 12, N max is the number of limiting coupling actions of RCASCC in indoor environment, M is the maximum number of natural sulfate freeze-thaw actions that can be tolerated in outdoor environment within 1 year, M=120 times / year in Northeast China, M=84 times / year in North China, and M=118 times / year in Northwest China.

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