Method for analyzing reusability of waste concrete resources in substation construction

Abstract

The embodiment of the invention relates to a waste concrete resource reusability analysis method in transformer substation construction, which comprises the following steps: collecting first sample data of waste concrete, and detecting the strength of the waste concrete by using the first sample data and a strength prediction model to obtain a first detection result; according to the first detection result, collecting second sample data of the waste concrete, and performing life cycle consumption prediction on the waste concrete by using the second sample data and a life cycle consumption analysis model to obtain a second detection result; and recycling the waste concrete according to the second detection result. According to the technical scheme provided by the embodiment of the invention, the collected multi-source data is utilized, the strength prediction model and the life cycle consumption analysis model are adopted, the reusability of the waste concrete is objectively and comprehensively analyzed from different dimensions, and efficient reutilization of resources can be realized.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of resource reuse, and in particular, to a method for analyzing the recyclability of waste concrete resources in substation construction. Background Art

[0002] The aggregate of waste concrete in substation construction has a certain bearing capacity. Even after preliminary use, its main components still have utilizable value in terms of structural integrity. As a large-scale infrastructure, the construction scale of substations is large. If all new concrete is used, a large amount of natural aggregate resources will be consumed. The mining process not only has high energy consumption but also causes damage to the ecological environment. Reusing waste concrete can significantly reduce the demand for new aggregates and cement, reduce carbon dioxide emissions, and help substations achieve sustainable development goals.

[0003] Currently, the evaluation of the recyclability of waste concrete mostly relies on experience. The judgment criteria of different personnel vary greatly, and there is a lack of a unified quantitative basis. Moreover, the existing evaluation methods usually only focus on single indicators and ignore the complex relationships and long-term dynamic changes among various factors. Summary of the Invention

[0004] Based on the above situation of the prior art, the purpose of the embodiments of the present invention is to provide a method for analyzing the recyclability of waste concrete resources in substation construction. By using the collected multi-source data and adopting a strength prediction model and a life cycle consumption analysis model, the recyclability of waste concrete can be objectively and comprehensively analyzed from different dimensions, and efficient resource reuse can be achieved.

[0005] To achieve the above object, according to one aspect of the present invention, a method for analyzing the recyclability of waste concrete resources in substation construction is provided, including the steps of:

[0006] Collect the first sample data of the waste concrete, and use the first sample data and the strength prediction model to detect the strength of the waste concrete to obtain a first detection result;

[0007] According to the first detection result, collect the second sample data of the waste concrete, and use the second sample data and the life cycle consumption analysis model to predict the life cycle consumption of the waste concrete to obtain a second detection result;

[0008] Reuse the waste concrete according to the second detection result;

[0009] Wherein, the first sample data includes aggregate porosity and remaining cement content, the second sample data includes alkali content, aggregate activity grade, environmental humidity and time, and the life cycle consumption analysis model is expressed as:

[0010]

[0011] Among them, m represents the set service life, L m represents the total consumption during the use stage, D t represents the quantified value of the internal damage degree of the concrete structure caused by the alkali-aggregate reaction in the t-th year. A represents the alkali content, R represents the aggregate activity grade, H represents the environmental humidity, and t represents the time.

[0012] Furthermore, detecting the strength of waste concrete by using the first sample data and the strength prediction model includes:

[0013] Substituting the collected aggregate porosity and remaining cement content into the first vector matrix, and solving the first parameter by the least squares method;

[0014] Substituting the first parameter into the first prediction model, and obtaining the predicted value of the original data sequence based on the first prediction model;

[0015] Based on the predicted value of the original data sequence, obtaining the first strength prediction value.

[0016] Furthermore, the first vector matrix is expressed as:

[0017]

[0018] Among them, x 1 represents the aggregate porosity, x 2 represents the remaining cement content, z 1 represents the first adjacent mean generation sequence; the superscript (0) represents the collected original data, and the superscript (1) represents the first-order cumulative data obtained by accumulating the collected original data; a 1 represents the first development coefficient, b 1 represents the first grey action quantity, n represents the number of collected samples, and the first parameter includes the first development coefficient and the first grey action quantity.

[0019] Furthermore, the first prediction model is expressed as:

[0020]

[0021] Among them, k = 1, 2, …… n-1, represents the estimated value of the original data sequence of the aggregate porosity of the (k + 1)-th sample.

[0022] Furthermore, based on the predicted value of the original data sequence, obtaining the first strength prediction value includes obtaining the first strength prediction value by using the following formula:

[0023]

[0024] Among them, represents the first strength prediction value, c 1 and c 2 represent strength fitting coefficients, which are obtained by fitting through known strength sample data.

[0025] Furthermore, the first sample data further includes the historical average ambient temperature and the cumulative load action time; using the first sample data and the strength prediction model to detect the strength of waste concrete further includes:

[0026] Substitute the first strength prediction value, the collected historical average ambient temperature, and the cumulative load action time into the second vector matrix, and solve the second parameter by the least squares method;

[0027] Substitute the second parameter into the second prediction model, and obtain the second strength prediction value based on the second prediction model.

[0028] Furthermore, the second vector matrix is expressed as:

[0029]

[0030]

[0031] Among them, x 3 represents the historical average ambient temperature, x 4 represents the cumulative load action time, z 2 represents the second adjacent mean generation sequence; the superscript (0) represents the collected original data, and the superscript (1) represents the first-order cumulative data obtained by accumulating the collected original data; a 2 represents the second development coefficient; b 2 represents the second grey action quantity, b 3 represents the third grey action quantity, n represents the number of collected samples, and the second parameter includes the second development coefficient, the second grey action quantity, and the third grey action quantity.

[0032] Furthermore, the second prediction model is expressed as:

[0033]

[0034] Among them, k = 1, 2, …… n - 1, represents the second strength prediction value.

[0035] Furthermore, the quantification value D of the internal damage degree of the concrete structure caused by the alkali-aggregate reaction in the t-th year t is expressed as:

[0036]

[0037] Among them, m 0 、m 1 、m 2 、m 3 and m 4 are fitting parameters for the damage degree.

[0038] Furthermore, the recycled use of waste concrete is carried out according to the second detection result, including:

[0039] When the total consumption in the usage stage calculated is within the preset threshold range, the waste concrete is recycled;

[0040] When the total consumption in the usage stage calculated exceeds the preset threshold range, after taking inhibitory measures on the waste concrete, it is re-evaluated whether it can be recycled.

[0041] In summary, the embodiment of the present invention provides a method for analyzing the recyclability of waste concrete resources in substation construction, including the steps of: collecting the first sample data of the waste concrete, detecting the strength of the waste concrete by using the first sample data and the strength prediction model to obtain the first detection result; according to the first detection result, collecting the second sample data of the waste concrete, predicting the life cycle consumption of the waste concrete by using the second sample data and the life cycle consumption analysis model to obtain the second detection result; and recycling the waste concrete according to the second detection result. The technical solution provided by the embodiment of the present invention uses the collected multi-source data, adopts the strength prediction model and the life cycle consumption analysis model, and objectively and comprehensively analyzes the recyclability of waste concrete from different dimensions, and can realize the efficient recycling of resources. Description of the Drawings

[0042] Figure 1 is a flowchart of the method for analyzing the recyclability of waste concrete resources in substation construction provided by the embodiment of the present invention. Detailed Embodiments

[0043] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with the specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present invention.

[0044] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in one or more embodiments of the present invention should have the ordinary meaning understood by those of ordinary skill in the art to which the present invention pertains. The terms "first", "second" and similar terms used in one or more embodiments of the present invention do not denote any order, quantity or importance, but are only used to distinguish different components. Words such as "including" or "comprising" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. Words such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect.

[0045] The technical solutions of the present invention will be described in detail below with reference to the accompanying drawings. An embodiment of the present invention provides a method for analyzing the recyclability of waste concrete resources in substation construction. Figure 1 The flowchart of the method for analyzing the recyclability of waste concrete resources in substation construction provided by the embodiment of the present invention is shown in Figure 1 As shown, the method includes the following steps:

[0046] S202. Collect the first sample data of the waste concrete, and use the first sample data and the strength prediction model to detect the strength of the waste concrete to obtain the first detection result.

[0047] In an embodiment of the present invention, the first sample data includes, for example, the aggregate porosity and the remaining cement content. Using the first sample data and the strength prediction model to detect the strength of the waste concrete includes the following steps:

[0048] S2021. Substitute the collected aggregate porosity and remaining cement content into the first vector matrix, and solve the first parameter by the least squares method. The aggregate porosity can be collected, for example, by a mercury intrusion porosimeter or a gas adsorption method device, and the remaining cement content can be obtained by a chemical analysis method. For example, after acid dissolution treatment of the waste concrete sample, chemical titration or spectral analysis is used to determine. Let the collected aggregate porosity be x 1 , and the remaining cement content be x 2 , and the collected data can be normalized.

[0049] Based on the grey system theory, a first vector matrix is constructed. The first vector matrix can be expressed as:

[0050]

[0051] Wherein,

[0052]

[0053] Among them, n represents the number of collected samples, the superscript (0) represents the original data collected, and the superscript (1) represents the first-order accumulated data obtained by accumulating the collected original data. For example represents the original data of the second sample of the aggregate porosity, k = 1, 2, ……, n. z 1 represents the first adjacent mean generation sequence, k = 2, 3, ……, n. By calculating the first-order accumulated data through the accumulation generation operation, the randomness of the original data is weakened, and the trend of the data is shown; while the adjacent mean generation sequence can make the change trend of the data more obvious and stable by taking the average of the adjacent two accumulated values. a 1 represents the first development coefficient, b 1 represents the first grey action quantity. The first parameter includes the first development coefficient and the first grey action quantity, and can be solved by the least squares method based on the above first vector matrix.

[0054] S2022. Substitute the first parameter into the first prediction model, and obtain the predicted value of the original data sequence based on the first prediction model. The first prediction model is expressed as:

[0055]

[0056] where k = 1, 2, ……, n - 1, represents the estimated value of the original data sequence of the aggregate porosity of the (k + 1)-th sample. Based on the first parameter that has been obtained, the above first prediction model is constructed based on the grey system theory to predict the result after the accumulation generation operation of the original data. The first prediction model reflects the internal dynamic change law of the first sample data and can predict the future trend of the first sample data. The predicted value of the original data sequence obtained through the inverse accumulation generation operation:

[0057]

[0058] where k = 2, 3, ……, n. The data with superscript (0) in step S2021 represents the actual observed values in the current state. The information contained in these data is unrefined and is not accurate enough to be directly used in the strength prediction model. In the embodiments of the present invention, the predicted value of the original data sequence is obtained through steps such as the accumulation generation operation, the inverse accumulation generation operation, and constructing the first vector matrix to solve the parameters, realizing the noise reduction and feature extraction of the original data, and can improve the accuracy of the model prediction result.

[0059] S2023. Based on the predicted value of the original data sequence, obtain the first strength predicted value, and calculate the first strength predicted value according to the following formula:

[0060]

[0061] Among them, represents the first strength prediction value, c 1 and c 2 represent the strength fitting coefficients, which can be obtained by fitting through known strength sample data. The first strength prediction value can be used as the first detection result.

[0062] According to some optional embodiments, the first sample data further includes the historical average ambient temperature and the cumulative load application time. In the above first prediction model, the current data of waste concrete is considered, which can initially reflect the current structural characteristics and potential strength of waste concrete. Further, in order to consider the influence of historical data on the performance of waste concrete, by constructing a second prediction model and incorporating historical data into the second prediction model, the strength state of waste concrete under complex working conditions can be restored, and the accuracy of the model prediction result can be improved. Detecting the strength of waste concrete using the first sample data and the strength prediction model further includes the following steps:

[0063] S2031. Substitute the first strength prediction value, the collected historical average ambient temperature, and the cumulative load application time into the second vector matrix, and solve the second parameter by the least squares method. Both the historical average ambient temperature and the cumulative load application time can be extracted from historical data. For example, extract the average ambient temperature within a preset time period in historical data, and extract the cumulative load application time of concrete during its past use in historical data. The second vector matrix is expressed as:

[0064]

[0065] Among them, x 3 represents the historical average ambient temperature, x 4 represents the cumulative load application time. Similarly, the above data can be normalized; the superscript (0) represents the collected original data, and the superscript (1) represents the first-order cumulative data obtained by accumulating the collected original data. z 2 represents the second adjacent mean generation sequence. The first strength prediction value, the collected historical average ambient temperature, and the cumulative load application time are used to construct a new sequence The second adjacent mean generation sequence is generated based on the above new sequence according to the same formula as the above first adjacent mean generation sequence generated. a 2 represents the second development coefficient; b 2 represents the second grey action quantity, b 3 represents the third grey action quantity, n represents the number of collected samples, and the second parameter includes the second development coefficient, the second grey action quantity, and the third grey action quantity, which can be solved by the least squares method and the above second vector matrix.

[0066] S2032. Substitute the second parameter into the second prediction model, and obtain a second strength prediction value based on the second prediction model. The second prediction model is expressed as:

[0067]

[0068] where k = 1, 2, …… n - 1, represents the second strength prediction value. In this embodiment, the second strength prediction value is used as the first detection result.

[0069] S204. According to the first detection result, collect the second sample data of the waste concrete, and use the second sample data and the life cycle consumption analysis model to predict the life cycle consumption of the waste concrete, and obtain a second detection result. When the strength prediction value in the first detection result is within the preset threshold range, it indicates that the waste concrete can meet the strength requirements for reuse, and the life cycle consumption of the waste concrete can be predicted in combination with other indicators to optimize the reuse plan; when the strength prediction value in the first detection result exceeds the preset threshold range, it indicates that the waste concrete does not meet the strength requirements for reuse, and other treatments can be performed on it. In this embodiment of the present invention, the second sample data includes, for example, the alkali content, the aggregate activity level, the environmental humidity, and time. The life cycle consumption analysis model is expressed as:

[0070]

[0071] where m represents the set service life, L m represents the total consumption in the use stage, D t represents the quantification value of the internal damage degree of the concrete structure caused by the alkali-aggregate reaction in the t-th year. A represents the alkali content, and the alkali content in the waste concrete can be determined by the combination of high-precision flame photometry and ion chromatography. R represents the aggregate activity level, and the aggregate activity level of the waste concrete can be obtained by the scanning electron microscope energy spectrum analysis method. H represents the environmental humidity, and the environmental humidity data can be obtained from the meteorological data of the reuse area. t represents time, and the unit is, for example, year. The quantification value D of the internal damage degree of the concrete structure caused by the alkali-aggregate reaction in the t-th year t is expressed as:

[0072]

[0073] where m 0 、m 1 、m 2 、m 3 and m 4 are damage degree fitting parameters.

[0074] S206. Reuse the waste concrete according to the second test result. When the total consumption in the usage stage calculated is within the preset threshold range, it indicates that the risk of alkali-aggregate reaction in the waste concrete is controllable at the loss level, and the waste concrete is suitable for reuse; when the total consumption in the usage stage calculated exceeds the preset threshold range, it indicates that inhibitory measures need to be taken for the waste concrete and then it is evaluated whether it can be reused. For example, mineral admixtures such as fly ash and slag can be added to the waste concrete. The addition ratio is that the mass ratio of fly ash in the total admixtures is 12%-28%, and the mass ratio of slag in the total admixtures is 22%-45%. After adding the mineral admixtures, the total consumption in the usage stage is predicted again.

[0075] In summary, the embodiment of the present invention relates to a method for analyzing the recyclability of waste concrete in substation construction, including the steps of: collecting the first sample data of the waste concrete, using the first sample data and the strength prediction model to detect the strength of the waste concrete to obtain the first test result; according to the first test result, collecting the second sample data of the waste concrete, using the second sample data and the life cycle consumption analysis model to predict the life cycle consumption of the waste concrete to obtain the second test result; and reusing the waste concrete according to the second test result. The technical solution provided by the embodiment of the present invention uses the collected multi-source data, adopts the strength prediction model and the life cycle consumption analysis model, and objectively and comprehensively analyzes the recyclability of the waste concrete from different dimensions, and can achieve the efficient reuse of resources.

[0076] It should be understood that the discussion of any above embodiment is only exemplary, and is not intended to imply that the scope of the present invention (including the claims) is limited to these examples; under the idea of the present invention, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes in different aspects of one or more embodiments of the present invention as described above, and they are not provided in detail for the sake of brevity. The above specific embodiments of the present invention are only used for exemplary illustration or explanation of the principle of the present invention, and do not constitute a limitation to the present invention. Therefore, any modification, equivalent replacement, improvement, etc. made without departing from the spirit and scope of the present invention shall be included within the protection scope of the present invention. In addition, the appended claims of the present invention are intended to cover all changes and modification examples falling within the scope and boundary of the appended claims, or equivalent forms of such scope and boundary.

Claims

1. A method for analyzing the reusability of waste concrete resources in substation construction, characterized in that: Includes steps: Collecting first sample data of the waste concrete, and testing the strength of the waste concrete using the first sample data and a strength prediction model to obtain a first test result; According to the first detection result, second sample data of the waste concrete is collected, and life cycle consumption prediction of the waste concrete is performed using the second sample data and a life cycle consumption analysis model to obtain a second detection result; Reusing the waste concrete according to the second detection result; The first sample data includes aggregate porosity and residual cement content, the second sample data includes alkali content, aggregate activity level, ambient humidity and time, and the life cycle consumption analysis model is expressed as: Where m represents the set service life, L m represents the total consumption in the use phase, D t It represents the quantitative value of the internal damage degree of the concrete structure caused by alkali-aggregate reaction in the tth year, A represents the alkali content, R represents the aggregate activity level, H represents the ambient humidity, and t represents time.

2. The method according to claim 1, characterized in that Using the first sample data and the strength prediction model to detect the strength of waste concrete includes: Substituting the collected aggregate porosity and residual cement content into the first vector matrix, solving the first parameter by the least square method; Substituting the first parameter into a first prediction model, and obtaining a prediction value of the original data sequence based on the first prediction model; Based on the predicted value of the original data sequence, a first intensity prediction value is obtained.

3. The method according to claim 2, characterized in that The first vector matrix is ​​expressed as: Among them, x1 represents the aggregate porosity, x2 represents the residual cement content, z1 represents the first adjacent mean generation sequence; the superscript (0) represents the collected original data, and the superscript (1) represents the accumulated data obtained by accumulating the collected original data; a1 represents the first development coefficient, b1 represents the first grey action, n represents the number of samples collected, and the first parameter includes the first development coefficient and the first grey action.

4. The method according to claim 3, characterized in that: The first prediction model is expressed as: Where k = 1, 2, ... n-1, Represents the estimated value of the raw data series of aggregate porosity of the k+1th sample.

5. The method according to claim 4, characterized in that Based on the predicted value of the original data sequence, obtaining a first intensity prediction value includes obtaining the first intensity prediction value using the following formula: in, represents the first intensity prediction value, c1 and c2 represent intensity fitting coefficients, which are obtained by fitting the known intensity sample data.

6. The method according to claim 5, characterized in that The first sample data also includes the historical average ambient temperature and the accumulated load action time; using the first sample data and the strength prediction model to test the strength of the waste concrete, further includes: Substituting the first strength prediction value and the collected historical average ambient temperature and cumulative load action time into the second vector matrix, and solving the second parameter by the least square method; Substitute the second parameter into a second prediction model, and obtain a second intensity prediction value based on the second prediction model.

7. The method according to claim 6, characterized in that The second vector matrix is ​​expressed as: Among them, x3 represents the historical average ambient temperature, x4 represents the cumulative load action time, z2 represents the second adjacent mean generation sequence; the superscript (0) represents the collected original data, and the superscript (1) represents the accumulated data obtained by accumulating the collected original data; a2 represents the second development coefficient; b2 represents the second gray action, b3 represents the third gray action, n represents the number of samples collected, and the second parameter includes the second development coefficient, the second gray action and the third gray action.

8. The method according to claim 7, characterized in that The second prediction model is expressed as: Where k = 1, 2, ... n-1, Represents the second intensity prediction value.

9. The method according to claim 1, characterized in that: Quantitative value D of the internal damage degree of concrete structure caused by alkali-aggregate reaction in year t t It is expressed as: Among them, m0, m1, m2, m3 and m4 are the damage degree fitting parameters.

10. The method according to claim 9, characterized in that Reusing the waste concrete according to the second test result includes: When the calculated total consumption in the use phase is within a preset threshold range, the waste concrete is reused; When the calculated total consumption in the use phase exceeds a preset threshold range, suppression measures are taken on the waste concrete, and then it is evaluated whether it can be reused.

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