Method for analyzing reusability of waste concrete resources in substation construction

Abstract

The embodiment of the present application relates to a kind of substations construction in waste concrete resource reusability analysis method, comprising the following steps: collecting the first sample data of the waste concrete, the intensity of waste concrete is detected using the first sample data and strength prediction model, and first detection result is obtained;According to the first detection result, the second sample data of the waste concrete is collected, and the life cycle consumption of waste concrete is predicted using the second sample data and life cycle consumption analysis model, and second detection result is obtained;According to the second detection result, the waste concrete is reused.The technical scheme provided in the embodiment of the present application, using the collected multi-source data, using strength prediction model and life cycle consumption analysis model, the reusability of waste concrete is objectively and comprehensively analyzed from different dimensions, and the efficient reuse of resources can be realized.

Description

Technical Field

[0001] This invention relates to the field of resource reuse technology, and in particular to a method for analyzing the reusability of waste concrete resources in substation construction. Background Technology

[0002] In substation construction, the aggregates from waste concrete possess a certain load-bearing capacity, and even after prior use, their main components still retain value in terms of structural integrity. Substations are large-scale infrastructure projects; if all new concrete were used, it would consume vast amounts of natural aggregate resources. The extraction process is not only energy-intensive but also environmentally damaging. Reusing waste concrete significantly reduces the demand for new aggregates and cement, lowers carbon dioxide emissions, and helps substations achieve sustainable development goals.

[0003] Current assessments of the reusability of waste concrete rely heavily on experience, with varying judgment standards among different personnel and a lack of standardized quantitative criteria. Furthermore, existing assessment methods typically focus on single indicators while ignoring the complex relationships between factors and their long-term dynamic changes. Summary of the Invention

[0004] Based on the above-mentioned situation of the prior art, the purpose of this embodiment of the invention is to provide a method for analyzing the reusability of waste concrete resources in substation construction. By using multi-source data and employing an intensity prediction model and a life cycle consumption analysis model, the reusability of waste concrete is analyzed objectively and comprehensively from different dimensions, which can achieve efficient reuse of resources.

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

[0006] First sample data of the waste concrete is collected, and the strength of the waste concrete is tested using the first sample data and the strength prediction model to obtain the first test result;

[0007] Based on the first test result, second sample data of the waste concrete is collected, and the waste concrete is predicted to have a life cycle consumption using the second sample data and the life cycle consumption analysis model to obtain the second test result.

[0008] The waste concrete is reused based on the second test result;

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

[0010]

[0011] Where m represents the set service life, L m D represents the total consumption during the usage phase. t This represents the quantitative value of the degree of internal damage to the concrete structure caused by the alkali-aggregate reaction in year t, where A represents the alkali content, R represents the aggregate activity level, H represents the ambient humidity, and t represents the time.

[0012] Furthermore, the strength of the waste concrete is tested using the first sample data and the strength prediction model, including:

[0013] Substitute the collected aggregate porosity and remaining cement content into the first vector matrix, and solve for the first parameter using the least squares method;

[0014] 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;

[0015] Based on the predicted values ​​of the original data sequence, a first intensity predicted value is obtained.

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

[0017]

[0018] Where x1 represents aggregate porosity, x2 represents remaining cement content, z1 represents the first nearest neighbor mean generation sequence; superscript (0) represents the collected raw data, superscript (1) represents the accumulated data obtained by accumulating the collected raw data; a1 represents the first development coefficient, b1 represents the first gray action amount, n represents the number of samples collected, and the first parameter includes the first development coefficient and the first gray action amount.

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

[0020]

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

[0022] Furthermore, based on the predicted values ​​of the original data sequence, a first intensity predicted value is obtained, including obtaining the first intensity predicted value using the following formula:

[0023]

[0024] in, c1 and c2 represent the first intensity prediction value, and c1 and c2 represent the intensity fitting coefficients, which are obtained by fitting known intensity sample data.

[0025] Furthermore, the first sample data also includes historical average ambient temperature and cumulative load duration; the strength detection of waste concrete using the first sample data and the strength prediction model also includes:

[0026] Substitute the first intensity prediction value, the collected historical average ambient temperature, and the cumulative load duration into the second vector matrix, and solve the second parameter using the least squares method.

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

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

[0029]

[0030]

[0031] Where x3 represents the historical average ambient temperature, x4 represents the cumulative load duration, z2 represents the second nearest neighbor mean generation sequence; superscript (0) represents the collected raw data, superscript (1) represents the accumulated data obtained by accumulating the collected raw data; a2 represents the second development coefficient; b2 represents the second gray action amount, b3 represents the third gray action amount, n represents the number of samples collected, and the second parameter includes the second development coefficient, the second gray action amount and the third gray action amount.

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

[0033]

[0034] Where k = 1, 2, ..., n-1, This represents the second intensity prediction value.

[0035] Furthermore, the quantitative value D of the degree of internal damage to the concrete structure caused by the alkali-aggregate reaction in year t. t Represented as:

[0036]

[0037] Where m0, m1, m2, m3 and m4 are fitting parameters for the degree of damage.

[0038] Furthermore, based on the second test results, the waste concrete is reused, including:

[0039] When the total consumption during the calculated usage phase is within a preset threshold range, the waste concrete is reused.

[0040] When the total consumption during the calculated usage phase exceeds the preset threshold range, suppression measures are taken on the waste concrete, and then the feasibility of reuse is evaluated.

[0041] In summary, this invention provides a method for analyzing the reusability of waste concrete resources in substation construction, comprising the following steps: collecting first sample data of the waste concrete; using the first sample data and a strength prediction model to test the strength of the waste concrete, obtaining a first test result; based on the first test result, collecting second sample data of the waste concrete; using the second sample data and a life cycle consumption analysis model to predict the life cycle consumption of the waste concrete, obtaining a second test result; and reusing the waste concrete based on the second test result. The technical solution provided by this invention utilizes multi-source data and employs a strength prediction model and a life cycle consumption analysis model to objectively and comprehensively analyze the reusability of waste concrete from different dimensions, enabling efficient resource reuse. Attached Figure Description

[0042] Figure 1 This is a flowchart of the method for analyzing the reusability of waste concrete resources in substation construction, provided in an embodiment of the present invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0044] It should be noted that, unless otherwise defined, the technical or scientific terms used in one or more embodiments of the present invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in one or more embodiments of the present invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the element or object listed following the word and its equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

[0045] The technical solution 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 reusability of waste concrete resources in substation construction. Figure 1 The flowchart illustrates a method for analyzing the reusability of waste concrete resources in substation construction, as provided in an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

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

[0047] In this embodiment of the invention, the first sample data includes, for example, aggregate porosity and residual cement content. The strength of waste concrete is tested using the first sample data and a strength prediction model, including the following steps:

[0048] S2021. Substitute the collected aggregate porosity and residual cement content into the first vector matrix, and solve for the first parameter using the least squares method. Aggregate porosity can be collected, for example, using mercury porosimetry or gas adsorption equipment, while residual cement content can be obtained through chemical analysis methods, such as acid dissolution treatment of waste concrete samples followed by chemical titration or spectral analysis. Let the collected aggregate porosity be x1 and the residual cement content be x2, then the collected data can be normalized.

[0049] Based on grey system theory, the first vector matrix is ​​constructed, which can be represented as:

[0050]

[0051] in,

[0052]

[0053] Where n represents the number of samples collected, superscript (0) represents the original data collected, and superscript (1) represents the accumulated data obtained by summing the original data collected, for example... This represents the raw data for the aggregate porosity of the second sample. k = 1, 2, ..., n. z1 represents the first nearest neighbor mean-generated sequence. k = 2, 3, ..., n. The cumulative generation operation calculates the accumulated data once, weakening the randomness of the original data and revealing its trend. Furthermore, the adjacent mean generation sequence, by taking the average of two adjacent accumulated values, makes the data's trend more obvious and stable. a1 represents the first development coefficient, b1 represents the first gray action quantity, and the first parameters, including the first development coefficient and the first gray action quantity, can be solved using the least squares method based on the aforementioned 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, This represents the estimated value of the original aggregate porosity data sequence for the (k+1)th sample. Based on the already calculated first parameter, the above first prediction model is constructed based on grey system theory to predict the result after the original data has undergone an accumulation generation operation. This first prediction model reflects the inherent 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 accumulation and subtraction generation operation is as follows:

[0057]

[0058] Where k = 2, 3, ..., n. The data with superscript (0) in step S2021 represents the actual observed values ​​in the current state. These data contain unrefined information, which is not accurate enough for direct use in the intensity prediction model. In this embodiment of the invention, the predicted values ​​of the original data sequence are obtained through cumulative generation operations, cumulative subtraction generation operations, and the construction of the first vector matrix to solve for parameters. This achieves noise reduction and feature extraction of the original data, which can improve the accuracy of the model prediction results.

[0059] S2023. Based on the predicted values ​​of the original data sequence, the first intensity predicted value is obtained, calculated according to the following formula:

[0060]

[0061] in, c1 and c2 represent the intensity prediction value, respectively, and are intensity fitting coefficients obtained by fitting known intensity sample data. The first intensity prediction value can be used as the first detection result.

[0062] According to certain optional embodiments, the first sample data also includes historical average ambient temperature and cumulative load duration. The first prediction model described above considers current data on the waste concrete, which can initially reflect the current structural characteristics and potential strength of the waste concrete. Furthermore, to account for the influence of historical data on the performance of the waste concrete, a second prediction model is constructed, incorporating historical data to reconstruct the strength state of the waste concrete under complex working conditions, thereby improving the accuracy of the model's prediction results. The strength detection of waste concrete using the first sample data and the strength prediction model also includes the following steps:

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

[0064]

[0065] Where x3 represents the historical average ambient temperature, and x4 represents the cumulative load duration, the above data can be normalized; the superscript (0) represents the original data collected, and the superscript (1) represents the accumulated data obtained by accumulating the original data collected. z2 represents the second nearest neighbor mean generation sequence, which constructs a new sequence by combining the first intensity prediction value with the collected historical average ambient temperature and cumulative load duration. The second nearest neighbor mean generation sequence follows the same formula as the first nearest neighbor mean generation sequence, based on the new sequence. Generate. a2 represents the second development coefficient; b2 represents the second gray action amount; b3 represents the third gray action amount; n represents the number of samples collected. The second parameter includes the second development coefficient, the second gray action amount, and the third gray action amount. The second parameter can be solved by the least squares method and the above-mentioned second vector matrix.

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

[0067]

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

[0069] S204. Based on the first test result, collect second sample data of waste concrete. Use the second sample data and the life cycle consumption analysis model to predict the life cycle consumption of the waste concrete, and obtain the second test result. When the predicted strength value in the first test result is within the preset threshold range, it indicates that the waste concrete can meet the strength requirements for reuse. Other indicators can be combined to predict the life cycle consumption of the waste concrete to optimize the reuse plan. When the predicted strength value in the first test 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 applied. In this embodiment of the invention, the second sample data includes, for example, alkali content, aggregate activity level, environmental humidity, and time. The life cycle consumption analysis model is expressed as follows:

[0070]

[0071] Where m represents the set service life, L m D represents the total consumption during the usage phase. t This represents the quantitative value of the degree of internal damage to the concrete structure caused by the alkali-aggregate reaction in year t. A represents the alkali content, which can be determined using high-precision flame photometry coupled with ion chromatography in waste concrete. R represents the aggregate activity level, which can be obtained using scanning electron microscopy with energy dispersive spectroscopy. H represents the ambient humidity, which can be obtained from meteorological data of the reuse area. t represents time, in units such as years. The quantitative value of the degree of internal damage to the concrete structure caused by the alkali-aggregate reaction in year t is D. t Represented as:

[0072]

[0073] Where m0, m1, m2, m3 and m4 are fitting parameters for the degree of damage.

[0074] S206. Based on the second test results, the waste concrete is reused. When the calculated total consumption during the usage stage 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 calculated total consumption during the usage stage exceeds the preset threshold range, it indicates that inhibitory measures need to be taken for the waste concrete before reassessing whether it can be reused. For example, fly ash, slag, and other mineral admixtures can be added to the waste concrete, with the addition ratio being 12%-28% of the total admixture mass and 22%-45% of the total admixture mass. After adding the mineral admixtures, the total consumption during the usage stage is predicted again.

[0075] In summary, this invention relates to a method for analyzing the reusability of waste concrete resources in substation construction, comprising the following steps: collecting first sample data of the waste concrete; using the first sample data and a strength prediction model to test the strength of the waste concrete, obtaining a first test result; based on the first test result, collecting second sample data of the waste concrete; using the second sample data and a life cycle consumption analysis model to predict the life cycle consumption of the waste concrete, obtaining a second test result; and reusing the waste concrete based on the second test result. The technical solution provided by this invention utilizes multi-source data and employs a strength prediction model and a life cycle consumption analysis model to objectively and comprehensively analyze the reusability of waste concrete from different dimensions, enabling efficient resource reuse.

[0076] It should be understood that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention (including the claims) is limited to these examples. Within the framework of this invention, technical features of the above embodiments or different embodiments can also be combined, steps can be implemented in any order, and many other variations exist regarding different aspects of one or more embodiments of the invention as described above, which are not provided in the details for the sake of brevity. The specific embodiments described above are merely illustrative or explanatory of the principles of the invention and do not constitute a limitation thereof. Therefore, any modifications, equivalent substitutions, improvements, etc., made without departing from the spirit and scope of the invention should be included within the protection scope of the invention. Furthermore, the appended claims are intended to cover all variations and modifications falling within the scope and boundaries of the appended claims, or equivalent forms of such scope and boundaries.

Claims

1. A method for analyzing the reusability of waste concrete resources in substation construction, characterized in that, Including the following steps: First sample data of the waste concrete is collected, and the strength of the waste concrete is tested using the first sample data and the strength prediction model to obtain the first test result; Based on the first test result, second sample data of the waste concrete is collected, and the waste concrete is predicted to have a life cycle consumption using the second sample data and the life cycle consumption analysis model to obtain the second test result. The waste concrete is reused based on the second test result; The first sample data includes aggregate porosity, residual cement content, historical average ambient temperature, and cumulative load duration. The strength of the waste concrete is tested using this first sample data and a strength prediction model, including: The collected aggregate porosity and residual cement content are substituted into the first vector matrix, and the first parameter is solved by the least squares method; the first parameter is substituted into the first prediction model, and the predicted value of the original data sequence is obtained based on the first prediction model; the first strength prediction value is obtained based on the predicted value of the original data sequence. Substitute the first intensity prediction value, the collected historical average ambient temperature, and the cumulative load duration into the second vector matrix, and solve for the second parameter using the least squares method; substitute the second parameter into the second prediction model, and obtain the second intensity prediction value based on the second prediction model; The second sample data includes alkali content, aggregate activity level, environmental humidity, and time. The life cycle consumption analysis model is expressed as follows: in, This indicates the set service life. This indicates the total consumption during the usage phase. This indicates that the alkali-aggregate reaction is in the first stage. Quantitative values ​​of the degree of internal damage to concrete structures caused by annual damage. Indicates alkali content, Indicates the aggregate activity level. Indicates ambient humidity. Indicates time; the alkali-aggregate reaction occurs in the [number]th [day]. Quantitative values ​​of the degree of internal damage to concrete structures caused by annual factors Represented as: in, , , , and The parameters are fitted to the degree of damage.

2. The method according to claim 1, characterized in that, The first vector matrix is ​​represented as: in, Indicates aggregate porosity. Indicates the remaining cement content. Indicates the sequence generated by the first nearest neighbor mean; superscript Indicates the raw data collected, superscript This represents the accumulated data obtained by summing the collected raw data; Indicates the first development coefficient. This represents the first gray action quantity. The first parameter represents the number of samples collected, and includes a first development coefficient and a first gray effect.

3. The method according to claim 2, characterized in that, The first prediction model is represented as: in, , Indicates the first Estimates of the original data sequence of aggregate porosity for each sample.

4. The method according to claim 3, characterized in that, Based on the predicted values ​​of the original data sequence, a first intensity predicted value is obtained, including obtaining the first intensity predicted value using the following formula: in, This represents the first intensity prediction value. and This represents the strength fitting coefficient, which is obtained by fitting known strength sample data.

5. The method according to claim 4, characterized in that, The second vector matrix is ​​represented as: in, This represents the historical average ambient temperature. Indicates the cumulative load duration. Indicates the second nearest neighbor mean generation sequence; superscript Indicates the raw data collected, superscript This represents the accumulated data obtained by summing the collected raw data; Indicates the second development coefficient; This represents the second gray action quantity. This represents the third gray action quantity. The second parameter represents the number of samples collected, and includes the second development coefficient, the second gray effect, and the third gray effect.

6. The method according to claim 5, characterized in that, The second prediction model is expressed as: in, , This represents the second intensity prediction value.

7. The method according to claim 1, characterized in that, Based on the second test results, the waste concrete was reused, including: When the total consumption during the calculated usage phase is within a preset threshold range, the waste concrete is reused. When the total consumption during the calculated usage phase exceeds the preset threshold range, suppression measures are taken on the waste concrete, and then the feasibility of reuse is evaluated.

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