Construction waste repairing and recycling evaluation method based on chaos theory and operational research

Abstract

The invention relates to the field of construction waste resource utilization, and discloses a construction waste restoration and reutilization evaluation method based on a chaos theory and operational research, and the method comprises the steps: S1, obtaining performance evaluation index data of regenerated aggregate formed after the construction waste is subjected to restoration treatment; s2, preprocessing the performance evaluation index data; s3, constructing a chaotic evolution model based on the normalized index data; s4, performing evolution analysis on the chaotic system; s5, determining an index weight corresponding to each normalized index based on an operational research method; and S6, outputting an engineering application suggestion corresponding to the repairing and reusing grade. A chaos theory is introduced into a construction waste recycled aggregate evaluation process, and a chaos evolution model is constructed and evolution analysis is performed on a system state, so that the evaluation method can describe a nonlinear coupling relationship among multiple performance indexes and potential mutation characteristics, and the comprehensive description capability of complex material performance behaviors is improved.

Description

Technical Field

[0001] This invention relates to the field of construction waste resource utilization, specifically to an evaluation method for the repair and reuse of construction waste based on chaos theory and operations research. Background Technology

[0002] Evaluation methods for the repair and reuse of construction waste refer to the methods used to analyze and judge the performance level, utilization potential, and applicable scenarios of recycled materials formed after the repair and treatment of construction waste through certain evaluation means. With the increasing demand for the resource utilization of construction waste, how to scientifically evaluate the recycled aggregates after repair has become an important part of the construction waste reuse process. The evaluation method for the repair and reuse of construction waste based on chaos theory and operations research is based on the above evaluation methods. It introduces chaos theory to analyze the nonlinear relationship of multiple indicators and combines operations research methods to determine weights and conduct comprehensive evaluation, so as to achieve a systematic assessment of the level of repair and reuse of construction waste. At present, the evaluation of the repair and reuse of construction waste usually adopts an evaluation method based on multiple performance indicators. After testing the mechanical properties, physical properties, and other indicators of recycled aggregates, the evaluation results are obtained through linear weighting or empirical judgment.

[0003] However, in current technologies, the evaluation of construction waste repair and reuse is usually based on the analysis of multiple performance indicators, and the evaluation results are comprehensively processed through linear or empirical methods. When there are nonlinear correlations between multiple performance indicators, the existing evaluation methods are difficult to uniformly characterize the complex correlation characteristics between the indicators, thus affecting the degree to which the evaluation results reflect the performance characteristics of recycled materials. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an evaluation method for the repair and reuse of construction waste based on chaos theory and operations research. This method solves the problem that existing evaluation methods struggle to uniformly model and accurately characterize the complex performance characteristics of recycled materials when multiple performance indicators exhibit nonlinear correlations.

[0005] To achieve the above objectives, this invention provides the following technical solution: an evaluation method for the remediation and reuse of construction waste based on chaos theory and operations research, comprising: S1. Obtain performance evaluation index data of recycled aggregate formed after the repair and treatment of construction waste. The performance evaluation index data includes cylinder compressive strength, crushing value, water absorption rate and needle-like particle content. S2. The performance evaluation index data is preprocessed, and the index is standardized according to the positive or negative attributes of each performance evaluation index to obtain normalized index data within a unified scale range. S3. Construct a chaotic evolution model based on the normalized index data, and introduce at least three indices from the normalized index data into the chaotic system as state variables; S4. Perform evolutionary analysis on the chaotic system to obtain chaotic analysis results that reflect the nonlinear coupling relationship and performance fluctuation characteristics between the performance evaluation indicators. The chaotic analysis results are a set of feature data calculated from the state variable evolution data. S5. Determine the index weights corresponding to each normalized index based on operations research methods, and perform comprehensive weighted calculation on the normalized index data according to the index weights to obtain the comprehensive evaluation results of recycled aggregates. S6. Determine the repair and reuse level of the recycled aggregate based on the comprehensive evaluation results, and output engineering application suggestions corresponding to the repair and reuse level.

[0006] Preferably, the acquisition of the S1 performance evaluation index data includes: Samples are taken from the recycled aggregate formed after the repair and treatment of construction waste, and recycled aggregate from different batches or sources is collected in accordance with the preset sampling rules. Mechanical properties and structural characteristics were tested on the collected recycled aggregate samples to obtain raw test data reflecting the load-bearing capacity, crush resistance, moisture content and particle morphology of the recycled aggregates. The original detection data is organized and verified, and abnormal or incomplete data is removed to form a performance evaluation index dataset for subsequent evaluation and analysis.

[0007] Preferably, the S2 step of standardizing the performance evaluation index data includes: The performance evaluation index data is attribute-identified, and the performance evaluation indexes are classified into positive and negative indicators according to the value direction of each performance evaluation index in the evaluation system. For the positive and negative indicators, corresponding standardization methods are applied to transform the performance evaluation indicator data into dimensionless data to obtain standardized indicator data. The standardized indicator data is then processed to unify its range, resulting in normalized indicator data for subsequent steps.

[0008] Preferably, S3, which constructs a chaotic evolution model based on normalized index data, includes: A multidimensional chaotic system is selected as the basic model for the chaotic evolution model; At least three performance evaluation indicators are selected from the normalized index data, and the selected performance evaluation indicators are respectively introduced into the multidimensional chaotic system as state variables of the chaotic system. The chaotic system is initialized based on the state variables to form a chaotic evolution model for subsequent evolution calculations.

[0009] Preferably, the chaotic evolution model is a three-dimensional chaotic system, which is composed of multiple state variables; The state variables are constructed by introducing different performance evaluation indicators from the normalized index data.

[0010] Preferably, S4, which involves evolutionary analysis of the chaotic system, includes: Based on the aforementioned chaotic evolution model, iterative calculations are performed on the chaotic system to obtain state change data of the chaotic system within a preset calculation interval; The state change data are recorded and organized to form a data sequence reflecting the evolution process of the chaotic system; Evolutionary feature data of the chaotic system is extracted based on the data sequence, and the evolutionary feature data is used as input data for subsequent steps.

[0011] Preferably, S5, which determines the indicator weights based on operations research methods, includes: Based on the pre-set evaluation index system, an index hierarchy structure is constructed, and the relative relationships between the various performance evaluation indicators are described. Based on the hierarchical structure of the indicators, obtain the weight calculation data corresponding to each performance evaluation indicator, and organize the weight calculation data. The weights of each performance evaluation index are calculated based on the weight calculation data to obtain the index weights corresponding to each performance evaluation index.

[0012] Preferably, the process of determining the indicator weights includes a weight calculation step based on the hierarchical relationship between performance evaluation indicators, and a weight calculation step based on the data distribution characteristics of the performance evaluation indicators, and the weight results obtained from different weight calculation steps are combined.

[0013] Preferably, step S5, which involves performing a comprehensive evaluation calculation on the normalized index data, includes: Obtain the index weights corresponding to the performance evaluation indexes, and match the index weights with the corresponding normalized index data; The normalized index data are weighted according to the index weights to obtain evaluation data that reflects the overall situation of each performance evaluation index; The evaluation data are summarized and processed to form a comprehensive evaluation result of recycled aggregates.

[0014] Preferably, step S6, which determines the repair and reuse level based on the comprehensive evaluation results, includes the following steps: Obtain the comprehensive evaluation result of the recycled aggregate, and compare the comprehensive evaluation result with the preset grading rules; Based on the comparison results, the recycled aggregates are classified into the corresponding repair and reuse levels; The repair and reuse level is associated with a preset engineering application type, and the engineering application information corresponding to the repair and reuse level is output.

[0015] This invention provides an evaluation method for the remediation and reuse of construction waste based on chaos theory and operations research. It has the following beneficial effects: 1. This invention introduces chaos theory into the evaluation process of recycled aggregates from construction waste. By constructing a chaotic evolution model and performing evolutionary analysis on the system state, the evaluation method can characterize the nonlinear coupling relationship and potential mutation characteristics between multiple performance indicators, thereby improving the comprehensive characterization of the performance behavior of complex materials.

[0016] 2. In the evaluation process, this invention introduces a multi-source weight determination mechanism based on hierarchical relationships and data distribution characteristics, and combines the calculation results of different weights to make the weight allocation process of each performance evaluation index more comprehensive, which is conducive to obtaining stable and consistent comprehensive evaluation results.

[0017] 3. This invention achieves quantitative grading of the repair and reuse level of recycled construction waste aggregates by combining comprehensive evaluation calculation with grading determination. It can also continuously analyze the calculation results of different evaluation stages, providing a quantitative reference for the application of recycled materials in different road structure levels.

[0018] 4. The evaluation method proposed in this invention is based on the technical idea of ​​combining multi-index data processing, chaotic evolution analysis and operations research decision-making. While keeping the overall framework of the method unchanged, it can be adapted to different evaluation objects and index systems and is applicable to a variety of material or environmental comprehensive evaluation scenarios. Attached Figure Description

[0019] Figure 1 This is a flowchart of the evaluation method for the repair and reuse of construction waste based on chaos theory and operations research, as presented in this invention. Detailed Implementation

[0020] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see the appendix Figure 1This invention provides an evaluation method for the remediation and reuse of construction waste based on chaos theory and operations research, including: S1. Obtain performance evaluation index data of recycled aggregate formed after the repair and treatment of construction waste. The performance evaluation index data includes cylinder compressive strength, crushing value, water absorption rate and needle-like and flaky particle content. Furthermore, the acquisition of S1 performance evaluation index data includes: Samples are taken from the recycled aggregate formed after the repair and treatment of construction waste, and recycled aggregate from different batches or sources is collected in accordance with the preset sampling rules. Mechanical properties and structural characteristics were tested on the collected recycled aggregate samples to obtain raw test data reflecting the load-bearing capacity, crush resistance, moisture content and particle morphology of the recycled aggregates. The original test data is organized and verified, and abnormal or incomplete data is removed to form a performance evaluation index dataset for subsequent evaluation and analysis.

[0022] Specifically, recycled aggregates from construction waste originating from different demolition projects or different repair batches are used as evaluation objects. They are categorized and sampled according to preset sampling rules. These rules may include differentiation based on the source project, repair process type, or treatment batch. Representative recycled aggregate samples are randomly selected from each category to ensure that subsequent evaluation data can accurately reflect the overall performance of the corresponding recycled aggregates, thereby forming several groups of recycled aggregate samples to be tested.

[0023] Subsequently, the collected recycled aggregate samples were subjected to mechanical property and structural characteristic tests, namely, cylinder compressive strength test, crushing value test, water absorption rate test, and needle-like and flaky particle content test, to obtain raw test data reflecting the load-bearing capacity, crushing resistance, moisture content and particle morphology characteristics of the recycled aggregate. The above test process can be completed using conventional testing equipment and procedures in this field. The test results are recorded in the form of experimentally measured values, thereby forming raw test data corresponding to each performance evaluation index. After the testing is completed, the raw test data is organized and verified. First, all raw test data obtained during the testing process are summarized, and data with obvious abnormal fluctuations or incomplete records are removed. Consistency verification is performed on repeated test results of the same recycled aggregate sample to ensure the reliability of the data source. After organization and verification, a complete and valid performance evaluation index dataset is formed. This dataset serves as input data for subsequent index standardization and evaluation analysis. For example, recycled aggregate from a demolition project that has undergone crushing, screening, and repair is sampled and classified according to processing batches. The recycled aggregate samples are then tested, obtaining raw test data showing a compressive strength of 6.2 MPa, a crushing value of 22%, a water absorption rate of 4.2%, and a needle-like / flaky particle content of 14%. After organizing and verifying the above test data, a corresponding performance evaluation index dataset is formed for subsequent index standardization and evaluation calculations.

[0024] S2. Preprocess the performance evaluation index data, and standardize the index according to the positive or negative attributes of each performance evaluation index to obtain normalized index data within a unified scale range. Furthermore, S2 performs standardization processing on the performance evaluation index data, including: Attribute identification is performed on the performance evaluation index data, and the performance evaluation indexes are divided into positive and negative indicators according to the value direction of each performance evaluation index in the evaluation system. For positive and negative indicators, corresponding standardization methods are used to transform the performance evaluation indicator data into dimensionless data. The standardized indicator data is then processed to unify its range, resulting in normalized indicator data for subsequent steps.

[0025] Specifically, based on the relationship between the direction of change of each performance evaluation index in the evaluation system and the performance of recycled aggregate, the performance evaluation indexes are classified and identified. Among them, the index that corresponds to an increase in the performance level of recycled aggregate when the index value increases is identified as a positive index; the index that corresponds to a decrease in the performance level of recycled aggregate when the index value increases is identified as a negative index. In this embodiment, the cylinder compressive strength is identified as a positive index, while the crushing value, water absorption rate, and needle-like particle content are identified as negative indexes. Through the above attribute identification process, a basis is provided for the subsequent adoption of different standardization treatment methods. Subsequently, corresponding standardization methods were applied to positive and negative indicators respectively, and the performance evaluation indicator data were transformed into dimensionless form. In specific implementation, for positive indicators, a standardization formula based on the maximum and minimum values ​​was used, the expression of which is: ; in, Indicates the first The original test data of each positive performance evaluation index and These represent the maximum and minimum values ​​of the indicator in the evaluation sample, respectively. This indicates the corresponding standard value.

[0026] For negative indicators, a reverse standardization method is used, and its expression is: ; The meaning of each symbol is consistent with the above positive index standardization formula. Through the above standardization process, the performance evaluation index data of different dimensions and orders of magnitude are converted into dimensionless form to obtain the standardized index data. After completing the dimensionless transformation, the standardized index data is subjected to range unification processing, that is, the numerical range of each performance evaluation index data after standardization is verified to ensure that each standardized index data falls within the preset unified numerical range. In this embodiment, the unified numerical range is a closed interval from zero to one. After range unification processing, normalized index data is formed for subsequent chaotic evolution modeling and comprehensive evaluation calculation. For example, based on the original test data of recycled aggregate, where the compressive strength is 6.2 MPa, the crushing value is 22%, the water absorption rate is 4.2%, and the content of needle-like and flaky particles is 14%, assuming that the maximum compressive strength in the same batch of samples is 7.0 MPa and the minimum is 5.0 MPa, then after positive standardization, the normalized index value of the compressive strength of the recycled aggregate is approximately 0.6. At the same time, if the maximum value of the crushing value in the sample is 28% and the minimum value is 18%, then after negative standardization, the normalized index value of the crushing value of the recycled aggregate is approximately 0.6. The other performance evaluation indicators are also standardized in the same way, finally forming a complete normalized index dataset for subsequent steps.

[0027] S3. Construct a chaotic evolution model based on normalized index data, and introduce at least three indices from the normalized index data into the chaotic system as state variables; Furthermore, S3 constructs a chaotic evolution model based on normalized index data, including: A multidimensional chaotic system is selected as the basic model for the chaotic evolution model; Select at least three performance evaluation indicators from the normalized index data, and introduce the selected performance evaluation indicators into the multidimensional chaotic system as state variables of the chaotic system. Among them, the chaotic evolution model is a three-dimensional chaotic system, which is composed of multiple state variables; The state variables are constructed by introducing different performance evaluation indicators from the normalized index data; The chaotic system is initialized based on state variables to form a chaotic evolution model for subsequent evolution calculations.

[0028] Specifically, in this embodiment, after obtaining the normalized performance evaluation index data, a chaotic evolution model is constructed based on the normalized index data, that is, a multidimensional chaotic system is first selected as the basic model of the chaotic evolution model. In specific implementation, a multidimensional chaotic system that can characterize the evolution characteristics of a nonlinear dynamic system is selected from the chaotic dynamic model as the basic model of the chaotic evolution model. The multidimensional chaotic system is used to describe the coupling relationship between multiple variables that changes over time. Its system state is determined by multiple state variables. In this embodiment, a three-dimensional chaotic system is selected as a specific implementation of the multidimensional chaotic system to meet the need for joint modeling of multiple performance evaluation indicators. Subsequently, at least three performance evaluation indicators are selected from the normalized index data and introduced into the chaotic system as state variables. From the normalized index data after index standardization, at least three performance evaluation indicators that can represent the performance characteristics of recycled aggregates are selected, and the selected performance evaluation indicators are mapped to the state variables of the chaotic system respectively. In this embodiment, the normalized index data corresponding to cylinder compressive strength, crushing value and water absorption rate can be selected as the three state variables of the chaotic system to construct a three-dimensional chaotic system. In the above way, multiple performance evaluation indicators of recycled aggregates are introduced into the same chaotic evolution framework in the form of state variables. After introducing the state variables, the chaotic system is initialized to form a chaotic evolution model. Specifically, based on the selected state variables, the initial state of the chaotic system is set to the normalized index value corresponding to the state variables at the initial moment. System parameters matching the three-dimensional chaotic system are then selected to complete the initialization of the chaotic system. After initialization, the chaotic system can be used as the chaotic evolution model for subsequent chaotic evolution calculations. In one specific embodiment, the three-dimensional chaotic system is described by chaotic dynamic equations of the following form: ; Where x, y, and z represent the three state variables of the chaotic system; in this embodiment, the state variables x, y, and z are constructed by introducing normalized index data corresponding to cylinder compressive strength, crushing value, and water absorption rate; σ, ρ, and β are the system parameters of the chaotic system, and their values ​​can be determined according to the conventional parameter settings of the selected chaotic system; t represents the system evolution time; For example, based on the normalized index data, the normalized index values ​​corresponding to cylinder compressive strength, crushing value, and water absorption rate are 0.6, 0.6, and 0.7, respectively. These three normalized index values ​​can be used as the state variable values ​​of the chaotic system at the initial moment, introduced into the three-dimensional chaotic system, and the construction of the chaotic evolution model can be completed, providing a basic model for the subsequent evolution calculation of the chaotic system.

[0029] S4. Perform evolutionary analysis on the chaotic system to obtain the chaotic analysis results that reflect the nonlinear coupling relationship between performance evaluation indicators and the characteristics of performance fluctuations. The chaotic analysis results are a set of characteristic data calculated from the state variable evolution data. Furthermore, S4 performs evolutionary analysis on chaotic systems, including: Based on the chaotic evolution model, iterative calculations are performed on the chaotic system to obtain the state change data of the chaotic system within a preset calculation interval; Record and organize the state change data to form a data sequence that reflects the evolution process of the chaotic system; Evolutionary feature data of chaotic systems are extracted based on data sequences, and this evolutionary feature data is used as input data for subsequent steps.

[0030] Specifically, firstly, the chaotic system is iteratively calculated based on the aforementioned chaotic evolution model to obtain state change data. In specific implementation, based on the aforementioned constructed three-dimensional chaotic system model, numerical iterative calculations are performed on the chaotic system within a preset calculation interval. In each calculation step, the system state is updated according to the dynamic relationship of the chaotic system, thereby obtaining the state data of each state variable of the chaotic system changing with time within the calculation interval. Subsequently, the state change data is recorded and organized to form a data sequence reflecting the evolution of the chaotic system. This involves recording the values ​​of each state variable obtained during the iterative calculation at different time points and organizing them in chronological order to form a data sequence of state variables changing over time. The data sequence can include time series data of a single state variable or multidimensional time series data composed of multiple state variables. After obtaining the data sequence, the evolutionary feature data of the chaotic system is extracted based on the data sequence. Specifically, the state changes of the chaotic system are calculated and processed according to the data sequence to extract the amount of data used to characterize the evolution process of the chaotic system. In this embodiment, the statistical feature quantity can be calculated based on the time series of the state variables. The obtained evolutionary feature data is used as the input data for subsequent index weight determination and comprehensive evaluation calculation steps.

[0031] S5. Based on operations research methods, determine the index weights corresponding to each normalized index, and perform comprehensive weighted calculation on the normalized index data according to the index weights to obtain the comprehensive evaluation results of recycled aggregates. Furthermore, S5 determines the indicator weights based on operations research methods, including: Based on the pre-set evaluation index system, an index hierarchy structure is constructed, and the relative relationships between the various performance evaluation indicators are described. Based on the hierarchical structure of the indicators, obtain the weight calculation data corresponding to each performance evaluation indicator, and organize the weight calculation data. The weights of each performance evaluation index are calculated based on the weight calculation data to obtain the index weights corresponding to each performance evaluation index. The process of determining the weights of the indicators includes a weight calculation step based on the hierarchical relationship between performance evaluation indicators and a weight calculation step based on the data distribution characteristics of performance evaluation indicators. The weight results obtained from different weight calculation steps are then combined.

[0032] Specifically, after obtaining the evolutionary characteristic data and normalized index data of the chaotic system, the index weights of each performance evaluation index are determined based on the operations research method. First, an index hierarchy structure is constructed based on the preset evaluation index system, and the relative relationships between each performance evaluation index are described. In practice, the performance evaluation target of recycled aggregate is used as the top target layer, and an index layer is set below the target layer. The index layer includes performance evaluation indicators such as cylinder compressive strength, crushing value, water absorption rate and needle-like and flaky particle content. The index hierarchy structure consisting of the target layer and the index layer is constructed in the above way. The relative relationship between each index is described according to the relative importance of each performance evaluation indicator in the evaluation system, so as to provide a structural basis for subsequent weight calculation. Subsequently, based on the hierarchical structure of the indicators, the weight calculation data corresponding to each performance evaluation indicator was obtained, and the weight calculation data was organized. In practice, based on the hierarchical structure of indicators, a judgment matrix is ​​constructed to describe the relative relationship between each performance evaluation indicator. By quantifying the relative relationship between different indicators, weight calculation data corresponding to each performance evaluation indicator is formed. At the same time, the weight calculation data is checked and organized to ensure that the weight calculation data meets the basic requirements for subsequent weight calculation. In one specific embodiment, a judgment matrix can be constructed based on hierarchical relationships. ,in, Indicates the first The performance evaluation index is relative to the first The importance of each performance evaluation indicator is determined by processing the judgment matrix to obtain weight calculation data that reflects the hierarchical relationship of each performance evaluation indicator. After obtaining the weight calculation data, the weights of each performance evaluation index are calculated based on the weight calculation data. In specific implementation, based on the judgment matrix, the hierarchical relationship weights corresponding to each performance evaluation index are obtained through eigenvector calculation or normalization. The hierarchical relationship weights are used to reflect the relative importance of each performance evaluation index in the evaluation system.

[0033] Meanwhile, the process of determining the weights of the indicators also includes a weight calculation step based on the distribution characteristics of the performance evaluation indicator data; Based on the aforementioned normalized index data or chaotic system evolution characteristic data, the distribution characteristics of each performance evaluation index in the sample data are calculated, and the corresponding objective weights are determined according to the distribution characteristics. In a specific embodiment, the entropy weight method can be used to calculate the data dispersion of each performance evaluation index, and the calculation process can be expressed as follows: ; in, Indicates the first The information entropy value of each performance evaluation metric Indicates the first The sample at the th Normalized weighting under each performance evaluation metric Indicates the number of samples. The normalization coefficient is used to calculate the objective weights of each performance evaluation index based on the information entropy value. Finally, the weight results obtained from different weight calculation steps are combined to obtain the final weight of each performance evaluation index. In practice, the hierarchical relationship weights obtained based on the hierarchical relationship of the indicators are combined with the objective weights obtained based on the distribution characteristics of the indicator data to form the final indicator weights corresponding to each performance evaluation indicator. The final indicator weights are used as input data for subsequent comprehensive evaluation calculation steps. For example, for the four performance evaluation indicators of cylinder compressive strength, crushing value, water absorption rate and needle-like particle content, their hierarchical relationship weights and objective weights based on data distribution characteristics are calculated respectively, and the two types of weights are combined to obtain the corresponding indicator weights, which are used in the subsequent comprehensive evaluation calculation process.

[0034] Furthermore, S5 performs comprehensive evaluation calculations on normalized index data, including: Obtain the weights of the performance evaluation metrics and match the weights with the corresponding normalized metric data. The normalized index data are weighted according to the index weights to obtain evaluation data that reflects the overall situation of each performance evaluation index; The evaluation data are summarized and processed to form a comprehensive evaluation result of recycled aggregates.

[0035] Specifically, firstly, the weights of the performance evaluation indicators are obtained, and then the weights are matched with the corresponding normalized indicator data. That is, the final weights of each performance evaluation indicator obtained in the previous steps are called, and each weight is matched with the corresponding normalized indicator data according to the correspondence of the performance evaluation indicators. For example, the weight of the indicator corresponding to the cylinder compressive strength is matched with the normalized indicator data of the cylinder compressive strength, and the weights of the indicators corresponding to the crushing value, water absorption rate and needle-like particle content are matched with their normalized indicator data respectively, thereby forming a correspondence between weights and indicator data. Subsequently, the normalized indicator data is weighted according to the indicator weights to obtain the evaluation data. Specifically, the normalized indicator data of each performance evaluation indicator is weighted according to its corresponding indicator weight to reflect the weight contribution of different performance evaluation indicators in the comprehensive evaluation. In this embodiment, the weighting process can be performed by weighted summation, and the comprehensive evaluation calculation formula is expressed as follows: ; Where V represents the comprehensive evaluation value of recycled aggregate; This represents the weight of the j-th performance evaluation index, and its value comes from the aforementioned index weight determination step. This represents the normalized index data corresponding to the j-th performance evaluation index; m represents the number of performance evaluation indicators involved in the comprehensive evaluation.

[0036] Through the above weighted calculation, evaluation data reflecting the overall situation of each performance evaluation index is obtained; After weighting, the evaluation data is summarized to form a comprehensive evaluation result of recycled aggregate. Specifically, the evaluation data obtained by the above weighted calculation is recorded and output as the comprehensive evaluation result of recycled aggregate. The comprehensive evaluation result is in the form of a single numerical value, which is used to characterize the comprehensive evaluation level of recycled aggregate under multiple performance evaluation indicators and serves as input data for the subsequent repair and reuse level determination step. For example, for the four performance evaluation indicators of compressive strength, crushing value, water absorption rate, and needle-like particle content, assuming that their corresponding indicator weights are 0.35, 0.30, 0.20, and 0.15 respectively, and their normalized indicator data are 0.82, 0.76, 0.61, and 0.71 respectively, the comprehensive evaluation value of recycled aggregate can be calculated by the above weighted summation method, which can be used for subsequent determination of the level of repair and reuse.

[0037] S6. Determine the repair and reuse level of recycled aggregates based on the comprehensive evaluation results, and output engineering application suggestions corresponding to the repair and reuse level.

[0038] Furthermore, S6 determines the level of repair and reuse based on the comprehensive evaluation results, including the following steps: Obtain the comprehensive evaluation results of recycled aggregates and compare the comprehensive evaluation results with the preset grading rules; Based on the comparison results, the recycled aggregates are classified into the corresponding repair and reuse levels; The repair and reuse level is associated with a preset engineering application type, and the engineering application information corresponding to the repair and reuse level is output.

[0039] Specifically, firstly, the comprehensive evaluation results of recycled aggregates are obtained and compared with the preset grading rules. That is, the comprehensive evaluation results of recycled aggregates obtained in the aforementioned comprehensive evaluation calculation steps are called and the comprehensive evaluation results are input into the preset grading rules for comparison. The grading rules are used to divide the comprehensive evaluation results into different interval ranges. Each interval range corresponds to a different repair and reuse level. The grading rules can be preset according to engineering application needs or historical data and expressed in the form of interval thresholds. Subsequently, based on the comparison results, the recycled aggregates are classified into the corresponding repair and reuse levels. In specific implementation, the repair and reuse level of the recycled aggregates is determined according to the range of the comprehensive evaluation results. For example, when the comprehensive evaluation results fall into a certain preset range, the recycled aggregates are classified into the corresponding repair and reuse levels, thereby completing the determination of the repair and reuse level of the recycled aggregates. After determining the repair and reuse level, the repair and reuse level is associated with preset engineering application types, and the corresponding engineering application information is output. Specifically, different repair and reuse levels are associated with pre-defined engineering application types. For example, different levels of recycled aggregates are associated with different road structure levels or engineering application scenarios, and engineering application information corresponding to the repair and reuse level is output. The engineering application information is used to characterize the applicable types of recycled aggregates in engineering applications. For example, in a specific embodiment, when the comprehensive evaluation result of a certain recycled aggregate is 0.743, and the preset grading rules correspond the interval of comprehensive evaluation result greater than or equal to 0.70 and less than 0.85 to the secondary repair and reuse grade, the recycled aggregate is classified as secondary, and the secondary repair and reuse grade is associated with engineering application types such as the subbase of a second-highest grade highway or the base of a municipal road, and the corresponding engineering application information is output.

[0040] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An evaluation method for the remediation and reuse of construction waste based on chaos theory and operations research, characterized in that, include: S1. Obtain performance evaluation index data of recycled aggregate formed after the repair and treatment of construction waste. The performance evaluation index data includes cylinder compressive strength, crushing value, water absorption rate and needle-like particle content. S2. The performance evaluation index data is preprocessed, and the index is standardized according to the positive or negative attributes of each performance evaluation index to obtain normalized index data within a unified scale range. S3. Construct a chaotic evolution model based on the normalized index data, and introduce at least three indices from the normalized index data into the chaotic system as state variables; S4. Perform evolutionary analysis on the chaotic system to obtain chaotic analysis results that reflect the nonlinear coupling relationship and performance fluctuation characteristics between the performance evaluation indicators. The chaotic analysis results are a set of feature data calculated from the state variable evolution data. S5. Determine the index weights corresponding to each normalized index based on operations research methods, and perform comprehensive weighted calculation on the normalized index data according to the index weights to obtain the comprehensive evaluation results of recycled aggregates. S6. Determine the repair and reuse level of the recycled aggregate based on the comprehensive evaluation results, and output engineering application suggestions corresponding to the repair and reuse level.

2. The evaluation method for the remediation and reuse of construction waste based on chaos theory and operations research as described in claim 1, characterized in that, The acquisition of the S1 performance evaluation index data includes: Samples are taken from the recycled aggregate formed after the repair and treatment of construction waste, and recycled aggregate from different batches or sources is collected in accordance with the preset sampling rules. Mechanical properties and structural characteristics were tested on the collected recycled aggregate samples to obtain raw test data reflecting the load-bearing capacity, crush resistance, moisture content and particle morphology of the recycled aggregates. The original detection data is organized and verified, and abnormal or incomplete data is removed to form a performance evaluation index dataset for subsequent evaluation and analysis.

3. The evaluation method for the remediation and reuse of construction waste based on chaos theory and operations research as described in claim 1, characterized in that, The S2 step of standardizing the performance evaluation index data includes: The performance evaluation index data is attribute-identified, and the performance evaluation indexes are classified into positive and negative indicators according to the value direction of each performance evaluation index in the evaluation system. For the positive and negative indicators, corresponding standardization methods are applied to transform the performance evaluation indicator data into dimensionless data to obtain standardized indicator data. The standardized indicator data is then processed to unify its range, resulting in normalized indicator data for subsequent steps.

4. The evaluation method for the remediation and reuse of construction waste based on chaos theory and operations research as described in claim 1, characterized in that, The S3 model, which constructs a chaotic evolution model based on normalized index data, includes: A multidimensional chaotic system is selected as the basic model for the chaotic evolution model; At least three performance evaluation indicators are selected from the normalized index data, and the selected performance evaluation indicators are respectively introduced into the multidimensional chaotic system as state variables of the chaotic system. The chaotic system is initialized based on the state variables to form a chaotic evolution model for subsequent evolution calculations.

5. The evaluation method for the remediation and reuse of construction waste based on chaos theory and operations research as described in claim 4, characterized in that, The chaotic evolution model is a three-dimensional chaotic system, which consists of multiple state variables; The state variables are constructed by introducing different performance evaluation indicators from the normalized index data.

6. The evaluation method for the remediation and reuse of construction waste based on chaos theory and operations research as described in claim 1, characterized in that, The S4 method for evolutionary analysis of chaotic systems includes: Based on the aforementioned chaotic evolution model, iterative calculations are performed on the chaotic system to obtain state change data of the chaotic system within a preset calculation interval; The state change data are recorded and organized to form a data sequence reflecting the evolution process of the chaotic system; Evolutionary feature data of the chaotic system is extracted based on the data sequence, and the evolutionary feature data is used as input data for subsequent steps.

7. The evaluation method for the remediation and reuse of construction waste based on chaos theory and operations research as described in claim 1, characterized in that, The S5 method for determining indicator weights based on operations research includes: Based on the pre-set evaluation index system, an index hierarchy structure is constructed, and the relative relationships between the various performance evaluation indicators are described. Based on the hierarchical structure of the indicators, obtain the weight calculation data corresponding to each performance evaluation indicator, and organize the weight calculation data. The weights of each performance evaluation index are calculated based on the weight calculation data to obtain the index weights corresponding to each performance evaluation index.

8. The evaluation method for the remediation and reuse of construction waste based on chaos theory and operations research as described in claim 7, characterized in that, The process of determining the weights of the indicators includes a weight calculation step based on the hierarchical relationship between the performance evaluation indicators and a weight calculation step based on the data distribution characteristics of the performance evaluation indicators. The weight results obtained from the different weight calculation steps are then combined.

9. The evaluation method for the remediation and reuse of construction waste based on chaos theory and operations research as described in claim 1, characterized in that, The S5 step of performing a comprehensive evaluation calculation on the normalized index data includes: Obtain the index weights corresponding to the performance evaluation indexes, and match the index weights with the corresponding normalized index data; The normalized index data are weighted according to the index weights to obtain evaluation data that reflects the overall situation of each performance evaluation index; The evaluation data are summarized and processed to form a comprehensive evaluation result of recycled aggregates.

10. The evaluation method for the remediation and reuse of construction waste based on chaos theory and operations research according to claim 1, characterized in that, The S6 step of determining the repair and reuse level based on the comprehensive evaluation results includes the following steps: Obtain the comprehensive evaluation result of the recycled aggregate, and compare the comprehensive evaluation result with the preset grading rules; Based on the comparison results, the recycled aggregates are classified into the corresponding repair and reuse levels; The repair and reuse level is associated with a preset engineering application type, and the engineering application information corresponding to the repair and reuse level is output.

Images