Comprehensive evaluation method and system for quality control effect of water gate detection

Abstract

The present application relates to the technical field of water conservancy project detection quality effect evaluation, in particular to a kind of sluice detection quality control effect comprehensive evaluation method and system, systematically collate engineering construction throughout raw material inspection, intermediate product quality control and engineering entity quality each stage detection data, complete the overall collection of historical detection data;Select fuzzy comprehensive evaluation algorithm to evaluate sluice detection quality control effect, establish comprehensive evaluation index system;The collected detection data are analyzed, and each evaluation index is quantitatively calculated;Using expert scoring strategy, the weight of each index in the established comprehensive evaluation index system is quantitatively assigned;According to the evaluation index quantization result and index system weight assignment result, fuzzy comprehensive evaluation calculation is carried out on sluice detection quality control effect, and the grade is judged according to the preset grade division table.The present application realizes the scientific quantization, comprehensive and accurate evaluation of sluice detection quality control effect.

Description

Technical Field

[0001] This invention relates to the field of quality evaluation technology for water conservancy project testing, specifically to a comprehensive evaluation method and system for the quality control effect of sluice gate testing. Background Technology

[0002] Quality inspection throughout the entire process of water conservancy projects is a crucial means of comprehensively controlling project quality. The main types of inspections in water conservancy project construction quality management include self-inspection by the construction unit, parallel inspection by the supervision unit, full-process inspection by the legal entity, unannounced inspections by quality supervision agencies, and sampling inspections during final acceptance. The sources and types of inspection reports issued by various testing units at each stage of construction are diverse and complex. Therefore, it is difficult for the legal entity to fully grasp the control of quality inspection throughout the entire project process and to accurately evaluate the effectiveness of the inspection quality.

[0003] To systematically construct a deep analysis system for water conservancy project quality data, accurately assess the quality control effectiveness of each project, and strengthen the scientific decision-making support for testing schemes, it is urgent to introduce a scientific and reasonable method for evaluating the effectiveness of engineering quality control that is tailored to the characteristics of water conservancy project construction. Through multi-dimensional and comprehensive systematic analysis of testing report data throughout the entire project construction cycle, a comprehensive quantitative evaluation of aspects such as testing qualification, testing item coverage, and the rationality of testing schemes can be achieved. This will provide solid data support and directional guidance for the subsequent optimization of water conservancy project quality testing schemes and the improvement of the testing system.

[0004] In recent years, domestic research on the evaluation and optimization of quality inspection management in water conservancy projects has mainly focused on summarizing engineering experience to assess the effectiveness of inspections and proposing corresponding optimization suggestions based on encountered problems. However, systematic research on the rationality and adaptability of evaluation methods has not been conducted, particularly regarding the optimization of quality inspection schemes for specialized types of water conservancy projects such as sluice gates. To address issues such as insufficient sampling frequency, omissions of indicators, and lack of representativeness in the quality inspection of sluice gate projects, it is particularly necessary to conduct comprehensive evaluation and method optimization research on quality inspection management and control. Summary of the Invention

[0005] To address the lack of a systematic and quantitative comprehensive evaluation method for the quality control effect of sluice gate inspection in existing technologies, which makes it difficult to accurately and comprehensively assess the quality control level of sluice gate engineering inspection, this invention proposes a comprehensive evaluation method and system for the quality control effect of sluice gate inspection. This method achieves scientific quantification and comprehensive and accurate evaluation of the quality control effect of sluice gate inspection, providing reliable technical support for optimizing the quality control of sluice gate inspection.

[0006] To achieve the above objectives, the technical solution adopted is:

[0007] This invention provides a comprehensive evaluation method for the quality control effect of sluice gate inspection, comprising the following steps:

[0008] Step 1: Systematically organize the testing data throughout the various stages of the project construction, including raw material inspection, intermediate product quality control, and the quality of the project entity, and complete the comprehensive collection of historical testing data;

[0009] Step 2: Select the fuzzy comprehensive evaluation algorithm to evaluate the quality control effect of sluice gate inspection and establish a comprehensive evaluation index system;

[0010] Step 3: Analyze the collected test data and quantify each evaluation index;

[0011] Step 4: Employ an expert scoring strategy to quantify and assign values ​​to the weights of each indicator in the established comprehensive evaluation index system;

[0012] Step 5: Based on the quantitative results of each evaluation indicator and the weight assignment results of the indicator system, perform fuzzy comprehensive evaluation calculation on the quality control effect of sluice gate detection, and judge the level according to the preset level classification table.

[0013] According to the comprehensive evaluation method for the quality control effect of sluice gate inspection of the present invention, in step 2, the established comprehensive evaluation index system includes eight evaluation indicators: inspection item coverage rate, inspection index coverage rate, inspection frequency compliance rate, inspection index pass rate, inspection data variation coefficient, inspection data deviation coefficient, coverage of engineering parts, and the planning rationality and implementation rationality of the inspection plan.

[0014] According to the comprehensive evaluation method for the quality control effect of sluice gate inspection of the present invention, in step 3, when quantifying the coverage rate of inspection items, the coverage rate of inspection items = (number of completed inspection items / total number of inspection items required for sluice gate project) × 100%, and the total number of inspection items required for sluice gate project is determined according to the sluice gate project design documents and relevant regulations and specifications; when quantifying the coverage rate of inspection indicators, the coverage rate of inspection indicators = (number of completed inspection indicators / total number of inspection indicators required for inspection items) × 100%.

[0015] According to the comprehensive evaluation method of the quality control effect of sluice gate inspection of the present invention, in step 3, when quantifying the compliance rate of inspection frequency, the compliance rate of inspection frequency = (actual inspection frequency of the inspection item / inspection frequency required by the procedure and specification of the inspection item) × 100%; when quantifying the pass rate of inspection indicators, the pass rate of inspection indicators = (number of qualified inspection indicators / total number of inspection indicators) × 100%.

[0016] According to the comprehensive evaluation method for the quality control effect of sluice gate inspection of the present invention, further, in step 3, when quantifying the coefficient of variation of the test data, the compaction degree of earthwork filling, the compressive strength of concrete specimens, and the tensile strength of hot-rolled ribbed steel bars are selected for analysis and the coefficient of variation is calculated respectively. The coefficient of variation is obtained by the entropy weight method. In step 3, when quantifying the deviation coefficient of the test data, the compaction degree of earthwork filling, the compressive strength of concrete specimens, and the tensile strength of hot-rolled ribbed steel bars are selected for analysis and the deviation coefficient is calculated respectively. The formula for calculating the deviation coefficient of a certain test index is as follows: ,in, This represents the average value of the test data. This represents the design value.

[0017] According to the comprehensive evaluation method of the quality control effect of sluice gate inspection of the present invention, in step 3, when quantifying the coverage of the engineering part, the planning rationality and implementation rationality of the inspection plan, several experts with associate senior or above titles are randomly selected to review all the inspection reports of each sluice gate and simulate scoring the two evaluation indicators, with the scores corresponding one-to-one with the grade classification.

[0018] According to the comprehensive evaluation method for the quality control effect of sluice gate inspection of the present invention, in step 4, the expert scoring strategy is as follows: first, a weight consultation table is prepared, then multiple experts are selected to score each evaluation indicator on a percentage basis, the average score of each evaluation indicator is calculated, and the weight ratio of each evaluation indicator is calculated accordingly, and the sum of the weights of all indicators is 1.

[0019] According to the comprehensive evaluation method for the quality control effect of sluice gate inspection of the present invention, further, in step 5, the fuzzy comprehensive evaluation calculation specifically includes:

[0020] Construct a fuzzy comprehensive evaluation index system, using index domains express;

[0021] Constructing weight vectors , where a1, a2, ..., a8 are the weights of the eight evaluation indicators involved in the evaluation, and their sum is 1;

[0022] By establishing membership functions, a framework can be constructed from the index domain. To the evaluation set Membership matrix ,in Indicate each indicator Belonging to various evaluation levels Membership degree;

[0023] The comprehensive evaluation result vector B = A·R is obtained, and the product of the comprehensive evaluation result vector B and the evaluation vector V is taken as the final score.

[0024] According to the comprehensive evaluation method for the quality control effect of sluice gate inspection of the present invention, further, in the preset grade classification table in step 5, the correspondence between the final score and the grade assessment is as follows: excellent corresponds to [95, 100] points; good corresponds to [85, 95) points; qualified corresponds to [75, 85) points; poor corresponds to [0, 75) points.

[0025] Furthermore, the present invention also provides a comprehensive evaluation system for the quality control effect of sluice gate inspection, comprising:

[0026] The data collection module is used to systematically organize the testing data throughout the various stages of engineering construction, including raw material inspection, intermediate product quality control, and engineering entity quality, and to complete the comprehensive collection of historical testing data.

[0027] The indicator system construction module is used to select a fuzzy comprehensive evaluation algorithm to evaluate the quality control effect of sluice gate detection and establish a comprehensive evaluation indicator system.

[0028] The indicator quantification module is used to analyze the collected detection data and quantify and calculate each evaluation indicator.

[0029] The weight assignment module is used to quantify and assign weights to each indicator in the established comprehensive evaluation index system using an expert scoring strategy.

[0030] The comprehensive evaluation calculation module is used to perform fuzzy comprehensive evaluation calculation on the quality control effect of sluice gate detection based on the quantitative results of each evaluation indicator and the weight assignment results of the indicator system, and to judge the level according to the preset level classification table.

[0031] The beneficial effects achieved by adopting the above technical solution are:

[0032] The comprehensive evaluation method for the quality control effect of sluice gate inspection provided by this invention first systematically organizes the inspection data throughout the various stages of engineering construction, including raw material inspection, intermediate product quality control, and the quality of the engineering entity, completing a comprehensive collection of historical inspection data. Then, a fuzzy comprehensive evaluation algorithm is selected to evaluate the quality control effect of sluice gate inspection, establishing a comprehensive evaluation index system. Next, the collected inspection data is analyzed, and each evaluation index is quantified and calculated. An expert scoring strategy is then used to quantify and assign values ​​to the weights of each index in the established comprehensive evaluation index system. Finally, based on the quantification results of each evaluation index and the weight assignment results of the index system, a fuzzy comprehensive evaluation calculation is performed on the quality control effect of sluice gate inspection, and the level is judged according to a preset level classification table. Through the implementation of this method, the quality control effect of sluice gate engineering inspection can be scientifically, rationally, accurately, and effectively evaluated. The evaluation index system adopted can reflect the impact of various aspects, from the preparation to implementation of the inspection plan, on the quality control effect, providing technical support for the implementation and scientific management of sluice gate engineering inspection. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. The drawings are merely illustrative of some embodiments of the present invention and are not intended to limit the scope of the present invention to all embodiments.

[0034] Figure 1 This is a flowchart illustrating the comprehensive evaluation method for the quality control effect of sluice gate detection according to an embodiment of the present invention.

[0035] Figure 2 This is a flowchart of the fuzzy comprehensive evaluation calculation according to an embodiment of the present invention;

[0036] Figure 3 This is an evaluation result diagram of the comprehensive evaluation of the quality control effect of the Yellow River downstream diversion sluice gate reconstruction project (Shandong section) in an embodiment of the present invention. Detailed Implementation

[0037] The exemplary solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art.

[0038] like Figure 1 As shown in the figure, this invention discloses a comprehensive evaluation method for the quality control effect of sluice gate inspection, including the following steps:

[0039] Step S1: Systematically organize the testing data throughout the various stages of the project construction, including raw material inspection, intermediate product quality control, and project entity quality, and complete the comprehensive collection of historical testing data.

[0040] To solidify the foundation of the engineering quality traceability database, a systematic collection and organization of historical testing data throughout the entire process was conducted. Focusing on the entire lifecycle of water conservancy project construction, the data precisely covers all stages of construction, including raw material inspection, intermediate product quality control, and the quality of the engineering entity itself. Original testing reports from various sources, including parallel testing by supervisors and full-process testing by legal entities, were comprehensively reviewed. Historical testing data was collected and organized using batch data extraction and processing methods. All testing data underwent rigorous post-processing and data cleaning to ensure that the collected data complies with relevant regulations and standards and is accurate.

[0041] Step S2: Select the fuzzy comprehensive evaluation algorithm to evaluate the quality control effect of sluice gate detection and establish a comprehensive evaluation index system.

[0042] Fuzzy comprehensive evaluation method is a comprehensive evaluation method based on fuzzy mathematics, proposed by American automatic control expert Richard in 1965. This method transforms qualitative evaluation into quantitative evaluation through membership theory, enabling it to handle uncertain decision-making problems under multiple constraints. It is characterized by strong systematicity and is suitable for multi-dimensional evaluation scenarios such as price, technology, and service.

[0043] This method constructs an evaluation factor system and divides it into levels, then uses membership functions to determine the correspondence between evaluation values ​​and factor values. Weight allocation follows the principle that the sum of the weights of each level of factors is 1, and is set using empirical methods or the analytic hierarchy process (AHP). The application process includes establishing a membership matrix, synthesizing a weight matrix, calculating the comprehensive evaluation value level by level, and finally determining the optimal solution based on the sum of the weighted evaluation values.

[0044] The most significant features of the fuzzy comprehensive evaluation method are: (1) Mutual comparison: the optimal evaluation factor value is taken as the benchmark, and its evaluation value is 1; the other suboptimal evaluation factors are evaluated according to their degree of suboptimality. (2) Functional relationship: the functional relationship (i.e., membership function) between the evaluation value and the evaluation factor value can be determined based on the characteristics of various evaluation factors. There are many ways to determine this functional relationship (membership function), such as F-statistics, various types of F-distributions, etc. Of course, experienced evaluation experts can also be invited to conduct the evaluation and directly give the evaluation value.

[0045] Due to the complexity of evaluation factors, the hierarchical nature of evaluation objects, the ambiguity in evaluation standards, and the ambiguity or uncertainty of influencing factors, as well as the difficulty in quantifying qualitative indicators, it is difficult to accurately describe objective reality with absolute "either / or" statements. A "both / and" ambiguity often exists, and descriptions are frequently expressed using natural language. The most prominent characteristic of natural language is its ambiguity, which is difficult to uniformly measure using classical mathematical models. Therefore, the fuzzy comprehensive evaluation method, based on fuzzy sets, comprehensively evaluates the hierarchical status of the evaluated object from multiple indicators. It divides the range of change of the evaluated object, taking into account the hierarchical nature of the object and reflecting the ambiguity of evaluation standards and influencing factors. Furthermore, it fully utilizes human experience in the evaluation process, making the evaluation results more objective and consistent with reality. Fuzzy comprehensive evaluation can combine qualitative and quantitative factors, expand the amount of information, improve the evaluation accuracy, and make the evaluation conclusions credible.

[0046] There are many traditional comprehensive evaluation methods, and they are widely used. However, no single method is suitable for all situations and can solve all problems. Each method has its own focus and main application area. If we want to solve new problems arising in new fields, the fuzzy comprehensive method is obviously more appropriate.

[0047] This paper summarizes engineering experience and, based on the entire process of water conservancy project construction, systematically reviews the key points and problem-solving approaches in the implementation of each testing scheme. It comprehensively reviews, refines, and summarizes the testing experience in engineering construction. The comprehensive evaluation index system established in this embodiment includes eight evaluation indicators: testing item coverage rate, testing indicator coverage rate, testing frequency compliance rate, testing indicator pass rate, testing data variation coefficient, testing data deviation coefficient, coverage of engineering parts, and the planning and implementation rationality of the testing scheme. These eight evaluation indicators comprehensively reflect all aspects of the testing quality control effect, forming a comprehensive evaluation index system.

[0048] Step S3: Analyze the collected testing data and quantify and calculate evaluation indicators such as testing item coverage, testing indicator coverage, testing frequency compliance rate, and testing indicator pass rate. The quantitative analysis process for the eight evaluation indicators is given below.

[0049] ① Inspection Item Coverage: Quantitative analysis of inspection item coverage. Quality inspection items for sluice gate engineering construction are mainly divided into three categories: raw materials, intermediate products, and structures, components, and entities. Based on engineering experience and referring to the "Standard for Acceptance of Construction Quality of Unit Projects in Water Conservancy and Hydropower Engineering" (SL / T 631.1-2025~631.4-2025), the inspection items in the water conservancy engineering inspection process can generally be classified as follows: Raw materials include cement, steel, admixtures, fly ash, waterproofing materials, waterstops, geotextiles, decorative materials, and other raw materials; intermediate products refer to finished and semi-finished products of civil engineering such as sand and gravel aggregates, stones, concrete mixes, mortar mixes, and precast concrete components used in the construction; structures, components, and entities include metal structures, hoists, electromechanical equipment, and completed and functional engineering entities. For different unit sluice gate projects, the required inspection items will vary due to differences in design and construction schemes. Based on the comprehensive description of the sluice gate engineering design report and the general description of the design details, and with reference to relevant regulations and standards, the total number of items that need to be tested during the design process of the sluice gate engineering can be determined. In addition, based on the comprehensive sorting and statistics of the test data in step S1, the number of test items to be tested during the construction phase can be obtained. Thus, by comparing the number of items that have been tested with the number of items that need to be tested, the test item coverage rate during the construction process of the sluice gate engineering can be obtained. Its mathematical expression is: Test item coverage rate = (Number of completed test items / Total number of test items required for the sluice gate engineering) × 100%.

[0050] ② Coverage of Testing Indicators: Quantitative analysis of the coverage of testing indicators. Similarly, referring to relevant regulations and standards, the testing indicators for the required testing items during the construction of the sluice gate project can be determined; based on the statistics of the testing data in step S1, the completion status of the testing indicators in the issued testing report can be obtained; thus, by comparing the completion status of the tested indicators with the total number of required testing indicators, the coverage rate of testing indicators during the construction of the sluice gate project can be obtained. Its mathematical expression is: Coverage rate of testing indicators = (Number of completed testing indicators / Total number of testing indicators required for the testing items) × 100%.

[0051] ③ Testing Frequency Compliance Rate: Based on relevant regulations and specifications, determine the required testing frequencies for each testing item by the construction unit's self-inspection, the supervision unit's parallel testing, and the legal entity's full-process testing. Based on the statistical analysis of testing data in step S1, the testing frequencies for each testing item by each testing unit in the issued testing reports can be obtained. Therefore, by comparing the testing frequencies of each testing unit with the required testing frequencies for each testing item as stipulated in relevant regulations and specifications, the testing frequency compliance rate during the sluice gate construction process can be obtained. Its mathematical expression is: Testing Frequency Compliance Rate = (Actual Testing Frequency for the Testing Item / Testing Frequency Required by Regulations and Specifications for the Testing Item) × 100%.

[0052] ④ Inspection index pass rate: By extracting and analyzing the inspection data during the construction of the sluice gate project, it is determined whether the inspection values ​​corresponding to the inspection indexes meet the design standards and specifications. The inspection index pass rate is calculated based on the number of inspection indexes that meet the specifications and the total number of inspection indexes. The mathematical expression is: Inspection index pass rate = (number of qualified inspection indexes / total number of inspection indexes) × 100%.

[0053] ⑤ Coefficient of Variation of Test Data: A coefficient of variation analysis was performed on the test data during the construction of the sluice gate project. The analysis primarily focused on the compaction degree of earthwork fill, compressive strength of concrete specimens, and tensile strength of hot-rolled ribbed steel bars, which had a relatively large number of test groups. The coefficient of variation for each test indicator was calculated using the entropy weight method, which included data standardization, constructing a decision matrix, calculating the entropy value, and calculating the coefficient of variation.

[0054] ⑥ Deviation Coefficient of Test Data: Deviation coefficient analysis is performed on the test data during the construction of the sluice gate project. The analysis primarily focuses on the earthwork compaction degree, concrete specimen compressive strength, and hot-rolled ribbed steel tensile strength, which have a relatively large number of test groups. The deviation coefficient for each test indicator is calculated using the following formula: ,in, This represents the average value of the test data. This represents the design value.

[0055] ⑦ Coverage of Engineering Parts: The effectiveness of quality control in sluice gate engineering inspections is scored based on the coverage of engineering parts. Because the labeling of engineering parts in inspection reports is often not standardized, it is difficult to accurately classify the sampling locations of different test samples using keyword indexing or terminology classification. Therefore, an expert scoring method is used to score the coverage of engineering parts in sluice gate inspections. Five experts (with associate senior or higher professional titles) are randomly selected to review all inspection reports for each sluice gate and simulate scoring the coverage of engineering parts. The scores correspond one-to-one with the grade classification.

[0056] ⑧ Reasonableness of the Testing Plan and its Implementation: The effectiveness of the quality control of the sluice gate project testing will be scored based on the reasonableness of the testing plan and its implementation. Expert scoring will also be used to score the reasonableness of the testing plan and its implementation. Five experts (with associate senior or higher professional titles) will be randomly selected to review all testing reports for each sluice gate and conduct simulated scoring, with scores corresponding to the grade classifications.

[0057] Step S4: Employ an expert scoring strategy to quantify and assign values ​​to the weights of each indicator in the established comprehensive evaluation index system.

[0058] As described in step S3, the eight indicators in the evaluation index system were quantitatively analyzed sequentially. Before comprehensively evaluating the quality control effect of the sluice gate project, it is necessary to allocate the weight proportions of the eight indicators in the fuzzy comprehensive evaluation algorithm evaluation index system. In this embodiment, an expert scoring strategy is used to allocate the weight proportion of each indicator. First, a weight consultation table is prepared as shown in Table 1. Then, 10 experts are selected to score each evaluation indicator on a percentage basis, and the average score of each evaluation indicator is calculated to obtain the weight proportion of each indicator.

[0059]

[0060] Step S5: Based on the quantitative results of each evaluation indicator and the weight assignment results of the indicator system, perform fuzzy comprehensive evaluation calculation on the quality control effect of sluice gate detection, and judge the level according to the preset level classification table.

[0061] The evaluation set, also known as the comment set or evaluation rating, is the set of all evaluation results generated by fuzzy evaluation on the evaluated object, which can be represented as: ,in, This represents several possible evaluation results, j=1, 2, ..., n. This embodiment uses a four-level system; therefore, in the fuzzy comprehensive evaluation model, the evaluation set is defined as having four levels, namely... The corresponding percentage ranges are: v1 represents excellent (100 points); v2 represents good (90 points); v3 represents satisfactory (80 points); and v4 represents poor (70 points). By determining the evaluation set, a fuzzy evaluation vector for fuzzy comprehensive evaluation is obtained. This fuzzy vector represents the degree of membership of the evaluated object to each evaluation level, thus reflecting the fuzziness of the evaluation.

[0062] like Figure 2 As shown, the fuzzy comprehensive evaluation calculation process specifically includes the following steps:

[0063] (1) Constructing a fuzzy comprehensive evaluation index system

[0064] As described in step S3, this embodiment constructs a fuzzy comprehensive evaluation index system containing eight evaluation indicators, using the index domain. express:

[0065]

[0066] (2) Constructing the weight vector

[0067] The weight vector A of the evaluation factors is defined as follows:

[0068]

[0069] Where a1, a2, ..., a8 are the weights of the eight evaluation indicators, and their sum is 1. As described in step S4, this embodiment determines the weights of each evaluation indicator through an expert scoring strategy.

[0070] (3) Constructing the evaluation matrix

[0071] Establish suitable membership functions to construct a fuzzy relation matrix. For the i-th index, the membership degree to each comment is a fuzzy subset of V. The membership matrix for each indicator is as follows:

[0072]

[0073] in, Indicate each indicator Belonging to various evaluation levels Membership degree; It is a membership matrix from U to V.

[0074] (4) Comprehensive evaluation

[0075] If there is a fuzzy relationship from U to V Then, using R, we can obtain a fuzzy transform: This transformation yields the comprehensive evaluation result vector B = A·R. The comprehensive evaluation can be viewed as a fuzzy vector on V, denoted as: The comprehensive evaluation results will be vectorized The product of the normalized score and the evaluation vector V is used as the final score. The correspondence between the final score and the grade rating is as follows:

[0076]

[0077] Corresponding to the above method, embodiments of the present invention disclose a comprehensive evaluation system for the quality control effect of sluice gate inspection, comprising:

[0078] The data collection module is used to systematically organize the testing data throughout the various stages of engineering construction, including raw material inspection, intermediate product quality control, and engineering entity quality, and to complete the comprehensive collection of historical testing data.

[0079] The indicator system construction module is used to select a fuzzy comprehensive evaluation algorithm to evaluate the quality control effect of sluice gate detection and establish a comprehensive evaluation indicator system.

[0080] The indicator quantification module is used to analyze the collected detection data and quantify and calculate each evaluation indicator.

[0081] The weight assignment module is used to quantify and assign weights to each indicator in the established comprehensive evaluation index system using an expert scoring strategy.

[0082] The comprehensive evaluation calculation module is used to perform fuzzy comprehensive evaluation calculation on the quality control effect of sluice gate detection based on the quantitative results of each evaluation indicator and the weight assignment results of the indicator system, and to judge the level according to the preset level classification table.

[0083] The following detailed implementation examples of the comprehensive evaluation method for the quality control effect of sluice gate inspection involved in this invention, with reference to the accompanying drawings, are described in detail.

[0084] The comprehensive evaluation method for the quality control effect of sluice gate inspection disclosed in this embodiment includes the following steps: (1) comprehensive collection of historical inspection data; (2) establishment of a comprehensive evaluation index system; (3) quantitative analysis of evaluation indicators; (4) weight assignment of evaluation indicators; and (5) execution of fuzzy comprehensive evaluation calculation. The method described above is used to conduct a comprehensive evaluation of the quality control effect of parallel inspections by the supervision unit for 22 sluice gates in the Yellow River downstream diversion culvert reconstruction project (Shandong section).

[0085] First, each indicator is evaluated sub-indicators based on the established indicator evaluation system:

[0086] (1) Coverage of testing items

[0087] Based on the statistical ranking and comparison of the coverage rates of various culvert inspection items, the classification of each culvert using inspection item coverage rate as the evaluation indicator can be derived, as shown in Table 2. The 22 culverts were classified into four levels: Excellent, Good, Pass, and Poor. An equal distribution method was used to classify the different culvert levels: culverts ranked 1-5 were classified as "Excellent," those ranked 6-10 as "Good," those ranked 11-15 as "Pass," and the remaining culverts as "Poor."

[0088]

[0089] (2) Coverage of detection indicators

[0090] Based on the statistical ranking and comparison of the coverage rates of various culvert inspection indicators, the classification of each culvert based on the coverage rate of inspection items can be derived, as shown in Table 3. Consistent with the above classification method, the 22 culverts are divided into 4 levels according to the coverage rate of inspection indicators.

[0091]

[0092] (3) Detection frequency compliance rate

[0093] The quality inspection of each culvert involves multiple testing items, including earthwork filling, concrete test blocks, and reinforcement. The "Code for Construction Supervision of Water Conservancy Projects" (SL 288-2014) stipulates that: the number of concrete samples tested in parallel should be no less than 3% of the contractor's total testing quantity, with at least one set of samples taken for each grade of concrete in important locations; the number of earthwork samples should be no less than 5% of the contractor's total testing quantity, with at least three sets taken for important locations. The "Standard for Acceptance of Construction Quality of Unit Projects in Water Conservancy and Hydropower Engineering, Part 1: Earthwork Engineering" (SL 631.1-2025) also stipulates the following regarding the compaction quality of earthwork filling: the number of parallel inspections by the supervision unit should be no less than 5% of the number of self-inspections by the construction unit. Furthermore, the "Standard for Acceptance and Evaluation of Construction Quality of Unit Projects in Water Conservancy and Hydropower Engineering" (SL 631.1-2025~631.4-2025) also specifies the frequency of parallel testing required by the supervision unit for different testing indicators.

[0094] Therefore, the evaluation of the compliance rate of different culvert and gate testing frequencies involves different testing items. Since the specifications clearly stipulate the testing frequency of earthwork filling and concrete samples by the supervision unit, the compliance rate of parallel testing of earthwork filling and concrete samples is reflected by judging whether the parallel testing frequency of the supervision unit meets the specified requirements.

[0095] In this embodiment, since the frequency of parallel testing of earthwork filling and concrete compressive strength of each culvert by the supervision unit meets the requirements, the culvert is classified as excellent based on this indicator.

[0096] (4) Pass rate of test indicators

[0097] By extracting, organizing, and analyzing the test data of each culvert and gate, it can be concluded that the test data of each culvert and gate meet the design standards and specifications. Therefore, when classifying the grades based on the pass rate of the test indicators, each culvert and gate is classified as excellent.

[0098] (5) Coefficient of variation of test data

[0099] When the sample size of the test data is small, the analysis results using the entropy weight method or deviation coefficient analysis model will be greatly affected by the variability of a single sample, making it difficult to reflect the true characteristics of the sample. Therefore, it is necessary to select test indicators with a larger sample size for entropy weight method quantitative analysis. Since the frequency of testing for indicators such as earthwork compaction, concrete test block compressive strength, and steel reinforcement physical properties of each culvert is relatively high, and the sample size of the test data is greater than 10, three test indicators—earthwork compaction, concrete test block compressive strength, and steel reinforcement tensile strength—are selected for entropy weight method quantitative analysis, and each culvert is classified into different grades based on the obtained coefficient of variation.

[0100] Table 4 shows the results of sorting and classifying the weight coefficients of each culvert and gate after analyzing the data from different detection indicators.

[0101]

[0102] (6) Deviation coefficient of test data

[0103] Similar to the calculation of the "coefficient of variation", three test indicators, namely earthwork compaction degree, concrete test block compressive strength and steel bar tensile strength, were selected for deviation coefficient analysis. Based on the deviation coefficients obtained from the analysis, each culvert was classified into different grades.

[0104] Table 5 shows the results of sorting and classifying the deviation coefficients of each culvert and gate after analyzing the test data using different test indicators as samples.

[0105]

[0106] (7) Coverage of the project area

[0107] Five experts (with associate senior or higher professional titles) were randomly selected to review the parallel inspection reports of each culvert and gate and simulate the coverage of their engineering parts. The simulation scoring results are shown in Table 6, and the scores correspond one-to-one with the grade classification.

[0108]

[0109] (8) The rationality of the testing plan and its implementation

[0110] Five experts (with associate senior or higher professional titles) were randomly selected to review the parallel inspection reports of each culvert and to simulate scoring the rationality of their inspection plans. The simulated scoring results are shown in Table 7, with scores corresponding to grade classifications.

[0111]

[0112] Next, a comprehensive evaluation calculation will be performed based on the evaluation results of the sub-indicators. Taking the Xinxiezhai Sluice Gate as an example, a fuzzy comprehensive evaluation calculation will be conducted.

[0113] First, construct the membership matrix. The membership matrix is ​​represented as:

[0114]

[0115] The membership matrix is ​​derived from the hierarchical classification comprehensive statistical matrix. The hierarchical classification comprehensive statistical matrix is ​​expressed as:

[0116]

[0117] The comprehensive statistical matrix for grade classification consists of 8 rows and 4 columns, with each row corresponding to an evaluation indicator and each column corresponding to a grade.

[0118] Based on the statistical results of the grading of evaluation index 1 (coverage of testing items), the Xinxiezhai Sluice Gate is classified as "Good" according to evaluation index 1. .

[0119] According to the statistical results of the grading of evaluation index 2 (coverage rate of detection index), the Xinxiezhai Sluice Gate is classified as "Excellent" based on evaluation index 2. .

[0120] According to the statistical results of the grading of evaluation index 3 (compliance rate of testing frequency), the Xinxiezhai Sluice Gate is classified as "Excellent" based on evaluation index 3-1 (compliance rate of earthwork compaction testing frequency), 3-2 (compliance rate of concrete test block compressive strength testing frequency), and 3-3 (compliance rate of raw material and intermediate product testing frequency). .

[0121] Based on the statistical results of the grading of evaluation index 4 (pass rate of test data), the Xinxiezhai Sluice Gate is classified as "Excellent" according to evaluation index 4. .

[0122] This indicates that, based on the statistical classification of evaluation index 5 (coefficient of variation of test data), the Xinxiezhai Sluice Gate is classified as "poor" according to evaluation index 5-1 (analysis based on earthwork compaction test data), 5-2 (analysis based on concrete test block compressive strength test data), and 5-3 (analysis based on hot-rolled ribbed steel bar tensile strength test data). .

[0123] According to the statistical classification of evaluation index 6 (deviation coefficient of test data), the Xinxiezhai Sluice Gate is classified as "Good" based on 6-1 (analysis based on earthwork compaction test data), "Qualified" based on 6-2 (analysis based on concrete test block compressive strength test data), and "Poor" based on 6-3 (analysis based on hot-rolled ribbed steel bar tensile strength test data). .

[0124] Based on the statistical results of the grading of evaluation index 7 (coverage of engineering parts), the Xinxiezhai Sluice Gate was graded as "Good, Good, Excellent, Excellent, Excellent" according to the evaluations of experts 1 to 5. .

[0125] According to the statistical results of the grading of evaluation index 8 (the rationality of the testing plan and the rationality of its implementation), the Xinxiezhai Sluice Gate was graded as "Good, Good, Excellent, Good, Excellent" based on the evaluations of experts 1 to 5. .

[0126] Therefore, combining S1 to S8, the comprehensive statistical matrix for the classification of the Xinxiezhai sluice gate is as follows:

[0127]

[0128] Membership degree calculation is performed by dividing each element in the comprehensive statistical matrix by the sum of the elements in that row, resulting in the membership degree matrix of Xinxiezhai Gate:

[0129]

[0130] Based on the membership matrix R and the weight vector A, the comprehensive evaluation result vector of Xinxiezhai Gate can be obtained as B = A·R, that is... The product of B and the evaluation vector V is the final result of the fuzzy comprehensive evaluation. The final score of the fuzzy comprehensive evaluation of the Xinxiezhai Sluice Gate is 91.25, and the final grade is rated as good.

[0131] Similarly, sub-indicators can be used to evaluate the remaining culverts and gates, and a membership matrix can be established. Then, the quality control effectiveness of the inspection for each culvert and gate project can be assessed. The evaluation results are as follows: Figure 3 As shown. Based on the comprehensive scores of each culvert, the comprehensive rating results of each culvert can be obtained. The comprehensive rating of Mazhazi Sluice Gate is excellent; the comprehensive rating of 14 culverts including Xinxiezhai Sluice Gate, Gaocun Sluice Gate, Yangji Sluice Gate, Guolina Sluice Gate, Taochengpu Sluice Gate, and Weishan Sluice Gate is good; and the comprehensive rating of 7 culverts including Jiucheng Sluice Gate, Zhangqiao Sluice Gate, and Bailongwan Sluice Gate is qualified.

[0132] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A comprehensive evaluation method for the quality control effect of sluice gate inspection, characterized in that, Includes the following steps: Step 1: Systematically organize the testing data throughout the various stages of the project construction, including raw material inspection, intermediate product quality control, and the quality of the project entity, and complete the comprehensive collection of historical testing data; Step 2: Select the fuzzy comprehensive evaluation algorithm to evaluate the quality control effect of sluice gate inspection and establish a comprehensive evaluation index system; Step 3: Analyze the collected test data and quantify each evaluation index; Step 4: Employ an expert scoring strategy to quantify and assign values ​​to the weights of each indicator in the established comprehensive evaluation index system; Step 5: Based on the quantitative results of each evaluation indicator and the weight assignment results of the indicator system, perform fuzzy comprehensive evaluation calculation on the quality control effect of sluice gate detection, and judge the level according to the preset level classification table.

2. The comprehensive evaluation method for the quality control effect of sluice gate inspection according to claim 1, characterized in that, In step 2, the established comprehensive evaluation index system includes eight evaluation indicators: coverage rate of testing items, coverage rate of testing indicators, compliance rate of testing frequency, pass rate of testing indicators, coefficient of variation of testing data, coefficient of deviation of testing data, coverage of engineering parts, and rationality of the testing plan and implementation.

3. The comprehensive evaluation method for the quality control effect of sluice gate inspection according to claim 2, characterized in that, In step 3, when quantifying the coverage rate of the testing items, the coverage rate of the testing items = (number of completed testing items / total number of testing items required for the sluice gate project) × 100%. The total number of testing items required for the sluice gate project is determined based on the sluice gate project design documents and relevant regulations and specifications. When quantifying the coverage rate of the testing indicators, the coverage rate of the testing indicators = (number of completed testing indicators / total number of testing indicators required for the testing items) × 100%.

4. The comprehensive evaluation method for the quality control effect of sluice gate inspection according to claim 2, characterized in that, In step 3, when quantifying the compliance rate of the testing frequency, the compliance rate of the testing frequency = (actual testing frequency of the testing item / testing frequency required by the procedure and specification of the testing item) × 100%; when quantifying the pass rate of the testing indicators, the pass rate of the testing indicators = (number of qualified testing indicators / total number of testing indicators) × 100%.

5. The comprehensive evaluation method for the quality control effect of sluice gate inspection according to claim 2, characterized in that, In step 3, when quantifying the coefficient of variation of the test data, the compaction degree of earthwork filling, the compressive strength of concrete specimens, and the tensile strength of hot-rolled ribbed steel bars are selected for analysis, and their coefficients of variation are calculated separately. These coefficients of variation are obtained using the entropy weight method. In step 3, when quantifying the deviation coefficient of the test data, the compaction degree of earthwork filling, the compressive strength of concrete specimens, and the tensile strength of hot-rolled ribbed steel bars are selected for analysis, and their deviation coefficients are calculated separately. The formula for calculating the deviation coefficient of a certain test index is as follows: ,in, This represents the average value of the test data. This represents the design value.

6. The comprehensive evaluation method for the quality control effect of sluice gate inspection according to claim 2, characterized in that, In step 3, when quantifying the coverage of the engineering parts and the rationality of the testing plan and implementation, several experts with associate senior or higher professional titles were randomly selected to review all the testing reports of each sluice gate and simulate scoring the two evaluation indicators. The scores corresponded one-to-one with the grade classification.

7. The comprehensive evaluation method for the quality control effect of sluice gate inspection according to claim 1, characterized in that, In step 4, the expert scoring strategy is as follows: First, a weighted consultation table is created. Then, multiple experts are selected to score each evaluation indicator on a 100-point scale. The average score of each evaluation indicator is calculated, and the weight percentage of each evaluation indicator is calculated accordingly. The sum of the weights of all indicators is 1.

8. The comprehensive evaluation method for the quality control effect of sluice gate inspection according to claim 1, characterized in that, Step 5, the fuzzy comprehensive evaluation calculation specifically includes: Construct a fuzzy comprehensive evaluation index system, using index domains express; Constructing weight vectors , where a1, a2, ..., a8 are the weights of the eight evaluation indicators involved in the evaluation, and their sum is 1; By establishing membership functions, a framework can be constructed from the index domain. To the evaluation set Membership matrix ,in Indicate each indicator Belonging to various evaluation levels Membership degree; The comprehensive evaluation result vector B = A·R is obtained, and the product of the comprehensive evaluation result vector B and the evaluation vector V is taken as the final score.

9. The comprehensive evaluation method for the quality control effect of sluice gate inspection according to claim 8, characterized in that, In the preset grading table in step 5, the correspondence between the final score and the grade assessment is as follows: Excellent corresponds to [95, 100] points; Good corresponds to [85, 95) points; Pass corresponds to [75, 85) points; Poor corresponds to [0, 75) points.

10. A comprehensive evaluation system for the quality control effect of sluice gate inspection, characterized in that, include: The data collection module is used to systematically organize the testing data throughout the various stages of engineering construction, including raw material inspection, intermediate product quality control, and engineering entity quality, and to complete the comprehensive collection of historical testing data. The indicator system construction module is used to select a fuzzy comprehensive evaluation algorithm to evaluate the quality control effect of sluice gate detection and establish a comprehensive evaluation indicator system. The indicator quantification module is used to analyze the collected detection data and quantify and calculate each evaluation indicator. The weight assignment module is used to quantify and assign weights to each indicator in the established comprehensive evaluation index system using an expert scoring strategy. The comprehensive evaluation calculation module is used to perform fuzzy comprehensive evaluation calculation on the quality control effect of sluice gate detection based on the quantitative results of each evaluation indicator and the weight assignment results of the indicator system, and to judge the level according to the preset level classification table.

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