Engineering quality evaluation method suitable for unmanned asphalt pavement construction

Abstract

The invention relates to the technical field of asphalt pavement construction, and discloses a project quality evaluation method suitable for unmanned asphalt pavement construction, which comprises the following steps: acquiring informatization monitoring parameters of an asphalt mixture production and transportation link, operation parameters of an unmanned paving and rolling link and quality parameters of a post-construction detection link in real time; based on an expert questionnaire survey method and an analytic hierarchy process, weights of three links in the comprehensive quality score and key index weights corresponding to the three links are determined; grading and scoring the key indexes of the three links to obtain grading and scoring results of the key indexes; according to the grading scoring result of each key index and the weight of the key index, carrying out weighted calculation to obtain the quality score of each link; according to the quality score of each link and the weight of each link, performing weighted calculation to obtain a comprehensive quality score; and quality grades are divided according to the comprehensive quality score, and an unmanned asphalt pavement construction quality three-level evaluation system is constructed.

Description

Technical Field

[0001] This invention relates to the field of asphalt pavement construction technology, and specifically to an engineering quality evaluation method applicable to unmanned asphalt pavement construction. Background Technology

[0002] Currently, my country has made significant progress in the fields of intelligent construction technology for asphalt pavement, such as information-based monitoring of asphalt and asphalt mixture production and transportation, unmanned paving and compaction of asphalt pavement, and rapid non-destructive testing of asphalt pavement.

[0003] Compared to traditional asphalt pavement construction techniques that rely on manual command and operation, unmanned asphalt pavement construction technology boasts a higher degree of automation and intelligence, resulting in differences in pavement quality compared to traditional methods. Furthermore, unmanned construction machinery such as intelligent monitoring systems for asphalt mixing plants, unmanned pavers, and unmanned rollers automatically generate and output numerous process parameters during construction, making it difficult to evaluate the quality of unmanned asphalt pavement construction solely through post-construction testing. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention proposes an engineering quality evaluation method suitable for unmanned asphalt pavement construction, thereby solving the aforementioned technical problems.

[0005] A method for evaluating the engineering quality of unmanned asphalt pavement construction, comprising: Real-time data collection of information monitoring parameters for asphalt mixture production and transportation, operational parameters for unmanned paving and compaction, and quality parameters for post-construction testing; The weights of the three stages in the comprehensive quality score and the weights of the key indicators corresponding to each stage were determined based on the expert questionnaire survey method and the analytic hierarchy process. The key indicators of the three stages are scored separately and graded to obtain the graded scores of each key indicator. Based on the graded scoring results of each key indicator and the weight of each key indicator, the quality score of each stage is calculated by weighted calculation. The overall quality score is calculated by weighting the scores of each stage and the weight of each stage. A three-level evaluation system for the quality of unmanned asphalt pavement construction was established by classifying quality levels based on comprehensive quality scores.

[0006] Furthermore, the three-level evaluation system includes: The first-level evaluation layer consists of graded scoring of key indicators in the asphalt mixture information monitoring, unmanned paving and compaction and post-construction testing stages. The second-level evaluation layer consists of quality scores for each stage of the asphalt mixture information monitoring, unmanned paving and compaction, and post-construction testing. The third-level evaluation layer is a comprehensive quality score that integrates three aspects: information monitoring of asphalt mixtures, unmanned paving and compaction, and post-construction testing. The quality level is then determined based on the comprehensive quality score.

[0007] Furthermore, the weight allocation for each stage is as follows: The weight of the unmanned paving and compaction process is 0.35-0.45; The weight of the information-based monitoring link for asphalt mixtures is 0.30-0.35; The weight of the post-process testing stage is 0.20-0.30.

[0008] Furthermore, the key indicators for the information-based monitoring of asphalt mixtures include: Oil-stone ratio, target sieve aperture passing rate, mixing temperature, and transportation time.

[0009] Furthermore, the key indicators for the unmanned paving and compaction process include: The factors to consider include paving uniformity, paving temperature, compaction temperature, paving thickness, number of compaction passes, and paving and compaction speed.

[0010] Furthermore, the key indicators for the post-process inspection stage include: Road surface compaction, key sieve passing rate of gradation, asphalt content, permeability coefficient, road surface thickness and smoothness.

[0011] Furthermore, the key indicators of the three stages are graded and scored separately, including: Set three threshold ranges: fully compliant, borderline compliant, and non-compliant; The highest score is given to the fully compliant range, the medium score is given to the borderline compliant range, and the zero score is given to the non-compliant range.

[0012] Furthermore, the formula for calculating the comprehensive quality score is: P = W1·P1 + W2·P2 + W3·P3 Among them, W1, W2, and W3 are the weights of unmanned paving and compaction, asphalt mixture monitoring, and post-construction testing, respectively, and P1, P2, and P3 are the scores of each stage.

[0013] Furthermore, the quality level includes: Excellent level: Overall score ≥ first threshold; Good level: First threshold > Overall score ≥ Second threshold; Pass / Fail Level: Second threshold > Overall score ≥ Third threshold; Unqualified: Overall score < third threshold.

[0014] Furthermore, it also includes: The weights of each stage are dynamically adjusted based on construction process data.

[0015] The invention employing the above technical solution has the following advantages: This invention constructs a three-level evaluation system by conducting on-site inspections of unmanned asphalt pavement construction sections and combining monitoring data from asphalt mixture production, transportation, and unmanned paving and compaction. This system integrates key indicators from three stages: asphalt mixture information monitoring, unmanned paving and compaction, and post-construction inspection. It also includes quality scores for each stage and a comprehensive quality score. Furthermore, the algorithm for each level of evaluation indicators is determined, thereby better serving the process control of unmanned asphalt pavement construction quality and guiding the acceptance of pavement engineering quality. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention, the accompanying drawings used in the specific embodiments will be briefly described below. In all the drawings, the elements or parts are not necessarily drawn to scale.

[0017] Figure 1 This is a general framework diagram of an engineering quality evaluation method applicable to unmanned asphalt pavement construction according to the present invention; Figure 2 This is a questionnaire display diagram (I) showing the importance of each indicator in the engineering quality evaluation method applicable to unmanned asphalt pavement construction of the present invention. Figure 3 This is a questionnaire display diagram (II) showing the importance of each indicator in the engineering quality evaluation method applicable to unmanned asphalt pavement construction of the present invention. Figure 4 This is a questionnaire display of the importance level evaluation of each link in the engineering quality evaluation method applicable to unmanned asphalt pavement construction of the present invention; Figure 5 This is a diagram of a three-level evaluation system in an engineering quality evaluation method applicable to unmanned asphalt pavement construction according to the present invention; Figure 6 This is a structural diagram of an engineering quality evaluation device suitable for unmanned asphalt pavement construction according to the present invention. Detailed Implementation

[0018] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. These embodiments are merely illustrative of the technical solution of the present invention and are therefore intended to limit the scope of protection of the present invention.

[0019] like Figures 1-6 As shown, the present invention provides an engineering quality evaluation method applicable to unmanned asphalt pavement construction, comprising: Step S01: Real-time collection of information monitoring parameters for asphalt mixture production and transportation, operational parameters for unmanned paving and compaction, and quality parameters for post-work inspection. Step S02: Determine the weights of the three stages in the comprehensive quality score and the weights of the key indicators corresponding to each stage based on the expert questionnaire survey method and the analytic hierarchy process. Step S03: Grade and score the key indicators of the three stages respectively to obtain the grade and score results of each key indicator. Step S04: Based on the graded scoring results of each key indicator and the weight of each key indicator, calculate the quality score of each stage by weighting. Step S05: Calculate the overall quality score by weighting the scores of each stage and the weight of each stage. Step S06: Based on the comprehensive quality score, the quality levels are divided, and a three-level evaluation system for the construction quality of unmanned asphalt pavement is constructed.

[0020] Specifically, by conducting on-site inspections of unmanned asphalt pavement construction sections and combining monitoring data from asphalt mixture production, transportation, and unmanned paving and compaction, a three-level evaluation system was constructed. This system integrates key indicators from three stages: asphalt mixture information monitoring, unmanned paving and compaction, and post-construction inspection. It also includes quality scores for each stage and a comprehensive quality score. Furthermore, the algorithms for each level of evaluation indicators were determined. This system better serves the process control of unmanned asphalt pavement construction quality and guides the acceptance of pavement engineering quality.

[0021] In this embodiment, the three-level evaluation system includes: The first-level evaluation layer consists of graded scoring of key indicators in the asphalt mixture information monitoring, unmanned paving and compaction and post-construction testing stages. The second-level evaluation layer consists of quality scores for each stage of the asphalt mixture information monitoring, unmanned paving and compaction, and post-construction testing. The third-level evaluation layer is a comprehensive quality score that integrates three aspects: information monitoring of asphalt mixtures, unmanned paving and compaction, and post-construction testing. The quality level is then determined based on the comprehensive quality score.

[0022] In this embodiment, the weight allocation for each stage is as follows: The weight of the unmanned paving and compaction process is 0.35-0.45; The weight of the information-based monitoring link for asphalt mixtures is 0.30-0.35; The weight of the post-process testing stage is 0.20-0.30.

[0023] In this embodiment, the key indicators for the information-based monitoring of asphalt mixtures include: Oil-stone ratio, target sieve aperture passing rate, mixing temperature, and transportation time.

[0024] In this embodiment, the key indicators for the unmanned paving and compaction process include: The factors to consider include paving uniformity, paving temperature, compaction temperature, paving thickness, number of compaction passes, and paving and compaction speed.

[0025] In this embodiment, the key indicators for post-process testing include: Road surface compaction, key sieve passing rate of gradation, asphalt content, permeability coefficient, road surface thickness and smoothness.

[0026] Specifically, due to the large number of parameters involved in asphalt pavement construction, an expert survey form was developed by reviewing literature and expert experience, and in accordance with the provisions of the "Technical Specification for Construction of Highway Asphalt Pavement" (JTG F40). This form listed the indicators that affect the quality of unmanned asphalt pavement construction projects. Experts selected from these indicators, and based on the selection results, the key indicators with a significant impact on the quality of unmanned asphalt pavement construction projects were determined. The format of the expert survey form is shown in Table 1-1.

[0027] Table 1-1 Expert Opinion Consultation Form Through expert questionnaire feedback, we can understand the importance of different indicators in the three stages of asphalt mixture information monitoring, unmanned paving and compaction, and post-construction testing on the quality of unmanned asphalt pavement construction projects.

[0028] Based on the statistical analysis of questionnaires regarding the importance of various indicators in unmanned asphalt pavement construction, this survey collected 56 expert survey responses from construction units, design units, supervision units, construction units, and research units. The importance of each indicator was determined by its frequency of selection; a higher frequency indicates a more important indicator, which served as the basis for identifying key indicators.

[0029] Determination of Key Indicators and Their Weights in the Information Monitoring of Asphalt Mixtures Based on the questionnaire survey results, the importance of each indicator in the information-based monitoring of asphalt mixtures is as follows.

[0030] Table 1-2 Statistical Analysis of the Importance of Various Indicators in the Information-Based Monitoring of Asphalt Mixtures According to the expert opinion survey, the oil-stone ratio is the most important, followed by the 0.075mm throughput, 4.75mm throughput, 2.36mm throughput, transportation time, and mixing temperature.

[0031] Therefore, the key indicators selected for the information monitoring process are oil-stone ratio, 0.075mm throughput, 2.36mm throughput, 4.75mm throughput, transportation time, and mixing temperature.

[0032] The weights of key indicators in the information monitoring process were calculated using the Analytic Hierarchy Process (AHP). Based on the AHP process and approach, the expert opinion survey results were transformed into the judgment matrix required by the AHP and plotted in the table below.

[0033] Table 1-3 Key Indicators of Asphalt Mixture Information Monitoring Based on the key indicator indices of the asphalt mixture information monitoring process, the AHP (Analog-HP) results were calculated.

[0034] Table 1-4 Results of AHP Hierarchical Analysis of Asphalt Mixture Information Monitoring Links Based on the AHP analytic hierarchy process results, a consistency check was performed. The corresponding RI value was found to be 1.25 in the RI table, and the CR value can be calculated from CI and RI.

[0035] Table 1-5 Consistency Inspection Results of Asphalt Mixture Information Monitoring Link In summary, the key indicators for quality evaluation in the information-based monitoring of asphalt mixtures are shown in Table 1-6.

[0036] Table 1-6 Key Indicators, Control Scope, and Weights of Information-Based Monitoring of Asphalt Mixtures 2) Determination of key indicators and their weights in unmanned paving and compaction processes Based on the questionnaire survey results, the importance of each indicator in the unmanned paving and compaction process is as follows.

[0037] Table 1-7 Statistics on the Importance of Various Indicators in the Unmanned Paving and Compaction Process The expert opinion survey shows that paving uniformity is the most important indicator, followed by compaction temperature, paving thickness, number of compaction passes, paving speed, and compaction speed. Therefore, the key indicators for unmanned paving and compaction are selected as paving uniformity, compaction temperature, paving thickness, number of compaction passes, paving speed, and compaction speed.

[0038] The weights of key indicators in the unmanned paving and compaction process were calculated using the analytic hierarchy process (AHP). Based on the AHP process and approach, the results of the expert opinion survey were transformed into the judgment matrix required by the AHP and plotted in the table below.

[0039] Table 1-8 Key Indicators of Unmanned Paving and Compaction Process Based on the key indexes of the unmanned paving and compaction process, the results of the AHP hierarchical analysis were calculated.

[0040] Table 1-9 Results of AHP Hierarchical Analysis of Unmanned Paving and Compaction Process Based on the AHP (Analog-Philosophical Hierarchy Process) results, a consistency check was performed. The corresponding RI value was found to be 1341 in the RI table, and the CR value can be calculated from CI and RI.

[0041] Table 1-10 Consistency Inspection Results of Unmanned Paving and Compaction Process In summary, the key indicators for unmanned paving and compaction are shown in Table 1-11.

[0042] Table 1-11 Key Indicators, Control Scope, and Weights of Unmanned Paving and Compaction Processes 3) Determination of key indicators and their weights in post-process testing Based on the questionnaire survey results, the importance of each indicator in the post-construction testing process is as follows.

[0043] Table 1-12 Statistics on the Importance of Various Indicators in Post-Production Testing The expert opinion survey shows that pavement compaction is the most important indicator, followed by gradation, asphalt content, permeability coefficient, pavement thickness, and pavement smoothness. Therefore, the key indicators selected for post-construction testing are pavement compaction, gradation, asphalt content, permeability coefficient, pavement thickness, and pavement smoothness.

[0044] The weights of key indicators in the post-process testing stage were calculated using the analytic hierarchy process (AHP). Based on the AHP process and approach, the results of the expert opinion survey were transformed into the judgment matrix required by the AHP and plotted in the table below.

[0045] Table 1-13 Key Indicators for Post-Production Testing The results of the AHP (Analog-Philosophical Analysis) were calculated based on the key indicator indices of the post-construction testing process.

[0046] Table 1-14 Results of AHP Hierarchical Analysis of Post-Production Inspection Process Based on the AHP analytic hierarchy process results, a consistency check was performed. The corresponding RI value was found to be 1.25 in the RI table, and the CR value can be calculated from CI and RI.

[0047] Table 1-15 Consistency Inspection Results of Post-Production Testing In summary, the key indicators for post-process testing are shown in Table 1-16.

[0048] Table 1-16 Key Indicators, Control Scope, and Weights for Post-Process Testing Note: In the gradation, the main focus is on the sieve aperture passing rates of 0.075mm, 2.36mm, and 4.75mm, and their weights are allocated according to the principle of equal distribution.

[0049] 4) Determination of the weights of each stage in unmanned asphalt pavement construction To understand the impact of each stage in unmanned asphalt pavement construction on the quality of the finished pavement, an expert survey was developed, listing the stages that affect the quality of unmanned asphalt pavement construction projects. Experts scored the selected stages, and the importance of each stage in unmanned asphalt pavement construction was determined based on the scoring results.

[0050] Based on the statistical analysis of questionnaires regarding the importance levels of each stage of unmanned asphalt pavement construction, this survey collected 49 expert feedback responses from construction units, design units, supervision units, construction units, and research units. Based on these expert feedback responses, a study was conducted on the quality evaluation of unmanned asphalt pavement construction.

[0051] Based on the results of the questionnaire survey, the importance levels of each stage in unmanned asphalt pavement construction are evaluated as follows.

[0052] Table 1-17 Statistics on the Importance of Each Stage in Unmanned Asphalt Pavement Construction The expert opinion survey revealed that the unmanned paving and compaction stage had the highest importance level, post-construction testing had the lowest, and the information-based monitoring of asphalt mixtures had a slightly higher importance level than post-construction testing. The weights of each stage were calculated using the Analytic Hierarchy Process (AHP). Based on the AHP process and methodology, the expert opinion survey results were transformed into the judgment matrix required for AHP, and the matrix is ​​presented in the table below.

[0053] Table 1-18 Index of Each Stage of Unmanned Asphalt Pavement Construction The AHP (Analog-HP) analysis results were calculated based on the indices of each stage of unmanned asphalt pavement construction.

[0054] Table 1-19 AHP Hierarchical Analysis Results for Each Stage of Unmanned Asphalt Pavement Construction Based on the AHP (Analog-Philosophical Hierarchy Process) results, a consistency check was performed. The corresponding RI value of 0.58 was found in the RI table, and the CR value can be calculated from CI and RI.

[0055] Table 1-20 Consistency Inspection Results of Each Stage in Unmanned Asphalt Pavement Construction In summary, post-construction testing accounts for 0.26% of the weight, information-based monitoring of asphalt mixtures accounts for 0.33%, and unmanned paving and compaction accounts for 0.41%.

[0056] In this embodiment, the key indicators of the three stages are graded and scored, including: Set three threshold ranges: fully compliant, borderline compliant, and non-compliant; The highest score is given to the fully compliant range, the medium score is given to the borderline compliant range, and the zero score is given to the non-compliant range.

[0057] In this embodiment, the comprehensive quality score is calculated as follows: P = W1·P1 + W2·P2 + W3·P3 Among them, W1, W2, and W3 are the weights of unmanned paving and compaction, asphalt mixture monitoring, and post-construction testing, respectively, and P1, P2, and P3 are the scores of each stage.

[0058] In this embodiment, the quality level includes: Excellent level: Overall score ≥ first threshold; Good level: First threshold > Overall score ≥ Second threshold; Pass / Fail Level: Second threshold > Overall score ≥ Third threshold; Unqualified: Overall score < third threshold.

[0059] In this embodiment, it also includes: The weights of each stage are dynamically adjusted based on construction process data.

[0060] Specifically, the following table shows the key indicator scoring rules for the three stages of asphalt mixture information monitoring, unmanned paving and compaction, and post-construction testing. To facilitate calculation and improve differentiation, the scoring standards for each indicator are optimized and graded based on the specifications and construction experience. Full marks are awarded if the requirements are fully met, and zero marks are awarded if some indicators exceed the range. Within the allowable range, the classification is divided into two levels: good and pass, with scores of 85 and 60 points respectively.

[0061] Key Indicator Scoring in the Information Monitoring of Asphalt Mixtures Table 2-1 Key Indicator Scoring Rules for Asphalt Mixture Information Monitoring Scoring of key indicators in the information-based monitoring of asphalt mixtures: P 油 = P 0.075 = P 2.36 = P 4.75 = P 运 = P 拌 = Note: P1, P2, ..., P n Let n be the quality score for each indicator, and n be the total data collected for each indicator.

[0062] The final calculation of the quality score P for the information-based monitoring of asphalt mixtures 沥青混合料 : P 沥青混合料 =0.29×P 油 +0.24×P 0.075 +0.19×P 4.75 +0.13×P 2.36 +0.09×P 运 +0.06×P 拌 2) Key indicator scoring for unmanned paving and compaction process Table 2-2 Key Indicator Scoring Rules for Unmanned Paving and Compaction Processes Scoring of key indicators for unmanned paving and compaction: P 摊（均） = P 碾（温） = P 摊（温） = P 摊（厚） = P 碾（遍） = P 摊（速） = P 碾（速） = Note: P1, P2, ..., P n Let n be the quality score for each indicator, and n be the total data collected for each indicator.

[0063] The final calculation of the quality score P for the unmanned paving and compaction process 摊铺碾压 : P 摊铺碾压 =0.24×P 摊（均） +0.17×P 碾（温） +0.16×P 摊（温） +0.16×P 摊（厚） +0.10×P 碾（遍） +0.09×P 摊（速） +0.08×P 碾（速） 3) Scoring of key indicators in post-construction testing Table 2-3 Key Indicator Scoring Rules for Post-Production Testing Scoring of key indicators in the post-construction inspection process: P 压 = P 0.075 = P 4.75 = P 2.36 = P 沥 = P 渗 = P 厚 = P 平 = Note: P1, P2, ..., P n Let n be the quality score for each indicator, and n be the total data collected for each indicator.

[0064] Finally, calculate the quality score P for the post-work inspection stage. 工后检测 : P 工后检测 =0.29×P 压 +0.08×P 0.075 +0.08×P 4.75 +0.08×P 2.36 +0.19×P 沥 +0.15×P 渗 +0.09×P 厚 +0.05×P 平 Based on the quality scores and weighting of the three stages of asphalt mixture information monitoring, unmanned paving and compaction, and post-construction testing, the final quality score of the unmanned asphalt pavement construction project is calculated.

[0065] Quality rating of unmanned asphalt pavement construction project: P = 0.41 × P 摊铺碾压 +0.33×P 沥青混合料 +0.26×P 工后检测 Ultimately, an engineering quality evaluation system for unmanned asphalt pavement construction was obtained.

[0066] After construction, a quantitative analysis of the quality of unmanned asphalt pavement construction was conducted based on the evaluation system, and the quality score of specific construction sections was calculated. Based on current industry standards and relevant requirements, combined with collected historical monitoring data and project experience, the final grading standards for the quality score of unmanned asphalt pavement construction were determined, as shown in Table 2-4.

[0067] Table 2-4 Quality Grading Standards for Unmanned Asphalt Pavement Construction Projects Outlier handling Perform data cleaning before scoring key metrics: Set a scrolling time window (e.g., the last 50 data sets). Calculate the mean μ and standard deviation σ of the dataset. Remove outlier samples that satisfy |x_i - μ|>3σ Specifically, |x_i - μ|>K·σ; Where: x_i is the real-time monitoring value of a single key indicator, and K is the strictness coefficient (default 3.0). A device calibration alarm is triggered when the rejection rate exceeds 20%. Note: This method is only applicable to high-frequency monitoring indicators with a sample size of ≥10. Example: As shown in Table 2-1 before the oil-stone ratio score: Taking 100 consecutive sets of test data from the same day, we calculated μ=4.85% and σ=0.12%. Exclusion range: Outliers <4.49% or >5.21% The compliance rate is calculated based on valid data.

[0068] Dynamic weight adjustment The weight is updated after each 1km construction section is completed: W_i' = W_i × K_i Where W_i is the initial stage weight. K_i is a dynamic adjustment coefficient, K_i = max(0.6, 1 - α·r) r = Failure rate (number of samples exceeding the limit / total number of samples) α = Sensitivity coefficient (0.5-1.0) The lower limit of K_i is set to 0.6 to avoid excessive weight decay.

[0069] Table 2-5 Reference Values ​​for Dynamic Adjustment Coefficients Note: Actual values ​​are calculated using a formula; this table is for quick reference only. Self-learning optimization When the road segment score P≥95, record the dynamic weight configuration of the road segment; When constructing new road sections, priority should be given to using historically successful configurations with similar working conditions.

[0070] In other embodiments, a computer device is provided, including a processor, an input device, an output device, and a memory, which are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to invoke the program instructions to execute steps of an engineering quality evaluation method suitable for unmanned asphalt pavement construction, as described in any of the preceding embodiments.

[0071] In other embodiments, a computationally readable storage medium is provided, and a computer program product includes a non-transitory computer-readable storage medium storing a computer program that causes a computer to perform some or all of the steps of any of the engineering quality evaluation methods applicable to unmanned asphalt pavement construction as described in the above method embodiments.

[0072] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0073] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0074] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0075] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0076] Furthermore, the functional units in the various embodiments of the application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software program module.

[0077] If the integrated unit is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0078] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk, or an optical disk, etc.

[0079] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. An engineering quality evaluation method suitable for unmanned asphalt pavement construction, characterized by, The application relates to a three-level evaluation system for the quality of unmanned asphalt pavement construction. The system comprises the following steps: Real-time acquisition of informationized monitoring parameters of an asphalt mixture production and transportation link, operation parameters of an unmanned paving and rolling link and quality parameters of a post-construction detection link; Determination of the weights of the three links and the weights of the respective key indicators of the three links based on expert questionnaire survey and analytic hierarchy process; Grading scoring of the key indicators of the three links to obtain grading scoring results of the key indicators; Weighted calculation of the quality scores of the links according to the grading scoring results of the key indicators and the weights of the key indicators; Weighted calculation of the comprehensive quality score according to the quality scores of the links and the weights of the links; 2. The method for evaluating the quality of works according to claim 1, characterized in that, Division of quality grades according to the comprehensive quality score to construct the three-level evaluation system for the quality of unmanned asphalt pavement construction. The three-level evaluation system comprises the following steps: The first-level evaluation layer is grading scoring of the respective key indicators of the informationized monitoring link, the unmanned paving and rolling link and the post-construction detection link; The second-level evaluation layer is the quality scores of the informationized monitoring link, the unmanned paving and rolling link and the post-construction detection link; 3. The method for evaluating the quality of works according to claim 1, characterized in that, The third-level evaluation layer is the comprehensive quality score of the informationized monitoring link, the unmanned paving and rolling link and the post-construction detection link, and the quality grades are divided according to the comprehensive quality score. The weight distribution of the links is as follows: The weight of the unmanned paving and rolling link is 0.35-0.45; The weight of the informationized monitoring link of the asphalt mixture is 0.30-0.35; 4. The method for evaluating the quality of works according to claim 2, characterized in that, The weight of the post-construction detection link is 0.20-0.

30. The key indicators of the informationized monitoring link of the asphalt mixture include the oil-stone ratio, the target sieve aperture passing rate, the mixing temperature and the transportation time.

5. The method for evaluating the quality of works according to claim 2, characterized in that, The key indicators of the unmanned paving and rolling link include the paving uniformity, the paving temperature, the rolling temperature, the paving thickness, the rolling frequency and the paving and rolling speed. The key indicators of the post-construction detection link include the pavement compactness, the key sieve aperture passing rate of the gradation, the asphalt dosage, the water permeation coefficient, the pavement thickness and the flatness.

6. The method for evaluating the quality of works according to claim 2, characterized in that, The grading scoring of the key indicators of the three links comprises the following steps: Setting of three threshold value intervals of complete compliance, critical compliance and non-compliance; 7. The method for evaluating the quality of works according to claim 1, characterized in that, The complete compliance interval is given the highest score, the critical compliance interval is given a medium score and the non-compliance interval is given zero. The comprehensive quality score calculation formula is P=W1.P1+W2.P2+W3.P3, wherein W1, W2 and W3 are the weights of the unmanned paving and rolling link, the informationized monitoring link of the asphalt mixture and the post-construction detection link respectively, and P1, P2 and P3 are the scores of the links. The quality grades include the following steps:

8. The method for evaluating the quality of works according to claim 1, characterized in that, Excellent grade: the comprehensive score is greater than or equal to the first threshold value; Good grade: the first threshold value is greater than the comprehensive score and the comprehensive score is greater than or equal to the second threshold value; Qualified grade: the second threshold value is greater than the comprehensive score and the comprehensive score is greater than or equal to the third threshold value; 9. The method for evaluating the quality of works according to claim 1, characterized in that, Unqualified grade: the comprehensive score is less than the third threshold value. The application further comprises the following steps: Dynamic adjustment of the weights of the links based on construction process data. ​ ​ 10. The method for evaluating the quality of works according to claim 1, characterized in that, ​ ​

Images