Construction management system and method for asphalt concrete pavement

Abstract

The application discloses a kind of asphalt concrete pavement construction management system and method, it is related to road construction technical field, the application first divides temperature, thickness, compactness and proportioning as each key parameter according to parameter type, divides two kinds of ways of continuous and batch, each key parameter is collected by distributed equipment, then parameter-quality dynamic evaluation model and abnormal parameter influence degree evaluation model are constructed, calculate quality comprehensive index, identify potential defects and associated factors, then combine real-time parameter and quality evaluation result, filter the construction link and parameter needing optimization, finally optimize process, integrate regional information, realize collaborative control and generate construction archives.The scheme significantly improves the uniformity, durability and efficiency of asphalt pavement construction through real-time monitoring, intelligent analysis and closed-loop control, reduces the maintenance cost in later period, and is suitable for high-grade pavement engineering of expressway and municipal road.

Description

Technical Field

[0001] This invention relates to the field of road construction technology, specifically to a construction management system and method for asphalt concrete pavement. Background Technology

[0002] With the high-quality development of transportation infrastructure, over 80% of the newly added highway mileage each year is asphalt pavement, leading to increasingly higher industry requirements for refined, standardized, and intelligent construction. However, current asphalt concrete pavement construction management still generally suffers from problems such as extensive control, low efficiency, and prominent quality risks, making traditional management models inadequate for the development needs of modern engineering construction.

[0003] Existing technologies, such as the invention patent application with publication number CN119044462A, disclose a method for testing and analyzing the construction quality of asphalt concrete pavement. This method extracts joint marking data from the target pavement, and on the one hand, plans each transverse joint area, each longitudinal joint area, and each conventional area, and tests their corresponding smoothness and density to accurately assess the basic construction quality of the target pavement. On the other hand, it analyzes the driving stability of the target pavement under various simulated vehicle conditions at different humidity levels to accurately assess the basic driving quality of the target pavement. In addition, it conducts corresponding temperature adaptability tests on each expansion joint and contraction joint section of the target pavement to assess the environmental adaptability quality of the target pavement. By comprehensively evaluating the basic construction quality, basic driving quality, and environmental adaptability quality of the target pavement, a highly accurate and objective target pavement construction quality test score is output, providing strong support for road construction and maintenance.

[0004] As can be seen from the above solutions, existing asphalt concrete pavement construction management systems and methods generally suffer from insufficient accuracy and lag in the acquisition of key construction parameters. They rely on manual intervention for quality assessment and decision-making, resulting in low efficiency, large errors, and prominent data silos. Furthermore, the coordination between construction planning, resource scheduling, and on-site construction management is disconnected, and there is a lack of intelligent dynamic early warning and automatic process adjustment mechanisms. The regional closed-loop management capability is weak, making it difficult to achieve precise and coordinated management of construction quality, progress, and resources, which can easily lead to problems such as construction quality hazards and project delays. Summary of the Invention

[0005] In view of the above-mentioned technical deficiencies, the purpose of this invention is to provide a construction management system and method for asphalt concrete pavement.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: In the first aspect, the present invention provides a construction management system for asphalt concrete pavement, including the following modules: Acquisition module: for a single construction area, the key parameters are divided into asphalt mixture and pavement temperature parameters, paving layer thickness parameters, pavement compaction parameters and asphalt-aggregate material ratio parameters according to parameter type, and the distributed acquisition device is used to collect the key parameters of the area in real time.

[0007] Analysis module: Used to construct a dual model based on the correlation between key parameters and construction quality. The dual model includes a parameter-quality dynamic evaluation model and an abnormal parameter influence degree evaluation model. It calculates the comprehensive quality index of the construction area and determines whether the construction quality meets the standards.

[0008] Control module: When construction quality fails to meet standards, it extracts construction quality parameters and comprehensive quality index, filters out construction links that need to be optimized and adjusted and their corresponding construction quality parameters, analyzes the deviation of construction quality parameters, and identifies potential construction defects and defect-related factors in the area.

[0009] Verification module: Used to monitor construction quality parameters again after adjustment and determine whether the construction quality meets the standards. For construction links that do not meet the standards and their corresponding construction quality parameters, the process is optimized.

[0010] In a second aspect, the present invention provides a construction management method for asphalt concrete pavement, comprising the following steps: S1, for a single construction area, the key parameters are divided into asphalt mixture and pavement temperature parameters, paving layer thickness parameters, pavement compaction parameters, and asphalt-aggregate material ratio parameters according to parameter type, and the key parameters of the area are collected in real time using distributed acquisition equipment.

[0011] S2. Based on the correlation between each key parameter and construction quality, a dual model is constructed. The dual model includes a parameter-quality dynamic evaluation model and an abnormal parameter influence degree evaluation model. The comprehensive quality index of the construction area is calculated to determine whether the construction quality meets the standards.

[0012] S3. When the construction quality is substandard, extract the construction quality parameters and comprehensive quality index, screen out the construction links that need to be optimized and adjusted and the corresponding construction quality parameters, analyze the deviation of the construction quality parameters, and identify potential construction defects and defect-related factors in the area.

[0013] S4. After the adjustment is completed, monitor the construction quality parameters again and determine whether the construction quality meets the standards. For construction links that do not meet the standards and the corresponding construction quality parameters, optimize the process.

[0014] The beneficial effects of this invention are as follows: This invention provides a construction management system and method for asphalt concrete pavement. Firstly, it categorizes key parameters by parameter type—temperature, thickness, compaction degree, and mix proportion—and collects these parameters using distributed devices in both continuous and batch modes. Then, it constructs a parameter-quality dynamic evaluation model and an abnormal parameter influence assessment model to calculate a comprehensive quality index, identify potential defects and related factors, and combine real-time parameters with quality evaluation results to screen for construction steps and parameters requiring optimization. Finally, it optimizes the process, integrates regional information, achieves collaborative management, and generates construction archives. This solution, through real-time monitoring, intelligent analysis, and closed-loop control, significantly improves the uniformity, durability, and efficiency of asphalt pavement construction, reduces subsequent maintenance costs, and is suitable for high-grade pavement projects on highways and municipal roads. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the system structure connection of the present invention.

[0017] Figure 2 This is a schematic diagram of the implementation steps of the method of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] See Figure 1 As shown, a construction management system for asphalt concrete pavement includes the following modules: Acquisition module: for a single construction area, key parameters are divided into asphalt mixture and pavement temperature parameters, paving layer thickness parameters, pavement compaction parameters, and asphalt-aggregate material ratio parameters according to parameter type. At the same time, distributed acquisition devices are used to collect the key parameters of the area in real time.

[0020] In a specific embodiment, the data acquisition module performs the following process: for the acquisition of asphalt mixture and pavement temperature parameters, paving layer thickness parameters and pavement compaction parameters, continuous acquisition is carried out with the construction stage as the acquisition unit, and the acquisition frequency is not less than once per minute. For the asphalt-aggregate material ratio parameters, a batch acquisition method is adopted, and the frequency of batch acquisition matches the batching cycle of mixture production.

[0021] The parameters collected by the distributed acquisition device are all key parameters corresponding to the standard target parameters preset in the design phase of the construction area. During the acquisition process, the acquisition time of each key parameter and the corresponding construction stage are recorded synchronously.

[0022] It should be noted that the temperature parameters of asphalt mixture and pavement include the temperature of the mixture leaving the plant, the temperature during transportation, the temperature at the paving site, and the temperature of the pavement after compaction.

[0023] Paving layer thickness parameters: including the thickness set by the paving equipment and the actual paving thickness on site.

[0024] Road surface compaction parameters include compaction values ​​corresponding to the number of compaction passes and compaction distribution values ​​in different areas of the road surface.

[0025] Asphalt-aggregate mix proportions include aggregate gradation ratio, mass ratio of asphalt to aggregate, and admixture dosage ratio.

[0026] The batching cycle of asphalt mixture production refers to the complete time cycle from the start of the batching equipment, the addition of aggregates and asphalt in the preset ratio, to the completion of mixing and output of the batch of asphalt mixture. Its duration is determined by the capacity of the asphalt mixture production equipment, the total production volume of a single batch, and the batching process. The frequency of batch data collection is completely synchronized with this cycle (i.e., one batching cycle corresponds to one data collection of the proportioning parameters).

[0027] Distributed data acquisition equipment: refers to a cluster of specialized equipment deployed at different work sites in the construction area, with independent data acquisition and transmission functions, including temperature sensors: deployed on the mix transport vehicles, paving site and compacted road surface; thickness gauges: installed on paving equipment; compaction gauges: mounted on compaction equipment; and batching and metering sensors: deployed in mix production batching bins.

[0028] Preset standard target parameters: These refer to the standard values ​​or ranges of key parameters that are predetermined during the design phase of the asphalt concrete pavement construction area, based on engineering design specifications, pavement usage requirements, and material performance standards. These include, but are not limited to, the standard values ​​for asphalt mixture factory temperature (150-170℃), the standard values ​​for paving layer design thickness (e.g., 4cm), the standard values ​​for pavement compaction (≥95%), and the standard values ​​for asphalt-aggregate mass ratio (e.g., 5%).

[0029] Analysis module: Used to construct a dual model based on the correlation between key parameters and construction quality. The dual model includes a parameter-quality dynamic evaluation model and an abnormal parameter influence degree evaluation model. It calculates the comprehensive quality index of the construction area and determines whether the construction quality meets the standards.

[0030] In a specific embodiment, the specific process of constructing the parameter-quality dynamic evaluation model is as follows: a standard test environment is constructed based on the preset standard target parameters of the construction area design stage. In the standard test environment, a control group and several experimental groups are set. The control group adopts all preset standard target parameters, and each experimental group sets only one independent variable key parameter that is different from the control group. The remaining parameters are consistent with the control group. After the control variables complete multiple parallel tests, the basic construction quality data corresponding to each key parameter are generated as the basic training data for constructing the parameter-quality dynamic evaluation model.

[0031] Both the control group and the experimental group were processed into asphalt concrete pavement samples of identical specifications. After the samples were allowed to stand until their performance stabilized, several construction quality indicators of each sample were tested to obtain the corresponding construction quality parameters. The construction quality parameters included pavement hardness, density, and bond strength. After testing, each sample was rated and divided into three levels: excellent, medium, and poor. The independent variable parameters of the experimental group corresponding to the poor quality samples were the initial abnormal parameters. The parameter differences between each abnormal parameter and the standard parameters of the control group were recorded simultaneously.

[0032] Calculate the parameter difference rate of abnormal parameters in each experimental group relative to the standard parameters of the control group. The calculation formula is: Parameter difference rate = |Actual parameter value - Standard parameter value| ÷ Standard parameter value.

[0033] The mass difference rate of each experimental group of pavement samples relative to the corresponding mass parameters of the control group samples was calculated. The mass difference rate of a single index is calculated as: |actual mass parameter value - standard mass parameter value| ÷ standard mass parameter value. Based on the importance of each mass parameter, a weight was assigned, and the weighted calculation yielded the comprehensive mass difference rate: pavement hardness parameter difference rate × 0.4 + density parameter difference rate × 0.3 + bond strength parameter difference rate × 0.3.

[0034] A mapping table between parameter difference rate and overall quality difference rate is established. The overall quality difference rate is divided into three quality levels: excellent, medium, and poor, corresponding to the ranges of 0-0.3, 0.3-0.7, and 0.7-1.0, respectively. The threshold values ​​of the overall quality index corresponding to each level are set: excellent quality ≥ 0.8, medium quality 0.5-0.8, and poor quality < 0.5. A parameter-quality dynamic evaluation model is formed. The model takes construction quality parameters as input and outputs the overall quality index and corresponding quality level of the construction area. Then, an evaluation model of the impact of abnormal parameters is constructed to calculate the overall quality index of the construction area and determine whether the construction quality meets the standards.

[0035] It should be noted that the standard testing environment refers to a simulated testing scenario built based on the pre-set standard target parameters during the design phase of the asphalt concrete pavement construction area. This scenario is completely identical to the actual construction scenario in terms of environmental conditions, equipment configuration, and operating procedures. Its core characteristics include: environmental temperature and humidity controlled within the allowable range of actual construction (e.g., temperature 15-30℃ and humidity 40%-60%), construction equipment models consistent with actual construction, and the operation procedures of material preparation, mixing, paving, and compaction synchronized with actual construction specifications. This ensures that the test data can truly reflect the correlation between parameters and quality in actual construction.

[0036] Control group: Refers to the benchmark group in which asphalt concrete pavement samples are prepared and tested in a standard testing environment using all the standard target parameters preset during the construction area design phase. These parameters include, but are not limited to: asphalt mixture factory temperature of 160℃ (standard value), paving layer thickness of 4cm (standard value), pavement compaction degree of 96% (standard value), and asphalt-aggregate mass ratio of 5% (standard value). This serves as a comparison benchmark for the experimental group, providing reference data for "standard parameters - standard mass".

[0037] Experimental group: This refers to a test group that, in a standard testing environment, adjusts only one key parameter as the independent variable, while keeping all other parameters consistent with the control group. It is used to analyze the impact of changes in a single parameter on construction quality. The adjustment of the independent variable parameter must cover the deviation range that may occur in actual construction (e.g., adjusting the temperature parameter to 140℃, 150℃, 170℃, and 180℃), and each group of adjusted independent variable values ​​must have three parallel samples to avoid random errors affecting the validity of the data.

[0038] Construction quality basic data: refers to the data set corresponding to "key parameter values ​​- quality index values" collected through parallel tests of the control group and the experimental group. It includes: actual values ​​of key parameters for each group, pavement hardness test values, density test values, bond strength test values, parameter difference rate, single index quality difference rate, comprehensive quality difference rate, and quality rating results, etc. It is the core training data for constructing the parameter-quality dynamic evaluation model.

[0039] Initial abnormal parameters: These refer to the "key independent parameters" corresponding to the pavement samples in the experimental group that are judged as "poor quality" by the quality rating. For example, if an experimental group adjusts the asphalt-aggregate mass ratio to 3% (independent variable) and keeps the other parameters the same as the control group, and the prepared samples are rated as "poor quality", then "asphalt-aggregate mass ratio 3%" is the initial abnormal parameter, and the difference between it and the standard value of the control group (5%) is the basis for abnormal deviation.

[0040] Mapping table: This refers to a table that establishes a correspondence between "parameter difference rate" and "overall quality difference rate" based on test data. It clarifies the range of the overall quality difference rate corresponding to different parameter difference rates. For example, when the temperature parameter difference rate of asphalt mixture is 0.1-0.2, the corresponding overall quality difference rate is 0.1-0.3 (excellent quality); when the difference rate is 0.6-0.8, the corresponding overall quality difference rate is 0.7-1.0 (poor quality).

[0041] Preferably, the specific process of constructing the abnormal parameter influence assessment model is as follows: the 0-1 interval of parameter difference rate is divided into 5 continuous intervals, namely 0-0.2, 0.2-0.4, 0.4-0.6, 0.6-0.8 and 0.8-1.0, corresponding to influence levels 1-5. A larger level value indicates that the abnormal parameter has a higher degree of influence on construction quality.

[0042] Based on the parameter difference rate and overall quality difference rate data obtained during the construction of the parameter-quality dynamic assessment model, a correspondence table of parameter difference rate, impact level and overall quality difference rate is established to clarify the range of change of overall quality difference rate of each key parameter in each impact level interval, and an assessment model of the degree of influence of abnormal parameters is formed.

[0043] The model takes the parameter difference rate of the construction quality parameters as input and outputs the influence level of the corresponding parameters and the degree of quality impact.

[0044] It should be noted that the three-way correspondence table refers to a three-dimensional correlation table established based on the test data collected during the construction of the parameter-quality dynamic assessment model, which shows the "parameter difference rate interval - influence level - range of change in the overall quality difference rate". The table clearly records the specific fluctuation range of the overall quality difference rate of each key parameter under different parameter difference rate intervals (corresponding influence levels). For example, when the asphalt-aggregate material ratio parameter has a parameter difference rate of 0.4-0.6 (influence level 3), the corresponding range of change in the overall quality difference rate is 0.4-0.6.

[0045] Preferably, the specific process for calculating the comprehensive quality index is as follows: using a weighted summation formula, specifically: The comprehensive construction quality index is obtained, where... This represents the overall quality index. Indicates the first Standardized values ​​of each construction quality parameter Indicates the first The influence weight of each construction quality parameter The number of construction quality parameters in this plan is [number]. =3, It is a positive integer.

[0046] The standardized value of a certain quality parameter is compared with the preset anomaly judgment threshold of 0.8. When the standardized value of a certain quality parameter is less than the anomaly judgment threshold of 0.8, the construction quality parameter corresponding to that quality parameter is determined to be an abnormal parameter.

[0047] It should be noted that, =3: This represents three construction quality parameters: pavement hardness, density, and bond strength.

[0048] The preset anomaly judgment threshold is a critical value used to determine whether a single quality parameter is abnormal. It is derived and determined by technical personnel based on the standard target parameters preset in the design stage of the construction area. In this scheme, the value is 0.8.

[0049] Preferably, the specific process for determining whether the construction quality meets the standards is as follows: the calculated comprehensive quality index of the construction area is compared with the comprehensive quality index threshold of 0.8. If the comprehensive quality index is ≥ the comprehensive quality index threshold of 0.8, the construction quality is determined to meet the standards, and there is no need to start the abnormal parameter influence assessment model; if the comprehensive quality index is < the comprehensive quality index threshold of 0.8, the construction quality is determined to be substandard, and the abnormal parameter influence assessment model is started.

[0050] Extract the actual values ​​of each key parameter in the construction area, calculate the parameter difference rate for each parameter, and combine the abnormal parameters identified during the calculation of the comprehensive quality index to determine the core abnormal parameters.

[0051] Input the parameter difference rate of the core abnormal parameters into the abnormal parameter impact assessment model to obtain the impact level and quality impact degree of each core abnormal parameter.

[0052] Output a judgment on whether the construction quality meets the standards. If it does not meet the standards, output the type of core abnormal parameters, the impact level of each abnormal parameter, and the degree of impact on quality.

[0053] It should be noted that the comprehensive quality index threshold is a critical value used to determine whether the overall construction quality meets the standards. It is set by technical personnel based on the benchmark level of the comprehensive quality index of the control group samples. In this scheme, the value is explicitly set to 0.8.

[0054] Core anomaly parameters: These refer to the parameters that have the most critical impact on construction quality when the overall quality index is less than 0.8, selected from all anomaly parameters. The selection criteria are based on two conditions: first, the standardized value of the corresponding quality parameter must be less than the anomaly threshold of 0.8 (indicating it is already an anomaly parameter); second, the parameter's variation rate must be relatively higher than all anomaly parameters, and after verification by the anomaly parameter impact assessment model, its impact level must be higher (usually ≥ level 3). For example, the variation rate of the asphalt-aggregate mix proportion parameter is 0.7 (impact level 4), significantly higher than other anomaly parameters, thus qualifying as a core anomaly parameter.

[0055] Control module: When construction quality fails to meet standards, it extracts construction quality parameters and comprehensive quality index, filters out construction links that need to be optimized and adjusted and their corresponding construction quality parameters, analyzes the deviation of construction quality parameters, and identifies potential construction defects and defect-related factors in the area.

[0056] In a specific embodiment, the specific process of the control module is as follows: when it is determined that the construction quality is substandard, the construction quality parameter deviation data and the comprehensive quality index of the construction area are extracted. The construction quality parameter deviation data are the parameter difference rate of the construction quality parameters relative to the design preset standard parameters and the corresponding impact level.

[0057] The impact level of construction quality parameters is determined from high to low based on the impact level, with level 5 being the highest impact level. The construction process corresponding to the abnormal parameter with the highest impact level is selected first.

[0058] For the selected construction processes that require optimization and adjustment, potential construction defects and their associated factors are identified by combining the types of abnormal parameters. Corresponding control measures are then implemented for the construction defects and their associated factors: abnormal asphalt mixture and pavement temperature parameters are associated with defects in heating and insulation construction processes; abnormal paving layer thickness parameters are associated with defects in paving equipment operation construction processes; abnormal pavement compaction parameters are associated with defects in rolling construction processes; and abnormal asphalt-aggregate material ratio parameters are associated with defects in batching and mixing construction processes.

[0059] It should be noted that potential construction defects refer to deviations in process execution or operation that may exist during construction, derived from the abnormal parameter types. These are direct construction problems that lead to parameter abnormalities. For example, abnormal asphalt mixture and pavement temperature parameters correspond to defects such as "inaccurate heating temperature control" and "inadequate insulation measures," while abnormal paving layer thickness parameters correspond to defects such as "incorrect paving equipment gear adjustment" and "uneven paving speed." These defects directly point to specific problems in the construction process.

[0060] Defect-related factors: These refer to indirect factors that cause potential construction defects, including equipment factors, operational factors, and environmental factors. They are the root causes of construction defects. For example, the related factors for the defect of "inaccurate heating temperature control" may be "heating equipment sensor failure" (equipment factor) and "operator failure to monitor temperature according to specifications" (operational factor); the related factors for the defect of "uneven paving thickness" may be "paving equipment travel system failure" (equipment factor) and "uneven ground surface at the construction site" (environmental factor).

[0061] Preferably, the specific process of implementing corresponding control measures for construction defects and defect-related factors is as follows: Temperature parameter anomaly control: When the temperature parameters of asphalt mixture and pavement are abnormal, if the parameter difference rate is negative, that is, the actual temperature is lower than the standard value, the set temperature of the heating equipment is increased by the temperature control instrument, and the thickness of the insulation covering layer is increased to extend the insulation time; if the parameter difference rate is positive, that is, the actual temperature is higher than the standard value, the set temperature of the heating equipment is reduced, the transportation time of the mixture is adjusted, and the temperature rise time during transportation is shortened.

[0062] Abnormal control of paving thickness parameters: When the paving layer thickness parameters are abnormal, if the actual thickness is less than the standard value, adjust the paving thickness setting of the paving equipment, increase the paving height, and slow down the paving speed; if the actual thickness is greater than the standard value, decrease the paving thickness setting of the paving equipment and speed up the paving speed. During the adjustment process, monitor the paving thickness in real time to ensure that the thickness deviation is controlled within ±5mm.

[0063] Control of abnormal compaction parameters: When the road compaction parameters are abnormal, if the actual compaction is lower than the standard value, increase the number of compaction passes of the rolling equipment by 1-2 passes each time; increase the rolling pressure by no more than 10%; and adjust the rolling route to ensure no compaction dead spots. If the actual compaction is higher than the standard value, reduce the number of compaction passes and reduce the rolling pressure to avoid over-compaction that could lead to road cracking.

[0064] Control of abnormal asphalt-aggregate material proportioning parameters: When the asphalt-aggregate material proportioning parameters are abnormal, adjust the feeding ratio of the batching equipment according to the parameter difference rate. For example, when the aggregate gradation ratio is abnormal, adjust the discharge speed of the batching bin; when the asphalt content is abnormal, adjust the delivery flow rate of the asphalt pump to ensure that the parameter difference rate between the adjusted proportioning parameters and the standard target parameters is ≤0.1.

[0065] Verification module: Used to monitor construction quality parameters again after adjustment and determine whether the construction quality meets the standards. For construction links that do not meet the standards and their corresponding construction quality parameters, the process is optimized.

[0066] In a specific embodiment, the verification module performs the following process: after the control measures are implemented, the construction quality parameters of the construction process after optimization and adjustment in the construction area are collected again in real time.

[0067] The newly collected construction quality parameters are input into the dual model of the analysis module to recalculate the comprehensive quality index and determine whether the construction quality meets the standards.

[0068] If the construction quality meets the standards, the control process for that stage ends; if the construction quality still does not meet the standards, the construction process for that stage is optimized, the process parameters are adjusted, and the above data collection, analysis, and control steps are repeated until the construction quality meets the standards.

[0069] It should be noted that process optimization refers to the systematic improvement of the corresponding construction process, operating procedures, and equipment parameter settings when the construction quality still fails to meet the standards after the implementation of control measures. This differs from single parameter adjustments and focuses more on addressing the root causes of problems at the process level. For example, if repeated adjustments to the paving thickness still fail to meet the standards, the plan for the paving equipment's travel route and the pre-treatment process for the base course flatness before paving can be optimized; if the compaction degree is insufficient, the temperature range control for the initial compaction, intermediate compaction, and final compaction, and the combination of compaction equipment can be optimized.

[0070] Process parameters: These refer to the core technical parameters that need to be strictly controlled during the execution of the construction process. They are the specific adjustment targets for process optimization and are directly related to the previous key parameters, but with a greater emphasis on the details of process execution. For example, paving process parameters include paving speed range, paving equipment vibration frequency, and screed preheating temperature; compaction process parameters include compaction overlap width, travel speed at different compaction stages, and compaction pressure gradient settings, etc.

[0071] The database is used to store asphalt mixture and pavement temperature, paving layer thickness, pavement compaction degree, asphalt-aggregate material ratio parameters, basic construction quality data of control and experimental groups, data related to dual model construction, construction quality parameter deviation data, control measures records, process optimization schemes and construction management archive data. It is also used to store comprehensive quality index thresholds and anomaly judgment thresholds.

[0072] See Figure 2 As shown, a construction management method for asphalt concrete pavement includes the following steps: S1. For a single construction area, the key parameters are divided into asphalt mixture and pavement temperature parameters, paving layer thickness parameters, pavement compaction parameters, and asphalt-aggregate material ratio parameters according to parameter type. At the same time, distributed acquisition devices are used to collect each key parameter of the area in real time.

[0073] S2. Based on the correlation between each key parameter and construction quality, a dual model is constructed. The dual model includes a parameter-quality dynamic evaluation model and an abnormal parameter influence degree evaluation model. The comprehensive quality index of the construction area is calculated to determine whether the construction quality meets the standards.

[0074] S3. When the construction quality is substandard, extract the construction quality parameters and comprehensive quality index, screen out the construction links that need to be optimized and adjusted and the corresponding construction quality parameters, analyze the deviation of the construction quality parameters, and identify potential construction defects and defect-related factors in the area.

[0075] S4. After the adjustment is completed, monitor the construction quality parameters again and determine whether the construction quality meets the standards. For construction links that do not meet the standards and the corresponding construction quality parameters, optimize the process.

[0076] The examples described in this invention are not limited to the specific embodiments listed above. The examples are merely illustrative to facilitate understanding of the invention and do not constitute a limitation on the scope of protection of this invention. Any modifications, equivalent substitutions, etc., made within the spirit and principles of this invention should be included within the scope of protection.

[0077] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the protection scope of the present invention.

Claims

1. A construction management system for asphalt concrete pavement, characterized in that, Includes the following modules: The data acquisition module is used to classify key parameters for a single construction area into asphalt mixture and pavement temperature parameters, paving layer thickness parameters, pavement compaction parameters, and asphalt-aggregate mix ratio parameters, based on parameter type. At the same time, it uses distributed acquisition devices to collect the key parameters of the area in real time. Analysis module: Used to construct a dual model based on the correlation between each key parameter and construction quality. The dual model includes a parameter-quality dynamic evaluation model and an abnormal parameter influence degree evaluation model. It calculates the comprehensive quality index of the construction area and determines whether the construction quality meets the standards. Control module: When construction quality fails to meet standards, it extracts construction quality parameters and comprehensive quality index, filters out construction links that need to be optimized and adjusted and their corresponding construction quality parameters, analyzes the deviation of construction quality parameters, and identifies potential construction defects and defect-related factors in the area. Verification module: Used to monitor construction quality parameters again after adjustment and determine whether the construction quality meets the standards. For construction links that do not meet the standards and their corresponding construction quality parameters, the process is optimized.

2. The construction management system for asphalt concrete pavement according to claim 1, characterized in that, The specific process of the data acquisition module is as follows: For the collection of asphalt mixture and pavement temperature parameters, paving layer thickness parameters and pavement compaction parameters, continuous collection is carried out with the construction stage as the collection unit, and the collection frequency is no less than once per minute. For the asphalt-aggregate material ratio parameters, a batch collection method is adopted, and the frequency of batch collection is matched with the batching cycle of mixture production. The parameters collected by the distributed acquisition device are all key parameters corresponding to the standard target parameters preset in the design phase of the construction area. During the acquisition process, the acquisition time of each key parameter and the corresponding construction stage are recorded synchronously.

3. The construction management system for asphalt concrete pavement according to claim 1, characterized in that, The specific process for constructing the parameter-quality dynamic evaluation model is as follows: A standard test environment is constructed based on the preset standard target parameters in the construction area design stage. A control group and several experimental groups are set in the standard test environment. The control group adopts all preset standard target parameters. Each experimental group sets only one independent variable key parameter that is different from the control group. The remaining parameters are consistent with the control group. After the control variables complete multiple parallel tests, the basic construction quality data corresponding to each key parameter are generated as the basic training data for the construction of the parameter-quality dynamic evaluation model. Both the control group and the experimental group were processed into asphalt concrete pavement samples of identical specifications. After the samples were allowed to stand until their performance stabilized, several construction quality indicators of each sample were tested to obtain the corresponding construction quality parameters. The construction quality parameters included pavement hardness parameters, density parameters, and bond strength parameters. After testing, each sample was rated and divided into three levels: excellent, medium, and poor. The independent variable parameters of the experimental group corresponding to the poor quality samples were the initial abnormal parameters. The parameter differences between each abnormal parameter and the standard parameters of the control group were recorded simultaneously. Calculate the parameter difference rate of abnormal parameters in each experimental group relative to the standard parameters of the control group. The calculation formula is: Parameter difference rate = |actual parameter value - standard parameter value| ÷ standard parameter value; The mass difference rate of each experimental group of pavement samples relative to the corresponding mass parameters of the control group samples was calculated. The mass difference rate of a single index is calculated as: |actual mass parameter value - standard mass parameter value| ÷ standard mass parameter value. Based on the importance of each mass parameter, a weight was assigned, and the weighted calculation yielded the comprehensive mass difference rate: pavement hardness parameter difference rate × 0.4 + density parameter difference rate × 0.3 + bond strength parameter difference rate × 0.

3. A mapping table between parameter difference rate and overall quality difference rate is established. The overall quality difference rate is divided into three quality levels: excellent, medium, and poor, corresponding to the ranges of 0-0.3, 0.3-0.7, and 0.7-1.0, respectively. The threshold values ​​of the overall quality index corresponding to each level are set: excellent quality ≥ 0.8, medium quality 0.5-0.8, and poor quality < 0.

5. A parameter-quality dynamic evaluation model is formed. The model takes construction quality parameters as input and outputs the overall quality index and corresponding quality level of the construction area. Then, an evaluation model of the impact of abnormal parameters is constructed to calculate the overall quality index of the construction area and determine whether the construction quality meets the standards.

4. The construction management system for asphalt concrete pavement according to claim 3, characterized in that, The specific process for constructing the model for assessing the impact of abnormal parameters is as follows: The 0-1 interval of parameter difference rate is divided into 5 continuous intervals, namely 0-0.2, 0.2-0.4, 0.4-0.6, 0.6-0.8 and 0.8-1.0, corresponding to influence levels 1-5. A larger level value indicates that the abnormal parameter has a higher degree of influence on construction quality. Based on the parameter difference rate and overall quality difference rate data obtained during the construction of the parameter-quality dynamic assessment model, a correspondence table of parameter difference rate, impact level and overall quality difference rate is established to clarify the range of change of overall quality difference rate of each key parameter in each impact level interval, and an assessment model of the degree of influence of abnormal parameters is formed. The model takes the parameter difference rate of the construction quality parameters as input and outputs the influence level of the corresponding parameters and the degree of quality impact.

5. The construction management system for asphalt concrete pavement according to claim 3, characterized in that, The specific process for calculating the comprehensive quality index is as follows: The weighted summation formula is as follows: The comprehensive construction quality index is obtained, where... This represents the overall quality index. Indicates the first Standardized values ​​of each construction quality parameter Indicates the first The influence weight of each construction quality parameter The number of construction quality parameters in this plan is [number]. =3, It is a positive integer; The standardized value of a certain quality parameter is compared with a preset anomaly detection threshold. When a standardized value of a quality parameter is less than the anomaly threshold of 0.8, the construction quality parameter corresponding to that quality parameter is determined to be an abnormal parameter.

6. The construction management system for asphalt concrete pavement according to claim 3, characterized in that, The specific process for determining whether the construction quality meets the standards is as follows: The calculated comprehensive quality index of the construction area is compared with the comprehensive quality index threshold of 0.

8. If the comprehensive quality index is greater than or equal to the comprehensive quality index threshold of 0.8, the construction quality is deemed to meet the standards, and there is no need to activate the abnormal parameter influence assessment model. If the comprehensive quality index is less than the comprehensive quality index threshold of 0.8, the construction quality is deemed to fail to meet the standards, and the abnormal parameter influence assessment model is activated. Extract the actual values ​​of each key parameter in the construction area, calculate the parameter difference rate of each parameter, and combine the abnormal parameters identified during the calculation of the comprehensive quality index to determine the core abnormal parameters. Input the parameter difference rate of the core abnormal parameters into the abnormal parameter impact assessment model to obtain the impact level and quality impact of each core abnormal parameter; Output a judgment on whether the construction quality meets the standards. If it does not meet the standards, output the type of core abnormal parameters, the impact level of each abnormal parameter, and the degree of impact on quality.

7. The construction management system for asphalt concrete pavement according to claim 1, characterized in that, The specific process of the control module is as follows: When it is determined that the construction quality is substandard, the construction quality parameter deviation data and the comprehensive quality index of the construction area are extracted. The construction quality parameter deviation data are the parameter difference rate of the construction quality parameters relative to the design preset standard parameters and the corresponding impact level. The impact level of construction quality parameters is determined from high to low based on the impact level, with level 5 being the highest impact level. The construction process corresponding to the abnormal parameter with the highest impact level is selected first. For the selected construction processes that require optimization and adjustment, potential construction defects and their associated factors are identified by combining the types of abnormal parameters. Corresponding control measures are then implemented for the construction defects and their associated factors: abnormal asphalt mixture and pavement temperature parameters are associated with defects in heating and insulation construction processes; abnormal paving layer thickness parameters are associated with defects in paving equipment operation construction processes; abnormal pavement compaction parameters are associated with defects in rolling construction processes; and abnormal asphalt-aggregate material ratio parameters are associated with defects in batching and mixing construction processes.

8. The construction management system for asphalt concrete pavement according to claim 7, characterized in that, The specific process for implementing corresponding control measures for construction defects and related factors is as follows: Temperature parameter anomaly control: When the temperature parameters of asphalt mixture and pavement are abnormal, if the parameter difference rate is negative, that is, the actual temperature is lower than the standard value, the set temperature of the heating equipment is increased by the temperature control instrument, and the thickness of the insulation covering layer is increased to extend the insulation time. If the parameter difference rate is positive, meaning the actual temperature is higher than the standard value, reduce the set temperature of the heating equipment, adjust the transportation time of the mixture, and shorten the time of temperature rise during transportation. Abnormal control of paving thickness parameters: When the paving layer thickness parameters are abnormal, if the actual thickness is less than the standard value, adjust the paving thickness setting of the paving equipment, increase the paving height, and slow down the paving speed; if the actual thickness is greater than the standard value, decrease the paving thickness setting of the paving equipment and speed up the paving speed. During the adjustment process, monitor the paving thickness in real time to ensure that the thickness deviation is controlled within ±5mm. Control of abnormal compaction parameters: When the road compaction parameters are abnormal, if the actual compaction degree is lower than the standard value, increase the number of compaction passes of the rolling equipment by 1-2 passes each time; increase the rolling pressure by no more than 10%; and adjust the rolling route to ensure no compaction dead spots. If the actual compaction degree is higher than the standard value, reduce the number of compaction passes and reduce the rolling pressure to avoid over-compaction that could lead to road cracking. Control of abnormal asphalt-aggregate material proportioning parameters: When the asphalt-aggregate material proportioning parameters are abnormal, adjust the feeding ratio of the batching equipment according to the parameter difference rate. For example, when the aggregate gradation ratio is abnormal, adjust the discharge speed of the batching bin; when the asphalt content is abnormal, adjust the delivery flow rate of the asphalt pump to ensure that the parameter difference rate between the adjusted proportioning parameters and the standard target parameters is ≤0.

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9. The construction management system for asphalt concrete pavement according to claim 1, characterized in that, The specific process of the verification module is as follows: After the control measures are implemented, the construction quality parameters of the construction process in the construction area after optimization and adjustment are collected again in real time. The newly collected construction quality parameters are input into the dual model of the analysis module to recalculate the comprehensive quality index and determine whether the construction quality meets the standards. If the construction quality meets the standards, the control process for that stage ends; if the construction quality still does not meet the standards, the construction process for that stage is optimized, the process parameters are adjusted, and the above data collection, analysis, and control steps are repeated until the construction quality meets the standards.

10. A method for implementing the construction management system for asphalt concrete pavement according to any one of claims 1-9, characterized in that, Includes the following steps: S1. For a single construction area, the key parameters are divided into asphalt mixture and pavement temperature parameters, paving layer thickness parameters, pavement compaction parameters, and asphalt-aggregate material ratio parameters according to parameter type. At the same time, distributed acquisition devices are used to collect the key parameters of the area in real time. S2. Construct a dual model based on the correlation between each key parameter and construction quality. The dual model includes a parameter-quality dynamic evaluation model and an abnormal parameter influence degree evaluation model. Calculate the comprehensive quality index of the construction area and determine whether the construction quality meets the standards. S3. When the construction quality is substandard, extract the construction quality parameters and comprehensive quality index, screen out the construction links that need to be optimized and adjusted and the corresponding construction quality parameters, analyze the deviation of the construction quality parameters, and identify potential construction defects and defect-related factors in the area. S4. After the adjustment is completed, monitor the construction quality parameters again and determine whether the construction quality meets the standards. For construction links that do not meet the standards and the corresponding construction quality parameters, optimize the process.

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