Method and system for controlling construction quality of asphalt pavement of municipal road reconstruction project

Abstract

The invention belongs to the technical field of asphalt pavement construction, and discloses a municipal road reconstruction engineering asphalt pavement construction quality control method, which specifically comprises the following steps: 1, carrying out three-dimensional laser scanning surveying and mapping on longitudinal and transverse joints and crack distribution of an original cement concrete pavement in the treatment research of the original pavement; the geometrical morphology and modulus characteristics of an original pavement are accurately analyzed through three-dimensional laser scanning and a dynamic modulus test, the thickness design of an asphalt layer is optimized, adaptive materials are intelligently screened according to climate characteristics and traffic load grades of a construction area, and the performance stability of an asphalt mixture is improved; meanwhile, by means of the intelligent paving technology, the flatness and thickness uniformity of a paving layer are guaranteed, the paving temperature is accurately controlled, the paving performance of the mixture is improved, rolling parameters can be automatically adjusted according to key information such as the paving thickness and the temperature, and the uniformity of the compaction quality is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of asphalt pavement construction, and in particular relates to a method and system for controlling the construction quality of asphalt pavement in a municipal road reconstruction project. Background Art

[0002] Asphalt pavement is a pavement structure that uses asphalt as a binder, mixed with mineral materials (such as stone, sand, etc.) in a certain proportion, and formed through processes such as paving and compaction. It has the advantages of a smooth surface, no joints, comfortable driving, low vibration, low noise, short construction period, and easy maintenance. It is widely used in transportation infrastructure such as highways at all levels, urban roads, airport runways, etc. It can effectively withstand vehicle loads and environmental factors, providing safe and smooth driving conditions for vehicles.

[0003] Quality control is particularly important when constructing asphalt pavements in municipal road reconstruction projects, as it affects the service life of the asphalt pavement. Existing technologies for quality control of asphalt pavements have significant shortcomings. First, when selecting materials, the climate characteristics and traffic load levels of the construction area are not fully considered, resulting in a lack of targeted material selection and difficulty in ensuring the performance stability of the asphalt mixture in different environments. Second, during the paving process, traditional methods rely primarily on worker experience to adjust construction parameters, which can easily lead to uneven material distribution or localized excessively high or low temperatures during paving, making it difficult to ensure the flatness and thickness uniformity of the paved layer. Furthermore, imprecise control of paving temperature affects the paving performance of the mixture. Furthermore, during compaction, existing technologies often use fixed rolling parameters and lack the ability to automatically adjust rolling parameters based on key information such as paving thickness and temperature, resulting in uneven compaction quality. Finally, in terms of quality inspection, traditional methods often rely on a single inspection method, which makes it difficult to comprehensively and accurately reflect the quality of the pavement. Furthermore, the inspection data lacks effective integration and analysis, making it difficult to form scientific maintenance decisions. Therefore, improvements are needed. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for controlling the construction quality of asphalt pavement in a municipal road reconstruction project, so as to solve the problems raised in the above-mentioned background technology.

[0005] In order to achieve the above-mentioned object, the present invention provides the following technical solution: a method for controlling the construction quality of asphalt pavement in a municipal road reconstruction project, comprising the following specific steps:

[0006] Step 1: Study on original road surface treatment

[0007] First, three-dimensional laser scanning was performed to map the longitudinal and transverse joints and crack distribution of the original cement concrete pavement. A database of the original pavement geometry was established. In combination with the climatic characteristics of the construction area, dynamic modulus tests were used to determine the viscoelastic parameters of the asphalt mixture under different temperature gradients. A modulus matching model for the asphalt surface layer and cement base layer was established. Finite element analysis software was used to simulate the distribution of interlayer shear stress under vehicle dynamic loads to determine the optimal asphalt layer thickness. The problem of reflective cracks was addressed by installing a modified asphalt stress absorption layer.

[0008] Step 2: Assessment and treatment of original pavement defects

[0009] Conduct a graded assessment of existing pavement damage, using ground-penetrating radar and a drop weight deflectometer for non-destructive testing of the entire road section. Establish a quantitative damage evaluation system that includes indicators such as crack width, base layer voids, and joint misalignment. Grouting reinforcement is implemented for severe damage detected during testing (cracks greater than 5 mm in width and accompanied by base layer voids).

[0010] Step 3: Intelligent material matching

[0011] The physicochemical property data of asphalt, aggregate, and filler are integrated, including parameters such as asphalt viscosity-temperature curve, aggregate alkalinity / silica content, and filler specific surface area, to form a standardized material performance archive. During construction, suitable materials are intelligently screened from the database based on the climate characteristics of the construction area (extreme temperatures, precipitation intensity) and traffic load levels. Based on the matching material combinations, a neural network model is trained using historical engineering data to generate a recommended range for the asphalt-to-stone ratio that meets the target parameters, achieving precise mix control.

[0012] Step 4: Intelligent paving

[0013] Through the intelligent paving system, construction parameters such as paving speed, material level, vibration frequency, etc. are collected in real time;

[0014] During the asphalt pavement paving process, by installing a high-precision GNSS receiver on the paver, GNSS technology can accurately obtain the paver's location information and determine the paver's three-dimensional coordinates (longitude, latitude and elevation) in real time;

[0015] The non-contact balance beam intelligent leveling unit in the intelligent paving system measures the distance between the road surface and the sensors in real time by installing multiple ultrasonic or laser sensors in front of the paver screed. The sensors then feed the distance signals back to the paver control system, which automatically adjusts the screed height based on these signals to ensure the flatness and thickness uniformity of the paving layer. The accuracy of the flatness and thickness uniformity of the road surface reaches ±1mm, effectively avoiding flatness defects such as waves and potholes on the road surface.

[0016] The paver is equipped with an intelligent heating control and monitoring unit in the intelligent paving system, which automatically adjusts the heating power of the screed plate based on real-time temperature monitoring data. When the mixture temperature is slightly lower than the optimal paving temperature, the heating power is increased to raise the screed plate temperature, thereby maintaining good paving performance of the mixture. At the same time, an infrared temperature sensor is installed on the paver to monitor the paving temperature of the asphalt mixture in real time. Through real-time temperature monitoring, construction personnel can promptly detect temperature anomalies. For example, if the temperature is too low, it may make it difficult to compact the mixture. In this case, construction personnel can promptly detect and take measures to adjust the situation.

[0017] Step 5: Intelligent Compaction Collaborative Control

[0018] After a section of pavement is completed, data is shared between intelligent paving units, and the rolling equipment can automatically adjust rolling parameters, such as rolling speed and number of rolling passes, based on key information such as the paving thickness and temperature of that section. During the rolling process, the compaction quality of the road surface can be detected in real time through the compaction detection sensor installed on the roller. The compaction quality data is fed back to the intelligent paving system, which adjusts the parameters of subsequent paving sections based on the feedback information. For example, if the compaction degree of a certain area is found to be insufficient, the paving system can adjust the paving thickness of the next section or the gradation of the mixture to improve the overall road surface quality.

[0019] Step 6: Quality Inspection

[0020] During the construction process, a mobile laboratory is deployed to conduct quality tracking inspections. A laser profiler is used to check the flatness every 50 meters, and a nuclear density meter is used to check the compaction every 100 meters. At the same time, a three-dimensional quality file is established, including indicators such as the interlayer bonding condition detected by infrared thermal imaging and the internal void ratio distribution detected by geological radar. In addition, a data detection and evaluation technology based on big data is developed to analyze and process the detection data. After the detection is completed, maintenance recommendations are generated to form a digital maintenance decision support system.

[0021] Preferably, the intelligent matching of materials in step three automatically matches modified asphalt of PG76 or above with basalt aggregate with a silica content of >65% in high-temperature and rainy areas, and preferentially matches a combination of high modulus asphalt and diabase aggregate with a cubic particle ratio of >90% in heavy traffic areas.

[0022] Preferably, the GNSS technology is used in step 4 to accurately obtain the position information of the paver, and at the same time, an inertial navigation system INS is combined as a supplement to the GNSS positioning system. When the GNSS signal is blocked or interfered with, such as in complex terrain environments such as tunnels and mountainous areas, the INS can rely on its own sensors such as accelerometers and gyroscopes to calculate the position and attitude of the paver.

[0023] Preferably, the intelligent compaction collaborative control in step five adopts a vibration compaction value (CMV) and infrared temperature fusion control strategy. When the CMV value is lower than 45 and the surface temperature of the mixture is greater than 145°C, the high-frequency vibration mode (40-45Hz) is automatically triggered and two additional compaction times are added.

[0024] Preferably, the roller in step five is equipped with a GNSS receiver to control the lateral rolling overlap width to be within the range of 15-20 cm.

[0025] Preferably, the quality inspection described in step six adopts multi-source data fusion technology, performs three-dimensional spatial registration on the inspection results of the laser profiler, nuclear density meter and infrared thermal imaging equipment, constructs a full-section digital quality model, and automatically identifies quality abnormality areas through deep learning algorithms to generate inspection results and maintenance recommendations.

[0026] Preferably, in the grouting reinforcement treatment described in step 2, the grouting material is selected from nano-silica composite cement-based material, the grouting pressure is controlled in the range of 0.8 to 1.2 MPa, and the filling effect is judged by real-time monitoring of the grouting volume change curve; the surface treatment adopts a full-section milling process, the milling depth is 3 to 5 cm, the residue particle size is controlled at 0 to 5 mm, and high-pressure water jet technology is used to thoroughly remove interface pollutants, the pressure is set to 35 MPa, and it is ensured that the structural depth reaches more than 0.8 mm.

[0027] A municipal road reconstruction project asphalt pavement construction quality control system, including the following modules:

[0028] Original pavement analysis module: used to establish an original pavement geometry database, combine dynamic modulus testing with finite element simulation to analyze interlayer stress distribution, optimize asphalt layer thickness and design stress absorption layers, and output modulus matching models and structural defect repair plans;

[0029] Defect assessment module: Establish a quantitative defect assessment system based on ground penetrating radar and drop weight deflectometer test data;

[0030] Material matching module: Integrates the physical and chemical property data of asphalt, aggregate, and filler, selects suitable materials through a climate-load adaptive algorithm, generates a recommended range for asphalt-to-aggregate ratio using a neural network model, and outputs a material combination plan and gradation verification report;

[0031] Intelligent Paving Module: Integrates GNSS positioning technology and various sensors to monitor paving speed, thickness, and temperature in real time;

[0032] Collaborative rolling module: Automatically adjusts the rolling speed and number of passes according to paving parameters, optimizes the process by combining real-time feedback data from compaction sensors, and achieves compaction defect traceability and cross-device parameter collaboration.

[0033] Quality Inspection and Maintenance Module: Builds a three-dimensional quality archive and develops a big data maintenance decision-making model, outputting defect location maps and digital maintenance priority plans.

[0034] Preferably, the intelligent paving module includes a GNSS positioning unit, an intelligent leveling unit and an intelligent heating control and monitoring unit. The GNSS positioning unit can obtain the position information of the paver and roller in real time; the intelligent leveling unit automatically adjusts the height of the ironing plate to ensure the flatness and thickness uniformity of the paving layer; the intelligent heating control and monitoring unit automatically adjusts the heating power of the ironing plate according to real-time temperature monitoring data, and the operator can monitor the temperature of the mixture in real time.

[0035] The beneficial effects of the present invention are as follows:

[0036] Through three-dimensional laser scanning and dynamic modulus tests, the original pavement geometry and modulus characteristics are accurately analyzed, the asphalt layer thickness design is optimized, and the adaptive materials are intelligently screened according to the climate characteristics and traffic load levels of the construction area, thereby improving the performance stability of the asphalt mixture. At the same time, through intelligent paving technology, the flatness and thickness uniformity of the paving layer are guaranteed, and the paving temperature is precisely controlled to improve the paving performance of the mixture. The rolling parameters can be automatically adjusted according to key information such as paving thickness and temperature, thereby improving the uniformity of the compaction quality. Finally, through big data analysis and other means, the detection data are effectively integrated and analyzed to form scientific maintenance decisions, further improving the overall level of asphalt pavement construction quality in municipal road reconstruction projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a system block diagram of the present invention. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0039] like Figure 1 As shown, the embodiment of the present invention provides a method for controlling the construction quality of asphalt pavement in a municipal road reconstruction project, and the specific steps are as follows:

[0040] Step 1: Study on original road surface treatment

[0041] First, three-dimensional laser scanning was performed to map the longitudinal and transverse joints and crack distribution of the original cement concrete pavement. A database of the original pavement geometry was established. In combination with the climatic characteristics of the construction area, dynamic modulus tests were used to determine the viscoelastic parameters of the asphalt mixture under different temperature gradients. A modulus matching model for the asphalt surface layer and cement base layer was established. Finite element analysis software was used to simulate the distribution of interlayer shear stress under vehicle dynamic loads to determine the optimal asphalt layer thickness. The problem of reflective cracks was addressed by installing a modified asphalt stress absorption layer.

[0042] Step 2: Assessment and treatment of original pavement defects

[0043] Conduct a graded assessment of existing pavement damage, using ground-penetrating radar and a drop weight deflectometer for non-destructive testing of the entire road section. Establish a quantitative damage evaluation system that includes indicators such as crack width, base layer voids, and joint misalignment. Grouting reinforcement is implemented for severe damage detected during testing (cracks greater than 5 mm in width and accompanied by base layer voids).

[0044] Step 3: Intelligent material matching

[0045] The physicochemical property data of asphalt, aggregate, and filler are integrated, including parameters such as asphalt viscosity-temperature curve, aggregate alkalinity / silica content, and filler specific surface area, to form a standardized material performance archive. During construction, suitable materials are intelligently screened from the database based on the climate characteristics of the construction area (extreme temperatures, precipitation intensity) and traffic load levels. Based on the matching material combinations, a neural network model is trained using historical engineering data to generate a recommended range for the asphalt-to-stone ratio that meets the target parameters, achieving precise mix control.

[0046] Step 4: Intelligent paving

[0047] Through the intelligent paving system, construction parameters such as paving speed, material level, vibration frequency, etc. are collected in real time;

[0048] During the asphalt pavement paving process, by installing a high-precision GNSS receiver on the paver, GNSS technology can accurately obtain the paver's location information and determine the paver's three-dimensional coordinates (longitude, latitude and elevation) in real time;

[0049] The non-contact balance beam intelligent leveling unit in the intelligent paving system measures the distance between the road surface and the sensors in real time by installing multiple ultrasonic or laser sensors in front of the paver screed. The sensors then feed the distance signals back to the paver control system, which automatically adjusts the screed height based on these signals to ensure the flatness and thickness uniformity of the paving layer. The accuracy of the flatness and thickness uniformity of the road surface reaches ±1mm, effectively avoiding flatness defects such as waves and potholes on the road surface.

[0050] The paver is equipped with an intelligent heating control and monitoring unit in the intelligent paving system, which automatically adjusts the heating power of the screed plate based on real-time temperature monitoring data. When the mixture temperature is slightly lower than the optimal paving temperature, the heating power is increased to raise the screed plate temperature, thereby maintaining good paving performance of the mixture. At the same time, an infrared temperature sensor is installed on the paver to monitor the paving temperature of the asphalt mixture in real time. Through real-time temperature monitoring, construction personnel can promptly detect temperature anomalies. For example, if the temperature is too low, it may make it difficult to compact the mixture. In this case, construction personnel can promptly detect and take measures to adjust the situation.

[0051] Step 5: Intelligent Compaction Collaborative Control

[0052] After a section of pavement is completed, data is shared between intelligent paving units, and the rolling equipment can automatically adjust rolling parameters, such as rolling speed and number of rolling passes, based on key information such as the paving thickness and temperature of that section. During the rolling process, the compaction quality of the road surface can be detected in real time through the compaction detection sensor installed on the roller. The compaction quality data is fed back to the intelligent paving system, which adjusts the parameters of subsequent paving sections based on the feedback information. For example, if the compaction degree of a certain area is found to be insufficient, the paving system can adjust the paving thickness of the next section or the gradation of the mixture to improve the overall road surface quality.

[0053] Step 6: Quality Inspection

[0054] During the construction process, a mobile laboratory is deployed to conduct quality tracking inspections. A laser profiler is used to check the flatness every 50 meters, and a nuclear density meter is used to check the compaction every 100 meters. At the same time, a three-dimensional quality file is established, including indicators such as the interlayer bonding condition detected by infrared thermal imaging and the internal void ratio distribution detected by geological radar. In addition, a data detection and evaluation technology based on big data is developed to analyze and process the detection data. After the detection is completed, maintenance recommendations are generated to form a digital maintenance decision support system.

[0055] Through 3D laser scanning and dynamic modulus tests, a geometric database of the original pavement and a modulus matching model were established. Finite element simulation was combined to optimize the asphalt layer thickness and set a stress absorption layer to prevent reflective cracks. At the same time, grouting reinforcement was implemented for serious damage based on ground penetrating radar and deflectometer detection. Material physical and chemical data were integrated, and asphalt and aggregate were intelligently screened based on climate and load, and a neural network was used to generate a recommended range for the oil-stone ratio. During construction, GNSS positioning, automatic leveling, and infrared temperature control systems were used to achieve stable paving thickness and temperature, and compaction uniformity was improved through coordinated regulation of compaction parameters and CMV-temperature fusion strategies. Finally, multi-source detection data such as laser profilers and nuclear density meters were integrated to construct a 3D quality archive, and digital maintenance decisions were generated through big data analysis to form a closed-loop control system for the entire process.

[0056] Among them, the intelligent material matching in step three automatically matches modified asphalt above PG76 with basalt aggregate with a silica content of more than 65% in high-temperature and rainy areas, and preferentially matches a combination of high-modulus asphalt and diabase aggregate with a cubic particle ratio of more than 90% in heavy traffic areas.

[0057] By automatically matching high-grade modified asphalt with specific mineral aggregates according to climate and load conditions, it ensures anti-rutting and anti-skid properties in hot and rainy areas, while providing high-load structural support for heavy traffic areas, ensuring the durability and adaptability of the road surface from the source of the material.

[0058] Among them, in step four, GNSS technology is used to accurately obtain the position information of the paver. At the same time, it is combined with the inertial navigation system INS as a supplement to the GNSS positioning system. When the GNSS signal is blocked or interfered with, such as in complex terrain environments such as tunnels and mountainous areas, the INS can rely on its own sensors such as accelerometers and gyroscopes to calculate the position and attitude of the paver.

[0059] INS has high short-term positioning accuracy. By combining with the GNSS system, it can achieve more stable and precise positioning, thus ensuring the continuity of paving quality.

[0060] Among them, the intelligent compaction collaborative control in step five adopts the vibration compaction value (CMV) and infrared temperature fusion control strategy. When the CMV value is lower than 45 and the surface temperature of the mixture is greater than 145°C, the high-frequency vibration mode (40-45Hz) is automatically triggered and two times of compaction are added.

[0061] The dual-parameter fusion control strategy of vibration compaction value and temperature is adopted to achieve dynamic optimization of the compaction process. It can not only prevent aggregate crushing caused by low-temperature overpressure, but also accurately reinforce weak areas, significantly improving compaction uniformity.

[0062] In step five, the roller is equipped with a GNSS receiver to control the lateral rolling overlap width within the range of 15-20 cm.

[0063] By controlling the roller's transverse overlap width through GNSS positioning, the overlap deviation caused by traditional manual marking is eliminated, ensuring the consistency of the compaction density across the entire section and avoiding structural differences at the longitudinal joints.

[0064] Among them, the quality inspection in step six uses multi-source data fusion technology to perform three-dimensional spatial alignment on the inspection results of the laser profiler, nuclear density meter and infrared thermal imaging equipment, build a full-section digital quality model, and automatically identify quality abnormality areas through deep learning algorithms to generate inspection results and maintenance recommendations.

[0065] Through three-dimensional spatial registration and deep learning analysis of multi-source inspection data, a three-dimensional quality evaluation system has been constructed, which can quickly identify hidden defects and associate them with maintenance decisions, greatly improving the predictability and scientific nature of quality control.

[0066] Among them, in step 2, during the grouting reinforcement treatment, the grouting material is nano-silica composite cement-based material, the grouting pressure is controlled in the range of 0.8 to 1.2 MPa, and the filling effect is judged by real-time monitoring of the grouting volume change curve; the surface treatment adopts a full-section milling process with a milling depth of 3 to 5 cm, the residue particle size is controlled at 0 to 5 mm, and high-pressure water jet technology is used to thoroughly remove interface pollutants. The pressure is set to 35 MPa to ensure that the structural depth reaches more than 0.8 mm.

[0067] The application of nano-composite grouting materials and high-pressure water jet technology can optimize the interlayer bonding interface while repairing base defects. By precisely controlling the grouting pressure and surface structure depth, the development path of reflective cracks can be effectively blocked and the structural integrity can be enhanced.

[0068] A municipal road reconstruction project asphalt pavement construction quality control system, including the following modules:

[0069] Original pavement analysis module: used to establish an original pavement geometry database, combine dynamic modulus testing with finite element simulation to analyze interlayer stress distribution, optimize asphalt layer thickness and design stress absorption layers, and output modulus matching models and structural defect repair plans;

[0070] Defect assessment module: Establish a quantitative defect assessment system based on ground penetrating radar and drop weight deflectometer test data;

[0071] Material matching module: Integrates the physical and chemical property data of asphalt, aggregate, and filler, selects suitable materials through a climate-load adaptive algorithm, generates a recommended range for asphalt-to-aggregate ratio using a neural network model, and outputs a material combination plan and gradation verification report;

[0072] Intelligent Paving Module: Integrates GNSS positioning technology and various sensors to monitor paving speed, thickness, and temperature in real time;

[0073] Collaborative rolling module: Automatically adjusts the rolling speed and number of passes according to paving parameters, optimizes the process by combining real-time feedback data from compaction sensors, and achieves compaction defect traceability and cross-device parameter collaboration.

[0074] Quality Inspection and Maintenance Module: Builds a three-dimensional quality archive and develops a big data maintenance decision-making model, outputting defect location maps and digital maintenance priority plans.

[0075] The intelligent paving module relies on high-precision positioning and multi-parameter collaborative control to ensure paving flatness and temperature uniformity. The collaborative rolling module eliminates compaction blind spots and improves interlayer bonding strength through real-time feedback of compaction data and process linkage adjustment.

[0076] Among them, the intelligent paving module includes a GNSS positioning unit, an intelligent leveling unit and an intelligent heating control and monitoring unit. The GNSS positioning unit can obtain the location information of the paver and roller in real time; the intelligent leveling unit automatically adjusts the height of the ironing plate to ensure the flatness and thickness uniformity of the paving layer; the intelligent heating control and monitoring unit automatically adjusts the heating power of the ironing plate according to real-time temperature monitoring data, and the operator can monitor the temperature of the mixture in real time.

[0077] By integrating the three core units of GNSS positioning, intelligent leveling and heating control, an intelligent paving system with full-factor coordination has been constructed. The synergistic effect of the three significantly improves the geometric accuracy of the paving layer and the workability of the material, creating a high-quality working surface for the subsequent compaction process.

[0078] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0079] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for controlling the construction quality of asphalt pavement in a municipal road reconstruction project, characterized in that: The specific steps are as follows: Step 1: Study on original road surface treatment First, three-dimensional laser scanning was performed to map the longitudinal and transverse joints and crack distribution of the original cement concrete pavement. A database of the original pavement geometry was established. In combination with the climatic characteristics of the construction area, dynamic modulus tests were used to determine the viscoelastic parameters of the asphalt mixture under different temperature gradients. A modulus matching model for the asphalt surface layer and cement base layer was established. Finite element analysis software was used to simulate the distribution of interlayer shear stress under vehicle dynamic loads to determine the optimal asphalt layer thickness. The problem of reflective cracks was addressed by installing a modified asphalt stress absorption layer. Step 2: Assessment and treatment of original pavement defects Conduct a graded assessment of existing pavement damage, using ground-penetrating radar and a drop weight deflectometer for non-destructive testing of the entire road section. Establish a quantitative damage evaluation system that includes indicators such as crack width, base layer voids, and joint misalignment. Grouting reinforcement is implemented for severe damage detected during testing (cracks greater than 5 mm in width and accompanied by base layer voids). Step 3: Intelligent material matching The physicochemical property data of asphalt, aggregate, and filler are integrated, including parameters such as asphalt viscosity-temperature curve, aggregate alkalinity / silica content, and filler specific surface area, to form a standardized material performance archive. During construction, suitable materials are intelligently screened from the database based on the climate characteristics of the construction area (extreme temperatures, precipitation intensity) and traffic load levels. Based on the matching material combinations, a neural network model is trained using historical engineering data to generate a recommended range for the asphalt-to-stone ratio that meets the target parameters, achieving precise mix control. Step 4: Intelligent paving Through the intelligent paving system, construction parameters such as paving speed, material level, vibration frequency, etc. are collected in real time; During the asphalt pavement paving process, by installing a high-precision GNSS receiver on the paver, GNSS technology can accurately obtain the paver's location information and determine the paver's three-dimensional coordinates (longitude, latitude and elevation) in real time; The non-contact balance beam intelligent leveling unit in the intelligent paving system measures the distance between the road surface and the sensors in real time by installing multiple ultrasonic or laser sensors in front of the paver screed. The sensors then feed the distance signals back to the paver control system, which automatically adjusts the screed height based on these signals to ensure the flatness and thickness uniformity of the paving layer. The accuracy of the flatness and thickness uniformity of the road surface reaches ±1mm, effectively avoiding flatness defects such as waves and potholes on the road surface. The paver is equipped with an intelligent heating control and monitoring unit in the intelligent paving system, which automatically adjusts the heating power of the screed plate based on real-time temperature monitoring data. When the mixture temperature is slightly lower than the optimal paving temperature, the heating power is increased to raise the screed plate temperature, thereby maintaining good paving performance of the mixture. At the same time, an infrared temperature sensor is installed on the paver to monitor the paving temperature of the asphalt mixture in real time. Through real-time temperature monitoring, construction personnel can promptly detect temperature anomalies. For example, if the temperature is too low, it may make it difficult to compact the mixture. In this case, construction personnel can promptly detect and take measures to adjust the situation. Step 5: Intelligent Compaction Collaborative Control After a section of pavement is completed, data is shared between intelligent paving units, and the rolling equipment can automatically adjust rolling parameters, such as rolling speed and number of rolling passes, based on key information such as the paving thickness and temperature of that section. During the rolling process, the compaction quality of the road surface can be detected in real time through the compaction detection sensor installed on the roller. The compaction quality data is fed back to the intelligent paving system, which adjusts the parameters of subsequent paving sections based on the feedback information. For example, if the compaction degree of a certain area is found to be insufficient, the paving system can adjust the paving thickness of the next section or the gradation of the mixture to improve the overall road surface quality. Step 6: Quality Inspection During the construction process, a mobile laboratory is deployed to conduct quality tracking inspections. A laser profiler is used to check the flatness every 50 meters, and a nuclear density meter is used to check the compaction every 100 meters. At the same time, a three-dimensional quality file is established, including indicators such as the interlayer bonding condition detected by infrared thermal imaging and the internal void ratio distribution detected by geological radar. In addition, a data detection and evaluation technology based on big data is developed to analyze and process the detection data. After the detection is completed, maintenance recommendations are generated to form a digital maintenance decision support system.

2. A method for controlling the construction quality of asphalt pavement in a municipal road reconstruction project according to claim 1, characterized in that: The intelligent material matching described in step three automatically matches modified asphalt of PG76 or above with basalt aggregate with a silica content of >65% in high-temperature and rainy areas, and preferentially matches a combination of high modulus asphalt and diabase aggregate with a cubic particle ratio of >90% in heavy traffic areas.

3. A method for controlling the construction quality of asphalt pavement in a municipal road reconstruction project according to claim 1, characterized in that: As described in step 4, GNSS technology is used to accurately obtain the position information of the paver. At the same time, it is combined with the inertial navigation system INS as a supplement to the GNSS positioning system. When the GNSS signal is blocked or interfered with, such as in complex terrain environments such as tunnels and mountainous areas, the INS can rely on its own sensors such as accelerometers and gyroscopes to calculate the position and attitude of the paver.

4. A method for controlling the construction quality of asphalt pavement in a municipal road reconstruction project according to claim 1, characterized in that: The intelligent compaction collaborative control described in step five adopts a vibration compaction value (CMV) and infrared temperature fusion control strategy. When the CMV value is lower than 45 and the surface temperature of the mixture is greater than 145°C, the high-frequency vibration mode (40-45Hz) is automatically triggered and two additional compaction passes are added.

5. The method for controlling the construction quality of asphalt pavement in a municipal road reconstruction project according to claim 1, characterized in that: The roller described in step 5 is equipped with a GNSS receiver to control the lateral rolling overlap width within the range of 15-20 cm.

6. A method for controlling the construction quality of asphalt pavement in a municipal road reconstruction project according to claim 1, characterized in that: The quality inspection described in step six uses multi-source data fusion technology to perform three-dimensional spatial alignment on the inspection results of the laser profiler, nuclear density meter, and infrared thermal imaging equipment, build a full-section digital quality model, and automatically identify quality abnormality areas through deep learning algorithms to generate inspection results and maintenance recommendations.

7. The method for controlling the construction quality of asphalt pavement in a municipal road reconstruction project according to claim 1, characterized in that: During the grouting reinforcement treatment described in step 2, the grouting material is a nano-silica composite cement-based material, the grouting pressure is controlled in the range of 0.8 to 1.2 MPa, and the filling effect is judged by real-time monitoring of the grouting volume change curve; the surface treatment adopts a full-section milling process with a milling depth of 3 to 5 cm, the residue particle size is controlled at 0 to 5 mm, and high-pressure water jet technology is used to thoroughly remove interface pollutants. The pressure is set to 35 MPa to ensure that the structural depth reaches more than 0.8 mm.

8. A municipal road reconstruction project asphalt pavement construction quality control system, characterized in that: Includes the following modules: Original pavement analysis module: used to establish an original pavement geometry database, combine dynamic modulus testing with finite element simulation to analyze interlayer stress distribution, optimize asphalt layer thickness and design stress absorption layers, and output modulus matching models and structural defect repair plans; Defect assessment module: Establish a quantitative defect assessment system based on ground penetrating radar and drop weight deflectometer test data; Material matching module: Integrates the physical and chemical property data of asphalt, aggregate, and filler, selects suitable materials through a climate-load adaptive algorithm, generates a recommended range for asphalt-to-aggregate ratio using a neural network model, and outputs a material combination plan and gradation verification report; Intelligent Paving Module: Integrates GNSS positioning technology and various sensors to monitor paving speed, thickness, and temperature in real time; Collaborative rolling module: Automatically adjusts the rolling speed and number of passes according to paving parameters, optimizes the process by combining real-time feedback data from compaction sensors, and achieves compaction defect traceability and cross-device parameter collaboration. Quality Inspection and Maintenance Module: Builds a three-dimensional quality archive and develops a big data maintenance decision-making model, outputting defect location maps and digital maintenance priority plans.

9. The asphalt pavement construction quality control system for municipal road reconstruction projects according to claim 8, characterized in that: The intelligent paving module includes a GNSS positioning unit, an intelligent leveling unit, and an intelligent heating control and monitoring unit. The GNSS positioning unit can obtain the position information of the paver and roller in real time; the intelligent leveling unit automatically adjusts the height of the screed to ensure the flatness and thickness uniformity of the paving layer; the intelligent heating control and monitoring unit automatically adjusts the heating power of the screed based on real-time temperature monitoring data, and the operator can monitor the temperature of the mixture in real time.

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