Highway asphalt pavement intelligent construction system and method based on multi-technology fusion

Abstract

The application relates to the technical field of road engineering construction, and discloses a highway asphalt pavement intelligent construction system and method based on multi-technology fusion. The construction system adopts a three-layer distributed architecture. A data acquisition layer collects data such as terrain, temperature and construction parameters in real time through a UAV and multiple sensors. A data processing and analysis layer relies on cloud computing, BIM three-dimensional modeling and big data analysis to realize construction parameter calculation and temperature decay prediction. A control execution layer receives instructions through 5G communication, intelligently adjusts parameters of a paver and a road roller, and forms a closed-loop management and control in combination with a construction management decision module. The construction method comprises BIM modeling and UAV terrain scanning in the preparation stage, precise paving and intelligent compaction in the implementation stage, UAV inspection and deviation rectification in the management and control stage. The application realizes intelligent management and control of the whole construction life cycle, improves compaction uniformity, reduces rework rate and construction cost, and solves problems such as quality detection lag and low construction accuracy in traditional construction.

Description

Technical Field

[0001] This invention relates to the field of road engineering construction technology, and in particular to an intelligent construction system and method for highway asphalt pavement based on the integration of multiple technologies. Background Technology

[0002] As a core component of transportation infrastructure, the construction quality of asphalt pavement directly affects the road's traffic capacity, durability, and safety.

[0003] However, current highway asphalt pavement construction still faces many technical bottlenecks:

[0004] 1. Traditional asphalt mixture quality control relies on sampling and testing, which cannot monitor key indicators such as temperature decay and segregation of the mixture in real time throughout the mixing, transportation and paving process. This leads to fluctuations in the performance of the mixture, which in turn causes early cracking and loosening of the pavement and shortens the service life of the road.

[0005] 2. The operation of pavers and rollers relies heavily on human experience. The error in controlling the longitudinal and transverse slopes usually exceeds 5mm, and the unevenness of compaction reaches ±3%. This not only affects the smoothness of the road surface and driving comfort, but also reduces the structural strength of the road surface and increases the cost of later maintenance. Summary of the Invention

[0006] To overcome the above shortcomings, this invention provides an intelligent construction system and method for highway asphalt pavement based on the integration of multiple technologies. Through collaborative innovation of multiple technologies, it realizes intelligent management and control of the entire life cycle of asphalt pavement construction.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: an intelligent construction system for highway asphalt pavement based on multi-technology integration, comprising a data acquisition layer, a data processing and analysis layer, and a control execution layer; the data acquisition layer consists of UAV measuring equipment, asphalt mixing plant temperature sensors, transport vehicle temperature sensors, paver multi-parameter sensors, and roller multi-parameter sensors; the UAV measuring equipment collects topographic point cloud data of the construction area; the asphalt mixing plant temperature sensors and transport vehicle temperature sensors monitor the temperature data of the asphalt during mixing and transportation; the paver multi-parameter sensors and roller multi-parameter sensors collect various operational parameters in real time during the construction process; these data and parameters are transmitted to the data processing and analysis layer via a communication network;

[0008] The data processing and analysis layer includes a cloud computing platform, a BIM 3D model management module, and a big data analysis engine. The cloud computing platform receives various data and parameters transmitted from the data acquisition layer, analyzes and processes them, and then transmits them to the BIM 3D model management module and the big data analysis engine. The BIM 3D model management module and the big data analysis engine analyze and process the data transmitted from the cloud computing platform, perform model predictions, and output decision instructions to the control execution layer.

[0009] The control execution layer consists of a 5G communication module, a paver intelligent control system, a roller intelligent control system, and a construction management decision module. Each layer achieves millisecond-level real-time data interaction through the 5G communication module. The paver intelligent control system and the roller intelligent control system receive instructions from the data processing and analysis layer and make specific adjustments to the construction parameters. The construction management decision module automatically generates rectification work orders or data archives based on the analysis results of the big data analysis engine, realizing intelligent management and control of the entire construction process.

[0010] Furthermore: the multi-parameter sensor of the road roller integrates an infrared temperature sensor, a pressure sensor, a speed sensor, and a vibration frequency sensor, which are used to collect temperature, pressure, travel speed, and vibration frequency data of the road roller in real time during the compaction process; the UAV measurement equipment acquires topographic point cloud data of the construction area through oblique photography, providing basic data support for terrain modeling and construction parameter calculation.

[0011] Furthermore: the BIM 3D model management module constructs a full-section BIM model containing the subgrade, base course, and surface course based on the design drawings, and decomposes the pavement into standardized construction units. Each unit is associated with operational parameters such as material mix proportions, paving thickness, and number of compaction passes, as well as the mixture temperature data output by temperature sensors from the asphalt mixing plant and transport vehicles. The big data analysis engine incorporates a heat conduction model and a machine learning model to integrate multi-source data to calculate construction parameters and predict temperature decay trends.

[0012] Furthermore: the 5G communication module supports 5G base stations and BeiDou / GNSS dual-mode positioning systems to provide real-time positioning for the intelligent paver; the 5G communication module supports millisecond-level data transmission to ensure that positioning data is synchronized with the actions of construction equipment in real time; the paver intelligent control system can automatically adjust the screed elevation angle, tamping hammer frequency, and auger spreader speed; the road roller intelligent control system can automatically adjust the vibration motor parameters, travel speed, and wheel load pressure.

[0013] The intelligent construction method for highway asphalt pavement based on the integration of multiple technologies includes the following steps:

[0014] Step S1, Construction Preparation Stage: A full-section BIM model is established through the BIM 3D model management module and decomposed into standardized construction units. After associating the operation parameters, the model is transmitted to the BIM model server. Terrain point cloud data is acquired using UAV surveying equipment and then fused with the BIM model through a big data analysis engine to calculate the construction parameters for each mileage segment.

[0015] Step S2, Construction Implementation Stage: Real-time positioning of the intelligent paver and 3D intelligent paving are achieved through 5G+BeiDou / GNSS dual-mode positioning; paving speed and compaction time are dynamically adjusted based on the heat conduction model; a compaction degree prediction model is established through machine learning model to automatically adjust the roller parameters;

[0016] Step S3, Construction Completion and Control Phase: The drone collects orthophotos at a preset frequency, compares them with the BIM model to generate a 3D deviation cloud map, and the construction management decision module performs parameter adjustments, rectifications or data archiving based on the deviation analysis to form a closed-loop management.

[0017] Further: In step S1, the specific steps for establishing the full-section BIM model are as follows: In the BIM three-dimensional model management module, associate the material data of the asphalt mixing plant temperature sensor and the transport vehicle temperature sensor with each standardized construction unit, including the asphalt mixture mix proportion data; and the operating parameters of the intelligent paver, vibration motor adjustment, driving speed adjustment, and wheel load pressure adjustment, including paving thickness and number of compaction passes.

[0018] Furthermore, the specific steps of 5G paver positioning and 3D intelligent paving in step S2 are as follows: the 5G base station and Beidou / GNSS provide real-time positioning data through the 5G communication module; the multi-parameter sensor group collects screed status data, and the paving parameters are automatically adjusted by the paver intelligent control system; in the curved and transition sections, the BIM model server generates a smooth paving trajectory, and the intelligent paver performs slope paving according to the trajectory.

[0019] Further: The specific steps for dynamically adjusting the paving speed and compaction time in step S2 are as follows: Temperature sensors at the asphalt mixing plant and transport vehicles collect temperature data in real time. A big data analysis engine, combined with environmental parameters such as ambient temperature and wind speed, predicts the temperature decay trend using a heat conduction model. This prediction is used to dynamically adjust the paving speed of the intelligent paver and the compaction time of the road roller, ensuring that the asphalt mixture is compacted within a suitable temperature range. The calculation formula is based on Fourier's law of heat conduction. In the formula Constant temperature of the mixture; Initial temperature of the mixture; Thermal conductivity of asphalt mixture; : Exposed surface area of ​​the mixture; : Density of the mixture; Specific heat capacity of the mixture; : Mixture volume; Wind speed correction factor; Ambient wind speed; Cooling time; Transportation distance; The difference between the ambient temperature and the initial temperature of the mixture.

[0020] Furthermore: the calculation formula for the compaction degree prediction model in step S2 is as follows: In the formula Predicting compaction degree; Temperature of the mixture at the moment of compaction; : Roller wheel load pressure; : The speed of the road roller; : Vibration frequency of the road roller; Fitting coefficient; calculates compaction degree in real time using sensor data, providing quantitative instructions for adjusting roller parameters.

[0021] Furthermore, the specific steps for forming closed-loop management in step S3 are as follows: if the deviation analysis determines that the deviation exceeds the standard, the construction management decision module automatically generates a rectification work order, issues a construction parameter adjustment instruction through the 5G communication module, and re-constructs the defective area; if the deviation determines that the standard is met, the construction data is archived for subsequent construction optimization; after the rectification is completed, the process of UAV orthophoto acquisition, image recognition processing, BIM model comparison, and deviation analysis is repeated until the quality meets the standard.

[0022] The present invention has the following beneficial effects:

[0023] 1. In this invention, a complete closed loop of data collection, analysis, decision-making, execution, and feedback is constructed. The BIM model runs through the entire cycle of design, construction, and management to ensure the consistency and traceability of parameters at each stage of construction. Distributed temperature sensors enable real-time monitoring of the entire process of asphalt mixture from mixing and transportation to paving. Combined with a heat conduction model to predict temperature decay, temperature segregation is effectively avoided, early pavement defects are reduced, and the service life of the road is extended.

[0024] 2. In this invention, drone inspection, BIM model comparison, and automated data statistics replace traditional manual inspection and data processing, shortening the inspection cycle, enabling real-time feedback and dynamic adjustment of construction quality, reducing rework rate, and saving construction costs; relying on 5G and Beidou / GNSS dual-mode positioning to achieve centimeter-level paving positioning, combined with modular management of the BIM model and automatic adjustment of intelligent equipment, the longitudinal slope and cross slope errors are controlled within 2mm, the compaction unevenness is significantly reduced, and the road surface smoothness and structural strength are greatly improved. Attached Figure Description

[0025] Figure 1 This is a diagram of the overall architecture of the intelligent construction system.

[0026] Figure 2 This is a flowchart of the entire construction process;

[0027] Figure 3 Schematic diagram of 5G paver positioning and 3D paving principle;

[0028] Figure 4 Construction drawings for multi-parameter intelligent compaction of road rollers;

[0029] Figure 5 This is a flowchart for construction progress and quality control. Detailed Implementation

[0030] 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.

[0031] Example 1

[0032] Reference Figure 1-5 The present invention provides an embodiment of an intelligent construction system for highway asphalt pavement based on the integration of multiple technologies, comprising a data acquisition layer, a data processing and analysis layer, and a control execution layer; each layer achieves millisecond-level real-time data interaction through a 5G communication module;

[0033] like Figure 1 and Figure 2 As shown, the data acquisition layer provides basic data support for the entire system process, consisting of UAV surveying equipment, asphalt mixing plant temperature sensors, transport vehicle temperature sensors, paver multi-parameter sensors, and roller multi-parameter sensors. The UAV surveying equipment acquires topographic point cloud data of the construction area using oblique photography technology, with an accuracy of ±5cm, providing basic data for terrain modeling and construction parameter calculation. The asphalt mixing plant temperature sensors monitor the temperature data during the mixing process in real time, ensuring that the initial temperature of the mixture meets construction requirements. The transport vehicle temperature sensors collect real-time temperature changes during transport to prevent temperature segregation. The paver multi-parameter sensors collect operational parameters such as screed temperature, paving speed, and auger distributor speed. The roller multi-parameter sensors integrate infrared temperature sensors, pressure sensors, speed sensors, and vibration frequency sensors (e.g., [missing information]). Figure 4 As shown in the figure, the temperature, pressure, travel speed and vibration frequency data of the road roller are collected in real time during the compaction process, providing data support for intelligent compaction;

[0034] like Figure 1 and Figure 2 As shown, the data processing and analysis layer is the "core brain" of the system, including a cloud computing platform, a BIM 3D model management module, and a big data analysis engine. The BIM 3D model management module constructs a full-section BIM model containing the subgrade, base course, and surface course based on design drawings. It decomposes the pavement into standardized construction units at 100m / module, and each unit is associated with operational parameters such as material mix proportions, paving thickness, and number of compaction passes. The big data analysis engine incorporates a heat conduction model and a machine learning model, integrates real-time data from the data acquisition layer with BIM model data, calculates construction parameters such as asphalt usage and paving thickness for each mileage section, and predicts the temperature decay trend of the mixture. The cloud computing platform performs distributed computing processing on multi-source heterogeneous data, generates construction parameter adjustment instructions, and provides decision-making basis for the control execution layer.

[0035] like Figure 1 , Figure 3 and Figure 5 As shown, the control execution layer is the system's "execution terminal," consisting of a 5G communication module, a paver intelligent control system, a roller intelligent control system, and a construction management decision module. The 5G communication module supports 5G base stations and a BeiDou / GNSS dual-mode positioning system, providing real-time positioning with an accuracy of ±2cm for the intelligent paver, and supports millisecond-level data transmission to ensure real-time synchronization of positioning data, construction instructions, and equipment actions. The paver intelligent control system receives instructions from the data processing and analysis layer and automatically adjusts parameters such as the screed elevation angle, tamping hammer frequency, and auger distributor speed. The roller intelligent control system receives instructions from the data processing and analysis layer and automatically adjusts parameters such as the vibratory motor parameters, travel speed, and wheel load pressure to ensure uniform compaction. Based on the deviation analysis results from the big data analysis engine, the construction management decision module automatically generates rectification work orders or data archives, achieving closed-loop management of construction quality.

[0036] like Figure 1-5 As shown, the construction method of the present invention is based on the above system and includes three core processes: construction preparation stage, construction implementation stage, and construction completion and control stage.

[0037] I. Construction Preparation Stage:

[0038] 1. BIM 3D modular modeling (combined with...) Figure 2 , Figure 3 ):

[0039] 1.1 System Interaction: The BIM model constructed by the BIM 3D model management module is transmitted to the BIM model server through the 5G communication module to provide paving benchmark data for the intelligent paver; each standardized construction unit is associated with the material mix ratio data output by the temperature sensors of the asphalt mixing plant and the temperature sensors of the transport vehicles, as well as the paving thickness, number of compaction passes, and other operational parameters of the intelligent paver, vibration motor adjustment, travel speed adjustment, and wheel load pressure adjustment.

[0040] 1.2 Implementation Steps: After receiving the design input files, a full-section BIM model is established through the BIM 3D model management module, and decomposed into standardized construction units according to 100m mileage. In the BIM 3D model management module, material data from temperature sensors of the asphalt mixing plant and temperature sensors of transport vehicles are associated with each standardized construction unit. The material data includes asphalt mixture proportion data, as well as operating parameters for intelligent paver, vibration motor adjustment, travel speed adjustment, and wheel load pressure adjustment. The operating parameters include paving thickness and number of compaction passes. A digital construction blueprint is formed, realizing refined modeling of the construction area, providing an accurate data foundation for subsequent intelligent construction, and improving the accuracy of construction planning.

[0041] 2. UAV terrain modeling and parameter calculation (combined with...) Figure 2 , Figure 3 ):

[0042] 2.1 System Collaboration: The terrain point cloud data acquired by the UAV surveying equipment is transmitted to the big data analysis engine through the 5G communication module, and is fused with the BIM model data of the BIM 3D model management module to generate the optimal construction parameters.

[0043] 2.2 Implementation Process: The construction area is photographed obliquely using UAV surveying equipment to obtain topographic point cloud data and transmit it to the big data analysis engine. The big data analysis engine processes the data and generates a 3D terrain model. Combined with the BIM model data, construction parameters such as asphalt usage and paving thickness for each mileage section are calculated. The calculation results are synchronized to the BIM model server as the benchmark data for intelligent paver paving operations. Construction parameters are automatically generated through multi-source data fusion to improve construction planning efficiency.

[0044] II. Construction Implementation Phase

[0045] 1. 5G paver positioning and 3D intelligent paving (combined) Figure 3 ):

[0046] 1.1 Technological Integration: The 5G base station and BeiDou / GNSS provide real-time positioning data to the intelligent paver through the 5G communication module. The multi-parameter sensor group collects screed status data, which is then processed by the paver's intelligent control system to automatically adjust the paving parameters.

[0047] 1.2 Implementation Process: The intelligent paver receives module construction parameter instructions sent by the BIM model server and automatically adjusts parameters such as screed elevation angle, tamping hammer frequency, and auger spreader speed. In complex areas such as curves and transition sections, the BIM model server generates a smooth transition paving trajectory based on the BIM model. The intelligent paver executes slope paving according to the instructions, controlling the longitudinal and transverse slope errors to ≤2mm, achieving centimeter-level paving accuracy and improving road surface smoothness.

[0048] 2. Intelligent temperature field analysis and multi-parameter intelligent compaction of road rollers (combined) Figure 2 , Figure 4 ):

[0049] 2.1 Data Closed Loop: The temperature data of the asphalt mixture collected by the temperature sensor at the asphalt mixing plant and the multi-parameter sensor on the road roller are transmitted to the big data analysis engine via a 5G communication module. After analysis by the heat conduction model, the generated adjustment commands are transmitted via the 5G communication module to the intelligent paver and vibration motor for adjustment, travel speed adjustment, and wheel load pressure adjustment. The heat conduction model is based on the calculation formula of Fourier's law of heat conduction. In the formula Temperature of the mixture at any given time (°C); Initial temperature of the mixture (discharge temperature of the mixing plant, °C); Thermal conductivity of asphalt mixture ( (Values ​​range from 0.8 to 1.2). : Exposed surface area of ​​the mixture (㎡, heat dissipation area of ​​transport vehicles / paving stockpile); : Mixture density (kg / m³, typically 2400~2500); Specific heat capacity of the mixture ( (Values ​​range from 900 to 1100). : Mixture volume (m³); Wind speed correction factor (0.01~0.03, which increases with increasing wind speed). Ambient wind speed (m / s); Heat dissipation time (s, total time from mixing to transportation to paving); Transport distance (m); The difference between ambient temperature and initial temperature of the mixture (°C). ); accurately calculate the temperature of the mixture under different time and environmental conditions.

[0050] 2.2 Implementation steps: During the preparation, transportation and paving of asphalt mixture, temperature sensors at the asphalt mixing plant and temperature sensors on the transport vehicles collect temperature data in real time. The big data analysis engine uses a heat conduction model, combined with parameters such as ambient temperature and wind speed, to predict the temperature decay trend and dynamically adjust the paving speed of the intelligent paver and the compaction time of the road roller.

[0051] The machine learning model establishes a compaction degree prediction model based on compaction data such as temperature, pressure, velocity, and vibration frequency collected by infrared temperature sensors and vibration frequency sensors. The calculation formula is as follows: In the formula Predicted compaction degree (%, target ≥96%); Temperature of the mixture at the moment of compaction (°C); : Roller wheel load pressure (kPa, 300~400); : Roller travel speed (km / h, 3-5); : Vibration frequency of the road roller (Hz, 40-50); Fitting coefficients (calibrated using field test data, example: a=0.12, b=0.08, c=-1.5, d=0.3, e=65); automatically calculates and adjusts parameters such as vibration motor adjustment, travel speed adjustment, and wheel load pressure adjustment; effectively controls parameters such as asphalt mixture temperature, paver feeder speed, screed tamping frequency, travel speed, and roller compaction, thereby improving the compaction quality of asphalt mixture.

[0052] III. Digital Management of Construction Progress and Quality (Combined with...) Figure 5 )

[0053] Intelligent decision-making process: After the orthophoto data acquired by the UAV is processed by image recognition, it is compared with the BIM model on the BIM model server to generate a three-dimensional deviation cloud map. The construction management decision module triggers the corresponding response based on the deviation analysis results.

[0054] Implementation Process: UAV orthophotos are collected on the constructed road sections at a preset inspection frequency to extract paving thickness and smoothness, which are then compared with the BIM model. If deviation analysis determines that the deviation exceeds the acceptable range, the construction management decision module automatically generates a rectification work order and issues automatic adjustment instructions for construction parameters via the 5G communication module. If the deviation is deemed acceptable, the data is archived to guide subsequent construction optimization. This process enables real-time monitoring and dynamic optimization of construction quality, improving quality inspection efficiency, reducing rework rates, and enhancing construction management efficiency.

[0055] Multi-source data interaction mechanism: Sensor data from the data acquisition layer is transmitted in real time to the data processing and analysis layer via a 5G communication module. The analysis results from the big data analysis engine are synchronized to the intelligent paver and the vibration motor adjustment-wheel load pressure adjustment, forming a complete closed loop of "perception-analysis-execution" (e.g., Figure 1 This ensures intelligent control of the construction process;

[0056] BIM Model Full Lifecycle Drive: The BIM model in the BIM 3D model management module runs through the entire construction process, from module decomposition in the design stage to deviation analysis in the construction stage, realizing digital control of the entire life cycle of asphalt pavement construction, and ensuring the accuracy and traceability of the construction process.

[0057] 5G communication collaborative optimization: The 5G communication module supports millisecond-level data transmission, ensuring that the positioning data of the 5G base station is synchronized with the paving actions of the intelligent paver in real time (e.g., Figure 3 This provides reliable communication support for high-precision construction, improving the system's response speed and construction accuracy.

[0058] Example 2

[0059] Reference Figure 1-4 The deployment and debugging process of the intelligent construction system provided by this invention is as follows:

[0060] I. Equipment Selection and Deployment

[0061] Unmanned aerial vehicle (UAV) measurement equipment: A multi-rotor UAV with oblique photography capability is selected, equipped with a high-definition camera and positioning module to ensure point cloud data accuracy of ±5cm;

[0062] Temperature sensor: A high-precision infrared temperature sensor is selected, with a measurement range of -40℃ to 300℃ and an error of ≤±1℃. It is installed at the discharge port of the asphalt mixing plant, inside the truck bed of the transport vehicle, and in the hopper of the paver.

[0063] Multi-parameter sensors for pavers: These sensors integrate speed, temperature, and pressure sensors and are installed on key components such as the paver's screed and auger spreader.

[0064] Multi-parameter sensors for road rollers: integrating infrared temperature sensors, pressure sensors, speed sensors, and vibration frequency sensors, installed on the road roller drum and body;

[0065] 5G communication module: An industrial-grade communication module supporting 5G SA mode is selected, which is compatible with Beidou / GNSS dual-mode positioning and has a positioning accuracy of ±2cm.

[0066] Cloud computing platform: It adopts a distributed server cluster, supports the processing capacity of more than 100,000 data entries per second, and deploys BIM model management software and big data analysis algorithms.

[0067] II. System Debugging

[0068] BIM Model Construction and Debugging: Import design drawings into BIM software to construct a full-section BIM model. After decomposing it into 100m modules, associate parameters such as material mix ratio and paving thickness. Test the transmission delay of model data through the 5G communication module to ≤10ms.

[0069] Sensor calibration: Calibrate all temperature sensors, pressure sensors, etc., to ensure that the data acquisition accuracy meets the requirements;

[0070] Equipment linkage test: Simulate construction scenarios to test the linkage response time of drone point cloud data transmission, BIM model parameter distribution, and automatic adjustment of paver / roller, to ensure that the actions of each piece of equipment are synchronized.

[0071] Reference Figure 2-5 The present invention provides a specific intelligent construction process for highway asphalt pavement:

[0072] Taking the construction of the asphalt surface layer of a highway as an example, the construction section is 5km long, and the system and method of this invention are used for construction:

[0073] Step A, Construction Preparation Stage:

[0074] Step a1, Design Input and BIM Modeling: After receiving the design drawings, a full-section BIM model including the subgrade, base course, and surface course is created through the BIM 3D model management module. The model is decomposed into 50 standardized construction units per 100m. Each unit is associated with parameters such as the mix proportion of AC-13 type asphalt mixture (asphalt content 4.8%), paving thickness 4cm, and number of compaction passes 6. The model data is then transmitted to the BIM model server.

[0075] Step a2, UAV terrain scanning: Use a UAV to conduct oblique photography of the 5km construction area to obtain terrain point cloud data. After transmitting it to the big data analysis engine, combine it with the BIM model to calculate the asphalt usage of each unit (the average asphalt usage per unit is about 2.3t) and the fine-tuning value of the paving thickness (adjusted within ±0.5cm according to the terrain undulation).

[0076] Step a3, Equipment Deployment: Complete the installation and debugging of sensors for the mixing plant, transport vehicles, pavers, and road rollers to ensure normal data acquisition.

[0077] Step B, Construction Implementation Phase:

[0078] Step b1, Mixture Preparation and Transportation: The mixing plant produces the mixture according to the mix proportion associated with the BIM model. Temperature sensors monitor the discharge temperature in real time (controlled between 160℃ and 170℃). The transport vehicles are equipped with temperature sensors to upload temperature data during transportation in real time. The big data analysis engine combines ambient temperature (25℃) and wind speed (3m / s) to predict temperature decay and dynamically adjust the transportation route to ensure that the temperature of the mixture upon arrival is ≥150℃.

[0079] Step b2, 5G paver positioning and paving: The 5G base station and Beidou / GNSS provide real-time positioning for the intelligent paver. The paver receives parameter instructions from the BIM model server and automatically adjusts the screed elevation angle (initial elevation angle 1.2°), tamping hammer frequency (30Hz), and auger spreader speed (20r / min). In curved areas, the BIM model server generates a smooth paving trajectory, and the paver performs variable slope paving according to the trajectory. The measured longitudinal slope and cross slope errors are both ≤1.5mm.

[0080] Step b3, Multi-parameter compaction of the road roller: After the road roller is started, multi-parameter sensors collect data on compaction temperature (initial compaction temperature 145℃), pressure (350kPa), travel speed (4km / h), and vibration frequency (45Hz) in real time. The machine learning model establishes a compaction degree prediction model based on the data. When it detects that the compaction degree of a certain area is insufficient (below 96%), it automatically adjusts the vibration frequency to 50Hz and reduces the travel speed to 3km / h to ensure the uniformity of compaction degree (error ≤ ±1%).

[0081] Step C, Construction Completion and Control Phase:

[0082] Step c1, UAV inspection: After the daily construction is completed, the UAV will collect orthophotos of the constructed road section. After the images are processed, the paving thickness (measured value 3.9~4.1cm) and flatness (IRI value ≤2.0m / km) will be extracted.

[0083] Step c2, Deviation Analysis and Closed-Loop Management: Compare the measured data with the BIM model to generate a 3D deviation cloud map. After analysis by the construction management decision module, it is determined that there are no areas exceeding the tolerance, and the data is archived. For the local flatness deviation (IRI value 2.3m / km) detected in the 4.7km~4.9km area, a rectification work order is automatically generated, and parameter adjustment instructions are issued (the frequency of the paver tamper is adjusted to 32Hz). After re-paving, the standard is re-measured and met.

[0084] Step c3, Data Archiving: After construction is completed, the system automatically archives all process data (BIM model, sensor data, adjustment records, quality inspection reports) to the cloud computing platform to form a construction quality traceability archive.

[0085] Construction effect verification:

[0086] The construction section in this embodiment has been tested, and all indicators are superior to those of traditional construction methods:

[0087] Road surface smoothness: The average IRI value is 1.8 m / km, which is 28% higher than that of traditional construction (average 2.5 m / km);

[0088] Compaction degree: Average compaction degree 97.5%, non-uniformity ≤ ±1%, which is significantly improved compared with traditional construction (non-uniformity ±3%);

[0089] Construction efficiency: The average daily construction distance is 1.2km, which is 50% higher than that of traditional construction (0.8km per day).

[0090] Rework rate: The rework rate is only 0.8%, which is significantly lower than that of traditional construction (5% rework rate).

[0091] This invention achieves intelligent construction of highway asphalt pavement through the integration of multiple technologies, effectively solving many defects of traditional construction, and has significant technical advantages and application value.

Claims

1. An intelligent construction system for highway asphalt pavement based on the integration of multiple technologies, characterized in that: It includes a data acquisition layer, a data processing and analysis layer, and a control execution layer; The data acquisition layer consists of UAV surveying equipment, asphalt mixing plant temperature sensors, transport vehicle temperature sensors, paver multi-parameter sensors, and roller multi-parameter sensors. The UAV surveying equipment collects topographic point cloud data of the construction area; the asphalt mixing plant temperature sensors and transport vehicle temperature sensors monitor the temperature data of the asphalt during mixing and transportation; the paver multi-parameter sensors and roller multi-parameter sensors collect various operational parameters in real time during construction; and these data and parameters are transmitted to the data processing and analysis layer via a communication network. The data processing and analysis layer includes a cloud computing platform, a BIM 3D model management module, and a big data analysis engine. The cloud computing platform receives various data and parameters transmitted from the data acquisition layer, analyzes and processes them, and then transmits them to the BIM 3D model management module and the big data analysis engine. The BIM 3D model management module and the big data analysis engine analyze and process the data transmitted from the cloud computing platform, perform model predictions, and output decision instructions to the control execution layer. The control execution layer consists of a 5G communication module, a paver intelligent control system, a road roller intelligent control system, and a construction management decision module. Each layer achieves millisecond-level real-time data interaction through the 5G communication module. The paver intelligent control system and the road roller intelligent control system receive instructions from the data processing and analysis layer and make specific adjustments to the construction parameters. The construction management decision module automatically generates rectification work orders or data archives based on the analysis results of the big data analysis engine.

2. The intelligent construction system for highway asphalt pavement based on multi-technology integration as described in claim 1, characterized in that: The multi-parameter sensor for the road roller integrates an infrared temperature sensor, a pressure sensor, a speed sensor, and a vibration frequency sensor, which are used to collect temperature, pressure, travel speed, and vibration frequency data of the road roller in real time during the compaction process; the UAV measurement equipment acquires topographic point cloud data of the construction area through oblique photography, providing basic data support for terrain modeling and construction parameter calculation.

3. The intelligent construction system for highway asphalt pavement based on multi-technology integration according to claim 1, characterized in that: The BIM 3D model management module constructs a full-section BIM model containing the subgrade, base course, and surface course based on the design drawings. It decomposes the pavement into standardized construction units, with each unit associated with operational parameters such as material mix proportions, paving thickness, and number of compaction passes, as well as mixture temperature data output by temperature sensors from the asphalt mixing plant and transport vehicles. The big data analysis engine incorporates a heat conduction model and a machine learning model to integrate multi-source data to calculate construction parameters and predict temperature decay trends.

4. The intelligent construction system for highway asphalt pavement based on multi-technology integration as described in claim 1, characterized in that: The 5G communication module supports 5G base stations and Beidou / GNSS dual-mode positioning systems to provide real-time positioning for intelligent pavers. The 5G communication module supports millisecond-level data transmission to ensure that positioning data is synchronized with the actions of construction equipment in real time. The intelligent control system of the paver can automatically adjust the screed elevation angle, tamping hammer frequency, and auger spreader speed. The intelligent control system of the road roller can automatically adjust the vibration motor parameters, travel speed, and wheel load pressure.

5. A smart construction method for highway asphalt pavement based on multi-technology integration, characterized in that: Includes the following steps: Step S1, Construction Preparation Stage: A full-section BIM model is established through the BIM 3D model management module and decomposed into standardized construction units. After associating the operation parameters, the model is transmitted to the BIM model server. Terrain point cloud data is acquired using UAV surveying equipment and then fused with the BIM model through a big data analysis engine to calculate the construction parameters for each mileage segment. Step S2, Construction Implementation Phase: Real-time positioning of the intelligent paver and 3D intelligent paving are achieved through 5G+BeiDou / GNSS dual-mode positioning; paving speed and compaction time are dynamically adjusted based on the heat conduction model; A compaction degree prediction model is established using machine learning models to automatically adjust the parameters of the road roller. Step S3, Construction Completion and Control Phase: The drone collects orthophotos at a preset frequency, compares them with the BIM model to generate a 3D deviation cloud map, and the construction management decision module performs parameter adjustments, rectifications or data archiving based on the deviation analysis to form a closed-loop management.

6. The intelligent construction method for highway asphalt pavement based on multi-technology integration according to claim 5, characterized in that: In step S1, the specific steps for establishing the full-section BIM model are as follows: In the BIM three-dimensional model management module, associate the material data of the temperature sensor of the asphalt mixing plant and the temperature sensor of the transport vehicle with each standardized construction unit, including the asphalt mixture mix proportion data; and the operating parameters of the intelligent paver, vibration motor adjustment, driving speed adjustment, and wheel load pressure adjustment, including paving thickness and number of compaction passes.

7. The intelligent construction method for highway asphalt pavement based on multi-technology integration according to claim 5, characterized in that: The specific steps of 5G paver positioning and 3D intelligent paving in step S2 are as follows: 5G base station and Beidou / GNSS provide real-time positioning data through 5G communication module; multi-parameter sensor group collects screed status data, and the paver intelligent control system automatically adjusts the paving parameters; in the curve and transition section areas, the BIM model server generates a smooth paving trajectory, and the intelligent paver performs slope paving according to the trajectory.

8. The intelligent construction method for highway asphalt pavement based on multi-technology integration according to claim 5, characterized in that: The specific steps for dynamically adjusting the paving speed and compaction time in step S2 are as follows: Temperature sensors at the asphalt mixing plant and on the transport vehicles collect temperature data in real time. A big data analysis engine, combined with environmental parameters such as ambient temperature and wind speed, predicts the temperature decay trend using a heat conduction model. This prediction is then used to dynamically adjust the paving speed of the intelligent paver and the compaction time of the road roller, ensuring that the asphalt mixture is compacted within a suitable temperature range. The calculation formula is based on Fourier's law of heat conduction. In the formula Constant temperature of the mixture; Initial temperature of the mixture; Thermal conductivity of asphalt mixture; : Exposed surface area of ​​the mixture; : Density of the mixture; Specific heat capacity of the mixture; : Mixture volume; Wind speed correction factor; Ambient wind speed; Cooling time; Transportation distance; The difference between the ambient temperature and the initial temperature of the mixture.

9. The intelligent construction method for highway asphalt pavement based on multi-technology integration according to claim 8, characterized in that: The calculation formula for the compaction degree prediction model in step S2 is as follows: In the formula Predicting compaction degree; Temperature of the mixture at the moment of compaction; : Roller wheel load pressure; : The speed of the road roller; : Vibration frequency of the road roller; Fitting coefficient; calculates compaction degree in real time using sensor data, providing quantitative instructions for adjusting roller parameters.

10. The intelligent construction method for highway asphalt pavement based on multi-technology integration according to claim 5, characterized in that: The specific steps for forming closed-loop management in step S3 are as follows: if the deviation analysis determines that the deviation exceeds the standard, the construction management decision module automatically generates a rectification work order, issues a construction parameter adjustment instruction through the 5G communication module, and re-constructs the defective area; if the deviation determines that the standard is met, the construction data is archived for subsequent construction optimization; after the rectification is completed, the process of UAV orthophoto acquisition, image recognition processing, BIM model comparison, and deviation analysis is repeated until the quality meets the standard.

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