Bituminous pavement 3D intelligent paving construction method based on BIM three-dimensional design

Abstract

The invention relates to the technical field of intelligent construction, in particular to an asphalt pavement 3D intelligent paving construction method based on BIM three-dimensional design, and the method comprises the following steps: determining a target construction area needing to be paved, and obtaining corresponding topographic surveying and mapping data, underground facility distribution, climate conditions and traffic prediction flow based on the target construction area; creating a corresponding three-dimensional digital model based on topographic surveying and mapping data, underground facility distribution, climate conditions and traffic prediction flow; a corresponding optimal paving path is determined, working parameters of the paver are adjusted, and the paver is controlled to perform paving according to the optimal paving path; in the paving process, real-time feedback data is obtained, whether the optimal paving path and the working parameters of the paver need to be adjusted or not is judged based on the real-time feedback data, if not, the paver is controlled to continue paving, and if not, the paver is controlled to continue paving based on the adjusted optimal paving path and the working parameters of the paver. According to the invention, the efficiency of road construction can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent construction, and in particular to a 3D intelligent paving construction method for asphalt pavement based on BIM three-dimensional design. Background Art

[0002] With the acceleration of urbanization, the demand for road construction is increasing. Traditional asphalt pavement paving construction methods mainly rely on two-dimensional drawing design and manual experience judgment, which has problems such as low precision, low efficiency, and serious material waste. In addition, due to the lack of effective real-time monitoring methods in the traditional construction process, it is difficult to ensure the consistency of the thickness and flatness of the paving layer, resulting in high maintenance costs in the later stage and a significant impact on the environment.

[0003] In recent years, Building Information Modeling (BIM) technology has been gradually applied to the field of construction engineering, providing comprehensive information support for the planning, design, construction and operation of engineering projects through three-dimensional digital modeling. However, in asphalt pavement construction, the application of BIM is still in its early stages, and a complete set of BIM-based intelligent construction solutions has not yet been formed. Although the existing 3D intelligent paving technology has improved construction accuracy to a certain extent, it is not deeply integrated with the BIM system and has not fully utilized the advantages of the combination of the two.

[0004] In view of this, the present invention proposes a 3D intelligent paving construction method for asphalt pavement based on BIM three-dimensional design, aiming to solve the above problems and provide a more efficient road construction solution. Summary of the invention

[0005] In order to improve the efficiency of road construction, the present application provides a 3D intelligent paving construction method for asphalt pavement based on BIM three-dimensional design.

[0006] In the first aspect, the present application provides a 3D intelligent paving construction method for asphalt pavement based on BIM three-dimensional design, which adopts the following technical solutions: A 3D intelligent paving construction method for asphalt pavement based on BIM three-dimensional design, comprising: determining a target construction area to be paved, acquiring corresponding topographic surveying and mapping data, underground facility distribution, climate conditions, and traffic forecast flow based on the target construction area, and creating a corresponding three-dimensional digital model based on the topographic surveying and mapping data, underground facility distribution, climate conditions, and traffic forecast flow; acquiring real-time construction site conditions corresponding to the target construction area, determining a corresponding optimal paving path based on the real-time construction site conditions, adjusting a paver working parameter based on the three-dimensional digital model and the optimal paving path, and controlling the paver to pave according to the optimal paving path; during the paving process, introducing an environmental monitoring sensor network to acquire corresponding real-time feedback data in real time, judging whether it is necessary to adjust the optimal paving path and the working parameters of the paver based on the real-time feedback data, and if not, controlling the paver to continue paving, otherwise, controlling the paver to continue paving based on the adjusted optimal paving path and the working parameters of the paver, wherein the real-time feedback data includes air quality, speed, temperature, and thickness.

[0007] By adopting the above technical solutions, an accurate 3D digital model is created by integrating multi-source data, and the paving path and parameters are dynamically optimized using real-time feedback data. This method achieves precise planning and automatic adjustment, reduces manual intervention and rework, significantly improves construction efficiency and quality, and ensures that road construction is more efficient, intelligent and environmentally friendly.

[0008] Optionally, when creating a three-dimensional digital model, the method also includes: obtaining material property data corresponding to the asphalt mixture used in the target construction area, wherein the characteristic data includes asphalt type, particle size distribution, grading ratio, and binder content; predicting corresponding pavement paving performance indicators under different paving parameters based on the material property data; and creating a corresponding three-dimensional digital model based on the material property data and the pavement paving performance indicators.

[0009] By adopting the above technical solution, when creating a three-dimensional digital model, the method also includes obtaining material property data of the asphalt mixture in the target construction area (such as asphalt type, particle size distribution, grading ratio, binder content), and predicting the pavement performance indicators under different paving parameters based on these data, thereby optimizing the three-dimensional digital model and ensuring the optimization of paving effect and quality.

[0010] Optionally, the method also includes: collecting historical traffic flow data of the target construction area, and determining corresponding peak hours and non-peak hours based on the historical traffic flow data; obtaining real-time traffic information of the target construction area, and predicting traffic flow distribution in the future based on the real-time traffic information, peak hours and non-peak hours; formulating a time scheduling strategy for paving operations according to the traffic flow distribution, giving priority to paving during time periods with lower traffic flow.

[0011] By adopting the above technical solutions, including collecting historical traffic flow data in the target construction area, determining peak and off-peak hours, and combining real-time traffic information to predict future traffic flow, a scheduling strategy for paving operations is formulated based on these predictions, giving priority to paving during periods of low traffic flow to reduce the impact on surrounding traffic and improve construction efficiency.

[0012] Optionally, the method also includes: acquiring corresponding real-time data based on a high-precision sensor network, wherein the real-time data includes continuously monitoring the ground and underground conditions of the construction site; judging whether there are construction obstacles based on the real-time data, and if so, immediately starting a dynamic path adjustment algorithm to recalculate the optimal paving path.

[0013] By adopting the above-mentioned technical solutions, including real-time monitoring of the ground and underground conditions of the construction site through a high-precision sensor network, once a construction obstacle is detected, a dynamic path adjustment algorithm is immediately activated to recalculate the optimal paving path to ensure construction continuity and efficiency.

[0014] Optionally, the method also includes: after construction, using vehicle-mounted high-precision measuring equipment to conduct a comprehensive inspection of the newly paved road surface to obtain corresponding quality indicators, wherein the quality indicators include flatness, thickness, compaction, and skid resistance; based on the quality indicators, edge computing is used to identify quality problem areas that do not meet design standards, and the corresponding quality problem situations are determined; based on the quality problem situations and the quality problem areas, a corresponding detailed repair plan is generated, and the quality problem areas are repaired based on the detailed repair plan.

[0015] By adopting the above technical solutions, including using high-precision vehicle-mounted measurement equipment to check the quality indicators of the newly paved road (such as flatness, thickness, compaction, and skid resistance) after construction, and using edge computing to identify areas that do not meet design standards, a detailed repair plan is generated based on the identified problems to guide the precise repair of quality problem areas and ensure the overall construction quality.

[0016] Optionally, during the construction process, the method also includes: determining key parameters that have a significant impact on the paving quality, wherein the key parameters include paving layer thickness, flatness, compaction, temperature, and humidity; setting corresponding upper and lower limit thresholds for each of the key parameters, retrieving corresponding real-time site conditions based on the real-time data, and adjusting the upper and lower limit thresholds of each key parameter based on the real-time site conditions; matching each key parameter in the real-time data with the corresponding upper and lower limit thresholds, and if there are unmatched problem key parameters, determining a corresponding warning level based on the problem key parameters, and determining corresponding remedial measures based on the warning level.

[0017] By adopting the above technical solutions, including determining key parameters (such as paving layer thickness, flatness, compaction degree, temperature, humidity), setting upper and lower threshold values for each parameter, and dynamically adjusting these thresholds according to real-time data. When the real-time data exceeds the set range, the system automatically matches and triggers the corresponding warning level, and at the same time determines and executes the corresponding remedial measures to ensure that the construction quality always meets the standards.

[0018] Optionally, the method further includes: dividing the construction process into multiple corresponding construction stages based on the overall progress and task arrangement of the paving project. Among them, the construction stages include a preparation stage, a preliminary paving stage, a fine adjustment stage, and a final acceptance stage; determining the corresponding key quality control points for each construction stage, where the key quality control points include material preparation, paving thickness, flatness, and compaction degree; setting corresponding quality control objectives for each construction stage based on the key quality control points, and monitoring various parameters in the construction process based on the real-time data and the current corresponding construction.

[0019] By adopting the above technical solutions, including dividing the construction process into multiple stages such as preparation, preliminary paving, fine adjustment, and final acceptance according to the progress of the paving project, determining the key quality control points (such as material preparation, paving thickness, flatness, compaction degree) for each stage, and setting specific quality control objectives for each stage. Monitoring various parameters in the construction process through real-time data to ensure that the quality control objectives of each stage are achieved.

[0020] In a second aspect, the present application provides an asphalt pavement 3D intelligent paving construction system based on BIM three-dimensional design, adopting the following technical solutions: An asphalt pavement 3D intelligent paving construction system based on BIM three-dimensional design, characterized in that it includes: A three-dimensional digital model creation module, determining the target construction area to be paved, obtaining the corresponding topographic survey data, underground facility distribution, climate conditions, and traffic predicted flow based on the target construction area, and using the topographic survey data, underground facility distribution, climate conditions, and traffic predicted flow to create a corresponding three-dimensional digital model; An optimal paving path determination module, obtaining the real-time construction site conditions corresponding to the target construction area, determining the corresponding optimal paving path based on the real-time construction site conditions, adjusting the working parameters of the paver based on the three-dimensional digital model and the optimal paving path, and controlling the paver to perform paving according to the optimal paving path; A parameter adjustment judgment module introduces an environmental monitoring sensor network during the paving process to obtain corresponding real-time feedback data in real time. The real-time feedback data is used to determine whether it is necessary to adjust the optimal paving path and the working parameters of the paver. If not, the paver is controlled to continue paving. Otherwise, the paver is controlled to continue paving based on the adjusted optimal paving path and the working parameters of the paver. The real-time feedback data includes air quality, speed, temperature, and thickness.

[0021] In the third aspect, the present application provides a 3D intelligent paving construction method for asphalt pavement based on BIM three-dimensional design, which adopts the following technical solutions: A method for 3D intelligent paving construction of an asphalt pavement based on BIM three-dimensional design, comprising a processor in which a program of any one of the above-mentioned methods for 3D intelligent paving construction of an asphalt pavement based on BIM three-dimensional design is run.

[0022] In a fourth aspect, the present application provides a storage medium, which adopts the following technical solution: A storage medium stores a program of any one of the above-mentioned methods for 3D intelligent paving of asphalt pavement based on BIM three-dimensional design.

[0023] In summary, the present application includes at least one of the following beneficial technical effects: 1. Create accurate 3D digital models by integrating multi-source data, and dynamically optimize paving paths and parameters using real-time feedback data. This approach enables precise planning and automatic adjustment, reduces manual intervention and rework, significantly improves construction efficiency and quality, and ensures that road construction is more efficient, intelligent, and environmentally friendly.

[0024] 2. During the construction process, the method uses a high-precision sensor network to monitor the ground and underground conditions of the construction site, as well as key parameters (such as paving layer thickness, flatness, compaction, temperature, and humidity) in real time. Once a construction obstacle is detected or a parameter exceeds the set threshold, the system immediately starts a dynamic path adjustment algorithm or triggers an early warning mechanism, and quickly takes remedial measures to ensure construction continuity and efficiency.

[0025] 3. According to the overall progress of the project, the construction process is divided into multiple stages, and specific key quality control points and goals are set for each stage. The newly paved road surface is fully inspected by on-board high-precision measurement equipment, and non-compliant areas are identified based on edge computing to generate detailed repair plans. This phased quality control and instant repair mechanism ensures that each construction stage can achieve the best results, further improving the overall construction efficiency and final road quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1It is a flow chart of a method for 3D intelligent paving construction of asphalt pavement based on BIM three-dimensional design according to an exemplary embodiment.

[0027] Figure 2 It is a structural block diagram of an asphalt pavement 3D intelligent paving construction system based on BIM three-dimensional design according to an exemplary embodiment. DETAILED DESCRIPTION

[0028] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings.

[0029] In the description of this specification, the description with reference to the terms "certain embodiments", "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0030] The present application embodiment discloses a 3D intelligent paving construction method for asphalt pavement based on BIM three-dimensional design, referring to Figure 1 ,include: S100, determine the target construction area that needs to be paved, obtain corresponding topographic surveying and mapping data, underground facility distribution, climate conditions, and traffic forecast flow based on the target construction area, and create a corresponding three-dimensional digital model based on the topographic surveying and mapping data, underground facility distribution, climate conditions, and traffic forecast flow.

[0031] First, the project team conducts on-site surveys and planning to identify specific areas where asphalt pavement needs to be laid. This step includes coordinating with relevant departments to ensure that the selection of construction areas meets urban planning and traffic needs. Then, collect topographic mapping data, underground facility distribution maps (such as water pipes, cables, etc.), climate conditions (such as temperature, humidity, rainfall), and traffic forecasts (such as peak traffic). These data can be obtained through existing geographic information systems (GIS), weather stations, traffic monitoring systems, and other channels.

[0032] In addition, computer-aided design (CAD) software or a specialized BIM (Building Information Modeling for Infrastructure) platform is used to integrate the above multi-source data to generate a three-dimensional digital model that accurately reflects the current status of the construction area. It should be pointed out here that the three-dimensional digital model not only includes terrain features, but also includes the location of underground facilities, climate influencing factors and traffic flow simulation.

[0033] By integrating multi-source data to create a detailed three-dimensional digital model, a scientific basis is provided for subsequent paving path planning and parameter adjustment, ensuring the accuracy and rationality of the construction plan.

[0034] When creating the three-dimensional digital model, the method further comprises: S101, obtaining material property data corresponding to the asphalt mixture used in the target construction area.

[0035] First, the project team needs to conduct a detailed investigation and sampling of the asphalt mixture used in the target construction area, which includes identifying the specific asphalt type (such as modified asphalt, emulsified asphalt, etc.), and obtaining samples from suppliers or collecting samples directly on site. The collected asphalt mixture samples are sent to a professional laboratory for a series of physical and chemical property tests, which may include but are not limited to: Asphalt type identification: Determine the specific type of asphalt and its characteristic parameters such as viscosity, ductility and aging properties through composition analysis.

[0036] Particle size distribution: Measuring the particle size distribution of aggregates using sieving or other particle analysis techniques ensures that the proportion of different particle size fractions is known, which is critical to the density of the paving layer.

[0037] Grading ratio assessment: Based on the particle size distribution results, calculate the reasonable ratio between coarse and fine aggregates to ensure good workability and compaction effect.

[0038] Binder Content Determination: Accurately measure the amount of binder added to the asphalt mixture, which affects the adhesion and water resistance of the mixture.

[0039] S102: predicting corresponding pavement paving performance indicators under different paving parameters based on material property data.

[0040] Among them, a prediction model is constructed based on the collected material property data. The prediction model can simulate the mechanical behavior and long-term performance of asphalt mixture under different paving parameters (such as temperature, speed, thickness, and compaction degree). In addition, considering multiple influencing factors, such as paving temperature, paving speed, paving layer thickness, compaction degree, etc., different parameter combinations are set and input into the prediction model for simulation.

[0041] The model is used to predict pavement performance indicators under different paving parameter combinations, such as durability, skid resistance, load-bearing capacity, fatigue resistance, etc. The prediction results are compared and verified with historical project data or data from small-scale test sites, and the prediction model is continuously adjusted and optimized to improve its accuracy.

[0042] S103, creating a corresponding three-dimensional digital model based on the material property data and the pavement paving performance index.

[0043] Among them, the material property data and predicted pavement paving performance indicators are integrated into the existing three-dimensional digital model, which means that not only the terrain characteristics and the location of underground facilities should be reflected, but also the material properties and the expected pavement performance should be added; and multi-scale modeling technology is used to comprehensively describe the material properties and their impact on the paving effect from the micro level (inter-particle interaction, pore structure) to the macro level (overall mechanical properties, thermal properties).

[0044] It should be pointed out here that it is necessary to ensure that the three-dimensional digital model has the ability to be dynamically updated and can be automatically adjusted according to real-time feedback data (such as changes in construction site conditions and newly acquired material test results) to maintain the accuracy and timeliness of the model.

[0045] Through the acquisition of detailed material property data, accurate performance indicator prediction and the creation of three-dimensional digital models, not only the accuracy of construction planning is improved and the uncertainty in the construction process is reduced, but also the dynamic update mechanism ensures that the construction plan is always in the optimal state, thereby significantly improving the efficiency and final quality of road construction.

[0046] S110, obtaining the real-time construction site conditions corresponding to the target construction area, determining the corresponding optimal paving path based on the real-time construction site conditions, adjusting the working parameters of the paver based on the three-dimensional digital model and the optimal paving path, and controlling the paver to pave according to the optimal paving path.

[0047] Among them, before and during construction, a high-precision sensor network (such as GPS positioning system, laser scanner, environmental monitoring sensor) is deployed to continuously obtain real-time data of the construction site, including ground conditions, changes in underground facilities, weather changes, etc.; in this embodiment of the application, it is necessary to use edge computing devices or cloud servers to process real-time data, combined with a three-dimensional digital model for comprehensive analysis, and evaluate the impact of current construction conditions on paving operations.

[0048] Based on the results of real-time data analysis, the path planning algorithm (Dijkstra algorithm) is used to calculate the most economical and efficient paving path that can avoid obstacles and reduce interference. At the same time, according to the construction progress and duration requirements, the path is optimized to ensure that the task is completed on time. According to the optimal paving path and real-time data, the working parameters of the paver (such as speed, temperature, compaction, etc.) are adjusted to ensure the best paving effect.

[0049] Through real-time data collection and intelligent analysis, the optimal paving path is dynamically determined and working parameters are adjusted, reducing manual intervention and improving construction efficiency and quality.

[0050] S120, during the paving process, an environmental monitoring sensor network is introduced to obtain corresponding real-time feedback data in real time, and based on the real-time feedback data, it is determined whether it is necessary to adjust the optimal paving path and the working parameters of the paver. If not, the paver is controlled to continue paving. Otherwise, based on the adjusted optimal paving path and the working parameters of the paver, the paver is controlled to continue paving.

[0051] Among them, the environmental monitoring sensor network covers various types such as air quality sensors, thermometers, hygrometers, thickness gauges, etc., ensuring comprehensive coverage of key monitoring points. The sensor network continuously collects various indicator data during the construction process and transmits them to the central control system in real time through wireless communication modules.

[0052] The central control system automatically determines whether any adjustment is needed based on real-time feedback data. For example, if it detects that the thickness of a section of the road is uneven or the temperature is abnormal, the adjustment mechanism is triggered. When adjustment is needed, the system immediately starts the dynamic path adjustment algorithm, recalculates the optimal paving path, and adjusts the paver's operating parameters accordingly to ensure construction quality and continuity. If no adjustment is needed, the paver is controlled to continue paving according to the original path and parameters. If necessary, the adjusted plan is implemented until all problems are resolved.

[0053] Through precise planning, real-time monitoring and dynamic adjustment, the efficiency of road construction has been significantly improved. From creating a detailed three-dimensional digital model to optimizing the paving path and parameters in real time, to introducing an environmental monitoring sensor network for real-time feedback control, each step is closely centered around the goals of improving construction accuracy, reducing manual intervention, shortening construction cycles and ensuring high quality. This approach not only improves construction efficiency, but also enhances the controllability and environmental friendliness of the project.

[0054] In the embodiment of the present application, for the optimal paving path, the method further includes: S111, acquiring corresponding real-time data based on a high-precision sensor network.

[0055] Among them, real-time data includes continuous monitoring of the ground and underground conditions of the construction site; the sensors of the high-precision sensor network include but are not limited to: Ground sensors: such as laser scanners, 3D cameras, ultrasonic rangefinders, etc., are used to monitor changes in surface morphology, paving layer thickness and flatness.

[0056] Underground sensors: such as geological radar (GPR), underground detectors, fiber optic sensors, etc., are used to detect underground facilities (such as pipelines, cables) and soil structure changes.

[0057] Environmental sensors: such as thermometers, hygrometers, air quality sensors, etc., are used to monitor the environmental conditions at the construction site to ensure construction safety and quality.

[0058] The sensor network continuously collects various types of real-time data and transmits the data to the central control system or cloud server through wireless communication modules (such as LoRa, Zigbee, 5G). The data collection frequency is set according to specific needs to ensure that any changes are captured in time. Some data can be initially processed on the edge computing device on site to reduce transmission delays and improve response speed. For example, the edge computing device can quickly identify abnormal situations and trigger local alarms.

[0059] S112, based on the real-time data, determine whether there is a construction obstacle. If so, immediately start the dynamic path adjustment algorithm to recalculate the optimal paving path.

[0060] After the central control system or cloud server receives real-time data from the sensor network, it uses advanced data analysis algorithms (such as machine learning and deep learning) to process and analyze the data. This step includes: Anomaly detection: Apply statistical methods or pattern recognition techniques to identify abnormal patterns in the data and distinguish between normal construction status and sudden failure situations.

[0061] Threshold monitoring: Set reasonable threshold ranges for key parameters (such as ground height changes and underground facility location offsets) and trigger alarms once the thresholds are exceeded.

[0062] Intelligent early warning mechanism: Based on the real-time data analysis results, the intelligent early warning system is automatically triggered. The early warning signal can be notified to relevant managers through multiple channels (such as SMS, email, mobile application push) and provide preliminary handling suggestions.

[0063] Obstacle confirmation: The management personnel will conduct further confirmation based on the warning information and the actual situation on site. If necessary, technical personnel will be dispatched to the site to verify and ensure the accuracy of the warning.

[0064] In addition, once a construction obstacle is confirmed, the system immediately initiates a dynamic path adjustment algorithm based on the latest real-time data, taking into account the new obstacle location, size and other influencing factors.

[0065] The path adjustment algorithm generates multiple possible alternative paths and evaluates the feasibility and efficiency of each path. Evaluation indicators include but are not limited to: whether the new path avoids all known obstacles to ensure the safe operation of the paver and other equipment; whether the new path is the shortest and most cost-effective, reducing unnecessary detours and waste of resources; whether the new path meets the design standards and does not affect the quality and performance of the paving layer.

[0066] Based on the evaluation results, the optimal alternative path is selected and the paving path planning in the 3D digital model is updated. According to the new path characteristics and site conditions, the paver's operating parameters (such as speed, temperature, compaction degree, etc.) are automatically adjusted to adapt to the changed construction environment; the adjusted optimal paving path and operating parameters are sent to the paver control system to ensure that it continues paving operations along the new path.

[0067] Through the deployment of a high-precision sensor network, continuous monitoring of the ground and underground conditions of the construction site is achieved. Combined with intelligent analysis and early warning mechanisms, construction obstacles can be quickly identified and responded to. The dynamic path adjustment algorithm ensures that when obstacles are encountered, the optimal paving path can be quickly recalculated to maintain the continuity and efficiency of construction.

[0068] In the embodiment of the present application, during the construction process, the method further includes: S1211, collecting historical traffic flow data of the target construction area, and determining corresponding peak hours and non-peak hours based on the historical traffic flow data.

[0069] Among them, historical traffic flow data of the target construction area is collected from multiple channels, including but not limited to: obtaining officially released traffic statistics, such as daily, weekly and monthly traffic records; using existing intelligent traffic monitoring systems (such as cameras, sensors) to obtain real-time and historical traffic flow information; referring to historical traffic data provided by map service providers (such as Google Maps, Amap), which usually have rich user-contributed data.

[0070] Use data analysis tools (such as statistical analysis libraries in Python and R) to clean, organize and analyze the collected data. This step includes: identifying the traffic flow variation patterns in different time periods (such as weekdays, weekends, and holidays), especially the specific time range of morning and evening peak hours; discovering potential change patterns through trend analysis of long-term data, such as the impact of seasonal fluctuations or special events (such as large-scale events and holidays) on traffic flow; identifying and eliminating outliers (such as temporary traffic surges caused by traffic accidents and road construction) to ensure the accuracy of the analysis results.

[0071] Based on the above analysis results, peak hours (such as 7:00-9:00 am and 17:00-19:00 pm on weekdays) and off-peak hours (such as late night and weekends) are clearly defined. These time periods will serve as the basis for subsequent planning.

[0072] S1212, obtaining real-time traffic information of the target construction area, and predicting the traffic flow distribution in the future based on the real-time traffic information, peak hours and non-peak hours.

[0073] Among them, real-time traffic monitoring equipment (such as cameras, radars, and license plate recognition systems) are deployed to continuously obtain current traffic flow information in the target construction area. At the same time, real-time traffic status updates provided by mobile applications (such as navigation software) are used.

[0074] Fusion of real-time traffic data from different sources to ensure data integrity and accuracy, use edge computing devices to quickly process part of the data on-site to reduce transmission delays. Build a dynamic traffic prediction model based on historical data and real-time data. Commonly used algorithms include but are not limited to: ARIMA model, used to predict future short-term traffic flow changes; random forest, gradient boosting tree (GBDT), which can handle complex nonlinear relationships and provide more accurate predictions; long short-term memory network (LSTM), which is particularly suitable for processing long time series data and capturing periodic and sudden changes in traffic flow.

[0075] Considering the changes in traffic flow under different scenarios, such as weather conditions (sunny, rainy days), emergencies (traffic accidents, temporary activities), etc., multi-scenario simulation is carried out to evaluate the possible traffic flow distribution under various circumstances; and a detailed traffic flow forecast report is generated, including the expected traffic flow per hour in the next few days, congestion level forecast, etc.

[0076] S1213, formulate a time scheduling strategy for paving operations based on the traffic flow distribution, giving priority to paving during time periods with lower traffic flow.

[0077] Among them, multiple time scheduling plans for paving operations are generated based on the traffic flow forecast results. Factors that should be considered for each plan include but are not limited to: ensuring that the paving operation is completed within the specified time without affecting the overall project progress; reasonably arranging the deployment of personnel, equipment and materials to ensure efficient use of resources; giving priority to time periods with low traffic flow (such as late at night and weekends) to minimize the impact on surrounding traffic; conducting a cost-benefit analysis for each plan to evaluate its comprehensive impact on construction costs, traffic interference, social impact, etc. When selecting the optimal plan, not only direct costs but also indirect costs (such as social and economic losses caused by traffic jams) should be considered.

[0078] Develop corresponding emergency measures for each plan to deal with possible emergencies (such as weather changes, equipment failures). Ensure that even in the event of unexpected situations, the plan can be adjusted quickly to maintain construction continuity. By collecting and analyzing historical and real-time traffic flow data, accurately predict future traffic flow distribution, and formulate a scientific and reasonable paving operation time scheduling strategy based on this. This method not only significantly reduces the impact on surrounding traffic and avoids congestion problems caused by construction during peak hours, but also improves construction efficiency, ensures the smooth progress of the project and the effective use of social resources. By optimizing the time schedule, a win-win situation for construction and traffic management is achieved.

[0079] In addition, the overall construction process is divided into multiple stages, so the method also includes: S1221, dividing the construction process into multiple corresponding construction phases based on the overall progress and task arrangement of the paving project.

[0080] Among them, according to the overall progress and task arrangement of the paving project, the entire construction process is divided into several clear stages. These stages usually include: Preparation phase: covers preparatory work such as construction site cleaning, material transportation, equipment commissioning, and personnel training. Preliminary paving phase: large-scale paving of asphalt mixture, focusing on paving efficiency and basic quality indicators (such as thickness and flatness). Fine adjustment phase: fine adjustment of the pavement after preliminary paving to ensure that key indicators such as flatness and compaction of the final pavement meet the design standards. Final acceptance phase: comprehensive quality inspection and repair work to ensure that all performance indicators meet the highest standards and pass the relevant acceptance procedures.

[0081] Develop a detailed timetable for each stage, clarify the start and end time of each stage and the expected completion date, which will help to allocate resources reasonably and ensure that the project proceeds as planned. At the same time, it is also necessary to identify the main person in charge and participants of each stage, clarify their respective responsibilities and tasks, ensure that each link has a dedicated person in charge, and improve work efficiency.

[0082] S1222, determine the key quality control points corresponding to each construction stage.

[0083] Among them, according to the characteristics of each construction stage, identify the key quality control points that need special attention. For example: Preparation stage: material selection, transportation route planning, equipment calibration, and on-site cleaning effects.

[0084] Initial paving stage: initial assessment of paving speed, temperature control, thickness uniformity, and flatness.

[0085] Fine-tuning phase: final smoothness, compaction, edge treatment, surface texture.

[0086] Final acceptance stage: surface friction coefficient, skid resistance, durability testing, and overall aesthetics.

[0087] During the overall construction process, it is necessary to evaluate the impact of these key points on the overall construction effect to ensure that the work at each stage lays a good foundation for the subsequent stages. For example, the quality of material preparation directly affects the performance of the paving layer, while the flatness and compaction determine the service life of the road surface; by identifying and determining the key quality control points of each construction stage, ensure that the key work of each stage is given full attention.

[0088] S1223, setting corresponding quality control targets for each construction stage based on key quality control points, and determining various parameters in the monitoring construction process based on real-time data and the current corresponding construction.

[0089] Among them, set specific quality control goals for each key quality control point to ensure that these goals can reflect the unique needs and challenges of this stage. For example: Preparation stage: Ensure that the material quality and equipment status are optimal; the site is thoroughly cleaned and free of debris. Preliminary paving stage: Ensure that the thickness and flatness of the paving layer meet the preliminary requirements; control the temperature within the appropriate range. Fine adjustment stage: Improve the overall flatness and density of the road surface; ensure that the edges are properly processed. Final acceptance stage: Ensure that all performance indicators (such as skid resistance and durability) meet the design standards.

[0090] Flexibly adjust goals according to actual conditions to adapt to different construction conditions and technical requirements. For example, paving speed or temperature control parameters may need to be adjusted under extreme weather conditions. Communicate the set quality control goals to all relevant personnel to ensure that everyone is clear about their tasks and goals. Through regular meetings and training, ensure that team members have a deep understanding of quality control goals.

[0091] In an embodiment of the present application, a high-precision sensor network is used to continuously collect various data from the construction site, including but not limited to the thickness, flatness, compaction, temperature, humidity, etc. of the paving layer; at the same time, edge computing devices or cloud servers are used to quickly process and analyze the collected data to provide instant feedback. For example, if it is detected that the thickness of a section of the road is uneven or the temperature is abnormal, an alarm is triggered immediately.

[0092] According to the results of real-time data analysis, various parameters in the construction process are dynamically adjusted. For example, if the compaction is insufficient, the working parameters of the roller are adjusted; if the flatness does not meet the requirements, the speed and operation mode of the paver are readjusted. Through continuous monitoring and feedback of real-time data, various parameters in the construction process are continuously optimized to ensure that the quality control goals of each stage are achieved. At the same time, valuable experience is accumulated to gradually improve the level of future construction management.

[0093] The construction process is divided into several clear stages, the key quality control points of each stage are determined, and specific goals are set for them. Combined with real-time data monitoring and dynamic adjustment mechanisms, it ensures that the work of each stage can be completed efficiently and accurately. This method not only improves the consistency and reliability of construction quality, but also significantly improves construction efficiency, reduces rework and resource waste, and realizes refined management and scientific control of the construction process.

[0094] During the construction process, the method also includes: S1231, determine the key parameters that have a significant impact on paving quality.

[0095] Among them, based on the professional knowledge and practical experience of paving engineering, key parameters that have a significant impact on paving quality are determined, and each key parameter is weighted and evaluated to clarify its impact on the overall paving quality. For example, the thickness and flatness of the paving layer may have a greater impact on the final pavement performance, while the impact of humidity is relatively small but still important. These parameters usually include but are not limited to: Paving layer thickness: Ensure that the asphalt layer reaches the design thickness and avoid being too thin or too thick.

[0096] Smoothness: Ensure the smoothness of the road surface, reduce driving bumps, and improve driving comfort and safety.

[0097] Compaction: Ensure the density of the paving layer and enhance the bearing capacity and durability of the road surface.

[0098] Temperature: Control the temperature during paving and compaction to ensure the performance of the asphalt mixture is stable.

[0099] Humidity: Monitor the humidity of the construction environment to avoid excessive humidity affecting the paving effect.

[0100] S1232, setting corresponding upper and lower limit thresholds for each key parameter, retrieving corresponding real-time on-site conditions based on real-time data, and adjusting the upper and lower limit thresholds of each key parameter based on the real-time on-site conditions.

[0101] Among them, reasonable upper and lower thresholds are set for each key parameter based on design standards, industry specifications and historical experience. Taking into account different construction conditions and technical requirements, flexible adjustments are allowed within a certain range. For example, under extreme weather conditions, the temperature threshold may need to be appropriately relaxed. For example: Paving layer thickness: set within ±5 mm.

[0102] Flatness: set within ±3 mm.

[0103] Compaction degree: set between 95% and 98%.

[0104] Temperature: Set between 120°C-160°C (the specific range depends on the material type).

[0105] Humidity: Set between 40% and 60%.

[0106] In addition, a high-precision sensor network is used to continuously collect various data from the construction site, including paving layer thickness, flatness, compaction, temperature, humidity, etc. The collected data can be quickly processed and analyzed using edge computing devices or cloud servers to provide instant feedback. For example, if an abnormal temperature is detected on a certain section of the road, an alarm is triggered immediately.

[0107] Based on the results of real-time data analysis, the upper and lower thresholds of each key parameter are dynamically adjusted. For example, if the construction environment temperature is low, the lower limit of the temperature threshold can be appropriately lowered; if the humidity is high, the upper limit of the humidity threshold can be appropriately relaxed; when the real-time data approaches or exceeds the set threshold, the early warning mechanism is automatically activated to remind managers to pay attention to potential problems and prepare to take corresponding measures.

[0108] S1233, match each key parameter in the real-time data with the corresponding upper and lower thresholds. If there are mismatched problem key parameters, determine the corresponding warning level based on the problem key parameters, and determine the corresponding remedial measures based on the warning level.

[0109] Each key parameter in the real-time data is matched with the set upper and lower thresholds one by one to check whether there is a situation that exceeds the threshold. For example, if the paving layer thickness exceeds the set range of ±5 mm, it is marked as mismatched. The corresponding warning level is determined according to the severity of the mismatched parameters.

[0110] In the embodiment of the present application, common warning levels include: Warning level: Slight deviation from the threshold, prompting management attention.

[0111] Alert level: Significant deviation from threshold, immediate corrective action is recommended.

[0112] Emergency level: Severe deviation from the threshold, construction must be stopped immediately and a comprehensive inspection must be carried out.

[0113] Corresponding remedial measures are formulated for different warning levels. For example, at the warning level, the monitoring frequency needs to be increased and the trend of changes needs to be closely observed; at the alarm level, the working parameters of the paver, such as speed and temperature, need to be adjusted to ensure that the parameters return to the normal range; at the emergency level, construction needs to be suspended, technicians need to be dispatched to check the cause of the problem, and the affected area needs to be re-paved if necessary.

[0114] Record each warning event and its handling process in detail to form a complete warning management document. Through regular analysis of the warning log, identify common problems and improvement points, and gradually optimize future construction management and quality control strategies.

[0115] By determining key parameters and setting upper and lower thresholds, combined with real-time data collection and dynamic adjustment mechanisms, refined control of paving quality is achieved. Through parameter matching and early warning mechanisms, problems that exceed the threshold can be discovered and handled in a timely manner to ensure that construction quality always meets standards.

[0116] Correspondingly, after the construction, the method further comprises: S1241, use vehicle-mounted high-precision measuring equipment to conduct a comprehensive inspection of the newly paved road surface and obtain the corresponding quality indicators.

[0117] Among them, high-precision vehicle-mounted measurement equipment is selected, such as laser scanners, 3D cameras, ultrasonic rangefinders, etc. Before use, ensure that the equipment is strictly calibrated to ensure the accuracy of the data; formulate a detailed inspection route to ensure that the entire newly paved road area is covered. The inspection route should include straight and curved sections, as well as areas of special concern (such as intersections and ramps).

[0118] Utilize on-board equipment to drive along the predetermined route and collect various quality indicators of the road surface in real time, including but not limited to: Flatness: record the undulations of the road surface through a laser scanner or inertial measurement unit (IMU) to generate a flatness profile; Thickness: use a radar detector or core sampling method to measure the actual thickness of the paving layer to ensure that it meets the design requirements; Compactness: evaluate the density of the road surface through a nuclear density meter or other non-destructive testing methods to ensure that it meets the specified compaction standards; Anti-skid performance: use a friction coefficient tester or texture depth meter to evaluate the anti-skid performance of the road surface to ensure driving safety.

[0119] S1242, based on various quality indicators, identify quality problem areas that do not meet design standards through edge computing, and determine the corresponding quality problem situations.

[0120] Among them, the raw data collected on the edge computing device is preliminarily processed, including data cleaning, format conversion, and outlier removal, to ensure the integrity and accuracy of the data; a quality assessment model is constructed based on preset design criteria (such as flatness ±3 mm, thickness ±5 mm, compaction degree 95% - 98%, skid resistance ≥0.4). This model can be a rule-based threshold judgment or a machine learning model (such as support vector machine, random forest) for automatically identifying quality problems.

[0121] In addition, the processed data is input into the quality assessment model to automatically identify areas that do not meet the design criteria. For example, if the flatness of a certain section of the road surface exceeds the range of ±3 mm, it is marked as a problem area. Further analyze the specific quality problems in each problem area, such as uneven flatness, insufficient thickness, insufficient compaction degree, or poor skid resistance. This step can be achieved through pattern recognition algorithms to help accurately classify the types of problems.

[0122] Finally, present the identified problem areas and their specific situations to the management personnel in an intuitive way, such as generating a heat map, a 3D model, or a report document, to facilitate their quick understanding of the problem distribution and severity.

[0123] S1243, generate a corresponding detailed repair plan based on the quality problem situation and the quality problem area, and repair the quality problem area based on the detailed repair plan.

[0124] Among them, according to the identified problem types and specific locations, combined with construction experience and best practices, automatically generate a detailed repair plan. The repair plan should include but not be limited to: Material selection: Recommend asphalt mixtures or other materials suitable for patching to ensure good compatibility between the patched part and the original road surface; Tools and equipment: List the required patching tools and equipment, such as small rollers, cutters, spray guns, etc., to facilitate the preparation of construction personnel; Operation steps: Provide detailed patching operation steps, including specific technological processes and technical points, such as cutting the damaged part, cleaning the surface, laying new materials, compaction, etc.; Time estimation: Give the expected repair time to help reasonably arrange the construction progress and ensure the completion of the repair work on time.

[0125] In addition, intuitive operation guides can also be provided to construction personnel through augmented reality (AR) technology or other visualization means, such as overlaying virtual instructions on the actual road surface to help them perform patching more accurately; during the repair process, the system continuously monitors the repair progress and adjusts subsequent operation parameters according to real-time feedback to ensure that the repair effect meets expectations.

[0126] Restoration record archiving: All restoration activities and their results are recorded and archived in detail to form complete quality control documents for later review and experience accumulation.

[0127] By using high-precision measuring equipment to conduct a comprehensive inspection of the newly paved road surface, combined with edge computing technology, the quality problem areas that do not meet the design standards are quickly identified and classified, and detailed repair plans are automatically generated to guide construction personnel to make precise repairs. This method not only improves the speed and accuracy of problem discovery and repair, reduces the possibility of rework, but also ensures the consistency and reliability of construction quality, significantly improving the overall efficiency and final quality of road construction. In this way, high-quality assurance and efficient management after construction are achieved.

[0128] The present application embodiment discloses a 3D intelligent asphalt pavement paving construction system based on BIM three-dimensional design. Figure 2 , the system includes but is not limited to: The three-dimensional digital model creation module 200 determines the target construction area to be paved, obtains corresponding topographic surveying and mapping data, underground facility distribution, climatic conditions, and traffic forecast flow based on the target construction area, and creates the corresponding three-dimensional digital model based on the topographic surveying and mapping data, underground facility distribution, climatic conditions, and traffic forecast flow; The optimal paving path determination module 210 obtains the real-time construction site conditions corresponding to the target construction area, determines the corresponding optimal paving path based on the real-time construction site conditions, adjusts the working parameters of the paver based on the three-dimensional digital model and the optimal paving path, and controls the paver to pave according to the optimal paving path; The parameter adjustment judgment module 220 introduces an environmental monitoring sensor network during the paving process to obtain corresponding real-time feedback data in real time. The real-time feedback data is used to determine whether it is necessary to adjust the optimal paving path and the working parameters of the paver. If not, the paver is controlled to continue paving. Otherwise, the paver is controlled to continue paving based on the adjusted optimal paving path and the working parameters of the paver, wherein the real-time feedback data includes air quality, speed, temperature, and thickness.

[0129] An embodiment of the present application also discloses a 3D intelligent paving construction method for asphalt pavement based on BIM three-dimensional design, including a processor in which a program of any one of the above-mentioned 3D intelligent paving construction methods for asphalt pavement based on BIM three-dimensional design is running.

[0130] An embodiment of the present application also discloses a storage medium storing a program of any one of the above-mentioned methods for intelligent paving construction of asphalt pavement based on BIM three-dimensional design.

[0131] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A 3D intelligent paving construction method for asphalt pavement based on BIM three-dimensional design, characterized in that: include: Determine the target construction area to be paved, obtain corresponding topographic surveying and mapping data, underground facility distribution, climatic conditions, and traffic forecast flow based on the target construction area, and create a corresponding three-dimensional digital model based on the topographic surveying and mapping data, underground facility distribution, climatic conditions, and traffic forecast flow; Acquire real-time construction site conditions corresponding to the target construction area, determine a corresponding optimal paving path based on the real-time construction site conditions, adjust working parameters of the paver based on the three-dimensional digital model and the optimal paving path, and control the paver to pave according to the optimal paving path; During the paving process, an environmental monitoring sensor network is introduced to obtain corresponding real-time feedback data in real time. Based on the real-time feedback data, it is determined whether it is necessary to adjust the optimal paving path and the working parameters of the paver. If not, the paver is controlled to continue paving. Otherwise, based on the adjusted optimal paving path and the working parameters of the paver, the paver is controlled to continue paving. The real-time feedback data includes air quality, speed, temperature, and thickness.

2. The asphalt pavement 3D intelligent paving construction method based on BIM three-dimensional design according to claim 1 is characterized in that: When creating the three-dimensional digital model, the method further comprises: Obtain material property data corresponding to the asphalt mixture used in the target construction area, where the property data includes asphalt type, particle size distribution, gradation ratio, and binder content; Predicting the pavement paving performance index corresponding to different paving parameters based on the material property data; A corresponding three-dimensional digital model is created based on the material property data and the pavement paving performance index.

3. The asphalt pavement 3D intelligent paving construction method based on BIM three-dimensional design according to claim 1 is characterized in that: The method also includes: Collect historical traffic flow data of the target construction area, and determine corresponding peak hours and non-peak hours based on the historical traffic flow data; Acquire real-time traffic information of the target construction area, and predict traffic flow distribution in the future based on the real-time traffic information, peak hours and non-peak hours; A time scheduling strategy for paving operations is formulated based on the traffic flow distribution, with priority given to paving during time periods with lower traffic flows.

4. The asphalt pavement 3D intelligent paving construction method based on BIM three-dimensional design according to claim 3 is characterized in that: The method also includes: Acquire corresponding real-time data based on a high-precision sensor network, where real-time data includes continuous monitoring of ground and underground conditions at the construction site; Based on the real-time data, it is determined whether there are construction obstacles. If so, the dynamic path adjustment algorithm is immediately started to recalculate the optimal paving path.

5. The asphalt pavement 3D intelligent paving construction method based on BIM three-dimensional design according to claim 1 is characterized in that: The method also includes: After construction, the newly paved road surface is fully inspected using high-precision vehicle-mounted measuring equipment to obtain the corresponding quality indicators, including flatness, thickness, compaction, and skid resistance; Based on the various quality indicators, edge computing is used to identify quality problem areas that do not meet the design standards, and to determine the corresponding quality problem situations; A corresponding detailed repair plan is generated based on the quality problem situation and the quality problem area, and the quality problem area is repaired based on the detailed repair plan.

6. The asphalt pavement 3D intelligent paving construction method based on BIM three-dimensional design according to claim 4 is characterized in that: During the construction process, the method also includes: Determine the key parameters that have a significant impact on paving quality, including paving layer thickness, flatness, compaction, temperature, and humidity; Setting corresponding upper and lower thresholds for each of the key parameters, retrieving corresponding real-time on-site conditions based on the real-time data, and adjusting the upper and lower thresholds for each of the key parameters based on the real-time on-site conditions; Match each key parameter in the real-time data with the corresponding upper and lower thresholds. If there are mismatched problem key parameters, determine the corresponding warning level based on the problem key parameters, and determine the corresponding remedial measures based on the warning level.

7. The asphalt pavement 3D intelligent paving construction method based on BIM three-dimensional design according to claim 1 is characterized in that: The method also includes: Based on the overall progress and task arrangement of the paving project, the construction process is divided into multiple corresponding construction stages, among which the construction stages include the preparation stage, the preliminary paving stage, the fine adjustment stage, and the final acceptance stage; Determine the key quality control points corresponding to each construction stage, among which the key quality control points include material preparation, paving thickness, flatness, and compaction; Based on the key quality control points, corresponding quality control targets are set for each construction stage, and various parameters in the monitoring construction process are determined based on the real-time data and the current corresponding construction.

8. The asphalt pavement 3D intelligent paving construction system based on BIM three-dimensional design according to claim 1 is characterized in that: include: A three-dimensional digital model creation module determines the target construction area to be paved, obtains corresponding topographic surveying and mapping data, underground facility distribution, climatic conditions, and traffic forecast flow based on the target construction area, and creates a corresponding three-dimensional digital model based on the topographic surveying and mapping data, underground facility distribution, climatic conditions, and traffic forecast flow; An optimal paving path determination module obtains real-time construction site conditions corresponding to the target construction area, determines the corresponding optimal paving path based on the real-time construction site conditions, adjusts the working parameters of the paver based on the three-dimensional digital model and the optimal paving path, and controls the paver to pave according to the optimal paving path; A parameter adjustment judgment module introduces an environmental monitoring sensor network during the paving process to obtain corresponding real-time feedback data in real time. The real-time feedback data is used to determine whether it is necessary to adjust the optimal paving path and the working parameters of the paver. If not, the paver is controlled to continue paving. Otherwise, the paver is controlled to continue paving based on the adjusted optimal paving path and the working parameters of the paver. The real-time feedback data includes air quality, speed, temperature, and thickness.

9. A 3D intelligent paving construction method for asphalt pavement based on BIM three-dimensional design, characterized in that: It includes a processor, in which runs a program of the asphalt pavement 3D intelligent paving construction method based on BIM three-dimensional design as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: A program for the asphalt pavement 3D intelligent paving construction method based on BIM three-dimensional design as described in any one of claims 1 to 7 is stored.

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