Road construction deformation joint detection management method and system based on BIM

Abstract

The invention relates to the technical field of road deformation joint detection management, in particular to a BIM-based road construction deformation joint detection management method and system. The method comprises the following steps: obtaining road construction scheme data; performing highway structure semantic extraction on the highway construction scheme data to obtain highway construction structure semantic data; performing three-dimensional digital model construction on the semantic data of the road construction structure to generate a road construction BIM model; and performing construction simulation on the road construction BIM model to generate road construction simulation data. Through systematic information integration, real-time monitoring, scientific inspection, data-driven damage assessment and repair strategy formulation and flexible management process adjustment, the comprehensiveness and real-time performance of BIM-based road construction deformation joint detection management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of highway deformation joint detection management, and in particular to a highway construction deformation joint detection management method and system based on BIM. Background Art

[0002] In the early days, highway construction mainly relied on traditional design drawings and manual inspection, which led to poor information transmission and lack of data sharing during the construction process, affecting the quality and safety of the project. With the rapid development of computer technology, BIM technology has gradually emerged. Through digital modeling, BIM provides a visual platform for the design, construction and operation and maintenance of the project, which can effectively integrate information from multiple parties. In the mid-2000s, BIM technology was widely used in the construction field and then gradually expanded to highway construction. In recent years, with the rapid development of sensor technology and the Internet of Things, real-time data collection and analysis have become possible. Combining BIM with sensor data can realize dynamic monitoring of expansion joints. By establishing a three-dimensional model of the expansion joint and combining it with real-time monitoring data, managers can timely identify the changes in the expansion joint and formulate corresponding maintenance strategies, thereby improving the safety and durability of the highway. However, the current existing technology is usually lagging behind in identifying the areas prone to expansion joints during the construction stage, and potential problems cannot be discovered in time. At the same time, traditional inspection plans are often lacking in pertinence, resulting in waste of resources or missed inspections, which leads to a one-sided and lack of real-time detection and management of expansion joint damage. Summary of the invention

[0003] Based on this, it is necessary to provide a BIM-based highway construction deformation joint detection management method and system to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a BIM-based highway construction deformation joint detection management method is provided, the method comprising the following steps: Step S1: Obtain highway construction plan data; perform highway structure semantic extraction on the highway construction plan data to obtain highway construction structure semantic data; construct a three-dimensional digital model of the highway construction structure semantic data to generate a highway construction BIM model; Step S2: Perform construction simulation on the highway construction BIM model to generate highway construction simulation data; perform virtual reality identification of deformation joint prone areas on the highway construction BIM model based on the highway construction simulation data to obtain virtual deformation joint prone area identification data and deformation joint detection area data; perform regional matching inspection division on the virtual deformation joint prone area identification data and the deformation joint detection area data to generate a first deformation joint inspection plan and a second deformation joint inspection plan; Step S3: periodic inspection data collection is performed on the highway construction BIM model through the first deformation joint inspection plan and the second deformation joint inspection plan to obtain periodic deformation joint inspection data; deformation joint damage assessment is performed on the periodic deformation joint inspection data to generate periodic deformation joint inspection damage assessment data; deformation joint repair strategy is formulated according to the periodic deformation joint inspection damage assessment data to generate deformation joint abnormal repair strategy; Step S4: Update the deformation joint status of the highway construction BIM model according to the deformation joint abnormal repair strategy, and generate deformation joint status inspection update data; adjust the detection management process through the deformation joint status inspection update data, and generate a highway construction deformation joint detection management adjustment plan to execute the highway construction deformation joint detection management operation.

[0005] The present invention lays the foundation for subsequent processing by preliminarily collecting all data related to construction. By analyzing the scheme data, the structural information in the highway construction is extracted, and the components and functions of the highway can be clearly defined. The extracted structural semantic data is converted into a BIM model, which provides a visual three-dimensional expression and facilitates subsequent construction and management. By simulating the construction process, it is possible to predict the problems and the reasonable allocation of resources, thereby improving construction efficiency. Virtual reality technology is used to identify the problem area where the expansion joint occurs, which enhances the forward-looking identification ability of potential risks. The detection area is subdivided into different inspection plans, which improves the pertinence and effectiveness of the inspection and provides a scientific basis for subsequent periodic inspections. Regular inspection data collection ensures dynamic monitoring of the highway status. The collected data is analyzed to generate damage assessment data, which can quickly and accurately identify the damage of the expansion joint. Based on the damage assessment results, a corresponding repair strategy is formulated to ensure that a rapid response and processing can be made when the problem occurs. The status of the BIM model is updated according to the repair strategy to ensure the timeliness and accuracy of the model and provide the latest data for subsequent decision-making. Based on the latest status data, the detection management process is optimized, the management efficiency is improved, and the deformation joint detection during highway construction is always in the best state. Through systematic construction simulation and real-time inspections, potential risks can be identified at an early stage, thereby reducing the probability of accidents. The implementation of each step provides reliable data support for subsequent decision-making, ensuring the scientificity and rationality of the decision. By recycling data, resource waste is reduced, the efficiency of management and construction is improved, and costs are reduced. Therefore, the present invention improves the comprehensiveness and real-time nature of BIM-based highway construction deformation joint detection management through systematic information integration, real-time monitoring, scientific inspections, data-driven damage assessment and repair strategy formulation, and flexible management process adjustments.

[0006] Preferably, step S1 comprises the following steps: Step S11: Obtaining highway construction plan data; Step S12: preprocessing the highway construction plan data to generate standard highway construction plan data, wherein the data preprocessing includes data cleaning, data missing value filling and data standardization; Step S13: extracting highway structure semantics from the standard highway construction plan to obtain highway construction structure semantic data; Step S14: Use BIM technology to construct a three-dimensional digital model of the highway construction structure semantic data to generate a highway construction BIM model.

[0007] The present invention ensures that the initial data source of the project construction is authentic and comprehensive by acquiring the highway construction plan data, and can provide basic guarantee for the subsequent digital processing. This step lays the data foundation for the entire process. The process of data cleaning and standardization can remove redundant and erroneous information in the data, and fill in missing values ​​to ensure the consistency and integrity of the data. By generating standardized highway construction plan data, the accuracy and reliability of the subsequent analysis and modeling process are guaranteed. By semantically extracting standard data, the structural information in the highway construction plan can be better understood. This step can effectively convert complex construction plan data into recognizable highway structure semantic information, providing an accurate structural foundation for subsequent model construction. Using BIM (Building Information Modeling) technology, the semantic data of highway construction is converted into a three-dimensional digital model to generate a highway construction BIM model. This step enables the project to move from conceptual design to actual visualization, which can better carry out construction planning and management, and improve the efficiency and quality of the construction process. This step S1 not only improves the data accuracy and availability of the highway construction plan through standardized data processing, highway structure semantic extraction and BIM modeling, but also enables the project to more intuitively manage and optimize the highway construction process in a three-dimensional visualization environment, which helps to reduce design errors, improve construction efficiency, and support subsequent operation and management work.

[0008] Preferably, step S14 includes the following steps: Step S141: classifying the functional structural components of the highway construction structure semantic data to generate highway construction functional facility classification data, wherein the functional structural component classification includes road structure classification, bridge structure classification, tunnel structure classification and roadbed structure classification; Step S142: performing IFC standard format conversion on the highway construction functional facility classification data to generate highway construction format conversion data; performing structural semantic mapping on the highway construction format conversion data using BIM technology to generate highway construction structural semantic mapping data; Step S143: performing three-dimensional geometric modeling according to the highway construction structure semantic mapping data to generate three-dimensional geometric modeling data for highway construction; performing GIS geographic information integration on the three-dimensional geometric modeling data for highway construction to generate a preliminary three-dimensional structural model; Step S144: Associating model components of the preliminary three-dimensional structure model based on the highway construction structure semantic data to generate three-dimensional structure attribute association data; inputting the three-dimensional structure attribute association data into the preliminary three-dimensional structure model to fill in model component information to generate a highway construction BIM model.

[0009] The present invention can classify complex highway facilities according to functional structures (roads, bridges, tunnels, roadbeds, etc.) by classifying and processing highway construction structural semantic data, and generate clear classification data. This step ensures that the structural attributes of various facilities can be accurately distinguished, facilitates subsequent modeling and analysis, and improves the efficiency and accuracy of systematic processing. By converting the classification data of highway construction functional facilities into the internationally accepted IFC (Industry Foundation Classes) standard format, the interoperability of data between different systems is achieved. Subsequently, structural semantic mapping is performed using BIM technology to accurately match semantic information to the structural model, which not only improves the standardization and compatibility of data, but also ensures the integrity and availability of semantic information. On the basis of three-dimensional geometric modeling, spatial geographic information integration is combined with GIS (geographic information system) to realize the positioning and association of highway construction models in actual geographical environments. This step combines the highway construction model with its real environment, and the generated preliminary three-dimensional structural model not only has three-dimensional geometric information, but also has spatial positioning and geographical attributes, which greatly improves the reality and practicality of the model. The association processing of model components is performed based on structural semantic data to ensure that the components of the three-dimensional model have correct attribute associations. Then, by filling in the attribute information of the model components, the final highway construction BIM model is generated. This step not only realizes the digitization and semantic association of the components, but also provides rich attribute information for subsequent project management, construction planning, and maintenance, further improving the integrity and practicality of the BIM model. Through functional classification, standardized conversion, 3D modeling, geographic information integration, and information filling, an accurate, comprehensive, and visual highway construction BIM model is gradually established. This BIM model not only reflects the geometric structure of highway construction, but also integrates rich semantic, functional, and geographic information, which facilitates efficient collaboration and management during the design, construction, and maintenance process, which will help optimize the project's construction process, improve construction efficiency, and reduce potential errors and rework.

[0010] Preferably, step S2 comprises the following steps: Step S21: performing construction simulation on the highway construction BIM model to generate highway construction simulation data; based on the highway construction simulation data, performing virtual deformation joint prone area identification on the highway construction BIM model to obtain virtual deformation joint prone area identification data; Step S22: Based on the virtual deformation joint prone area identification data, a total station is used to perform deformation joint actual detection, thereby generating deformation joint actual detection data; the deformation joint actual detection data is divided into deformation joint areas, thereby generating deformation joint detection area data; Step S23: performing regional matching calculation on the deformation joint detection area data and the virtual deformation joint prone area identification data to obtain the deformation joint area detection matching degree; Step S24: Compare the deformation joint area detection matching degree with the preset area detection matching threshold. When the deformation joint area detection matching degree is less than or equal to the preset area detection matching threshold, a first deformation joint inspection plan is generated; when the deformation joint area detection matching degree is greater than the preset area detection matching threshold, a second deformation joint inspection plan is generated.

[0011] The present invention can predict and analyze potential problems in the construction process in advance, especially the prone areas of deformation joints, by simulating the construction of the highway construction BIM model. Virtual deformation joint prone area identification can find the parts of the structure that are deformed before construction, and help take preventive measures in advance, which helps to reduce the risk of deformation joints in actual construction and improve construction accuracy and safety. Based on the virtual recognition results, the deformation joint detection in reality is carried out using precision equipment such as total stations, and the virtual simulation can be combined with the actual detection to ensure the accuracy of the detection results. By dividing the deformation joint detection data into regions, the specific location and range of the deformation joint can be clearly defined, which is convenient for construction management and subsequent processing. The matching degree of the deformation joint detection area data and the virtual recognition data is calculated to evaluate the degree of difference between the virtual simulation and the actual detection. This step can effectively verify the accuracy of the virtual prediction, help find errors or omissions in the virtual model, and provide a basis for subsequent construction decisions. According to the matching degree result, it is compared with the preset regional detection matching threshold, and the inspection plan can be dynamically generated. If the matching degree is low, it means that there is a big difference between the virtual prediction and reality, and more frequent and strict inspection measures need to be taken (the first inspection plan); if the matching degree is high, it means that the virtual prediction is accurate and the inspection frequency can be reduced (the second inspection plan). This intelligent inspection plan generation mechanism not only improves the inspection efficiency, but also reduces unnecessary resource waste. Through construction simulation, reality detection, matching degree calculation and the formulation of intelligent inspection plans, accurate detection and management of deformation joints in virtual and real construction processes are achieved. This step can identify potential risks in advance and improve construction quality and safety; at the same time, the inspection plan dynamically generated according to the matching results improves work efficiency, reduces rework and waste during construction, and provides a reliable guarantee for the smooth implementation of the project.

[0012] Preferably, the construction simulation of the highway construction BIM model includes: Decompose the construction tasks of highway construction BIM model data to generate construction task planning data; perform resource consumption analysis on the construction task planning data to generate resource use analysis data; perform resource allocation optimization processing on the resource use analysis data to generate construction resource optimization data; According to the construction resource optimization data, the construction task planning data is processed in time axis association to generate the construction schedule data; the construction schedule data is simulated in three dimensions to generate the construction dynamic simulation data; the construction dynamic simulation data is tested for construction collision to generate the construction simulation collision detection data; The construction dynamic simulation data and the construction simulation conflict detection data are integrated to generate highway construction simulation data.

[0013] The present invention can refine the entire construction process by decomposing the construction tasks of the highway construction BIM model data, decompose the complex construction tasks into specific and operable task items, and the generated construction task planning data helps to clarify the requirements and goals of each construction stage, ensure that the construction steps are carried out in an orderly manner, and reduce the confusion and delays in construction. Resource consumption analysis can quantify the resource requirements (such as materials, equipment, manpower, etc.) for each construction task, thereby generating detailed resource usage analysis data. By optimizing the resource allocation of these data, resource waste can be reduced, costs can be reduced, and the reasonable allocation of resources can be ensured. The generated construction resource optimization data can enable the project to be carried out smoothly under resource constraints, thereby improving the overall efficiency and benefits of the construction. Based on the optimized resource allocation data, the time axis association processing is performed, and the generated construction schedule data can accurately plan the time arrangement of each task. Reasonable time management can not only shorten the construction period, but also avoid time conflicts and delays between tasks, ensure that the construction progress is carried out as planned, and improve the time management ability of the project. The three-dimensional construction simulation of the construction schedule can dynamically present the construction process and provide intuitive progress display and simulation effects. The construction dynamic simulation data generated by simulation can comprehensively evaluate the feasibility and potential risks of the construction. The collision detection of construction simulation can detect conflicts or problems that occur during the construction process in advance (such as conflicts between structural components, limited equipment movement, etc.). The generated construction simulation conflict detection data provides a strong reference for further optimizing the construction process. Integrating the construction dynamic simulation data with the conflict detection data can comprehensively evaluate the simulation results and generate the final highway construction simulation data. This comprehensive data can provide valuable reference for the actual construction of the project, ensure that the construction process is efficient and conflict-free, and minimize potential construction risks. Through the steps of decomposition and planning of construction tasks, resource analysis and optimization, schedule formulation, three-dimensional dynamic simulation and collision detection, the construction simulation process can comprehensively evaluate the feasibility and risks of the project, which not only improves the accuracy of the construction plan, reduces resource waste and time delays, but also detects and handles conflict problems in construction in advance, ensuring an efficient and smooth construction process. The final generated construction simulation data provides a reliable basis for actual construction, helping the smooth implementation and management optimization of the project.

[0014] Preferably, step S23 includes the following steps: Step S231: Binarization processing is performed on the deformation joint detection area data and the virtual deformation joint prone area identification data to generate deformation joint detection area binary data and virtual deformation joint prone area binary data; Step S232: performing pixel-by-pixel intersection area comparison calculation on the binary data of the deformation joint detection area and the binary data of the virtual deformation joint prone area to generate intersection area data; performing region overlapping pixel calculation on the intersection area data to obtain the region intersection area; Step S233: performing pixel-by-pixel union region comparison calculation on the binary data of the deformation joint detection region and the binary data of the virtual deformation joint prone region to generate union region data; performing regional non-overlapping pixel calculation on the union region data to obtain the regional union area; Step S234: Calculate the intersection-and-union ratio of the region intersection area and the region union area, thereby generating a deformation joint region detection matching degree.

[0015] The present invention can simplify complex regional data into binary form by binarizing the deformation joint detection area data and the virtual deformation joint prone area identification data, so that subsequent calculations are more efficient and accurate. By generating the deformation joint detection area binary data and the virtual deformation joint prone area binary data, the area of ​​interest can be clearly separated and the foundation for the subsequent pixel-level comparison is laid. This processing improves the operability and analysis efficiency of the data. By performing pixel-by-pixel intersection area comparison on the binarized data, the overlapping area in the virtual identification and the actual detection data can be accurately determined. The generation of intersection area data helps to evaluate the consistency between the actual deformation joint and the predicted area, and the calculation of the intersection area quantifies this consistency and provides a basis for further judgment and optimization. This process can reveal potential errors or undetected areas, thereby improving the accuracy of matching. The pixel-by-pixel union area comparison calculation helps to determine the maximum value of the entire deformation joint coverage range, which includes both the virtual prediction area and the actual detection area. The generated union area data provides a basis for subsequent regional difference analysis, and the calculation of the union area quantifies the non-overlapping area of ​​the virtual and actual data. Through this process, the coverage of virtual prediction can be better evaluated and the virtual model can be optimized. The final intersection-and-union ratio calculation step generates the deformation joint area detection matching degree by calculating the ratio of the intersection area and the union area. This ratio can accurately reflect the fit between the virtual prediction and the real detection. Through the quantitative representation of the matching degree, the prediction accuracy of the virtual model can be intuitively judged, providing data support for subsequent construction decisions and providing direction for optimizing virtual modeling. By binarizing the deformation joint detection area data and the virtual deformation joint prone area data, calculating the intersection and union, and analyzing the intersection-and-union ratio, accurate comparison and matching degree evaluation of virtual and real data are achieved. This step not only improves the detection accuracy, but also quantifies the matching degree between virtual prediction and actual detection, helping to discover potential risk areas and errors. The generated matching degree data provides a strong basis for subsequent construction optimization, risk management and decision-making, and ultimately improves the safety and efficiency of the construction process.

[0016] Preferably, step S24 includes the following steps: Step S241: comparing the deformation joint area detection matching degree with a preset area detection matching threshold, when the deformation joint area detection matching degree is less than or equal to the preset area detection matching threshold, marking the key deformation joint risk area for the deformation joint actual detection data according to the deformation joint area detection matching degree, and obtaining the key deformation joint risk marking area; Step S242: Perform high-frequency inspection on the key deformation joint risk mark area to generate high-frequency inspection frequency data, and perform non-destructive inspection of hidden deformation joints on the key deformation joint risk mark area using an ultrasonic detector according to the high-frequency inspection frequency data to generate non-destructive inspection data of hidden deformation joints; integrate the non-destructive inspection data of hidden deformation joints into the highway construction BIM model for inspection visualization, and generate a first deformation joint inspection plan; Step S243: when the deformation joint area detection matching degree is greater than a preset area detection matching threshold, the deformation joint actual detection data is marked as a basic deformation joint risk area according to the deformation joint area detection matching degree to obtain a basic deformation joint risk marking area; Step S244: Perform low-frequency inspections on key deformation joint risk marked areas to generate low-frequency inspection frequency data, and use strain gauges to perform explicit deformation joint detection on foundation deformation joint risk marked areas based on the low-frequency inspection frequency data to generate explicit deformation joint detection data; integrate the explicit deformation joint detection data into the highway construction BIM model for inspection visualization, and generate a second deformation joint inspection plan.

[0017] The present invention can effectively judge the actual condition of the expansion joint by comparing the detection matching degree of the expansion joint area with the preset matching threshold. When the matching degree is less than or equal to the preset threshold, emphasizing the marking of the risk area helps to identify potential risk points. The generation of such key risk marking areas enables subsequent detection and monitoring work to be more centralized, thereby improving the overall inspection efficiency and pertinence, and reducing losses caused by failure to discover problems in a timely manner. High-frequency inspections are performed on key risk marking areas, and the high-frequency inspection frequency data generated can ensure the timely detection of potential problems. Combined with the use of ultrasonic detectors, non-destructive testing of hidden expansion joints can deeply evaluate the integrity and safety of the structure. This process can not only discover hidden problems in advance and reduce safety hazards, but also integrate the non-destructive testing data of hidden expansion joints into the BIM model to achieve visual inspections, providing important data support for subsequent decision-making. The generated first expansion joint inspection plan provides detailed guidance for construction and maintenance. When the matching degree is greater than the preset threshold, the risk marking area of ​​the foundation deformation joint is generated, so that the risks of the relevant areas can be reasonably classified. This classification method helps to clarify the handling strategies of different risk levels and ensure that high-risk areas are monitored and maintained first when resources are limited, thereby optimizing resource allocation and improving management efficiency. Low-frequency inspections are carried out on key risk marking areas. By combining the generated low-frequency inspection frequency data with the use of strain gauges, explicit deformation joint detection can be carried out to more comprehensively understand the health status of the area. The acquisition of explicit deformation joint detection data provides a substantial information basis for risk management and ensures effective monitoring of different risk levels. These data are integrated into the BIM model for inspection visualization, and the second deformation joint inspection plan is generated, making the inspection work systematic and visualized, which is conducive to subsequent construction monitoring and maintenance decisions. Through the comparison and marking of regional matching degrees, the implementation of high-frequency and low-frequency inspections, and the combination of explicit and implicit deformation joint detection, all-round support is provided for the safety management of highway construction. By identifying and monitoring key risk areas, we can not only improve construction safety and reduce potential risks, but also ensure that problems are discovered and dealt with in a timely manner during the maintenance process, ultimately promoting efficient and safe operation of highway construction. The generated inspection plan provides detailed guidance and decision-making support for the construction team, helps optimize management processes, and improves the overall quality and safety of highway construction.

[0018] Preferably, step S3 comprises the following steps: Step S31: collecting periodic inspection data of the highway construction BIM model through the first deformation joint inspection plan and the second deformation joint inspection plan to obtain periodic deformation joint inspection data; Step S32: performing deformation joint damage assessment on the periodic deformation joint inspection data to generate periodic deformation joint inspection damage assessment data; Step S33: Performing automatic early warning according to the periodic deformation joint inspection damage assessment data to generate periodic deformation joint inspection early warning data; Step S34: Formulate a deformation joint repair strategy based on the periodic deformation joint inspection warning data to generate a deformation joint abnormality repair strategy.

[0019] The present invention can systematically collect the status data of expansion joints by periodically inspecting the highway construction BIM model through the first and second expansion joint inspection plans. Regular data collection ensures the timely updating and comprehensiveness of information, and helps to form long-term monitoring records, thereby providing a solid foundation for subsequent analysis and decision-making. Damage assessment of periodic expansion joint inspection data helps to timely identify potential problems and their severity of expansion joints. This assessment can not only clearly show the current health status of expansion joints, but also provide an important basis for subsequent management and maintenance work, making targeted maintenance possible and avoiding the development of small problems into major hidden dangers. Automated early warning based on damage assessment data can achieve real-time monitoring and response. This mechanism improves the efficiency of expansion joint management, reduces the time and workload of manual evaluation, and enables any potential risks to be discovered and handled in the shortest time. The automated early warning system enhances the reliability of inspection work, ensures that timely response measures can be taken, and thus reduces the risk of accidents. Based on the early warning data of periodic deformation joint inspections, the abnormal deformation joint repair strategy is formulated to ensure that prompt action is taken after the problem is discovered. This strategy formulation process provides clear guidance for the construction and maintenance teams, so that the repair work can be carried out efficiently and orderly, thereby reducing the additional losses caused by improper maintenance. The systematization of repair strategies can also promote the improvement of construction quality and extend the service life of deformation joints. Through the collection of periodic inspection data, damage assessment, automatic early warning and the formulation of repair strategies, a complete deformation joint monitoring and management mechanism is provided for highway construction. This mechanism not only improves the safety and reliability of deformation joints, but also optimizes resource allocation and management processes. Through effective monitoring and timely repair, the safety hazards in highway construction can be significantly reduced, the overall construction quality can be improved, and the long-term safe operation of highways can be ensured. This systematic management method has laid a solid foundation for the management of highway construction.

[0020] Preferably, step S32 includes the following steps: Step S321: screening the historical deformation joint abnormal data on the periodic deformation joint inspection data to obtain the historical deformation joint abnormal inspection data; extracting abnormal inspection features from the historical deformation joint abnormal inspection data to obtain abnormal inspection feature data; Step S322: dividing the abnormal inspection feature data into data sets to generate a model training set and a model test set; using a convolutional neural network algorithm to perform model training on the model training set to generate a deformation joint damage assessment pre-model; Step S323: perform model optimization iteration on the expansion joint damage assessment pre-model according to the model test set, so as to generate the expansion joint damage assessment model; import the periodic expansion joint inspection data into the expansion joint damage assessment model to perform expansion joint damage assessment, and generate the periodic expansion joint inspection damage assessment data.

[0021] The present invention can clearly identify the deformation joint problems that occurred in the past by screening the historical deformation joint abnormal inspection data, and then extract the abnormal inspection features of these data. This feature extraction process helps to understand the manifestation and law of deformation joint abnormalities, and provides key basic data for subsequent analysis and modeling. By analyzing historical data, potential risks can be better identified, and a good foundation for model training can be laid. The abnormal inspection feature data is divided into a model training set and a test set to ensure the scientificity and rationality of model training. Using the convolutional neural network algorithm to train the model training set can improve the recognition ability and accuracy of the model. Convolutional neural networks perform well in processing images and time series data. Therefore, applying its algorithm in this step can effectively capture the complex features of deformation joint abnormalities, thereby generating a preliminary damage assessment pre-model. Optimizing and iterating the deformation joint damage assessment pre-model according to the model test set can improve the performance and generalization ability of the model. Through this optimization process, the model can better adapt to new inspection data, reduce the risk of overfitting, and ensure the reliability of the model in practical applications. In this process, the inspection data of periodic deformation joints are imported into the optimized model for evaluation, which can ensure the scientificity and practicality of the evaluation results and generate periodic deformation joint inspection damage evaluation data. Through the analysis of historical data, feature extraction, reasonable division of data sets, and model training and optimization, a scientific deformation joint damage evaluation system is constructed. This system not only improves the monitoring ability of deformation joint status, but also enhances the ability to predict deformation joint damage risks. Through efficient data processing and model training, potential problems of deformation joints can be discovered and handled in a timely manner, thereby reducing the probability of accidents and ensuring the safety and reliability of highway construction.

[0022] In this specification, a highway construction deformation joint detection management system based on BIM is provided, which is used to execute the above-mentioned highway construction deformation joint detection management method based on BIM. The highway construction deformation joint detection management system based on BIM includes: The BIM model building module is used to obtain highway construction plan data; extract highway structure semantics from highway construction plan data to obtain highway construction structure semantic data; construct a three-dimensional digital model of highway construction structure semantic data to generate a highway construction BIM model; The expansion joint inspection module is used to perform construction simulation on the highway construction BIM model and generate highway construction simulation data; perform virtual reality identification of expansion joint prone areas on the highway construction BIM model based on the highway construction simulation data to obtain virtual expansion joint prone area identification data and expansion joint detection area data; perform area matching inspection division on the virtual expansion joint prone area identification data and expansion joint detection area data to generate the first expansion joint inspection plan and the second expansion joint inspection plan; The damage assessment module is used to collect periodic inspection data of the highway construction BIM model through the first deformation joint inspection plan and the second deformation joint inspection plan to obtain periodic deformation joint inspection data; perform deformation joint damage assessment on the periodic deformation joint inspection data to generate periodic deformation joint inspection damage assessment data; formulate deformation joint repair strategies based on the periodic deformation joint inspection damage assessment data to generate deformation joint abnormal repair strategies; The detection management optimization module is used to update the deformation joint status of the highway construction BIM model according to the deformation joint abnormal repair strategy, and generate deformation joint status inspection update data; adjust the detection management process through the deformation joint status inspection update data, and generate a highway construction deformation joint detection management adjustment plan to execute highway construction deformation joint detection management operations.

[0023] The beneficial effect of the present invention is that by obtaining highway construction plan data, the accuracy and completeness of basic data are ensured, and a reliable basis is provided for subsequent analysis and modeling. The complex construction plan is converted into structured data, which is convenient for information management and processing, generates a visual BIM model, enhances the understanding and communication of highway construction projects, and facilitates collaboration between different participants. The data generated by construction simulation provides a virtual environment for the actual construction process, identifies potential problems through simulation analysis, and predicts construction risks in advance. Virtual reality identification of prone areas can efficiently identify areas prone to deformation joints, reduce the possibility of missed detection and false detection, and improve construction safety. Scientifically divide the inspection plan to ensure the rational allocation and use of resources, and improve the pertinence and effectiveness of the inspection. Through regular data collection, a monitoring database for deformation joints is established to form a continuous tracking of the construction status. The deformation joint damage assessment provides a scientific basis, timely discovers and evaluates the damage of the deformation joint, and helps to take maintenance measures in advance. According to the evaluation results, a targeted repair strategy is formulated to ensure the effectiveness and timeliness of the repair work and extend the service life of the deformation joint. The deformation joint status in the BIM model is updated in real time to ensure that all relevant parties obtain the latest information and reduce communication errors. By dynamically adjusting the management process, we can adapt to the progress of the project and the changes in the state of the expansion joints, improve management efficiency, and ensure construction safety. We can implement standardized management solutions, improve the systematicness and standardization of expansion joint detection, and provide guarantees for the safety of highway construction. Therefore, the present invention improves the comprehensiveness and real-time nature of BIM-based highway construction expansion joint detection management through systematic information integration, real-time monitoring, scientific inspections, data-driven damage assessment and repair strategy formulation, and flexible management process adjustments. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of the steps of a BIM-based highway construction deformation joint detection management method; Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart; Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG. The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0025] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, and the term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0028] To achieve this, please refer to Figures 1 to 3 , a highway construction deformation joint detection management method based on BIM, the method comprising the following steps: Step S1: Obtain highway construction plan data; perform highway structure semantic extraction on the highway construction plan data to obtain highway construction structure semantic data; construct a three-dimensional digital model of the highway construction structure semantic data to generate a highway construction BIM model; Step S2: Perform construction simulation on the highway construction BIM model to generate highway construction simulation data; perform virtual reality identification of deformation joint prone areas on the highway construction BIM model based on the highway construction simulation data to obtain virtual deformation joint prone area identification data and deformation joint detection area data; perform regional matching inspection division on the virtual deformation joint prone area identification data and the deformation joint detection area data to generate a first deformation joint inspection plan and a second deformation joint inspection plan; Step S3: periodic inspection data collection is performed on the highway construction BIM model through the first deformation joint inspection plan and the second deformation joint inspection plan to obtain periodic deformation joint inspection data; deformation joint damage assessment is performed on the periodic deformation joint inspection data to generate periodic deformation joint inspection damage assessment data; deformation joint repair strategy is formulated according to the periodic deformation joint inspection damage assessment data to generate deformation joint abnormal repair strategy; Step S4: Update the deformation joint status of the highway construction BIM model according to the deformation joint abnormal repair strategy, and generate deformation joint status inspection update data; adjust the detection management process through the deformation joint status inspection update data, and generate a highway construction deformation joint detection management adjustment plan to execute the highway construction deformation joint detection management operation.

[0029] The present invention lays the foundation for subsequent processing by preliminarily collecting all data related to construction. By analyzing the scheme data, the structural information in the highway construction is extracted, and the components and functions of the highway can be clearly defined. The extracted structural semantic data is converted into a BIM model, which provides a visual three-dimensional expression and facilitates subsequent construction and management. By simulating the construction process, it is possible to predict the problems and the reasonable allocation of resources, thereby improving construction efficiency. Virtual reality technology is used to identify the problem area where the expansion joint occurs, which enhances the forward-looking identification ability of potential risks. The detection area is subdivided into different inspection plans, which improves the pertinence and effectiveness of the inspection and provides a scientific basis for subsequent periodic inspections. Regular inspection data collection ensures dynamic monitoring of the highway status. The collected data is analyzed to generate damage assessment data, which can quickly and accurately identify the damage of the expansion joint. Based on the damage assessment results, a corresponding repair strategy is formulated to ensure that a rapid response and processing can be made when the problem occurs. The status of the BIM model is updated according to the repair strategy to ensure the timeliness and accuracy of the model and provide the latest data for subsequent decision-making. Based on the latest status data, the detection management process is optimized, the management efficiency is improved, and the deformation joint detection during highway construction is always in the best state. Through systematic construction simulation and real-time inspections, potential risks can be identified at an early stage, thereby reducing the probability of accidents. The implementation of each step provides reliable data support for subsequent decision-making, ensuring the scientificity and rationality of the decision. By recycling data, resource waste is reduced, the efficiency of management and construction is improved, and costs are reduced. Therefore, the present invention improves the comprehensiveness and real-time nature of BIM-based highway construction deformation joint detection management through systematic information integration, real-time monitoring, scientific inspections, data-driven damage assessment and repair strategy formulation, and flexible management process adjustments.

[0030] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a highway construction deformation joint detection management method based on BIM of the present invention. In this example, the highway construction deformation joint detection management method based on BIM includes the following steps: Step S1: Obtain highway construction plan data; perform highway structure semantic extraction on the highway construction plan data to obtain highway construction structure semantic data; construct a three-dimensional digital model of the highway construction structure semantic data to generate a highway construction BIM model; In the embodiment of the present invention, the source of the highway construction plan data is determined, including: design drawings (such as CAD drawings), engineering planning documents (such as design specifications, project bidding documents), and geographic information system (GIS) data (such as topographic maps, soil properties). If the data is a paper document or drawing, a scanner is used to digitize it. Import existing digital format (such as DWG, DXF, Shapefile, etc.) files into the data processing system. Convert the data into a unified format (such as GeoJSON, CSV, etc.) for subsequent processing. Check the integrity and accuracy of the data, and correct missing values ​​and error information. Based on relevant standards for highway construction (such as "Highway Engineering Technical Standards"), a semantic model of the highway structure is constructed to define the categories of various components (such as roadbed, pavement, bridges, culverts, etc.) and their attributes (such as materials, dimensions, bearing capacity, etc.). Natural language processing (NLP) technology is used to extract key information such as material type, structural form, and design parameters from text data such as project bidding documents and design specifications. For drawing data, image recognition technology (such as image segmentation and feature extraction) is used to extract structural information from CAD drawings and identify each component and its corresponding attributes. Integrate the extracted semantic data with other data (such as GIS data) to form a comprehensive highway structure semantic data set. Select specific building information modeling (BIM) software (such as Revit, Navisworks or SketchUp) to build the 3D model. According to the highway structure semantic data, build 3D models of each component in the BIM software, including roadbed, pavement, bridge, etc. According to the extracted semantic data, assign attributes such as material, size, construction process, etc. to each component in the model to make it rich in information. Perform geometric checks on the generated 3D model to ensure its accuracy and compliance with design standards. Use the simulation function of the BIM software to analyze the model, such as construction sequence simulation, cost estimation, and environmental impact assessment. Export the completed highway construction BIM model to common formats (such as IFC, OBJ, FBX) for subsequent use and sharing.

[0031] Step S2: Perform construction simulation on the highway construction BIM model to generate highway construction simulation data; perform virtual reality identification of deformation joint prone areas on the highway construction BIM model based on the highway construction simulation data to obtain virtual deformation joint prone area identification data and deformation joint detection area data; perform regional matching inspection division on the virtual deformation joint prone area identification data and the deformation joint detection area data to generate a first deformation joint inspection plan and a second deformation joint inspection plan; In the embodiment of the present invention, the entire construction process is divided into multiple construction tasks (such as roadbed construction, bridge construction, pavement paving, etc.) according to the design drawings and construction plans of highway construction. Each construction task is further refined into specific construction steps, such as earthwork, concrete pouring, pavement paving, etc. According to the requirements of each construction task, the required construction resources (such as manpower, machinery, materials, etc.) are analyzed. A resource allocation plan for each construction task is formulated to ensure the rational allocation and efficient use of resources. A specific time node is assigned to each construction task, and a construction schedule is set in the BIM software. BIM software (such as Navisworks, Revit, etc.) is used to perform dynamic construction simulation to observe the execution of each construction task, including time schedule, resource usage and mutual influence. Key data in the construction simulation process are recorded, including construction time, resource usage and bottlenecks. The construction simulation results are analyzed to generate highway construction simulation data. A three-dimensional virtual environment of the construction site is constructed using virtual reality technology to truly reproduce the details of highway construction. Design functions that users can interact with in the virtual environment, such as viewing different construction stages, material application and deformation joint locations. Based on the construction simulation data, identify the factors that cause expansion joints (such as construction loads, temperature changes, soil settlement, etc.). Through data analysis and model simulation, determine the areas prone to expansion joints, and mark these areas in the virtual environment to obtain virtual expansion joint prone area identification data and expansion joint detection area data for subsequent use. Compare and analyze the virtual expansion joint prone area identification data with the historical expansion joint detection area data to evaluate the matching degree of each area. Define the criteria for matching evaluation, such as similarity threshold, overlap, accuracy of historical monitoring data, etc. Based on the matching analysis results, select high-risk expansion joint prone areas and formulate the first expansion joint inspection plan. Determine the frequency and time schedule of the inspection to ensure timely detection of problems. For areas with low matching, formulate a second expansion joint inspection plan for low-frequency inspections. Set up a feedback mechanism to compare the inspection results with the inspection plan and dynamically adjust the inspection strategy.

[0032] Step S3: periodic inspection data collection is performed on the highway construction BIM model through the first deformation joint inspection plan and the second deformation joint inspection plan to obtain periodic deformation joint inspection data; deformation joint damage assessment is performed on the periodic deformation joint inspection data to generate periodic deformation joint inspection damage assessment data; deformation joint repair strategy is formulated according to the periodic deformation joint inspection damage assessment data to generate deformation joint abnormal repair strategy; In the embodiment of the present invention, regular inspection time is arranged according to the first deformation joint inspection plan and the second deformation joint inspection plan to ensure that all relevant areas are inspected. A professional inspection team is formed to collect and record on-site inspection data. According to the plan, the inspection team conducts on-site inspections on the calibrated deformation joint prone areas, including visual inspections and data collection using professional detection equipment (such as laser rangefinders, strain gauges, etc.). The deformation joint conditions observed during the inspection process are recorded, including information such as crack width, deformation conditions and the impact of the surrounding environment, to generate periodic deformation joint inspection data. The data collected on-site are sorted, including data format unification, information completion, etc., to ensure the integrity and availability of the data. The sorted data is stored in the BIM system for subsequent analysis and use. Based on industry specifications and historical data, standards and indicators for deformation joint damage assessment, such as crack width, deformation amplitude, influencing factors, etc., are formulated. The periodic deformation joint inspection data is cleaned to remove outliers to ensure data quality. Machine learning or deep learning algorithms (such as convolutional neural networks) are applied to damage assessment of periodic deformation joint inspection data. Generate periodic deformation joint inspection damage assessment data based on the analysis results, including assessment conclusions (such as normal, slightly damaged, and severely damaged) and recommended treatment measures. Based on the periodic deformation joint inspection damage assessment data, identify deformation joints with abnormalities, including areas with slight damage and severe damage. Develop corresponding repair strategies for different damage levels. Repair strategies include: Minor damage: Regular monitoring, and material filling is recommended. Moderate damage: Local repair and use of repair materials. Severe damage: After a comprehensive assessment, structural reinforcement or replacement is required. Integrate the repair strategies to generate a detailed deformation joint abnormality repair strategy, including repair methods, required materials, construction time, personnel arrangements and other information. After implementing the repair strategy, continuously monitor the deformation joint status and feed back the monitoring results to the BIM system so that the repair strategy can be dynamically adjusted and optimized based on the new data.

[0033] Step S4: Update the deformation joint status of the highway construction BIM model according to the deformation joint abnormal repair strategy, and generate deformation joint status inspection update data; adjust the detection management process through the deformation joint status inspection update data, and generate a highway construction deformation joint detection management adjustment plan to execute the highway construction deformation joint detection management operation.

[0034] In the embodiment of the present invention, by collecting and integrating the previous deformation joint status, inspection results and repair strategy information, all data are ensured to be accessible in one platform. The initial state is set for each deformation joint, including whether there is damage, repair record, monitoring frequency, etc. According to the abnormal repair strategy of the deformation joint, the state of the deformation joint in the highway construction BIM model is updated. The update content includes: identifying which deformation joints have been repaired and which are still under monitoring. The historical state changes, repair time and method of each deformation joint are recorded in detail, and the deformation joint state inspection update data is generated, including the current state, historical state changes, repair records and subsequent monitoring plans. The updated deformation joint state is visualized in the BIM model to ensure that all parties have a clear understanding of the current situation. The existing deformation joint detection management process is evaluated to identify the deficiencies or room for improvement in the process. Based on the deformation joint state inspection update data, the repair state, monitoring frequency and resource allocation of different deformation joints are analyzed to drive management decisions. According to the evaluation results, a new deformation joint detection management process is formulated, including: adjusting the inspection frequency for deformation joints in different states. For the repaired expansion joints, the inspection frequency can be reduced; for the expansion joints with potential risks, the inspection frequency can be increased. Based on the status of the expansion joints, optimize the allocation of human and material resources to improve the inspection efficiency. Evaluate the existing detection equipment and technical means, and update or introduce new equipment and technology according to needs. Organize the adjustment plan into a document, including information such as flow charts, step instructions, responsible persons, time nodes and expected results. Submit the adjustment plan for review to ensure the recognition and approval of the plan by relevant management. According to the approved adjustment plan, formulate a detailed implementation plan and arrange the implementation of various tasks, including personnel training and equipment adjustment. Establish a feedback mechanism to ensure that feedback from all parties is collected during the implementation process so that the plan can be adjusted and optimized in a timely manner.

[0035] Preferably, step S1 comprises the following steps: Step S11: Obtaining highway construction plan data; Step S12: preprocessing the highway construction plan data to generate standard highway construction plan data, wherein the data preprocessing includes data cleaning, data missing value filling and data standardization; Step S13: extracting highway structure semantics from the standard highway construction plan to obtain highway construction structure semantic data; Step S14: Use BIM technology to construct a three-dimensional digital model of the highway construction structure semantic data to generate a highway construction BIM model.

[0036] In an embodiment of the present invention, highway construction plan data from different channels are collected, including information provided by the design unit and relevant data in the project management software. The data includes information such as project name, construction site, design drawings, bill of quantities, construction plan, etc. Check whether there are duplicate records in the data set and delete them. Ensure that the data format is consistent, such as date format, numerical unit, etc. Fill missing values ​​according to project type, historical data or statistical methods such as mean and median. Keep records for subsequent analysis. Normalize numerical data to ensure that different features are on the same scale. Convert categorical variables to numerical types, use methods such as unique hot encoding, and generate standard highway construction plan data for use in subsequent steps. Use natural language processing (NLP) technology and machine learning algorithms to analyze standard highway construction plan data, extract structural semantic information of highways, structural elements such as bridges, tunnels, pavements, drainage systems, etc., and design parameters such as width, height, slope, materials, etc. Save the extracted highway construction structure semantic information in a structured form to facilitate subsequent model construction. Select appropriate BIM software (such as Revit, Civil 3D, etc.) for modeling. Import the semantic data of highway construction structure into BIM software. Design the three-dimensional digital model of the highway based on the semantic data, including the geometry, position, material properties, etc. of each structural element. Adjust the model in detail according to the design requirements and construction specifications to generate the final highway construction BIM model, which is convenient for visualization, collision detection and construction simulation.

[0037] Preferably, step S14 includes the following steps: Step S141: classifying the functional structural components of the highway construction structure semantic data to generate highway construction functional facility classification data, wherein the functional structural component classification includes road structure classification, bridge structure classification, tunnel structure classification and roadbed structure classification; Step S142: performing IFC standard format conversion on the highway construction functional facility classification data to generate highway construction format conversion data; performing structural semantic mapping on the highway construction format conversion data using BIM technology to generate highway construction structural semantic mapping data; Step S143: performing three-dimensional geometric modeling according to the highway construction structure semantic mapping data to generate three-dimensional geometric modeling data for highway construction; performing GIS geographic information integration on the three-dimensional geometric modeling data for highway construction to generate a preliminary three-dimensional structural model; Step S144: Associating model components of the preliminary three-dimensional structure model based on the highway construction structure semantic data to generate three-dimensional structure attribute association data; inputting the three-dimensional structure attribute association data into the preliminary three-dimensional structure model to fill in model component information to generate a highway construction BIM model.

[0038] In an embodiment of the present invention, different types of highways are classified, such as main roads, secondary roads and branch roads. Bridges are classified according to their forms (such as beam bridges, arch bridges and suspension bridges). Different types of tunnels (such as highway tunnels and railway tunnels) are distinguished, including classifications of soil foundations, stone foundations and concrete foundations, and functional facility classification data for highway construction is generated to record detailed information and characteristics of various functional structural components. The IFC (Industry Foundation Classes) standard is used for data conversion to ensure compatibility with BIM tools. The functional facility classification data is converted into the IFC format, including information such as the geometric shape, material properties and component relationships of the defined components. Using BIM technology, the converted highway construction format conversion data is subjected to structural semantic mapping to generate highway construction structural semantic mapping data. This process includes associating IFC data with highway construction semantic information to ensure data consistency and availability. According to the highway construction structural semantic mapping data, three-dimensional geometric modeling is performed in the BIM software to construct three-dimensional models of each functional structural component. The generated 3D geometric modeling data of highway construction is integrated with GIS (Geographic Information System) data to obtain the geographical location information (such as terrain and environment) of the highway project to generate a preliminary 3D structural model, which provides a basis for subsequent model construction and refinement. Based on the semantic data of highway construction structure, the relationship and association between each model component are analyzed to generate 3D structural attribute association data, including information such as material properties, bearing capacity, and usage functions. The generated 3D structural attribute association data is input into the preliminary 3D structural model to fill in the model components. Ensure that each component contains the necessary attribute information for subsequent analysis and optimization, and generate the final highway construction BIM model, which contains complete geometric shapes and attribute information, and can support visualization, collision detection, construction simulation and other functions.

[0039] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: performing construction simulation on the highway construction BIM model to generate highway construction simulation data; based on the highway construction simulation data, performing virtual deformation joint prone area identification on the highway construction BIM model to obtain virtual deformation joint prone area identification data; Step S22: Based on the virtual deformation joint prone area identification data, a total station is used to perform deformation joint actual detection, thereby generating deformation joint actual detection data; the deformation joint actual detection data is divided into deformation joint areas, thereby generating deformation joint detection area data; Step S23: performing regional matching calculation on the deformation joint detection area data and the virtual deformation joint prone area identification data to obtain the deformation joint area detection matching degree; Step S24: Compare the deformation joint area detection matching degree with the preset area detection matching threshold. When the deformation joint area detection matching degree is less than or equal to the preset area detection matching threshold, a first deformation joint inspection plan is generated; when the deformation joint area detection matching degree is greater than the preset area detection matching threshold, a second deformation joint inspection plan is generated.

[0040] In an embodiment of the present invention, a construction simulation of a highway construction BIM model is performed by using BIM software (such as Navisworks, Tekla, etc.). A construction schedule is set, including the start and end time of each stage. Various tasks in the construction process are simulated, such as earthwork, roadbed paving, bridge construction, etc. Necessary resources (such as personnel, equipment, and materials) are allocated to ensure the smooth progress of the construction, generate highway construction simulation data, and record key parameters and potential locations of deformation joints in the construction process. Based on the construction simulation data, an analysis algorithm is applied to identify the area where deformation occurs. Numerical simulation methods, finite element analysis, and other technologies can be used to predict the location of deformation joints and obtain virtual deformation joint prone area identification data, including the location information of potential deformation joints and their degree of influence. A total station is used to measure and detect actual deformation joints. The total station can provide high-precision three-dimensional coordinate data. A total station is set up in an area prone to deformation joints for measurement. Record the actual position, shape and change of the expansion joint, generate the actual expansion joint detection data, including the actual expansion joint information detected and its precise position, generate the expansion joint detection area data, including the actual expansion joint information detected and its precise position, and generate the expansion joint detection area data, which is convenient for subsequent analysis and comparison. Use the regional matching algorithm (such as overlap rate, distance measurement, etc.) to compare the expansion joint detection area data and the virtual expansion joint prone area identification data. Calculate the matching degree between the two sets of data, judge the overlap degree between the actual detection area and the virtual prone area, obtain the expansion joint area detection matching degree, and quantify the relationship between the two. Set the preset regional detection matching threshold as the standard for judging the expansion joint inspection plan. If the expansion joint area detection matching degree is less than or equal to the preset regional detection matching threshold, generate the first expansion joint inspection plan, and focus on the prone area for detailed inspection. If the matching degree is greater than the preset regional detection matching threshold, generate the second expansion joint inspection plan, including expanding the inspection range or taking other monitoring measures, and generate two different expansion joint inspection plans, respectively for different matching degree situations, to ensure effective monitoring of the expansion joint.

[0041] Preferably, the construction simulation of the highway construction BIM model includes: Decompose the construction tasks of highway construction BIM model data to generate construction task planning data; perform resource consumption analysis on the construction task planning data to generate resource use analysis data; perform resource allocation optimization processing on the resource use analysis data to generate construction resource optimization data; According to the construction resource optimization data, the construction task planning data is processed in time axis association to generate the construction schedule data; the construction schedule data is simulated in three dimensions to generate the construction dynamic simulation data; the construction dynamic simulation data is tested for construction collision to generate the construction simulation collision detection data; The construction dynamic simulation data and the construction simulation conflict detection data are integrated to generate highway construction simulation data.

[0042] In the embodiment of the present invention, a complete highway construction BIM model including geometric information, material properties and component information is obtained from the design team. According to the BIM model, the main tasks in the construction process are identified, such as foundation construction, pavement paving, bridge construction, etc. Each main task is further refined into subtasks, such as earth excavation and foundation pouring in foundation construction. According to the decomposed tasks, a detailed construction task list is formed, including task name, task description and estimated duration. The resources required for each construction task are determined, including manpower, machinery and equipment, and materials. Based on the nature of the construction task, the resource consumption of each task is calculated using historical data and industry standards. A resource consumption analysis report is formed, covering the consumption and cost of various types of resources. The resource consumption data is analyzed to identify bottleneck resources or high-consumption tasks. Using optimization methods such as linear programming and genetic algorithms, a resource allocation optimization model is designed to minimize cost and duration. The optimization model is implemented to generate optimized construction resource data to ensure the reasonable allocation and efficient use of resources. Time parameters are defined for each task, including start time, end time and duration. Associate resource optimization data with construction task planning data to generate a timeline, generate a complete construction schedule, and list the time schedule of each task and its resource allocation. Select specific BIM construction simulation software (such as Navisworks, Tekla, BIM 360, etc.) and import construction schedule data and BIM models. Perform construction simulation according to the schedule and generate construction dynamic simulation data to display construction progress and resource usage. Set collision detection parameters in the simulation software, such as detection accuracy and type (such as collision between equipment and components). Run the collision detection algorithm to identify potential conflicts in the construction dynamic simulation data and generate a conflict detection report that lists all conflicts and their locations. Use data management tools or scripts to integrate construction dynamic simulation data and construction simulation conflict detection data. Form a final construction simulation data report that integrates construction progress, resource usage, and collision detection results to provide support for decision-making.

[0043] Preferably, step S23 includes the following steps: Step S231: Binarization processing is performed on the deformation joint detection area data and the virtual deformation joint prone area identification data to generate deformation joint detection area binary data and virtual deformation joint prone area binary data; Step S232: performing pixel-by-pixel intersection area comparison calculation on the binary data of the deformation joint detection area and the binary data of the virtual deformation joint prone area to generate intersection area data; performing region overlapping pixel calculation on the intersection area data to obtain the region intersection area; Step S233: performing pixel-by-pixel union region comparison calculation on the binary data of the deformation joint detection region and the binary data of the virtual deformation joint prone region to generate union region data; performing regional non-overlapping pixel calculation on the union region data to obtain the regional union area; Step S234: Calculate the intersection-and-union ratio of the region intersection area and the region union area, thereby generating a deformation joint region detection matching degree.

[0044] In an embodiment of the present invention, deformation joint detection area data (such as high-resolution images, point cloud data, etc.) is collected. Virtual deformation joint prone area identification data (generated based on models or historical data) is collected. The collected detection area data and prone area data are converted into grayscale images. A suitable threshold (T) is selected, which can be determined by the Otsu method or an adaptive threshold. The pixel value in the grayscale image is compared with the threshold: if the pixel value is greater than T, it is set to 1 (foreground). If the pixel value is less than or equal to T, it is set to 0 (background), and the deformation joint detection area binary data and the virtual deformation joint prone area binary data are generated and saved in binary image format respectively. The deformation joint detection area binary data and the virtual deformation joint prone area binary data are compared pixel by pixel, and the intersection area is calculated: the intersection area is a set of pixel points where both are 1. The intersection area data is generated from the pixel points of the intersection area and stored as a new binary image. The number of pixel values ​​​​of 1 in the intersection area data is counted to obtain the area of ​​regional intersection (A_intersect). Compare the binary data of the deformation joint detection area and the binary data of the virtual deformation joint prone area pixel by pixel, and calculate the union area: the union area is a set of pixels where either of them is 1. Generate the union area data from the pixels of the union area and store it as a new binary image. Count the number of pixels with a value of 1 in the union area data to obtain the union area (A_union). Use the formula to calculate the intersection-and-union ratio (Jaccard Index): In the formula, Indicates the matching degree of deformation joint area detection, is expressed as the area of ​​the region intersection, Expressed as the area of ​​the regional union. Output the calculation results to form a data report of the deformation joint area detection matching degree, including the matching degree value and the corresponding regional data. Analyze the matching degree value, judge the effectiveness of the deformation joint detection, and generate a corresponding feedback report to provide a basis for subsequent deformation joint processing and repair.

[0045] Preferably, step S24 includes the following steps: Step S241: comparing the deformation joint area detection matching degree with a preset area detection matching threshold, when the deformation joint area detection matching degree is less than or equal to the preset area detection matching threshold, marking the key deformation joint risk area for the deformation joint actual detection data according to the deformation joint area detection matching degree, and obtaining the key deformation joint risk marking area; Step S242: Perform high-frequency inspection on the key deformation joint risk mark area to generate high-frequency inspection frequency data, and perform non-destructive inspection of hidden deformation joints on the key deformation joint risk mark area using an ultrasonic detector according to the high-frequency inspection frequency data to generate non-destructive inspection data of hidden deformation joints; integrate the non-destructive inspection data of hidden deformation joints into the highway construction BIM model for inspection visualization, and generate a first deformation joint inspection plan; Step S243: when the deformation joint area detection matching degree is greater than a preset area detection matching threshold, the deformation joint actual detection data is marked as a basic deformation joint risk area according to the deformation joint area detection matching degree to obtain a basic deformation joint risk marking area; Step S244: Perform low-frequency inspections on key deformation joint risk marked areas to generate low-frequency inspection frequency data, and use strain gauges to perform explicit deformation joint detection on foundation deformation joint risk marked areas based on the low-frequency inspection frequency data to generate explicit deformation joint detection data; integrate the explicit deformation joint detection data into the highway construction BIM model for inspection visualization, and generate a second deformation joint inspection plan.

[0046] In an embodiment of the present invention, by collecting the deformation joint area detection matching degree data (J), a preset area detection matching threshold (T) is set. The deformation joint area detection matching degree J is compared with the threshold T: if J≤T, the key deformation joint risk area is marked. According to the deformation joint area detection matching degree J, the actual deformation joint detection data is analyzed, the key risk area is identified, and the key deformation joint risk marking area data is generated and visualized. A high-frequency inspection plan is formulated according to the key deformation joint risk marking area. The high-frequency inspection frequency is set (for example: every day, every week, etc.). The high-frequency inspection frequency data is recorded and generated for subsequent analysis. The hidden deformation joint detection is performed on the key deformation joint risk marking area using an ultrasonic detector: the detection parameters (such as frequency, wavelength, etc.) are set, non-destructive testing is performed, and the detection data is collected. The hidden deformation joint non-destructive detection data is integrated into the highway construction BIM model. The inspection results are visualized in the BIM model, and the first deformation joint inspection plan is generated. When the deformation joint area detection matching degree J>T, the basic deformation joint risk area is marked. According to the deformation joint area detection matching degree J, the actual deformation joint detection data is analyzed to identify the basic risk area, generate the foundation deformation joint risk marking area data, and visualize it. Develop a low-frequency inspection plan based on the foundation deformation joint risk marking area. Set the low-frequency inspection frequency (for example: monthly, quarterly, etc.). Record and generate low-frequency inspection frequency data for subsequent analysis. Use strain gauges to perform explicit deformation joint detection on the foundation deformation joint risk marking area: set detection parameters (such as sensitivity, response time, etc.). Perform explicit deformation joint detection and collect detection data. Integrate the explicit deformation joint detection data into the highway construction BIM model. Visualize the inspection results in the BIM model and generate a second deformation joint inspection plan.

[0047] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: Step S31: collecting periodic inspection data of the highway construction BIM model through the first deformation joint inspection plan and the second deformation joint inspection plan to obtain periodic deformation joint inspection data; Step S32: performing deformation joint damage assessment on the periodic deformation joint inspection data to generate periodic deformation joint inspection damage assessment data; Step S33: Performing automatic early warning according to the periodic deformation joint inspection damage assessment data to generate periodic deformation joint inspection early warning data; Step S34: Formulate a deformation joint repair strategy based on the periodic deformation joint inspection warning data to generate a deformation joint abnormality repair strategy.

[0048] In an embodiment of the present invention, regular inspections are arranged according to the first deformation joint inspection plan and the second deformation joint inspection plan. An inspection schedule is set to ensure periodic execution (e.g., daily, weekly, or monthly). During the inspection process, information such as the deformation joint status, hidden and explicit damage, and the impact of the surrounding environment is recorded. Sensors, photographic equipment, and data recorders are used to collect data to ensure the accuracy and integrity of the data. The collected deformation joint status data are integrated to generate a periodic deformation joint inspection data set. The data includes: deformation joint location, detection time, damage, inspection personnel information, etc. A damage assessment standard is formulated, including the acceptable damage degree of the deformation joint, risk level classification, etc. Evaluation indicators are defined, such as crack width, depth, displacement, etc. The periodic deformation joint inspection data is analyzed, and the actual detection results are compared with the set evaluation standards. Data analysis tools (e.g., statistical analysis software, machine learning models) are used to process the data. The evaluation results generate periodic deformation joint inspection damage assessment data, including: deformation joint damage level (minor, medium, severe), detailed report of damage location and degree, and risk impact assessment. Set automatic early warning rules based on the damage assessment results, for example, automatically trigger an early warning when the damage level reaches "serious". Set different early warning thresholds for specific types of damage (such as displacement, cracks, etc.). Connect the damage assessment data to the automated monitoring system to monitor the state of the expansion joint in real time. Implement dynamic updates of monitoring data to ensure the real-time and accuracy of the data. According to the set early warning rules, the system automatically generates periodic expansion joint inspection early warning data, including: early warning type (such as hidden damage, explicit damage), early warning level (such as attention, warning, emergency), and related recommended measures or treatment plans. According to the periodic expansion joint inspection early warning data, analyze the characteristics and repair needs of various types of damage. Considering the time, cost and resource availability of the repair, formulate a reasonable repair strategy, and generate an abnormal expansion joint repair strategy, including: repair methods (such as grouting, reinforcement, replacement, etc.), expected repair time frame and resource allocation, expected results and risk assessment. Integrate the repair strategy into the highway construction BIM model for visual management. After the repair is completed, track and monitor the repair effect, feedback the effectiveness of the repair strategy, and optimize future repair plans.

[0049] Preferably, step S32 includes the following steps: Step S321: screening the historical deformation joint abnormal data on the periodic deformation joint inspection data to obtain the historical deformation joint abnormal inspection data; extracting abnormal inspection features from the historical deformation joint abnormal inspection data to obtain abnormal inspection feature data; Step S322: dividing the abnormal inspection feature data into data sets to generate a model training set and a model test set; using a convolutional neural network algorithm to perform model training on the model training set to generate a deformation joint damage assessment pre-model; Step S323: perform model optimization iteration on the expansion joint damage assessment pre-model according to the model test set, so as to generate the expansion joint damage assessment model; import the periodic expansion joint inspection data into the expansion joint damage assessment model to perform expansion joint damage assessment, and generate the periodic expansion joint inspection damage assessment data.

[0050] In an embodiment of the present invention, the inspection data of periodic deformation joints in the past are collected, including deformation joint status, damage records, environmental impact, etc. The standard of abnormal data is defined, such as exceeding a specific crack width, displacement, etc. The threshold method or statistical analysis (such as Z-score) is used for screening. The historical deformation joint abnormal inspection data set that meets the abnormal standard is output. The features related to deformation joint damage are determined, including crack width, displacement, deformation rate, environmental conditions, etc. The raw data is preprocessed, including standardization, normalization and data enhancement, to improve the model performance, generate abnormal inspection feature data, and construct a feature matrix. The abnormal inspection feature data is randomly divided into training set and test set, with a common ratio of 80% training set and 20% test set. Ensure that the samples of each abnormal type in the training set are balanced to avoid bias in model training. Use a deep learning framework (such as TensorFlow or PyTorch) to build a CNN model, including an input layer, a hidden layer, and an output layer. Design a suitable network structure, such as a convolutional layer, a pooling layer, and a fully connected layer. Select a specific loss function (such as cross entropy loss) to evaluate the difference between the model prediction and the actual label. Use optimization algorithms (such as Adam or SGD) to adjust the model weights to minimize the loss. Perform multiple iterations of training on the training set until the loss function converges to generate a pre-model for deformation joint damage assessment. Input the model test set into the deformation joint damage assessment pre-model to obtain the model prediction results. Use evaluation indicators (such as accuracy, precision, recall, F1 score) to evaluate the model performance and determine the generalization ability of the model. According to the evaluation results, perform optimization iterations of the model, including: adjusting hyperparameters (such as learning rate, batch size), increasing the data set or using data enhancement technology to improve the robustness of the model, and adopting a more complex model structure (such as deep CNN or using transfer learning). Save the optimized model in a format that can be used for real-time evaluation (such as HDF5 or ONNX format). Import the periodic deformation joint inspection data into the final deformation joint damage assessment model, execute the evaluation process, and generate the periodic deformation joint inspection damage assessment data.

[0051] In this specification, a highway construction deformation joint detection management system based on BIM is provided, which is used to execute the above-mentioned highway construction deformation joint detection management method based on BIM. The highway construction deformation joint detection management system based on BIM includes: The BIM model building module is used to obtain highway construction plan data; extract highway structure semantics from highway construction plan data to obtain highway construction structure semantic data; construct a three-dimensional digital model of highway construction structure semantic data to generate a highway construction BIM model; The expansion joint inspection module is used to perform construction simulation on the highway construction BIM model and generate highway construction simulation data; perform virtual reality identification of expansion joint prone areas on the highway construction BIM model based on the highway construction simulation data to obtain virtual expansion joint prone area identification data and expansion joint detection area data; perform area matching inspection division on the virtual expansion joint prone area identification data and expansion joint detection area data to generate the first expansion joint inspection plan and the second expansion joint inspection plan; The damage assessment module is used to collect periodic inspection data of the highway construction BIM model through the first deformation joint inspection plan and the second deformation joint inspection plan to obtain periodic deformation joint inspection data; perform deformation joint damage assessment on the periodic deformation joint inspection data to generate periodic deformation joint inspection damage assessment data; formulate deformation joint repair strategies based on the periodic deformation joint inspection damage assessment data to generate deformation joint abnormal repair strategies; The detection management optimization module is used to update the deformation joint status of the highway construction BIM model according to the deformation joint abnormal repair strategy, and generate deformation joint status inspection update data; adjust the detection management process through the deformation joint status inspection update data, and generate a highway construction deformation joint detection management adjustment plan to execute highway construction deformation joint detection management operations.

[0052] The beneficial effect of the present invention is that by obtaining highway construction plan data, the accuracy and completeness of basic data are ensured, and a reliable basis is provided for subsequent analysis and modeling. The complex construction plan is converted into structured data, which is convenient for information management and processing, generates a visual BIM model, enhances the understanding and communication of highway construction projects, and facilitates collaboration between different participants. The data generated by construction simulation provides a virtual environment for the actual construction process, identifies potential problems through simulation analysis, and predicts construction risks in advance. Virtual reality identification of prone areas can efficiently identify areas prone to deformation joints, reduce the possibility of missed detection and false detection, and improve construction safety. Scientifically divide the inspection plan to ensure the rational allocation and use of resources, and improve the pertinence and effectiveness of the inspection. Through regular data collection, a monitoring database for deformation joints is established to form a continuous tracking of the construction status. The deformation joint damage assessment provides a scientific basis, timely discovers and evaluates the damage of the deformation joint, and helps to take maintenance measures in advance. According to the evaluation results, a targeted repair strategy is formulated to ensure the effectiveness and timeliness of the repair work and extend the service life of the deformation joint. The deformation joint status in the BIM model is updated in real time to ensure that all relevant parties obtain the latest information and reduce communication errors. By dynamically adjusting the management process, we can adapt to the progress of the project and the changes in the state of the expansion joints, improve management efficiency, and ensure construction safety. We can implement standardized management solutions, improve the systematicness and standardization of expansion joint detection, and provide guarantees for the safety of highway construction. Therefore, the present invention improves the comprehensiveness and real-time nature of BIM-based highway construction expansion joint detection management through systematic information integration, real-time monitoring, scientific inspections, data-driven damage assessment and repair strategy formulation, and flexible management process adjustments.

[0053] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0054] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A BIM-based highway construction deformation joint detection and management method, characterized in that: The following steps are involved: Step S1: Obtaining highway construction plan data; Extract highway structure semantics from highway construction plan data to obtain highway construction structure semantic data; construct a three-dimensional digital model of highway construction structure semantic data to generate a highway construction BIM model; Step S2: Perform construction simulation on the highway construction BIM model to generate highway construction simulation data; perform virtual reality identification of deformation joint prone areas on the highway construction BIM model based on the highway construction simulation data to obtain virtual deformation joint prone area identification data and deformation joint detection area data; perform regional matching inspection division on the virtual deformation joint prone area identification data and the deformation joint detection area data to generate a first deformation joint inspection plan and a second deformation joint inspection plan; Step S3: collecting periodic inspection data of the highway construction BIM model through the first deformation joint inspection plan and the second deformation joint inspection plan to obtain periodic deformation joint inspection data; Conduct deformation joint damage assessment on periodic deformation joint inspection data to generate periodic deformation joint inspection damage assessment data; formulate deformation joint repair strategies based on periodic deformation joint inspection damage assessment data to generate deformation joint abnormality repair strategies; Step S4: updating the deformation joint status of the highway construction BIM model according to the deformation joint abnormal repair strategy, and generating deformation joint status inspection update data; By updating data through expansion joint status inspection, the inspection management process is adjusted, and an adjustment plan for highway construction expansion joint inspection management is generated to execute highway construction expansion joint inspection management operations.

2. The BIM-based highway construction deformation joint detection and management method according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Obtaining highway construction plan data; Step S12: preprocessing the highway construction plan data to generate standard highway construction plan data, wherein the data preprocessing includes data cleaning, data missing value filling and data standardization; Step S13: extracting highway structure semantics from the standard highway construction plan to obtain highway construction structure semantic data; Step S14: Use BIM technology to construct a three-dimensional digital model of the highway construction structure semantic data to generate a highway construction BIM model.

3. The BIM-based highway construction deformation joint detection and management method according to claim 2 is characterized in that: Step S14 includes the following steps: Step S141: classifying the functional structural components of the highway construction structure semantic data to generate highway construction functional facility classification data, wherein the functional structural component classification includes road structure classification, bridge structure classification, tunnel structure classification and roadbed structure classification; Step S142: performing IFC standard format conversion on the highway construction functional facility classification data to generate highway construction format conversion data; performing structural semantic mapping on the highway construction format conversion data using BIM technology to generate highway construction structural semantic mapping data; Step S143: performing three-dimensional geometric modeling according to the highway construction structure semantic mapping data to generate three-dimensional geometric modeling data for highway construction; performing GIS geographic information integration on the three-dimensional geometric modeling data for highway construction to generate a preliminary three-dimensional structural model; Step S144: Associating model components of the preliminary three-dimensional structure model based on the highway construction structure semantic data to generate three-dimensional structure attribute association data; inputting the three-dimensional structure attribute association data into the preliminary three-dimensional structure model to fill in model component information to generate a highway construction BIM model.

4. The BIM-based highway construction deformation joint detection and management method according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: performing construction simulation on the highway construction BIM model to generate highway construction simulation data; based on the highway construction simulation data, performing virtual deformation joint prone area identification on the highway construction BIM model to obtain virtual deformation joint prone area identification data; Step S22: Based on the virtual deformation joint prone area identification data, a total station is used to perform deformation joint actual detection, thereby generating deformation joint actual detection data; the deformation joint actual detection data is divided into deformation joint areas, thereby generating deformation joint detection area data; Step S23: calculating the regional matching degree of the deformation joint detection area data and the virtual deformation joint prone area identification data to obtain the deformation joint area detection matching degree; Step S24: Compare the deformation joint area detection matching degree with the preset area detection matching threshold. When the deformation joint area detection matching degree is less than or equal to the preset area detection matching threshold, a first deformation joint inspection plan is generated; when the deformation joint area detection matching degree is greater than the preset area detection matching threshold, a second deformation joint inspection plan is generated.

5. The BIM-based highway construction deformation joint detection and management method according to claim 4 is characterized in that: Construction simulation of highway construction BIM models includes: Decompose the construction tasks of highway construction BIM model data to generate construction task planning data; perform resource consumption analysis on the construction task planning data to generate resource use analysis data; perform resource allocation optimization processing on the resource use analysis data to generate construction resource optimization data; According to the construction resource optimization data, the construction task planning data is processed in time axis association to generate the construction schedule data; the construction schedule data is simulated in three dimensions to generate the construction dynamic simulation data; the construction dynamic simulation data is tested for construction collision to generate the construction simulation collision detection data; The construction dynamic simulation data and the construction simulation conflict detection data are integrated to generate highway construction simulation data.

6. The BIM-based highway construction deformation joint detection and management method according to claim 4 is characterized in that: Step S23 includes the following steps: Step S231: Binarization processing is performed on the deformation joint detection area data and the virtual deformation joint prone area identification data to generate deformation joint detection area binary data and virtual deformation joint prone area binary data; Step S232: performing pixel-by-pixel intersection area comparison calculation on the binary data of the deformation joint detection area and the binary data of the virtual deformation joint prone area to generate intersection area data; performing region overlapping pixel calculation on the intersection area data to obtain the region intersection area; Step S233: performing pixel-by-pixel union region comparison calculation on the binary data of the deformation joint detection region and the binary data of the virtual deformation joint prone region to generate union region data; performing regional non-overlapping pixel calculation on the union region data to obtain the regional union area; Step S234: Calculate the intersection-and-union ratio of the region intersection area and the region union area, thereby generating a deformation joint region detection matching degree.

7. The BIM-based highway construction deformation joint detection and management method according to claim 4 is characterized in that: Step S24 includes the following steps: Step S241: comparing the deformation joint area detection matching degree with a preset area detection matching threshold, when the deformation joint area detection matching degree is less than or equal to the preset area detection matching threshold, marking the key deformation joint risk area for the deformation joint actual detection data according to the deformation joint area detection matching degree, and obtaining the key deformation joint risk marking area; Step S242: Perform high-frequency inspection on the key deformation joint risk mark area to generate high-frequency inspection frequency data, and perform non-destructive inspection of hidden deformation joints on the key deformation joint risk mark area using an ultrasonic detector according to the high-frequency inspection frequency data to generate non-destructive inspection data of hidden deformation joints; integrate the non-destructive inspection data of hidden deformation joints into the highway construction BIM model for inspection visualization, and generate a first deformation joint inspection plan; Step S243: when the deformation joint area detection matching degree is greater than a preset area detection matching threshold, the deformation joint actual detection data is marked as a basic deformation joint risk area according to the deformation joint area detection matching degree to obtain a basic deformation joint risk marking area; Step S244: Perform low-frequency inspections on key deformation joint risk marked areas to generate low-frequency inspection frequency data, and use strain gauges to perform explicit deformation joint detection on foundation deformation joint risk marked areas based on the low-frequency inspection frequency data to generate explicit deformation joint detection data; integrate the explicit deformation joint detection data into the highway construction BIM model for inspection visualization, and generate a second deformation joint inspection plan.

8. The BIM-based highway construction deformation joint detection and management method according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: collecting periodic inspection data of the highway construction BIM model through the first deformation joint inspection plan and the second deformation joint inspection plan to obtain periodic deformation joint inspection data; Step S32: performing deformation joint damage assessment on the periodic deformation joint inspection data to generate periodic deformation joint inspection damage assessment data; Step S33: Performing automatic early warning according to the periodic deformation joint inspection damage assessment data to generate periodic deformation joint inspection early warning data; Step S34: Formulate a deformation joint repair strategy based on the periodic deformation joint inspection warning data to generate a deformation joint abnormality repair strategy.

9. The BIM-based highway construction deformation joint detection and management method according to claim 1 is characterized in that: Step S32 includes the following steps: Step S321: screening the historical deformation joint abnormal data on the periodic deformation joint inspection data to obtain the historical deformation joint abnormal inspection data; extracting abnormal inspection features from the historical deformation joint abnormal inspection data to obtain abnormal inspection feature data; Step S322: dividing the abnormal inspection feature data into data sets to generate a model training set and a model test set; using a convolutional neural network algorithm to perform model training on the model training set to generate a deformation joint damage assessment pre-model; Step S323: perform model optimization iteration on the expansion joint damage assessment pre-model according to the model test set, so as to generate the expansion joint damage assessment model; import the periodic expansion joint inspection data into the expansion joint damage assessment model to perform expansion joint damage assessment, and generate the periodic expansion joint inspection damage assessment data.

10. A BIM-based highway construction expansion joint detection and management system, characterized in that: Used to execute the highway construction deformation joint detection management method based on BIM as claimed in claim 1, the highway construction deformation joint detection management system based on BIM comprises: The BIM model building module is used to obtain highway construction plan data; extract highway structure semantics from highway construction plan data to obtain highway construction structure semantic data; construct a three-dimensional digital model of highway construction structure semantic data to generate a highway construction BIM model; The expansion joint inspection module is used to perform construction simulation on the highway construction BIM model and generate highway construction simulation data; perform virtual reality identification of expansion joint prone areas on the highway construction BIM model based on the highway construction simulation data to obtain virtual expansion joint prone area identification data and expansion joint detection area data; perform area matching inspection division on the virtual expansion joint prone area identification data and expansion joint detection area data to generate the first expansion joint inspection plan and the second expansion joint inspection plan; The damage assessment module is used to collect periodic inspection data of the highway construction BIM model through the first deformation joint inspection plan and the second deformation joint inspection plan to obtain periodic deformation joint inspection data; perform deformation joint damage assessment on the periodic deformation joint inspection data to generate periodic deformation joint inspection damage assessment data; formulate deformation joint repair strategies based on the periodic deformation joint inspection damage assessment data to generate deformation joint abnormal repair strategies; The detection management optimization module is used to update the deformation joint status of the highway construction BIM model according to the deformation joint abnormal repair strategy, and generate deformation joint status inspection update data; adjust the detection management process through the deformation joint status inspection update data, and generate a highway construction deformation joint detection management adjustment plan to execute highway construction deformation joint detection management operations.

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