BIM (Building Information Modeling)-based tunnel construction digital construction method, system, equipment and medium

Abstract

The invention relates to the technical field of tunnel engineering construction, and discloses a BIM-based tunnel construction digital construction method, system and equipment and a medium, a high-precision BIM construction model is constructed by integrating environmental data, and visual simulation and precise planning of a construction process under complex geological conditions are realized; real-time data sharing and synchronous change of participants such as design, construction and supervision are achieved based on a multi-party cooperation platform, and cooperation efficiency and decision scientificity are improved; a risk prediction model is established and an optimization scheme is generated by combining a digital twinning technology and a deep learning algorithm, and a closed-loop intelligent monitoring system is formed by monitoring surrounding rock displacement, support stress and equipment operation data in real time, dynamically correcting construction parameters and verifying an adjustment effect; an intelligent management module and a sensor network are integrated, total element dynamic optimization is supported, data collection and model interaction are achieved, and multi-dimensional construction data and algorithm models are stored.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel engineering construction, and in particular to a BIM-based tunnel construction digital construction method, system, equipment and medium. Background Art

[0002] In the field of modern tunnel engineering construction, with the acceleration of urbanization and the growing demand for infrastructure, tunnel construction often faces complex and diverse construction conditions. The Baitashan section of the tunnel project in the Yellow River Basin of Lanzhou City is a high-risk area for geological disasters. The stratum structure is extremely complex due to the influence of the Jinchengguan Fault Zone. The tunnel surrounding rock is mostly Class IV or V, with poor stability. The ultra-shallow buried section is long, and there are also problems such as dual topographic and geological bias and small deflection angles when entering the mountain. At the same time, as a key cultural relic protection area in Gansu Province, this area is dotted with numerous ancient buildings. These buildings are ancient and extremely valuable, and are extremely sensitive to vibration and deformation during construction. Any minor improper construction can cause irreversible damage to them. In addition, the construction site is narrow and the available space is limited, which brings great inconvenience to the parking of construction equipment, the stacking of materials, and the operation of personnel.

[0003] Traditional tunnel construction technology and management models, when dealing with these complex conditions, have exposed numerous significant flaws. At the construction management level, significant issues exist: asynchrony between business activity and data collection, between business data and financial data, and between progress, revenue, and costs. This severely hinders information flow during the construction process, preventing all parties from obtaining timely and accurate data. This leads to inefficient project management and a lack of scientific basis for decision-making, which in turn impacts construction progress and cost control.

[0004] Traditional methods for construction monitoring rely primarily on tools such as total stations, levels, cross-section gauges, and feeler gauges, performing measurements at individual sections and measuring points. This approach is not only labor-intensive, requiring significant manpower and time, but also suffers from inaccuracies that are easily affected by factors such as lighting and the operator's skill level, resulting in a low level of intelligence. In complex construction environments, comprehensive and accurate monitoring of tunnel construction is difficult, making it difficult to detect potential safety hazards in a timely manner and ensuring construction safety and project quality.

[0005] Although the application of BIM technology in tunnel engineering is gradually gaining attention, its application in the field of highway tunnels is still in its developmental stage. The relevant modeling standards have not yet been officially promulgated, and the lack of unified specifications has led to uneven quality of BIM models across different projects. Furthermore, current research on tunnel BIM technology is insufficiently in-depth, and its application during the construction phase is primarily focused on schedule simulation. This fails to fully tap the enormous potential of BIM technology for digital management of the entire tunnel construction process under complex conditions, making it difficult to meet the needs of modern tunnel engineering construction. Therefore, there is an urgent need for an innovative technology that can effectively address the many challenges faced by tunnel construction under complex conditions, realize digital and intelligent construction throughout the entire tunnel construction process, and ensure the safe, efficient, and high-quality completion of the project. Summary of the Invention

[0006] In view of the above existing problems, the present invention is proposed.

[0007] Therefore, the present invention provides a BIM-based tunnel construction digital construction method and system, aiming to build a comprehensive, efficient and intelligent tunnel construction digital construction system. It uses BIM technology and deeply integrates cutting-edge technologies such as digital twins and deep learning algorithms to achieve digital management and precise control of the entire tunnel construction process under complex conditions, effectively solving the problems faced by traditional construction technologies and significantly improving the quality, safety and efficiency of tunnel construction.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0009] In a first aspect, the present invention provides a BIM-based digital construction method for tunnel construction, comprising:

[0010] Integrate environmental data and build construction models;

[0011] Collaborative management and control based on construction models, real-time data sharing, and synchronous design changes;

[0012] Combine digital technology to build a model, train the prediction model through algorithms, and obtain the first-level solution;

[0013] Dynamically modify construction parameters based on real-time monitoring data to optimize monitoring.

[0014] As a preferred solution of the BIM-based tunnel construction digital construction method of the present invention, the environmental data is integrated to establish a construction model, including:

[0015] Based on the construction process, summarize the key technologies of tunnel construction;

[0016] Use technology to drive intelligent construction of composite structures and establish a construction model.

[0017] As a preferred solution of the BIM-based tunnel construction digital construction method of the present invention, the collaborative management and control based on the construction model includes:

[0018] Based on the construction model, a multi-party collaborative work network platform is established to conduct data sharing and analysis;

[0019] Carry out intelligent management and conduct intelligent monitoring of the entire construction process and all elements.

[0020] As a preferred solution of the BIM-based tunnel construction digital construction method of the present invention, the real-time sharing of data and synchronization of design changes include:

[0021] Participate in real-time data communication and sharing through a multi-party collaborative work network platform;

[0022] Intelligent analysis results based on real-time data feedback.

[0023] As a preferred solution of the BIM-based tunnel construction digital construction method of the present invention, the model construction based on digital technology includes:

[0024] Use digital technology to build models and synchronously map the physical quantities of the construction scene in real time;

[0025] Intelligently manage information involved in the construction process.

[0026] As a preferred solution of the BIM-based tunnel construction digital construction method of the present invention, wherein: the prediction model is trained by an algorithm to obtain a first-level solution, including:

[0027] Based on historical construction data and real-time monitoring data, deep learning algorithms are used to train risk prediction models to identify potential risk factors and predict construction risks;

[0028] Generate multiple risk control plans based on the prediction results, and verify the effectiveness of the plans through digital twin technology simulation;

[0029] Combining the simulation results with the actual construction constraints, the optimal solution is selected as the first-level solution and dynamically optimized and implemented.

[0030] As a preferred solution of the BIM-based tunnel construction digital construction method of the present invention, wherein: dynamically correcting construction parameters according to real-time monitoring data to perform monitoring optimization includes:

[0031] Compare and analyze the surrounding rock displacement, support structure stress, and equipment operation data collected in real time by sensors with the standard parameters of the construction model to identify abnormal deviations;

[0032] Generate construction parameter adjustment plans through deep learning algorithms combined with risk prediction models;

[0033] The digital twin model is used to simulate the implementation effect of the adjustment plan, and the construction model parameters are dynamically updated after feasibility is verified to achieve real-time optimization and closed-loop risk control of the construction process.

[0034] In a second aspect, the present invention provides a BIM-based tunnel construction digital construction system, comprising:

[0035] Environmental modeling module, integrating environmental data and building construction models;

[0036] Collaborative management and control module, based on construction model collaborative management and control, real-time data sharing, and synchronous design changes;

[0037] The risk prediction module combines digital technology to build a model, trains the prediction model through algorithms, and obtains a first-level solution;

[0038] The dynamic control module dynamically modifies construction parameters based on real-time monitoring data and performs monitoring optimization.

[0039] In a third aspect, the present invention provides an electronic device, comprising:

[0040] memory and processor;

[0041] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the BIM-based tunnel construction digital construction method are implemented.

[0042] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the BIM-based tunnel construction digital construction method.

[0043] Compared with the existing technology, the beneficial effects of the present invention are as follows: by integrating environmental data and constructing a high-precision construction model, the present invention provides multi-dimensional visual guidance and dynamic simulation capabilities for tunnel construction, significantly improving the accuracy of construction planning under complex geological conditions; based on a multi-party collaborative platform, it realizes real-time data sharing and synchronization of design changes, breaks through the information barriers of each participant, and enhances the collaborative efficiency and scientific decision-making of construction management; by integrating digital twin technology and deep learning algorithms, it constructs a risk prediction model and generates an optimization plan, and dynamically corrects construction parameters based on real-time monitoring data, realizing full-cycle intelligent monitoring and closed-loop control of surrounding rock stability, equipment operating status and construction risks. Ultimately, this technical system comprehensively optimizes construction quality and safety levels, effectively reduces construction costs, shortens construction periods, and promotes the digital and intelligent transformation of tunnel projects under complex conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 This is a schematic diagram of the overall process of a BIM-based tunnel construction digital construction method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0047] Example 1, reference Figure 1 , as one embodiment of the present invention, provides a BIM-based digital construction method for tunnel construction, comprising:

[0048] S1: Integrate environmental data and build a construction model;

[0049] S2: Collaborative management and control based on the construction model, real-time data sharing, and synchronized design changes;

[0050] S3: Build a model by combining digital technology, train the prediction model through algorithms, and obtain the first-level solution;

[0051] S4: Dynamically modify construction parameters based on real-time monitoring data to perform monitoring optimization.

[0052] It should be noted that traditional tunnel construction technology faces core problems such as information fragmentation, low coordination efficiency, insufficient risk prediction and delayed dynamic adjustment under complex geological conditions, which makes it difficult to ensure construction quality, safety and progress.

[0053] Therefore, in response to the above-mentioned problems such as difficulty in integrating construction data, inefficient multi-party collaboration, insufficient risk prediction accuracy, and weak dynamic optimization capabilities of the construction process, through steps S1-S4, a high degree of integration of environmental data and construction models was achieved, a real-time collaboration and information sharing mechanism among multiple parties was opened up, a risk prediction and solution optimization system based on digital twins and deep learning was constructed, and closed-loop control was formed through real-time monitoring and dynamic parameter correction. Ultimately, the level of digital management of the entire process of tunnel construction under complex conditions was improved, risks were effectively avoided, resource allocation was optimized, and systematic technical support was provided for the safe and efficient advancement of the project.

[0054] Example 2, reference Figure 1 , which is an embodiment of the present invention, provides a BIM-based tunnel construction digital construction method based on the above embodiment.

[0055] In the implementation mode of the present application, in step S1, environmental data is integrated and a construction model is established by forming an interdisciplinary team to collect geological, topographic and surrounding environmental data, and using BIM technology to construct a three-dimensional high-precision model including stratum distribution, mechanical parameters and ancient building structure. By simulating the impact of different excavation methods and support schemes on the stability of the surrounding rock, accurate modeling and dynamic visualization of the construction process under complex geological conditions are achieved.

[0056] In an optional embodiment, the environmental data is integrated in step S1, and the construction model can also be established by constructing a geological model through manual mapping and two-dimensional drawings combined with finite element analysis software, relying only on static geological reports and local sensor data, manually inputting formation parameters and performing single-dimensional mechanical simulation.

[0057] In another optional embodiment, the environmental data is integrated in step S1, and the construction model is established by quickly acquiring terrain point cloud data through drone aerial photography and lidar scanning, and combining it with general modeling software to generate a simplified three-dimensional scene, but the refined parametric modeling of construction equipment, support structures and surrounding environment is not integrated.

[0058] In the embodiment of the present application, the environmental data is integrated in step S1 to establish a construction model, including:

[0059] Forming a professional team and collecting data: At the beginning of the project, a professional interdisciplinary modeling team was established, consisting of tunnel engineering experts, BIM technical engineers, geological engineers, and surveying engineers. Tunnel engineering experts, with their rich experience, provided professional guidance on tunnel construction technology and processes. BIM technical engineers were responsible for proficiently using BIM software to build and maintain the model. Geological engineers conducted in-depth research on the geological conditions of the project area to provide accurate geological data for the model. Surveying engineers obtained precise data on topography, landforms, and the surrounding environment through field measurements.

[0060] Establish and optimize the model: Utilizing professional BIM software such as Revit and Bentley, a three-dimensional tunnel model was constructed based on the collected data. For complex geological conditions, stratigraphic information was accurately entered, including the distribution, thickness, and mechanical parameters of different soil and rock layers. For special geological structures such as faults and caves, detailed modeling was performed and corresponding characteristics were assigned. For surrounding ancient buildings, precise dimensions and structural information were obtained through field surveys and laser scanning, and fully presented in the model. During the simulation process, construction difficulties under complex conditions were fully considered. For example, in areas with poor geological conditions, the effects of different excavation methods (such as benching, CD, and CRD) and support methods (steel support, shotcrete, etc.) on the stability of the surrounding rock were simulated. In the simulation of soft soil layers, when the step method is combined with steel support, the top of the surrounding rock sinks and deforms greatly after the upper step is excavated, and the stress is concentrated on the vault; when combined with shotcrete, the deformation is even greater and the stress is dispersed; the CD method combined with steel support can reduce the deformation of the surrounding rock and distribute the stress evenly; the effect of combining with shotcrete is slightly worse; the CRD method combined with steel support has the least disturbance to the surrounding rock, and the deformation and stress concentration are best controlled. By comparison, it can be seen that the CRD method + steel support is the optimal solution for soft soil layers; in fault zones, whether the step method is combined with steel support or shotcrete, the surrounding rock dislocation deformation and stress concentration are serious; the CD method combined with steel support has the least disturbance to the surrounding rock, and the deformation and stress concentration are best controlled. Steel support can relatively reduce disturbance and achieve reasonable stress distribution; the effect of using it with shotcrete is poor; the CRD method with steel support has the least disturbance to the fault, ensuring tunnel stability, and is the optimal solution for the fault zone; the simulation situation in the cave section is similar, and the surrounding rock deformation of the step method is large and the stress is concentrated at the edge of the cave; the CD method can reduce some disturbance; the CRD method with steel support has the least disturbance to the surrounding rock around the cave, can effectively control stress concentration, and is the optimal solution for the cave section; by comparing and analyzing various solutions under different adverse geological conditions, the optimal construction solution for specific conditions can be determined.

[0061] In the implementation mode of the present application, in step S2, collaborative management and control based on the construction model is carried out by building a multi-party collaborative work network platform, integrating participants such as construction units, design units, supervision units and owner units, to achieve real-time sharing and dynamic synchronization of construction progress, quality data and design change information, and combine BIM technology and artificial intelligence algorithms to conduct intelligent monitoring and early warning analysis of all construction elements, effectively improving cross-departmental collaboration efficiency and decision-making accuracy.

[0062] In an optional implementation, the collaborative management and control based on the construction model in step S2 can also enhance data credibility and collaboration process automation by introducing blockchain technology, encrypting construction progress, quality acceptance records and design change information on the chain, and using smart contracts to automatically trigger the responsible party response mechanism to ensure that the data cannot be tampered with and the collaboration process is transparent and traceable.

[0063] In another optional implementation, the collaborative management and control based on the construction model in step S2 can also achieve local real-time decision-making by deploying edge computing nodes, processing the surrounding rock displacement and equipment operation data collected by sensors at the construction site, and combining the lightweight BIM model with the AI ​​reasoning engine to quickly generate early warning signals and adjustment suggestions, reducing cloud dependence and improving the timeliness and stability of collaborative response.

[0064] In the embodiment of the present application, step S2 includes collaborative management and control based on the construction model, real-time data sharing, and synchronous design changes, including:

[0065] Model application and updating during the construction process: During the construction process, various types of sensors such as displacement sensors, stress sensors, inclinometers, etc. are arranged in the tunnel surrounding rock, support structures and surrounding buildings to collect data in real time. With the help of wireless transmission technology, the sensors immediately feed back the collected data to the BIM model, thereby promoting dynamic updating of the model. Based on the visualization features of the BIM model, construction personnel can intuitively understand the actual status of the construction.

[0066] For example, if displacement sensors installed in the surrounding rock of a tunnel vault show a rapid increase in displacement over three days from the normal daily increment of 0.5 mm to 1.2 mm, 1.5 mm, and 1.8 mm, approaching the 2 mm warning value, this is likely due to excessive excavation, resulting in insufficient time for the surrounding rock to stabilize. In this case, construction personnel should promptly adjust their construction strategy, reducing the excavation rate from 2 meters per day to 1 meter, while also accelerating the support process and increasing the number of steel supports installed daily from two to three.

[0067] If the stress sensor is placed on the support steel and the reported stress value reaches 80% of the design bearing stress (assuming the design bearing stress is 200MPa and the current value is 160MPa), it is likely caused by stress concentration in the surrounding rock. In this case, I18 I-beam braces can be added as temporary supports to enhance the stability of the support structure. At the same time, grouting reinforcement operations can be implemented in the surrounding rock to improve its overall strength.

[0068] When an inclinometer is installed near the foundation of a surrounding ancient building and it is monitored that the foundation inclination angle increases from 0.0005 to 0.002 within a month, approaching the allowable value of 0.003, the excavation method should be immediately changed from the step method to the CRD method, which causes less disturbance to the surrounding environment, and the foundation of the ancient building should be reinforced with tree root piles to ensure construction safety and project quality.

[0069] Construction Site Data Collection and Transmission: Various monitoring equipment, including total stations, levels, pressure sensors, and temperature and humidity sensors, were deployed throughout the construction site. Total stations and levels were used to accurately measure the tunnel's geometry, position, and deformation; pressure sensors monitored the pressure on the supporting structure; and temperature and humidity sensors collected construction environment data. These devices were connected to a BIM-based management system via IoT technology, enabling automated data collection and real-time transmission, ensuring that all parties had timely access to the latest construction information.

[0070] Build a collaborative platform and set permissions: Build a multi-party collaborative work network platform based on cloud computing, select a cloud service provider with stable performance, and ensure that the platform has strong data storage, processing and transmission capabilities; assign independent accounts to participants such as construction units, design units, supervision units and owners, and set strict permissions according to their respective responsibilities; construction units can upload construction progress, quality inspection data, equipment operating status, etc.; design units have the right to publish design change information and provide explanations; supervision units can monitor the construction process in real time and issue rectification instructions; owners can view the overall progress of the project, but their direct modification permissions to construction data are restricted to ensure data security and orderly progress of the project.

[0071] Construction Site Data Collection and Transmission: Various monitoring equipment, including total stations, levels, pressure sensors, and temperature and humidity sensors, are deployed throughout the construction site. Total stations and levels are used to accurately measure the tunnel's geometry, position, and deformation; pressure sensors monitor the pressure on the supporting structure; and temperature and humidity sensors collect construction environment data. Through IoT technology, connected to a BIM-based management system, automatic data collection and real-time transmission are achieved, ensuring that all parties have timely access to the latest construction information.

[0072] Collaborative Management and Intelligent Analysis: Construction units must enter detailed data, including construction progress and quality inspection results, into the system daily. These data include tunnel excavation length, lining construction progress, and material inspection reports. Design changes must be promptly published through the platform, along with updated drawings and detailed explanations, to ensure all parties understand the changes and their impact. Supervision units utilize the system to monitor the construction process in real time. Upon discovering any quality or safety issues, such as concrete pouring defects or illegal construction worker practices, they will immediately issue rectification notices and track the progress of these changes.

[0073] In the implementation mode of the present application, in step S3, a model is constructed by combining digital technology, and a prediction model is trained through an algorithm to construct a high-fidelity virtual construction scene model based on digital twin technology, and physical quantities such as surrounding rock stress and strain, support structure force and equipment operation status are mapped in real time; historical construction data and real-time monitoring data are trained using deep learning algorithms to explore potential risk patterns and establish a risk prediction model to identify risk factors such as surrounding rock instability, water gushing and equipment failure; a variety of risk control schemes are generated through the model, and the effectiveness of the schemes is simulated and verified with the help of digital twin technology. The optimal scheme is selected as the first-level scheme based on the actual constraints of the project, and the implementation path is dynamically optimized to achieve forward-looking prediction and precise control of construction risks.

[0074] In an optional embodiment, in step S3, a model is constructed by combining digital technology, and the prediction model can be trained by algorithm. The prediction model can also be trained by method. The surrounding rock microstrain and temperature gradient data can also be collected by distributed fiber optic sensing technology. The local risk features are extracted by combining traditional machine learning algorithms (such as random forests), and a preliminary risk warning signal is generated. Then, a control plan is formed after correction through artificial experience.

[0075] In another optional embodiment, in step S3, a model is constructed by combining digital technology, and the prediction model can be trained through an algorithm. The prediction model can also be trained through an algorithm. The static simulation analysis of the surrounding rock stress field can also be performed through finite element simulation software. The expert system database is combined with historical cases to generate risk response suggestions, and construction parameters are adjusted through manual decision-making.

[0076] In the embodiment of the present application, in step S3, a model is constructed by combining digital technology, and a prediction model is trained by an algorithm to obtain a first-level solution, including:

[0077] Artificial intelligence algorithms are used to conduct in-depth analysis of collected data. To predict construction progress trends using machine learning algorithms, the system first collects extensive historical tunnel construction project data, including average daily excavation rates under different geological conditions, the time contribution of each construction phase, and downtime due to weather or equipment failures. For example, during the initial construction phase of a tunnel project currently under construction, the system determined that the tunnel was located in medium-hard rock formations and that past projects under similar geological conditions averaged 5 meters per day. As construction progressed, the system continuously collected real-time data. By the 30th day of excavation, the actual daily excavation length was 4 meters, and the cumulative equipment maintenance time due to failures in the past week had reached 8 hours. The machine learning algorithm combines this historical data with real-time analysis and predicts that if the current construction status continues, the overall construction period will be extended by 15 days beyond the original plan. Based on this prediction, an automatic warning is issued, prompting the construction unit to promptly troubleshoot equipment issues and optimize the construction process. This includes adding backup equipment to minimize the impact of equipment failures on the construction progress and rationally adjusting the working hours of each construction team to improve efficiency, ensuring that the construction progress remains on track.

[0078] To monitor construction site safety hazards through image recognition technology, the system deploys cameras at key locations on the construction site to continuously collect image data. For example, if the image recognition algorithm detects that construction workers in a certain area are not wearing safety helmets as required in five consecutive frames of images, and the area is a high-risk operation area, the system immediately issues a safety warning, alerting supervisors to safety violations in that area. Furthermore, if image recognition detects large-scale leakage in the pouring area during concrete pouring, the system will automatically identify and issue a warning, prompting the construction unit to promptly stop pouring operations, inspect the pouring equipment and formwork sealing, and implement appropriate repair measures to ensure construction quality and safety. Based on the analysis results, the system automatically issues a warning, reminding relevant personnel to take timely measures.

[0079] Building a digital twin model and data acquisition: High-precision measurement technologies, such as 3D laser scanning and geological radar, are used to obtain precise topographic, geological, and structural data from the construction site, creating a digital twin model that is highly consistent with the real-world scenario. 3D laser scanning technology comprehensively scans the tunnel construction site and surrounding terrain, generating a highly accurate 3D terrain model. Geological radar acquisition captures underground geological structure information, incorporating data such as the distribution of different strata and fault locations into the model. Based on this, the model maps various physical quantities during tunnel construction in real time, including surrounding rock stress and strain, groundwater level fluctuations, and the operating status of construction equipment. Numerous sensors are deployed at the construction site, such as fiber optic sensors placed within and on the tunnel surrounding rock to monitor surrounding rock stress and strain; water level sensors are installed in groundwater observation holes around the tunnel to collect real-time groundwater level data; and vibration sensors are installed at key locations on construction equipment to monitor vibration during operation. The sensors transmit the collected data to the digital twin model in real time via wireless transmission technology, ensuring that the model remains synchronized with actual construction and achieving high fidelity. Then, a physical model of tunnel construction is established, and the stress and strain conditions of the surrounding rock are intuitively presented in the digital twin model through a mechanical analysis model; a hydrological model is used to simulate changes in groundwater levels; and the control system of the construction equipment is connected to obtain the equipment operating status in real time, such as the propulsion speed and cutterhead speed of the shield machine, and display it in the model.

[0080] In the embodiment of the present application, in step S4, the construction parameters are dynamically corrected according to the real-time monitoring data to perform monitoring optimization, including:

[0081] Simulated risk scenarios: Based on the characteristics of the construction project, these scenarios can be divided into two main parts: surrounding rock instability risk simulation and water inrush risk simulation. In the digital twin model, the surrounding rock mechanical parameters in a certain area of ​​the fault fracture zone are artificially set to suddenly deteriorate (simulating unforeseen weak interlayers encountered during actual construction). Through model calculation and feedback, the changes in surrounding rock stress and strain are observed, as well as the impact on surrounding structures and construction equipment. Water inrush risk simulation: Based on geological data, it is assumed that the groundwater level suddenly rises due to factors such as rainfall. The groundwater level parameters are adjusted in the model to simulate the impact of water inrush on tunnel construction, including flooding of areas and further weakening of surrounding rock stability.

[0082] In summary, the present invention integrates geological, topographical, and ancient architectural data through an interdisciplinary team to construct a high-precision BIM model, accurately simulating the impact of construction under different geological conditions; relying on a multi-party collaborative platform to achieve real-time sharing of construction data and intelligent decision-making; integrating digital twins with deep learning technology to build a risk prediction model, generate and verify optimization solutions; dynamically correcting construction parameters based on real-time monitoring data, simulating risk scenarios such as surrounding rock instability and water gushing, and forming a closed-loop intelligent control. Compared with traditional methods that rely on static analysis or manual intervention, this technology significantly improves construction safety, efficiency, and economy through full-factor dynamic modeling, intelligent algorithm drive, and risk prediction optimization, providing a systematic digital solution for complex tunnel projects.

[0083] Example 3. The above is an illustrative solution for a BIM-based tunnel construction digital construction method. It should be noted that the technical solution of this BIM-based tunnel construction digital construction system and the technical solution of the BIM-based tunnel construction digital construction method described above share the same concept. For details not described in detail in the technical solution of the BIM-based tunnel construction digital construction system in this example, please refer to the description of the technical solution of the BIM-based tunnel construction digital construction method described above.

[0084] This embodiment also provides a BIM-based tunnel construction digital construction system, including:

[0085] Environmental modeling module, integrating environmental data and building construction models;

[0086] Collaborative management and control module, based on construction model collaborative management and control, real-time data sharing, and synchronous design changes;

[0087] The risk prediction module combines digital technology to build a model, trains the prediction model through algorithms, and obtains a first-level solution;

[0088] The dynamic control module dynamically modifies construction parameters based on real-time monitoring data and performs monitoring optimization.

[0089] This embodiment also provides an electronic device suitable for BIM-based tunnel construction digital construction, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the BIM-based tunnel construction digital construction method proposed in the above embodiment.

[0090] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the BIM-based tunnel construction digital construction method proposed in the above embodiment.

[0091] The storage medium proposed in this embodiment and the method for realizing digital construction of BIM-based tunnel construction proposed in the above embodiment belong to the same inventive concept. For technical details not fully described in this embodiment, please refer to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0092] Through the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented with the help of software and necessary general hardware, and of course can also be implemented by hardware. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0093] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A BIM-based digital construction method for tunnel construction, characterized in that: include: Integrate environmental data and build construction models; Collaborative management and control based on construction models, real-time data sharing, and synchronous design changes; Combine digital technology to build a model, train the prediction model through algorithms, and obtain the first-level solution; Dynamically modify construction parameters based on real-time monitoring data to optimize monitoring.

2. The BIM-based tunnel construction digital construction method according to claim 1, characterized in that: Integrate environmental data to build a construction model, including: Based on the construction process, summarize the key technologies of tunnel construction; Use technology to drive intelligent construction of composite structures and establish a construction model.

3. The BIM-based tunnel construction digital construction method according to claim 2, characterized in that: The collaborative management and control based on the construction model includes: Based on the construction model, a multi-party collaborative work network platform is established to conduct data sharing and analysis; Carry out intelligent management and conduct intelligent monitoring of the entire construction process and all elements.

4. The BIM-based tunnel construction digital construction method according to claim 3, characterized in that: The real-time data sharing and synchronous design changes include: Participate in real-time data communication and sharing through a multi-party collaborative work network platform; Intelligent analysis results based on real-time data feedback.

5. The BIM-based digital construction method for tunnel construction according to claim 4, characterized in that: The method of combining digital technology to build a model includes: Use digital technology to build models and synchronously map the physical quantities of the construction scene in real time; Intelligently manage information involved in the construction process.

6. The BIM-based tunnel construction digital construction method according to claim 5, characterized in that: The algorithm is used to train the prediction model to obtain a first-level solution, including: Based on historical construction data and real-time monitoring data, deep learning algorithms are used to train risk prediction models to identify potential risk factors and predict construction risks; Generate multiple risk control plans based on the prediction results, and verify the effectiveness of the plans through digital twin technology simulation; Combining the simulation results with the actual construction constraints, the optimal solution is selected as the first-level solution and dynamically optimized and implemented.

7. The BIM-based digital construction method for tunnel construction according to claim 6, characterized in that: Dynamically modify construction parameters based on real-time monitoring data to optimize monitoring, including: Compare and analyze the surrounding rock displacement, support structure stress, and equipment operation data collected in real time by sensors with the standard parameters of the construction model to identify abnormal deviations; Generate construction parameter adjustment plans through deep learning algorithms combined with risk prediction models; The digital twin model is used to simulate the implementation effect of the adjustment plan, and the construction model parameters are dynamically updated after feasibility is verified to achieve real-time optimization and closed-loop risk control of the construction process.

8. A BIM-based tunnel construction digital construction system, applying the method according to any one of claims 1 to 7, characterized in that: include: Environmental modeling module, integrating environmental data and building construction models; Collaborative management and control module, based on construction model collaborative management and control, real-time data sharing, and synchronous design changes; The risk prediction module combines digital technology to build a model, trains the prediction model through algorithms, and obtains a first-level solution; The dynamic control module dynamically modifies construction parameters based on real-time monitoring data to perform monitoring optimization.

9. An electronic device comprising: memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the BIM-based tunnel construction digital construction method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, implement the steps of the BIM-based tunnel construction digital construction method according to any one of claims 1 to 7.

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