A BIM-based tunnel construction deformation monitoring method, system and device

Abstract

The application relates to the technical field of deformation monitoring, in particular to a tunnel construction deformation monitoring method, system and equipment based on BIM. The method comprises the following steps: acquiring three-dimensional laser point cloud data and synchronous photogrammetry image data of a tunnel construction site to be monitored, and generating a real scene three-dimensional model reflecting a current construction state; performing deformation alignment on the real scene three-dimensional model and a preset design BIM model, determining a deformation tunnel section, acquiring environmental influence data, and performing construction simulation on the deformation tunnel section based on the environmental influence data and a to-be-executed construction operation, to obtain a simulated construction result; identifying simulated cumulative deformation and a simulated deformation acceleration index in the simulated construction result, and determining a deformation score; when the deformation score is higher than a preset deformation threshold, determining that the deformation tunnel section is a risk section, superimposing the risk section on a display scene of the preset design BIM model, and generating visual early warning information. The application facilitates improving the tunnel construction deformation monitoring precision.

Description

Technical Field

[0001] This application relates to the field of deformation monitoring technology, and in particular to a BIM-based method, system, and equipment for monitoring deformation during tunnel construction. Background Technology

[0002] During tunnel construction, the original stress field of the surrounding rock redistributes due to excavation operations. Combined with external factors such as groundwater seepage, surface loads, construction machinery disturbance, and ground bias, the tunnel lining, initial support, and surrounding soil and rock are highly susceptible to continuous deformation. Tunnel deformation is a core indicator reflecting the structural safety status. If the deformation exceeds the design allowable range or the deformation rate exhibits an abnormally rapid trend, it may trigger major safety accidents such as surrounding rock collapse and surface subsidence. Therefore, conducting routine and meticulous deformation monitoring throughout the entire tunnel construction process, real-time understanding of structural deformation status, early prediction of risk development trends, and timely issuance of safety warnings are essential measures for the safety management and quality assurance of underground engineering construction. Currently, tunnel monitoring primarily employs either manual measurement or automated sensor monitoring. Manual monitoring relies on equipment such as total stations, levels, and convergence meters to periodically collect cross-sectional data, and manual analysis is used to determine deformation. Automated monitoring utilizes sensors such as displacement gauges and strain gauges to automatically collect and transmit data, reducing manual labor. However, under conditions of complex geology, multi-section deformation, and interference from the surrounding environment, existing technologies reveal numerous shortcomings and struggle to meet the high-precision, intelligent monitoring requirements of tunnel engineering. Summary of the Invention

[0003] To improve the accuracy of tunnel construction deformation monitoring, this application provides a BIM-based method, system, and equipment for tunnel construction deformation monitoring.

[0004] Firstly, this application provides a BIM-based method for monitoring tunnel construction deformation, employing the following technical solution: A BIM-based method for monitoring tunnel construction deformation includes: Acquire three-dimensional laser point cloud data and synchronous photogrammetric image data of the construction site of the tunnel to be monitored, and generate a real-scene three-dimensional model reflecting the current construction status based on the three-dimensional laser point cloud data and the synchronous photogrammetric image data. The real-scene 3D model is deformed and aligned with the preset design BIM model corresponding to the tunnel to be monitored. Deformed tunnel sections are determined from the tunnel to be monitored. The deformed tunnel sections are tunnel sections with multi-dimensional deformation deviation data. Environmental impact data of the area surrounding the deformed tunnel section is obtained, and based on the environmental impact data and the construction operations to be performed, a construction simulation is conducted on the deformed tunnel section to obtain the simulated construction results. Identify the simulated cumulative deformation and simulated deformation acceleration index in the simulated construction results, and determine the deformation score based on the simulated cumulative deformation and the simulated deformation acceleration index; When the deformation score is higher than the preset deformation threshold, the deformed tunnel section is determined to be a risk section, and the risk section is superimposed on the display scene of the preset design BIM model to generate visual early warning information.

[0005] By adopting the above technical solutions and integrating 3D laser scanning, photogrammetry, real-scene modeling, and BIM technology, the actual construction form of the tunnel can be accurately reproduced. Comparison and analysis between the real-scene 3D model and the pre-designed BIM model facilitates the efficient identification of deformed tunnel sections with multi-dimensional deformation deviations, enabling a comprehensive assessment of the overall tunnel deformation. Construction simulation is conducted by combining data on the surrounding environment of the deformed area with upcoming construction operations, allowing for a full reproduction of the deformation evolution process under the coupled effects of multiple factors. Simultaneously, two core indicators—simulated cumulative deformation and simulated deformation acceleration index—are extracted from the simulation results and comprehensively calculated to obtain a deformation score for risk assessment. This significantly improves the comprehensiveness and accuracy of risk identification. The identified risk sections are overlaid and displayed in the BIM model, generating visual early warning information that intuitively presents the risk location and related status, enabling managers to quickly grasp the on-site risk distribution and take timely control measures.

[0006] In one possible implementation, when two adjacent deformed tunnel sections are detected, the method further includes: The simulated deformation trajectories corresponding to the construction simulation results of the two adjacent deformed tunnel sections are extracted respectively, and the average trajectory spacing, trajectory contour fit and trajectory direction fitting degree between the two simulated deformation trajectories are calculated. Identify the original deformation score of each adjacent deformed tunnel segment and determine the correction weight corresponding to each adjacent deformed tunnel segment; Based on the average trajectory spacing, the trajectory contour fit, the trajectory direction fitting degree, and each correction weight, the correction coefficient corresponding to each adjacent deformed tunnel section is determined. Based on the correction coefficients of each adjacent deformed tunnel section, the original deformation scores corresponding to each section are optimized.

[0007] By adopting the above technical solution, the simulated deformation trajectories of adjacent sections are extracted, and the correlation between the deformation of the two sections is quantified in multiple dimensions, including the average trajectory spacing, trajectory contour fit, and trajectory direction fitting. Then, the correction coefficient is solved by combining the correction weights corresponding to each section, thereby optimizing and correcting the original deformation score. This facilitates accurate analysis of the mutual interference between adjacent sections, effectively corrects the score deviation caused by ignoring the deformation linkage effect, and avoids underestimating the overall instability risk caused by continuous deformation.

[0008] In one possible implementation, the step of deformably aligning the real-world 3D model with the preset design BIM model corresponding to the tunnel to be monitored, and determining the deformed tunnel section from the tunnel to be monitored, includes: Spatial coordinate registration and grid node mapping are performed between the real-scene 3D model and the preset design BIM model. The coordinate difference of the corresponding grid node is extracted to obtain the deformation deviation value of a single node. According to the preset tunnel section zoning rules, the tunnel to be monitored is divided into several continuous tunnel sections. Based on the single-node deformation deviation value of all grid nodes in each tunnel section, the section section settlement deviation, section section convergence deviation and section spatial torsion deviation of the corresponding tunnel section are calculated. The section cross-sectional settlement deviation, section cross-sectional convergence deviation, and section spatial torsion deviation of each tunnel section are integrated to form the corresponding section multidimensional deformation deviation data. Tunnel sections whose multidimensional deformation deviation data exceed the preset static deviation threshold are identified as deformable tunnel sections.

[0009] By adopting the above technical solution, precise spatial coordinate registration and grid node mapping are carried out between the real-scene 3D model and the pre-designed BIM model. The deformation deviation of a single node is obtained by quantifying the difference in grid node coordinates, which facilitates the fine extraction of tunnel micro-deformation data. After dividing the continuous tunnel sections according to the cross-sectional zoning rules, all node data are integrated, and three typical deformation indices of cross-sectional settlement, cross-sectional convergence, and spatial torsion are calculated respectively, thereby forming complete multi-dimensional deformation deviation data of the section. This facilitates the comprehensive characterization of different types of tunnel deformation states. Deformed tunnel sections are screened based on the preset static deviation threshold, which facilitates the rapid and accurate location of deformed tunnel sections with abnormal deformation.

[0010] In one possible implementation, acquiring environmental impact data of the area surrounding the deformed tunnel section includes: The comprehensive deformation variable is calculated based on the multidimensional deformation deviation data of the deformed tunnel section, and the deformation level of the deformed tunnel section is determined based on the comprehensive deformation variable. Identify the dominant deformation dimension from the multidimensional deformation deviation data; Environmental impact data of the area surrounding the deformed tunnel section are obtained based on the deformation level and the dominant deformation dimension.

[0011] By adopting the above technical solution, and by calculating the comprehensive deformation variables and classifying the deformation levels based on multidimensional deformation deviation data, it is easy to intuitively quantify the severity of the overall deformation of the deformation tunnel section. At the same time, by identifying the dominant deformation dimension from the multidimensional deformation deviation data, it is easy to clarify the core deformation type that causes abnormal tunnel deformation and lock in the corresponding key environmental factors. Using the deformation level combined with the dominant deformation dimension as a dual basis for environmental impact data collection, it is easy to plan the scope and content of data collection according to the severity and type of deformation, thereby improving the matching degree between environmental data and the actual deformation conditions of the tunnel.

[0012] In one possible implementation, obtaining environmental impact data of the area surrounding the deformed tunnel section based on the deformation level and the dominant deformation dimension includes: Based on the deformation level and the deformation tunnel section, the collection area is determined, the collection area is divided into multiple annular spatial regions, and a regional influence weight is set for each annular spatial region based on the tunnel interval between each annular spatial region and the deformation tunnel section. Based on the dominant deformation dimension, core monitoring factors and auxiliary monitoring factors are determined; Based on the deformation level and the regional influence weight of each annular spatial region, the core acquisition parameters and auxiliary acquisition parameters of each annular spatial region are determined. Based on each core acquisition parameter, the core monitoring factors in each annular spatial region are collected to obtain core impact data; Based on each auxiliary acquisition parameter, the auxiliary monitoring factors in each annular spatial region are collected to obtain auxiliary impact data; The core impact data and the auxiliary impact data constitute the environmental impact data.

[0013] By adopting the above technical solution, the environmental data collection range is accurately delineated based on the deformation level and the deformed tunnel section. By dividing the space into annular regions and assigning regional influence weights at spatial intervals, the environmental impact contribution of different regions is effectively quantified based on the deformation attenuation law of the strata, achieving spatial differentiation of influence. At the same time, core monitoring factors and auxiliary monitoring factors are differentiated according to the dominant deformation dimension. The core collection parameters and auxiliary collection parameters are matched with the deformation level and regional influence weights, which facilitates the adaptive matching of the priority of monitoring factors and the collection intensity. Finally, by collecting core influence data and auxiliary influence data respectively, a complete environmental dataset is constructed, which can comprehensively and accurately reflect the real state of the environment around the tunnel on the deformed section.

[0014] In one possible implementation, when at least two dominant deformation dimensions are included, the acquisition area is divided into multiple annular spatial regions, and a regional influence weight is assigned to each annular spatial region based on the tunnel interval between each annular spatial region and the deformation tunnel segment, including: Based on the multidimensional deformation deviation data, the deformation amplitude and deformation direction of at least two dominant deformation dimensions are identified, a deformation vector group is constructed, and the deformation vector group is vector-synthesized and fitted to determine the comprehensive fitted deformation direction and deformation bias amplitude. Centered on the deformed tunnel section, based on the deformation deviation amplitude, the partition extension range is expanded along the comprehensive fitting deformation direction and the partition extension range is contracted in the opposite direction to divide multiple non-standard annular spatial regions with asymmetrical shapes. The regional influence weight of each non-standard annular space region is calculated based on the shortest spatial distance between each non-standard annular space region and the deformed tunnel section, as well as the deviation angle of each non-standard annular space region relative to the comprehensive fitted deformation direction.

[0015] By adopting the above technical solution, the deformation direction and deformation deviation amplitude are obtained by vector synthesis and fitting of the deformation vector group. This facilitates the accurate characterization of the overall deformation trend and deviation characteristics after multi-dimensional coupled deformation. Based on the deformation deviation amplitude, the acquisition area is asymmetrically expanded and shrunk to form a non-standard annular spatial region that adapts to the actual deformation distribution. This makes the monitoring zoning shape no longer limited to a regular circular structure, but rather fits the asymmetric stratigraphic influence range generated by multi-deformation coupling. This helps to eliminate the monitoring blind spots and regional adaptation deviations existing in traditional standard annular zoning. At the same time, the regional influence weight is calculated by combining the shortest spatial distance and the deviation angle of the relative fitted deformation direction as dual parameters. This greatly improves the scientificity and accuracy of regional weight assignment, so that the influence weight of each annular region can truly reflect the strength of the contribution of environmental factors in different directions to the tunnel coupling deformation.

[0016] In one possible implementation, the construction simulation of the deformed tunnel section based on the environmental impact data and the construction operations to be performed, to obtain the simulated construction results, includes: Based on the type of construction operation to be performed, a corresponding set of construction disturbance parameters is determined. The set of construction disturbance parameters includes at least disturbance intensity, disturbance frequency, disturbance duration, and disturbance direction. The regional impact data of each annular spatial region in the environmental impact data is mapped to the regional load boundary conditions corresponding to each annular spatial region according to the corresponding regional impact weight. Using the current multidimensional deformation deviation data of the deformed tunnel section as the initial displacement field, and using the construction disturbance parameter set and all regional load boundary conditions as dynamic inputs, construction simulation is performed. During the construction simulation, the cumulative deformation at each time step is recorded in real time, and the first-order difference of the cumulative deformation sequence is used to obtain the simulated deformation rate sequence. The second-order difference of the simulated deformation rate sequence is used to obtain the simulated deformation acceleration index sequence. When the simulation reaches the expected end time of the construction operation to be performed, the peak values ​​of the final simulated cumulative deformation sequence and the final simulated deformation acceleration index sequence are output as the simulation construction result.

[0017] By adopting the above technical solutions, the corresponding set of construction disturbance parameters is matched according to the type of construction operation to be performed, which facilitates the comprehensive and accurate reproduction of the on-site disturbance effects brought about by different construction procedures. At the same time, the environmental impact data of each annular space area is mapped to the corresponding regional load boundary conditions by combining the regional impact weight, which facilitates the accurate matching of the degree of environmental impact with the simulated boundary conditions. By using the current multidimensional deformation deviation data of the deformed tunnel section as the initial displacement field for simulation, and combining the set of construction disturbance parameters with the regional load boundary conditions to carry out dynamic time-step construction simulation, it is easy to realistically reproduce the deformation evolution process of the deformed tunnel section under the coupling of multiple factors such as the existing deformed foundation, environmental load and construction disturbance. In addition, by performing first-order and second-order difference operations on the cumulative deformation amount step by step, the deformation rate sequence and deformation acceleration index sequence are solved respectively, which facilitates the in-depth mining of the dynamic evolution characteristics of the cumulative deformation magnitude, change rate and development acceleration. Finally, the peak data at the end of the simulation is extracted as the simulation construction result, which facilitates the accurate capture of extreme deformation risks in the construction process and effectively improves the scientificity and accuracy of subsequent risk assessment.

[0018] Secondly, this application provides a monitoring system, which adopts the following technical solution: A monitoring system comprising: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: execute the above-described BIM-based tunnel construction deformation monitoring method.

[0019] Thirdly, this application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium includes: a computer program stored thereon that can be loaded by a processor and executed by the above-described BIM-based tunnel construction deformation monitoring method.

[0020] Fourthly, this application provides a computer program product, which adopts the following technical solution: A computer program product includes a computer program that, when executed by a processor, implements the aforementioned BIM-based tunnel construction deformation monitoring method.

[0021] In summary, this application includes at least one of the following beneficial technical effects: By integrating 3D laser scanning, photogrammetry, real-scene modeling, and BIM technology, the actual construction form of the tunnel can be accurately reproduced. Comparison and analysis between the real-scene 3D model and the pre-designed BIM model facilitates the efficient identification of deformed tunnel sections with multi-dimensional deformation deviations, enabling a comprehensive assessment of the overall tunnel deformation. Construction simulations are conducted by combining data on the surrounding environment of the deformed area with upcoming construction operations, allowing for a full reproduction of the deformation evolution process under the coupled effects of multiple factors. Simultaneously, two core indicators—simulated cumulative deformation and simulated deformation acceleration index—are extracted from the simulation results and comprehensively calculated to obtain a deformation score for risk assessment. This significantly improves the comprehensiveness and accuracy of risk identification. The identified risk sections are overlaid and displayed in the BIM model, generating visual early warning information that intuitively presents the risk location and related status, enabling managers to quickly grasp the on-site risk distribution and take timely control measures.

[0022] By extracting the simulated deformation trajectories of adjacent segments, and combining the average trajectory spacing, trajectory contour fit, and trajectory direction fitting degree to quantify the correlation between the deformation of the two segments in multiple dimensions, and then solving the correction coefficient with the corresponding correction weights of each segment, the original deformation score is optimized and corrected. This facilitates accurate analysis of the mutual interference between adjacent segments, effectively corrects the score deviation caused by ignoring the deformation linkage effect, and avoids underestimating the overall instability risk caused by continuous deformation. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating a BIM-based tunnel construction deformation monitoring method in an embodiment of this application. Figure 2 This is a schematic diagram of a deformation score optimization process in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a monitoring system according to an embodiment of this application. Detailed Implementation

[0024] The following is in conjunction with the appendix Figures 1 to 3 This application will be described in further detail.

[0025] After reading this specification, those skilled in the art may make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0026] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] It should be noted that, in the optional embodiments of this application, the data related to object information, when applied to specific products or technologies, requires the permission or consent of the object. Furthermore, the collection, use, and processing of this data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. In other words, if the embodiments of this application involve data related to an object, it must be obtained with the object's authorization and consent, the authorization and consent of relevant departments, and in accordance with the relevant laws, regulations, and standards of the country and region. If the embodiments involve personal information, the acquisition of all personal information requires the individual's consent. If sensitive information is involved, the separate consent of the information subject is required. The embodiments also need to be implemented with the object's authorization and consent.

[0028] Specifically, this application provides a BIM-based method for monitoring tunnel construction deformation, executed by a monitoring system. This monitoring system can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, and this application does not impose any limitations on this.

[0029] refer to Figure 1 , Figure 1 This is a flowchart illustrating a BIM-based tunnel construction deformation monitoring method according to an embodiment of this application. The method includes steps S110-S150, wherein: Step S110: Acquire the three-dimensional laser point cloud data and synchronous photogrammetric image data of the construction site of the tunnel to be monitored, and generate a real-scene three-dimensional model reflecting the current construction status based on the three-dimensional laser point cloud data and synchronous photogrammetric image data.

[0030] Specifically, the tunnel to be monitored is the tunnel that requires deformation monitoring. Three-dimensional laser point cloud data refers to a high-density, high-precision set of spatial coordinate points obtained by scanning the entire construction site of the tunnel to be monitored using a three-dimensional laser scanning device. This data accurately records the three-dimensional spatial coordinates and morphological information of the tunnel's surrounding rock, support structure, and surrounding area. Synchronous photogrammetric image data refers to a high-definition sequence of images obtained by taking multi-angle photographs of the construction area of ​​the tunnel to be monitored during the same period as the laser scanning operation using the three-dimensional laser scanning device. This includes, but is not limited to, on-site texture, color, structural details, and spatial correlation information.

[0031] The 3D laser point cloud data undergoes preprocessing such as denoising, registration, stitching, and simplification to remove construction debris, floating noise, and invalid redundant points, resulting in a regular spatial point cloud skeleton. Then, feature point extraction, image matching, and aerial triangulation are performed on the synchronous photogrammetric image data to establish an image spatial coordinate system. Finally, the preprocessed 3D laser point cloud data is fused and coupled with the synchronous photogrammetric image data to accurately map the image texture information onto the spatial grid structure constructed from the point cloud, automatically generating a real-world 3D model that can completely, realistically, and with high precision reflect the current construction outline, structural morphology, and on-site conditions of the tunnel to be monitored.

[0032] Step S120: Align the real-world 3D model with the preset design BIM model corresponding to the tunnel to be monitored for deformation, and determine the deformed tunnel section from the tunnel to be monitored. The deformed tunnel section is the tunnel section with multi-dimensional deformation deviation data.

[0033] Specifically, the pre-designed BIM model is a standardized digital model built based on tunnel construction drawings, geological survey data, and relevant design specifications. It fully records the tunnel's theoretical shape, structural dimensions, spatial location, and component arrangement, representing the standard form of the tunnel under completed and normal construction conditions. It can be determined by relevant personnel based on actual construction needs and uploaded to the monitoring system in advance. It can also be extracted from the monitoring system based on the tunnel's identifier or tunnel number. The real-world 3D model is deformably aligned with the corresponding pre-designed BIM model. That is, through coordinate transformation, spatial registration, and other technical means, the real-world 3D model and the pre-designed BIM model are unified to the same spatial coordinate system, achieving precise correspondence in the spatial location and outline boundaries of the two types of models. After registration, the overall model comparison is transformed into a refined mesh node comparison, realizing the quantitative extraction of micro-deformation deviations. Furthermore, to facilitate rapid and accurate location of deformed tunnel sections with abnormal deformation, the method provided in this application embodiment, when deformably aligning the real-world 3D model with the corresponding pre-designed BIM model of the tunnel to be monitored to determine the deformed tunnel section, specifically involves the following process: Spatial coordinate registration and one-to-one mapping of grid nodes are performed between the real-world 3D model and the pre-designed BIM model. The coordinate difference of the corresponding grid nodes is extracted to obtain the single-node deformation deviation value. According to the pre-set tunnel cross-section zoning rules, the tunnel to be monitored is divided into several continuous tunnel segments. Based on the single-node deformation deviation value of all grid nodes in each tunnel segment, the segment cross-section settlement deviation, segment cross-section convergence deviation, and segment spatial torsion deviation of the corresponding tunnel segment are calculated. The segment cross-section settlement deviation, segment cross-section convergence deviation, and segment spatial torsion deviation of each tunnel segment are integrated to form the corresponding segment multidimensional deformation deviation data. The tunnel segment whose segment multidimensional deformation deviation data exceeds the pre-set static deviation threshold is identified as a deformed tunnel segment.

[0034] Specifically, firstly, global spatial coordinate registration is performed between the real-world 3D model and the pre-designed BIM model. Based on the tunnel's stable reference structure, attitude correction and coordinate unification are completed. Then, the tunnel surfaces of the two models are meshed with equal precision, and a one-to-one mapping relationship between the mesh nodes of the two types of models is constructed. The 3D coordinate difference is extracted from each matched node to obtain the single-node deformation deviation value at all locations. The single-node deformation deviation value is the 3D coordinate offset of the mesh node in the real-world 3D model relative to the mesh node in the pre-designed BIM model at the same spatial location, which can reflect the microscopic deformation of local points in the tunnel. Based on the preset tunnel cross-section zoning rules, the entire tunnel to be monitored is divided into several continuous and regular tunnel sections according to the construction mileage. Statistical analysis is performed on the single-node deformation deviation values ​​of all grid nodes within each tunnel section, and the corresponding tunnel section cross-section settlement deviation, cross-section convergence deviation, and spatial torsional deviation are calculated by fitting. The cross-section settlement deviation value, cross-section convergence deviation value, and spatial torsional deviation value refer to the degree of vertical settlement deformation of the tunnel section as a whole, the degree of lateral opening and closing deformation of the cross-section as a whole, and the degree of spatial attitude deflection deformation of the tunnel as a whole, respectively. The specific fitting method is not specifically limited in this embodiment.

[0035] The preset static deviation threshold is a critical deformation judgment standard pre-calibrated according to tunnel design specifications and construction safety limits. It is used to distinguish between normal construction deformation and abnormal risk deformation. It can be set in advance by relevant personnel according to actual needs. The preset static deviation threshold corresponds to the multidimensional deformation deviation data of the section. That is, the preset static deviation threshold includes the preset settlement deviation threshold, the preset convergence deviation threshold, and the preset torsional deviation threshold. During the judgment stage, if any dimension of the multidimensional deformation deviation data of a certain tunnel section exceeds the corresponding preset static deviation threshold, the tunnel section is determined to be a deformed tunnel section.

[0036] Step S130: Obtain environmental impact data of the area surrounding the deformed tunnel section, and based on the environmental impact data and the construction operations to be performed, conduct a construction simulation of the deformed tunnel section to obtain the simulation construction results.

[0037] Specifically, environmental impact data refers to a set of monitoring data and parameters that characterize the impact of external environmental factors such as the surrounding soil and rock, groundwater, surface load, and adjacent structures on the deformation of the surrounding rock and support structure of the deformed tunnel section. Since tunnel deformation during construction is not solely determined by the deformation characteristics of the structure itself, but rather by the combined effects of multiple fields of the surrounding geological environment, external loads, and dynamic disturbances during construction, accurately collecting real environmental impact parameters around the deformed section and combining this with the upcoming construction procedures allows for the reconstruction of the stress conditions the deformed tunnel section may face during a future construction period. This facilitates the prediction of the dynamic deformation development trend of the deformed tunnel section during actual construction. When acquiring environmental impact data, sensors can be deployed directly at fixed points around the deformed tunnel section. For example, environmental parameters such as groundwater, soil and rock pressure, and surface settlement can be collected uniformly and indiscriminately, supplemented by manual on-site readings and data aggregation to ultimately obtain the environmental monitoring data. Furthermore, to improve the matching degree between environmental impact data and actual tunnel deformation conditions, the method provided in this application embodiment, when acquiring environmental impact data of the area surrounding the deformed tunnel section, may specifically include: The comprehensive deformation variable is calculated based on the multidimensional deformation deviation data of the deformed tunnel section, and the deformation level of the deformed tunnel section is determined based on the comprehensive deformation variable; the dominant deformation dimension is identified from the multidimensional deformation deviation data; and the environmental impact data of the surrounding area of ​​the deformed tunnel section is obtained based on the deformation level and the dominant deformation dimension.

[0038] Specifically, firstly, multidimensional deformation deviation data of the deformed tunnel section can be retrieved. Then, using preset weighting coefficients, the section cross-sectional settlement deviation, section cross-sectional convergence deviation, and section spatial torsional deviation from the multidimensional deformation deviation data are fused and calculated to obtain a comprehensive deformation variable that characterizes the overall deformation degree of the section. Based on a preset deformation level mapping relationship, the deformation level corresponding to the comprehensive deformation variable is determined. This preset deformation level mapping relationship is the correspondence between the comprehensive deformation variable and the morphological level; the higher the comprehensive deformation variable, the higher the corresponding deformation level. The specific content of this mapping relationship is not specifically limited in this embodiment.

[0039] Subsequently, the deformation amplitude of a single deformation dimension relative to the corresponding preset static deviation threshold in the multidimensional deformation deviation data is calculated, and the deformation dimension with the highest deformation amplitude is identified as the dominant deformation dimension. Finally, based on the determined deformation level, the collection range, accuracy, and frequency of environmental impact data can be defined. Furthermore, based on the identified dominant deformation dimension, differentiated influencing factors are extracted within the collection range, enabling adaptive matching of the environmental monitoring scheme according to the actual deformation state of the tunnel, effectively improving the effectiveness and matching degree of the environmental impact data.

[0040] Furthermore, in order to comprehensively and accurately reflect the true impact of the surrounding environment on the deformed section, this application provides a specific implementation process for obtaining environmental impact data of the area surrounding the deformed tunnel section based on the deformation level and the dominant deformation dimension, including: The data collection area was determined based on the deformation level and the deformed tunnel section. This area was divided into multiple annular spatial regions, and a regional influence weight was assigned to each annular spatial region based on the tunnel interval between it and the deformed tunnel section. Core monitoring factors and auxiliary monitoring factors were determined based on the dominant deformation dimension. Core collection parameters and auxiliary collection parameters for each annular spatial region were determined based on the deformation level and the regional influence weight. Core monitoring factors in each annular spatial region were collected based on each core collection parameter to obtain core influence data. Auxiliary monitoring factors in each annular spatial region were collected based on each auxiliary collection parameter to obtain auxiliary influence data. The core influence data and auxiliary influence data constituted the environmental impact data.

[0041] Specifically, the monitoring center location can be determined based on the spatial mileage of the deformed tunnel section. Then, the corresponding deformation level is combined with the corresponding monitoring radiation radius; the higher the deformation level, the larger the corresponding monitoring radiation radius and the larger the corresponding collection area, thus completing the adaptive collection area definition. Subsequently, using the center point of the deformed tunnel section as the center, the overall collection area is divided layer by layer from the inside out according to a preset interval, forming multiple continuous, non-overlapping annular spatial areas. This is used to achieve layered and refined monitoring of the surrounding environmental impact. The specific preset interval is not specifically limited in this embodiment and can be set by relevant personnel according to actual needs. The shortest tunnel interval between the boundary point of each annular spatial area and the center point of the deformed tunnel section is calculated one by one. Based on the preset influence weight mapping relationship, a higher regional influence weight is assigned to nearby annular spatial areas, and a lower regional influence weight is assigned to distant annular spatial areas, completing the weight assignment for all annular spatial areas. The preset influence weight mapping relationship is the correspondence between tunnel intervals and regional influence weights; the smaller the tunnel interval, the greater the corresponding regional influence weight. The specific content of this mapping relationship is not specifically limited in this embodiment.

[0042] Based on preset deformation-environmental factor correlation mapping rules, core monitoring factors and auxiliary monitoring factors corresponding to the dominant deformation dimension can be selected. Core monitoring factors are surrounding environmental factors that are directly and strongly correlated with the mechanical response of the dominant deformation dimension and can directly induce or aggravate the deformation of the corresponding tunnel section. Auxiliary monitoring factors are environmental factors that are indirectly correlated with the dominant deformation dimension and have a secondary disturbance effect on the deformation of the tunnel section. For example, when the dominant deformation dimension is cross-sectional settlement, the corresponding core monitoring factors are groundwater seepage and surrounding rock and soil compaction, while the corresponding auxiliary monitoring factors are temporary surface loads, regional temperature changes, small displacements of surrounding shallow soil, and atmospheric precipitation recharge.

[0043] Subsequently, combining the overall risk level represented by the deformation grade with the regional impact weights corresponding to each annular spatial region, differentiated core and auxiliary acquisition parameters are configured for each annular spatial region. Core acquisition parameters include the acquisition density and frequency when collecting data on core monitoring factors. Higher deformation grades and greater regional weights result in higher acquisition densities and more frequent acquisition frequencies for core monitoring factors, thereby strengthening the monitoring of high-risk areas and key environmental factors. Auxiliary acquisition parameters include the acquisition density and frequency when collecting data on auxiliary monitoring factors. Since auxiliary monitoring factors can indirectly lead to further deformation in the deformed tunnel section, lower acquisition densities and frequencies can be used. For example, the acquisition density used for core monitoring factors is 'a', and the acquisition density used for auxiliary monitoring factors is 'b', where 'a > b'. Finally, the core and auxiliary impact data corresponding to all annular spatial regions of the deformed tunnel section are summarized, organized, and integrated to form complete environmental impact data that covers the entire monitoring area, distinguishes impact weights, and reflects the actual deformation conditions of the deformed tunnel section.

[0044] Since the subsequent deformation of the tunnel structure is a comprehensive result of the long-term effects of the surrounding environment and the dynamic disturbances of on-site construction, environmental impact data can reconstruct the existing external boundary conditions, the additional disturbance loads and action modes generated by the construction process corresponding to the construction operations to be performed. Therefore, after obtaining the environmental impact data of the area surrounding the deformed tunnel section, it is necessary to comprehensively analyze the development of the existing deformation of the deformed tunnel section in the future in conjunction with the construction operations to be performed. The construction operations to be performed are the tunnel construction procedures and corresponding process parameters that will be carried out in the deformed tunnel section, such as excavation, support, lining, and surrounding rock reinforcement. These can be extracted from the preset construction plan or uploaded to the monitoring system by relevant personnel according to the actual construction plan. This application uses a tunnel digital carrier constructed from a real-world 3D model and a BIM model as its foundation. It transforms real-collected environmental impact data into boundary constraints and load conditions for the simulation model. Combined with disturbance parameters corresponding to construction procedures, it constructs dynamic simulation conditions. Through numerical calculations, it simulates the mechanical response and deformation development of the tunnel structure under the superposition of multiple factors, thereby predicting the deformation characteristics and risk status throughout the construction process. The specific process of simulating the construction of deformable tunnel sections based on environmental impact data and the construction operations to be performed is as follows: Based on the type of construction operation to be performed, a corresponding set of construction disturbance parameters is determined. This set includes at least disturbance intensity, disturbance frequency, disturbance duration, and disturbance direction. The regional impact data for each annular spatial region in the environmental impact data is mapped to the corresponding regional load boundary conditions for each annular spatial region according to their respective regional impact weights. Using the current multidimensional deformation deviation data of the deformed tunnel section as the initial displacement field, and the set of construction disturbance parameters and all regional load boundary conditions as dynamic inputs, construction simulation is performed. During the simulation, the cumulative deformation at each time step is recorded in real time. The cumulative deformation sequence is then subjected to first-order difference to obtain the simulated deformation rate sequence, and the simulated deformation rate sequence is subjected to second-order difference to obtain the simulated deformation acceleration index sequence. When the simulation reaches the expected end time of the construction operation to be performed, the peak values ​​of the final simulated cumulative deformation sequence and the final simulated deformation acceleration index sequence are output as the simulation construction result.

[0045] Specifically, based on feature recognition algorithms, task identifiers can be determined from the construction operations to be performed, and then the corresponding task types can be determined based on the task identifiers. Task types include, but are not limited to, drilling and blasting excavation, mechanical milling, initial support, shotcrete, rebar tying, secondary lining pouring, and surrounding rock grouting reinforcement. Different task types have significant differences in disturbance intensity, frequency, duration, and direction of action. Therefore, different task types correspond to different sets of construction disturbance parameters. There is a task type-disturbance mapping relationship between task types and construction disturbance parameter sets. This mapping relationship can be determined by relevant personnel based on historical task data and uploaded to the monitoring system in advance. Subsequently, the regional impact data and regional impact weights corresponding to each annular spatial area are extracted sequentially. The environmental impact data are converted and corrected according to the weight ratio, and the processed data are transformed one by one into regional load boundary conditions that the model can recognize. Then, the current multidimensional deformation deviation data of the deformed tunnel section is imported into the preset simulation model to construct the initial displacement field required for simulation calculation. The initial displacement field is a spatial displacement distribution model constructed based on the current actual deformation state of the deformed tunnel section. It can reflect the offset shape of each position of the deformed tunnel section at the beginning of the preset simulation model. Finally, all regional load boundary conditions and construction disturbance parameter sets are loaded into the spatial displacement distribution model as dynamic input items to carry out the full-process construction simulation. The construction simulation is a calculation process that relies on numerical simulation technology to reproduce the deformation evolution process of the deformed tunnel section structure and surrounding rock under multiple external forces.

[0046] Throughout the construction simulation iterative calculation process, the calculation is advanced sequentially in units of preset simulation time steps. After each time step of mechanical iteration calculation is completed, the overall cumulative deformation of the current deformed tunnel section is collected and recorded in real time. All sampled data are stored sequentially according to time, forming a complete cumulative deformation sequence—that is, a time-series data set composed of the cumulative deformation corresponding to all simulation time steps, ordered chronologically. After each round of time step data updates, a first-order difference operation is performed on the real-time updated cumulative deformation sequence. Using time as the independent variable and deformation as the dependent variable, the rate of change is solved to generate a simulated deformation rate sequence for the corresponding time period. In other words, the deformation time-series change data obtained through the first-order difference operation characterizes the rate of tunnel deformation development over simulation time. A second-order difference operation is continuously performed on the obtained simulated deformation rate sequence to solve for the rate gradient, generating a simulated deformation acceleration index sequence in real time to dynamically track the accelerating evolution trend of tunnel deformation. The simulation calculation continues iteratively until the preset expected end time of the construction operation to be performed is reached, at which point the time step iteration and data collection cease.

[0047] Subsequently, extreme value screening was performed on the cumulative deformation sequence and the simulated deformation acceleration index sequence recorded throughout the process. The peak data of the two sequences were extracted, and the final complete cumulative deformation sequence and the peak value of the deformation acceleration index were combined to form the simulated construction result of this simulation and output. By selecting the peak values ​​of the final simulated cumulative deformation sequence and the final simulated deformation acceleration index sequence as the output results, it is convenient to accurately identify the maximum deformation and the most dangerous acceleration mutation node during the construction process, realize the extreme value quantitative evaluation of construction risk, and ensure the comprehensiveness and risk orientation of the simulation results.

[0048] Step S140: Identify the simulated cumulative deformation and simulated deformation acceleration index in the simulated construction results, and determine the deformation score based on the simulated cumulative deformation and simulated deformation acceleration index.

[0049] Specifically, the simulated cumulative deformation and simulated deformation acceleration index in the simulated construction results can be directly normalized to eliminate the calculation interference caused by the inconsistency of the numerical range and physical units of the two types of parameters. Then, according to the preset deformation weight ratio, the simulated cumulative deformation and simulated deformation acceleration index are respectively assigned preset deformation weights. The two normalized parameters are coupled and calculated through a weighted fusion formula, and finally the deformation score that can comprehensively characterize the degree of deformation and instability trend of the tunnel section is obtained.

[0050] Step S150: When the deformation score is higher than the preset deformation threshold, the deformed tunnel section is identified as a risk section, and the risk section is superimposed on the display scene of the preset design BIM model to generate visual early warning information.

[0051] Specifically, the calculated deformation score is compared with a preset deformation threshold. When the deformation score is higher than the preset threshold, the deformed tunnel section can be identified as a risk section, meaning that there is a significant safety hazard when relevant construction work is carried out in this section in the future. The specific preset deformation threshold is not limited in this embodiment and can be set by relevant personnel according to actual needs. Subsequently, the 3D display scene of the preset design BIM model is retrieved. According to the construction mileage and spatial range corresponding to the risk section, the corresponding structural position is accurately matched in the model, and exclusive graphic markers, color differentiation, and text descriptions are superimposed here as warning elements. Finally, the model body and the newly added risk labeling content are integrated to generate and display complete visual warning information, completing the location and warning push of the tunnel risk area.

[0052] In this embodiment of the application, by integrating 3D laser scanning, photogrammetry, real-scene modeling, and BIM technology, the actual construction form of the tunnel site can be accurately reproduced. By comparing and analyzing the real-scene 3D model with the preset design BIM model, it is easy to efficiently identify the deformed tunnel sections with multi-dimensional deformation deviations, and to achieve a comprehensive survey of the overall deformation status of the tunnel. By combining the environmental impact data of the deformation area with the upcoming construction operations, construction simulation can be carried out, which can fully restore the deformation evolution process under the coupling effect of multiple factors on site. At the same time, the two core indicators of simulated cumulative deformation and simulated deformation acceleration index are extracted from the simulation results, and the deformation score is calculated to carry out risk assessment, which can significantly improve the comprehensiveness and accuracy of risk identification. The identified risk sections are overlaid and displayed in the BIM model to generate visual early warning information, intuitively presenting the risk location and related status, which can help managers quickly grasp the on-site risk distribution and take timely control measures.

[0053] When two adjacent deformed tunnel sections are detected, the method provided in this application embodiment may further include steps S210-S240, such as... Figure 2 As shown, where: Step S210: Extract the simulated deformation trajectories corresponding to the construction simulation results of two adjacent deformed tunnel sections, and calculate the average trajectory spacing, trajectory profile fit, and trajectory direction fitting degree between the two simulated deformation trajectories.

[0054] Specifically, for two spatially adjacent deformed tunnel sections, complete construction simulation results for each are retrieved. Deformation coordinates of each monitoring point within the entire simulation cycle are extracted from the simulation data, and two independent simulated deformation trajectories are generated. Then, corresponding sampling point groups are selected on both simulated deformation trajectories according to a unified rule. The straight-line distance between each sampling point is calculated, and the average of all distance values ​​is obtained to determine the average trajectory spacing. The average trajectory spacing is the arithmetic mean of the straight-line distances between corresponding sampling points on the two simulated deformation trajectories, used to characterize the overall spatial deviation of the two simulated deformation trajectories. Next, the curvature, undulations, and other geometric features of the two simulated deformation trajectories are compared, and the trajectory contour fit is calculated using a preset algorithm. The trajectory contour fit is a quantitative indicator measuring the similarity of the geometric shape and undulation features of the two trajectories. Finally, the overall extension angle and deformation development direction of the two simulated deformation trajectories are analyzed. The trajectory direction fit is obtained through direction fitting calculation. The trajectory direction fit is a characteristic parameter for evaluating the consistency of the extension direction and deformation development direction of the two trajectories.

[0055] Step S220: Identify the original deformation score of each adjacent deformed tunnel segment and determine the correction weight corresponding to each adjacent deformed tunnel segment.

[0056] Specifically, the correction weight for each deformed tunnel segment can be determined based on a preset correction weight mapping relationship and each original deformation score. The preset correction weight mapping relationship is the correspondence between the original deformation score and the correction weight; the higher the original deformation score, the higher the corresponding correction weight. The specific content of this mapping relationship is not specifically limited in this embodiment. The original deformation score is the uncorrected deformation score.

[0057] Step S230: Based on the average trajectory spacing, trajectory profile fit, trajectory direction fitting degree, and each correction weight, determine the correction coefficient corresponding to each adjacent deformed tunnel section.

[0058] Specifically, for any adjacent deformed tunnel segment, the three calculated trajectory feature indicators are first processed to unify their dimensions, eliminating calculation biases caused by differences in numerical ranges and physical dimensions of different indicators. Then, the normalized feature indicators are weighted and summed according to preset feature indicator weights to obtain the feature indicator scores of adjacent deformed tunnel segments. Based on the feature indicator scores and corresponding correction weights, the correction coefficients corresponding to adjacent deformed tunnel segments are calculated. The preset feature indicator weights can be set in advance by relevant personnel according to actual needs. Based on the above method, the correction coefficients corresponding to each adjacent deformed tunnel segment can be obtained.

[0059] Step S240: Optimize the original deformation score corresponding to each adjacent deformed tunnel section based on the correction coefficient.

[0060] Specifically, the original deformation score can be optimized by directly multiplying the correction coefficient with the original deformation score.

[0061] In this embodiment of the application, the simulated deformation trajectories of adjacent segments are extracted, and the correlation between the deformation of the two segments is quantified in multiple dimensions, including the average trajectory spacing, trajectory contour fit, and trajectory direction fitting. Then, the correction coefficient is solved by combining the correction weights corresponding to each segment, thereby optimizing and correcting the original deformation score. This facilitates accurate analysis of the mutual interference between adjacent segments, effectively corrects the score deviation caused by ignoring the deformation linkage effect, and avoids underestimating the overall instability risk caused by continuous deformation.

[0062] Furthermore, to improve the scientific rigor and accuracy of regional weight assignment, when at least two dominant deformation dimensions are included, the collection area is divided into multiple annular spatial regions. A regional influence weight is assigned to each annular spatial region based on the tunnel interval between each annular spatial region and the deformation tunnel segment. Specifically, this may include: Based on multidimensional deformation deviation data, the deformation amplitude and direction of at least two dominant deformation dimensions are identified, a deformation vector group is constructed, and the deformation vector group is vector-synthesized and fitted to determine the comprehensive fitted deformation direction and deformation bias amplitude. Taking the deformed tunnel section as the center, the extension range of the partition is expanded along the comprehensive fitted deformation direction based on the deformation bias amplitude, and the extension range of the partition is contracted in the opposite direction to obtain multiple non-standard annular spatial regions with asymmetrical shapes. According to the shortest spatial distance between each non-standard annular spatial region and the deformed tunnel section, and the bias angle of each non-standard annular spatial region relative to the comprehensive fitted deformation direction, the regional influence weight of each non-standard annular spatial region is calculated.

[0063] Specifically, when multiple dominant deformation dimensions exist in a deformed tunnel section, the overall deformation may no longer exhibit a uniform radial offset characteristic, but rather a significant spatial directional bias. Regular annular symmetrical partitioning methods cannot match the non-uniform distribution patterns of surrounding rock disturbance and environmental influences. In this case, it is necessary to first identify all dominant deformation dimensions within the deformed tunnel section, extract the deformation amplitude and direction corresponding to each dominant deformation dimension, and establish single-dimensional deformation vectors one by one according to vector construction rules. These vectors are then combined to form the deformation vector group for that section. The deformation amplitude is the difference between the deformation degree of the dominant deformation dimension in the multidimensional deformation deviation data and the preset static deviation threshold of the corresponding dominant deformation dimension. For example, when the dominant deformation dimension is cross-sectional settlement, its corresponding deformation amplitude is the cross-sectional settlement deviation value of the section in the multidimensional deformation deviation data and the preset settlement deviation threshold. The deformation direction is the spatial offset orientation of each dominant deformation dimension based on the tunnel's unified engineering coordinate system, used to characterize the deformation development trend. For each dominant deformation dimension, an independent spatial deformation vector is constructed, with the corresponding deformation amplitude as the vector magnitude and the deformation orientation as the vector direction. All vectors are then aggregated to form the deformation vector group for the current deformed tunnel segment. Subsequently, vector decomposition, component superposition, and overall fitting operations are performed on all spatial deformation vectors within the deformation vector group to solve for the optimal composite orientation and composite magnitude after multi-vector coupling. Finally, the overall spatial orientation obtained from the fitting is output as the comprehensive fitted deformation direction, and the total deformation intensity after synthesis is output as the deformation bias amplitude.

[0064] Using the center point of the deformed tunnel section as the origin of the zoning reference, and combining it with the determined comprehensive fitting deformation direction, the reference direction is delineated. Simultaneously, the magnitude of the range adjustment is determined with reference to the deformation deviation amplitude. Along the comprehensive fitting deformation direction, the radial extension distance of each layer of annular space area is increased step by step according to the proportional coefficient corresponding to the amplitude, expanding the zoning coverage area in that direction. On the side opposite to the comprehensive fitting deformation direction, the radial extension distance of each layer of annular space area is simultaneously reduced, shrinking the corresponding zoning range. The range adjustment and boundary delineation of each layer of annular space area are completed sequentially from the inside out, ultimately forming several non-standard annular space areas with different radial spans, spatial shapes, and an overall asymmetrical layout.

[0065] For each non-standard annular spatial region that has been divided, the shortest spatial distance from the region to the center point of the deformation tunnel section is first measured. Then, the deviation angle of the region's center orientation relative to the comprehensive fitting deformation direction is calculated. According to the preset distance attenuation function and angle attenuation function, the two parameters are quantified and converted to obtain the distance influence factor and angle influence factor. Then, the two types of influence factors are summed to obtain the regional influence weight of each non-standard annular spatial region. The smaller the distance and the smaller the deviation angle, the higher the calculated regional influence weight, and vice versa.

[0066] In this embodiment of the application, the deformation direction and deformation deviation amplitude are obtained by vector synthesis and fitting of the deformation vector group. This facilitates the accurate characterization of the overall deformation trend and deviation characteristics after multi-dimensional coupled deformation. The acquisition area is asymmetrically expanded and shrunk according to the deformation deviation amplitude to form a non-standard annular spatial region that adapts to the actual deformation distribution. This makes the monitoring partition shape no longer limited to a regular circular structure, but fits the asymmetric stratum influence range generated by multi-deformation coupling. This helps to eliminate the monitoring blind spots and regional adaptation deviations existing in traditional standard annular partitions. At the same time, the regional influence weight is calculated by combining the shortest spatial distance and the deviation angle of the relative fitted deformation direction as dual parameters. This greatly improves the scientificity and accuracy of the regional weight assignment, so that the influence weight of each annular region can truly reflect the strength of the contribution of environmental factors in different directions to the tunnel coupling deformation.

[0067] This application provides a monitoring system, such as Figure 3 As shown, Figure 3 The monitoring system 300 shown includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the monitoring system 300 may also include a transceiver 304. It should be noted that in practical applications, the transceiver 304 is not limited to one type, and the structure of this monitoring system 300 does not constitute a limitation on the embodiments of this application.

[0068] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0069] Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by only one line, but this does not mean that there is only one bus or one type of bus.

[0070] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0071] The memory 303 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0072] The monitoring system includes, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. It can also include servers. Figure 3 The monitoring system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0073] This application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0074] This application provides a computer program product including a computer program that, when executed by a processor, implements the methods described in any of the above embodiments.

[0075] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0076] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A BIM-based method for monitoring tunnel construction deformation, characterized in that, include: Acquire three-dimensional laser point cloud data and synchronous photogrammetric image data of the construction site of the tunnel to be monitored, and generate a real-scene three-dimensional model reflecting the current construction status based on the three-dimensional laser point cloud data and the synchronous photogrammetric image data. The real-scene 3D model is deformed and aligned with the preset design BIM model corresponding to the tunnel to be monitored. Deformed tunnel sections are determined from the tunnel to be monitored. The deformed tunnel sections are tunnel sections with multi-dimensional deformation deviation data. Environmental impact data of the area surrounding the deformed tunnel section is obtained, and based on the environmental impact data and the construction operations to be performed, a construction simulation is conducted on the deformed tunnel section to obtain the simulated construction results. Identify the simulated cumulative deformation and simulated deformation acceleration index in the simulated construction results, and determine the deformation score based on the simulated cumulative deformation and the simulated deformation acceleration index; When the deformation score is higher than the preset deformation threshold, the deformed tunnel section is determined to be a risk section, and the risk section is superimposed on the display scene of the preset design BIM model to generate visual early warning information.

2. The BIM-based tunnel construction deformation monitoring method according to claim 1, characterized in that, When two adjacent deformed tunnel sections are detected, the following is also included: The simulated deformation trajectories corresponding to the construction simulation results of the two adjacent deformed tunnel sections are extracted respectively, and the average trajectory spacing, trajectory contour fit and trajectory direction fitting degree between the two simulated deformation trajectories are calculated. Identify the original deformation score of each adjacent deformed tunnel segment and determine the correction weight corresponding to each adjacent deformed tunnel segment; Based on the average trajectory spacing, the trajectory contour fit, the trajectory direction fitting degree, and each correction weight, the correction coefficient corresponding to each adjacent deformed tunnel section is determined. Based on the correction coefficients of each adjacent deformed tunnel section, the original deformation scores corresponding to each section are optimized.

3. The BIM-based tunnel construction deformation monitoring method according to claim 1, characterized in that, The step of aligning the real-world 3D model with the preset design BIM model corresponding to the tunnel to be monitored, and determining the deformed tunnel section from the tunnel to be monitored, includes: Spatial coordinate registration and grid node mapping are performed between the real-scene 3D model and the preset design BIM model. The coordinate difference of the corresponding grid node is extracted to obtain the deformation deviation value of a single node. According to the preset tunnel section zoning rules, the tunnel to be monitored is divided into several continuous tunnel sections. Based on the single-node deformation deviation value of all grid nodes in each tunnel section, the section section settlement deviation, section section convergence deviation and section spatial torsion deviation of the corresponding tunnel section are calculated. The section cross-sectional settlement deviation, section cross-sectional convergence deviation, and section spatial torsion deviation of each tunnel section are integrated to form the corresponding section multidimensional deformation deviation data. Tunnel sections whose multidimensional deformation deviation data exceed the preset static deviation threshold are identified as deformed tunnel sections.

4. The BIM-based method for monitoring tunnel construction deformation according to claim 1, characterized in that, The acquisition of environmental impact data for the area surrounding the deformed tunnel section includes: The comprehensive deformation variable is calculated based on the multidimensional deformation deviation data of the deformed tunnel section, and the deformation level of the deformed tunnel section is determined based on the comprehensive deformation variable. Identify the dominant deformation dimension from the multidimensional deformation deviation data; Environmental impact data of the area surrounding the deformed tunnel section are obtained based on the deformation level and the dominant deformation dimension.

5. The BIM-based tunnel construction deformation monitoring method according to claim 4, characterized in that, The environmental impact data of the area surrounding the deformed tunnel section obtained based on the deformation level and the dominant deformation dimension includes: Based on the deformation level and the deformation tunnel section, the collection area is determined, the collection area is divided into multiple annular spatial regions, and a regional influence weight is set for each annular spatial region based on the tunnel interval between each annular spatial region and the deformation tunnel section. Based on the dominant deformation dimension, core monitoring factors and auxiliary monitoring factors are determined; Based on the deformation level and the regional influence weight of each annular spatial region, the core acquisition parameters and auxiliary acquisition parameters of each annular spatial region are determined. Based on each core acquisition parameter, the core monitoring factors in each annular spatial region are collected to obtain core impact data; Based on each auxiliary acquisition parameter, the auxiliary monitoring factors in each annular spatial region are collected to obtain auxiliary impact data; The core impact data and the auxiliary impact data constitute the environmental impact data.

6. The BIM-based tunnel construction deformation monitoring method according to claim 5, characterized in that, When at least two dominant deformation dimensions are included, the acquisition area is divided into multiple annular spatial regions, and a regional influence weight is assigned to each annular spatial region based on the tunnel interval between each annular spatial region and the deformation tunnel segment, including: Based on the multidimensional deformation deviation data, the deformation amplitude and deformation direction of at least two dominant deformation dimensions are identified, a deformation vector group is constructed, and the deformation vector group is vector-synthesized and fitted to determine the comprehensive fitted deformation direction and deformation bias amplitude. Centered on the deformed tunnel section, based on the deformation deviation amplitude, the partition extension range is expanded along the comprehensive fitting deformation direction and the partition extension range is contracted in the opposite direction to divide multiple non-standard annular spatial regions with asymmetrical shapes. The regional influence weight of each non-standard annular space region is calculated based on the shortest spatial distance between each non-standard annular space region and the deformed tunnel section, as well as the deviation angle of each non-standard annular space region relative to the comprehensive fitted deformation direction.

7. The BIM-based tunnel construction deformation monitoring method according to claim 5, characterized in that, Based on the environmental impact data and the construction operations to be performed, a construction simulation is conducted on the deformed tunnel section to obtain the simulated construction results, including: Based on the type of construction operation to be performed, a corresponding set of construction disturbance parameters is determined. The set of construction disturbance parameters includes at least the disturbance intensity, disturbance frequency, disturbance duration, and disturbance direction. The regional impact data of each annular spatial region in the environmental impact data is mapped to the regional load boundary conditions corresponding to each annular spatial region according to the corresponding regional impact weight. Using the current multidimensional deformation deviation data of the deformed tunnel section as the initial displacement field, and using the construction disturbance parameter set and all regional load boundary conditions as dynamic inputs, construction simulation is performed. During the construction simulation, the cumulative deformation at each time step is recorded in real time, and the first-order difference of the cumulative deformation sequence is used to obtain the simulated deformation rate sequence. The second-order difference of the simulated deformation rate sequence is used to obtain the simulated deformation acceleration index sequence. When the simulation reaches the expected end time of the construction operation to be performed, the peak values ​​of the final simulated cumulative deformation sequence and the final simulated deformation acceleration index sequence are output as the simulation construction result.

8. A monitoring system, characterized in that, The monitoring system includes: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: perform a BIM-based tunnel construction deformation monitoring method according to any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, include: The computer program is stored and can be loaded by a processor and executed as described in any one of claims 1-7, which is a BIM-based method for monitoring tunnel construction deformation.

10. A computer program product, characterized in that, The method includes a computer program that, when executed by a processor, implements the steps of a BIM-based tunnel construction deformation monitoring method according to any one of claims 1-7.

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