Intelligent monitoring and control system and method for construction precision of large-section prefabricated assembly type channel

Abstract

The invention relates to the technical field of construction control, and particularly discloses a large-section prefabricated assembly type channel construction precision intelligent monitoring and control system and method, and the system comprises a multi-source data monitoring module which obtains multi-dimensional construction process monitoring data; the data fusion analysis module generates a comprehensive deviation feature vector based on the multi-dimensional construction process monitoring data, and judges a deviation level; the mechanical property evaluation module updates a component position, a joint state and a foundation settlement boundary condition in a preset large-section prefabricated assembly type channel finite element model based on the comprehensive deviation feature vector to obtain a current large-section prefabricated assembly type channel refined finite element model; component stress distribution analysis, joint force transmission performance analysis and structure linear stability analysis are carried out on the model, and a mechanical influence degree score is obtained; the self-adaptive adjustment control module triggers a self-adaptive adjustment control process based on the deviation level and the mechanical influence degree score; the construction precision and quality are powerfully guaranteed, and the construction intelligence and the controllable level are improved.

Description

Technical Field

[0001] This invention relates to the field of construction control technology, and in particular to an intelligent monitoring and control system and method for the construction accuracy of large-section prefabricated modular tunnels. Background Technology

[0002] Currently, large-section prefabricated modular tunnels are widely used in modern infrastructure construction, particularly in highway and municipal engineering projects, due to their advantages such as fast construction speed and easy quality control. However, ensuring construction accuracy is crucial for guaranteeing the structural safety and performance of prefabricated modular tunnels. Insufficient construction accuracy can lead to problems such as joint misalignment and alignment deviations. These issues not only affect the appearance of the tunnel but may also negatively impact its mechanical properties, ultimately threatening its long-term stability and safety. Therefore, developing an intelligent monitoring and control system and method for the construction accuracy of large-section prefabricated modular tunnels is of significant practical importance. With the construction industry's pursuit of green, efficient, and intelligent construction, such intelligent monitoring and control systems are expected to become standard equipment in future prefabricated modular structure construction, driving the industry towards intelligent and digital development, and possessing broad application prospects and development potential.

[0003] However, existing monitoring and control technologies for large-section prefabricated modular tunnel construction cannot promptly detect potential construction deviations and struggle to quantify their severity. Regarding the impact of construction deviations on structural mechanical properties, current technologies are ill-equipped to accurately analyze component stress distribution, joint load transfer performance, and structural linear stability, failing to obtain accurate mechanical impact scores. This results in insufficient understanding of the potential mechanical performance changes caused by construction deviations. Furthermore, in terms of deviation mitigation measures, existing technologies cannot promptly trigger adaptive adjustment control processes based on deviation levels and mechanical impact scores, hindering timely and effective adjustments to construction equipment. Consequently, they cannot effectively guarantee construction accuracy, impacting the construction quality and structural safety of prefabricated modular tunnels.

[0004] Therefore, this invention proposes an intelligent monitoring and control system and method for the construction accuracy of large-section prefabricated modular tunnels. Summary of the Invention

[0005] This invention provides an intelligent monitoring and control system and method for the construction accuracy of large-section prefabricated modular passageways. By intelligently monitoring and controlling the construction process, key data can be acquired in real time, and construction deviations can be detected and corrected in a timely manner, thereby significantly improving assembly accuracy. This not only helps reduce manual intervention and labor intensity, but also effectively avoids structural defects caused by construction errors, improves project quality, and extends the service life of the passageway.

[0006] This invention provides an intelligent monitoring and control system for the construction accuracy of large-section prefabricated modular tunnels, comprising: The multi-source data monitoring module is used to obtain multi-dimensional construction process monitoring data based on monitoring components deployed on prefabricated components of large-section prefabricated assembled channels. The data fusion and analysis module is used to generate a comprehensive deviation feature vector based on multi-dimensional construction process monitoring data, and to determine the deviation level based on the modulus of the comprehensive deviation feature vector; The mechanical performance evaluation module is used to update the component positions, joint states, and foundation settlement boundary conditions in the preset large-section prefabricated assembled passage finite element model based on the comprehensive deviation feature vector, obtain the current large-section prefabricated assembled passage refined finite element model, and perform component stress distribution analysis, joint force transmission performance analysis, and structural linear stability analysis on the current large-section prefabricated assembled passage refined finite element model to obtain the mechanical influence score; The adaptive adjustment control module is used to trigger the adaptive adjustment control process based on the deviation level and mechanical influence score.

[0007] Preferably, the multi-source data monitoring module includes: The strain monitoring submodule is used to collect strain data during the hoisting and assembly of prefabricated components based on multiple wireless strain sensors deployed at the four corners and mid-span of the top slab of the large-section prefabricated assembly channel. The attitude monitoring submodule is used to monitor the hoisting attitude angle of prefabricated components in real time based on a pair of dual-axis tilt sensors installed at the upper and lower ends of the sidewall of the large-section prefabricated assembly channel. The hoisting attitude angle of the prefabricated components includes pitch angle and yaw angle. The settlement monitoring submodule is used to monitor uneven settlement of the foundation in real time based on displacement sensors that are deployed longitudinally at preset intervals along the foundation of the large-section prefabricated assembled channel, and to generate a foundation settlement record sequence. The misalignment monitoring submodule is used to collect lateral misalignment data, longitudinal misalignment data, and joint gap of prefabricated component joints based on multiple laser displacement sensors installed at the joints of prefabricated components in large-section prefabricated assembled channels. The model generation submodule is used to capture continuous images of the prefabricated component hoisting process using multiple industrial-grade binocular vision cameras installed in the construction area and whose camera intrinsic and extrinsic parameters have been calibrated using the Zhang's calibration method, and to generate a 3D point cloud model of the component using a motion recovery structure algorithm. The multidimensional construction process monitoring data includes strain data during the hoisting and assembly of precast components, hoisting attitude angles of precast components, foundation settlement record sequences, lateral misalignment data of precast component joints, longitudinal misalignment data, joint gaps, and three-dimensional point cloud models of components.

[0008] Preferably, the data fusion and analysis module includes: The strain deviation analysis submodule is used to convert strain data from the precast component hoisting and assembly process in the multidimensional construction process monitoring data into strain deviation feature vectors. The geometric deviation analysis submodule is used to convert the precast component hoisting attitude angle, foundation settlement record sequence, lateral misalignment data of precast component joints, longitudinal misalignment data of precast component joints, and joint gap of precast component joints in the multidimensional construction process monitoring data into geometric deviation feature vectors. The linear deviation analysis submodule is used to downsample and register the three-dimensional point cloud model of the components in the multi-dimensional construction process monitoring data and extract the deviation value between the actual coordinates and the design coordinates of the precast components. Based on the deviation value between the actual coordinates and the design coordinates of the precast components, the planar linear deviation, longitudinal slope deviation and cross-sectional dimension deviation are determined, and the linear deviation feature vector is generated based on the planar linear deviation, longitudinal slope deviation and cross-sectional dimension deviation. The deviation feature fusion submodule is used to fuse the strain deviation feature vector, geometric deviation feature vector, and linear deviation feature vector to obtain a comprehensive deviation feature vector.

[0009] Preferably, the data fusion and analysis module further includes: The multi-level threshold setting submodule is used to import component design dimensions, joint allowable deviations, linear design parameters, and foundation allowable settlement as construction design parameters, and set multi-level deviation warning thresholds based on the construction design parameters. The deviation level determination submodule is used to determine the deviation level based on the magnitude of the comprehensive deviation feature vector and the multi-level deviation threshold.

[0010] Preferably, the mechanical performance evaluation module includes: The model update submodule is used to update the component positions, joint states, and foundation settlement boundary conditions in the preset large-section prefabricated assembled passage finite element model based on the comprehensive deviation feature vector, so as to obtain the current refined finite element model of the large-section prefabricated assembled passage. The performance analysis submodule is used to perform component stress distribution analysis, joint force transmission performance analysis, and structural linear stability analysis on the current large-section prefabricated assembled channel refined finite element model, and obtain stress over-limit coefficient, force under-transmission coefficient, and linear instability coefficient. The comprehensive scoring submodule is used to determine the mechanical influence score based on the stress over-limit coefficient, the force transmission insufficiency coefficient, and the linear instability coefficient.

[0011] Preferably, the performance analysis submodule includes: The stress exceeding the standard area calibration unit is used to perform component stress distribution analysis on the current large-section prefabricated assembled channel refined finite element model and mark all stress exceeding the standard areas; The critical stress area calibration unit is used to calibrate all critical stress areas based on the current refined finite element model of large-section prefabricated assembled channels. The insufficient force transmission joint screening unit is used to analyze the joint force transmission performance of the current large-section prefabricated assembled channel refined finite element model, obtain the actual force transmission value of the shear key at the joint of the large-section prefabricated assembled channel, and calculate the ratio of the actual force transmission value of the shear key at the joint of the large-section prefabricated assembled channel to the design value, which is used as the basis value for judging insufficient force transmission at the corresponding joint. Based on the basis value for judging insufficient force transmission at each joint and the preset threshold value for judging insufficient force transmission, the unit screens out the joints with insufficient force transmission from all joints. The unstable linear section screening unit is used to perform structural linear stability analysis on the current large-section prefabricated assembled channel refined finite element model to obtain the actual linear shape of each segmented linear section. The deviation curve between the actual linear shape and the design linear shape is fitted by cubic spline interpolation algorithm. Based on the first derivative of the deviation curve of each segmented linear section and the preset rate of change threshold, the unstable linear section is screened out from all segmented linear sections. The mechanical performance coefficient determination unit is used to determine the stress excess coefficient, the force deficiency coefficient, and the linear instability coefficient based on the stress excess area and critical stress area of ​​all precast components, the total number of all insufficient force transmission joints, and the segment length of all unstable linear intervals.

[0012] Preferably, the mechanical property coefficient determination unit includes: The stress exceedance coefficient determination sub-unit is used as the ratio of the total projected area of ​​the stress exceedance area of ​​all precast components to the total projected area of ​​the critical stress area of ​​all precast components as the stress exceedance coefficient. The force transmission deficiency coefficient determination sub-unit is used as the ratio of the total number of all force transmission deficiency joints to the total number of joints in the large-section prefabricated assembled channel as the force transmission deficiency coefficient. The linear instability coefficient determination sub-unit is used as the ratio of the segment length of all instable linear intervals to the total length of the channel in the current construction section as the linear instability coefficient.

[0013] Preferably, the stress-exceeding area calibration unit includes: The component calibration sub-unit is used to map the current large-section prefabricated assembled channel refined finite element model to a three-dimensional rectangular coordinate system and extract the three-dimensional coordinate range of all key stress-bearing components. Mesh subdivision unit is used to mesh all key load-bearing components based on the three-dimensional coordinate range of all key load-bearing components, and obtain the three-dimensional coordinate range of all sub-mesh spatial regions of each key load-bearing component; The stress analysis sub-element is used to construct a stress calculation matrix based on the material constitutive parameters of the current refined finite element model of large-section prefabricated assembled channels. Based on the stress calculation matrix, the stress balance equation of each sub-mesh spatial region of each key stress-bearing component is constructed. The stress balance equation is solved by Gaussian elimination to obtain the displacement vector output value. The strain tensor of each sub-mesh spatial region is calculated by combining the geometric equations. Finally, the stress tensor of each sub-mesh spatial region is determined by combining the physical equations. The cloud map generation sub-element is used to perform maximum principal stress calculation on the stress tensor of all sub-mesh spatial regions of each critical stress component, and generate stress cloud maps for each critical stress component; The coordinate filtering sub-unit is used to determine all stress-exceeding coordinates and construct a set of stress-exceeding coordinates based on the stress cloud map of each key stress-bearing component. The region calibration sub-unit is used to cluster the stress-exceeding coordinate set using the DBSCAN clustering algorithm to obtain multiple stress-exceeding clusters. The minimum bounding box of each stress-exceeding cluster is calculated, and the three-dimensional spatial region corresponding to each minimum bounding box is regarded as all stress-exceeding regions.

[0014] Preferably, the adaptive adjustment control module includes: The adjustment trigger judgment submodule is used to determine whether to trigger the adaptive adjustment control process based on the deviation level and mechanical influence score. The adjustment strategy generation submodule is used to generate an adjustment strategy based on the control parameters of the construction equipment if the adaptive adjustment control process is triggered.

[0015] This invention provides an intelligent monitoring and control method for the construction accuracy of large-section prefabricated modular tunnels, including: Based on monitoring components deployed on prefabricated components of large-section prefabricated assembled passages, multi-dimensional construction process monitoring data is obtained. A comprehensive deviation feature vector is generated based on multi-dimensional construction process monitoring data, and the deviation level is determined based on the modulus of the comprehensive deviation feature vector. Based on the comprehensive deviation feature vector, update the component positions, joint states and foundation settlement boundary conditions in the preset large-section prefabricated assembled passage finite element model to obtain the current large-section prefabricated assembled passage refined finite element model. Then, perform component stress distribution analysis, joint force transmission performance analysis and structural linear stability analysis on the current large-section prefabricated assembled passage refined finite element model to obtain the mechanical influence score. The adaptive adjustment control process is triggered based on the deviation level and mechanical influence score.

[0016] The beneficial effects of this invention compared to existing technologies are as follows: By utilizing monitoring components deployed on prefabricated components to acquire multi-dimensional construction process monitoring data, comprehensive and accurate data support is provided for system analysis. Based on this data, a comprehensive deviation feature vector is generated, and its modulus is used to determine the deviation level, achieving a quantitative assessment of the degree of construction deviation. The component position, joint state, and foundation settlement boundary conditions of the finite element model are updated according to the comprehensive deviation feature vector to construct a refined model. Through analysis of component stress distribution, joint force transmission performance, and structural linear stability, a mechanical influence score is obtained, scientifically assessing the impact of deviations from a mechanical perspective. Then, based on the deviation level and mechanical influence score, an adaptive adjustment control process is triggered, intelligently taking adjustment measures for different situations, effectively ensuring construction accuracy and quality, and improving the level of construction intelligence and controllability.

[0017] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in this application.

[0018] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0019] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the intelligent monitoring and control system for the construction accuracy of large-section prefabricated assembled tunnels in an embodiment of the present invention; Figure 2 This is a flowchart illustrating the sub-modules of the multi-source data monitoring module in an embodiment of the present invention. Figure 3 This is a flowchart of a sub-module of the mechanical performance evaluation module in an embodiment of the present invention. Detailed Implementation

[0020] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0021] like Figure 1 As shown, this invention provides an implementation method for an intelligent monitoring and control system for the construction accuracy of large-section prefabricated modular tunnels, including: The multi-source data monitoring module is used to obtain multi-dimensional construction process monitoring data based on monitoring components deployed on prefabricated components of large-section prefabricated assembled channels. The data fusion and analysis module is used to generate a comprehensive deviation feature vector based on multi-dimensional construction process monitoring data, and to determine the deviation level based on the modulus of the comprehensive deviation feature vector; The mechanical performance evaluation module is used to update the component positions, joint states, and foundation settlement boundary conditions in the preset large-section prefabricated assembled passage finite element model based on the comprehensive deviation feature vector, obtain the current large-section prefabricated assembled passage refined finite element model, and perform component stress distribution analysis, joint force transmission performance analysis, and structural linear stability analysis on the current large-section prefabricated assembled passage refined finite element model to obtain the mechanical influence score; The adaptive adjustment control module is used to trigger the adaptive adjustment control process based on the deviation level and mechanical influence score.

[0022] In this embodiment, large-section prefabricated assembled passage refers to a prefabricated assembled passage with a cross-sectional size of ≥6×4.5m. During the construction of such passages, specific intelligent monitoring and control methods are used to ensure construction accuracy.

[0023] In this embodiment, prefabricated components are the constituent parts of the large-section prefabricated assembled passageway, including the top slab, side walls, and bottom slab. These components are prefabricated in the early stages of construction and then hoisted and assembled.

[0024] In this embodiment, the monitoring components are devices used to acquire various relevant data during construction, distributed across precast components and their corresponding locations. These include wireless strain sensors attached to the four corners and mid-span of the roof slab, dual-axis tilt sensors installed at the upper and lower ends of the outer sidewalls, GNSS displacement sensors deployed in the foundation of the passageway, and laser displacement sensors installed at component joints. Each component is responsible for collecting different types of data, such as strain data, component hoisting attitude angles, uneven foundation settlement, and lateral / longitudinal misalignment data at joints.

[0025] In this embodiment, the comprehensive deviation feature vector is formed by fusing preprocessed multi-source monitoring data into a unified feature vector via a data fusion analysis unit. Specifically, strain data is first converted into a strain deviation feature vector, attitude angle, joint displacement, settlement, and other data are integrated into a geometric deviation feature vector, and the three-dimensional deviation extracted from the visual point cloud is converted into a linear deviation feature vector. Then, a weighted fusion algorithm is used to calculate the comprehensive deviation feature vector, where the weights are determined by the analytic hierarchy process and are dynamically adjusted according to the construction stage. This vector can comprehensively reflect the construction deviation status; alternatively, the comprehensive deviation feature vector can be obtained by directly concatenating the strain deviation feature vector, geometric deviation feature vector, and linear deviation feature vector.

[0026] In this embodiment, the deviation level is determined by three levels based on the modulus of the comprehensive deviation feature vector, used to judge the degree of construction deviation. Level 1 warning indicates a minor deviation: the modulus of the comprehensive deviation feature vector is <5mm, requiring no adjustment. Level 2 warning indicates a moderate deviation: 5mm ≤ the modulus of the comprehensive deviation feature vector is <10mm, requiring manual verification. Level 3 warning indicates a severe deviation: when the modulus of the comprehensive deviation feature vector is ≥10mm, the adaptive control process is triggered. For example, if the calculated modulus of the comprehensive deviation feature vector is 3mm, it is determined to be a minor deviation.

[0027] In this embodiment, the pre-built finite element model of the large-section prefabricated assembled passageway is a pre-constructed model used to simulate the mechanical properties of the large-section prefabricated assembled passageway. Built using ABAQUS software, the model includes soil-structure interaction and has element sizes ≤100mm. This model provides the basic framework for analyzing the mechanical properties of the passageway, similar to a virtual passageway model built in a computer, allowing for various mechanical analyses.

[0028] In this embodiment, the component positions, joint states, and foundation settlement boundary conditions in the preset large-section prefabricated assembled passage finite element model are updated based on the comprehensive deviation feature vector to obtain the current refined finite element model of the large-section prefabricated assembled passage. The comprehensive deviation feature vector is used as input parameters to modify the component positions, joint states, and foundation settlement boundary conditions in the pre-set finite element model. For example, in actual construction, if a component position deviates, the comprehensive deviation feature vector is used to adjust the position of the component in the model, making the model more consistent with the actual construction situation, thus obtaining a refined finite element model that more accurately reflects the mechanical performance of the passage.

[0029] In this embodiment, the mechanical influence score is obtained by conducting component stress distribution analysis, joint force transmission performance analysis, and structural linear stability analysis on a refined finite element model of the current large-section prefabricated assembled passage. The specific calculation method is: Influence Score = (Stress Excess Coefficient × 0.4 + Force Insufficiency Coefficient × 0.3 + Linear Instability Coefficient × 0.3). This score is used to assess the degree of impact of construction deviations on the mechanical performance of the passage. A control command is triggered when the score is ≥ 0.6.

[0030] In this embodiment, the adaptive adjustment control process is triggered when the deviation level reaches a level three warning (severe deviation) or the mechanical impact score is ≥0.6. The adaptive control unit receives control commands from the mechanical performance evaluation unit and, in conjunction with the control parameters of construction equipment such as the truck crane and hydraulic jacks, generates corresponding adjustment strategies. For example, to address deviations in the lifting posture, a control signal is sent to the truck crane to adjust the boom angle; for misaligned joints, the hydraulic jacks are controlled to push and adjust, etc. These methods correct construction deviations and ensure construction accuracy.

[0031] like Figure 2 As shown, in order to provide comprehensive and accurate data for subsequent construction analysis, a multi-source data monitoring module is proposed, including: The strain monitoring submodule is used to collect strain data during the hoisting and assembly of prefabricated components based on multiple wireless strain sensors deployed at the four corners and mid-span of the top slab of the large-section prefabricated assembly channel. The attitude monitoring submodule is used to monitor the hoisting attitude angle of prefabricated components in real time based on a pair of dual-axis tilt sensors installed at the upper and lower ends of the sidewall of the large-section prefabricated assembly channel. The hoisting attitude angle of the prefabricated components includes pitch angle and yaw angle. The settlement monitoring submodule is used to monitor uneven settlement of the foundation in real time based on displacement sensors that are deployed longitudinally at preset intervals along the foundation of the large-section prefabricated assembled channel, and to generate a foundation settlement record sequence. The misalignment monitoring submodule is used to collect lateral misalignment data, longitudinal misalignment data, and joint gap of prefabricated component joints based on multiple laser displacement sensors installed at the joints of prefabricated components in large-section prefabricated assembled channels. The model generation submodule is used to capture continuous images of the prefabricated component hoisting process using multiple industrial-grade binocular vision cameras installed in the construction area and whose camera intrinsic and extrinsic parameters have been calibrated using the Zhang's calibration method, and to generate a 3D point cloud model of the component using a motion recovery structure algorithm. The multidimensional construction process monitoring data includes strain data during the hoisting and assembly of precast components, hoisting attitude angles of precast components, foundation settlement record sequences, lateral misalignment data of precast component joints, longitudinal misalignment data, joint gaps, and three-dimensional point cloud models of components.

[0032] In this embodiment, strain data during the hoisting and assembly of prefabricated components refers to the measured data corresponding to the relative deformation of the component's interior caused by external forces during the process from hoisting to completion of installation. This data is collected by wireless strain sensors attached to the four corners and mid-span of the top slab, with a measurement range of -2000 to 2000 με and an accuracy of ±1 με. For example, during the process of hoisting the top slab and installing it in the designated position, the sensors acquire real-time strain data of the top slab caused by gravity, hoisting operations, etc., helping to understand the stress and deformation of the component during this period.

[0033] In this embodiment, the prefabricated component hoisting attitude angles, including pitch and yaw angles, are used to describe the spatial attitude of the prefabricated component during hoisting. This is monitored in real time by installing two dual-axis tilt sensors at the upper and lower ends of the outer sidewall. For example, during the hoisting of the sidewall, the tilt sensors can promptly report whether the sidewall exhibits tilting, twisting, or other attitude changes during the hoisting process, ensuring that the component's hoisting attitude meets the installation requirements.

[0034] In this embodiment, uneven settlement of the foundation is monitored, and a foundation settlement record sequence is generated: GNSS displacement sensors, one deployed every 5m along the longitudinal direction of the foundation, are used to monitor the settlement at different locations of the foundation. Due to differences in geological conditions and other factors, the settlement at different points of the foundation may vary, i.e., uneven settlement. The sensors measure and record the data in real time, forming a foundation settlement record sequence in chronological order.

[0035] In this embodiment, the data on lateral misalignment, longitudinal misalignment, and joint gap of the prefabricated component joints are collected: During the splicing and installation of prefabricated components, lateral or longitudinal positional offsets and changes in joint width may occur at the joints. These data are collected by installing four laser displacement sensors at the component joints, with a measurement range of 0~500mm and an accuracy of ±0.1mm. For example, when splicing two prefabricated wall panels, the lateral and longitudinal offset distances of the joint and the size of the joint gap can be accurately determined to determine whether the installation meets the standards and ensure the quality of the splicing.

[0036] In this embodiment, the construction area refers to the site area for constructing the large-section prefabricated modular passageway. Within this area, work such as hoisting and assembling prefabricated components and deploying various monitoring equipment will be carried out.

[0037] In this embodiment, multiple industrial-grade binocular vision cameras are used, with their internal and external parameters calibrated using Zhang's calibration method. Multiple (e.g., two) industrial-grade binocular vision cameras are installed in the construction area, and Zhang's calibration method is used to determine the cameras' internal parameters (such as focal length and principal point position) and external parameters (such as the camera's position and orientation in the world coordinate system). After calibration, the cameras can accurately measure the position and orientation of objects in space. For example, the calibrated binocular vision cameras can accurately capture the spatial position and orientation information of prefabricated components during the hoisting process.

[0038] In this embodiment, continuous images of the prefabricated component hoisting process are captured, and a 3D point cloud model of the component is generated using the Structure from Motion (SfM) algorithm. An industrial-grade binocular vision camera captures continuous images of the prefabricated component hoisting process at a frequency of 1 image per 2 seconds. Based on these images, the SfM algorithm is used to recover 3D spatial information from the 2D image information, generating a 3D point cloud model of the component. This model consists of a large number of discrete points, describing the 3D shape and position of the component, and is used to analyze component hoisting attitude deviations. For example, by comparing the 3D point cloud model with the design model, the angular and positional differences of the component during the hoisting process can be calculated.

[0039] To facilitate a comprehensive reflection of construction deviations from different dimensions, a data fusion analysis module is proposed, including: The strain deviation analysis submodule is used to convert strain data from the precast component hoisting and assembly process in the multidimensional construction process monitoring data into strain deviation feature vectors. The geometric deviation analysis submodule is used to convert the precast component hoisting attitude angle, foundation settlement record sequence, lateral misalignment data of precast component joints, longitudinal misalignment data of precast component joints, and joint gap of precast component joints in the multidimensional construction process monitoring data into geometric deviation feature vectors. The linear deviation analysis submodule is used to downsample and register the three-dimensional point cloud model of the components in the multi-dimensional construction process monitoring data and extract the deviation value between the actual coordinates and the design coordinates of the precast components. Based on the deviation value between the actual coordinates and the design coordinates of the precast components, the planar linear deviation, longitudinal slope deviation and cross-sectional dimension deviation are determined, and the linear deviation feature vector is generated based on the planar linear deviation, longitudinal slope deviation and cross-sectional dimension deviation. The deviation feature fusion submodule is used to fuse the strain deviation feature vector, geometric deviation feature vector, and linear deviation feature vector to obtain a comprehensive deviation feature vector.

[0040] In this embodiment, strain data from the hoisting and assembly of precast components in the multi-dimensional construction process monitoring data are transformed into a strain deviation feature vector. During construction, strain data during hoisting and assembly is acquired by wireless strain sensors attached to the precast components (such as the four corners and mid-span of the top slab). These actually measured strain values ​​are subtracted from the designed expected strain values ​​to obtain the strain difference at each measurement point. These differences are arranged in a certain order to form a vector, namely the strain deviation feature vector. It reflects the deviation between the actual strain and the designed strain of the precast component. For example, if the actual strain value at a certain point is 500 με and the designed strain value is 400 με, then the difference in the vector corresponding to that point is 100 με.

[0041] In this embodiment, the precast component hoisting attitude angles, foundation settlement record sequences, lateral misalignment data of precast component joints, longitudinal misalignment data of precast component joints, and joint gaps of precast component joints from the multi-dimensional construction process monitoring data are transformed into a geometric deviation feature vector. First, the hoisting attitude angles of precast components (including pitch and yaw angles, monitored by a dual-axis tilt sensor), foundation settlement record sequences (generated by a GNSS displacement sensor), and lateral misalignment data, longitudinal misalignment data, and joint gaps at the precast component joints (collected by a laser displacement sensor) are collected. The actual geometric state information reflected by these data is compared with the corresponding geometric state specified in the design to obtain the attitude angle deviation, settlement difference, and lateral and longitudinal misalignment deviations and joint gap deviations at the joints. These deviation values ​​are integrated according to specific rules to form a vector, namely the geometric deviation feature vector, which comprehensively reflects the deviations between the geometric aspects and design requirements during construction. For example, if the design requires that the lateral misalignment of the joint not exceed 1mm, and the actual measurement is 3mm, then this deviation value will be reflected in the vector.

[0042] In this embodiment, the 3D point cloud model of the precast component in the multi-dimensional construction process monitoring data is downsampled and registered, and the deviation between the actual coordinates and design coordinates of the precast component is extracted. Images of the precast component hoisting process are captured using an industrial-grade binocular vision camera, and a motion recovery structure algorithm is used to generate the 3D point cloud model of the component. Downsampling refers to reducing the number of points in this point cloud model according to certain rules (such as selecting one point at certain intervals) while preserving as many of the model's main features as possible; the voxel size is set to 5mm. Registration is based on the designed 3D model, matching the generated 3D point cloud model with its spatial position and orientation so that the two can be compared in the same coordinate system. After registration, the coordinates of each point in the actual 3D point cloud model (i.e., the actual coordinates of the precast component) are compared with the coordinates of the corresponding point in the design model (i.e., the design coordinates), and the coordinate differences in the X, Y, and Z directions are calculated. These differences are the deviation between the actual coordinates and the design coordinates of the precast component. For example, if the actual coordinates of a point in the model are (100, 200, 300) and the design coordinates are (98, 202, 301), then the deviation values ​​are 2 in the X direction, -2 in the Y direction, and -1 in the Z direction.

[0043] In this embodiment, design coordinates refer to the ideal spatial coordinate values ​​set for each point of the prefabricated component during the design phase of the large-section prefabricated assembly channel, based on engineering planning and design requirements. These coordinates determine the precise position and shape of the prefabricated component within the overall channel structure and serve as a standard for measuring the accuracy of the actual component's position during construction. For example, when designing a prefabricated wall panel, the coordinates of a certain corner point of the wall panel in the entire channel coordinate system are specified as (X=5, Y=10, Z=2). These (X=5, Y=10, Z=2) are the design coordinates of that point.

[0044] In this embodiment, the planar alignment deviation, longitudinal slope deviation, and cross-sectional dimension deviation are determined based on the deviation values ​​between the actual coordinates and design coordinates of the precast components. According to the extracted deviation values ​​between the actual and design coordinates of the precast components, the difference in alignment between the actual and design positions of the components in the horizontal plane (XY plane) is analyzed to obtain the planar alignment deviation, which reflects whether the component deviates from the designed alignment trajectory in the horizontal direction. For example, in the design, a beam should be arranged in a straight line on the horizontal plane. However, due to installation deviations, its alignment becomes curved. This degree of curvature can be calculated using the coordinate deviation, which is the planar alignment deviation. The longitudinal slope deviation refers to the difference between the actual slope and the design slope in the longitudinal direction of the passage (generally set as the X direction), determined by the change in coordinate deviation in the longitudinal direction. For example, if the designed longitudinal slope of the passage is 2%, but due to factors such as foundation settlement, the actual slope is calculated to be 2.5%, then the longitudinal slope deviation is 0.5%. The cross-sectional dimension deviation refers to the difference between the actual cross-sectional dimensions (such as width, height, etc.) of the precast component and the designed cross-sectional dimensions, calculated using the corresponding coordinate deviation. For example, if the designed width of a precast column is 500mm, but the actual measurement and coordinate deviation calculation show that the width is 505mm, then the deviation of the cross-sectional dimension is 5mm.

[0045] In this embodiment, a linear deviation feature vector is generated based on planar alignment deviation, longitudinal slope deviation, and cross-sectional dimension deviation. The determined planar alignment deviation, longitudinal slope deviation, and cross-sectional dimension deviation are combined in a specific order to form a vector, namely the linear deviation feature vector. This vector comprehensively reflects the deviations of the precast component from the design alignment requirements in terms of shape and position. For example, a planar alignment deviation of 3mm, a longitudinal slope deviation of 0.3%, a cross-sectional width deviation of 4mm, and a height deviation of -2mm are all arranged in a prescribed order to form a vector.

[0046] In this embodiment, the strain deviation feature vector, geometric deviation feature vector, and linear deviation feature vector are fused to obtain a comprehensive deviation feature vector. A weighted fusion algorithm is used to fuse the strain deviation feature vector, geometric deviation feature vector, and linear deviation feature vector. The weights are determined using the analytic hierarchy process (AHP), with initial values ​​of 0.3, 0.4, and 0.3, and the weights are dynamically adjusted during the construction phase. This comprehensive deviation feature vector V comprehensively reflects the deviations from design requirements in multiple aspects during construction.

[0047] To achieve quantitative grading of construction deviations and provide a clear basis for subsequent decision-making, a data fusion analysis module is proposed, which also includes: The multi-level threshold setting submodule is used to import component design dimensions, joint allowable deviations, linear design parameters, and foundation allowable settlement as construction design parameters, and set multi-level deviation warning thresholds based on the construction design parameters. The deviation level determination submodule is used to determine the deviation level based on the magnitude of the comprehensive deviation feature vector and the multi-level deviation threshold.

[0048] In this embodiment, component design dimensions refer to the specific size and specifications planned for each prefabricated component (such as the roof slab, side walls, and floor slab) in the design scheme of the large-section prefabricated assembly channel, including the values ​​of dimensions such as length, width, and wall thickness. These dimensions are important bases for the construction and fabrication of prefabricated components and subsequent installation, and must be strictly controlled according to the design requirements. For example, if the design specifies that the roof slab is 10 meters long, 5 meters wide, and 0.3 meters thick, these are the component design dimensions of the roof slab.

[0049] In this embodiment, the allowable deviation for joints refers to the acceptable range of positional deviations at the joints between prefabricated components when designing large-section prefabricated assembly channels. Specifically, the allowable deviation for transverse joints is no more than 5mm, and the allowable deviation for longitudinal joints is no more than 3mm. This standard ensures the overall quality and stability of the assembled prefabricated components. If the joint deviation exceeds the allowable range, it may affect the structural performance and appearance of the channel. For example, when assembling two prefabricated wall panels, a transverse joint deviation within 5mm and a longitudinal joint deviation within 3mm meet the design requirements.

[0050] In this embodiment, the alignment design parameters mainly relate to the design specifications for the horizontal alignment and longitudinal slope of large-section prefabricated assembled tunnels, specifically including the horizontal alignment radius and longitudinal slope. The horizontal alignment radius determines the degree of curvature of the tunnel on the horizontal plane, while the longitudinal slope represents the degree of inclination of the tunnel in the longitudinal direction. These parameters have a significant impact on the tunnel's drainage, traffic safety, and overall structural stress. For example, the design specifications for a certain section of the tunnel may specify a horizontal alignment radius of 500 meters and a longitudinal slope of 2%, which constitutes the alignment design parameters for that section of the tunnel.

[0051] In this embodiment, the allowable foundation settlement is the upper limit set for foundation settlement in the design of large-section prefabricated modular tunnels, taking into account factors such as geological conditions. It is specified that uneven foundation settlement cannot exceed 10mm per 10m. Controlling the allowable foundation settlement ensures the stability and safety of the tunnel structure, preventing problems such as tunnel deformation and cracking due to excessive foundation settlement. For example, if the foundation settlement is less than or equal to 10mm over a 10m length, the design requirements are met.

[0052] In this embodiment, multi-level deviation warning thresholds are set based on construction design parameters: different levels of deviation warning limits are established according to construction design parameters such as component design dimensions, allowable joint deviations, alignment design parameters, and allowable foundation settlement. These thresholds serve as the basis for determining whether deviations during construction are within a reasonable range. For example, combining the above parameters, when the deviation reflected by the comprehensive deviation feature vector approaches or exceeds the allowable range of these design parameters, different levels of warning thresholds are set accordingly to promptly detect potential problems during construction.

[0053] In this embodiment, the deviation level is determined based on the modulus of the comprehensive deviation feature vector and multi-level deviation thresholds: the modulus of the comprehensive deviation feature vector, obtained by fusing strain deviation feature vectors, geometric deviation feature vectors, and linear deviation feature vectors, is calculated. This modulus is then compared with multi-level deviation thresholds set based on construction design parameters to determine the level of the construction deviation. When the modulus of the comprehensive deviation feature vector is < 5mm, it is classified as a Level 1 warning (minor deviation), indicating a small construction deviation that requires no adjustment; when 5mm ≤ modulus of the comprehensive deviation feature vector < 10mm, it is classified as a Level 2 warning (moderate deviation), requiring manual review to further confirm the deviation; when the modulus of the comprehensive deviation feature vector is ≥ 10mm, it is classified as a Level 3 warning (serious deviation), requiring the triggering of an adaptive control process to correct the deviation and ensure that the construction accuracy meets design requirements.

[0054] like Figure 3 As shown, in order to scientifically evaluate the impact of deviations on the channel structure from a mechanical perspective, a mechanical performance evaluation module is further proposed, including: The model update submodule is used to update the component positions, joint states, and foundation settlement boundary conditions in the preset large-section prefabricated assembled passage finite element model based on the comprehensive deviation feature vector, so as to obtain the current refined finite element model of the large-section prefabricated assembled passage. The performance analysis submodule is used to perform component stress distribution analysis, joint force transmission performance analysis, and structural linear stability analysis on the current large-section prefabricated assembled channel refined finite element model, and obtain stress over-limit coefficient, force under-transmission coefficient, and linear instability coefficient. The comprehensive scoring submodule is used to determine the mechanical influence score based on the stress over-limit coefficient, the force transmission insufficiency coefficient, and the linear instability coefficient.

[0055] In this embodiment, the mechanical influence score is determined based on the stress excess coefficient, the force transmission deficiency coefficient, and the linear instability coefficient. These coefficients are calculated according to specific weights, i.e., mechanical influence score = (stress excess coefficient × 0.4 + force transmission deficiency coefficient × 0.3 + linear instability coefficient × 0.3). When it is ≥ 0.6, a control command will be triggered so that corresponding measures can be taken to adjust the construction deviation and ensure that the mechanical performance of the channel meets the design requirements.

[0056] To accurately quantify the impact of deviations on the channel's mechanical properties, a performance analysis submodule is proposed, including: The stress exceeding the standard area calibration unit is used to perform component stress distribution analysis on the current large-section prefabricated assembled channel refined finite element model and mark all stress exceeding the standard areas; The critical stress area calibration unit is used to calibrate all critical stress areas based on the current refined finite element model of large-section prefabricated assembled channels. The insufficient force transmission joint screening unit is used to analyze the joint force transmission performance of the current large-section prefabricated assembled channel refined finite element model, obtain the actual force transmission value of the shear key at the joint of the large-section prefabricated assembled channel, and calculate the ratio of the actual force transmission value of the shear key at the joint of the large-section prefabricated assembled channel to the design value, which is used as the basis value for judging insufficient force transmission at the corresponding joint. Based on the basis value for judging insufficient force transmission at each joint and the preset threshold value for judging insufficient force transmission, the unit screens out the joints with insufficient force transmission from all joints. The unstable linear section screening unit is used to perform structural linear stability analysis on the current large-section prefabricated assembled channel refined finite element model to obtain the actual linear shape of each segmented linear section. The deviation curve between the actual linear shape and the design linear shape is fitted by cubic spline interpolation algorithm. Based on the first derivative of the deviation curve of each segmented linear section and the preset rate of change threshold, the unstable linear section is screened out from all segmented linear sections. The mechanical performance coefficient determination unit is used to determine the stress excess coefficient, the force deficiency coefficient, and the linear instability coefficient based on the stress excess area and critical stress area of ​​all precast components, the total number of all insufficient force transmission joints, and the segment length of all unstable linear intervals.

[0057] In this embodiment, the area with excessive stress is defined as follows: When performing mechanical performance analysis on a large-section prefabricated modular tunnel, the maximum principal stress is extracted from locations such as the mid-span of the top slab and the connection between the side walls and the bottom slab. If this stress value exceeds the axial tensile strength of C50 concrete (2.64 MPa), then the areas where these stresses exceed the standard value are defined as areas with excessive stress. For example, when performing stress calculations on a certain part of the tunnel, if the principal stress in a certain area of ​​the mid-span of the top slab reaches 2.8 MPa, exceeding the axial tensile strength of C50 concrete, this area is considered an area with excessive stress. This means that the concrete in this area may be bearing significant tensile force, posing a potential risk of damage.

[0058] In this embodiment, the critical stress areas refer to specific locations in a large-section prefabricated modular tunnel that play a crucial supporting and force-transferring role in the overall structural mechanical performance. These mainly include the mid-span of the top slab and the connection points between the side walls and the bottom slab. These areas bear significant stress when the tunnel is subjected to various loads, and their mechanical properties directly affect the stability and safety of the tunnel. For example, during tunnel use, loads such as vehicle traffic can cause significant pressure on the mid-span area of ​​the top slab, which is then transferred to the foundation through the connection points between the side walls and the bottom slab. Therefore, these areas are considered critical stress areas and require close attention during mechanical analysis and monitoring.

[0059] In this embodiment, the joint force transmission performance of the current large-section prefabricated assembled passageway is analyzed using a refined finite element model. The actual force transmission value of the shear keys at the joints of the large-section prefabricated assembled passageway is obtained, and the ratio of the actual force transmission value to the design value is calculated. This ratio serves as the criterion for determining insufficient force transmission at the corresponding joint. Based on the criterion for insufficient force transmission at each joint and a preset threshold for insufficient force transmission, joints with insufficient force transmission are selected from all joints. When simulating the mechanical performance of the passageway using the current refined finite element model, the shear keys at the joints are specifically analyzed. The actual force that the shear keys at each joint can transmit is calculated using the model, i.e., the actual force transmission value. This actual force transmission value is divided by the force that the shear keys should transmit (design value) set during the design phase, resulting in a ratio. This ratio is the criterion for determining insufficient force transmission at the corresponding joint. Then, the judgment criterion value obtained at each joint is compared with the preset insufficient force transmission judgment threshold. If the judgment criterion value at a certain joint is less than the preset insufficient force transmission judgment threshold, the joint is considered to have insufficient force transmission and is filtered out from all joints. For example, if the actual force transmission value of the shear key at a certain joint is 8kN and the design value is 10kN, then the insufficient force transmission judgment criterion value is 0.8. If the preset insufficient force transmission judgment threshold is 0.85, since 0.8 < 0.85, the joint is judged as an insufficient force transmission joint.

[0060] In this embodiment, the preset threshold for determining insufficient force transmission is a standard value pre-set during the design phase of the large-section prefabricated assembly channel, based on factors such as the channel's usage requirements, structural characteristics, and relevant engineering standards, to determine whether the shear key at the joint has insufficient force transmission. When the calculated ratio of the actual force transmission value of the shear key at the joint to the design value is less than this preset threshold, the joint is considered to have insufficient force transmission.

[0061] In this embodiment, a structural alignment stability analysis is performed on the refined finite element model of the current large-section prefabricated assembled passage to obtain the actual alignment of each segmented alignment interval. A cubic spline interpolation algorithm is used to fit the deviation curve between the actual and designed alignment. Based on the first derivative of the deviation curve of each segmented alignment interval and a preset rate of change threshold, unstable alignment intervals are selected from all segmented alignment intervals. The structural alignment stability of the passage is analyzed using the refined finite element model of the current large-section prefabricated assembled passage. The passage is longitudinally divided into multiple equal-length segmented alignment intervals (each segment length...). =5m), the actual alignment of each segmented alignment interval is calculated using the model. Then, using a cubic spline interpolation algorithm, with the designed alignment as a reference, the deviation between the actual alignment of each segment and the designed alignment is mathematically fitted to form a deviation curve. The first derivative of the deviation curve reflects the rate of change of the segmented alignment. The first derivative of the deviation curve of each segmented alignment interval is compared with a preset rate of change threshold. If the first derivative of the deviation curve of a certain segmented alignment interval is greater than the preset rate of change threshold, the segmented alignment interval is determined to be an unstable alignment interval, that is, the structural alignment stability of this interval may have problems. For example, if the calculated value of the first derivative of the deviation curve of a certain segmented alignment interval is 0.006 / m, and the preset rate of change threshold is 0.005 / m, since 0.006 > 0.005, this segmented alignment interval is determined to be an unstable alignment interval.

[0062] In this embodiment, the preset rate of change threshold is a standard value set in advance during the design process of large-section prefabricated assembled tunnels, based on the tunnel's expected function, structural safety requirements, and relevant industry standards, to measure the structural linear stability. When analyzing the structural linear stability of the tunnel, the first derivative of the deviation curve between the actual linear shape fitted to each segment and the designed linear shape is compared with this threshold. If the first derivative of the deviation curve is greater than the preset rate of change threshold, it indicates that the segment's linear shape changes too rapidly, potentially affecting the tunnel's structural stability, and thus the segment is determined to be an unstable linear shape segment.

[0063] To accurately determine the stress excess coefficient, force insufficiency coefficient, and linear instability coefficient, a mechanical performance coefficient determination unit is proposed, including: The stress exceedance coefficient determination sub-unit is used as the ratio of the total projected area of ​​the stress exceedance area of ​​all precast components to the total projected area of ​​the critical stress area of ​​all precast components as the stress exceedance coefficient. The force transmission deficiency coefficient determination sub-unit is used as the ratio of the total number of all force transmission deficiency joints to the total number of joints in the large-section prefabricated assembled channel as the force transmission deficiency coefficient. The linear instability coefficient determination sub-unit is used as the ratio of the segment length of all instable linear intervals to the total length of the channel in the current construction section as the linear instability coefficient.

[0064] In this embodiment, the total projected area of ​​the stress-exceeding regions of the precast components is determined as follows: In the construction of large-section precast assembled passageways, stress-exceeding regions are identified through mechanical performance analysis of the precast components. These stress-exceeding regions are projected onto a specific plane (such as the horizontal XY plane for the top and bottom slabs; or the vertical XZ plane for the side walls), and the sum of the areas of all projected regions is calculated. This sum is the total projected area of ​​the stress-exceeding regions of the precast components. For example, if several stress-exceeding regions are identified on the top slab, after projecting them onto the XY plane, geometric calculations show that the sum of the areas of these projected regions is 5 square meters. This 5 square meters is the total projected area of ​​the stress-exceeding regions of the top slab.

[0065] In this embodiment, the total projected area of ​​the critical stress-bearing areas of the precast component is calculated. These critical stress-bearing areas mainly include the mid-span of the top slab and the connection points between the side walls and the bottom slab. These critical stress-bearing areas are also projected onto their respective planes, and the sum of the areas of all projected areas is obtained as the total projected area of ​​the critical stress-bearing areas of the precast component. For example, projecting critical stress-bearing areas such as the connection points between the side walls and the bottom slab onto a suitable plane yields a total projected area of ​​8 square meters. This area reflects the distribution range of the critical stress-bearing areas on the plane, which is of great significance for analyzing the overall stress condition of the precast component and also serves as important reference data when calculating parameters such as the stress exceedance coefficient.

[0066] In this embodiment, the segment length of the unstable linear section is as follows: When analyzing the linear stability of a large-section prefabricated tunnel structure, the tunnel is longitudinally divided into several equally long segmented linear sections. When the first derivative of the deviation curve between the actual linear shape and the design linear shape of a certain segmented linear section is greater than a preset rate of change threshold, the segment is determined to be an unstable linear section, and this 5m is the segment length of the unstable linear section. If a certain unstable linear section is exactly a complete segment, its segment length is 5m; if the unstable linear section crosses some other segments, its specific length needs to be determined by calculation based on the actual situation. For example, if the calculation shows that a certain unstable linear section crosses from 2m in the 3rd segment to 3m in the 4th segment, then the segment length of the unstable linear section is 5m (because each segment is 5m long).

[0067] In this embodiment, the total length of the passage in the current construction section refers to the overall length of the passage involved in the specific stage of construction during the construction of a large-section prefabricated assembled passage. For example, if the construction range extends from 0m to 300m from the starting point of the passage in a certain construction stage, then the total length of the passage in the current construction section is 300m.

[0068] To accurately calibrate areas where stress exceeds the limit, a calibration unit for stress exceeding the limit is proposed, including: The component calibration sub-unit is used to map the current large-section prefabricated assembled channel refined finite element model to a three-dimensional rectangular coordinate system and extract the three-dimensional coordinate range of all key stress-bearing components. Mesh subdivision unit is used to mesh all key load-bearing components based on the three-dimensional coordinate range of all key load-bearing components, and obtain the three-dimensional coordinate range of all sub-mesh spatial regions of each key load-bearing component; The stress analysis sub-element is used to construct a stress calculation matrix based on the material constitutive parameters of the current refined finite element model of large-section prefabricated assembled channels. Based on the stress calculation matrix, the stress balance equation of each sub-mesh spatial region of each key stress-bearing component is constructed. The stress balance equation is solved by Gaussian elimination to obtain the displacement vector output value. The strain tensor of each sub-mesh spatial region is calculated by combining the geometric equations. Finally, the stress tensor of each sub-mesh spatial region is determined by combining the physical equations. The cloud map generation sub-element is used to perform maximum principal stress calculation on the stress tensor of all sub-mesh spatial regions of each critical stress component, and generate stress cloud maps for each critical stress component; The coordinate filtering sub-unit is used to determine all stress-exceeding coordinates and construct a set of stress-exceeding coordinates based on the stress cloud map of each key stress-bearing component. The region calibration sub-unit is used to cluster the stress-exceeding coordinate set using the DBSCAN clustering algorithm to obtain multiple stress-exceeding clusters. The minimum bounding box of each stress-exceeding cluster is calculated, and the three-dimensional spatial region corresponding to each minimum bounding box is regarded as all stress-exceeding regions.

[0069] In this embodiment, the refined finite element model of the current large-section prefabricated assembled passageway is mapped to a three-dimensional Cartesian coordinate system, and the three-dimensional coordinate range of all key load-bearing components is extracted. To more accurately analyze the mechanical properties of the key load-bearing components of the large-section prefabricated assembled passageway, the refined finite element model updated based on actual construction deviations is placed in a three-dimensional Cartesian coordinate system. This coordinate system acts like a spatial frame, with the X-axis representing the longitudinal direction of the passageway, the Y-axis representing the transverse direction, and the Z-axis representing the vertical direction. Then, the position range of all key load-bearing components (such as the top slab, side walls, and bottom slab) in the model is determined in this three-dimensional coordinate system. For example, the three-dimensional coordinate range of the top slab may be from (X1, Y1, Z1) to (X2, Y2, Z2), and similarly for components such as the side walls and bottom slab. This clearly defines the position and size of each key load-bearing component in space.

[0070] In this embodiment, all key load-bearing components are meshed based on their three-dimensional coordinate ranges to obtain the three-dimensional coordinate ranges of all sub-mesh spatial regions for each key load-bearing component. After clarifying the three-dimensional coordinate ranges of the key load-bearing components, these components are meshed to study their internal mechanical properties in greater detail. This is analogous to cutting a large object into many small, regular blocks. For key load-bearing components such as the top plate, side walls, and bottom plate, they are divided into numerous sub-mesh spatial regions according to certain rules (e.g., the top and bottom plates are divided into hexahedral elements of 50mm×50mm×20mm, and the side walls into 50mm×20mm×50mm). Each sub-mesh spatial region has its corresponding three-dimensional coordinate range; for example, the coordinate range of a certain sub-mesh region in the coordinate system is from (Xa,Ya,Za) to (Xb,Yb,Zb). In this way, the mechanical analysis of the entire key load-bearing component is refined to each sub-mesh spatial region, enabling subsequent accurate calculation of mechanical parameters such as stress.

[0071] In this embodiment, a stress calculation matrix is ​​constructed based on the material constitutive parameters of the current refined finite element model of large-section prefabricated assembled passageways. Based on this matrix, stress equilibrium equations are built for each sub-grid spatial region of each key load-bearing component. The stress equilibrium equations are solved using Gaussian elimination to obtain displacement vector output values. The strain tensor of each sub-grid spatial region is calculated using geometric equations, and the stress tensor of each sub-grid spatial region is determined using physical equations. The stress calculation matrix is ​​established based on the characteristics of the materials used in the prefabricated components in the refined finite element model, such as the elastic modulus of C50 concrete (34.5 GPa) and Poisson's ratio (0.2). The stress calculation matrix is ​​closely related to the constitutive relationship of the material. For linear elastic materials, such as C50 concrete, the stress-strain relationship can be described by Hooke's law. In three-dimensional space, the stress calculation matrix D (also called the elasticity matrix) is related to the elastic modulus E and Poisson's ratio ν.

[0072] Example: For isotropic materials, the formula for calculating their elasticity matrix is: In other cases, the elastic modulus of C50 concrete is known. Poisson's ratio .

[0073] This matrix contains information related to materials and structure, and is crucial for subsequent calculations.

[0074] For each sub-mesh region of each critical load-bearing component, a stress balance equation is established based on the stress calculation matrix. In finite element analysis, each sub-mesh region must satisfy the force equilibrium condition. For a three-dimensional sub-mesh, the stress balance equation is: ,in It is the element stiffness matrix (related to the stress calculation matrix). It is the element displacement vector. This is the element's external force vector, including its own weight, lifting load, and backfill pressure. It is assumed here that the element stiffness matrix has already been determined based on the stress calculation matrix, etc.

[0075] Assume a subgrid space region, subject to the weight of the top plate. (Along the negative Z-axis direction) Lifting horizontal tension (Along the positive X-axis) Earth filling pressure (Along the negative Y-axis). Let the element stiffness matrix be... middle, ,other .

[0076] The stress balance equation is: in, , , These are displacement vectors, representing the displacements of the subgrid in the X, Y, and Z directions, respectively.

[0077] This equation describes the force balance relationship within the subgrid space region.

[0078] Then, the stress balance equation is solved using the Gaussian elimination method, yielding the displacement vector output value for each subgrid spatial region. The Gaussian elimination method transforms the augmented matrix of the linear equation system into a row echelon form matrix through elementary row operations, and then substitutes this matrix back to solve for the unknowns. This represents the displacement of the subgrid under stress.

[0079] Next, using geometric equations and displacement vectors, the strain tensor of each sub-mesh spatial region is calculated. The strain tensor reflects the degree of deformation of the sub-mesh. The geometric equations describe the relationship between displacement and strain. In three-dimensional space, the strain tensor... With displacement vector The relationship is , It is a geometric matrix related to the shape and size of the sub-mesh. The following example illustrates this using a sub-mesh in the XY plane under plane stress.

[0080] Finally, based on the physical equations and the strain tensor, the stress tensor of each subgrid spatial region is determined. The physical equations relate the strain tensor to the stress tensor. For linear elastic materials, the stress tensor... , This is the stress calculation matrix established earlier.

[0081] The stress tensor reflects the stress conditions experienced by the subgrid, thus providing a comprehensive understanding of the mechanical state of each subgrid spatial region.

[0082] In this embodiment, the maximum principal stress is calculated for the stress tensors of all sub-mesh spatial regions of each critical load-bearing component, generating a stress cloud map for each critical load-bearing component. After obtaining the stress tensors of all sub-mesh spatial regions of each critical load-bearing component, the maximum principal stress of each sub-mesh spatial region is calculated. The principal stress refers to the normal stress in a specific direction at a point; the maximum principal stress reflects the maximum value of the normal stress at that point in all directions. In three-dimensional space, the stress state of each sub-mesh spatial region can be described by a stress tensor, which reflects the stress situation in different directions of that region. To calculate the maximum principal stress, it is first necessary to know that the stress tensor contains information about stresses in different directions, such as the normal stresses along the three coordinate axes and the shear stresses between the coordinate axes.

[0083] During calculation, we need to find a direction where the normal stress reaches its maximum value; this maximum value is the maximum principal stress. First, we integrate and analyze all the stress information represented by the stress tensor. For example, in a simple case, we know the magnitudes of the normal stresses in the three coordinate axes of the submesh are A, B, and C, as well as the magnitudes of the shear stresses between the coordinate axes. Through a series of analyses (similar to considering the superposition effect of forces in different directions), we continuously try different directions to calculate the magnitudes of the normal stresses in these directions. After comparing the normal stresses in all directions, we finally determine the largest value; this value is the maximum principal stress of the submesh spatial region.

[0084] By processing the maximum principal stresses in all sub-grid spatial regions, a stress contour map is generated. The stress contour map is an intuitive visualization that uses different colors to represent different magnitudes of maximum principal stresses. For example, blue may represent lower stress values, and red may represent higher stress values. This allows for a clear view of the stress distribution within each critical load-bearing component, quickly identifying areas of stress concentration or high stress levels.

[0085] In this embodiment, based on the stress cloud map of each key load-bearing component, all stress exceeding coordinates are determined and a stress exceeding coordinate set is constructed: Observe the stress cloud map of each key load-bearing component and find the points with stress values ​​greater than 2.64 MPa; these points are the stress exceeding points. Determine the coordinates of these stress exceeding points in a three-dimensional Cartesian coordinate system, and collect the coordinates of all stress exceeding points to construct a set, namely the stress exceeding coordinate set.

[0086] In this embodiment, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm is used to cluster the stress-exceeding coordinate set, obtaining multiple stress-exceeding clusters. The minimum bounding box of each stress-exceeding cluster is calculated, and the three-dimensional spatial region corresponding to each minimum bounding box is considered as all stress-exceeding regions. The DBSCAN clustering algorithm is a density-based clustering algorithm that can group points with similar densities in the stress-exceeding coordinate set into the same cluster based on the density distribution of data points, thus obtaining multiple stress-exceeding clusters. For each stress-exceeding cluster, the minimum bounding box that completely contains all points within the cluster is calculated. This minimum bounding box has a specific range in three-dimensional space. The three-dimensional spatial region corresponding to each minimum bounding box is identified as a stress-exceeding region because the concentration of stress-exceeding points within these regions indicates relatively high stress levels, exceeding the normal range. This method can accurately identify areas in large-section prefabricated assembled channels that may have stress problems, providing a basis for subsequent appropriate measures.

[0087] To enable intelligent adjustments to construction based on construction deviations and mechanical effects, an adaptive adjustment control module is proposed, including: The adjustment trigger judgment submodule is used to determine whether to trigger the adaptive adjustment control process based on the deviation level and mechanical influence score. The adjustment strategy generation submodule is used to generate an adjustment strategy based on the control parameters of the construction equipment if the adaptive adjustment control process is triggered.

[0088] In this embodiment, the adaptive adjustment control process is triggered based on the deviation level and mechanical impact score. During the construction of large-section prefabricated modular tunnels, the deviation level is determined based on the magnitude of the comprehensive deviation characteristic vector, with three levels: Level 1 (minor deviation), Level 2 (moderate deviation), and Level 3 (serious deviation). The mechanical impact score is calculated by assigning specific weights to the stress exceedance coefficient, the force transmission deficiency coefficient, and the linear instability coefficient. When the deviation level reaches Level 3 (i.e., the magnitude of the comprehensive deviation characteristic vector ||V|| ≥ 10 mm), or the mechanical impact score ≥ 0.6, it indicates that the construction deviation has a significant impact on the accuracy and mechanical performance of the tunnel. In this case, the adaptive adjustment control process needs to be triggered to take measures to correct the deviation and ensure construction quality. For example, if the magnitude of the comprehensive deviation characteristic vector is calculated to be 12 mm, or the mechanical impact score is 0.7, the triggering condition is met, and the corresponding process should be initiated.

[0089] In this embodiment, if the adaptive adjustment control process is triggered, an adjustment strategy is generated based on the control parameters of the construction equipment. Once it is determined that the adaptive adjustment control process will be triggered, an adjustment strategy needs to be formulated based on the control parameters of the equipment used during construction. The construction equipment includes truck cranes, hydraulic jacks, and electric lifting supports. For example, for truck cranes, the control parameters include boom luffing angle and slewing angle; for hydraulic jacks, the control parameters include jacking force and jacking rate; and for electric lifting supports, the control parameters include height adjustment. Taking lifting posture adjustment as an example, if the dual-axis tilt sensor detects a posture angle deviation, the control parameters of the truck crane's luffing and slewing mechanisms are combined with the PID algorithm (proportional coefficient Kp=2.5, integral coefficient Ki=0.1, derivative coefficient Kd=0.05) to calculate the boom angle that needs to be adjusted, and a control signal is sent to the crane to bring the posture angle deviation back to within 0.2°. For example, when correcting joint misalignment, if the laser displacement sensor detects that the lateral or longitudinal misalignment exceeds the allowable deviation, the jacking force is dynamically adjusted based on the control parameters such as the jacking force and jacking rate of the hydraulic jack, combined with the real-time strain data from the wireless strain sensor. This controls the jacking force to make adjustments, ensuring that the joint misalignment meets the requirements and the strain does not exceed 1500με. By combining the equipment control parameters in this way, a specific adjustment strategy is generated to correct the construction deviation.

[0090] This invention provides an implementation method for intelligent monitoring and control of construction accuracy in large-section prefabricated modular tunnels, comprising: Based on monitoring components deployed on prefabricated components of large-section prefabricated assembled passages, multi-dimensional construction process monitoring data is obtained. A comprehensive deviation feature vector is generated based on multi-dimensional construction process monitoring data, and the deviation level is determined based on the modulus of the comprehensive deviation feature vector. Based on the comprehensive deviation feature vector, update the component positions, joint states and foundation settlement boundary conditions in the preset large-section prefabricated assembled passage finite element model to obtain the current large-section prefabricated assembled passage refined finite element model. Then, perform component stress distribution analysis, joint force transmission performance analysis and structural linear stability analysis on the current large-section prefabricated assembled passage refined finite element model to obtain the mechanical influence score. The adaptive adjustment control process is triggered based on the deviation level and mechanical influence score.

[0091] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of this invention and its equivalents, this invention also intends to include these modifications and variations.

Claims

1. A smart monitoring and control system for the construction accuracy of large-section prefabricated modular tunnels, characterized in that: include: The multi-source data monitoring module is used to obtain multi-dimensional construction process monitoring data based on monitoring components deployed on prefabricated components of large-section prefabricated assembled channels. The data fusion and analysis module is used to generate a comprehensive deviation feature vector based on multi-dimensional construction process monitoring data, and to determine the deviation level based on the modulus of the comprehensive deviation feature vector; The mechanical performance evaluation module is used to update the component positions, joint states, and foundation settlement boundary conditions in the preset large-section prefabricated assembled passage finite element model based on the comprehensive deviation feature vector, obtain the current large-section prefabricated assembled passage refined finite element model, and perform component stress distribution analysis, joint force transmission performance analysis, and structural linear stability analysis on the current large-section prefabricated assembled passage refined finite element model to obtain the mechanical influence score; The adaptive adjustment control module is used to trigger the adaptive adjustment control process based on the deviation level and mechanical influence score.

2. The intelligent monitoring and control system for construction accuracy of large-section prefabricated modular tunnels according to claim 1, characterized in that, The multi-source data monitoring module includes: The strain monitoring submodule is used to collect strain data during the hoisting and assembly of prefabricated components based on multiple wireless strain sensors deployed at the four corners and mid-span of the top slab of the large-section prefabricated assembly channel. The attitude monitoring submodule is used to monitor the hoisting attitude angle of prefabricated components in real time based on a pair of dual-axis tilt sensors installed at the upper and lower ends of the sidewall of the large-section prefabricated assembly channel. The hoisting attitude angle of the prefabricated components includes pitch angle and yaw angle. The settlement monitoring submodule is used to monitor uneven settlement of the foundation in real time based on displacement sensors that are deployed longitudinally at preset intervals along the foundation of the large-section prefabricated assembled channel, and to generate a foundation settlement record sequence. The misalignment monitoring submodule is used to collect lateral misalignment data, longitudinal misalignment data, and joint gap of prefabricated component joints based on multiple laser displacement sensors installed at the joints of prefabricated components in large-section prefabricated assembled channels. The model generation submodule is used to capture continuous images of the prefabricated component hoisting process using multiple industrial-grade binocular vision cameras installed in the construction area and whose camera intrinsic and extrinsic parameters have been calibrated using the Zhang's calibration method, and to generate a 3D point cloud model of the component using a motion recovery structure algorithm. The multidimensional construction process monitoring data includes strain data during the hoisting and assembly of precast components, hoisting attitude angles of precast components, foundation settlement record sequences, lateral misalignment data of precast component joints, longitudinal misalignment data, joint gaps, and three-dimensional point cloud models of components.

3. The intelligent monitoring and control system for construction accuracy of large-section prefabricated modular tunnels according to claim 1, characterized in that, The data fusion and analysis module includes: The strain deviation analysis submodule is used to convert strain data from the precast component hoisting and assembly process in the multidimensional construction process monitoring data into strain deviation feature vectors. The geometric deviation analysis submodule is used to convert the precast component hoisting attitude angle, foundation settlement record sequence, lateral misalignment data of precast component joints, longitudinal misalignment data of precast component joints, and joint gap of precast component joints in the multidimensional construction process monitoring data into geometric deviation feature vectors. The linear deviation analysis submodule is used to downsample and register the three-dimensional point cloud model of the components in the multi-dimensional construction process monitoring data and extract the deviation value between the actual coordinates and the design coordinates of the precast components. Based on the deviation value between the actual coordinates and the design coordinates of the precast components, the planar linear deviation, longitudinal slope deviation and cross-sectional dimension deviation are determined, and the linear deviation feature vector is generated based on the planar linear deviation, longitudinal slope deviation and cross-sectional dimension deviation. The deviation feature fusion submodule is used to fuse the strain deviation feature vector, geometric deviation feature vector, and linear deviation feature vector to obtain a comprehensive deviation feature vector.

4. The intelligent monitoring and control system for construction accuracy of large-section prefabricated modular tunnels according to claim 1, characterized in that, The data fusion and analysis module also includes: The multi-level threshold setting submodule is used to import component design dimensions, joint allowable deviations, linear design parameters, and foundation allowable settlement as construction design parameters, and set multi-level deviation warning thresholds based on the construction design parameters. The deviation level determination submodule is used to determine the deviation level based on the magnitude of the comprehensive deviation feature vector and the multi-level deviation threshold.

5. The intelligent monitoring and control system for construction accuracy of large-section prefabricated modular tunnels according to claim 1, characterized in that, The mechanical performance evaluation module includes: The model update submodule is used to update the component positions, joint states, and foundation settlement boundary conditions in the preset large-section prefabricated assembled passage finite element model based on the comprehensive deviation feature vector, so as to obtain the current refined finite element model of the large-section prefabricated assembled passage. The performance analysis submodule is used to perform component stress distribution analysis, joint force transmission performance analysis, and structural linear stability analysis on the current large-section prefabricated assembled channel refined finite element model, and obtain stress over-limit coefficient, force under-transmission coefficient, and linear instability coefficient. The comprehensive scoring submodule is used to determine the mechanical influence score based on the stress over-limit coefficient, the force transmission insufficiency coefficient, and the linear instability coefficient.

6. The intelligent monitoring and control system for construction accuracy of large-section prefabricated modular tunnels according to claim 5, characterized in that, The performance analysis submodule includes: The stress exceeding the standard area calibration unit is used to perform component stress distribution analysis on the current large-section prefabricated assembled channel refined finite element model and mark all stress exceeding the standard areas; The critical stress area calibration unit is used to calibrate all critical stress areas based on the current refined finite element model of large-section prefabricated assembled channels. The insufficient force transmission joint screening unit is used to analyze the joint force transmission performance of the current large-section prefabricated assembled channel refined finite element model, obtain the actual force transmission value of the shear key at the joint of the large-section prefabricated assembled channel, and calculate the ratio of the actual force transmission value of the shear key at the joint of the large-section prefabricated assembled channel to the design value, which is used as the basis value for judging insufficient force transmission at the corresponding joint. Based on the basis value for judging insufficient force transmission at each joint and the preset threshold value for judging insufficient force transmission, the unit screens out the joints with insufficient force transmission from all joints. The unstable linear section screening unit is used to perform structural linear stability analysis on the current large-section prefabricated assembled channel refined finite element model to obtain the actual linear shape of each segmented linear section. The deviation curve between the actual linear shape and the design linear shape is fitted by cubic spline interpolation algorithm. Based on the first derivative of the deviation curve of each segmented linear section and the preset rate of change threshold, the unstable linear section is screened out from all segmented linear sections. The mechanical performance coefficient determination unit is used to determine the stress excess coefficient, the force deficiency coefficient, and the linear instability coefficient based on the stress excess area and critical stress area of ​​all precast components, the total number of all insufficient force transmission joints, and the segment length of all unstable linear intervals.

7. The intelligent monitoring and control system for construction accuracy of large-section prefabricated modular tunnels according to claim 6, characterized in that, The unit for determining mechanical property coefficients includes: The stress exceedance coefficient determination sub-unit is used as the ratio of the total projected area of ​​the stress exceedance area of ​​all precast components to the total projected area of ​​the critical stress area of ​​all precast components as the stress exceedance coefficient. The force transmission deficiency coefficient determination sub-unit is used as the ratio of the total number of all force transmission deficiency joints to the total number of joints in the large-section prefabricated assembled channel as the force transmission deficiency coefficient. The linear instability coefficient determination sub-unit is used as the ratio of the segment length of all instable linear intervals to the total length of the channel in the current construction section as the linear instability coefficient.

8. The intelligent monitoring and control system for construction accuracy of large-section prefabricated modular tunnels according to claim 6, characterized in that, The calibration unit for areas with excessive stress includes: The component calibration sub-unit is used to map the current large-section prefabricated assembled channel refined finite element model to a three-dimensional rectangular coordinate system and extract the three-dimensional coordinate range of all key stress-bearing components. Mesh subdivision unit is used to mesh all key load-bearing components based on the three-dimensional coordinate range of all key load-bearing components, and obtain the three-dimensional coordinate range of all sub-mesh spatial regions of each key load-bearing component; The stress analysis sub-element is used to construct a stress calculation matrix based on the material constitutive parameters of the current refined finite element model of large-section prefabricated assembled channels. Based on the stress calculation matrix, the stress balance equation of each sub-mesh spatial region of each key stress-bearing component is constructed. The stress balance equation is solved by Gaussian elimination to obtain the displacement vector output value. The strain tensor of each sub-mesh spatial region is calculated by combining the geometric equations. Finally, the stress tensor of each sub-mesh spatial region is determined by combining the physical equations. The cloud map generation sub-element is used to perform maximum principal stress calculation on the stress tensor of all sub-mesh spatial regions of each critical stress component, and generate stress cloud maps for each critical stress component; The coordinate filtering sub-unit is used to determine all stress-exceeding coordinates and construct a set of stress-exceeding coordinates based on the stress cloud map of each key stress-bearing component. The region calibration sub-unit is used to cluster the stress-exceeding coordinate set using the DBSCAN clustering algorithm to obtain multiple stress-exceeding clusters. The minimum bounding box of each stress-exceeding cluster is calculated, and the three-dimensional spatial region corresponding to each minimum bounding box is regarded as all stress-exceeding regions.

9. The intelligent monitoring and control system for construction accuracy of large-section prefabricated modular tunnels according to claim 1, characterized in that, The adaptive adjustment control module includes: The adjustment trigger judgment submodule is used to determine whether to trigger the adaptive adjustment control process based on the deviation level and mechanical influence score. The adjustment strategy generation submodule is used to generate an adjustment strategy based on the control parameters of the construction equipment if the adaptive adjustment control process is triggered.

10. A method for intelligent monitoring and control of construction accuracy in large-section prefabricated modular tunnels, characterized in that: include: Based on monitoring components deployed on prefabricated components of large-section prefabricated assembled passages, multi-dimensional construction process monitoring data is obtained. A comprehensive deviation feature vector is generated based on multi-dimensional construction process monitoring data, and the deviation level is determined based on the modulus of the comprehensive deviation feature vector. Based on the comprehensive deviation feature vector, update the component positions, joint states and foundation settlement boundary conditions in the preset large-section prefabricated assembled passage finite element model to obtain the current large-section prefabricated assembled passage refined finite element model. Then, perform component stress distribution analysis, joint force transmission performance analysis and structural linear stability analysis on the current large-section prefabricated assembled passage refined finite element model to obtain the mechanical influence score. The adaptive adjustment control process is triggered based on the deviation level and mechanical influence score.

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