A method and system for monitoring deep horizontal displacement in foundation pits based on digital twins
By burying an automated inclinometer sensor array and an Internet of Things module around the foundation pit, and combining geometric projection and spatial interpolation algorithms to generate a three-dimensional digital twin model, the problems of low efficiency and unintuitive data in monitoring deep horizontal displacement of the foundation pit are solved, enabling real-time monitoring of foundation pit deformation and rapid identification of risks.
Patent Information
- Application Number
- CN202610136406.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-30
- Publication Date
- 2026-05-26
AI Technical Summary
Existing methods for monitoring deep horizontal displacement in foundation pits are inefficient, have limited data acquisition frequency, cannot achieve continuous real-time tracking, have lagging data processing and information transmission, and present data in a non-intuitive manner, making it difficult to reveal the distribution patterns and dynamic trends of displacement in three-dimensional space.
By burying an automated inclination sensor array around the boundary line on each side of the foundation pit and uploading the data to the cloud server using an Internet of Things module, high-precision three-dimensional point cloud data is generated by combining geometric projection and spatial interpolation algorithms. This allows for the construction and updating of a three-dimensional digital twin model of the foundation pit, achieving real-time synchronization and dynamic evolution between the physical entity and the virtual model.
It has achieved automated data acquisition and real-time processing of deep horizontal displacement in foundation pits, improved the efficiency of identifying abnormal displacement areas, facilitated rapid assessment of risk levels, provided timely and accurate decision support, and realized effective closed-loop management of engineering risks.
Smart Images
Figure CN122083873A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of foundation pit monitoring, specifically to a method and system for monitoring deep horizontal displacement of foundation pits based on digital twins. Background Technology
[0002] Foundation pit engineering is a fundamental component of all types of construction projects. The stability of its retaining structure during excavation and subsequent construction directly affects the safety of surrounding existing buildings, underground pipelines, and the overall project. Deep horizontal displacement monitoring (commonly known as inclinometer testing) is a core indicator for evaluating the deformation state and stability of the foundation pit retaining structure, playing a decisive role in preventing engineering accidents and ensuring construction safety.
[0003] Existing methods for monitoring deep horizontal displacement in foundation pits primarily rely on manual on-site operations, using equipment such as inclinometers for periodic measurements, and manually recording the data in paper or spreadsheets. These traditional methods have several inherent drawbacks: First, they are inefficient, with data collection frequency limited by manpower and weather conditions, typically once a day or every few days, failing to achieve continuous, real-time tracking of foundation pit deformation. Second, data processing and information transmission are significantly delayed; from on-site measurement to data analysis and then to information acquisition by decision-makers, a considerable amount of time is required, potentially missing the optimal window for risk warning. Finally, the data presentation is limited, often delivered in the form of two-dimensional graphs or data reports, lacking intuitiveness and failing to reveal the overall distribution pattern of displacement in three-dimensional space and its dynamic evolution over time, making it difficult for engineering managers to accurately identify risk areas and deformation patterns.
[0004] To overcome these shortcomings, automated monitoring technologies have emerged, enabling real-time acquisition of inclinometer data through the deployment of automated sensors. However, these technologies still have limitations in data presentation, with most deliverables remaining at the level of two-dimensional charts. In recent years, with the development of Building Information Modeling (BIM) and Geographic Information Systems (GIS) technologies, visualization solutions have emerged that overlay monitoring data onto static 3D engineering models. However, in this "static model + dynamic data" model, the data and model are essentially separate; the geometric shape of the model does not change with the actual deformation of the physical entity, failing to achieve real-time synchronization and dynamic evolution between the physical and virtual worlds.
[0005] Therefore, how to construct a dynamic three-dimensional virtual model that can truly reflect the real-time deformation state of the physical foundation pit and achieve deep integration and closed-loop interaction between the physical entity and the virtual model is a technical problem that urgently needs to be solved in the field of foundation pit safety monitoring. Summary of the Invention
[0006] One of the objectives of this invention is to provide a method for monitoring deep horizontal displacement of foundation pits based on digital twins, which can intuitively reveal the spatiotemporal evolution of displacement in the monitoring area, provide timely and accurate decision support for on-site construction, and help to effectively manage engineering risks in a closed loop.
[0007] One of the objectives of this invention is achieved through the following technical solution: A method for monitoring deep horizontal displacement in foundation pits based on digital twins includes the following steps: S1. Obtain the depth of the foundation pit and the plane coordinates of each corner point of the foundation pit. Based on the plane coordinates of the corner points of the foundation pit, determine the boundary lines of each side of the foundation pit, and establish an initial model of the foundation pit on the cloud server in combination with the depth of the foundation pit. S2. At least one automated inclinometer array is buried outside the boundary line on each side of the foundation pit. The automated inclinometer array is used to periodically or as needed to collect deep multi-point horizontal displacement data at its location. S3. The raw data of deep multi-point horizontal displacement collected by the automated inclinometer sensor array is sent to the cloud server through the Internet of Things transmission module; S4. The received original data of foundation pit displacement is processed on the cloud server, including mapping the spatial coordinates of the monitoring points corresponding to the automated inclinometer array and the displacement data collected to the elevation corresponding to the initial model of the foundation pit through a geometric projection algorithm to form multiple discrete elevation spatial coordinate points, and performing encrypted calculation on the multiple elevation spatial coordinate points using a spatial interpolation algorithm to generate high-precision three-dimensional point cloud data covering the elevation of the foundation pit. S5. Construct a three-dimensional digital twin model corresponding to the foundation pit based on the high-precision three-dimensional point cloud data, and update the geometry and / or state of the three-dimensional digital twin model when new displacement data is received; S6. Visualize the three-dimensional digital twin model through the application service layer and provide user interaction functions.
[0008] The second objective of this invention is to provide a deep horizontal displacement monitoring system for foundation pits based on digital twins, which is achieved through the following technical solution: A deep horizontal displacement monitoring system for foundation pits based on digital twins, comprising: The physical entity layer includes the foundation pit and the automated inclinometer sensor array buried around the foundation pit; The sensing and transmission layer includes a data acquisition module and an Internet of Things (IoT) module connected to the automated inclinometer array. The data acquisition module is used to read the displacement data of the automated inclinometer array, and the IoT module is used to upload the displacement data to a cloud server via a wireless network and receive control commands from the cloud server. The digital twin model layer, deployed on the cloud server, is used to perform geometric projection mapping and spatial interpolation processing on the received displacement data to generate three-dimensional point cloud data, and to construct or update the three-dimensional digital twin model of the foundation pit based on the three-dimensional point cloud data. The application and service layer serves as the user interface, used to visualize the three-dimensional digital twin model of the foundation pit and provide functions such as data query, historical backtracking, and control command issuance.
[0009] Compared with the prior art, the advantages of the present invention are as follows: By embedding an automated inclinometer array around the perimeter of each side of the foundation pit, and integrating it with an IoT transmission module to upload deep multi-point horizontal displacement data to a cloud server, the data acquisition, transmission, and processing are automated, reducing the lag caused by manual measurement. Simultaneously, an initial model of the foundation pit is established on the cloud server. The spatial coordinates and displacement data of the monitoring points are mapped to the corresponding facades using a geometric projection algorithm. Combined with a spatial interpolation algorithm, high-precision 3D point cloud data covering the foundation pit facades is generated. Based on this high-precision 3D point cloud data, a 3D digital twin model corresponding to the foundation pit is constructed to update the geometry and / or state of the 3D digital twin model upon receiving new displacement data. Furthermore, the visualization of the 3D digital twin model of the foundation pit effectively improves the identification efficiency of areas with abnormal displacement, facilitating rapid assessment of risk levels. Attached Figure Description
[0010] Figure 1 This is a schematic diagram of an engineering example of the present invention; Figure 2 This is a simplified schematic diagram of the color mapping cloud map in this invention; Figure 3 is A simplified schematic diagram of the automated sensor array of this invention projected onto the boundary line of the foundation pit; Figure 4 It is a simplified schematic diagram of a three-dimensional digital twin model; Figure 5 yes Figure 4 A simplified diagram illustrating the dynamic changes. Detailed Implementation
[0011] The present invention will now be described in detail with reference to the accompanying drawings and embodiments: A method for monitoring deep horizontal displacement in foundation pits based on digital twins includes the following steps: S1. Obtain the depth of the foundation pit and the plane coordinates of each corner point of the foundation pit. Based on the plane coordinates of the corner points of the foundation pit, determine the boundary lines of each side of the foundation pit, and establish an initial model of the foundation pit on the cloud server in combination with the depth of the foundation pit. S2. At least one automated inclinometer array is buried outside the boundary line on each side of the foundation pit. The automated inclinometer array is used to periodically or as needed to collect deep multi-point horizontal displacement data at its location. S3. The raw data of deep multi-point horizontal displacement collected by the automated inclinometer sensor array is sent to the cloud server through the Internet of Things transmission module; S4. The received original data of foundation pit displacement is processed on the cloud server, including mapping the spatial coordinates of the monitoring points corresponding to the automated inclinometer array and the displacement data collected to the elevation corresponding to the initial model of the foundation pit through a geometric projection algorithm to form multiple discrete elevation spatial coordinate points, and performing encrypted calculation on the multiple elevation spatial coordinate points using a spatial interpolation algorithm to generate high-precision three-dimensional point cloud data covering the elevation of the foundation pit. S5. Construct a three-dimensional digital twin model corresponding to the foundation pit based on the high-precision three-dimensional point cloud data, and update the geometry and / or state of the three-dimensional digital twin model when new displacement data is received; S6. Visualize the three-dimensional digital twin model through the application service layer and provide user interaction functions.
[0012] Furthermore, the geometric projection algorithm includes: The planar position of the automated inclinometer array is vertically projected onto the boundary line of the corresponding side of the foundation pit to obtain the projection parameters of the automated inclinometer array on the boundary line of the foundation pit on the corresponding side. Based on the projection parameters and the elevation information of each monitoring point of the automated inclinometer array, the elevation projection position of each monitoring point of the automated inclinometer on the corresponding elevation of the foundation pit is determined. Based on the facade projection position of the monitoring points and their associated displacement data, facade spatial coordinate points are constructed.
[0013] The spatial interpolation algorithm is a bicubic interpolation algorithm.
[0014] The three-dimensional digital twin model is built and rendered using the WebGL-based Three.js engine.
[0015] The visualization includes using color mapping technology to map the magnitude of displacement values to different colors, and displaying the displacement field distribution and risk level in the form of a cloud map.
[0016] Specifically, using high-precision 3D point cloud data, it is possible to draw... Figure 2 The color mapping cloud shown represents the deformation of the foundation pit facade.
[0017] Furthermore, to visually represent risk levels, color mapping technology is used to map displacement values to a gradient from blue to red. For example, blue can be set to represent a 0mm displacement, and red can be set to represent a warning threshold displacement (such as 30mm). Intermediate values are then proportionally interpolated by color; the larger the displacement, the more red the color, indicating a higher risk in that area.
[0018] The visualization also includes scaling up the geometric deformation of the 3D digital twin model to enhance the visualization of minute displacements.
[0019] The IoT transmission module uses the MQTT protocol for bidirectional communication. The user interaction function includes sending control commands to the IoT transmission module through the application service layer to achieve remote control of the automated inclinometer sensor array.
[0020] A deep horizontal displacement monitoring system for foundation pits based on digital twins, comprising: The physical entity layer includes the foundation pit and the automated inclinometer sensor array buried around the foundation pit; The sensing and transmission layer includes a data acquisition module and an Internet of Things (IoT) module connected to the automated inclinometer array. The data acquisition module is used to read the displacement data of the automated inclinometer array, and the IoT module is used to upload the displacement data to a cloud server via a wireless network and receive control commands from the cloud server. The digital twin model layer, deployed on the cloud server, is used to perform geometric projection mapping and spatial interpolation processing on the received displacement data to generate three-dimensional point cloud data, and to construct or update the three-dimensional digital twin model of the foundation pit based on the three-dimensional point cloud data. The application and service layer serves as the user interface, used to visualize the three-dimensional digital twin model of the foundation pit and provide functions such as data query, historical backtracking, and control command issuance.
[0021] Furthermore, the sensing and transmission layer also includes a solar energy storage module that powers the data acquisition module and the Internet of Things module.
[0022] Specifically, the automated inclinometer array is a buried inclinometer array, which includes a stainless steel tube shell, a measurement module, and cables. The measurement module has a built-in high-precision inclinometer chip and a geomagnetic chip, used to measure the tilt angle, dip, and azimuth, and to calculate the displacement data. The cables adopt RS-485 bus mode to connect the automated inclinometer array in parallel.
[0023] The data acquisition module is a data acquisition instrument that uses the address bus method to poll all automated inclinometer sensor arrays connected to the bus and read their displacement data.
[0024] To better illustrate the embodiments of the present invention, the following engineering example is used to illustrate the embodiments of the present invention: like Figure 1 As shown in Table 1 below, the perimeter of the foundation pit is about 195m and the excavation depth is about 4.8m, according to the engineering drawings. A plane coordinate system is established with the southwest corner point A1 of the foundation pit as the coordinate origin (0,0), and the plane coordinates of each corner point (A1-A5) of the foundation pit and the deployment points (C1-C7) of the automated inclinometer sensor array are extracted.
[0025] The boundary lines of each side of the foundation pit are determined based on the plane coordinates of the corner points of the foundation pit, and an initial model of the foundation pit is established on the cloud server in combination with the depth of the foundation pit. As shown in Table 2 below, the correspondence between the automated inclinometer array and the initial model elevation of the foundation pit is established based on the automated inclinometer array and its corresponding boundary line.
[0026] Displacement data from each monitoring point in the automated inclinometer array is collected and uploaded to a cloud server via an IoT module. The received raw data of the foundation pit displacement is then processed on the cloud server.
[0027] It should be noted that the automated inclinometer sensor array includes multiple monitoring points with different elevations. The planar coordinates of each monitoring point in the same automated inclinometer sensor array are the same, but their spatial coordinates are different.
[0028] Since automated inclinometer arrays are typically deployed at a certain distance from the foundation pit boundary line, this embodiment employs a geometric projection method to visually display the monitored displacement data on the initial elevation of the foundation pit, as detailed below: Reference Figure 3, For any monitoring point C(xc, yc), it needs to be projected onto the boundary line AB defined by the corner points A(xa, ya) and B(xb, yb) of the foundation pit, and then the elevation of its corresponding monitoring point is mapped onto the initial elevation of the foundation pit. The distance AP from the projection point P to the starting point A of the boundary line can be calculated using the following formula: This method can associate the deep horizontal displacement data (z value, i.e. deformation) of each physical monitoring point with a two-dimensional coordinate (x=AP, y=elevation) on the foundation pit elevation, forming a series of spatial scattered points P(x,y,z).
[0029] Subsequently, as Figure 4As shown, spatial interpolation algorithms are used to encrypt these scattered points, generating high-precision, regularly gridded 3D point cloud data covering the entire foundation pit facade.
[0030] When new monitoring data is uploaded through the IoT module, the system automatically triggers data processing again and updates the model, thereby enabling the model to evolve dynamically in real time.
[0031] Figure 4 and Figure 5 The example excavation pit is presented in 3D digital twin form on two consecutive dates. These two 3D digital twin models clearly demonstrate the spatial distribution of the displacement field using a "blue-green-yellow-red" color cloud map, where red areas indicate larger displacements and blue areas indicate smaller displacements.
[0032] By comparison Figure 4 and Figure 5 It can be observed that over two days, the central area of the north facade (M2) of the foundation pit, defined by corner points A2 and A3, changed significantly from yellow and green to a large area of red, indicating a sharp increase in horizontal displacement in that area within a single day. Monitoring personnel quickly located this area of abnormal displacement using the foundation pit's three-dimensional digital twin model, and immediately issued an early warning. Verification with the on-site construction team revealed that a large amount of steel reinforcement had been illegally piled up at the edge of the foundation pit in that area that day, leading to a surge in lateral earth pressure, which perfectly matched the deformation trend reflected in the foundation pit's three-dimensional digital twin model.
[0033] Based on the precise positioning information provided by the three-dimensional digital twin model of the foundation pit, on-site construction personnel quickly removed the overload. Subsequent monitoring data showed that the displacement rate in the area was effectively controlled, preventing a potential foundation pit instability accident. This example fully demonstrates that the digital twin-based deep horizontal displacement monitoring method and system for foundation pits provided by this invention can intuitively reveal the spatiotemporal evolution of displacement in the monitored area, providing timely and accurate decision support for on-site construction and achieving effective closed-loop management of engineering risks.
[0034] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.
Claims
1. A method for monitoring deep horizontal displacement in foundation pits based on digital twins, characterized in that, Includes the following steps: S1. Obtain the depth of the foundation pit and the plane coordinates of each corner point of the foundation pit. Based on the plane coordinates of the corner points of the foundation pit, determine the boundary lines of each side of the foundation pit, and establish an initial model of the foundation pit on the cloud server in combination with the depth of the foundation pit. S2. At least one automated inclinometer array is buried outside the boundary line on each side of the foundation pit. The automated inclinometer array is used to periodically or as needed to collect deep multi-point horizontal displacement data at its location. S3. The raw data of deep multi-point horizontal displacement collected by the automated inclinometer sensor array is sent to the cloud server through the Internet of Things transmission module; S4. The received original data of foundation pit displacement is processed on the cloud server, including mapping the spatial coordinates of the monitoring points corresponding to the automated inclinometer array and the displacement data collected to the elevation corresponding to the initial model of the foundation pit through a geometric projection algorithm to form multiple discrete elevation spatial coordinate points, and performing encrypted calculation on the multiple elevation spatial coordinate points using a spatial interpolation algorithm to generate high-precision three-dimensional point cloud data covering the elevation of the foundation pit. S5. Construct a three-dimensional digital twin model corresponding to the foundation pit based on the high-precision three-dimensional point cloud data, and update the geometry and / or state of the three-dimensional digital twin model when new displacement data is received; S6. Visualize the three-dimensional digital twin model through the application service layer and provide user interaction functions.
2. The method for monitoring deep horizontal displacement of foundation pits based on digital twins according to claim 1, characterized in that, The geometric projection algorithm includes: The planar position of the automated inclinometer array is vertically projected onto the boundary line of the corresponding side of the foundation pit to obtain the projection parameters of the automated inclinometer array on the boundary line of the foundation pit on the corresponding side. Based on the projection parameters and the elevation information of each monitoring point of the automated inclinometer array, the elevation projection position of each monitoring point of the automated inclinometer on the corresponding elevation of the foundation pit is determined. Based on the facade projection position of the monitoring points and their associated displacement data, facade spatial coordinate points are constructed.
3. The method for monitoring deep horizontal displacement of foundation pits based on digital twins according to claim 1, characterized in that: The spatial interpolation algorithm is a bicubic interpolation algorithm.
4. The method for monitoring deep horizontal displacement of foundation pits based on digital twins according to claim 1, characterized in that: The three-dimensional digital twin model is built and rendered using the WebGL-based Three.js engine.
5. The method for monitoring deep horizontal displacement of foundation pits based on digital twins according to claim 1, characterized in that: The visualization includes using color mapping technology to map the magnitude of displacement values to different colors, and displaying the displacement field distribution and risk level in the form of a cloud map.
6. The method for monitoring deep horizontal displacement of foundation pits based on digital twins according to claim 5, characterized in that: The visualization also includes scaling up the geometric deformation of the 3D digital twin model to enhance the visualization of minute displacements.
7. The method for monitoring deep horizontal displacement of foundation pits based on digital twins according to claim 1, characterized in that: The IoT transmission module uses the MQTT protocol for bidirectional communication. The user interaction function includes sending control commands to the IoT transmission module through the application service layer to achieve remote control of the automated inclinometer sensor array.
8. A deep horizontal displacement monitoring system for foundation pits based on digital twins, characterized in that, include: The physical entity layer includes the foundation pit and the automated inclinometer sensor array buried around the foundation pit; The sensing and transmission layer includes a data acquisition module and an Internet of Things (IoT) module connected to the automated inclinometer array. The data acquisition module is used to read the displacement data of the automated inclinometer array, and the IoT module is used to upload the displacement data to a cloud server via a wireless network and receive control commands from the cloud server. The digital twin model layer, deployed on the cloud server, is used to perform geometric projection mapping and spatial interpolation processing on the received displacement data to generate three-dimensional point cloud data, and to construct or update the three-dimensional digital twin model of the foundation pit based on the three-dimensional point cloud data. The application and service layer serves as the user interface, used to visualize the three-dimensional digital twin model of the foundation pit and provide functions such as data query, historical backtracking, and control command issuance.
9. The deep horizontal displacement monitoring system for foundation pits based on digital twins according to claim 8, characterized in that: The sensing and transmission layer also includes a solar energy storage module that powers the data acquisition module and the Internet of Things module.