GB2617453A - Three-dimensional intelligent filling construction management method of subgrade assisted by mixed reality technology - Google Patents

Three-dimensional intelligent filling construction management method of subgrade assisted by mixed reality technology Download PDF

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GB2617453A
GB2617453A GB2302343.5A GB202302343A GB2617453A GB 2617453 A GB2617453 A GB 2617453A GB 202302343 A GB202302343 A GB 202302343A GB 2617453 A GB2617453 A GB 2617453A
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filling
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Lv Xilin
Cheng Bowen
Xie Zhiwei
Lv Long
Zhu Changgen
Xu Kefeng
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Tongji University
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Abstract

The application provides a three-dimensional intelligent filling construction management method of subgrade assisted by mixed reality technology, which comprises the following steps: obtaining an MR three-dimensional realistic model; associating the absolute three-dimensional coordinates of the filling surface with the MR three-dimensional realistic model, and filling the subgrade based on an association result; obtaining a compaction degree of the filling subgrade based on the scanning characteristic image; obtaining a simplified calculation model based on the subgrade solid model, and inputting variable parameters of the filling subgrade into the simplified calculation model to obtain deformation data of the filling subgrade; and judging whether the subgrade deformation control index meets the design requirements based on the porosity, compaction degree and deformation data of filling subgrade.

Description

THREE-DIMENSIONAL INTELLIGENT FILLING CONSTRUCTION MANAGEMENT METHOD OF SUBGRADE ASSISTED BY MIXED REALITY TECHNOLOGY
TECHNICAL FIELD
The application belongs to the technical field of intelligent filling construction of earth-rock mixed subgrade in road construction, and in particular to a three-dimensional intelligent filling construction management method of subgrade assisted by mixed reality technology.
BACKGROUND
High fill engineering has become the main form of building facilities; meanwhile, the amount of filling is large, so in order to save costs and make full use of excavated waste materials, the earth-rock mixture is often used as high fill filler. However, the complex nature of the earth-rock mixture, the high filling height and the high self-weight of the high fill, and the large settlement after construction if it is not properly treated all affect the long-term operation stability of the high fill engineering. In the filling stage, the current method adopts block pouring paving, which fails to fill accurately according to the designed layer thickness, and less filling delays the construction period, while overfilling is easy to cause the layer thickness to be too large, which is not easy to compact. In the compaction stage, at present, experience or random spot check of compaction degree are mainly used to judge whether the subgrade currently filled with compacted layer has reached the preset compaction degree, which has large error, and it is easy to miss the judgment of potentially unconsolidated road sections. In the consolidation stage, as the current method, it is mainly to perform settlement observation of a few key points to ensure that the subgrade deformation may be controlled, which lacks a wide range of quick and convenient settlement management methods.
SUMMARY
In order to solve the above technical problems, the present application provides a three-dimensional intelligent filling construction management method of subgrade assisted by mixed reality technology. Based on the mixed reality technology, the present application provides an intelligent construction management method in subgrade filling, compaction and consolidation observation stages. In the subgrade filling stage, combining satellite positioning and fast scanning technology, the virtual image of the section characteristics of the filling subgrade is generated in real time, and compared with the preset filling section, so as to easily judge whether the filling volume meets the design requirements. In the subgrade compaction stage, the characteristic scanning image of the interior of the filling body is generated in real time by using the image feature extraction technology of electromagnetic wave scanning device and convolutional neural network, and the pixel block information corresponding to the pore is extracted, and then the compaction degree of the filling body is quickly estimated by calculating the porosity of the filling body. In the stage of subgrade settlement observation, the section form of the filling road section can be reasonably simplified based on the secondary development platform of finite element software, so as to quickly generate the finite element calculation model after compaction, calculate the subgrade deformation under different working conditions, extract the key settlement data and compare the key settlement data with the design standards, and the long-term stable operation of the earth-rock mixed subgrade can be further guaranteed. The method can provide a new idea for intelligent construction of earth-rock mixed filling road engineering and a certain reference for settlement management of earth-rock mixed filling engineering.
In order to achieve the above objectives, the present application provides a three-dimensional intelligent filling construction management method of subgrade assisted by mixed reality technology, including coupling a three-dimensional geological model of the filling road section with a subgrade solid model to obtain a mixed reality (MR) three-dimensional realistic model; obtaining real-time three-dimensional positioning information of a filling machinery and relative point cloud coordinates of control points on a surface of a filling body, coupling the real-time three-dimensional positioning information with the relative point cloud coordinates of the control points, obtaining absolute three-dimensional coordinates of a filling surface, associating the absolute three-dimensional coordinates of the filling surface with the MR three-dimensional realistic model, and filling the subgrade based on an association result; after the subgrade is filled, obtaining a scanning characteristic image of the filling subgrade based on electromagnetic wave detection, and obtaining a compaction degree of the filling subgrade based on the scanning characteristic image; carrying out a semi-structural analysis on the subgrade solid model to obtain a simplified calculation model of the filling subgrade, and inputting variable parameters of the filling subgrade into the simplified calculation model to obtain deformation data of the filling subgrade; judging whether a subgrade deformation control indicators meet design requirements based on the compaction degree of the filling subgrade and the deformation data of the filling subgrade; if not, re-obtaining the compaction degree of the filling subgrade until the subgrade deformation control indicators meet the design requirements.
Optionally, coupling the three-dimensional geological model of the filling road section with the subgrade solid model includes: building the three-dimensional geological model based on the section characteristics of a designed subgrade, building the subgrade solid model based on elevation information, coupling the three-dimensional geological model and the subgrade solid model, and associating a whole with a corresponding geodetic coordinate system.
Optionally, obtaining the real-time three-dimensional positioning information of the filling machinery and the relative point cloud coordinates of the control points on the surface of the filling body includes: the filling machinery is equipped with a UPS receiving antenna and a three-dimensional laser scanning device; in the filling process, a positioning satellite sends the three-dimensional space coordinate information of the filling machinery to the UPS receiving antenna in real time, and the real-time three-dimensional positioning information of the filling machinery is comprehensively generated by combining a differential signal sent by a base station, the relative point cloud coordinates of the control points on the surface of the filling body are obtained by the three-dimensional laser scanning device.
Optionally, obtaining the scanning characteristic image of subgrade based on electromagnetic wave detection includes: in the process of rolling and ramming the filling subgrade, electromagnetic waves are continuously emitted into the filling body by an electromagnetic wave emitting device to obtain the scanning characteristic image of the filling subgrade, wherein the filling subgrade is rolled and rammed in a serpentine-shaped superposition mode.
Optionally, obtaining the compaction degree of filling subgrade based on the scanning characteristic image includes: extracting pixel blocks smaller than a preset value in the scanning characteristic image based on a convolution neural method, calculating the ratios of all the pixel blocks in the scanning image to obtain a porosity of the filling subgrade, and obtaining the compaction degree of the filling subgrade based on the porosity.
Optionally, the convolution neural method is as follows: y(t) = x(t) * h(t) =J f f (x, y) * g(x + Ax, y + iy)dx dy, where f (x,y) is a value of (x, y) in the original picture, g(x + Ax, y + Ay) is a value of (x + Ax, y + Ay) after mapping and biasing an original function, Ax is an offset on the X axis of a picture pixel, Ay is the offset on the Y axis of the picture pixel, y(t) is a result function of x(t) and h(t), x(t) is an input function and h(t) is a response function. Optionally, extracting pixel blocks smaller than a preset value in the scanning characteristic image based on a convolution neural method includes: extracting different characteristics of the scanning characteristic image to obtain the original characteristic image, combining convolution kernels with different sizes or characteristics into a unit, and carrying out a convolution operation on the original characteristic image and different convolution kernels in the unit.
Optionally, carrying out a convolution operation on the original characteristic image and different convolution kernels in the unit includes: padding the scanning characteristic image before the convolution operation, and convolution kernels with different sizes may have different padding layers; in the convolution operation, a convolution step length is all set to 1, and outputting several images with a same pixel size as the original image respectively; using a Rectified Linear Unit (ReLU) activation function to process the output images to eliminate non-zero pixels generated in the convolution process, and then the output images are used as the input of the neural network to carry out a next convolution operation; in the convolution process, the pixel size of the image is always kept unchanged.
Optionally, the method to obtain the compaction degree of the filling subgrade based on the porosity is as follows: P = 1 ip where P is the compaction degree and cp is the porosity.
Optionally, obtaining deformation data of filling subgrade includes: obtaining a load condition, a material property, a section shape and a size of the filling subgrade in real time during the construction, then inputting the load condition, the material property, the section shape and the size into a filling subgrade calculation model, and outputting the deformation data of the filling subgrade, where the deformation data of the filling subgrade includes a displacement deformation and a stress distribution of the filling subgrade.
Compared with the prior art, the application has the following advantages and technical effects.
Firstly based on the mixed reality technology, the application puts forward an accurate filling construction technology of earth-rock mixed subgrade, which combines the existing mature fast scanning technology and satellite positioning technology, couples the real-time point cloud data of the filling surface into the virtual model of the filling subgrade, and intuitively judges whether the filling quantity meets the design requirements, and avoid the potential risks of delayed construction period due to under-filling and difficult compaction due to over-filling, Secondly in the process of subgrade compaction, the application continuously transmits electromagnetic waves into the interior of the filling body, receives the reflected signals, and converts the reflected signals into characteristic scanning pictures of the filling body by combining with color coding Through convolution neural method, different convolution kernels are used to extract the pore characteristics of scanning characteristic image, and then the porosity and compaction degree of compacted road sections can be quickly judged The method effectively makes up for the shortcomings of incomplete random sampling detection of compaction degree, time-consuming and labor-consuming in practical engineering, and can more efficiently remedy the unconsolidated road sections in the subgrade compaction stage, and reduce the diseases such as uneven settlement and subgrade cracks that may occur in the actual operation of the subgrade.
Thirdly the method simplifies the filling section, quickly models the filling road section through the finite element secondary development platform, generates a calculation model and performs calculation, so that the possible post-construction settlement data of the subgrade can be obtained based on numerical simulation in the consolidation settlement stage, and combined with the measured settlement data of a few observation points, the filling quality of the subgrade can be better judged, the post-construction settlement value may be predicted, and the basis may be provided for ensuring the long-term stable operation of the subgrade.
BRIEF DESCRIPTION OF THE DRAWINGS
The drawings that form a part of this application are used to provide a further understanding of this application. The illustrative embodiments of the application and the descriptions are used to explain this application, and do not constitute undue limitations on this application. The attached drawings are as follows: FIG. 1 is a flow diagram of a three-dimensional intelligent filling construction management method of subgrade assisted by mixed reality technology in the embodiment of the present application FIG. 2 is a schematic diagram of the three-dimensional intelligent filling construction of subgrade based on mixed reality technology in the embodiment of the present application FIG. 3 is a schematic flow diagram of estimating subgrade compaction degree based on electromagnetic wave scanning and convolutional neural network according to the embodiment of the present application.
FIG. 4 is a schematic diagram of the serpentine-shaped superimposed compaction 25 method adopted by the compacted subgrade path in the embodiment of the present application.
FIG. 5 is a schematic diagram of the numerical model of 15-layer filling of fill subgrade based on finite element secondary development platform according to the embodiment of the present application.
where 1. positioning satellites; 2. base station; 3. GPS receiving antenna; 4. electromagnetic wave scanning device; 5. construction machineryry; 6, three-dimensional laser scanning device; 7. filled subgrade; 8. virtual model of unfilled subgrade; 9. electromagnetic wave scanning characteristic image; 10. three-dimensional realistic image of filling subgrade; 11. finite element rapid modeling model; U. three-dimensional topographic map; 13. Cloud.
DETAILED DESCRIPTION OF THE EMBODIMENTS
It should be noted that the embodiments in this application and the characteristics in the embodiments may be combined with each other without conflicts. The application will be described in detail with reference to the drawings and examples.
It should be noted that the steps shown in the flowchart of the figure may be executed in a computer system such as a set of computer-executable instructions, and although a logical sequence is shown in the flowchart, in some cases, the steps shown or described may be executed in a sequence different from that here Embodiment This embodiment provides a three-dimensional intelligent filling construction management method of subgrade assisted by mixed reality technology, including: coupling a three-dimensional geological model of the filling road section with a subgrade solid model to obtain an MR three-dimensional realistic model; obtaining real-time three-dimensional positioning information of a filling machinery and relative point cloud coordinates of control points on a surface of a filling body, coupling the real-time three-dimensional positioning information with the relative point cloud coordinates of the control points, obtaining absolute three-dimensional coordinates of a filling surface, associating the absolute three-dimensional coordinates of the filling surface with the MR three-dimensional realistic model, and controlling the filling volume of the subgrade based on the association result; obtaining electromagnetic wave scanning characteristic image 9 of subgrade based on electromagnetic wave detection, and obtaining compaction degree of filling subgrade based on electromagnetic wave scanning characteristic image 9; base on that subgrade solid model, simplifying the filling body into a plane strain problem, taking semi-structural for analysis to obtain a simplified calculation model of the filling subgrade, and inputting variable parameters of the filling subgrade into the filling subgrade calculation model to obtain deformation data of the filling subgrade; obtaining a filling subgrade calculation model based on the subgrade solid model, inputting variable parameters of the filling subgrade into the filling subgrade calculation model, and obtaining deformation data of the filling subgrade; based on the porosity, compaction degree of filling subgrade and deformation data of filling subgrade, judging whether the subgrade deformation control index meets the design requirements, and if not, the porosity and compaction degree of filling subgrade are re-acquired.
Further, coupling the three-dimensional geological model of the filling road section with the subgrade solid model includes: building the three-dimensional geological model based on the section characteristics of a is designed subgrade, building the subgrade solid model based on elevation information, coupling the three-dimensional geological model and the subgrade solid model, and associating a whole with a corresponding geodetic coordinate system.
Further, obtaining the real-time three-dimensional positioning information of the filling machinery and the relative point cloud coordinates of the control points on the surface of the zo filling body includes: the filling machinery is equipped with a UPS receiving antenna 3 and a three-dimensional laser scanning device; in the filling process, a positioning satellite 1 sends the three-dimensional space coordinate information of the filling machinery to the GPS receiving antenna 3 in real time, and the real-time three-dimensional positioning information of the filling machinery is comprehensively generated by combining a differential signal sent by a base station 2; the relative point cloud coordinates of the control points on the surface of the filling body are obtained by the three-dimensional laser scanning device.
Further, obtaining the electromagnetic wave scanning characteristics map 9 based on electromagnetic wave detection includes: after the subgrade is filled, in the process of rolling and tamping the subgrade, the electromagnetic wave scanning device 4 continuously emits electromagnetic waves to the inside of the filling body to obtain the electromagnetic wave scanning characteristic image 9 of the subgrade, wherein the rolling and tamping of the subgrade adopts a serpentine-shaped superposition mode.
Further, obtaining the compaction degree of the filling subgrade based on the scanning characteristic image includes: extracting pixel blocks smaller than a preset value in the scanning characteristic image based on a convolution neural method, calculating the proportion of the pixel blocks in all pixel blocks in the scanning image to obtain a porosity of the filling subgrade, and obtaining the compaction degree of the filling subgrade based on the porosity.
Further, extracting pixel blocks smaller than a preset value in the scanning characteristic image based on a convolution neural method includes: extracting different characteristics of the scanning characteristic image to obtain the 15 original characteristic image, combining convolution kernels with different sizes or characteristics into a unit, and carrying out a convolution operation on the original characteristic image and different convolution kernels in the unit.
Further, carrying out a convolution operation on the original characteristic image and different convolution kernels in the unit includes: padding the scanning characteristic image before the convolution operation, and convolution kernels with different sizes may have different padding layers; in the convolution operation, a convolution step length is all set to I, and outputting several images with a same pixel size as the original image respectively; using a ReLU activation function to process the output images to eliminate non-zero pixels generated in the convolution process, and then the output images are used as the input of the neural network to carry out a next convolution operation; in the convolution process, the pixel size of the image is always kept unchanged. Further, obtaining deformation data of filling subgrade includes: obtaining a load condition, a material property, a section shape and a size of the filling subgrade in real time during the construction, then inputting the load condition, the material property, the section shape and the size into a filling subgrade calculation model, and outputting the deformation data of the filling subgrade; wherein, the deformation data of the filling subgrade includes a displacement deformation and a stress distribution of the filling subgrade.
As shown in FIG. 1-FIG. 2, this embodiment provides a three-dimensional intelligent filling construction management method of subgrade assisted by mixed reality technology, which specifically includes: Step 1: obtaining the three-dimensional realistic image 10 of the subgrade coupled with the natural terrain of the filling section based on the CAD three-dimensional modeling software, and importing the real-life model file into the presentation hardware of the MR terminal of the construction machinery.
Based on the contour topographic map of the filling road section and the characteristic elevation point data file of the total station, the elevation point information in the characteristic elevation point data file is extracted through the menu bar in the southern CASSIO software, selecting the drawing processing button-> and selecting elevation point.
Select mode 2, select all elevation points to generate data files. Then, through the Engineering Application button in the menu bar, the generated file will be saved as the uncoded elevation point data file in total station data format. Then, create a new blank document, use the contour button in the menu bar, draw the three-dimensional model command, and open the data file in the f uncoded elevation point data file in total station data format saved above. The elevation coefficient is 1.0, the grid spacing is 5 m, and the three-dimensional topographic map 12 of the filling road section is generated by fitting. Through the three-dimensional surround function, check whether the model coordinates are consistent with the elevation values, and finally save them as dwg files of three-dimensional terrain coordinates. Open CAD, and create a three-dimensional subgrade virtual model by using the finite element rapid modeling model 11. Draw the three-dimensional subgrade cross-section by the straight line command, and then generate the virtual subgrade entity by the stretching command. Then, open the generated three-dimensional terrain coordinate file and adjust the aspect ratio of the file pixels to ensure the correct scale of three-dimensional terrain map 12. Finally, the three-dimensional subgrade virtual model is coupled with the three-dimensional topographic map 12 in the software, and the whole is related to the corresponding geodetic coordinate system, and the allowable error is less than 1% of the actual feature size, and import the real-life model file into the presentation hardware of the MR terminal of the construction machinery.
Step 2: coupling filling machinery coordinates and filling surface coordinates in the filling process, and hiding the unfilled virtual subgrade base. Keep only the virtual model of the subgrade base currently being filled Through MR hardware terminal, the filling volume can be accurately controlled.
Each construction machinery 5 is equipped with a UPS receiving antenna 3 and a three-dimensional laser scanning device 6. During the subgrade filling process, the construction machinery 5 acquires the three-dimensional space coordinate information of the filling machinery sent by the positioning satellite 1 in real time through the GPS receiving antenna 3, and comprehensively generates centimeter-level three-dimensional positioning information of the filling machinery by combining the differential signal sent by the base station 2; through the three-dimensional laser scanning device 6, the relative point cloud coordinates of the control points are obtained, and the accuracy is controlled to be within 20 mm in vertical error and 50 mm in horizontal error. Coupling the three-dimensional position coordinates of the filling machinery and the point cloud data of the filling surface to the real-time absolute three-dimensional coordinates of the filling surface based on the corresponding geodetic coordinate system, and associating the point cloud data in real-time to the three-dimensional real-life model 10 of the filling subgrade obtained in Step 1. Based on the layered construction conditions of subgrade, in the filling stage, the unfilling road sections and layered subgrade models in the virtual model are hidden (the unfilling subgrade virtual model 8 is hidden), and only the virtual three-dimensional model of the filled subgrade 7 is kept. Through the MR hardware terminal, the difference between the current filling amount and the designed filling amount is observed in real time, so as to control the filling quality more accurately and conveniently, and avoid the potential risks of delayed construction period due to under-filling and difficult compaction due to over-filling.
Step 3 in the process of subgrade compaction, the scanning characteristic line of subgrade is converted into electromagnetic wave scanning characteristic image 9 by electromagnetic wave detection and color coding. The characteristic image is processed by using convolution neural method, and the proportion of black pixels representing pores in the pixel size of the whole scanned characteristic image, which is the so-called porosity, is obtained. Furthermore, the compaction degree of subgrade is estimated by porosity.
After the layered filling of the subgrade is completed, during the layered rolling and tamping of the subgrade, based on the control host on the compaction machinery, the electromagnetic wave scanning device 4 is controlled to continuously emit short pulse electromagnetic waves with a center frequency of 100 MHz and a pulse width of 0.15 ns into the filling body. The detection depth of electromagnetic waves can be ensured. Compaction path adopts serpentine-shaped superposition mode, which is convenient to determine the vertical and horizontal range of unconsolidated areas. The electromagnetic wave is received by the receiving device, and the propagation speed of the electromagnetic wave in different media is calculated according to the following formula: V = where Er is the dielectric constant of the medium, c is the speed of light, and (E,J2 v is the propagation speed of electromagnetic waves in the medium.
The depth of electromagnetic wave from the surface at the interface reflection is calculated according to the formula as follows: h = V *-' where V is the propagation time of the electromagnetic wave in this medium, 2 and t is the time from transmission to reception of the electromagnetic wave.
Every time an electromagnetic wave is emitted, a scanning point will be generated, and all scanning points will be connected to obtain a scanning line. Based on the color code (0-255 figures, each figure represents a color with different gray levels between pure white and pure black), each peak of the scanning line is mapped to the color code, and the peak length corresponds to one length of 0-255 (for example, the largest positive peak corresponds to 255, representing the pure white pixel, and the largest negative peak corresponds to 0, representing the pure black pixel). Then a scanning image of the internal characteristics of the filling body composed of different figures can be obtained after a period of scanning of the compacted subgrade.
Judging the propagation of electromagnetic waves in different media based on the following formula: R -VT1-r, where zi is the dielectric constant of the first medium through which electromagnetic waves pass, £2 is the dielectric constant of the second medium through which electromagnetic waves pass, and R is the reflection coefficient. If the reflection coefficient is positive, the main phase of the scanning line is positive, and the pixel is white, Otherwise, the pixel is black. When there is a section with insufficient compaction degree in the filling road section, there must be larger pores in the filling road section. Pore dielectric constant is small, filler dielectric constant is large, and reflection coefficient is negative. Therefore, the value of larger pores in the filling road section is small in the characteristic scanning image, approaching to 0. Therefore, only by extracting the pixel blocks with smaller values in the characteristic scanning image, the porosity of the filled-compacted road section can be calculated based on the proportion of the white pixel blocks in the pixel blocks of the whole scanned characteristic image, and the compaction degree of this road section can be quickly estimated through the porosity.
As shown in FIG. 4, the path of the compacted subgrade adopts the serpentine-shaped superposition method, with the aim of compacting all parts of the subgrade as evenly as possible, and enabling the scanning device to scan all parts of the subgrade at the same time. Therefore, the range of the internal pores of the subgrade can be more accurately determined, and the filling quality of the subgrade can be ensured.
In Step 3, based on the scanning characteristic image, the convolution neural method is used to extract the characteristic pixels representing pores in the scanning characteristic image.
Based on the following convolution formula: y(t) = x(t) * h(t) = f +. +00 f(x,y) * L. g(x + Ax, y + ay)dx dy, where f (x, y) is the value of the original image corresponding to (x, y), and g(x + Ax, y + Ay) is the value of (x + Ax, y + Ay) after mapping and biasing the original function.
In the convolution process, convolution kernels of different sizes can be used to observe the characteristic points in different ranges of the original image; or convolution kernels with one size but different corresponding position weights are adopted, so as to extract different characteristics of the original images. Convolutional kernels of different sizes or characteristics form a unit together. The convolution operation is carried out on the original characteristic image and different convolution kernels in the unit.
The convolution operation is carried out in python environment. numpy and cv packages are imported Imread method based on cv package is used to read the picture file, and the parameters are the absolute path string of the image and the sign of reading in the image. in this embodiment, the image sign is set to cv2IMREAD GRAYSCALE, which means reading in the grayscale image. A convolution kernel as shown in FIG. 3 is selected. Before convolution, the original scanning characteristic image is padded, the padding parameter is set to 1 and the convolution step size to 1, so as to ensure that the pixel size of the image remains unchanged after convolution operation. ReLU activation function is used to process the output image, and the pixel values generated in the convolution process are all positive integers. In the generated result file, pixels close to the value of 255 represent relatively dense sections, and pixels close to the value of 0 represent possible pores. Cycle operation is adopted to calculate the proportion of pixels less than the threshold of 10 in the pixel size of the whole image. As a result, the pixel size of the image is (1440, 2153), and the number of pixels less than the threshold of 10 is 168,844. The compaction degree of the filled-compacted subgrade can be quickly estimated based on the formula as follows: P = 1 -cp, where P represents compaction degree, and co represents porosity with the unit of %.
Then the porosity of the subgrade in this section is 168844/1440/2153 0.06, so the compaction degree of the subgrade in this detection section is 1 -0.06 = 0.94.
Step 4: simplifying the filling body into a two-dimensional plane strain problem, and automatically generating the calculation model of the filling subgrade based on the finite element secondary development module; submitting the calculation and automatically extracting the deformation data of filling subgrade.
Firstly, the model of the filling subgrade is simplified, and considering the large longitudinal dimension of the filling subgrade, the plane strain problem is used to analyze the deformation force. Based on the general finite element software secondary development platform, the corresponding programming language is adopted to realize the automatic and structured creation of model instances. Specifically, the method includes the following steps: firstly, importing the corresponding modules of finite element software in the development platform. For example, ABAQUS imports a series of modules such as section and part, Plaxis imports Plaxis modules, ANSYS imports ansys-mapdl-core, pyaedt and other modules; then creating a new project or a new instance according to the initialization method of the corresponding module of the programming language, and completing the operations such as size setting, structure division, and set assignment of the model by calling the sub-methods of the instance. For highly structured models, the for loop can be used to complete the repeated appeals of the instances. Furthermore, the material attributes of the model are set and given by the material module. The analysis step of the model is set by the analysis step module, the actual parameters of sub-methods in the analysis step module are changed, and the attributes such as the type, frequency, initial value and tolerance of the analysis step are set. The sub-methods under the case-based load module are called to complete the load application and boundary condition setting of the model. Finally, through the sub-method of case-based meshing module, the mesh generation and mesh attribute setting of the model are completed and submitted for calculation. According to the above method, only by setting the constitutive method and boundary conditions of the calculation model in advance, variables such as load, material properties, cross-section shape and size of the filling subgrade can be obtained in real time during the construction process, and then the variables can be used as actual parameters to be transmitted into the compiled finite element secondary development modeling program, so that the calculation model can be automatically generated, submitted for calculation and key data such as displacement, deformation and stress distribution of the filling subgrade based on the current external conditions can be obtained.
The longitudinal dimension of the filling subgrade is long, and the calculation of the three-dimensional model is large. In this embodiment, the calculation model is simplified as a two-dimensional plane strain problem. The top width of subgrade is 15 m, the bottom width is 120 m, the slope is 1: 2, and the height of subgrade is 35 m. The width of the foundation is m and the thickness is 40 m. The minimum layer thickness is 2 in Therefore, the subgrade needs to be filled in about 18 layers. This embodiment is based on the secondary development platform of the finite element software, which realizes the rapid modeling and analysis of simplified subgrade.
First, enter the program statement of the newly-building window, and set the transformation method of the model as index number search or coordinate point search through the corresponding program statement. Then, step 1: Create a component instance object, and set the instance dimension, instance window size, instance generation method and other parameters through formal parameters respectively. Then, according to the size of the fill subgrade, create the entity using the method of point, line or rectangle of the instance object, and use the formal parameters in the sub-method to create the entity, including: The top width of subgrade BI, bottom width of subgrade B2, filling height H1, foundation width B, foundation thickness H, etc. In the segmentation sub-method of the example, circular to statements are used to segment the entity and simulate the layered filling condition. Finally, the set of parameters is set, and the line set, surface set, ground set and fill set of the instance object are named in a structured form respectively. Such as fill-1, fill-2... Step 2: In the materials module, each part of the model instance is given different material properties through the sub-method provided in the finite element software material library. In this example, the Duncan-Chang model is used for the subgrade material, and the Mohr-Coulomb model is used for the foundation material. The layered subgrade uses circular statements to give properties to the material. Step 3: Create the analysis step of the model. Use the analysis step module of the finite element secondary development platform to create the analysis step required by the model, such as geostatic analysis, loading analysis and consolidation analysis.
For the filled subgrade, cyclic statements can be used to create the analysis step of layered filling load application and consolidation settlement, and the corresponding incremental step name, initial incremental step, minimum incremental step and maximum incremental step are set in the form of formal parameters in the sub-method of the analysis step module. Step 4: Aiming at the construction stage of filling subgrade, parameters are set in the corresponding analysis step to simulate killing or activating the filling subgrade by using the life-and-death unit method in the contact module of the finite element secondary development platform. Step 5: Based on the structured named set of Step 1, the displacement and load of the model can be set using circular statements; The boundary conditions are set by the displacement constraint module of the instance model, and the corresponding parameters of action time, load form and load size are set. Step 6: Set the mesh seed size with the judgment statement. If the filling layer thickness is greater than 2 m, the seed size is 1. Otherwise, set the seed size to 1/2 the thickness of the filling layer; Set the grid properties through the sub-method of the grid module. Finally, the grid generation method is used to generate the grid and submit the calculation. A finite element model generated before the calculation was submitted is shown in FIG 5.
Step 5: judging whether the engineering deformation control indicator of the filling body under the current conditions meets the requirements of the standard design and use. If not, go back to Step 3 and repeat the process until the engineering deformation control indicator meets the requirements.
fo Combine the porosity and compaction degree characteristics of the filling subgrade obtained in Step 3 with the deformation calculation results obtained in Step 4, comprehensively judge whether the subgrade deformation control indicator meets the design requirements, and transmit the final data to the cloud 13 for subsequent analysis. If the subgrade deformation control indicator does not meet the design requirements, it is necessary to repeatedly treat the unconsolidated section of subgrade reflected in the analysis results until the deformation control requirements are met. The overall construction schematic diagram is shown in FIG. 2.
The above are only the preferred embodiments of this application, but the scope of protection of this application is not limited to this. Any changes or substitutions that may be easily thought of by those skilled in the technical field within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims (10)

  1. Claims I. A three-dimensional intelligent filling construction management method of subgrade assisted by mixed reality (MR) technology, comprising--coupling a three-dimensional geological model of a filling road section with a subgrade solid model to obtain an MR three-dimensional realistic model; obtaining real-time three-dimensional positioning information of a filling machinery and relative point cloud coordinates of control points on a surface of a filling body, coupling the real-time three-dimensional positioning information with the relative point cloud coordinates of the control points, obtaining absolute three-dimensional coordinates of a filling surface, associating the absolute three-dimensional coordinates of the filling surface with the MR three-dimensional realistic model, and filling the subgrade based on an association result; obtaining a scanning characteristic image of the filling subgrade based on electromagnetic wave detection, and obtaining a compaction degree of the filling subgrade based on the scanning characteristic image after the subgrade is filled; carrying out a semi-structural analysis on the subgrade solid model to obtain a simplified calculation model of the filling subgrade, and inputting variable parameters of the filling subgrade into the simplified calculation model to obtain deformation data of the filling subgrade; and judging whether subgrade deformation control indicators meet design requirements based on the compaction degree of the filling subgrade and the deformation data of the filling subgrade, if not, re-obtaining the compaction degree of the filling subgrade until the subgrade deformation control indicators meet the design requirements.
  2. 2. The three-dimensional intelligent filling construction management method of subgrade assisted by mixed reality technology according to claim 1, wherein coupling the three-dimensional geological model of the filling road section with the subgrade solid model comprises: building the three-dimensional geological model based on the section characteristics of a designed subgrade, building the subgrade solid model based on elevation information, coupling the three-dimensional geological model and the subgrade solid model, and associating a whole with a corresponding geodetic coordinate system.
  3. 3 The three-dimensional intelligent filling constniction management method of subgrade assisted by mixed reality technology according to claim 1, wherein obtaining the real-time three-dimensional positioning information of the filling machinery and the relative point cloud coordinates of the control points on the surface of the filling body comprises: equipping the filling machinery with a global positioning system (GPS) receiving antenna and a three-dimensional laser scanning device; in the filling process, a sending three-dimensional space coordinate information of the filling machinery to the UPS receiving antenna in real time by a positioning satellite in the filling process, and comprehensively generating the real-time three-dimensional positioning information of the filling machinery by combining a differential signal sent by a base station; and obtaining the relative point cloud coordinates of the control points on the surface of the filling body by the three-dimensional laser scanning device.
  4. 4. The three-dimensional intelligent filling construction management method of subgrade assisted by mixed reality technology according to claim 1, wherein obtaining the scanning characteristic image of subgrade based on electromagnetic wave detection comprises: continuously emitting electromagnetic waves are into the filling body by an electromagnetic wave emitting device to obtain the scanning characteristic image of the filling subgrade in the process of rolling and ramming the filling subgrade, wherein the filling subgrade is rolled and rammed in a serpentine-shaped superposition mode.
  5. 5. The three-dimensional intelligent filling construction management method of subgrade assisted by mixed reality technology according to claim 1, wherein obtaining the compaction degree of filling subgrade based on the scanning characteristic image comprises: extracting pixel blocks smaller than a preset value in the scanning characteristic image based on a convolution neural method, calculating the ratios of all the pixel blocks in the scanning image to obtain a porosity of the filling subgrade, and obtaining the compaction degree of the filling subgrade based on the porosity.
  6. 6. The three-dimensional intelligent filling construction management method of subgrade assisted by mixed reality technology according to claim 5, wherein the convolution neural method is as follows: y(t) = x(t) * h(t) = -F: +.7 f (x,y) * g(x + Ax, y + Ay)dx dy, wherein f (x, y) is a value of (x,y) in the original picture, g(x + Ax,y + Ay) is a value of (x + Ax,y + Ay) after mapping and biasing an original function, Ax is an offset on the X axis of a picture pixel, Ay is the offset on the Y axis of the picture pixel, y(t) is a result function of x(t) and h(t), and x(t) is an input function and h(t) is a response function.
  7. 7. The three-dimensional intelligent filling construction management method of subgrade assisted by mixed reality technology according to claim 5, wherein extracting pixel blocks smaller than a preset value in the scanning characteristic image based on a convolution neural method comprises: extracting different characteristics of the scanning characteristic image to obtain the original characteristic image, combining convolution kernels with different sizes or characteristics into a unit, and carrying out a convolution operation on the original characteristic image and different convolution kernels in the unit.
  8. 8. The three-dimensional intelligent filling construction management method of subgrade assisted by mixed reality technology according to claim 7, wherein carrying out a convolution operation on the original characteristic image and different convolution kernels in the unit comprises: padding the scanning characteristic image before the convolution operation to make convolution kernels with different sizes corresponding to different padding layers, wherein a convolution step length is all set to 1 in the convolution operation, outputting several images with a same pixel size as the original image respectively; using a Rectified Linear Unit (ReLU) activation function to process output images to eliminate non-zero pixels generated in the convolution process, and then using the output images as input of a neural network to carry out a next convolution operation, wherein the pixel size of the image is always kept unchanged in the convolution process
  9. 9. The three-dimensional intelligent filling construction management method of subgrade assisted by mixed reality technology according to claim 5, wherein a formula to obtain the compaction degree of the filling subgrade based on the porosity is as follows: wherein P is the compaction degree and cp. is the porosity
  10. 10. The three-dimensional intelligent filling construction management method of subgrade assisted by mixed reality technology according to claim I, wherein obtaining deformation data of filling subgrade comprises: obtaining a load condition, a material property, a section shape and a size of the filling subgrade in real time during the construction, then inputting the load condition, the material property, the section shape and the size into a filling subgrade calculation model, and outputting the deformation data of the filling subgrade; wherein the deformation data of the filling subgrade comprises a displacement deformation and a stress distribution of the filling subgrade.
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