Method for establishing concrete in-situ mesoscopic finite element model based on CT (Computed Tomography) scanning technology

Abstract

The invention discloses a method for establishing a concrete in-situ mesoscopic finite element model based on a CT (Computed Tomography) scanning technology, which is applied to the technical field of mesoscopic simulation of a concrete structure. The method comprises the following steps: firstly, performing XCT scanning on a steel fiber ultra-high performance concrete test piece to obtain a two-dimensional sequence image of an internal structure of the steel fiber ultra-high performance concrete test piece; then, image processing is carried out through Avizo software, and three-dimensional space distribution information of a base body, steel fibers and pores is segmented and extracted; on the basis of the three-dimensional information, a Python program is utilized to generate a finite element mesh, an eight-node hexahedron unit is adopted in a model, and the established model can accurately reflect real mesoscopic characteristics such as fiber orientation, distribution and pore defects.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of concrete structure meso simulation, and particularly relates to a method for establishing a concrete in-situ meso finite element model based on CT scanning technology. BACKGROUND

[0002] Steel fiber reinforced concrete has excellent tensile strength, toughness, impact resistance and fatigue resistance, and is widely used in the field of construction. The internal structure of steel fiber reinforced concrete is complex, and its mechanical properties are not only determined by the properties of each component material, but also significantly affected by the microstructure characteristics such as fiber distribution, orientation, fiber-matrix interface characteristics and internal pore defects.

[0003] In meso simulation, steel fiber reinforced concrete is usually composed of three phases of matrix, pores and fibers. The existing meso modeling method mostly adopts random distribution assumption. Although this method considers the non-homogeneity of the material, it has a large difference from the actual internal structure of steel fiber reinforced concrete, and cannot accurately reflect the real spatial distribution of fibers. At the same time, the existing concrete numerical model generally ignores the pores or generates them by random method, which cannot accurately reflect the influence of real pores on mechanical properties. Finally, the image segmentation mostly adopts a single global threshold method, which has poor segmentation effect on the area where the matrix and micro-pores have close gray values, resulting in deviation of the model from the source. SUMMARY

[0004] The purpose of the present application is to provide a method for establishing a concrete in-situ meso finite element model based on CT scanning technology to solve the above problems.

[0005] To achieve the above purpose, the present application provides the following technical scheme: a method for establishing a concrete in-situ meso finite element model based on CT scanning technology, the specific steps are as follows: S1, performing X-ray computed tomography on a steel fiber reinforced concrete specimen to obtain a two-dimensional sequence image; S2, preprocessing the obtained two-dimensional sequence image, specifically including cropping the two-dimensional sequence image along the material outer boundary, and removing noise from the cropped image by using a median filtering method; S3, performing three-phase separation on the image preprocessed in S2, and extracting the pore phase and the fiber phase by image segmentation processing; wherein the pore phase segmentation adopts direct threshold segmentation and local image segmentation method, and then the pore phases separated by the two methods are merged, and a binary image of the pores is output; the fiber phase segmentation separates the fibers by using a local image segmentation method, and specifically includes skeletonizing the separated fibers to extract the centerline geometric parameters of the fibers; S4, based on the pore binary image obtained in S3, generating a matrix finite element grid by image processing algorithm, and outputting a matrix K file; S5, generating fiber beam element model through coordinate conversion algorithm based on the centerline geometric parameters of the fiber obtained in S3, and outputting a fiber K file; S6, integrating the matrix K file and the fiber K file in S4 and S5, adding corresponding material properties and cross-section properties, and constructing a complete steel fiber reinforced concrete mesoscopic finite element model.

[0006] Preferably, the image preprocessing in S2 specifically comprises selecting a suitable region through a Volume Edit command and cutting parts outside the selected range; and adopting a median filtering method to add a Media Filter command to filter and remove noise, thereby generating filtered data.

[0007] Preferably, the pore phase segmentation in S3 specifically comprises screening larger pores through direct threshold segmentation, and then screening smaller pores by searching for maximum values or minimum values in the image within a given gray value range through a local image segmentation method; using a Separate Objects command to separate the pore phase, merging the pore phases separated by the two methods, and outputting a binary image of the pores.

[0008] Preferably, the fiber phase segmentation in S3 specifically comprises separating fibers by using a local image segmentation method, adding an Interactive Top-Hat command to determine the segmentation threshold value based on the position of the peak and valley values in the image gray histogram, and separating the fiber phase with a fiber segmentation threshold value of about 120 (8-bit gray image); performing skeletonization operation on the separated fibers, using an automatic skeletonization algorithm (Auto-Skeleton) to generate fiber skeletons; obtaining spatial distribution information by spatial graph statistics, outputting fiber end point and length information, and saving the information in an Excel format file.

[0009] Preferably, the generation of the matrix finite element grid through the image processing algorithm in S4 specifically comprises reading the pore binary image output in S3 and constructing a three-dimensional body data; directly mapping the image voxels to eight-node hexahedral elements, and judging by gray threshold value to generate elements only for voxels corresponding to the matrix material, without generating grids for the pore region, thereby finally obtaining a three-dimensional finite element grid containing only the matrix material.

[0010] Preferably, the generation of the fiber beam element model through the coordinate conversion algorithm in S5 specifically comprises reading the Excel format file output in S3, extracting the length, starting node number and ending node number of each fiber; constructing a fiber end point coordinate matrix, and writing the XYZ coordinates of all fiber end points in sequence; assigning continuous node numbers to each fiber end point: node number, X coordinate, Y coordinate, Z coordinate; creating a beam element for each fiber: element number, material number, starting node, ending node, direction node; and generating a final fiber model K file.

[0011] Preferably, the finite element model in S6 is specifically fused by a unified spatial coordinate system, the matrix K file and the fiber K file; and the concrete constitutive model is defined for the matrix entity unit, the steel constitutive model and the beam section attribute are defined for the fiber beam unit, a complete steel fiber concrete mesoscopic finite element model is constructed, and finally the complete steel fiber concrete mesoscopic finite element model is exported in the form of a K file.

[0012] The technical effects and advantages of the present application are: 1. The existing steel fiber ultra-high performance concrete mesoscopic modeling only contains fiber and matrix components, rarely contains pore components, the modeling method provided by the present application realizes automatic generation from data processing to finite element model through a Python program, which not only ensures the modeling accuracy, but also greatly improves the work efficiency; the established model can accurately reflect the internal structural characteristics of steel fiber ultra-high performance concrete.

[0013] 2. The modeling method provided by the present application combines the global and local hybrid segmentation method, effectively overcomes the problem of over-segmentation or insufficient segmentation when a single global threshold method is used to segment the steel fiber concrete image, and can accurately separate the real structure including micron-sized pores, thereby effectively avoiding the phenomena of over-segmentation and insufficient segmentation; 3. The modeling method provided by the present application uses a Python program to generate a matrix finite element grid, which adopts a grid generation algorithm based on voxel direct mapping, and the algorithm is simple and efficient in logic; the existing concrete finite element modeling method usually generates a complete matrix grid first, and then performs complex grid cutting or deletion according to the pore position, which is tedious and prone to errors; in the present application, the gray scale is judged during the matrix grid generation process, and the unit is selectively not generated for the pore voxel, which is high in calculation efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 is the steel fiber ultra-high performance concrete XCT image denoising and cutting processing of the present application; Figure 2 is the steel fiber ultra-high performance concrete XCT image pore phase segmentation of the present application; Figure 3 is the steel fiber ultra-high performance concrete XCT image fiber phase segmentation of the present application; Figure 4 is the Python software reconstruction steel fiber ultra-high performance concrete finite element model flowchart of the present application; Figure 5 is the steel fiber ultra-high performance concrete three-dimensional finite element model of the present application. DETAILED DESCRIPTION

[0015] With reference to the drawings and the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of the present application.

[0016] The present application provides a method for establishing a concrete in-situ mesoscopic finite element model based on a CT scanning technology, and the specific steps are as follows: S1, performing X-ray computed tomography on a steel fiber reinforced concrete sample to obtain a two-dimensional sequence image; S2, preprocessing the obtained two-dimensional sequence image, specifically including cutting along the material outer boundary, and removing noise from the cut image by using a median filtering method; S3, performing three-phase separation on the image after S2 preprocessing, and extracting the pore phase and the fiber phase by image segmentation processing; wherein the pore phase is separated by using direct threshold segmentation and local image segmentation method, and then the pore phases separated by the two methods are combined, and a binary image of the pore is output; the fiber phase is separated by using the local image segmentation method, and specifically includes skeletonizing the separated fiber to extract the centerline geometric parameters of the fiber; S4, based on the binary image of the pore obtained in S3, generating a matrix finite element grid by an image processing algorithm, and outputting a matrix K file; S5, based on the centerline geometric parameters of the fiber obtained in S3, generating a fiber beam element model by a coordinate conversion algorithm, and outputting a fiber K file; S6, integrating the matrix K file and the fiber K file in S4 and S5, adding corresponding material properties and cross-section properties, and constructing a complete steel fiber reinforced concrete mesoscopic finite element model.

[0017] Specifically, the image preprocessing in S2 specifically selects a suitable region by a Volume Edit command, cuts the part outside the selected range, and adds a Media Filter command to filter and remove noise from the cut image, to generate filtered data.

[0018] Specifically, the pore phase segmentation in S3 specifically filters large pores by direct threshold segmentation, and then finds the maximum or minimum value in the given gray value range by local image segmentation method to filter small pores; the pore phase is separated by using a Separate Objects command, the pore phases separated by the two methods are combined, and a binary image of the pore is output.

[0019] Specifically, the fiber phase segmentation in S3 is specifically to separate the fibers by using a local image segmentation method, add an Interactive Top-Hat command, and determine the segmentation threshold value according to the position of the peak and valley value in the image gray histogram. The fiber segmentation threshold value is about 120 (8-bit gray image), and the fiber phase is separated out. The separated fibers are subjected to skeletonization operation, and an automatic skeletonization algorithm (Auto-Skeleton) is used to generate fiber skeletons. The spatial distribution information is obtained by spatial graph statistics, and the fiber end point and length information are output and saved as an Excel format file.

[0020] Specifically, the generation of the matrix finite element grid in S4 by the image processing algorithm is specifically to read the binary image of the pores output in S3 and construct a three-dimensional body data; the image voxel is directly mapped to an eight-node hexahedral element, and the gray threshold value is judged to generate only the element corresponding to the matrix material, and no grid is generated in the pore region, and finally a three-dimensional finite element grid containing only the matrix material is obtained.

[0021] Specifically, the generation of the fiber beam element model in S5 by the coordinate conversion algorithm is specifically to read the Excel format file output in S3, extract the length, starting node number and ending node number of each fiber; construct a fiber end point coordinate matrix, and write the XYZ coordinates of all fiber end points in order; assign a continuous node number to each fiber end point: node number, X coordinate, Y coordinate, Z coordinate; create a beam element for each fiber: element number, material number, starting node, ending node, direction node; and generate the final fiber model K file.

[0022] The finite element model in S6 is obtained by fusing the matrix K file and the fiber K file in a unified spatial coordinate system; and defining a concrete constitutive model for the matrix entity element, a steel constitutive model and beam section properties for the fiber beam element, constructing a complete steel fiber reinforced concrete mesoscopic finite element model, and finally exporting the complete steel fiber reinforced concrete mesoscopic finite element model in the form of a K file.

[0023] Example 1: Step 1: Threshold segmentation of each material phase of the XCT image by Avizo software Import the raw file obtained by XCT scanning into Avizo software, select 8-bit gray image, and fill in the voxel size according to the resolution of the data.

[0024] Step 1.1: Noise reduction and cutting processing of XCT image To avoid the influence of the outer boundary of the material on the threshold determination, the used image is cropped along the outer boundary of the material. The appropriate region is selected by the Volume Edit command, and the part outside the selected range is cut. The cropped image is filtered by the median filtering method, and the Media Filter command is added to filter and remove noise, generating filtered data. As shown in Figure 1 .

[0025] Step 1.2: Pore phase segmentation Combined with direct threshold segmentation (Interactive Thresholding) and local image segmentation method (Interactive Top-Hat), first, the larger pores are screened by direct threshold segmentation, and then the local image segmentation method is used to find the maximum or minimum value in the given gray value range to screen smaller pores. The SeparateObjects command is used to separate the pore phase, and the pore phase separated by the two methods is merged, and the binary image of the pore is output. The Label Analysis command is used to output the pore volume and frequency distribution. As shown in Figure 2 .

[0026] Step 1.3: Fiber phase segmentation The local image segmentation method is used to separate the fiber, and the Interactive Top-Hat command is added to select the appropriate threshold to separate the fiber phase. The skeletonization operation is performed on the separated fiber, and the Auto-Skeleton algorithm is used to generate the fiber skeleton. The spatial distribution information is obtained by Spatial Graph Statistics, and the fiber endpoint and length information is output and saved as an Excel format file. As shown in Figure 3 .

[0027] Step 2: Python-based steel fiber reinforced ultra-high performance concrete mesoscopic finite element model construction Figure 4 .

[0028] Step 2.1: The specific implementation process of the matrix finite element model is as follows: 1. Read the pore binary image sequence output in step 1.2; 2. Determine the original size of the three-dimensional data by reading the first image, and calculate the grid size after downsampling according to the set resampling ratio; 3. Convert each image to a grayscale image and normalize the grayscale value to the range of 0-255; 4. Stack the two-dimensional image slices in order to construct a three-dimensional data cube; 5. Downsample the three-dimensional data to reduce the grid density; 6. Differentiate the matrix and the pores by grayscale threshold, and mark the voxels with grayscale value greater than 100 as the matrix material; 7. Traverse all voxel units, and mark the corresponding 8 corner nodes of the matrix material as valid states; 8. Assign continuous numbers to all valid nodes, and calculate the actual physical coordinates of each node; 9. Create eight-node hexahedral units for each matrix voxel, and connect the corresponding 8 nodes; 10. Write the node and unit information in the LS-DYNA keyword format into the K file.

[0029] Finally, a three-dimensional finite element grid containing only the matrix material is obtained, and the pore region is deleted by threshold screening, realizing the complete conversion from a binary image to a finite element model.

[0030] Step 2.2: Steel fiber finite element model generation specific implementation process as follows: 1. Read the Excel file output in step 1.3, and extract the fiber length, starting node number and ending node number; 2. Initialize the fiber endpoint coordinate storage matrix, traverse each fiber, and extract the three-dimensional coordinates of the starting and ending points; 3. Convert the 0 starting index of Avizo software to 1 starting index of Python, and ensure that the coordinates are correctly matched; 4. Write the reorganized fiber endpoint coordinates into a text file, with each row containing the X, Y and Z coordinates of a node; 5. Assign continuous node numbers to each fiber endpoint, and write the node coordinate information; 6. Create beam units for each fiber, define the unit number, material number, starting node, ending node and direction node; 7. Write the node and unit information in the LS-DYNA keyword format into the K file.

[0031] Step 2.3: Steel fiber ultra-high performance concrete complete finite element model construction process as follows: Open the K files generated in 2.1 and 2.2 through LS-PREPOST software, as shown in Figure 5 Add beam section attribute definition (*SECTION_BEAM) for steel fibers, define material attributes (*MAT_) for the matrix and steel fibers respectively, and finally export the complete finite element model in the form of K file.

[0032] Finally, it should be noted that the above only describes the preferred embodiments of the present application and is not intended to limit the present application. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art will appreciate that the technical solutions described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalent features, and any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for establishing an in-situ micro-finite element model of concrete based on CT scanning technology, characterized in that: The specific steps are as follows: S1. X-ray computed tomography was performed on the steel fiber reinforced concrete specimens to obtain two-dimensional sequential images; S2. Preprocess the acquired two-dimensional sequence images, specifically including cropping the two-dimensional sequence images along the outer boundary of the material, and removing noise from the cropped images using median filtering. S3. Perform three-phase separation on the image preprocessed in S2, and extract the pore phase and fiber phase respectively through image segmentation processing; wherein, the pore phase segmentation adopts direct threshold segmentation and local image segmentation method, and then merges the pore phases separated by the two methods and outputs a binary image of the pores; the fiber phase segmentation adopts local image segmentation method to separate the fibers, specifically including skeletonization processing of the separated fibers to extract the centerline geometric parameters of the fibers; S4. Based on the pore binary image obtained in S3, generate the matrix finite element mesh through image processing algorithm and output the matrix K file; S5. Based on the fiber centerline geometric parameters obtained in S3, generate a fiber beam element model through a coordinate transformation algorithm and output the fiber K file; S6 integrates the matrix K-file and fiber K-file from S4 and S5, adds corresponding material properties and section properties, and constructs a complete steel fiber reinforced concrete mesoscopic finite element model.

2. The method for establishing an in-situ micro-finite element model of concrete based on CT scanning technology according to claim 1, characterized in that: The image preprocessing in S2 specifically involves selecting a suitable area using the Volume Edit command and cropping the portion outside the selected area; applying median filtering to the cropped image and adding the Media Filter command to filter and remove noise, generating filtered data.

3. The method for establishing an in-situ micro-finite element model of concrete based on CT scanning technology according to claim 1, characterized in that: The pore phase segmentation in S3 specifically involves filtering larger pores through direct threshold segmentation, and then using local image segmentation to find the maximum or minimum value in the image within a given grayscale range to filter smaller pores; the pore phase is separated using the Separate Objects command, the pore phases separated by the two methods are merged, and a binary image of the pores is output.

4. The method for establishing an in-situ micro-finite element model of concrete based on CT scanning technology according to claim 1, characterized in that: The fiber phase segmentation in S3 specifically involves using a local image segmentation method to separate fibers, adding an Interactive Top-Hat command, determining the segmentation threshold based on the position of peaks and valleys in the image grayscale histogram, and separating the fiber phase; performing skeletonization operations on the separated fibers, using an automatic skeletonization algorithm to generate the fiber skeleton; obtaining spatial distribution information through spatial graph statistics, outputting fiber endpoint and length information, and saving it as an Excel file.

5. The method for establishing an in-situ micro-finite element model of concrete based on CT scanning technology according to claim 3, characterized in that: In step S4, the image processing algorithm is used to generate the matrix finite element mesh. Specifically, the binary image of pores output in step S3 is read and three-dimensional volume data is constructed. The image voxels are directly mapped to eight-node hexahedral elements. Based on the grayscale threshold, only voxels corresponding to the matrix material are generated as elements. No mesh is generated in the pore region. Finally, a three-dimensional finite element mesh containing only the matrix material is obtained.

6. The method for establishing an in-situ micro-finite element model of concrete based on CT scanning technology according to claim 4, characterized in that: In step S5, the fiber beam element model is generated through a coordinate transformation algorithm by reading the Excel file output in step S3, extracting the length, start node number, and end node number of each fiber; constructing a fiber endpoint coordinate matrix, and writing the XYZ coordinates of all fiber endpoints in sequence; assigning consecutive node numbers to each fiber endpoint: node number, X coordinate, Y coordinate, Z coordinate; creating a beam element for each fiber: element number, material number, start node, end node, and direction node; and generating the final fiber model K file.

7. The method for establishing an in-situ micro-finite element model of concrete based on CT scanning technology according to claim 1, characterized in that: The finite element model in S6 specifically integrates the matrix K-file and the fiber K-file through a unified spatial coordinate system; it defines a concrete constitutive model for the matrix solid element, and a steel constitutive model and beam section properties for the fiber beam element, thus constructing a complete steel fiber reinforced concrete mesoscopic finite element model. Finally, the complete steel fiber reinforced concrete mesoscopic finite element model is exported in the form of a K-file.

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