CN111681185B - Finite element modeling method based on X-ray scanning image of asphalt mixture - Google Patents
Finite element modeling method based on X-ray scanning image of asphalt mixture Download PDFInfo
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- 239000011800 void material Substances 0.000 claims description 9
- 239000011159 matrix material Substances 0.000 claims description 8
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Abstract
The invention discloses a finite element modeling method based on an X-ray scanning image of a bituminous mixture, which comprises the following steps: extracting an X-ray scanning original image of the cross section of the asphalt mixture standard test piece; carrying out gray level conversion, smoothing, denoising and morphological processing on an original image in Matlab software to obtain a high-quality image with clear edges; performing threshold segmentation, watershed segmentation and vectorization processing on the high-quality image to respectively obtain accurate binary images of aggregates and gaps; then leading the vector diagrams of the aggregate and the gap into Auto CAD software, and merging by using a translation function; and finally, importing the merged images into finite element software for finite element modeling and analysis. The method can effectively improve the accuracy of the image segmentation of the asphalt mixture.
Description
Technical Field
The invention relates to a finite element modeling method based on an X-ray scanning image of an asphalt mixture, belonging to the field of road engineering.
Background
The road performance of asphalt road surfaces has been a hot topic in the field of road research for a long time, but when finite element software is used for analyzing the road performance, researchers mostly construct road models from a macroscopic perspective, which improves the working efficiency but neglects the internal structure of the asphalt mixture. Research finds that the distribution of aggregates and voids in the asphalt mixture has an important influence on the overall performance, so that it is necessary to reduce the internal structure of the asphalt mixture and consider the influence of the distribution of the aggregates and voids when constructing a road model. However, at present, no proper finite element modeling method is available, and a finite element model which is consistent with the actual structure of the asphalt mixture can be established.
Disclosure of Invention
Based on the defects of the prior art, the invention provides a finite element modeling method based on an X-ray scanning image of the asphalt mixture, so that a finite element model with the same structure as the asphalt mixture can be created, the internal structure of the asphalt mixture can be more accurately understood, and various characteristics of the asphalt mixture can be further researched.
In order to realize the purpose of the invention, the following technical scheme is adopted:
a finite element modeling method based on an X-ray scanning image of an asphalt mixture comprises the following steps:
step 1: scanning the cross section of the asphalt mixture standard test piece by using an X-ray scanner to obtain an original image of the cross section;
step 2: opening the original image in Matlab software for gray level conversion, and carrying out denoising and smoothing on the obtained gray level image to obtain a processed original image:
and step 3: in Matlab software, firstly performing morphological on operation on the processed original image, and then performing morphological off operation on the processed original image to obtain a high-quality image with clear edges;
and 4, step 4: in Matlab software, calculating the gray value of each pixel point in the high-quality image and the number of the pixel points corresponding to the same gray value to create a two-dimensional histogram of the image;
and 5: in Matlab software, calculating an optimal segmentation threshold of the aggregate based on the two-dimensional histogram, and then performing threshold segmentation on the high-quality image obtained in the step 3 based on the optimal segmentation threshold of the aggregate to obtain a preliminary binary image of the aggregate;
further segmenting the preliminary binary image of the aggregate by adopting a watershed segmentation method in Matlab software to obtain an accurate binary image of the aggregate;
finally, vectorizing the accurate binary image of the aggregate by using vectorizing software to obtain an aggregate vector image with a corresponding format, which can be guided into Auto CAD software for subsequent processing;
step 6: in Matlab software, calculating an optimal segmentation threshold of the gaps based on the two-dimensional histogram, and then directly performing threshold segmentation on the high-quality image obtained in the step 3 based on the optimal segmentation threshold of the gaps to obtain an accurate binary image of the gaps; compared with aggregate segmentation, the segmentation of the gaps is much simpler and more concise, and the segmentation effect can be realized by directly adopting a threshold segmentation method;
finally, vectorizing the accurate binary image of the gap by using vectorizing software to obtain a gap vector image of a corresponding format which can be guided into Auto CAD software for subsequent processing;
and 7: in Auto CAD software, accurately combining an aggregate vector diagram and a void vector diagram by using a translation function, and outputting an image with a format which can be identified by finite element software;
and 8: and (4) importing the image obtained in the step (7) into finite element software for finite element modeling, and obtaining a model for finite element analysis.
Further, the X-ray scanner is a medical X-ray CT scanner.
Further, the asphalt mixture standard test piece in the step 1 is a standard marshall test piece which is a cylindrical test piece, the diameter of the cylindrical test piece is 101.6mm, and the height of the cylindrical test piece is 63.5mm.
Further, the original image of the cross-section in step 1 only contains the common asphalt mixture components, i.e., aggregate, asphalt matrix and voids.
Further, the gray scale conversion, the denoising and the smoothing in the step 2, and the morphological on operation and the morphological off operation in the step 3 are all functions of an image processing tool kit carried by Matlab software.
Further, in step 5, the optimal segmentation threshold of the aggregate is determined by the maximum inter-class variance method, and the obtained preliminary binary image includes a black part and a white part, wherein the white part represents the aggregate.
Further, in step 6, the optimal segmentation threshold of the gap is determined by the maximum inter-class variance method, and the obtained accurate binary image includes black and white portions, wherein the black portion represents the gap.
Further, the WaterShed segmentation method and the maximum class variance method described in step 5 and step 6 are methods carried by Matlab software, namely, watercut Algorithm and Graythresh.
Further, the vectorization software in steps 5 and 6 is Vector Magic software.
Further, the aggregate vector diagram and the void vector diagram obtained in the steps 5 and 6 are images in a dxf format.
Further, the finite element software in step 7 can recognize the image in the format of. Iges.
Further, the finite element software is ABAQUS finite element software.
The invention has the beneficial effects that:
the cross section of the asphalt mixture standard test piece is scanned by a widely used medical scanner to obtain an X-ray CT image, the internal components of the asphalt mixture are divided by an image processing technology, and finally the components are led into finite element software for modeling and finite element analysis. The method can effectively improve the accuracy of image segmentation of the asphalt mixture, more clearly and accurately know the distribution condition of each component in the asphalt mixture, and simultaneously establish a model which is extremely consistent with the actual structure of the asphalt mixture from the viewpoint of understandings. In addition, a certain number of models with different cross sections are built, and a three-dimensional model which is consistent with an actual asphalt mixture standard test piece can be built, so that a very convenient tool is provided for subsequent performance research of asphalt mixtures, and the accuracy and the practicability of research results are greatly improved.
Drawings
FIG. 1 is a flow chart of a finite element modeling method based on an X-ray scanning image of an asphalt mixture according to the present invention;
FIG. 2 is an original image obtained by scanning the cross section of the standard test piece of the asphalt mixture in step 1 of the embodiment of the invention;
FIG. 3 is a diagram illustrating an image obtained by smoothing an original image in step 2 according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating an image after performing a morphological open operation in step 3 according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating an image after performing a morphological close operation in step 3 according to an embodiment of the present invention;
FIG. 6 is a preliminary binary image of the aggregate obtained after thresholding in step 5 in accordance with an embodiment of the present invention;
FIG. 7 is an ideal binary image of the aggregate obtained after watershed segmentation in step 5 according to an embodiment of the present invention;
FIG. 8 is an aggregate vector diagram obtained in step 5 of the present invention;
FIG. 9 is a diagram of the gap vector obtained in step 6 according to the embodiment of the present invention;
FIG. 10 is an image processed by Auto CAD software in step 7 according to an embodiment of the present invention;
FIG. 11 is a diagram of a finite element analysis model obtained in step 8 according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.
Example 1
In an actual asphalt mixture image, some low-contrast areas may exist, and the watershed image segmentation method adopted in the prior art cannot identify the boundary position of the low-contrast area, which may cause a part of pore contours in the pore area to be lost. In order to solve the technical problem, the method carries out dryness removal, smoothing and morphological treatment on the original image before carrying out threshold processing on the original gradient image, and carries out forced minimum operation in the watershed segmentation image process so as to filter some local minimum and further improve the accuracy of asphalt mixture image segmentation.
As shown in FIG. 1, a finite element modeling method based on an X-ray scanning image of a bituminous mixture comprises the following steps.
Step 1: an X-ray scanner is utilized to scan the cross section of an asphalt mixture standard test piece (a standard Marshall test piece with the grading of SMA-13 is selected, the Marshall test piece is a cylindrical test piece, the diameter is 101.6mm, and the height is 63.5 mm), and an original image of the cross section is obtained, wherein the original image specifically comprises an aggregate part, a void part and an asphalt matrix part.
As shown in fig. 2, the black color on the periphery is the background portion, the circle in the middle is the cross section of the test piece, the geometric figure with the outline in the circle is the aggregate portion, the black color in the circle is the void, and the rest in the circle is the asphalt matrix. As can be seen from fig. 2, the edges of the aggregate part outline in the original image are not clear, and the brightness of the middle part of the image relative to the outer side is dark, so that the image needs to be subjected to preliminary processing.
Step 2: opening the original image in Matlab software for gray level conversion, namely converting the original image into a gray level image by using a gray level conversion formula; since most of the collected original images contain noise interference, in this embodiment, the grayscale images are denoised and smoothed to obtain processed original images, as shown in fig. 3.
And 3, step 3: since the smoothed image contour is rough, the morphological opening operation is performed on the processed original image in Matlab software, as a result of which the image contour becomes smooth and the narrow connection is broken, as shown in fig. 4, and in addition, the large contour of the entire image is not shrunk and the position of the image is not changed. Then, a morphological off operation is performed, which also smoothes the image contour, but in contrast to the on operation, it fills small holes and closes narrow discontinuities, as shown in fig. 5, and a high quality image with sharp edges is obtained via the morphological off operation.
And 4, step 4: in Matlab software, calculating the gray value of each pixel point in the high-quality image and the number of the pixel points corresponding to the same gray value to create a two-dimensional histogram of the image;
in a digital image, the luminance of the image is a gray scale value, which ranges from (0, 255), a gray scale value of 0 indicating black and a gray scale value of 255 indicating white. The more the grey value is gradually increased, the closer the color is to white. In the asphalt mixture scanning image, the gray values corresponding to the aggregates, the asphalt matrix and the voids are all in different ranges, as shown in the off-operation image of fig. 5: the part with the maximum brightness in the circular section is the aggregate part, and the gray value of the part is also the maximum; secondly, the darker part is the asphalt matrix part, and the gray value of the asphalt matrix part is smaller; the darkest is the void fraction of pitch (without the surrounding background fraction).
And 5: in Matlab software, calculating an optimal segmentation threshold of the aggregate by a maximum inter-class variance method based on a two-dimensional histogram, and then performing threshold segmentation on the high-quality image obtained in step 3 based on the optimal segmentation threshold of the aggregate (the whole of which the gray value is smaller than the set threshold is changed into black, and the whole of which the gray value is greater than the set threshold is changed into white, so as to extract the aggregate part), so as to obtain a preliminary binary image of the aggregate, as shown in fig. 6, the preliminary binary image includes a black part and a white part, wherein the white part represents the aggregate; as can be seen from fig. 6, there are aggregates that are not completely separated from each other;
and further segmenting the preliminary binary image of the aggregate by a watershed segmentation method in Matlab software to obtain an accurate binary image of the aggregate, wherein the accurate binary image is shown in figure 7. Specifically, the watershed segmentation is directly applied, and the result contains a lot of segmentation, which results in a minimum local area. Therefore, in this embodiment, the specific manner of the watershed segmentation method is as follows: firstly, carrying out first WaterShed segmentation on Matlab software by using a WaterShed Algorithm method, then calling an Imextendmin function carried by the MATLAB software to filter out local minimum (the operation is called as 'forced minimum'), finally modifying a distance change result, and then carrying out second WaterShed segmentation by using the WaterShed Algorithm method to obtain an accurate binary image of the aggregate.
Finally, vector Magic vectorization software is used for vectorizing the accurate binary image of the aggregate, a 'smoothing' function is selected, the fuzzy edge of the image is smoothed, and an aggregate Vector diagram in a dxf format which can be led into Auto CAD software for subsequent processing is obtained, as shown in FIG. 8, the aggregate Vector diagram is the aggregate diagram after segmentation is completed, and white geometric figures in the diagram represent the aggregate.
Step 6: in Matlab software, calculating an optimal segmentation threshold of the gap by a maximum inter-class variance method based on a two-dimensional histogram, and then performing threshold segmentation on the high-quality image obtained in the step 3 based on the optimal segmentation threshold of the gap (the whole gray value smaller than the set threshold is changed into black, and the whole gray value larger than the set threshold is changed into white, so that a gap part is extracted), so as to obtain an accurate binary image of the gap, wherein the accurate binary image comprises a black part and a white part, and the black part represents the gap;
and finally, vectorizing the accurate binary diagram of the gap by utilizing Vector Magic software to obtain a gap Vector diagram in a dxf format, which can be introduced into Auto CAD software for subsequent processing, as shown in FIG. 9, the gap Vector diagram is a segmented gap diagram, and a black geometric figure in the diagram represents the gap.
And 7: in Auto CAD software, aggregate vector diagrams and void vector diagrams are accurately combined by using a translation function, and after the aggregate vector diagrams and the void vector diagrams are combined into a complete asphalt mixture cross-sectional diagram, images are output into images in Iges format which can be identified by Abaqus by using an output function of CAD, as shown in FIG. 10;
and 8: selecting 'Import' → 'Sketch' from Abaqus, and introducing the asphalt mixture section image in the Iges format obtained in the step 7 into ABAQUS finite element software to obtain a model for finite element analysis, wherein as shown in FIG. 11, the black part in the figure shows a gap, the dark gray part shows aggregate, and the remaining light gray part shows asphalt matrix.
The above-described embodiments are exemplary embodiments of the present invention, but the present invention is not limited to the above-described embodiments, and any obvious improvement, replacement or modification by those skilled in the art without departing from the spirit of the present invention is within the scope of the present invention.
Claims (9)
1. A finite element modeling method based on an X-ray scanning image of an asphalt mixture is characterized by comprising the following steps:
step 1: scanning the cross section of the asphalt mixture standard test piece by using an X-ray scanner to obtain an original image of the cross section;
step 2: opening the original image in Matlab software for gray level conversion, and carrying out denoising and smoothing on the obtained gray level image to obtain a processed original image:
and step 3: in Matlab software, firstly performing morphological on operation on the processed original image, and then performing morphological off operation on the processed original image to obtain a high-quality image with clear edges;
and 4, step 4: in Matlab software, calculating the gray value of each pixel point in the high-quality image and the number of the pixel points corresponding to the same gray value to create a two-dimensional histogram of the image;
and 5: in Matlab software, calculating an optimal segmentation threshold value of the aggregate based on the two-dimensional histogram, and then performing threshold segmentation on the high-quality image obtained in the step 3 based on the optimal segmentation threshold value of the aggregate to obtain a preliminary binary image of the aggregate;
further segmenting the preliminary binary image of the aggregate by adopting a watershed segmentation method in Matlab software to obtain an accurate binary image of the aggregate;
finally, vectorizing the accurate binary image of the aggregate by using vectorizing software to obtain an aggregate vector image with a corresponding format, which can be guided into Auto CAD software for subsequent processing;
step 6: in Matlab software, calculating an optimal segmentation threshold of the gap based on the two-dimensional histogram, and then directly performing threshold segmentation on the high-quality image obtained in the step 3 based on the optimal segmentation threshold of the gap to obtain an accurate binary image of the gap;
finally, vectorizing the accurate binary image of the gap by using vectorizing software to obtain a gap vector image of a corresponding format which can be guided into Auto CAD software for subsequent processing;
and 7: in Auto CAD software, accurately combining an aggregate vector diagram and a void vector diagram by using a translation function, and outputting the images in a format recognizable by finite element software;
and 8: and (4) importing the image obtained in the step (7) into finite element software for finite element modeling, and obtaining a model for finite element analysis.
2. The finite element modeling method based on the X-ray scanning image of the bituminous mixture according to claim 1, characterized in that: the asphalt mixture standard test piece in the step 1 is a standard Marshall test piece which is a cylindrical test piece with the diameter of 101.6mm and the height of 63.5mm.
3. The finite element modeling method based on the X-ray scanning image of the bituminous mixture according to claim 1, characterized in that: the original image of the cross section in the step 1 only comprises aggregates, asphalt matrix and voids which form the asphalt mixture.
4. The finite element modeling method based on the X-ray scanning image of the bituminous mixture according to claim 1, characterized in that: in step 5, the optimal segmentation threshold value of the aggregate is determined by the maximum inter-class variance method, and the obtained preliminary binary image comprises a black part and a white part, wherein the white part represents the aggregate.
5. The finite element modeling method based on the X-ray scanning image of the bituminous mixture according to claim 1, characterized in that: in step 6, the optimal segmentation threshold of the gap is determined by the maximum inter-class variance method, and the obtained accurate binary image comprises a black part and a white part, wherein the black part represents the gap.
6. The finite element modeling method based on the X-ray scanning image of the bituminous mixture according to claim 1, characterized in that: the vectorization software in the steps 5 and 6 is Vector Magic software.
7. The finite element modeling method based on the X-ray scanning image of the bituminous mixture according to claim 1, characterized in that: the aggregate vector diagram and the void vector diagram obtained in the steps 5 and 6 are images in a dxf format.
8. The finite element modeling method based on the X-ray scanning image of the bituminous mixture according to claim 1, characterized in that: the finite element software in step 7 can identify the format of the image as an images in Iges format.
9. The finite element modeling method based on the X-ray scanning image of the bituminous mixture according to claim 1, characterized in that: the finite element software is ABAQUS finite element software.
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