Application of digital image processing technology in geotechnical engineering

Application of Digital Image Processing Technology in Geotechnical EngineeringMeng CuiCollege of Civil and Transportation EngineeringHohai UniversityNanjing ,Chinacmyfwy@Baoning HongCollege of Civil and Transportation EngineeringHohai UniversityNanjing ,ChinaHongbaoning24@Qingjun FangCollege of Civil and Transportation EngineeringHohai UniversityNanjing ,Chinafanglin0601320@Abstract—Use the digital image processing method to describe the microstructure characteristics of different geotechnical materials. Firstly, the microstructure images of different geotechnical materials were obtained by Charged Coupled Device (CCD) camera and long distance microscope. Secondly, the clear parts of the same geotechnical material image which were taken at the same point but different focus were obtained by technology of multi-resolution wavelet transform in digital image processing method and combined to get a high definition image. And then the high definition images were enhanced by histogram equalization process to highlight the contrast ratio.Finally, the images were divided according to the threshold which was automatically extracted by the maximum deviation to extract the microstructure parameters of geotechnical material. After processing images of different geotechnical materials, it shows that the digital image processing technology which is studied could well apply to describe the microstructure characteristics of different geotechnical materials.Keywords-digital image; geotechnical material; wavelet transform; microstructure;microstructure parameterI.INTRODUCTIONDigital image processing technology is a new technology in the late 1980s, and with the digital image capture technology increasing, the digital image processing method also has been rapid development which was widely used in military, geography, medicine, biological engineering, geological engineering, and other fields. The application of digital image processing technology in civil engineering, especially in geotechnical engineering, is only in recent ten years developed.By the digital image processing to the microstructure geotechnical material, we can get a clearer image and make further processing about local area of interest to get the required parameters in the research. The microstructure parameters [2] describing geotechnical material mainly include the fractal dimension of the pore, the shape, grading, arrangement of the soil particles and so on [3]~[5],and by the research of the parameters, we can better explain the macroscopic mechanical behaviors in geotechnical engineering. So to explain the macroscopic mechanical behaviors more reasonably, it is necessary to capture the microstructure parameters of geotechnical material correctly and then the image processing method has a high applicability. In the following, with the method mentioned in the abstract, we process the microstructure images of two different geotechnical materials to show its applicability.II.THE TECHNOLOGY OF WAVELETTRANSFORM IMAGE FUSIONActually the section of geotechnical material is uneven, so the sample surface is also uneven to simulate the actual situation, but that causes the depth of field easily when shooting images. The main way to solve the depth of field is to shoot multiple images in the same location and magnification but different focuses, then use the multi-resolution wavelet transform technology to extract and regroup the clear parts of the images. Here we mainly analyze the applicability of wavelet transform in two different geotechnical materials.The images in Figure 1-Figure 3 are taken in the same location of red clay with different focus, and it is not necessary to measure focus in actual shooting, so here we use A, B, C to represent the three different focuses. We can find out that the three images all have local area blurred (marked with thick lines), which is not beneficial to the image analysis and further divided, and then image fusion is necessary. The concrete steps are as follows: firstly fuse the Figure 1 and Figure 2 to get Figure 4, and then fuse the Figure 4 and Figure 3 again to get Figure 5. Besides image fusion is achieved by using the calculation program called “wavelet” which is worked out by microstructure laboratory of geotechnical materials, Geo-Hohai Institute.In Figure 4 we can observe that the blurred area in Figure 1 has been significantly improved, but the upper right parts in both Figure 1 and 2 are blurred, so this area2011 International Conference on Transportation, Mechanical, and Electrical Engineering (TMEE) December 16-18, Changchun, Chinain Figure 4 has not any improvement, but in Figure 5 itshows the improvement.Figure 1.Image with focus A Figure 2. Image with focus BFigure 3. Image with focus C Figure 4. First fusion imageFigure5. Second fusion image Figure 6. Image with focus AFigure 7. Image with focus B Figure 8. Image with focus CFigure 9. First fusion image Figure 10. Second fusion imageThe images in Figure 6-Figure 8 are taken in the samelocation of lime-ash soil with different focuses, and we stilluse A, B, C to represents the three different focuses. Alsothe three images all have local area blurred (marked withthick lines), and we get the high-definition images (Figure9 and 10) in the same way, still the image in Figure 10 fusethe clear parts of Figure 6-Figure 8. Although theproperties between red clay and lime-ash soil are quitedifferent, both the treatment effects are idea from thefusion images, which just show that the use of multi-resolution wavelet transform technique for image fusionhas a high applicability in geotechnical materials.III.HISTOGRAM EQUALIZATIONENHANCEMENT THCHNOLOGYUsing histogram equalization method for imageprocessing is a relatively simple image enhancementtechnology, however, in some cases it could make goodresults, increasing the contrast ratio in some areas,reflecting the major information of the images moreintuitively. Following we will study the applicability ofusing histogram equalization method for imageenhancement in geotechnical materials.Figure 11 and 12 are the histograms of Figure 5 and 10who are fused. Now I will process Figure 5 and 10 with thehistogram equalization method, which is our own programwritten with matlab.Figure 13 and 14 are the processing results of Figure 5and 10 with the histogram equalization method, and wecan find that it is more easy to distinguish the pore andsolid particles in Figure 13 and 14, especially the contrastratio of the pore and solid particles is significantlyenhanced in Figure 14, making the boundaries moreobvious, which is significant for further analysis to themicrostructure of lame-ash soil. And from themicrostructure images of two different geotechnicalmaterials, the processing effect with histogramequalization method is satisfactory, and the method has a05010015020025050100150200250Figure 11. Histogram of Figure 5 Figure 12. Histogram of Figure 10(red clay) (lime-ash soil)Figure13. Equalization of Figure 5 Figure14. Equalization of Figure 10IV.IMAGE SEGMENTATION WITH THETHRESHOLD EXTRACTED BY THE MAXIMUMDEVIATIONImage segmentation is one important step in themicrostructure image processing of geotechnical materials,especially before extracting the structural parameters.Thepurpose of image segmentation is to segment the imagearea with different special meaning, and to microstructureimage of geotechnical materials, the most important is tosegment the pores, solid particles and connection area, andthen to extract the parameters. Now it is difficult to definethe connection area, and here the connection area will beignored and classified to the areas of pores or solid particles. In view of the different size and scope of grayscale in pores and solid particles, the primary segmentation method is to use the threshold, and there are many ways to extract threshold in which the maximum deviation is a more common one. The following we will study its applicability in microstructure image segmentation of geotechnical materials.The software in image segmentation is called “geoimage” who was established by microstructure laboratory of geotechnical materials, Geo-Hohai Institute. The following Figure 15 and 16 respectively are thesegmentation results of Figure 13 and 14.Figure15. Segmentation image Figure16. Segmentation image of Figure13 of Figure 14 The black areas in Figure 15 and 16 mean the pores, while the white areas mean solid particles, and bycomparing with Figure 13 and 14, we can observe that thepores and solid particles are all separated basically, andtheir distribution is not disturbed too much. It shows thatthe method of image segmentation with the threshold extracted by the maximum deviation has a strongapplicability in two different geotechnical materials, andthe effect is satisfactory.Image segmentation is one of the most important steps in the microstructure analysis of geotechnical materials. Inmany literatures, the results with different methods ofimage segmentation on the same geotechnical material were compared, but few compares the results with thesame method but different materials although thedifferences exist. By studying, the method of imagesegmentation with the threshold extracted by the maximum deviation has a strong applicability, which should berecommended in the microstructure analysis of geotechnical material. V.MICROSTRUCTURE PARAMETER EXTRACTING IN GEOTECHNICAL MATERIAL The microstructure changes of geotechnical material can be directly reflected in the macroscopic mechanical behavior, so it seems important to have comparative analysis on the microstructure parameter of geotechnical material, and above image processing work are also in order to get microstructure parameters more accurately. The structures of geotechnical material mainly include: 1. the shape, size, arrangement of space and texture characteristics of the pore; 2. the shape, size, arrangement of space and texture characteristics of the solid particle; 3.connection form (primary for the contact form among the particles and connectivity features). For quantitative analysis to the microstructure of geotechnical material, we must use mathematical tools to describe the three structural elements quantitatively. From the point of view of previous work, the microstructure parameters ofgeotechnical material mainly include: equivalent size of particle, particle entropy, fractal dimension of particle, directional fractal dimension of particle, orientation angle, distribution function of degree of orientation, Euler number and so on. These parameters have their own physical meanings and mathematical expressions, and now these more commonly used include: particle area, particle perimeter, particle circularity, particle degree of orientation, particle fractal dimension, particle orientation coefficient, particle orientation uniformity coefficient; pore area, pore perimeter, pore circularity, pore degree of orientation, pore fractal dimension, pore orientation coefficient, pore orientation uniformity coefficient and so on. With the mathematic expressions of these parameters, we can compile program or software for repeated use, and the software “geoimage” compiled by microstructure laboratory of geotechnical materials, Geo-Hohai Institute could Figure out the above microstructure parameters. The results that extracting microstructure parameters of Figure 15 and 16 with geoimage are shown in table 1 and 2.DŽIn table 1 and 2 , these extracted microstructureparameters can be used in mechanism study of structuredamage on geotechnical material, and coupled withcomparative analysis of macro-tests, the significance ishuge. TABLE I. P ARAMETERS E XTRACTED I N F IGURE 15 Particle area Pore area 55.70385 44.29615 Particle perimeter Pore perimeter36.852573 30.41811 Particle circularity Pore circularity 0.825759 1.062973 Particle degree of orientation Pore degree of orientation 0.704868 0.607319Particle fractal dimension Pore fractal dimension 1.435324 1.363748 Particle orientation coefficientPore orientation coefficient 0.974396 0.972876 Particle orientation uniformitycoefficientPore orientation uniformitycoefficient1.873943 1.973948 TABLE II. P ARAMETERS E XTRACTED I N F IGURE 16Particle areaPore area25.213396 74.786606 Particle perimeterPore perimeter14.337384 20.106337 Particle circularityPore circularity2.302809 0.387112Particle degree of orientationPore degree of orientation0.412375 0.841009Particle fractal dimensionPore fractal dimension1.286155 1.512165Particle orientation coefficientPore orientation coefficient1.013833 1.003299Particle orientation uniformity coefficient Pore orientation uniformitycoefficient2.245388 1.853201 VI.CONCLUSIONThe image processing method which was selected toprocess two very different geotechnical materials in soil properties has a strong applicability according to theprocessing results, and can be used as the method in extracting microstructure parameters of geotechnical material. 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