innovations in optical geo-characterization

Innovations in Optical Geo-Characterization

Roman D. Hryciw1, M. ASCE, Junxing Zheng2, Hyon-Sohk Ohm3, Jia Li4

1Professor, Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor; [email protected]

2Graduate Student Research Assistant, Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor; [email protected]

3Research Fellow, Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor; [email protected]

4Assistant in Research, Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor; [email protected]

ABSTRACT: With exponential increases in digital camera resolution, the last 15 years have witnessed major advances in the use of image analysis in geotechnical engineering. This paper highlights the history of photography and image analysis. It includes a database of 100 recent references describing the use of image analysis in four geotechnical areas: site characterization; earth mass characterization; particle characterization; motion and deformation. Also described are the authors’ development of image analysis systems for particle size and shape characterization. They include the Translucent Segregation Table (TST), Sedimaging, the Vision Cone Penetrometer (VisCPT), Stereophotography and In-situ Particle Tracking. Future research will lead to the estimation of intrinsic soil properties based on detailed assessment of particle size and shape distributions from images of both non-contacting and three-dimensional assemblies of particles.

INTRODUCTION If a picture is worth a thousand words, the 1820’s invention of photography eclipsed Guttenberg’s 15th century printing press in terms of its significance to science, the arts and man’s ability to learn from the past in order to advance more prosperously into the future. Analysis of photographic images naturally followed. By the 1850’s war photography was used to document battles and to plan for subsequent combat. Civil engineers also soon availed themselves of images; aerial photography from balloons was patented in the 1850’s by Gaspar Felix Tournachon in France for mapmaking and city planning. At about the same time, the 1851 World’s Fair in London showcased stereophotography.

The great civil engineering achievements of the next century were captured on film-based photographs. Testimony to the significance of image analysis to civil engineering was provided in the inaugural July, 1930 Volume 1, Number 1 issue of ASCE’s Civil Engineering magazine which featured on its front cover an aerial

97Geo-Congress 2014 Keynote Lectures, GSP 235 © ASCE 2014

Geo-Congress 2014 Keynote Lectures

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photograph of the Black Canyon of the Colorado River with the anticipated Boulder (now Hoover) Dam sketched-in at its anticipated location. At the other end of the image scale range, the early 1930’s also saw the invention of the scanning electron microscope (SEM) that soon allowed visual characterization of micron-sized clay particles.

Many of today’s image-based techniques utilized in geotechnical engineering were already used at the time of the First International Conference on Soil Mechanics and Foundation Engineering in 1936. Colored sand bands revealed dislocation patterns in soil masses, movement direction of particles beneath loaded model footings behind translucent walls was captured by long film exposures, flow lines identified by fluorescent dyes were photographed and 800X “micrographs” of soil particles revealed their shapes. However, absent digital cameras and computers, the image interpretations in these studies were performed manually.

Manual analysis of images continued for another 60 years while image processing methods were being developed in anticipation of digital cameras and personal computers. The 1969 invention of the charge coupled device (CCD) by Willard Boyle and George E. Smith of AT&T Bell Labs marked the beginning of digital photography. However, it was not until the early 1990’s when digital cameras became commonplace that engineers could avail themselves of the technology. Widespread image analysis would wait to until the mid-1990’s when desktop computers became powerful enough for the requisite digital computations and storage devices could preserve the images. Driven by both commercial and recreational demands, the 2000’s and early 2010’s saw exponential advances in digital single lens reflex (DSLR) camera resolutions at continuously decreasing costs. Geotechnical engineering and earth sciences were particularly blessed with the availability of the high resolutions that continue to improve yearly. Recent high resolution images of Mars provide unprecedented insight to the geomorphology of the red planet and image processing is allowing for interpretation of its composition and relief. NASA recently combined 900 i mages taken with a 1200 pi xel x 1200 pixel camera into a one-billion pixel panoramic view in which the sizes and shapes of even gravel particles can be determined. This paper, however, will focus on the advances in image collection and analysis that is transforming the way common terrestrial geotechnical tasks are performed today, with emphasis on l aboratory and in-situ soil characterization.

IMAGE ANALYSIS IN GEOTECHNICAL ENGINEERING Recent geotechnical and engineering geology literature is abundant on the topic of image-based analysis. The authors have prepared Table 1 which summarizes recent examples in a compact format. The table is organized by four areas with subsections:

1. Site Characterization: ground surface and stratigraphic analysis; 2. Mass Characterization: detection of fractures, defects, surfaces and fabric; 3. Particle Characterization: size, size distribution, shape and angularity; 4. Motion and Deformation: laboratory and field scales.



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Table 1. Image Analysis in Geotechnical Engineering Category/Reference Loc. Hard. Mat. Features of Interest Size Analysis Tools

1. Ground Surface Interpretation Sjostrom et al. (2001) A S R River beds m Edge detection Nyander et al. (2003) L LP S River beds mm Wavelet transformation Turel and Frost (2012) A S S Landslides km Feature detection

2. Stratigraphic Analysis Ehrlich et al. (1984) L M R Pore sizes mm Math. morphology Francus (1998) L M, E S Particle size variations mm Filtering and segmentation Ghalib et al. (2000) L, B C S Soil stratigraphy μm-mm Image texture analysis Nederbragt and Thurow (2001) L DC S Varves cm Smoothing Hryciw et al. (2009) L C S Thin layers and seams μm-mm Wavelet transformation Shaffner et al. (2009) B D R Rock stratigraphy m Image mapping

3. Fractures, Defects and Surfaces Bagde et al. (2002) L, S SC R Rock fractures cm-m Transform. & segmentation Chen et al. (2004) L, S M R Rock surface mineralogy mm Segmentation Take et al. (2007) A D G Wrinkles in geosynthetics cm-m Transform. & segmentation Piñuela et al. (2007) B M S Water migration pathways in clay m Wavelet transformation Amarasiri et al. (2009) S P Pavement cracks mm Ward’s reflection Benavente and Pina (2009) L SC R Rock surface cm Math. morphology Mlynarczuk (2010) L LP R Rock surface μm-mm Math. morphology Adu-Gyamfi et al. (2011) S P Pavement cracks mm Multi-resolution image mining Lee et al. (2013) L, S D R Tunnel liner cracks mm Image rectification

Location (camera): L=lab; A=aerial S=ground surface; B=below surface Hardware: S=satellite; SC=scanner; D=DSLR; C=CCD; M=microscope with digital camera; E=SEM; LP=laser profilometer;

N=neutron imager; X=X-ray; LD=laser diffraction; DC=digital camera back; EN=endoscope Material: S=soil; R=rock; P=pavement; G=geosynthetic



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Table 1. (continued) Image Analysis in Geotechnical Engineering

Category/Reference Loc. Hard. Mat. Features of Interest Size Analysis Tools

4. Fabric Bowman and Soga (2003) L M S Particle orientation and voids μm-mm Segmentation Hu et al. (2005) S M S Compacted clay μm Transform. & morph. operation Kim et al. (2012) L N S Pore water distribution μm-mm 3D watershed segmentation Kim et al. (2013) L X S Voids and particle sizes μm-mm 3D watershed segmentation Ohm and Hryciw (2014) L D S Soil fabric and particle orientation μm-mm Wavelet transformation

5. Particle Size Devaux et al. (1997) L C S Particle assembly μm-mm Math. morphology Shin and Hryciw (2004) L C S Particle assembly mm Wavelet transformation Breul and Gourves (2006) L EN S Particle assembly mm Textural analysis Amankwah and Aldrich (2011) L S, R Particle assembly mm Wavelet transformation Zheng and Hryciw (2014) L D S, R Contacting particles on a flat surface mm Stereophotography

6. Particle Size Distribution Raschke and Hryciw (1997) L C S Non-contacting particles on a flat surface μm-mm Segmentation Mora et al. (1998) L C S Non-contacting particles on a flat surface mm Segmentation Ghalib and Hryciw (1999) L C S Contacting particles on a flat surface μm-mm Watershed segmentation Hubner et al. (2001) L C S, R Particles falling through water mm Single particle tracking Aydilek et al. (2002) S M G Pores in geosynthetics μm Threshold. & slicing van den Berg et al. (2002) L M S Contacting particles on a flat surface μm Digital cutting method Banta et al. (2003) C S, R Non-contacting particles on a flat surface mm Edge detect. and Fourier trans. Fernlund (2005) L S Non-contacting particles on a flat surface mm Segmentation Sanchidrian et al. (2006) B R Particle assembly cm-m Transform. & segmentation





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Table 1. (continued) Image Analysis in Geotechnical Engineering

Category/Reference Loc. Hard. Mat. Features of Interest Size Analysis Tools Arasan et al. (2011a) L D S Non-contacting particles on a flat surface mm Thresholding Kumara et al. (2012) L D S Non-contacting particles on a flat surface mm-cm Segmentation Hryciw and Ohm (2012) L D S Particle assembly μm-mm Wavelet transformation Ohm and Hryciw (2013) L D S Contacting particles on a flat surface mm-cm Watershed segmentation

7. Particle Shape and Angularity Kuo et al. (1996) L C S Non-contacting particles on a flat surface μm-mm Segmentation Kuo and Freeman (2000) L, S C S Non-contacting particles on a flat surface μm-mm Segmentation Mora and Kwan (2000) L C S Non-contacting particles on a flat surface mm Fourier transformation Bowman et al. (2001) L E S Non-contacting particles on a flat surface μm-mm Fourier descriptors Sukumaran and Ashmawy (2003) L M S Non-contacting particles on a flat surface μm-mm Segmentation Chandan et al. (2004) L C S Non-contacting particles on a flat surface mm Wavelet transformation Wang et al. (2005) L M S Non-contacting particles on a flat surface mm Fourier transformation Wettimuny and Penumadu (2004) L C S Non-contacting particles on a flat surface μm-mm Fourier transformation Mahmoud and Masad (2007) L M S Non-contacting particles on a flat surface μm-mm Wavelet transformation Tutumluer and Pan (2008) L C S Non-contacting particles on a flat surface μm-mm Math. morphology Matsushima et al. (2009) S X S Non-contacting particles on a flat surface mm Watershed segmentation Gelinas and Vidal (2010) L M S Non-contacting particles on a flat surface μm Watershed segmentation Katagiri et al. (2010) S X S Non-contacting particles on a flat surface mm Not reported Mahmoud et al. (2010) L M S Non-contacting particles on a flat surface μm-mm Wavelet transformation Arasan et al. (2011b) L, S D S Non-contacting particles on a flat surface mm Fractal dimension Tafesse et al. (2012) L S Non-contacting particles on a flat surface mm Segmentation





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Table 1. (continued) Image Analysis in Geotechnical Engineering Category/Reference Loc. Hard. Mat. Features of Interest Size Analysis Tools

8. Laboratory Scale Motion and Deformation Alshibli and Sture (1999) L S Shear band formation mm Feature point detect. & tracking Guler et al. (1999) L S Particle tracking mm-cm 2D image correlation Sadek et al. (2003) L G Soil mass deformation mm-cm 2D image correlation Liu and Iskander (2004) L D S Soil mass deformation mm-cm 2D image correlation Medina-Cetina and Rechenmacher (2006) L D S Soil mass deformation mm-cm 3D image correlation

Sachan et al. (2006) L D S Soil mass deformation mm-cm 2D image correlation Westgate and DeJong (2006) L D S Particle tracking mm-cm Particle image velocimetry Sobhan et al. (2008) L D P Soil mass deformation mm-cm 2D image correlation

9. Field Scale Motion and Deformation Hattori et al. (2003) B D R Tunnel lining m Feature points tracking Hervas et al. (2003) S S Landslide km Change detect. & thresholding Collins and Sitar (2005) S SC S Slopes m Image correlation Guler et al. (2005) S C G Geotextiles mm Image correlation Leu and Chang (2005) B R Tunnel faces m Feature points tracking Ohnishi et al. (2006) S C S Slopes m Image rectification Su et al. (2006) B SC S Excavation m Image correlation Kemeny et al. (2008) S SC R Slopes m Image correlation Quiñones-Rozo et al. (2008) B SC S Excavation m Close-range photogrammetry Wang et al. (2009) L, S D R Tunnel liner m Image rectification Aksoy and Ercanoglu (2012) A S S Landslide km Multi-resolution segmentation Allan and Priest (2012) S SC S Landslide M Image correlation Conte and Coffman (2012) S D S Slopes M Image correlation Suncar et al. (2013) A S S Landslide movement Km Image correlation





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In keeping with the theme of GeoCongress 2014, the focus of this paper is on the characterization of soils in-situ and in the laboratory. The authors’ expertise in this area (or lack of it in other areas) further narrows the paper to image-based characterization of soil particles, their size distributions, particle shape and soil particle tracking.

Recent reports by Masad et al. (2007), Hryciw and Ohm (2012) and Rajagopal (2012) are excellent companions to the present paper as they detail commercial image-based systems for particle characterization. Two noteworthy systems are the Aggregate Image Measurement System (AIMS) (Fletcher et al. 2003) and the University of Illinois Aggregate Image Analyzer (UIAIA) (Rao and Tutumluer 2000). The technical, economic and environmental shortcomings of sieving for grain size analysis are documented by Ohm et al. (2013). They clearly point to the inevitable adoption of image based techniques for soil characterization.

MAJOR HURDLES FOR IMAGE ANALYSIS OF SOILS Unlike the medical, pharmaceutical, manufacturing and food processing industries where image analysis has been employed since the early 1990’s for diagnostic characterization or product quality control, earth materials have lagged behind in the use of digital image analysis. The reason is clear: whereas biological cells, pills and farm produce are relatively uniform in size and shape, particles of earth range over many orders of magnitude in size, from micron-sized clay to meter-sized boulders. As such, no camera can capture an image of, and no image analysis software can analyze the full range of particle sizes in typical soils. Even though pixel resolutions of common commercial cameras (e.g. Nikon, Canon, etc.) have doubled every 3 years since 1999 and are now approaching 40 megapixels, size and shape analysis of even relatively uniform soil specimens spanning two orders of magnitude in diameter requires even higher resolution cameras.

FIG. 1. Soil images: a) 3D assembly, b) 2D non-contacting, c) thresholded binary.

In addition to camera resolution issues, for soils containing particles spanning two orders of magnitude, a problem also exists in capturing an image which is representative of the soil so that the grain size distribution is accurately determined. Fig. 1 shows two extreme situations: Fig. 1(a) is a typical soil image as it would exist in-situ in a three dimensional assembly of multi-sized particles. Because only some of the particles are in full frontal view while others are obstructed by foreground



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particles, no traditional image analysis method can yet accurately determine the particle size distribution from such an image. Fig. 1(b) shows the same soil particles spread out on a flat surface and not in contact with each other. Elementary image analysis techniques such as thresholding and conversion to a binary image as shown in Fig. 1(c) can yield a size and 2D shape of every particle. However, the preparation of a sufficiently large specimen to be statistically valid in which all of the particles are detached and in camera view is practically impossible. In summary, Fig. 1 shows one image that can’t be analyzed and a second that can’t be prepared. The solution to this dilemma comes in the form of two novel laboratory systems for preparing soils for image capture and original image analysis software geared to soils.

THE TRANSLUCENT SEGREGATION TABLE (TST) Referring again to Fig. 1(b), four obstacles had to be overcome to using the flat surface approach to preparing the specimen, capturing an image and analyzing it. Recognizing that every particle in such a system must be individually analyzed (i.e. a deterministic analysis), the approach is presently realistic only for coarse-grained sands (diameter > 2 mm) and gravels so that the total number of particles would be manageable. Secondly, to avoid having smaller particles obstructed from view by larger particles, a nominal amount of segregation of the particles by size is needed. The Translucent Segregation Table (TST) shown in Figs. 2(a) to 2(c) was therefore developed by Ohm and Hryciw (2013). It is a 36 i n. x 36 in. (91 cm x 91 cm) translucent backlit plate which tilts upwards 35 degrees for specimen preparation. The soil is introduced at the top of the incline and the particles slide or roll downward passing beneath a series of “bridges” having progressively smaller underpass heights. Particle blockages behind the bridges can be disrupted by mild brushing of the grains with horizontal strokes. Following segregation, the TST is lowered, the bridges are removed and the backlit specimen is photographed by a ceiling-mounted camera. The TST backlighting enhances the contrast between the particles and the background.

The third obstacle is contacting particles which would result in overestimation of their actual sizes. The key feature of the TST system is a numerical algorithm called watershed segmentation which digitally separates contacting particles thereby eliminating the tedious task of physically separating the particles prior to image capture. Watershed segmentation was introduced for soil particle “detachment” by Ghalib and Hryciw (1999) and its use in the TST is described by Ohm and Hryciw (2013). The watershed segmentation sequence is illustrated in Figs. 2(d) to 2(f).

The final TST obstacle is determining the third dimension of each particle. The captured image will generally show the two larger particle dimensions (d1 and d2) while the smallest dimension (d3) will generally be the vertical. An approximation for the third dimension is provided by the underpass heights of the bridges. However, besides being only a crude estimate, this necessitates a much more careful preparation of the specimen to insure that the particles end up in the proper intervals between bridges. Some errors will be inevitable because not all particles will end up with their smallest dimension vertically. A solution to this problem will be presented shortly.



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FIG. 2. Translucent Segregation Table (TST) system: a) table and camera, b) top view, c) side view, d) binary image, e) Euclidean distance map, f) segmented particles (Ohm and Hryciw 2013).

CORRECTION FOR SIEVE-BASED PARTICLE SIZE There are many ways to define particle size with agreement occurring only for perfectly spherical grains. The “sieve size” is very specific and its long history of use by every soil classification system mandates that it be preserved. As such, image-based particle dimensions must be corrected to obtain an “equivalent sieve size”. Ohm and Hryciw (2013) rigorously showed that if a particle is assumed to be ellipsoidal with d1>d2>d3 the equivalent sieve opening size, de, will be:

( )2 22 3 / 2ed d d= + (1)

Fig. 3 shows a typical TST test results from Hubler et al. (2014), which includes a particle size distribution for pea gravel by Eq. (1) as well as distributions of particle convexity, sphericity and aspect ratio. The four distributions are shown as cumulative percent volume where the relative volumes of individual particles were computed as (d1)(d2)(d3). The aspect ratio is d1/d2. Convexity is the ratio of the observed projected particle area to the area of the smallest convex polygon that contains the particle. Sphericity is the ratio of the perimeter of a circle that has the same projected area as the particle to the actual particle perimeter.



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FIG. 3. TST results: a) particle size, b) aspect ratio, c) convexity, d) sphericity. STEREOPHOTOGRAPHY The shortcomings associated with using the TST bridge underpass heights to estimate the third particle dimension is pointing to the adoption of stereophotography to detail particle sizes and shapes in 3D. As mentioned earlier, stereophotography was already popular in the 1850’s. However, its full capabilities were on hold until recently when high resolution cameras and digital characterization became available.

FIG. 4. Stereophotography: a) contouring, b) 3D view (Zheng and Hryciw 2014).



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The contouring abilities of stereophotography to obtain detailed 3D particle shapes are shown in Fig. 4. Analyzing 600 particles having 2 mm to 40 mm sieve opening sizes, Zheng and Hryciw (2014) observed nearly perfect agreement between stereophotography and manual caliper measurements. The agreement with sieving by Eq. (1) was also perfect. Whereas sieve tests took 30 m inutes and the caliper measurements took days, stereophotography required only seconds to capture and analyze the images. At this time, commercial camera resolutions are approaching the needs of the TST to analyze stereo images of particles as small as 2 mm in a 36 in. (900 mm) field of view.

SEDIMAGING For medium to fine sands and certainly for silts, an individual accounting for every particle as done in the TST test is presently impossible except for small specimens captured at relatively high camera magnifications. As such, a different approach based on image capture of three-dimensional assemblies of particles was developed.

Shin and Hryciw (2004) developed a mathematical wavelet analysis method which yields the dominant particle size from images (or areas of an image) in which the particles are within a size range bracketed by two successive standard sieves. Hryciw et al. (2013) provide a detailed description and analysis of the wavelet method. In short, the analysis yields a wavelet index (CA) for a s quare incremental segment (typically 128 pi xels x 128 pixels) of an image. The wavelet index has been semi-empirically correlated to the dominant particle size in units of pixels per particle diameter (PPD). The same equation and empirical constants work for a wide range of soil types, grain sizes and camera magnifications:

1

log log CAPPD ACA

=

(2)

where CA1 = 2.4 and A = 5.1 for saturated sands photographed behind glass.

Specimen preparation involves segregating the particles by size prior to image capture. This is accomplished by sedimenting the soil through a column of water. The system is shown in Fig. 5. It features a 2 in. x 2 in. x 6 ft (5 cm x 5 cm x 180 cm) water filled column with a detachable sediment accumulator at the bottom which allows image capture through its glass window. A typical image of a sedimented soil is shown in Fig. 5. Hryciw and Ohm (2012) named the test “Sedimaging” (for sediment imaging). Sedimaging is presently used for particles in the 2 mm to 0.075 mm size range. However, the percentage of fines is also determined and the fines are conveniently recovered for Atterberg limits testing. A typical sedimaging test result with comparison to sieving is shown in Fig. 6.

It should be noted that the sedimaging hardware shown in Fig. 5 could be used with other image processing techniques including mathematical morphology (Ohm 2013) and edge pixel density (Jung 2010). However, these methods are not as developed as wavelet analysis.



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FIG. 5. Sedimaging system and a 2NS soil column (Hryciw and Ohm 2012).

FIG. 6. Size distribution based on 5490 sedimaging points for the soil from Fig. 5.

VISION CONE PENETRPOMETER (VisCPT) The senior author’s interest in analysis of soil images began in the mid 1990’s with development of the Vision Cone Penetrometer (Raschke and Hryciw 1997) shown in Fig. 7. It was conceived to capture a continuous stream of images of the soil in-situ and thus eliminate the shortcoming of having no soil specimen to inspect in the CPT. The VisCPT has demonstrated an ability to detect very thin soil seams, easily detect transitions between layers, confirm groundwater conditions and explain anomalous spikes in CPT logs (Ghalib et al. 2000; Hryciw et al. 2003; Hryciw and Shin 2004; Hryciw et al. 2005; Jung et al. 2008; Hryciw et al. 2009). Fig. 8 illustrates a segment of a VisCPT log showing its much higher resolving ability compared to the CPT alone. The Contrast and Homogeneity parameters in Fig. 8 are “image texture” indices developed by Haralick et al. (1973) and adopted for soils by Ghalib et al.



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(1998). For the reasons previously explained, grain size distribution from VisCPT images is not yet determinable. However, the textural indices do reveal changes in soil types.

The resolutions of miniature cameras have not increased since the 1990’s as they have for regular digital cameras. Furthermore, zooming abilities are still not available for such small formats. Therefore, the fixed camera magnifications have to be judiciously set at specific levels to discriminate particle sizes of most interest. For example, at 75 microns to discriminate sands from silts.

FIG. 7. The Vision Cone Penetrometer (VisCPT).

FIG. 8. Stratigraphic resolutions by CPT and VisCPT (after Ghalib 2001).



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SOIL PARTICLE TRACKING Despite its relatively low camera resolutions, the VisCPT is able to detect the movement or flow of soil particles during pauses in CPT advance. The flows result from dissipation of excess pore water pressures induced by the advancing CPT and are possibly indicators of soil liquefaction susceptibility. In some gap-graded soils, a piping of finer particles through a coarser soil skeleton has also been observed by Hryciw and Ohm (2013). An “optical flow” technique is being developed to quantify such motions by tracking changes in soil grain positions through successive video

frames. Fig. 9(a) shows a frame in which the migrations of soil particles was observed in the delineated area. Figs. 9(b) and 9(c) show the computed motion vectors and the areas of soil erosion. The authors believe that the sum of the absolute velocities of soil particles in the eroded area could lead to prediction of soil liquefaction and piping susceptibility.

FIG. 9. Particle tracking: a) single frame with observed motions; b) computed

motion vectors, c) eroded areas identified by tracking the soil particles.

FUTURE DIRECTIONS Particle sizes and shapes (just as consistency limits) are merely indices of potential engineering soil behavior. Particle shapes (and certainly not their distributions by size) are not even formally considered in classification systems. This partly explains the wide range of potential behaviors within soil groups. The ability to provide complete size and shape distributions from images of non-contacting particles leads to a narrowing of these estimated ranges. An ability to discern a soil’s intrinsic properties from three-dimensional assemblies is the next frontier. Once developed, even images captured in-situ may be analyzed and when combined with CPT penetration resistances will paint a complete picture of both the intrinsic and state properties and their stratigraphic distributions. With complete knowledge of intrinsic and state properties, the anticipated engineering behavior of a site will be more accurately predicted.



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CONCLUSIONS Image analysis will play an ever increasing role in geotechnical engineering, particularly in landform analysis using satellite images; aerial and surface monitoring of soil and rock mass stability; stratigraphic characterization by borehole cameras and the VisCPT; laboratory grain size and particle shape analysis; scanning electron microscopy of clays; and motion detection of particles in-situ to detect liquefaction and piping susceptibility. As camera resolutions and computing power increase, the optical methods described in this paper will make many mechanical measurement and testing systems less attractive and possibly obsolete. The optical methods provide much more information than their mechanical counterparts. For example, image based analysis of soils also provides detailed information on particle shapes. This holds the promise of estimating intrinsic soil properties based on information derived from high resolution images of geomaterials both in the lab and in-situ.

ACKNOWLEDGMENTS This material is based upon w ork supported by the National Science Foundation under Grant Nos. CMMI 0900105 and CMMI 1300010 and Michigan Department of Transportation Contract No. 2010-0296 Research No. ORE0908. ConeTec Investigations Ltd. and the ConeTec Education Foundation are acknowledged for their support to the Geotechnical Engineering Laboratories at the University of Michigan. Figure 2 is reprinted, with permission, from Geotechnical Testing Journal, Vol. 36, N o. 4, copyright ASTM International, 100 B arr Harbor Drive, West Conshohocken, PA 19428. Drs. Scott Raschke, Ali Ghalib, Seung-Cheol Shin and Yongsub Jung conducted the earlier research described in this paper while Merrick Burch and Robert Fischer helped with the design and construction of the described testing systems.

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