Quantification of Aggregate Packing in Asphalt Mixtures...
Transcript of Quantification of Aggregate Packing in Asphalt Mixtures...
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Quantification of Aggregate Packing in Asphalt Mixtures using 3D Image Processing and Artificial Neural
Networks
M. Emin Kutay, Ph.D., P.E.Assistant Professor
Department of Civil & Environmental Engineering
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Outline
IntroductionAggregate packing in HMA
3D X‐ray CT imaging and analysis methods Challenges in processing of AC images
Use of ANN‐based pattern recognition tool to process 3D X‐ray CT images
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Sieve Size (mm)
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Aggregate characteristics in Hot Mix Asphalt (HMA) pavements
Fine graded HMA
Coarse graded HMA
Stone Matrix Asphalt
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Study of Aggregate Packing: Top‐down vs. Bottom‐up
(Lab) (Lab) SPECIMENSPECIMEN
Individual Individual aggregateaggregate3D shapes3D shapes
XX--ray CTray CTImage Image ProcessingProcessing
OUTPUT:OUTPUT:Contact points, Contact points, Orientation, Orientation, Spatial distributionSpatial distribution
(Digital) (Digital) SPECIMENSPECIMEN
Individual Individual aggregateaggregate3D shapes3D shapes
TopTop--downdown(Experimentation)(Experimentation)
BottomBottom--upup(Simulation)(Simulation)
NumericalNumericalSimulationSimulation
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Study of Aggregate Packing: Lab + Image analysis
Varying aggregate type, Varying aggregate type, compaction characteristicscompaction characteristics
Specimen Prep.Specimen Prep.(Lab compacted / (Lab compacted / field core)field core)
Measurement of 3D Measurement of 3D internal geometryinternal geometry
XX--ray CT imagingray CT imaging
Rigorous image Rigorous image processing & analysisprocessing & analysis
Separation of aggregatesSeparation of aggregates
Individual aggregate props:Individual aggregate props:3D orientation, angularity, 3D orientation, angularity, Specific surface area Specific surface area ……etc.etc.
Calculation of contact Calculation of contact pointspoints
TopTop--downdown
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Study of Aggregate Packing: Numerical Simulation
Varying aggregate type, Varying aggregate type, CompactionCompactioncharacteristicscharacteristics
Digital specimen Digital specimen Preparation Preparation
(numerical simulation)(numerical simulation)
Measurement of 3D Measurement of 3D Aggregate shapesAggregate shapes
XX--ray CT imaging, ray CT imaging, LADAR, projection moire LADAR, projection moire interferometryinterferometry
BottomBottom--upup
Individual aggregate packing Individual aggregate packing props: 3D orientation, props: 3D orientation,
spatial distribution, contact spatial distribution, contact points points ……etc.etc.
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Study of Aggregate Packing: Top‐down vs. Bottom‐up
(Lab) (Lab) SPECIMENSPECIMEN
Individual Individual aggregateaggregate3D shapes3D shapes
XX--ray CTray CTImage Image ProcessingProcessing
OUTPUT:OUTPUT:Contact points, Contact points, Orientation, Orientation, Spatial distributionSpatial distribution
(Digital) (Digital) SPECIMENSPECIMEN
Individual Individual aggregateaggregate3D shapes3D shapes
TopTop--downdown(Experimentation)(Experimentation)
BottomBottom--upup(Simulation)(Simulation)
NumericalNumericalSimulationSimulation
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XX‐‐ray Computed Tomography setupray Computed Tomography setup
X-Ray Source
Detector
Specimen
AlgorithmsCT# (in)
CT# (Out)
Vertical shift
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3D reconstruction from image slicesX-ray CT
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Characteristics of aggregates packed in an asphalt mixture
3D Contact points (or influence zone)
3D OrientationSegregation: Spatial distribution of
different sizes3D Angularity, sphericity,
specific surface area and texture
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We want to study: Given : gradation and compaction
level Variation of contact points for
different aggregates: rectangular, flat , elongated and round
Locking point and aggregate degradation
Effect of number of contact pointson HMA performance
Mix design considering contact points and packing
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X‐ray CT Image 3D Analysis Tool
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Challenge in processing of X-ray CT images: separation of aggregates
IdealIdeal RealReal
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Thresholding and labeling (2D description)
Intensity Distribution
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Grayscale image Thresholded binary image
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Thresholding and labeling(2D description)
Binary image Labeled image
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Separation of aggregates:Separation of aggregates:3D image processing steps3D image processing steps
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Calculation of aggregate properties
3D Contact points 3D Orientation 3D Volume, Angularity, Specific surface area
Segregation: Spatial distribution of different sizes
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Calculation of contact points
Shortest distance between surface voxels
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Validation of the algorithms
Orig
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Sieve Size (mm)
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Original gradationImage-based gradation
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Internal structure change with compaction – (e.g., locking point)
NdesignN1 N2 N4 N5 Gyrations (N)N6
Height (mm)
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Good Poor
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Good Poor
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Image A
Rigorous Image Processing:Image noise filters (Gaussian, median …etc) H-max regional maxima filterWatershed transformThresholding
Good binary image
Image B Poor binary image
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Use of ANN to recognize aggregates in X‐ray CT images Trained to recognize coarse aggregates (>4mm) ANN Architecture was similar to those used in the
field of face detection
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Preparation of Training Dataset for the ANNManually crop and save boxes from the X‐ray CT images
Two types of training datasets were prepared: Aggregates: boxes encompassing only one whole coarse aggregate (the box may also include other smaller size material)
Non‐aggregates: random voxels which does not include whole aggregate
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p gANN
X-ray CT images of 11 different specimens were used to obtain: 20 aggregate and 20 non-aggregate input
boxes for each specimen A full dataset of 440 boxes.
Of the 11 specimens: 7 of them were utilized in training the
ANN (i.e., total 7 × 40 = 280 input boxes) and
4 of them were used for testing the ANN (i.e., total 4 × 40 = 160 input boxes).
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Preparation of Training Dataset for the ANN (Cont'd) Wide variety of aggregate sizes and shapes
A standard input dimension was needed Resizing (3D interpolation) → 20×20×20 voxels Reshaping → 8000×1 voxels
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Structure of the ANN A feed-forward (backpropagation) network
1 hidden layer of 160 neurons Output layer had 1 neuron
H160x8000W
H160x1b
1x8000p
H160x1n
o1x160W
ob
on
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Hidden layer Output layerInput
y
tansig tansig
)(tansig HHH bpWa )(tansig oHo by aW
Output
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Training ANN
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Training
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performance goal
H160x8000W
H160x1b
1x8000p
H160x1n
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Hidden layer Output layerInput
y
tansig tansig
)(tansig HHH bpWa )(tansig oHo by aW
Output
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Performance of the ANN
ANN was tested using the input dataset not utilized in the training
After the ANN was trained: Weights and bias vectors were used to
calculate the output scalars (y) of the testing images
The output was compared with the known target values (yt)
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Performance of the ANN
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Specimen: acl2
Correct = 100%
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Specimen: bcm1
Correct = 100%
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Specimen: 9.5C25
Correct = 95%
Incorrect = 5%
incorrect
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Specimen: 19F50
Correct = 95.24%
Incorrect = 4.76%
incorrect
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Ongoing Work on ANN Search within an X-ray CT
image of coarse aggregates with different sizes Get spatial distribution of
different sizes (segregation)
A further ANN to get the boundaries of the aggregate within each box
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M. Emin Kutay, PhD, PE Michigan State University
Department of Civil & Environmental Engineering
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3D numerical simulation of compaction3D numerical simulation of compactionusing Dissipative Particle Dynamics (DPD)using Dissipative Particle Dynamics (DPD)
Animation -1
Animation -2Animation-3
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Original Gaussian smoothed Hmax filtered
Watershed transformed image
Watershed transformed image
Watershed transformed image
Not good Not good Good
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Original Gaussian smoothed Hmax filtered
Watershed transformed imageInverted Watershed lines
Filtered watershed transformation
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Thresholding IsuesThresholding Isues……Different intensities
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Dynamic thresholding in 2DDynamic thresholding in 2D
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Dynamic thresholding in 3DDynamic thresholding in 3D
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