SmartDATA Lab Publications
2023
163.
Harsha Vardhan Tetali, Joel B Harley
Physics-Informed Guided Wavefield Data Completion Proceedings Article
In: Proc. of the International Workshop on Structural Health Monitoring, pp. 9, 2023.
@inproceedings{Tetali2023-xp,
title = {Physics-Informed Guided Wavefield Data Completion},
author = {Harsha Vardhan Tetali and Joel B Harley},
url = {https://www.dpi-proceedings.com/index.php/shm2023/article/view/36993},
doi = {10.12783/shm2023/36993},
year = {2023},
date = {2023-09-01},
booktitle = {Proc. of the International Workshop on Structural Health
Monitoring},
pages = {9},
abstract = {Ultrasonic wavefields are widely employed in nondestructive
testing and structural health monitoring to detect and evaluate
structural damage. However, measuring wavefields continuously
throughout space poses challenges and can be costly. To address
this, we propose a novel approach that combines the wave equation
with computer vision algorithms to visualize wavefields. Our
algorithm incorporates the wave equation, which encapsulates our
knowledge of wave propagation, to infer the wavefields in regions
where direct measurement is not feasible. Specifically, we focus
on reconstructing wavefields from partial measurements, where the
wavefield data from large continuous regions are missing. The
algorithm is tested on experimental data demonstrating its
effectiveness in reconstructing the wavefields at unmeasured
regions. This also benefits in reducing the need for expensive
equipment and enhancing the accuracy of structural health
monitoring at a lower cost. The results highlight the potential
of our approach to advance ultrasonic wavefield imaging
capabilities and open new avenues for Nondestructive testing and
structural health monitoring.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ultrasonic wavefields are widely employed in nondestructive
testing and structural health monitoring to detect and evaluate
structural damage. However, measuring wavefields continuously
throughout space poses challenges and can be costly. To address
this, we propose a novel approach that combines the wave equation
with computer vision algorithms to visualize wavefields. Our
algorithm incorporates the wave equation, which encapsulates our
knowledge of wave propagation, to infer the wavefields in regions
where direct measurement is not feasible. Specifically, we focus
on reconstructing wavefields from partial measurements, where the
wavefield data from large continuous regions are missing. The
algorithm is tested on experimental data demonstrating its
effectiveness in reconstructing the wavefields at unmeasured
regions. This also benefits in reducing the need for expensive
equipment and enhancing the accuracy of structural health
monitoring at a lower cost. The results highlight the potential
of our approach to advance ultrasonic wavefield imaging
capabilities and open new avenues for Nondestructive testing and
structural health monitoring.
testing and structural health monitoring to detect and evaluate
structural damage. However, measuring wavefields continuously
throughout space poses challenges and can be costly. To address
this, we propose a novel approach that combines the wave equation
with computer vision algorithms to visualize wavefields. Our
algorithm incorporates the wave equation, which encapsulates our
knowledge of wave propagation, to infer the wavefields in regions
where direct measurement is not feasible. Specifically, we focus
on reconstructing wavefields from partial measurements, where the
wavefield data from large continuous regions are missing. The
algorithm is tested on experimental data demonstrating its
effectiveness in reconstructing the wavefields at unmeasured
regions. This also benefits in reducing the need for expensive
equipment and enhancing the accuracy of structural health
monitoring at a lower cost. The results highlight the potential
of our approach to advance ultrasonic wavefield imaging
capabilities and open new avenues for Nondestructive testing and
structural health monitoring.
162.
Kang Yang, Sungwon Kim, Joel B Harley
Unsupervised long-term damage detection in an uncontrolled environment through optimal autoencoder Journal Article
In: Mechanical systems and signal processing, vol. 199, no. 110473, pp. 110473, 2023.
@article{Yang2023-dl,
title = {Unsupervised long-term damage detection in an uncontrolled
environment through optimal autoencoder},
author = {Kang Yang and Sungwon Kim and Joel B Harley},
url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=Isf8yn0AAAAJ:QyXJ3EUuO1IC},
doi = {10.1016/j.ymssp.2023.110473},
year = {2023},
date = {2023-09-01},
journal = {Mechanical systems and signal processing},
volume = {199},
number = {110473},
pages = {110473},
publisher = {Elsevier BV},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
161.
Kalyn M Kearney, Joel B Harley, Jennifer A Nichols
Inverse distance weighting to rapidly generate large simulation datasets Journal Article
In: Journal of biomechanics, vol. 158, no. 111764, pp. 111764, 2023.
@article{Kearney2023-lw,
title = {Inverse distance weighting to rapidly generate large simulation
datasets},
author = {Kalyn M Kearney and Joel B Harley and Jennifer A Nichols},
url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=Isf8yn0AAAAJ:ce2CqMG-AY4C},
doi = {10.1016/j.jbiomech.2023.111764},
year = {2023},
date = {2023-09-01},
journal = {Journal of biomechanics},
volume = {158},
number = {111764},
pages = {111764},
publisher = {Elsevier BV},
abstract = {Obtaining large biomechanical datasets for machine learning is an
ongoing challenge. Physics-based simulations offer one approach
for generating large datasets, but many simulation methods, such
as computed muscle control (CMC), are computationally costly. In
contrast, interpolation methods, such as inverse distance
weighting (IDW), are computationally fast. We examined whether
IDW is a low-cost and accurate approach for interpolating muscle
activations from CMC.IDW was evaluated using lateral pinch
simulations in OpenSim. Simulated pinch data were organized into
grids of varying sparsity (high, medium, and low density), where
each grid point represented the muscle activations associated
with a unique combination of mass and height of a young adult.
For each grid, muscle activations were calculated via CMC and IDW
for 108 random mass-height pairs that were not coincident with
simulation grid vertices. We evaluated the interpolation errors
from IDW for each grid, as well as the sensitivity of lateral
pinch force to these errors. The root mean square error (RMSE)
associated with interpolated muscle activations decreased with
increasing grid density and never exceeded 4%. While CMC
received a target thumb-tip force of 40 N, errors from the
interpolated muscle activations never impacted the simulated
force magnitude by more than 0.1 N. Furthermore, the computation
time for CMC simulations averaged 4.22 core-minutes, while IDW
averaged 0.95 core-seconds per mass-height pair.These results
indicate IDW is a practical approach for rapidly estimating
muscle activations from sparse CMC datasets. Future works could
adapt our IDW approach to evaluate other tasks, biomechanical
features, and/or populations.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Obtaining large biomechanical datasets for machine learning is an
ongoing challenge. Physics-based simulations offer one approach
for generating large datasets, but many simulation methods, such
as computed muscle control (CMC), are computationally costly. In
contrast, interpolation methods, such as inverse distance
weighting (IDW), are computationally fast. We examined whether
IDW is a low-cost and accurate approach for interpolating muscle
activations from CMC.IDW was evaluated using lateral pinch
simulations in OpenSim. Simulated pinch data were organized into
grids of varying sparsity (high, medium, and low density), where
each grid point represented the muscle activations associated
with a unique combination of mass and height of a young adult.
For each grid, muscle activations were calculated via CMC and IDW
for 108 random mass-height pairs that were not coincident with
simulation grid vertices. We evaluated the interpolation errors
from IDW for each grid, as well as the sensitivity of lateral
pinch force to these errors. The root mean square error (RMSE)
associated with interpolated muscle activations decreased with
increasing grid density and never exceeded 4%. While CMC
received a target thumb-tip force of 40 N, errors from the
interpolated muscle activations never impacted the simulated
force magnitude by more than 0.1 N. Furthermore, the computation
time for CMC simulations averaged 4.22 core-minutes, while IDW
averaged 0.95 core-seconds per mass-height pair.These results
indicate IDW is a practical approach for rapidly estimating
muscle activations from sparse CMC datasets. Future works could
adapt our IDW approach to evaluate other tasks, biomechanical
features, and/or populations.
ongoing challenge. Physics-based simulations offer one approach
for generating large datasets, but many simulation methods, such
as computed muscle control (CMC), are computationally costly. In
contrast, interpolation methods, such as inverse distance
weighting (IDW), are computationally fast. We examined whether
IDW is a low-cost and accurate approach for interpolating muscle
activations from CMC.IDW was evaluated using lateral pinch
simulations in OpenSim. Simulated pinch data were organized into
grids of varying sparsity (high, medium, and low density), where
each grid point represented the muscle activations associated
with a unique combination of mass and height of a young adult.
For each grid, muscle activations were calculated via CMC and IDW
for 108 random mass-height pairs that were not coincident with
simulation grid vertices. We evaluated the interpolation errors
from IDW for each grid, as well as the sensitivity of lateral
pinch force to these errors. The root mean square error (RMSE)
associated with interpolated muscle activations decreased with
increasing grid density and never exceeded 4%. While CMC
received a target thumb-tip force of 40 N, errors from the
interpolated muscle activations never impacted the simulated
force magnitude by more than 0.1 N. Furthermore, the computation
time for CMC simulations averaged 4.22 core-minutes, while IDW
averaged 0.95 core-seconds per mass-height pair.These results
indicate IDW is a practical approach for rapidly estimating
muscle activations from sparse CMC datasets. Future works could
adapt our IDW approach to evaluate other tasks, biomechanical
features, and/or populations.
160.
Kang Yang, Sungwon Kim, Joel B Harley
Unsupervised long-term damage detection in an uncontrolled environment through optimal autoencoder Journal Article
In: Mechanical systems and signal processing, vol. 199, no. 110473, pp. 110473, 2023.
@article{Yang2023-yy,
title = {Unsupervised long-term damage detection in an uncontrolled environment through optimal autoencoder},
author = {Kang Yang and Sungwon Kim and Joel B Harley},
url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Isf8yn0AAAAJ&sortby=pubdate&citation_for_view=Isf8yn0AAAAJ:QyXJ3EUuO1IC},
doi = {10.1016/j.ymssp.2023.110473},
year = {2023},
date = {2023-09-01},
urldate = {2023-09-01},
journal = {Mechanical systems and signal processing},
volume = {199},
number = {110473},
pages = {110473},
publisher = {Elsevier BV},
abstract = {Unsupervised damage detection in the presence of both regular
environmental variations, such as a daily change in temperature
and humidity, and irregular environmental variations, such as
rain and snow, remains a critical challenge in guided wave
structural health monitoring. This paper proposes an optimal
autoencoder-based damage detection strategy to solve this
problem. Although the autoencoder and other neural networks have
been used to detect anomalies in structural health monitoring,
the training data has been also required to be collected from a
known intact structure, which increases the time and labor cost
for inspection and limits the application of such methods.
Instead, our autoencoder is trained with guided wave data that
contain uncontrolled regular, irregular, and damage variations.
We investigate hyperparameters that will negatively influence
autoencoder-based damage detection, including the training time
and damage duration. We also propose sparsity, dropout, and
weight decay regularization strategies to enhance the robustness
of the autoencoder to these hyperparameters. Results show that
our optimal autoencoder method can achieve an area under the
receiver operating curve score near 0.92 for detecting damage
present in the last 16 days, which improves previous local
principal components analysis reconstruction methods with a score
of 0.88.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Unsupervised damage detection in the presence of both regular
environmental variations, such as a daily change in temperature
and humidity, and irregular environmental variations, such as
rain and snow, remains a critical challenge in guided wave
structural health monitoring. This paper proposes an optimal
autoencoder-based damage detection strategy to solve this
problem. Although the autoencoder and other neural networks have
been used to detect anomalies in structural health monitoring,
the training data has been also required to be collected from a
known intact structure, which increases the time and labor cost
for inspection and limits the application of such methods.
Instead, our autoencoder is trained with guided wave data that
contain uncontrolled regular, irregular, and damage variations.
We investigate hyperparameters that will negatively influence
autoencoder-based damage detection, including the training time
and damage duration. We also propose sparsity, dropout, and
weight decay regularization strategies to enhance the robustness
of the autoencoder to these hyperparameters. Results show that
our optimal autoencoder method can achieve an area under the
receiver operating curve score near 0.92 for detecting damage
present in the last 16 days, which improves previous local
principal components analysis reconstruction methods with a score
of 0.88.
environmental variations, such as a daily change in temperature
and humidity, and irregular environmental variations, such as
rain and snow, remains a critical challenge in guided wave
structural health monitoring. This paper proposes an optimal
autoencoder-based damage detection strategy to solve this
problem. Although the autoencoder and other neural networks have
been used to detect anomalies in structural health monitoring,
the training data has been also required to be collected from a
known intact structure, which increases the time and labor cost
for inspection and limits the application of such methods.
Instead, our autoencoder is trained with guided wave data that
contain uncontrolled regular, irregular, and damage variations.
We investigate hyperparameters that will negatively influence
autoencoder-based damage detection, including the training time
and damage duration. We also propose sparsity, dropout, and
weight decay regularization strategies to enhance the robustness
of the autoencoder to these hyperparameters. Results show that
our optimal autoencoder method can achieve an area under the
receiver operating curve score near 0.92 for detecting damage
present in the last 16 days, which improves previous local
principal components analysis reconstruction methods with a score
of 0.88.
159.
Kang Yang, Sungwon Kim, Joel B Harley
Guidelines for effective unsupervised guided wave compression and denoising in long-term guided wave structural health monitoring Journal Article
In: Structural health monitoring, vol. 22, no. 4, pp. 2516–2530, 2023.
@article{Yang2023-xw,
title = {Guidelines for effective unsupervised guided wave compression and
denoising in long-term guided wave structural health monitoring},
author = {Kang Yang and Sungwon Kim and Joel B Harley},
url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=Isf8yn0AAAAJ:KaMxkj08jr0C},
doi = {10.1177/14759217221124689},
year = {2023},
date = {2023-07-01},
journal = {Structural health monitoring},
volume = {22},
number = {4},
pages = {2516–2530},
publisher = {SAGE Publications},
abstract = {This paper studies the effectiveness of joint compression and
denoising strategies with realistic, long-term guided wave
structural health monitoring data. We leverage the high
correlation between nearby collections of guided waves in time to
create sparse and low-rank representations. While compression and
denoising schemes are not new, they are almost exclusively
designed and studied with relatively simple datasets. In
contrast, guided wave structural health monitoring datasets have
much more complex operational and environmental conditions, such
as temperature, that distort data and for which the requirements
to achieve effective compression and denoising are not well
understood. The paper studies how to optimize our data collection
and algorithms to best utilize guided wave data for compression,
denoising, and damage detection based on seven million guided
wave measurements collected over 2 years.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper studies the effectiveness of joint compression and
denoising strategies with realistic, long-term guided wave
structural health monitoring data. We leverage the high
correlation between nearby collections of guided waves in time to
create sparse and low-rank representations. While compression and
denoising schemes are not new, they are almost exclusively
designed and studied with relatively simple datasets. In
contrast, guided wave structural health monitoring datasets have
much more complex operational and environmental conditions, such
as temperature, that distort data and for which the requirements
to achieve effective compression and denoising are not well
understood. The paper studies how to optimize our data collection
and algorithms to best utilize guided wave data for compression,
denoising, and damage detection based on seven million guided
wave measurements collected over 2 years.
denoising strategies with realistic, long-term guided wave
structural health monitoring data. We leverage the high
correlation between nearby collections of guided waves in time to
create sparse and low-rank representations. While compression and
denoising schemes are not new, they are almost exclusively
designed and studied with relatively simple datasets. In
contrast, guided wave structural health monitoring datasets have
much more complex operational and environmental conditions, such
as temperature, that distort data and for which the requirements
to achieve effective compression and denoising are not well
understood. The paper studies how to optimize our data collection
and algorithms to best utilize guided wave data for compression,
denoising, and damage detection based on seven million guided
wave measurements collected over 2 years.
158.
Harsha Vardhan Tetali, Joel B Harley
Learning Tensor Representations to Improve Quality of Wavefield Data Journal Article
In: vol. 87202, pp. V001T05A002, 2023.
@article{Tetali2023-me,
title = {Learning Tensor Representations to Improve Quality of Wavefield
Data},
author = {Harsha Vardhan Tetali and Joel B Harley},
url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=Isf8yn0AAAAJ:O0nohqN1r9EC},
year = {2023},
date = {2023-07-01},
volume = {87202},
pages = {V001T05A002},
publisher = {American Society of Mechanical Engineers},
abstract = {Recent advancements in physics-informed machine learning have
contributed to solving partial differential equations through
means of a neural network. Following this, several
physics-informed neural network works have followed to solve
inverse problems arising in structural health monitoring. Other
works involving physics-informed neural networks solve the wave
equation with partial data and modeling wavefield data generator
for efficient sound data generation. While a lot of work has been
done to show that partial differential equations can be solved
and identified using a neural network, little work has been done
the same with more basic machine learning (ML) models. The
advantage with basic ML models is that the parameters learned in
a simpler model are both more interpretable and extensible. For
applications such as ultrasonic nondestructive evaluation, this
interpretability is essential for …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Recent advancements in physics-informed machine learning have
contributed to solving partial differential equations through
means of a neural network. Following this, several
physics-informed neural network works have followed to solve
inverse problems arising in structural health monitoring. Other
works involving physics-informed neural networks solve the wave
equation with partial data and modeling wavefield data generator
for efficient sound data generation. While a lot of work has been
done to show that partial differential equations can be solved
and identified using a neural network, little work has been done
the same with more basic machine learning (ML) models. The
advantage with basic ML models is that the parameters learned in
a simpler model are both more interpretable and extensible. For
applications such as ultrasonic nondestructive evaluation, this
interpretability is essential for …
contributed to solving partial differential equations through
means of a neural network. Following this, several
physics-informed neural network works have followed to solve
inverse problems arising in structural health monitoring. Other
works involving physics-informed neural networks solve the wave
equation with partial data and modeling wavefield data generator
for efficient sound data generation. While a lot of work has been
done to show that partial differential equations can be solved
and identified using a neural network, little work has been done
the same with more basic machine learning (ML) models. The
advantage with basic ML models is that the parameters learned in
a simpler model are both more interpretable and extensible. For
applications such as ultrasonic nondestructive evaluation, this
interpretability is essential for …
157.
Samuel Jerel Hansen, Evan Benoit, Cynthia Furse, Joel B Harley
Evaluation of Methods to Generate Spread Spectrum Time Domain Reflectometry Journal Article
In: pp. 281–282, 2023.
@article{Hansen2023-na,
title = {Evaluation of Methods to Generate Spread Spectrum Time Domain
Reflectometry},
author = {Samuel Jerel Hansen and Evan Benoit and Cynthia Furse and Joel B Harley},
url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Isf8yn0AAAAJ&sortby=pubdate&citation_for_view=Isf8yn0AAAAJ:kWvqk_afx_IC},
year = {2023},
date = {2023-07-01},
pages = {281–282},
publisher = {IEEE},
abstract = {Reflectometry is used extensively to measure electrical
characteristics such as impedance, fault location and detection,
and wire degradation due to aging. Reflectometry is often not
used on energized systems due to interference, the potential for
device damage, or poor SNR. These issues have been solved in
wireless communication systems using pseudo-noise (PN) codes.
Combining PN codes and reflectometry creates spread spectrum time
domain reflectometry (SSTDR). SSTDR enables reflectometry
measurements on energized systems. This paper explores the
tradeoffs in methods to implement SSTDR systems. Analog circuits
can be used for simplicity but come at the cost of size. ASIC
design is utilized for smaller circuits with less flexibility and
higher initial costs. FPGAs allow for greater flexibility but
typically don't go above 600 MHz. Current research is underway to
implement SSTDR with software-defined …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Reflectometry is used extensively to measure electrical
characteristics such as impedance, fault location and detection,
and wire degradation due to aging. Reflectometry is often not
used on energized systems due to interference, the potential for
device damage, or poor SNR. These issues have been solved in
wireless communication systems using pseudo-noise (PN) codes.
Combining PN codes and reflectometry creates spread spectrum time
domain reflectometry (SSTDR). SSTDR enables reflectometry
measurements on energized systems. This paper explores the
tradeoffs in methods to implement SSTDR systems. Analog circuits
can be used for simplicity but come at the cost of size. ASIC
design is utilized for smaller circuits with less flexibility and
higher initial costs. FPGAs allow for greater flexibility but
typically don't go above 600 MHz. Current research is underway to
implement SSTDR with software-defined …
characteristics such as impedance, fault location and detection,
and wire degradation due to aging. Reflectometry is often not
used on energized systems due to interference, the potential for
device damage, or poor SNR. These issues have been solved in
wireless communication systems using pseudo-noise (PN) codes.
Combining PN codes and reflectometry creates spread spectrum time
domain reflectometry (SSTDR). SSTDR enables reflectometry
measurements on energized systems. This paper explores the
tradeoffs in methods to implement SSTDR systems. Analog circuits
can be used for simplicity but come at the cost of size. ASIC
design is utilized for smaller circuits with less flexibility and
higher initial costs. FPGAs allow for greater flexibility but
typically don't go above 600 MHz. Current research is underway to
implement SSTDR with software-defined …
156.
C Tran Joel Harley, S Zafar
Tips for Effective Machine Learning in NDT/E Journal Article
In: Materials Evaluation, vol. 81, no. 7, pp. 43–47, 2023.
@article{Harley2023-yd,
title = {Tips for Effective Machine Learning in NDT/E},
author = {C Tran Joel Harley and S Zafar},
url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Isf8yn0AAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=Isf8yn0AAAAJ:sszUF3NjhM4C},
year = {2023},
date = {2023-07-01},
journal = {Materials Evaluation},
volume = {81},
number = {7},
pages = {43–47},
publisher = {ASNT},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
155.
Kang Yang, Sungwon Kim, Joel B Harley
Improving long-term guided wave damage detection with measurement resampling Journal Article
In: IEEE sensors journal, vol. 23, no. 7, pp. 7178–7187, 2023.
@article{Yang2023-xl,
title = {Improving long-term guided wave damage detection with measurement
resampling},
author = {Kang Yang and Sungwon Kim and Joel B Harley},
url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=Isf8yn0AAAAJ:OBSaB-F7qqsC},
doi = {10.1109/jsen.2023.3242259},
year = {2023},
date = {2023-04-01},
journal = {IEEE sensors journal},
volume = {23},
number = {7},
pages = {7178–7187},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
154.
Ishan D Khurjekar, Bryan Conry, Michael S Kesler, Michael R Tonks, Amanda R Krause, Joel B Harley
Automated, high-accuracy classification of textured microstructures using a convolutional neural network Journal Article
In: Frontiers in materials, vol. 10, pp. 25, 2023.
@article{Khurjekar2023-io,
title = {Automated, high-accuracy classification of textured
microstructures using a convolutional neural network},
author = {Ishan D Khurjekar and Bryan Conry and Michael S Kesler and Michael R Tonks and Amanda R Krause and Joel B Harley},
url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=Isf8yn0AAAAJ:MAUkC_7iAq8C},
doi = {10.3389/fmats.2023.1086000},
year = {2023},
date = {2023-01-01},
journal = {Frontiers in materials},
volume = {10},
pages = {25},
publisher = {Frontiers Media SA},
abstract = {Crystallographic texture is an important descriptor of material
properties but requires time-intensive electron backscatter
diffraction (EBSD) for identifying grain orientations. While some
metrics such as grain size or grain aspect ratio can distinguish
textured microstructures from untextured microstructures after
significant grain growth, such morphological differences are not
always visually observable. This paper explores the use of deep
learning to classify experimentally measured textured
microstructures without knowledge of crystallographic
orientation. A deep convolutional neural network is used to
extract high-order morphological features from binary images to
distinguish textured microstructures from untextured
microstructures. The convolutional neural network results are
compared with a statistical Kolmogorov–Smirnov tests with
traditional morphological metrics for describing microstructures.
Results show that the convolutional neural network achieves a
significantly improved classification accuracy, particularly at
early stages of grain growth, highlighting the capability of deep
learning to identify the subtle morphological patterns resulting
from texture. The results demonstrate the potential of a
convolutional neural network as a tool for reliable and automated
microstructure classification with minimal preprocessing.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Crystallographic texture is an important descriptor of material
properties but requires time-intensive electron backscatter
diffraction (EBSD) for identifying grain orientations. While some
metrics such as grain size or grain aspect ratio can distinguish
textured microstructures from untextured microstructures after
significant grain growth, such morphological differences are not
always visually observable. This paper explores the use of deep
learning to classify experimentally measured textured
microstructures without knowledge of crystallographic
orientation. A deep convolutional neural network is used to
extract high-order morphological features from binary images to
distinguish textured microstructures from untextured
microstructures. The convolutional neural network results are
compared with a statistical Kolmogorov–Smirnov tests with
traditional morphological metrics for describing microstructures.
Results show that the convolutional neural network achieves a
significantly improved classification accuracy, particularly at
early stages of grain growth, highlighting the capability of deep
learning to identify the subtle morphological patterns resulting
from texture. The results demonstrate the potential of a
convolutional neural network as a tool for reliable and automated
microstructure classification with minimal preprocessing.
properties but requires time-intensive electron backscatter
diffraction (EBSD) for identifying grain orientations. While some
metrics such as grain size or grain aspect ratio can distinguish
textured microstructures from untextured microstructures after
significant grain growth, such morphological differences are not
always visually observable. This paper explores the use of deep
learning to classify experimentally measured textured
microstructures without knowledge of crystallographic
orientation. A deep convolutional neural network is used to
extract high-order morphological features from binary images to
distinguish textured microstructures from untextured
microstructures. The convolutional neural network results are
compared with a statistical Kolmogorov–Smirnov tests with
traditional morphological metrics for describing microstructures.
Results show that the convolutional neural network achieves a
significantly improved classification accuracy, particularly at
early stages of grain growth, highlighting the capability of deep
learning to identify the subtle morphological patterns resulting
from texture. The results demonstrate the potential of a
convolutional neural network as a tool for reliable and automated
microstructure classification with minimal preprocessing.
153.
Kang Yang, Joel B Harley
An Investigation of the Effect of Measurement Interval on the Autoencoder Based Damage Detection in Uncontrolled Structural Health Monitoring Journal Article
In: STRUCTURAL HEALTH MONITORING 2023, 2023.
@article{Yang2023-ba,
title = {An Investigation of the Effect of Measurement Interval on the
Autoencoder Based Damage Detection in Uncontrolled Structural
Health Monitoring},
author = {Kang Yang and Joel B Harley},
url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=Isf8yn0AAAAJ:BrOSOlqYqPUC},
year = {2023},
date = {2023-01-01},
journal = {STRUCTURAL HEALTH MONITORING 2023},
abstract = {Unsupervised damage detection in uncontrolled, outdoor
environmental and operational conditions (EOCs) is crucial for
practical structural health monitoring. While previous research
has explored autoencoder-based unsupervised damage detection
methods, they require training data only from pristine conditions.
In long-term monitoring, irregular environmental and operational
conditions, as well as variations in damage, may make it difficult
to satisfy this requirement. In this paper, we propose a novel
autoencoderbased approach that uses training data containing
regular and irregular environmental and operational conditions, as
well as damage variations. We also investigate the impact of
various factors, such as training epoch, damage duration, and
measurement interval on the accuracy of damage detection. Our
results indicate that our proposed framework achieves an AUC score
of over 0.95 when the measurement interval is around 860 seconds
per measurement. Interestingly, this score decreases both when we
sampling faster and slower.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Unsupervised damage detection in uncontrolled, outdoor
environmental and operational conditions (EOCs) is crucial for
practical structural health monitoring. While previous research
has explored autoencoder-based unsupervised damage detection
methods, they require training data only from pristine conditions.
In long-term monitoring, irregular environmental and operational
conditions, as well as variations in damage, may make it difficult
to satisfy this requirement. In this paper, we propose a novel
autoencoderbased approach that uses training data containing
regular and irregular environmental and operational conditions, as
well as damage variations. We also investigate the impact of
various factors, such as training epoch, damage duration, and
measurement interval on the accuracy of damage detection. Our
results indicate that our proposed framework achieves an AUC score
of over 0.95 when the measurement interval is around 860 seconds
per measurement. Interestingly, this score decreases both when we
sampling faster and slower.
environmental and operational conditions (EOCs) is crucial for
practical structural health monitoring. While previous research
has explored autoencoder-based unsupervised damage detection
methods, they require training data only from pristine conditions.
In long-term monitoring, irregular environmental and operational
conditions, as well as variations in damage, may make it difficult
to satisfy this requirement. In this paper, we propose a novel
autoencoderbased approach that uses training data containing
regular and irregular environmental and operational conditions, as
well as damage variations. We also investigate the impact of
various factors, such as training epoch, damage duration, and
measurement interval on the accuracy of damage detection. Our
results indicate that our proposed framework achieves an AUC score
of over 0.95 when the measurement interval is around 860 seconds
per measurement. Interestingly, this score decreases both when we
sampling faster and slower.
152.
E Benoit, Samuel J Hansen, S Kingston, J B Harley, Cynthia M Furse
Capability of impedance measurement using spread spectrum time-domain reflectometry Journal Article
In: IEEE transactions on instrumentation and measurement, vol. 72, pp. 1–9, 2023.
@article{Benoit2023-ro,
title = {Capability of impedance measurement using spread spectrum
time-domain reflectometry},
author = {E Benoit and Samuel J Hansen and S Kingston and J B Harley and Cynthia M Furse},
url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Isf8yn0AAAAJ&sortby=pubdate&citation_for_view=Isf8yn0AAAAJ:mKu_rENv82IC},
doi = {10.1109/TIM.2023.3318677},
year = {2023},
date = {2023-01-01},
journal = {IEEE transactions on instrumentation and measurement},
volume = {72},
pages = {1–9},
publisher = {IEEE},
abstract = {Spread spectrum time-domain reflectometry (SSTDR) has been
proposed for measuring electrical impedance on live, energized
systems, in electrically noisy environments, or for measuring
multiple channels simultaneously. We evaluate the capability of
SSTDR for impedance measurement from near dc to 96 MHz and find
that they agree very well with measurements from a vector network
analyzer (VNA). Time gating is used to remove (calibrate out)
transmission line effects. We evaluate the frequency ranges over
which SSTDR at different modulation frequencies is accurate for
the measurement of series resistive–capacitive ( $RC$ ) loads and
observe that fusing measurements from multiple modulation
frequencies can broaden the effective measurement bandwidth.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Spread spectrum time-domain reflectometry (SSTDR) has been
proposed for measuring electrical impedance on live, energized
systems, in electrically noisy environments, or for measuring
multiple channels simultaneously. We evaluate the capability of
SSTDR for impedance measurement from near dc to 96 MHz and find
that they agree very well with measurements from a vector network
analyzer (VNA). Time gating is used to remove (calibrate out)
transmission line effects. We evaluate the frequency ranges over
which SSTDR at different modulation frequencies is accurate for
the measurement of series resistive–capacitive ( $RC$ ) loads and
observe that fusing measurements from multiple modulation
frequencies can broaden the effective measurement bandwidth.
proposed for measuring electrical impedance on live, energized
systems, in electrically noisy environments, or for measuring
multiple channels simultaneously. We evaluate the capability of
SSTDR for impedance measurement from near dc to 96 MHz and find
that they agree very well with measurements from a vector network
analyzer (VNA). Time gating is used to remove (calibrate out)
transmission line effects. We evaluate the frequency ranges over
which SSTDR at different modulation frequencies is accurate for
the measurement of series resistive–capacitive ( $RC$ ) loads and
observe that fusing measurements from multiple modulation
frequencies can broaden the effective measurement bandwidth.
2022
151.
Harsha Vardhan Tetali, Joel Harley
A physics-informed machine learning based dispersion curve estimation for non-homogeneous media Proceedings Article
In: Proc. of the Meeting on Acoustics, pp. A239–A239, Acoustical Society of America, 2022.
@inproceedings{Tetali2022-ia,
title = {A physics-informed machine learning based dispersion curve
estimation for non-homogeneous media},
author = {Harsha Vardhan Tetali and Joel Harley},
url = {https://asa.scitation.org/doi/abs/10.1121/10.0016136},
doi = {10.1121/10.0016136},
year = {2022},
date = {2022-10-01},
booktitle = {Proc. of the Meeting on Acoustics},
volume = {152},
pages = {A239–A239},
publisher = {Acoustical Society of America},
abstract = {Modern machine learning has been on the rise in many scientific
domains, such as acoustics. Many scientific problems face
challenges with limited data, which prevent the use of the many
powerful machine learning strategies. In response, the physics of
wave-propagation can be exploited to reduce the amount of data
necessary and improve performance of machine learning techniques.
Based on this need, we present a physics-informed machine
learning framework, known as wave-informed regression, to extract
dispersion curves from a guided wave wavefield data from
non-homogeneous media. Wave-informed regression blends matrix
factorization with known wave-physics by borrowing results from
optimization theory. We briefly derive the algorithm and discuss
a signal processing-based interpretability aspect of it, which
aids in extracting dispersion curves for non-homogenous media. We
show our results on a non-homogeneous media, where the dispersion
curves change as a function of space. We demonstrate our ability
to use wave-informed regression to extract spatially local
dispersion curves.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Modern machine learning has been on the rise in many scientific
domains, such as acoustics. Many scientific problems face
challenges with limited data, which prevent the use of the many
powerful machine learning strategies. In response, the physics of
wave-propagation can be exploited to reduce the amount of data
necessary and improve performance of machine learning techniques.
Based on this need, we present a physics-informed machine
learning framework, known as wave-informed regression, to extract
dispersion curves from a guided wave wavefield data from
non-homogeneous media. Wave-informed regression blends matrix
factorization with known wave-physics by borrowing results from
optimization theory. We briefly derive the algorithm and discuss
a signal processing-based interpretability aspect of it, which
aids in extracting dispersion curves for non-homogenous media. We
show our results on a non-homogeneous media, where the dispersion
curves change as a function of space. We demonstrate our ability
to use wave-informed regression to extract spatially local
dispersion curves.
domains, such as acoustics. Many scientific problems face
challenges with limited data, which prevent the use of the many
powerful machine learning strategies. In response, the physics of
wave-propagation can be exploited to reduce the amount of data
necessary and improve performance of machine learning techniques.
Based on this need, we present a physics-informed machine
learning framework, known as wave-informed regression, to extract
dispersion curves from a guided wave wavefield data from
non-homogeneous media. Wave-informed regression blends matrix
factorization with known wave-physics by borrowing results from
optimization theory. We briefly derive the algorithm and discuss
a signal processing-based interpretability aspect of it, which
aids in extracting dispersion curves for non-homogenous media. We
show our results on a non-homogeneous media, where the dispersion
curves change as a function of space. We demonstrate our ability
to use wave-informed regression to extract spatially local
dispersion curves.
150.
Kang Yang, Sungwon Kim, Joel B Harley
Guidelines for effective unsupervised guided wave compression and denoising in long-term guided wave structural health monitoring Journal Article
In: Structural health monitoring, vol. 22, no. 4, pp. 2516–2530, 2022.
@article{Yang2022-fo,
title = {Guidelines for effective unsupervised guided wave compression and
denoising in long-term guided wave structural health monitoring},
author = {Kang Yang and Sungwon Kim and Joel B Harley},
url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Isf8yn0AAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=Isf8yn0AAAAJ:KaMxkj08jr0C},
doi = {10.1177/14759217221124689},
year = {2022},
date = {2022-10-01},
journal = {Structural health monitoring},
volume = {22},
number = {4},
pages = {2516–2530},
publisher = {SAGE Publications},
abstract = {This paper studies the effectiveness of joint compression and
denoising strategies with realistic, long-term guided wave
structural health monitoring data. We leverage the high
correlation between nearby collections of guided waves in time to
create sparse and low-rank representations. While compression and
denoising schemes are not new, they are almost exclusively
designed and studied with relatively simple datasets. In
contrast, guided wave structural health monitoring datasets have
much more complex operational and environmental conditions, such
as temperature, that distort data and for which the requirements
to achieve effective compression and denoising are not well
understood. The paper studies how to optimize our data collection
and algorithms to best utilize guided wave data for compression,
denoising, and damage detection based on seven million guided
wave measurements collected over 2 years.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper studies the effectiveness of joint compression and
denoising strategies with realistic, long-term guided wave
structural health monitoring data. We leverage the high
correlation between nearby collections of guided waves in time to
create sparse and low-rank representations. While compression and
denoising schemes are not new, they are almost exclusively
designed and studied with relatively simple datasets. In
contrast, guided wave structural health monitoring datasets have
much more complex operational and environmental conditions, such
as temperature, that distort data and for which the requirements
to achieve effective compression and denoising are not well
understood. The paper studies how to optimize our data collection
and algorithms to best utilize guided wave data for compression,
denoising, and damage detection based on seven million guided
wave measurements collected over 2 years.
denoising strategies with realistic, long-term guided wave
structural health monitoring data. We leverage the high
correlation between nearby collections of guided waves in time to
create sparse and low-rank representations. While compression and
denoising schemes are not new, they are almost exclusively
designed and studied with relatively simple datasets. In
contrast, guided wave structural health monitoring datasets have
much more complex operational and environmental conditions, such
as temperature, that distort data and for which the requirements
to achieve effective compression and denoising are not well
understood. The paper studies how to optimize our data collection
and algorithms to best utilize guided wave data for compression,
denoising, and damage detection based on seven million guided
wave measurements collected over 2 years.
149.
Bryan Conry, Joel B Harley, Michael R Tonks, Michael S Kesler, Amanda R Krause
Engineering grain boundary anisotropy to elucidate grain growth behavior in alumina Journal Article
In: Journal of the European Ceramic Society, vol. 42, no. 13, pp. 5864–5873, 2022.
@article{Conry2022-lw,
title = {Engineering grain boundary anisotropy to elucidate grain growth
behavior in alumina},
author = {Bryan Conry and Joel B Harley and Michael R Tonks and Michael S Kesler and Amanda R Krause},
url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Isf8yn0AAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=Isf8yn0AAAAJ:q-HalDI95KYC},
doi = {10.1016/j.jeurceramsoc.2022.06.059},
year = {2022},
date = {2022-10-01},
journal = {Journal of the European Ceramic Society},
volume = {42},
number = {13},
pages = {5864–5873},
publisher = {Elsevier BV},
abstract = {Current grain growth models have evolved to account for the
relationship between grain boundary energy/mobility anisotropy
and the five degrees of grain boundary character. However, the
role of grain boundary networks on overall growth kinetics
remains poorly understood. To experimentally investigate this
problem, a highly textured Al2O3 was fabricated by colloidal
casting in a strong magnetic field to engineer a unique spatial
distribution of grain boundary character. Microstructural
evolution was quantified and compared to an untextured sample.
From this comparison, a prevalence of (0001)/(0001) terminated
grain boundaries with anisotropic networks were identified in the
textured sample. These boundaries and their networks were found
to be driving grain growth at a faster rate than predicted by
models. These findings will allow better modelling of grain
growth in real systems by experimentally exploring the impact
thereon of grain boundary plane anisotropy and relative
energy/mobility differences between neighboring boundaries.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Current grain growth models have evolved to account for the
relationship between grain boundary energy/mobility anisotropy
and the five degrees of grain boundary character. However, the
role of grain boundary networks on overall growth kinetics
remains poorly understood. To experimentally investigate this
problem, a highly textured Al2O3 was fabricated by colloidal
casting in a strong magnetic field to engineer a unique spatial
distribution of grain boundary character. Microstructural
evolution was quantified and compared to an untextured sample.
From this comparison, a prevalence of (0001)/(0001) terminated
grain boundaries with anisotropic networks were identified in the
textured sample. These boundaries and their networks were found
to be driving grain growth at a faster rate than predicted by
models. These findings will allow better modelling of grain
growth in real systems by experimentally exploring the impact
thereon of grain boundary plane anisotropy and relative
energy/mobility differences between neighboring boundaries.
relationship between grain boundary energy/mobility anisotropy
and the five degrees of grain boundary character. However, the
role of grain boundary networks on overall growth kinetics
remains poorly understood. To experimentally investigate this
problem, a highly textured Al2O3 was fabricated by colloidal
casting in a strong magnetic field to engineer a unique spatial
distribution of grain boundary character. Microstructural
evolution was quantified and compared to an untextured sample.
From this comparison, a prevalence of (0001)/(0001) terminated
grain boundaries with anisotropic networks were identified in the
textured sample. These boundaries and their networks were found
to be driving grain growth at a faster rate than predicted by
models. These findings will allow better modelling of grain
growth in real systems by experimentally exploring the impact
thereon of grain boundary plane anisotropy and relative
energy/mobility differences between neighboring boundaries.
148.
Weishi Yan, Joseph Melville, Vishal Yadav, Kristien Everett, Lin Yang, Michael S Kesler, Amanda R Krause, Michael R Tonks, Joel B Harley
A novel physics-regularized interpretable machine learning model for grain growth Journal Article
In: Materials & design, vol. 222, no. 111032, pp. 111032, 2022.
@article{Yan2022-ka,
title = {A novel physics-regularized interpretable machine learning model
for grain growth},
author = {Weishi Yan and Joseph Melville and Vishal Yadav and Kristien Everett and Lin Yang and Michael S Kesler and Amanda R Krause and Michael R Tonks and Joel B Harley},
url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Isf8yn0AAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=Isf8yn0AAAAJ:jFemdcug13IC},
doi = {10.1016/j.matdes.2022.111032},
year = {2022},
date = {2022-10-01},
journal = {Materials & design},
volume = {222},
number = {111032},
pages = {111032},
publisher = {Elsevier BV},
abstract = {Experimental grain growth observations often deviate from grain
growth simulations, revealing that the governing rules for grain
boundary motion are not fully understood. A novel deep learning
model was developed to capture grain growth behavior from
training data without making assumptions about the underlying
physics. The Physics-Regularized Interpretable Machine Learning
Microstructure Evolution (PRIMME) model consists of a multi-layer
neural network that predicts the likelihood of a point changing
to a neighboring grain. Here, we demonstrate PRIMME’s ability to
replicate two-dimensional normal grain growth by training it with
Monte Carlo Potts simulations. The trained PRIMME model’s grain
growth predictions in several test cases show good agreement with
analytical models, phase-field simulations, Monte Carlo Potts
simulations, and results from the literature. Additionally,
PRIMME’s adaptability to investigate irregular grain growth
behavior is shown. Important aspects of PRIMME like
interpretability, regularization, extrapolation, and overfitting
are also discussed.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Experimental grain growth observations often deviate from grain
growth simulations, revealing that the governing rules for grain
boundary motion are not fully understood. A novel deep learning
model was developed to capture grain growth behavior from
training data without making assumptions about the underlying
physics. The Physics-Regularized Interpretable Machine Learning
Microstructure Evolution (PRIMME) model consists of a multi-layer
neural network that predicts the likelihood of a point changing
to a neighboring grain. Here, we demonstrate PRIMME’s ability to
replicate two-dimensional normal grain growth by training it with
Monte Carlo Potts simulations. The trained PRIMME model’s grain
growth predictions in several test cases show good agreement with
analytical models, phase-field simulations, Monte Carlo Potts
simulations, and results from the literature. Additionally,
PRIMME’s adaptability to investigate irregular grain growth
behavior is shown. Important aspects of PRIMME like
interpretability, regularization, extrapolation, and overfitting
are also discussed.
growth simulations, revealing that the governing rules for grain
boundary motion are not fully understood. A novel deep learning
model was developed to capture grain growth behavior from
training data without making assumptions about the underlying
physics. The Physics-Regularized Interpretable Machine Learning
Microstructure Evolution (PRIMME) model consists of a multi-layer
neural network that predicts the likelihood of a point changing
to a neighboring grain. Here, we demonstrate PRIMME’s ability to
replicate two-dimensional normal grain growth by training it with
Monte Carlo Potts simulations. The trained PRIMME model’s grain
growth predictions in several test cases show good agreement with
analytical models, phase-field simulations, Monte Carlo Potts
simulations, and results from the literature. Additionally,
PRIMME’s adaptability to investigate irregular grain growth
behavior is shown. Important aspects of PRIMME like
interpretability, regularization, extrapolation, and overfitting
are also discussed.
147.
Kyle A Riding, Christopher C Ferraro, Trey Hamilton, Joel Harley, Raid S Alrashidi, Megan S Voss, Daniel Alabi
Ultra-High-Performance Concrete (UHPC) Use in Florida Structural Applications Journal Article
In: no. BDV31-977-130, 2022.
@article{Riding2022-qr,
title = {Ultra-High-Performance Concrete (UHPC) Use in Florida
Structural Applications},
author = {Kyle A Riding and Christopher C Ferraro and Trey Hamilton and Joel Harley and Raid S Alrashidi and Megan S Voss and Daniel Alabi},
url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=Isf8yn0AAAAJ:1DsIQWDZLl8C},
year = {2022},
date = {2022-09-01},
number = {BDV31-977-130},
publisher = {Florida. Department of Transportation},
abstract = {In order for UHPC to be implemented in Florida structures and
realize its potential benefits, research was needed to develop
specifications for UHPC materials not supplied as prebagged
materials. The research objective of this project was to
establish mixing, placing, curing and durability requirements and
test methods necessary to produce durable, non-proprietary UHPC,
made using locally-sourced raw materials, for different classes
of structural use and exposure conditions. This research program
was performed using four different concrete mixture designs with
different fiber contents and three different curing methods to
investigate the range of material properties possible and their
impact on strength, durability, and applicability of methods. As
part of this work, a new modified double punch test was developed
and is recommended for use in quality control, with direct
tension testing recommended for mixture qualification. All
mixtures with compressive strength above 15 ksi performed
excellent in freeze-thaw testing. Steam curing was found to
differentiate between good and worse performance of concrete
microstructure for transport property testing. It is recommended
that the UHPC fresh chloride limit be raised by 25% from 0.4
lb/yd3 to 0.5 lb/yd3. A modified rapid chloride migration test is
recommended to be used on steam-cured samples with a chloride
intrusion limit of 5 mm after 7 days. Steam curing samples gave
conservative results at 28 days compared to the long-term test
results and is recommended for acceptance testing purposes. A
nondestructive electromagnetic sensor based on inductive
principles quantify the fiber orientation in …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In order for UHPC to be implemented in Florida structures and
realize its potential benefits, research was needed to develop
specifications for UHPC materials not supplied as prebagged
materials. The research objective of this project was to
establish mixing, placing, curing and durability requirements and
test methods necessary to produce durable, non-proprietary UHPC,
made using locally-sourced raw materials, for different classes
of structural use and exposure conditions. This research program
was performed using four different concrete mixture designs with
different fiber contents and three different curing methods to
investigate the range of material properties possible and their
impact on strength, durability, and applicability of methods. As
part of this work, a new modified double punch test was developed
and is recommended for use in quality control, with direct
tension testing recommended for mixture qualification. All
mixtures with compressive strength above 15 ksi performed
excellent in freeze-thaw testing. Steam curing was found to
differentiate between good and worse performance of concrete
microstructure for transport property testing. It is recommended
that the UHPC fresh chloride limit be raised by 25% from 0.4
lb/yd3 to 0.5 lb/yd3. A modified rapid chloride migration test is
recommended to be used on steam-cured samples with a chloride
intrusion limit of 5 mm after 7 days. Steam curing samples gave
conservative results at 28 days compared to the long-term test
results and is recommended for acceptance testing purposes. A
nondestructive electromagnetic sensor based on inductive
principles quantify the fiber orientation in …
realize its potential benefits, research was needed to develop
specifications for UHPC materials not supplied as prebagged
materials. The research objective of this project was to
establish mixing, placing, curing and durability requirements and
test methods necessary to produce durable, non-proprietary UHPC,
made using locally-sourced raw materials, for different classes
of structural use and exposure conditions. This research program
was performed using four different concrete mixture designs with
different fiber contents and three different curing methods to
investigate the range of material properties possible and their
impact on strength, durability, and applicability of methods. As
part of this work, a new modified double punch test was developed
and is recommended for use in quality control, with direct
tension testing recommended for mixture qualification. All
mixtures with compressive strength above 15 ksi performed
excellent in freeze-thaw testing. Steam curing was found to
differentiate between good and worse performance of concrete
microstructure for transport property testing. It is recommended
that the UHPC fresh chloride limit be raised by 25% from 0.4
lb/yd3 to 0.5 lb/yd3. A modified rapid chloride migration test is
recommended to be used on steam-cured samples with a chloride
intrusion limit of 5 mm after 7 days. Steam curing samples gave
conservative results at 28 days compared to the long-term test
results and is recommended for acceptance testing purposes. A
nondestructive electromagnetic sensor based on inductive
principles quantify the fiber orientation in …
146.
Lin Yang, Floyd Hilty, Vivekanand Muralikrishnan, Kenneth Silva-Reyes, Joel B Harley, Amanda R Krause, Michael R Tonks
Calculating the grain boundary inclination of voxelated grain structures using a smoothing algorithm Journal Article
In: Scripta materialia, vol. 218, no. 114796, pp. 114796, 2022.
@article{Yang2022-ee,
title = {Calculating the grain boundary inclination of voxelated grain
structures using a smoothing algorithm},
author = {Lin Yang and Floyd Hilty and Vivekanand Muralikrishnan and Kenneth Silva-Reyes and Joel B Harley and Amanda R Krause and Michael R Tonks},
url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=Isf8yn0AAAAJ:DBa1UEJaJKAC},
doi = {10.1016/j.scriptamat.2022.114796},
year = {2022},
date = {2022-09-01},
journal = {Scripta materialia},
volume = {218},
number = {114796},
pages = {114796},
publisher = {Elsevier BV},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
145.
Samuel Jerel Hansen, Evan Benoit, Joseph Brignone, Joel B Harley, Cynthia M Furse
Measuring Impedance with Spread Spectrum Time Domain Reflectometry Journal Article
In: pp. 635–636, 2022.
@article{Hansen2022-ww,
title = {Measuring Impedance with Spread Spectrum Time Domain
Reflectometry},
author = {Samuel Jerel Hansen and Evan Benoit and Joseph Brignone and Joel B Harley and Cynthia M Furse},
url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Isf8yn0AAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=Isf8yn0AAAAJ:1Ye0OR6EYb4C},
year = {2022},
date = {2022-07-01},
pages = {635–636},
publisher = {IEEE},
abstract = {Spread spectrum time domain reflectometry (SSTDR) can be used to
measure circuit impedances similar to those measured by a vector
network analyzer (VNA). We describe a measurement setup and
calibration method to compare VNA and SSTDR measurements of a
complex load from 0.1 MHz to 100 MHz for the former and near DC
to 96 MHz for the later. The impedance for the SSTDR measurements
closely match the VNA and ideal impedance magnitude values from
10 MHz to 65 MHz, where the SSTDR signal has enough spectral
power to create an accurate reflected signal. These initial
measurements show the feasibility of using the SSTDR approach to
measure impedance.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Spread spectrum time domain reflectometry (SSTDR) can be used to
measure circuit impedances similar to those measured by a vector
network analyzer (VNA). We describe a measurement setup and
calibration method to compare VNA and SSTDR measurements of a
complex load from 0.1 MHz to 100 MHz for the former and near DC
to 96 MHz for the later. The impedance for the SSTDR measurements
closely match the VNA and ideal impedance magnitude values from
10 MHz to 65 MHz, where the SSTDR signal has enough spectral
power to create an accurate reflected signal. These initial
measurements show the feasibility of using the SSTDR approach to
measure impedance.
measure circuit impedances similar to those measured by a vector
network analyzer (VNA). We describe a measurement setup and
calibration method to compare VNA and SSTDR measurements of a
complex load from 0.1 MHz to 100 MHz for the former and near DC
to 96 MHz for the later. The impedance for the SSTDR measurements
closely match the VNA and ideal impedance magnitude values from
10 MHz to 65 MHz, where the SSTDR signal has enough spectral
power to create an accurate reflected signal. These initial
measurements show the feasibility of using the SSTDR approach to
measure impedance.
144.
Samuel Jerel Hansen, Evan Benoit, Joseph Brignone, Joel B Harley, Cynthia M Furse
Measuring Impedance with Spread Spectrum Time Domain Reflectometry Journal Article
In: pp. 635–636, 2022.
@article{Hansen2022-ai,
title = {Measuring Impedance with Spread Spectrum Time Domain
Reflectometry},
author = {Samuel Jerel Hansen and Evan Benoit and Joseph Brignone and Joel B Harley and Cynthia M Furse},
url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=Isf8yn0AAAAJ:1Ye0OR6EYb4C},
year = {2022},
date = {2022-07-01},
pages = {635–636},
publisher = {IEEE},
abstract = {Spread spectrum time domain reflectometry (SSTDR) can be used to
measure circuit impedances similar to those measured by a vector
network analyzer (VNA). We describe a measurement setup and
calibration method to compare VNA and SSTDR measurements of a
complex load from 0.1 MHz to 100 MHz for the former and near DC
to 96 MHz for the later. The impedance for the SSTDR measurements
closely match the VNA and ideal impedance magnitude values from
10 MHz to 65 MHz, where the SSTDR signal has enough spectral
power to create an accurate reflected signal. These initial
measurements show the feasibility of using the SSTDR approach to
measure impedance.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Spread spectrum time domain reflectometry (SSTDR) can be used to
measure circuit impedances similar to those measured by a vector
network analyzer (VNA). We describe a measurement setup and
calibration method to compare VNA and SSTDR measurements of a
complex load from 0.1 MHz to 100 MHz for the former and near DC
to 96 MHz for the later. The impedance for the SSTDR measurements
closely match the VNA and ideal impedance magnitude values from
10 MHz to 65 MHz, where the SSTDR signal has enough spectral
power to create an accurate reflected signal. These initial
measurements show the feasibility of using the SSTDR approach to
measure impedance.
measure circuit impedances similar to those measured by a vector
network analyzer (VNA). We describe a measurement setup and
calibration method to compare VNA and SSTDR measurements of a
complex load from 0.1 MHz to 100 MHz for the former and near DC
to 96 MHz for the later. The impedance for the SSTDR measurements
closely match the VNA and ideal impedance magnitude values from
10 MHz to 65 MHz, where the SSTDR signal has enough spectral
power to create an accurate reflected signal. These initial
measurements show the feasibility of using the SSTDR approach to
measure impedance.