Underground Infrastructure - May 2023 - 27

Conference Review
Artificial Neural Network (ANN) model for adaptation in
machine learning for various applications.
The investigation was on the performance of highly sensing
foam added smart cement and verified with various behavior
models. Highly sensing smart oil well cement with
water-to-cement (w/c) ratio of 0.38 was modified by adding
5 percent and 20 percent foam by weight. This was first characterized
using the impedance-frequency response to identify
the critical electrical property for monitoring. Based on the
Vipulanandan Impedance Model, electrical resistivity was the
monitoring property.
resistivity can be used and with 5 percent and 20 percent foam
addition, electrical resistivity increased by 14 percent and 94
percent and Vipulanandan correlation model was used to relate
the density to the resistivity.
In addition, rheological properties and piezoresistivity
(slurry) were investigated. With the addition of 20 percent
foam, the smart foam cement density reduced by 45 percent
and thermal conductivity reduced by 65 percent. Also, initial
resistivity increased by 94 percent, indicating a potential quality
control parameter for monitoring in the field. The thermal
conductivity and the initial resistivity were related to the density
using the Vipulanandan Correlation Model.
The slurries investigated were piezoresistive; when pressure
was applied, the electrical resistivity changed. The rheological
behavior of the smart foam cement slurries has been
quantified using the new Vipulanandan rheological model
and compared with Artificial Neural Network (ANN) Model.
The slurry piezoresistivity model was used to predict the
piezoresistive behavior and compared it to the ANN model.
Accuracies of all the model predictions were compared using
the statistical parameters, such as the root mean square error
(RMSE) and the coefficient of determination.
For quality control of the mixing in the field, the electrical Wrap-up
A reception followed the technical sessions during which posters
on research activities at CIGMAT and in the Department of
Civil and Environmental Engineering were displayed. A number
of grouting, pipe manufacturers, material suppliers, pipe
condition monitoring, geotechnical engineering and waste
water rehabilitation companies participated in the exhibition.
A CIGMAT 2023 Conference Proceedings was published
and is posted on the CIGMAT web site (http://cigmat.cive.
uh.edu).
The next CIGMAT conference (CIGMAT-2024) will be
held on March 1, 2024, at the University Hilton, University
of Houston. UI
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