Predicting of Punching Shear Capacity of Corroded Reinforced Concrete Slab-column Joints Using Artificial Intelligence Techniques
Lotfy, E., Gomaa, A., Hosny, S., Khafaga, S., Ahmed, M. (2023). Predicting of Punching Shear Capacity of Corroded Reinforced Concrete Slab-column Joints Using Artificial Intelligence Techniques. EKB Journal Management System, 2(2), 384-407. doi: 10.21608/msaeng.2023.291880
Ehab M. Lotfy; Ahmed mahmoud Abd ELKhalek Gomaa; Sally Hosny; Sherif A. Khafaga; Manar A. Ahmed. "Predicting of Punching Shear Capacity of Corroded Reinforced Concrete Slab-column Joints Using Artificial Intelligence Techniques". EKB Journal Management System, 2, 2, 2023, 384-407. doi: 10.21608/msaeng.2023.291880
Lotfy, E., Gomaa, A., Hosny, S., Khafaga, S., Ahmed, M. (2023). 'Predicting of Punching Shear Capacity of Corroded Reinforced Concrete Slab-column Joints Using Artificial Intelligence Techniques', EKB Journal Management System, 2(2), pp. 384-407. doi: 10.21608/msaeng.2023.291880
Lotfy, E., Gomaa, A., Hosny, S., Khafaga, S., Ahmed, M. Predicting of Punching Shear Capacity of Corroded Reinforced Concrete Slab-column Joints Using Artificial Intelligence Techniques. EKB Journal Management System, 2023; 2(2): 384-407. doi: 10.21608/msaeng.2023.291880

Predicting of Punching Shear Capacity of Corroded Reinforced Concrete Slab-column Joints Using Artificial Intelligence Techniques

MSA Engineering Journal
Volume 2, Issue 2 - Serial Number 6, March 2023, Page 384-407  XML PDF (888.01 K)
Document Type: Original Article
DOI: 10.21608/msaeng.2023.291880
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Authors
Ehab M. Lotfyorcid 1; Ahmed mahmoud Abd ELKhalek Gomaaorcid 2; Sally Hosnyorcid 1; Sherif A. Khafagaorcid 3; Manar A. Ahmedorcid 1
1civil engineering, Suez canal university, Egypt
2civil engineering, engineering, sues canal university, Ismailia, Egypt
3Building Materials Research and Quality Control Institute, Housing & Building National Research Center (HBRC), Cairo, Egypt
Abstract
 Rebars in reinforced concrete (RC) slab-column structures may corrode
under unfavourable conditions, making slab-column joints (SCJs) more susceptible
to punching shear (PS) failure. Moreover, PS failure is a common brittle failure,
which makes it more difficult to evaluate slab column systems' functioning and
failure probability. Thus, the prediction of PS resistance and the related reliability
analysis are key factors for building RC slab-column systems. In this study, a highfidelity finite-element model was created using Abaqus. A comprehensive
experimental record is compiled for corroded RC slab-column joints subjected to
punching shear loading. Then, effective parameters are established by applying  statistical technique principles. The text then provided a model of artificial
intelligence, an artificial neural network (ANN). In addition, it provided guidelines
for the future development of design codes by identifying the significance of each
variable on strength. In addition, it supplied an expression demonstrating the
intricate interdependence of affective variables. The results show that The ACI is
the most dependable standard, while the CSA is the least. The ANN model had an
average, coefficient of variation (COV), root mean square error (RMSE), and lower
95 % values of 0.93, 12.2 %, 1.8, and 0.82, respectively. As a result, the ANN
model was found to be more accurate, reliable, and design-safe than variable
uncertainty.

Keywords
RC slab-column structure; artificial neural network; Corrosion; Finite element; Punching shear capacity
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