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

Document Type : Original Article

Authors

1 civil engineering, Suez canal university, Egypt

2 civil engineering, engineering, sues canal university, Ismailia, Egypt

3 Building 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.

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MSA Engineering Journal
Volume 2, Issue 2 - Serial Number 6
Special issue for Artificial Intelligence & Sustainable Engineering
March 2023
Pages 384-407

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