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291880

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

Article

Last updated: 28 Dec 2024

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Subjects

-

Tags

Civil Engineering

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.

DOI

10.21608/msaeng.2023.291880

Keywords

RC slab-column structure, artificial neural network, Corrosion, Finite Element, punching shear capacity

Authors

First Name

Ehab

Last Name

Lotfy

MiddleName

M.

Affiliation

civil engineering, Suez canal university, Egypt

Email

ehablotfy2000@gmail.com

City

Ismailia

Orcid

0000000257191800

First Name

Ahmed

Last Name

Gomaa

MiddleName

mahmoud Abd ELKhalek

Affiliation

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

Email

ahmed_blace@eng.suez.edu.eg

City

Ismailia

Orcid

0009-0005-9507-3783

First Name

Sally

Last Name

Hosny

MiddleName

-

Affiliation

civil engineering, Suez canal university, Egypt

Email

sally_hosni@eng.suez.edu.eg

City

Ismailia

Orcid

0000000234523139

First Name

Sherif

Last Name

Khafaga

MiddleName

A.

Affiliation

Building Materials Research and Quality Control Institute, Housing & Building National Research Center (HBRC), Cairo, Egypt

Email

sh.khafaga@yahoo.com

City

Cairo

Orcid

0000000303942207

First Name

Manar

Last Name

Ahmed

MiddleName

A.

Affiliation

civil engineering, Suez canal university, Egypt

Email

manar_abdelshakour@eng.suez.edu.eg

City

Ismailia

Orcid

0000-0003-1566-1154

Volume

2

Article Issue

2

Related Issue

40382

Issue Date

2023-03-01

Receive Date

2023-03-23

Publish Date

2023-03-01

Page Start

384

Page End

407

Print ISSN

2812-5339

Online ISSN

2812-4928

Article File

MSAENG_Volume 2_Issue 2_Pages 384-407.pdf

PDF . 909.3kB

Link

https://msaeng.journals.ekb.eg/article_291880.html

Detail API

https://msaeng.journals.ekb.eg/service?article_code=291880

Order

291,880

Type

Original Article

Type Code

2,183

Publication Type

Journal

Publication Title

MSA Engineering Journal

Publication Link

https://msaeng.journals.ekb.eg/

MainTitle

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

Details

Type

Article

Created At

28 Dec 2024