Prediction of the Punching Load Strength of SCS Slabs with Stud-Bolt Shear Connectors Using Numerical Modeling and GEP Algorithm

Using bolt shear connectors in Steel-Concrete-Steel (SCS) slabs is very important due to producing a complete steel plates connection and adjusting the sandwich thickness desirably. Therefore, in the present research, a numerical study is conducted on the flexural behavior of SCS sandwich slabs with...

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Bibliographic Details
Main Authors: Mehdi Yousefi, Mohammad Golmohammadi, Seyed Hashem Khatibi, Majid Yaghoobi
Format: Article
Language:English
Published: Semnan University 2023-08-01
Series:Journal of Rehabilitation in Civil Engineering
Subjects:
Online Access:https://civiljournal.semnan.ac.ir/article_6932_712651c265d4cc7df9c2746bfd5fa9b1.pdf
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Summary:Using bolt shear connectors in Steel-Concrete-Steel (SCS) slabs is very important due to producing a complete steel plates connection and adjusting the sandwich thickness desirably. Therefore, in the present research, a numerical study is conducted on the flexural behavior of SCS sandwich slabs with stud-bolt shear connectors under the effect of the quasi-static concentrated load. For this purpose, first, the experimental specimens extracted from the previously published study were numerically modeled and quasi-statically analyzed using explicit dynamic analysis. Then based on the tests, the models were validated. Subsequently, the effect of the parameters, including the thickness of steel plates, stud-bolts diameter, the concrete core thickness, center-to-center distance of stud-bolt connectors, and the concrete core strength was evaluated based on the numerical models on the failure modes and the force-displacement curve. Finally, using the experimental setup and gene expression programming (GEP) algorithm, several numerical models were planned to predict the maximum strength of the slabs and a simple relation was proposed. The maximum strength resulting from the proposed relation and numerical models had an acceptable agreement with an error of 11% based on mean absolute percentage error (MAPE).
ISSN:2345-4415
2345-4423