Investigation of shear strength correlations and reliability assessments of sandwich structures by kriging method

Created by W.Langdon from gp-bibliography.bib Revision:1.8187

@Article{AMERYAN:2020:CS,

• author = "Ala Ameryan and Mansour Ghalehnovi and Mohsen Rashki",
• title = "Investigation of shear strength correlations and reliability assessments of sandwich structures by kriging method",
• journal = "Composite Structures",
• volume = "253",
• pages = "112782",
• year = "2020",
• ISSN = "0263-8223",
• DOI = "doi:10.1016/j.compstruct.2020.112782",
• URL = "http://www.sciencedirect.com/science/article/pii/S0263822320327082",
• keywords = "genetic algorithms, genetic programming, Structural reliability, Kriging, Sandwich structures, Finite element, Experimental data, Failure probability",
• abstract = "Steel-concrete-steel (SCS) sandwich composite structure with corrugated-strip connectors (CSC) is the promising structure which is applied in offshore and building structures. The behavior prediction of shear connections is of major importance in SCS structures. The present study evaluated the existing shear strength correlations of SCS sandwich structures exploiting experimental data and Finite Element Analysis (FEA). The considered system is a double steel skin sandwich structure with CSC (DSCS). Due to the limitation of the literature regarding CSC development, some new correlations were proposed and studied relying on several FEA results through the Genetic Programming method. The accuracy of the estimated shear strength predicted by the existing and proposed equations was evaluated using the FEA data and push-out test results. The FE models were verified through experimental data. Moreover, the correlations were investigated based on reliability assessment due to the high importance of the reliability analysis of such structures. Given that high accuracy in estimating the shear strength fails to necessarily lead to acceptable results in structural reliability analysis, the reliability of the existing and proposed equations was evaluated using the Kriging model by considering experimental data. This meta-model could predict accurate values with a limited number of initial training samples",

}

Genetic Programming entries for Ala Ameryan Mansour Ghalehnovi Mohsen Rashki

Citations