PREDICTING INTERSTORY DRIFT DISTRIBUTION OF RC STRUCTURES USING MACHINE-LEARNING METHODS

Benyamin Mohebi; Neda Asgarkhani; Farzin Kazemi; Natalia Lasowicz

PREDICTING INTERSTORY DRIFT DISTRIBUTION OF RC STRUCTURES USING MACHINE-LEARNING METHODS

Abstract

Improving the construction of Reinforced Concrete (RC) buildings make it clear that it is in the interest of owners due to cost-benefit and unique behavior of these systems. Therefore, in this study, the aim is to provide a Machine Learning (ML) model for estimating the Interstory Drift Distribution (IDD) of floor levels of RC buildings, which can ease the design procedure and help civil structural designers. To train the ML models, an enriched framework of 2-, to 10-story RC structures having one to five bays have been provided, and the conventional ML models have been improved with feature selection and data processing methods. The results demonstrated that the ETR method can estimate the IDD of 2-, to 10-story RC structures by accuracy of 97.12% and can be used for IDD prediction of RC structures.

Authors (4)

Cite as

Full text

full text is not available in portal

full content of the article see on external site open in new tab

Keywords

Details

Category:

Conference activity

Type:

publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)

Language:

English

Publication year:

2024

Bibliographic description:

Mohebi B., Asgarkhani N., Kazemi F., Lasowicz N.: PREDICTING INTERSTORY DRIFT DISTRIBUTION OF RC STRUCTURES USING MACHINE-LEARNING METHODS// / : , 2024,

Sources of funding: