Prediction of Critical Distance Between Two MDOF Systems Subjected to Seismic Excitation in Terms of Artificial Neural Networks

• Type: Article in International Scientific Journal
• Date: 2017

Authors

Naderpour, H (Author) Other; Khatami, SM (Author) Other; Rui Carneiro de Barros (Author) FEUP

Journal

Title: Periodica Polytechnica: Civil Engineering

Vol. 61

Pages: 516-529

ISSN: 0553-6626

Publisher: Budapesti Muszaki es Gazdasagtudomanyi Egyetem/Budapest University of Technology and Economics

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 12 Citations

Other information

Authenticus ID: P-00N-81M

DOI: 10.3311/ppci.9618

Abstract (EN): This study focuses on preventing collisions between structures during seismic excitation based on gap size. Several approximated equations in order to estimate separation distance between buildings are collected and evaluated to measure gap size in order to avoid impact between them when large lateral displacements occurred due to earthquake. Artificial neural networks are utilized to estimate the required distance between structures. The majority of building codes suggest separation distances based on maximum lateral displacements of each building or height of buildings in order to provide safety gap size between them. Subsequently, researchers have proposed several equations to predict the critical distance. In current study, some MDOF models are equivalently modelled and optimum gap size between buildings is approximately estimated and finally a new equation for separation distance is suggested and the accuracy of formula is numerically investigated.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14