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

  • Hosein Naderpour Semnan University
  • Seyed Mohammad Khatami
  • Rui Carneiro Barros University of Porto

Abstract

This study focuses on preventing collisions between structuresduring seismic excitation based on gap size. Several approximatedequations in order to estimate separation distancebetween buildings are collected and evaluated to measure gapsize in order to avoid impact between them when large lateraldisplacements occurred due to earthquake. Artificial neuralnetworks are utilized to estimate the required distance betweenstructures. The majority of building codes suggest separationdistances based on maximum lateral displacements of eachbuilding or height of buildings in order to provide safety gapsize between them. Subsequently, researchers have proposedseveral equations to predict the critical distance. In currentstudy, some MDOF models are equivalently modelled and optimumgap size between buildings is approximately estimatedand finally a new equation for separation distance is suggestedand the accuracy of formula is numerically investigated.

Keywords

critical distance, pounding, lateral displacement, seismic excitation
Published
31-01-2017
How to Cite
NADERPOUR, Hosein; KHATAMI, Seyed Mohammad; BARROS, Rui Carneiro. Prediction of Critical Distance Between Two MDOF Systems Subjected to Seismic Excitation in Terms of Artificial Neural Networks. Periodica Polytechnica Civil Engineering, [S.l.], v. 61, n. 3, p. 516-529, jan. 2017. ISSN 1587-3773. Available at: <https://pp.bme.hu/ci/article/view/9618>. Date accessed: 24 sep. 2017. doi: https://doi.org/10.3311/PPci.9618.
Section
Research Article