Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/33041
Title: DEVELOPING FUNDAMENTAL PERIOD FORMULAE FOR STEEL FRAMED STRUCTURES THROUGH MACHINE LEARNING AND AUTOMATED ALGORITHMS
Authors: Calitz, Duan 
Markou, George 
Bakas, Nikolaos P. 
Papadrakakis, Manolis 
Major Field of Science: Engineering and Technology
Field Category: Computer and Information Sciences;ENGINEERING AND TECHNOLOGY;Civil Engineering
Keywords: Earthquake Engineering;Finite Element Method;Fundamental Period;Soil-Structure Interaction;Steel-Framed Structure;Structural Dynamics
Issue Date: 1-Jan-2023
Source: 9th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, Athens, Greece, 12-–14 June 2023
Conference: ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering 
Abstract: The fundamental period of a structure is an important intrinsic dynamic property of the structural system response. Current design codes use simplified empirical formulae to estimate the fundamental period, that generally do not account for the influence of plan geometry, cross-sectional properties of the specific structural elements, nor the properties of the soil the structures are founded on, and thus the effects of soil-structure interaction (SSI) are not accounted for. This research work investigates the fundamental period of unbraced steel framed structures of various geometric configurations, and multiple different steel column members, as well as the SSI effect. A new algorithm is also developed for the automated construction and analysis of finite element models. The proposed automated procedure is used to create a dataset that is used to train and test the predictive formulae for computing the fundamental period of steel structures with and without accounting for the SSI effect. The proposed 40-feature formula was found to derive an optimum coefficient of determination (R2) with a value of 99.976% and an error that is less than 2%.
URI: https://hdl.handle.net/20.500.14279/33041
ISSN: 26233347
DOI: 10.7712/120123.10468.20613
Type: Conference Papers
Affiliation : University of Pretoria 
The Cyprus Institute 
National Technical University Of Athens 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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