Authors:
Nathan Carstens
1
;
George Markou
1
and
Nikolaos Bakas
2
Affiliations:
1
Department of Civil Engineering, University of Pretoria, South Africa
;
2
Department of RnD, RDC Informatics, Athens, Greece
Keyword(s):
Machine Learning Algorithms, Fundamental Mode Formulae, Modal Analysis, Soil-structure Interaction, Finite Element Method, Reinforced Concrete, Hybrid Modelling.
Abstract:
With the development of technology and building materials, the world is moving towards creating a better and safer environment. One of the main challenges for reinforced concrete structures is the capability to withstand the seismic loads produced by earthquake excitations, through using the fundamental period of the structure. However, it is well documented that the current design formulae fail to predict the natural frequency of the considered structures due to their inability to incorporate the soil-structure interaction and other features of the structures. This research work extends a dataset containing 475 modal analysis results developed through a previous research work. The extended dataset was then used to develop three predictive fundamental period formulae using a machine learning algorithm that utilizes a higher-order, nonlinear regression modelling framework. The predictive formulae were validated with 60 out-of-sample modal analysis results. The numerical findings concl
uded that the fundamental period formulae proposed in this study possess superior prediction ability, compared to all other international proposed formulae, for the under-studied types of buildings.
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