Development Of A New Fundamental Period Formula By Considering Soil-structure Interaction With The Use Of Machine Learning Algorithms

Vicky-Lee Taljaard, Dewald Z. Gravett, Christos Mourlas, George Markou, Nikolaos Bakas, Manolis Papadrakakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

The fundamental period of a structure is one of the key parameters utilized in the design phase to compute the seismic-resistant forces. Although the importance of seismic-resistant buildings is well understood it has been found that the current design code formulae, which are used to predict the fundamental period of reinforced concrete (RC) buildings are quite simplistic, failing to accurately predict the natural frequency, raising many concerns with regards to their reliability. The primary objective of this research project was to develop a formula that has the ability to compute the fundamental period of an RC structure, while taking into account the soil-structure interaction phenomenon. This was achieved by using a computationally efficient and robust 3D detailed modelling approach for modal analysis obtaining the numerically predicted fundamental period of 475 models, producing a dataset with numerical results. This dataset was then used to train a machine learning algorithm to formulate three fundamental period formulae using a higher-order, nonlinear regression modelling framework. The three newly proposed formulae were evaluated during the validation phase to investigate their performance using 60 new out-of-sample modal results, where, in this work, additional validation models are created and used to test the predictive abilities of the proposed fundamental period formulae. The findings of this research report suggest that the proposed fundamental period formulae exhibit exceptional predictive capabilities for the under-study RC multi-storey buildings, where they outperform all existing de-sign code fundamental period formulae currently in effect.
Original languageEnglish
Title of host publicationCOMPDYN 2021 Proceedings
EditorsM. Papadrakakis, M. Fragiadakis
PublisherECCOMAS Proceedia
Pages3801-3809
Number of pages9
Volume2
ISBN (Electronic)9786188507258
DOIs
Publication statusPublished - Jun 2021
EventCOMPDYN 2021: 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering - Athens, Greece
Duration: 28 Jun 202130 Jun 2021
https://2021.compdyn.org/

Publication series

Name
ISSN (Electronic)2623-3347

Conference

ConferenceCOMPDYN 2021
Country/TerritoryGreece
CityAthens
Period28/06/2130/06/21
Internet address

Keywords

  • Fundamental Period Formula
  • Soil-Structure Interaction
  • Machine Learning Algorithms
  • Modal Analysis
  • Finite Element Method
  • Reinforced Concrete

Fingerprint

Dive into the research topics of 'Development Of A New Fundamental Period Formula By Considering Soil-structure Interaction With The Use Of Machine Learning Algorithms'. Together they form a unique fingerprint.

Cite this