Improved Predictive Fundamental Period Formula for Reinforced Concrete Structures through the Use of Machine Learning Algorithms

Nathan Carstens, George Markou, Nikolaos Bakas

2022

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 concluded 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.

Paper Citation

in Harvard Style

Carstens N., Markou G. and Bakas N. (2022). Improved Predictive Fundamental Period Formula for Reinforced Concrete Structures through the Use of Machine Learning Algorithms. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-547-0, pages 647-652. DOI: 10.5220/0010984500003116

in Bibtex Style

@conference{icaart22,
author={Nathan Carstens and George Markou and Nikolaos Bakas},
title={Improved Predictive Fundamental Period Formula for Reinforced Concrete Structures through the Use of Machine Learning Algorithms},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2022},
pages={647-652},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010984500003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Improved Predictive Fundamental Period Formula for Reinforced Concrete Structures through the Use of Machine Learning Algorithms
SN - 978-989-758-547-0
AU - Carstens N.
AU - Markou G.
AU - Bakas N.
PY - 2022
SP - 647
EP - 652
DO - 10.5220/0010984500003116