Version 1
: Received: 11 September 2024 / Approved: 11 September 2024 / Online: 11 September 2024 (16:23:56 CEST)
How to cite:
Karampinis, I.; Morfidis, K.; Iliadis, L. Derivation of Analytical Equations for the Fundamental Period of Framed Structures Using Machine Learning and SHAP Values. Preprints2024, 2024090890. https://doi.org/10.20944/preprints202409.0890.v1
Karampinis, I.; Morfidis, K.; Iliadis, L. Derivation of Analytical Equations for the Fundamental Period of Framed Structures Using Machine Learning and SHAP Values. Preprints 2024, 2024090890. https://doi.org/10.20944/preprints202409.0890.v1
Karampinis, I.; Morfidis, K.; Iliadis, L. Derivation of Analytical Equations for the Fundamental Period of Framed Structures Using Machine Learning and SHAP Values. Preprints2024, 2024090890. https://doi.org/10.20944/preprints202409.0890.v1
APA Style
Karampinis, I., Morfidis, K., & Iliadis, L. (2024). Derivation of Analytical Equations for the Fundamental Period of Framed Structures Using Machine Learning and SHAP Values. Preprints. https://doi.org/10.20944/preprints202409.0890.v1
Chicago/Turabian Style
Karampinis, I., Konstaninos Morfidis and Lazaros Iliadis. 2024 "Derivation of Analytical Equations for the Fundamental Period of Framed Structures Using Machine Learning and SHAP Values" Preprints. https://doi.org/10.20944/preprints202409.0890.v1
Abstract
The fundamental period is one of the most important parameters for the design of new structures , as well as the estimation of the capacity of existing ones. However, its calculation requires the solution of an eigenvalue problem. Especially for new buildings, this can involve material and geometric parameters that are not known beforehand. Thus, to estimate it, various design codes and researchers have adopted a several approximate analytical equations based on a number of key structural parameters. To this end, the present study introduces a novel methodology for the derivation of analytical equations for the fundamental period of Reinforced Concrete (RC) structures. The methodology is based on Machine Learning (ML) explainability techniques and specifically the so-called SHapley Additive exPlanations (SHAP) values. The novel methodology allows these equations to be constructed sequentially and in an informed manner, controlling the balance between accuracy and complexity. An extended dataset comprised of 4026 data points is employed. The results showed excellent accuracy, with a coefficient of determination R2≈0.95. This demonstrates the potential applicability of the proposed methodology in a wide array of similar engineering challenges.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
