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Research Article

Dynamic response estimation of an equivalent single degree of freedom system using artificial neural network and nonlinear static procedure

Benbokhari Abdellatif1, Chikh Benazouz1, Mébarki Ahmed2, 3

1Laboratoire des Travaux publics, ingénierie de Transport, environnement, Ecole Nationale Supérieure des Travaux Publics (ENSTP), Kouba, Algiers, Algeria.
2University Gustave Eiffel, UPEC, CNRS, Laboratoire Modélisation et Simulation Multi Echelle (MSME 8208 UMR), Marne-la-Vallée, France
3Permanent Guest Professor within “High-Level Foreign Talents Programme” Grant, Nanjing Tech University, China

Keywords

Abstract


Nonlinear time history analysis;

 Nonlinear static analysis; 

 Artificial neural networks; 

 Seismic response prediction;

 Machine learning

This paper introduces an innovative methodology for predicting the maximum dynamic response of structures using capacity curves and artificial neural networks (ANNs). This novel approach offers a quick and accurate procedure for estimating target displacements, obviating the need for intricate supplementary computations. The method generates a comprehensive dataset encompassing the bilinear representation of a single-degree-of-freedom (SDOF) characteristic, with ground motion parameters as inputs and maximum inelastic displacement as the corresponding output. This dataset is used to train an ANN model, with meticulous calibration of hyperparameters to ensure optimal model performance and predictive precision. The findings of this study demonstrate that the ANN model showed operational efficacy in approximating dynamic displacements. It is notably revealed that the size of the dataset significantly influences the ANN's performance and predictive accuracy. Through comparative analysis with established methodologies such as the displacement coefficient method and the modified coefficient method adopted by the Federal Emergency Management Agency (FEMA), the ANN model emerges as a fast tool for precisely predicting the dynamic response of single-degree-of-freedom systems, particularly those characterized by vibration periods exceeding 0.5 seconds. Consequently, this research culminates in the assertion that the ANN, owing to its inherent simplicity and impressive precision, is an alternative tool for estimating target displacements.

© 2023 MIM Research Group. All rights reserved.

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2023 Reviewer Awards:

Please, visit Reviewer Awards section for the winners of the 2022 RESM reviewer awards.



2023 Best Paper Award:

The paper authored by Ferzan Fidan, Naim Aslan, Mümin Mehmet Koç entitled as “Morpho-structural and compressive mechanical properties of graphene oxide reinforced hydroxyapatite scaffolds for bone tissue applications” is awarded.



2023 Most Cited Paper Award:

The paper authored by Ercan Işık, Ehsan Harirchian, Hüseyin Bilgin, Kirti Jadhav entitled as “The effect of material strength and discontinuity in RC structures according to different site-specific design spectra" is awarded.


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