Intelligent Prediction of Seismic Displacements Computed Using the Newmark-Beta Method: A Comparison Between Random Forests and Artificial Neural Networks

International Journal of Civil Engineering

© 2025 by SSRG - IJCE Journal

Volume 12 Issue 7

Year of Publication : 2025

Authors : Mouna EL Mkhalet, Nouzha Lamdouar

10.14445/23488352/IJCE-V12I7P113

How to Cite?

Mouna EL Mkhalet, Nouzha Lamdouar, "Intelligent Prediction of Seismic Displacements Computed Using the Newmark-Beta Method: A Comparison Between Random Forests and Artificial Neural Networks," SSRG International Journal of Civil Engineering, vol. 12,  no. 7, pp. 145-158, 2025. Crossref, https://doi.org/10.14445/23488352/IJCE-V12I7P113

Abstract:

This research explores the application of Random Forest (RF) and Artificial Neural Networks (ANN) to determine the most effective method for predicting the displacement of ground-floor structures subjected to various seismic excitations, modeled using the Newmark-Beta method. The introduction first presents the Random Forest algorithm, which uses bagging (bootstrap aggregation) combined with variance-based splitting. Then, we introduce Artificial Neural Networks, detailing their structure and training steps. This is followed by a presentation of the Newmark-Beta method, which is used to compute the seismic response of the structures. In the results section, we analyze the range of variation in input parameters and the corresponding displacement outputs at the ground floor. Next, we apply both the Random Forest and ANN models using the results generated by the Newmark-Beta method. In the discussion, we compare the performance of both models using the Mean Squared Error (MSE). We also examine the sensitivity and non-linearity of each model to assess which method—Random Forest or ANN—provides more accurate predictions. Finally, we compare our results with similar studies in the literature and highlight our contribution as well as future research perspectives.

Keywords:

Seismic Displacements, Newmark-Beta Method, Random Forest, Artificial Neural Network, Predictive Modeling.

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