Version 1
: Received: 11 October 2024 / Approved: 11 October 2024 / Online: 14 October 2024 (10:00:55 CEST)
Version 2
: Received: 31 October 2024 / Approved: 31 October 2024 / Online: 4 November 2024 (12:31:53 CET)
How to cite:
Barkhordari, M. S.; Barkhordari, M. M. Fuzzy-Based Convolutional Neural Network Model for Structural Response Prediction Under Seismic Excitation. Preprints2024, 2024100956. https://doi.org/10.20944/preprints202410.0956.v2
Barkhordari, M. S.; Barkhordari, M. M. Fuzzy-Based Convolutional Neural Network Model for Structural Response Prediction Under Seismic Excitation. Preprints 2024, 2024100956. https://doi.org/10.20944/preprints202410.0956.v2
Barkhordari, M. S.; Barkhordari, M. M. Fuzzy-Based Convolutional Neural Network Model for Structural Response Prediction Under Seismic Excitation. Preprints2024, 2024100956. https://doi.org/10.20944/preprints202410.0956.v2
APA Style
Barkhordari, M. S., & Barkhordari, M. M. (2024). Fuzzy-Based Convolutional Neural Network Model for Structural Response Prediction Under Seismic Excitation. Preprints. https://doi.org/10.20944/preprints202410.0956.v2
Chicago/Turabian Style
Barkhordari, M. S. and Mohammad Mahdi Barkhordari. 2024 "Fuzzy-Based Convolutional Neural Network Model for Structural Response Prediction Under Seismic Excitation" Preprints. https://doi.org/10.20944/preprints202410.0956.v2
Abstract
This study addresses the challenge of predicting the dynamic behavior of the structures under seismic excitation. Accurate prediction of such systems' responses is critical for the design and evaluation of buildings and infrastructure. Traditional methods, including numerical models and differential equation solvers, often face significant computational burdens, especially with nonlinear hysteretic behaviors and large-scale problems. To overcome these limitations, a novel Fuzzy-based Convolutional Neural Network (FuzzyCNN) model is developed. This model integrates fuzzy logic principles with convolutional neural networks to effectively manage the uncertainties and complexities inherent in soil-structure interaction under seismic loads. The model's performance is validated through both numerical simulations and experimental data from a mid-rise concrete building subjected to seismic events. Comparative analysis with a traditional Physics-informed CNN (PhyCNN) model demonstrates the superior accuracy and robustness of the FuzzyCNN in predicting seismic responses. Key results show that the FuzzyCNN model not only enhances prediction accuracy but also handles uncertainties more effectively than the PhyCNN model. The findings suggest that the FuzzyCNN model can significantly improve the efficiency and accuracy of dynamic response predictions. This advancement offers valuable implications for engineering design, seismic risk assessment, and the development of more resilient infrastructure.
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.
