Machine learning in seismic structural design: an exploration of ANN and tabu-search optimization
Author:
Publisher
Springer Science and Business Media LLC
Subject
Civil and Structural Engineering
Link
https://link.springer.com/content/pdf/10.1007/s42107-023-00913-w.pdf
Reference43 articles.
1. Al Khazaleh, M., & Bisharah, M. (2023). Ann-based prediction of cone tip resistance with tabu-search optimization for geotechnical engineering applications. Asian Journal of Civil Engineering. https://doi.org/10.1007/s42107-023-00693-3
2. Al Yamani, W. H., Ghunimat, D. M., & Bisharah, M. M. (2023). Modeling and predicting high-performance concrete compressive strength sensitivity using machine learning methods. Asian Journal of Civil Engineering. https://doi.org/10.1007/s42107-023-00614-4
3. Ali, T., Eldin, M. N., & Haider, W. (2023). The effect of soil-structure interaction on the seismic response of structures using machine learning, finite element modeling and ASCE 7-16 methods. Sensors, 23(4), 2047. https://doi.org/10.3390/s23042047
4. Alkhdour, A., Khazaleh, M. A., Mnaseer, R. A., Bisharah, M., Alkhadrawi, S., & Al-Bdour, H. (2023). Optimizing soil settlement/consolidation prediction in Finland clays: Machine learning regressions with Bayesian hyperparameter selection. Asian Journal of Civil Engineering. https://doi.org/10.1007/s42107-023-00704-3
5. Almahameed, B. A., & Bisharah, M. (2023). Applying machine learning and particle swarm optimization for predictive modeling and cost optimization in construction project management. Asian Journal of Civil Engineering. https://doi.org/10.1007/s42107-023-00843-7
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