Artificial neural network and machine learning models for predicting the lateral cyclic response of post-tensioned base rocking steel bridge piers
Author:
Publisher
Springer Science and Business Media LLC
Subject
Civil and Structural Engineering
Link
https://link.springer.com/content/pdf/10.1007/s42107-023-00791-2.pdf
Reference22 articles.
1. Abidhan, B., Biswas, R., Kardani, N., Iqbal, M., Samui, P., Singh, M. P., & Asteris, G. (2022). A novel integrated approach of augmented grey wolf optimizer and ann for estimating axial load carrying-capacity of concrete-filled steel tube columns. Construction and Building Materials, 337, 127454. https://doi.org/10.1016/j.conbuildmat.2022.127454
2. Ahmad, R., Shahria, A. M., & Robert, T. (2021). Experimental Investigations on the Lateral Cyclic Response of Post-Tensioned Rocking Steel Bridge Piers. Journal of Structural Engineering, 147(12), 4021211. https://doi.org/10.1061/(ASCE)ST.1943-541X.0003197
3. Almustafa, M. K., & Nehdi, M. L. (2020). Machine Learning Model for Predicting Structural Response of RC Slabs Exposed to Blast Loading. Engineering Structures, 221, 111109. https://doi.org/10.1016/j.engstruct.2020.111109
4. Almustafa, M. K., & Nehdi, M. L. (2022). Machine Learning Model for Predicting Structural Response of RC Columns Subjected to Blast Loading. International Journal of Impact Engineering, 162, 104145. https://doi.org/10.1016/j.ijimpeng.2021.104145
5. ASTM A252/A252M-19. 2019 Standard Specification for Welded and Seamless Steel Pipe Piles. Doi: https://doi.org/10.1520/A0252_A0252M-19.
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