Optimization of an Artificial Neural Network Using Three Novel Meta-heuristic Algorithms for Predicting the Shear Strength of Soil
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geotechnical Engineering and Engineering Geology,Transportation,Civil and Structural Engineering,Environmental Engineering
Link
https://link.springer.com/content/pdf/10.1007/s40515-023-00343-w.pdf
Reference51 articles.
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2. Azam, A., Bardhan, A., Kaloop, M.R., Samui, R., Alanazi, F., Alzara, M., Yosri: A.M.: Modeling resilient modulus of subgrade soils using LSSVM optimized with swarm intelligence algorithms. Sci. Rep. 12, 14454 (2022). https://doi.org/10.1038/s41598-022-17429-z
3. Bardhan, A., Asteris, P.G.: Application of hybrid ANN paradigms built with nature inspired meta-heuristics for modelling soil compaction parameters. Transp. Geotech. 41, 100995 (2023). https://doi.org/10.1016/j.trgeo.2023.100995
4. Bardhan, A., Biswas, R., Kardani, N., Iqbal, M., Samui, P., Singh, M.P., Asteris, P.G.: A novel integrated approach of augmented grey wolf optimizer and ANN for estimating axial load carrying-capacity of concrete-filled steel tube columns. Construct. Build Mater. 337, 127454 (2022). https://doi.org/10.1016/j.conbuildmat.2022.127454
5. Bardhan, A., Alzo'ubi, A.K., Palanivelu, S., Hamidian, P., GuhaRay, A., Kumar, G., Tsoukalas, M.Z., Asteris, P.G.: A hybrid approach of ANN and improved PSO for estimating soaked CBR of subgrade soils of heavy-haul railway corridor. Int. J. Pavement Eng. 24(1), 2176494 (2023). https://doi.org/10.1080/10298436.2023.2176494
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