Prediction of the undrained shear strength of remolded soil with non-linear regression, fuzzy logic, and artificial neural network
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11629-024-8645-5.pdf
Reference62 articles.
1. Abu-Farsakh MY, Mojumder MAH (2020) Exploring artificial neural network to evaluate the undrained shear strength of soil from cone penetration test data. Transp Res Rec 2674(4):11–22. https://doi.org/10.1177/0361198120912426
2. Akan R, Keskin SN (2019) The effect of data size of ANFIS and MLR models on prediction of unconfined compression strength of clayey soils. SN Appl Sci 1(8):1–11. https://doi.org/10.1007/s42452-019-0883-8
3. Akan R, Keskin SN, Yazici MF (2017) The relationship between the water content and unconfined compressive strength of clayey soils. Kilis 7 December University Journal of Science and Engineering 1(2): 1–9.
4. Akbay Arama Z, Yücel M, Akın MS, Dalyan I (2021) A comparative study on the application of artificial intelligence networks versus regression analysis for the prediction of clay plasticity. Arab J Geosci 14(7):1–16. https://doi.org/10.1007/s12517-021-06894-x
5. ASTM D7928-17 (2017) Standard Test Method for Particle-Size Distribution (Gradation) of Fine-Grained Soils Using the Sedimentation (Hydrometer) Analysis. West Conshohocken, PA: ASTM International.
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