GUI-based platform for slope stability prediction under seismic conditions using machine learning algorithms
Author:
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s44150-024-00112-4.pdf
Reference44 articles.
1. Koopialipoor M, JahedArmaghani D, Hedayat A, Marto A, Gordan B (2019) Applying Various Hybrid Intelligent Systems to Evaluate and Predict Slope Stability under Static and Dynamic Conditions. Soft comput 23:5913–5929. https://doi.org/10.1007/s00500-018-3253-3
2. Gordan B, Armaghani DJ, Adnan AB, Rashid ASA (2016) A New Model for Determining Slope Stability Based on Seismic Motion Performance. Soil Mech Found Eng 53:344–351. https://doi.org/10.1007/s11204-016-9409-1
3. Shirkhanloo S, Najafi M, Kaushal V, Rajabi M (2021) A Comparative Study on the Effect of Class C and Class F Fly Ashes on Geotechnical Properties of High-Plasticity Clay. CivilEng 2:1009–1018. https://doi.org/10.3390/civileng2040054
4. Khosravi M, Tabasi S, Eldien HH, Motahari MR, Alizadeh SM (2022) Evaluation and Prediction of the Rock Static and Dynamic Parameters. J Appl Geophy 199:104581. https://doi.org/10.1016/j.jappgeo.2022.104581
5. Mahdiyar A, Hasanipanah M, Armaghani DJ, Gordan B, Abdullah A, Arab H, Majid MZA (2017) A Monte Carlo Technique in Safety Assessment of Slope under Seismic Condition. Eng Comput 33:807–817. https://doi.org/10.1007/s00366-016-0499-1
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