Accurate and generalizable soil liquefaction prediction model based on the CatBoost algorithm
Author:
Funder
Natural Science Foundation of Shandong Province
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11600-024-01381-9.pdf
Reference25 articles.
1. Akkaya İ, Ali Ö, Mutluhan A, Müge KA, Uğur Ö (2018) Comparison of SPT and V S-based liquefaction analyses: a case study in Erciş (Van, Turkey). Acta Geophys Pol 66(1):21–38. https://doi.org/10.1007/s11600-017-0103-0
2. Alla V, Sahoo UK, Behera RN (2023) Seismic liquefaction analysis of MCDM weighted SPT data using support vector machine classification. Iran J Sci Technol Trans Civ Eng. https://doi.org/10.1007/s40996-023-01293-6
3. Bolton Seed H, Tokimatsu K, Harder LF, Chung RM (1985) Influence of SPT procedures in soil liquefaction resistance evaluations. J Geotech Eng 111(12):1425–1445. https://doi.org/10.1061/(ASCE)0733-9410(1985)111:12(1425)
4. Cetin KO, Seed RB, Kayen RE, Moss RES, Bilge HT, Ilgac M, Chowdhury K (2018) SPT-based probabilistic and deterministic assessment of seismic soil liquefaction triggering hazard. Soil Dyn Earthq Eng 115:698–709. https://doi.org/10.1016/j.soildyn.2018.09.012
5. Chern S-G, Lee C-Y, Wang C-C (2008) CPT-BASED liquefaction assessment by using fuzzy-neural network. J Mar Sci Technol 16(2):6. https://doi.org/10.51400/2709-6998.2024
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