Soil liquefaction in seismic events: pioneering predictive models using machine learning and advanced regression techniques
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s12665-024-11480-x.pdf
Reference48 articles.
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2. Ahmad M, Tang X-W, Qiu J-N, Ahmad F (2019b) Evaluating seismic soil liquefaction potential using bayesian belief network and C4.5 decision tree approaches. Appl Sci 9(20):4226. https://doi.org/10.3390/app9204226
3. Ahmad M, Tang X-W, Qiu J-N, Wen-Jing G, Ahmad F (2020) Assessing the seismic soil liquefaction potential based on CPT through a hybrid approach utilizing Bayesian belief networks. J Cent South Univ 27(2):500–516. https://doi.org/10.1007/s11771-020-4312-3
4. Ahmad M, Tang X-W, Qiu J-N et al (2021) Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential. Front Struct Civ Eng 15(2):490–505. https://doi.org/10.1007/s11709-020-0669-5
5. Ahmad M, Amjad M, Al-Mansob RA, Kamiński P, Olczak P, Khan BJ, Alguno AC (2022) Forecasting liquefaction-induced lateral displacements through gaussian process regression. Appl Sci 12(4):1977. https://doi.org/10.3390/app12041977
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