The Adoption of Deep Belief Network Classifier with Shark Smell Optimizer to Predict the Soil Liquefaction
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-2676-3_28
Reference25 articles.
1. Javdanian H (2019) Evaluation of soil liquefaction potential using energy approach: experimental and statistical investigation. Bull Eng Geol Env 78(3):1697–1708. https://doi.org/10.1007/s10064-017-1201-6
2. Zuzulock ML, Taylor ODS, Maerz NH (2020) Soil fatigue hazard screening analyses framework for spacio-temporally clustered induced seismicity with examples of damage potential due to liquefaction. SN Appl Sci 2(6). https://doi.org/10.1007/s42452-020-2878-x
3. Seed HB, Idriss IM (1971) Simplified procedure for evaluating soil liquefaction potential. J Soil Mech Found Div 97(9):1249–1273
4. Hossain MD, Maksud Kamal ASM, Rahman MZ, Farazi AH, Mondal DR, Mahmud T, Ferdous N (2020) Assessment of soil liquefaction potential: a case study for Moulvibazar town, Sylhet, Bangladesh. SN Appl Sci 2:1–2
5. Youd TL, Idriss IM (2001) Liquefaction resistance of soils: summary report from the 1996 NCEER and 1998 NCEER/NSF workshops on evaluation of liquefaction resistance of soils. J Geotech Geoenviron Eng 127(4):297–313
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